Skip to content

GitLab

otbtf
otbtf
Commit 70004b8e authored by Cresson Remi's avatar Cresson Remi
Browse files
Options

Merge branch 'develop'

parents 5c0ec1a9 4d9c113a
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 925 additions and 745 deletions
README.md
View file @ 70004b8e
This diff is collapsed.
app/otbImageClassifierFromDeepFeatures.cxx
View file @ 70004b8e
......@@ -92,9 +92,11 @@ private:
ShareParameter("deepmodel", "tfmodel.model",
"Deep net model parameters", "Deep net model parameters");
ShareParameter("output", "tfmodel.output",
"Deep net outputs parameters", "Deep net outputs parameters");
ShareParameter("finetuning", "tfmodel.finetuning",
"Deep net fine tuning parameters","Deep net fine tuning parameters");
"Deep net outputs parameters",
"Deep net outputs parameters");
ShareParameter("optim", "tfmodel.optim",
"This group of parameters allows optimization of processing time",
"This group of parameters allows optimization of processing time");
// Classify shared parameters
ShareParameter("model" , "classif.model" , "Model file" , "Model file" );
......
app/otbTensorflowModelServe.cxx
View file @ 70004b8e
......@@ -106,14 +106,14 @@ public:
// Parameter group keys
ss_key_in << ss_key_group.str() << ".il";
ss_key_dims_x << ss_key_group.str() << ".fovx";
ss_key_dims_y << ss_key_group.str() << ".fovy";
ss_key_dims_x << ss_key_group.str() << ".rfieldx";
ss_key_dims_y << ss_key_group.str() << ".rfieldy";
ss_key_ph << ss_key_group.str() << ".placeholder";
// Parameter group descriptions
ss_desc_in << "Input image (or list to stack) for source #" << inputNumber;
ss_desc_dims_x << "Field of view width for source #" << inputNumber;
ss_desc_dims_y << "Field of view height for source #" << inputNumber;
ss_desc_dims_x << "Input receptive field (width) for source #" << inputNumber;
ss_desc_dims_y << "Input receptive field (height) for source #" << inputNumber;
ss_desc_ph << "Name of the input placeholder for source #" << inputNumber;
// Populate group
......@@ -182,22 +182,22 @@ public:
MandatoryOn ("output.names");
// Output Field of Expression
AddParameter(ParameterType_Int, "output.foex", "The output field of expression (x)");
SetMinimumParameterIntValue ("output.foex", 1);
SetDefaultParameterInt ("output.foex", 1);
MandatoryOn ("output.foex");
AddParameter(ParameterType_Int, "output.foey", "The output field of expression (y)");
SetMinimumParameterIntValue ("output.foey", 1);
SetDefaultParameterInt ("output.foey", 1);
MandatoryOn ("output.foey");
AddParameter(ParameterType_Int, "output.efieldx", "The output expression field (width)");
SetMinimumParameterIntValue ("output.efieldx", 1);
SetDefaultParameterInt ("output.efieldx", 1);
MandatoryOn ("output.efieldx");
AddParameter(ParameterType_Int, "output.efieldy", "The output expression field (height)");
SetMinimumParameterIntValue ("output.efieldy", 1);
SetDefaultParameterInt ("output.efieldy", 1);
MandatoryOn ("output.efieldy");
// Fine tuning
AddParameter(ParameterType_Group, "finetuning" , "Fine tuning performance or consistency parameters");
AddParameter(ParameterType_Bool, "finetuning.disabletiling", "Disable tiling");
MandatoryOff ("finetuning.disabletiling");
AddParameter(ParameterType_Int, "finetuning.tilesize", "Tile width used to stream the filter output");
SetMinimumParameterIntValue ("finetuning.tilesize", 1);
SetDefaultParameterInt ("finetuning.tilesize", 16);
AddParameter(ParameterType_Group, "optim" , "This group of parameters allows optimization of processing time");
AddParameter(ParameterType_Bool, "optim.disabletiling", "Disable tiling");
MandatoryOff ("optim.disabletiling");
AddParameter(ParameterType_Int, "optim.tilesize", "Tile width used to stream the filter output");
SetMinimumParameterIntValue ("optim.tilesize", 1);
SetDefaultParameterInt ("optim.tilesize", 16);
// Output image
AddParameter(ParameterType_OutputImage, "out", "output image");
......@@ -205,8 +205,8 @@ public:
// Example
SetDocExampleParameterValue("source1.il", "spot6pms.tif");
SetDocExampleParameterValue("source1.placeholder", "x1");
SetDocExampleParameterValue("source1.fovx", "16");
SetDocExampleParameterValue("source1.fovy", "16");
SetDocExampleParameterValue("source1.rfieldx", "16");
SetDocExampleParameterValue("source1.rfieldy", "16");
SetDocExampleParameterValue("model.dir", "/tmp/my_saved_model/");
SetDocExampleParameterValue("model.userplaceholders", "is_training=false dropout=0.0");
SetDocExampleParameterValue("output.names", "out_predict1 out_proba1");
......@@ -248,16 +248,16 @@ public:
m_TFFilter = TFModelFilterType::New();
m_TFFilter->SetGraph(m_SavedModel.meta_graph_def.graph_def());
m_TFFilter->SetSession(m_SavedModel.session.get());
m_TFFilter->SetOutputTensorsNames(GetParameterStringList("output.names"));
m_TFFilter->SetOutputTensors(GetParameterStringList("output.names"));
m_TFFilter->SetOutputSpacingScale(GetParameterFloat("output.spcscale"));
otbAppLogINFO("Output spacing ratio: " << m_TFFilter->GetOutputSpacingScale());
// Get user placeholders
TFModelFilterType::DictListType dict;
TFModelFilterType::StringList expressions = GetParameterStringList("model.userplaceholders");
TFModelFilterType::DictType dict;
for (auto& exp: expressions)
{
TFModelFilterType::DictType entry = tf::ExpressionToTensor(exp);
TFModelFilterType::DictElementType entry = tf::ExpressionToTensor(exp);
dict.push_back(entry);
otbAppLogINFO("Using placeholder " << entry.first << " with " << tf::PrintTensorInfos(entry.second));
......@@ -267,7 +267,7 @@ public:
// Input sources
for (auto& bundle: m_Bundles)
{
m_TFFilter->PushBackInputBundle(bundle.m_Placeholder, bundle.m_PatchSize, bundle.m_ImageSource.Get());
m_TFFilter->PushBackInputTensorBundle(bundle.m_Placeholder, bundle.m_PatchSize, bundle.m_ImageSource.Get());
}
// Fully convolutional mode on/off
......@@ -281,15 +281,15 @@ public:
FloatVectorImageType::SizeType foe;
foe[0] = GetParameterInt("output.foex");
foe[1] = GetParameterInt("output.foey");
m_TFFilter->SetOutputFOESize(foe);
m_TFFilter->SetOutputExpressionFields({foe});
otbAppLogINFO("Output field of expression: " << m_TFFilter->GetOutputFOESize());
otbAppLogINFO("Output field of expression: " << m_TFFilter->GetOutputExpressionFields()[0]);
// Streaming
if (GetParameterInt("finetuning.disabletiling")!=1)
if (GetParameterInt("optim.disabletiling")!=1)
{
// Get the tile size
const unsigned int tileSize = GetParameterInt("finetuning.tilesize");
const unsigned int tileSize = GetParameterInt("optim.tilesize");
otbAppLogINFO("Force tiling with squared tiles of " << tileSize)
// Update the TF filter to get the output image size
......
app/otbTensorflowModelTrain.cxx
View file @ 70004b8e
......@@ -53,7 +53,7 @@ public:
itkNewMacro(Self);
itkTypeMacro(TensorflowModelTrain, Application);
/** Typedefs for tensorflow */
/** Typedefs for TensorFlow */
typedef otb::TensorflowMultisourceModelTrain<FloatVectorImageType> TrainModelFilterType;
typedef otb::TensorflowMultisourceModelValidate<FloatVectorImageType> ValidateModelFilterType;
typedef otb::TensorflowSource<FloatVectorImageType> TFSource;
......@@ -75,8 +75,8 @@ public:
// Parameters keys
std::string m_KeyInForTrain; // Key of input image list (training)
std::string m_KeyInForValid; // Key of input image list (validation)
std::string m_KeyPHNameForTrain; // Key for placeholder name in the tensorflow model (training)
std::string m_KeyPHNameForValid; // Key for placeholder name in the tensorflow model (validation)
std::string m_KeyPHNameForTrain; // Key for placeholder name in the TensorFlow model (training)
std::string m_KeyPHNameForValid; // Key for placeholder name in the TensorFlow model (validation)
std::string m_KeyPszX; // Key for samples sizes X
std::string m_KeyPszY; // Key for samples sizes Y
};
......@@ -122,18 +122,19 @@ public:
// Parameter group keys
ss_key_tr_in << ss_key_tr_group.str() << ".il";
ss_key_val_in << ss_key_val_group.str() << ".il";
ss_key_dims_x << ss_key_tr_group.str() << ".fovx";
ss_key_dims_y << ss_key_tr_group.str() << ".fovy";
ss_key_dims_x << ss_key_tr_group.str() << ".patchsizex";
ss_key_dims_y << ss_key_tr_group.str() << ".patchsizey";
ss_key_tr_ph << ss_key_tr_group.str() << ".placeholder";
ss_key_val_ph << ss_key_val_group.str() << ".placeholder";
ss_key_val_ph << ss_key_val_group.str() << ".name";
// Parameter group descriptions
ss_desc_tr_in << "Input image (or list to stack) for source #" << inputNumber << " (training)";
ss_desc_val_in << "Input image (or list to stack) for source #" << inputNumber << " (validation)";
ss_desc_dims_x << "Field of view width for source #" << inputNumber;
ss_desc_dims_y << "Field of view height for source #" << inputNumber;
ss_desc_dims_x << "Patch size (x) for source #" << inputNumber;
ss_desc_dims_y << "Patch size (y) for source #" << inputNumber;
ss_desc_tr_ph << "Name of the input placeholder for source #" << inputNumber << " (training)";
ss_desc_val_ph << "Name of the input placeholder for source #" << inputNumber << " (validation)";
ss_desc_val_ph << "Name of the input placeholder "
"or output tensor for source #" << inputNumber << " (validation)";
// Populate group
AddParameter(ParameterType_Group, ss_key_tr_group.str(), ss_desc_tr_group.str());
......@@ -194,21 +195,25 @@ public:
AddParameter(ParameterType_StringList, "training.userplaceholders",
"Additional single-valued placeholders for training. Supported types: int, float, bool.");
MandatoryOff ("training.userplaceholders");
AddParameter(ParameterType_StringList, "training.targetnodesnames", "Names of the target nodes");
MandatoryOn ("training.targetnodesnames");
AddParameter(ParameterType_StringList, "training.outputtensorsnames", "Names of the output tensors to display");
MandatoryOff ("training.outputtensorsnames");
AddParameter(ParameterType_StringList, "training.targetnodes", "Names of the target nodes");
MandatoryOn ("training.targetnodes");
AddParameter(ParameterType_StringList, "training.outputtensors", "Names of the output tensors to display");
MandatoryOff ("training.outputtensors");
AddParameter(ParameterType_Bool, "training.usestreaming", "Use the streaming through patches (slower but can process big dataset)");
MandatoryOff ("training.usestreaming");
// Metrics
AddParameter(ParameterType_Group, "validation", "Validation parameters");
AddParameter(ParameterType_Group, "validation", "Validation parameters");
MandatoryOff ("validation");
AddParameter(ParameterType_Choice, "validation.mode", "Metrics to compute");
AddChoice ("validation.mode.none", "No validation step");
AddChoice ("validation.mode.class", "Classification metrics");
AddChoice ("validation.mode.rmse", "Root mean square error");
AddParameter(ParameterType_Choice, "validation.mode", "Metrics to compute");
AddChoice ("validation.mode.none", "No validation step");
AddChoice ("validation.mode.class", "Classification metrics");
AddChoice ("validation.mode.rmse", "Root mean square error");
AddParameter(ParameterType_StringList, "validation.userplaceholders",
"Additional single-valued placeholders for validation. Supported types: int, float, bool.");
MandatoryOff ("validation.userplaceholders");
AddParameter(ParameterType_Bool, "validation.usestreaming", "Use the streaming through patches (slower but can process big dataset)");
MandatoryOff ("validation.usestreaming");
// Input/output images
AddAnInputImage();
......@@ -220,15 +225,15 @@ public:
// Example
SetDocExampleParameterValue("source1.il", "spot6pms.tif");
SetDocExampleParameterValue("source1.placeholder", "x1");
SetDocExampleParameterValue("source1.fovx", "16");
SetDocExampleParameterValue("source1.fovy", "16");
SetDocExampleParameterValue("source1.patchsizex", "16");
SetDocExampleParameterValue("source1.patchsizey", "16");
SetDocExampleParameterValue("source2.il", "labels.tif");
SetDocExampleParameterValue("source2.placeholder", "y1");
SetDocExampleParameterValue("source2.fovx", "1");
SetDocExampleParameterValue("source2.fovy", "1");
SetDocExampleParameterValue("source2.patchsizex", "1");
SetDocExampleParameterValue("source2.patchsizex", "1");
SetDocExampleParameterValue("model.dir", "/tmp/my_saved_model/");
SetDocExampleParameterValue("training.userplaceholders", "is_training=true dropout=0.2");
SetDocExampleParameterValue("training.targetnodenames", "optimizer");
SetDocExampleParameterValue("training.targetnodes", "optimizer");
SetDocExampleParameterValue("model.saveto", "/tmp/my_saved_model_vars1");
}
......@@ -240,10 +245,10 @@ public:
// -Placeholders
// -PatchSize
// -ImageSource
// 2.Validation/Test
// 2.Learning/Validation
// -Placeholders (if input) or Tensor name (if target)
// -PatchSize (which is the same as for training)
// -ImageSource (depending if it's for test or validation)
// -ImageSource (depending if it's for learning or validation)
//
// TODO: a bit of refactoring. We could simply rely on m_Bundles
// if we can keep trace of indices of sources for
......@@ -262,12 +267,12 @@ public:
// Clear bundles
m_InputSourcesForTraining.clear();
m_InputSourcesForTest.clear();
m_InputSourcesForValidation.clear();
m_InputSourcesForEvaluationAgainstLearningData.clear();
m_InputSourcesForEvaluationAgainstValidationData.clear();
m_TargetTensorsNames.clear();
m_InputTargetsForValidation.clear();
m_InputTargetsForTest.clear();
m_InputTargetsForEvaluationAgainstValidationData.clear();
m_InputTargetsForEvaluationAgainstLearningData.clear();
// Prepare the bundles
......@@ -289,7 +294,7 @@ public:
m_InputPatchesSizeForTraining.push_back(patchSize);
otbAppLogINFO("New source:");
otbAppLogINFO("Field of view : "<< patchSize);
otbAppLogINFO("Patch size : "<< patchSize);
otbAppLogINFO("Placeholder (training) : "<< placeholderForTraining);
// Prepare validation sources
......@@ -314,8 +319,8 @@ public:
if (placeholderForValidation.compare(placeholderForTraining) == 0)
{
// Source
m_InputSourcesForValidation.push_back(bundle.tfSourceForValidation.Get());
m_InputSourcesForTest.push_back(bundle.tfSource.Get());
m_InputSourcesForEvaluationAgainstValidationData.push_back(bundle.tfSourceForValidation.Get());
m_InputSourcesForEvaluationAgainstLearningData.push_back(bundle.tfSource.Get());
// Placeholder
m_InputPlaceholdersForValidation.push_back(placeholderForValidation);
......@@ -330,8 +335,8 @@ public:
else
{
// Source
m_InputTargetsForValidation.push_back(bundle.tfSourceForValidation.Get());
m_InputTargetsForTest.push_back(bundle.tfSource.Get());
m_InputTargetsForEvaluationAgainstValidationData.push_back(bundle.tfSourceForValidation.Get());
m_InputTargetsForEvaluationAgainstLearningData.push_back(bundle.tfSource.Get());
// Placeholder
m_TargetTensorsNames.push_back(placeholderForValidation);
......@@ -350,13 +355,13 @@ public:
//
// Get user placeholders
//
TrainModelFilterType::DictListType GetUserPlaceholders(const std::string key)
TrainModelFilterType::DictType GetUserPlaceholders(const std::string key)
{
TrainModelFilterType::DictListType dict;
TrainModelFilterType::DictType dict;
TrainModelFilterType::StringList expressions = GetParameterStringList(key);
for (auto& exp: expressions)
{
TrainModelFilterType::DictType entry = tf::ExpressionToTensor(exp);
TrainModelFilterType::DictElementType entry = tf::ExpressionToTensor(exp);
dict.push_back(entry);
otbAppLogINFO("Using placeholder " << entry.first << " with " << tf::PrintTensorInfos(entry.second));
......@@ -414,16 +419,19 @@ public:
m_TrainModelFilter = TrainModelFilterType::New();
m_TrainModelFilter->SetGraph(m_SavedModel.meta_graph_def.graph_def());
m_TrainModelFilter->SetSession(m_SavedModel.session.get());
m_TrainModelFilter->SetOutputTensorsNames(GetParameterStringList("training.outputtensorsnames"));
m_TrainModelFilter->SetTargetNodesNames(GetParameterStringList("training.targetnodesnames"));
m_TrainModelFilter->SetOutputTensors(GetParameterStringList("training.outputtensors"));
m_TrainModelFilter->SetTargetNodesNames(GetParameterStringList("training.targetnodes"));
m_TrainModelFilter->SetBatchSize(GetParameterInt("training.batchsize"));
m_TrainModelFilter->SetUserPlaceholders(GetUserPlaceholders("training.userplaceholders"));
m_TrainModelFilter->SetUseStreaming(GetParameterInt("training.usestreaming"));
// Set input bundles
// Set inputs
for (unsigned int i = 0 ; i < m_InputSourcesForTraining.size() ; i++)
{
m_TrainModelFilter->PushBackInputBundle(m_InputPlaceholdersForTraining[i],
m_InputPatchesSizeForTraining[i], m_InputSourcesForTraining[i]);
m_TrainModelFilter->PushBackInputTensorBundle(
m_InputPlaceholdersForTraining[i],
m_InputPatchesSizeForTraining[i],
m_InputSourcesForTraining[i]);
}
// Train the model
......@@ -449,55 +457,55 @@ public:
m_ValidateModelFilter = ValidateModelFilterType::New();
m_ValidateModelFilter->SetGraph(m_SavedModel.meta_graph_def.graph_def());
m_ValidateModelFilter->SetSession(m_SavedModel.session.get());
m_ValidateModelFilter->SetOutputTensorsNames(m_TargetTensorsNames);
m_ValidateModelFilter->SetBatchSize(GetParameterInt("training.batchsize"));
m_ValidateModelFilter->SetUserPlaceholders(GetUserPlaceholders("validation.userplaceholders"));
// Evaluate the metrics against the learning data (test)
for (unsigned int i = 0 ; i < m_InputSourcesForTest.size() ; i++)
{
m_ValidateModelFilter->PushBackInputBundle(m_InputPlaceholdersForValidation[i],
m_InputPatchesSizeForValidation[i], m_InputSourcesForTest[i]);
}
for (unsigned int i = 0 ; i < m_TargetTensorsNames.size() ; i++)
// AS we use the learning data here, it's rational to use the same option as streaming during training
m_ValidateModelFilter->SetUseStreaming(GetParameterInt("training.usestreaming"));
// 1. Evaluate the metrics against the learning data
for (unsigned int i = 0 ; i < m_InputSourcesForEvaluationAgainstLearningData.size() ; i++)
{
m_ValidateModelFilter->PushBackInputReference(m_InputTargetsForTest[i], m_TargetPatchesSize[i]);
m_ValidateModelFilter->PushBackInputTensorBundle(
m_InputPlaceholdersForValidation[i],
m_InputPatchesSizeForValidation[i],
m_InputSourcesForEvaluationAgainstLearningData[i]);
}
m_ValidateModelFilter->SetOutputTensors(m_TargetTensorsNames);
m_ValidateModelFilter->SetInputReferences(m_InputTargetsForEvaluationAgainstLearningData);
m_ValidateModelFilter->SetOutputExpressionFields(m_TargetPatchesSize);
// Evaluate the model (test)
AddProcess(m_ValidateModelFilter, "Evaluate model (Test)");
// Update
AddProcess(m_ValidateModelFilter, "Evaluate model (Learning data)");
m_ValidateModelFilter->Update();
// Print some metrics
for (unsigned int i = 0 ; i < m_TargetTensorsNames.size() ; i++)
{
otbAppLogINFO("Metrics for target \"" << m_TargetTensorsNames[i] << "\":");
PrintClassificationMetrics(m_ValidateModelFilter->GetConfusionMatrix(i), m_ValidateModelFilter->GetMapOfClasses(i));
}
// Evaluate the metrics against the validation data
for (unsigned int i = 0 ; i < m_InputSourcesForValidation.size() ; i++)
{
m_ValidateModelFilter->SetInput(i, m_InputSourcesForValidation[i]);
}
m_ValidateModelFilter->ClearInputReferences();
for (unsigned int i = 0 ; i < m_TargetTensorsNames.size() ; i++)
// 2. Evaluate the metrics against the validation data
// Here we just change the input sources and references
for (unsigned int i = 0 ; i < m_InputSourcesForEvaluationAgainstValidationData.size() ; i++)
{
m_ValidateModelFilter->PushBackInputReference(m_InputTargetsForValidation[i], m_TargetPatchesSize[i]);
m_ValidateModelFilter->SetInput(i, m_InputSourcesForEvaluationAgainstValidationData[i]);
}
m_ValidateModelFilter->SetInputReferences(m_InputTargetsForEvaluationAgainstValidationData);
m_ValidateModelFilter->SetUseStreaming(GetParameterInt("validation.usestreaming"));
// Evaluate the model (validation)
AddProcess(m_ValidateModelFilter, "Evaluate model (Validation)");
// Update
AddProcess(m_ValidateModelFilter, "Evaluate model (Validation data)");
m_ValidateModelFilter->Update();
// Print some metrics
for (unsigned int i = 0 ; i < m_TargetTensorsNames.size() ; i++)
{
otbAppLogINFO("Metrics for target \"" << m_TargetTensorsNames[i] << "\":");
PrintClassificationMetrics(m_ValidateModelFilter->GetConfusionMatrix(i), m_ValidateModelFilter->GetMapOfClasses(i));
}
}
else if (GetParameterInt("validation.mode")==2) // rmse)
{
......@@ -511,22 +519,31 @@ public:
private:
tensorflow::SavedModelBundle m_SavedModel; // must be alive during all the execution of the application !
// Filters
TrainModelFilterType::Pointer m_TrainModelFilter;
ValidateModelFilterType::Pointer m_ValidateModelFilter;
tensorflow::SavedModelBundle m_SavedModel; // must be alive during all the execution of the application !
// Inputs
BundleList m_Bundles;
// Patches size
SizeList m_InputPatchesSizeForTraining;
SizeList m_InputPatchesSizeForValidation;
SizeList m_TargetPatchesSize;
// Placeholders and Tensors names
StringList m_InputPlaceholdersForTraining;
StringList m_InputPlaceholdersForValidation;
StringList m_TargetTensorsNames;
// Image sources
std::vector<FloatVectorImageType::Pointer> m_InputSourcesForTraining;
std::vector<FloatVectorImageType::Pointer> m_InputSourcesForTest;
std::vector<FloatVectorImageType::Pointer> m_InputTargetsForTest;
std::vector<FloatVectorImageType::Pointer> m_InputSourcesForValidation;
std::vector<FloatVectorImageType::Pointer> m_InputTargetsForValidation;
std::vector<FloatVectorImageType::Pointer> m_InputSourcesForEvaluationAgainstLearningData;
std::vector<FloatVectorImageType::Pointer> m_InputSourcesForEvaluationAgainstValidationData;
std::vector<FloatVectorImageType::Pointer> m_InputTargetsForEvaluationAgainstLearningData;
std::vector<FloatVectorImageType::Pointer> m_InputTargetsForEvaluationAgainstValidationData;
}; // end of class
......
app/otbTrainClassifierFromDeepFeatures.cxx
View file @ 70004b8e
......@@ -63,7 +63,6 @@ private:
}
void DoInit()
{
......@@ -91,7 +90,7 @@ private:
}
ShareParameter("model", "tfmodel.model", "Deep net model parameters", "Deep net model parameters");
ShareParameter("output", "tfmodel.output", "Deep net outputs parameters", "Deep net outputs parameters");
ShareParameter("finetuning", "tfmodel.finetuning", "Deep net fine tuning parameters", "Deep net fine tuning parameters");
ShareParameter("optim", "tfmodel.optim", "This group of parameters allows optimization of processing time", "This group of parameters allows optimization of processing time");
// Train shared parameters
ShareParameter("vd" , "train.io.vd" , "Input vector data list" , "Input vector data list" );
......
include/otbTensorflowGraphOperations.cxx
View file @ 70004b8e
......@@ -105,7 +105,9 @@ void GetTensorAttributes(const tensorflow::GraphDef & graph, std::vector<std::st
if (node.name().compare((*nameIt)) == 0)
{
found = true;
tensorflow::DataType ts_dt;
// Set default to DT_FLOAT
tensorflow::DataType ts_dt = tensorflow::DT_FLOAT;
// Default (input?) tensor type
auto test_is_output = node.attr().find("T");
......
include/otbTensorflowMultisourceModelBase.h
View file @ 70004b8e
......@@ -30,12 +30,36 @@ namespace otb
/**
* \class TensorflowMultisourceModelBase
* \brief This filter is base for TensorFlow model over multiple input images.
* \brief This filter is the base class for all TensorFlow model filters.
*
* The filter takes N input images and feed the TensorFlow model.
* Names of input placeholders must be specified using the
* SetInputPlaceholdersNames method
* This abstract class implements a number of generic methods that are used in
* filters that use the TensorFlow engine.
*
* The filter has N input images (Input), each one corresponding to a placeholder
* that will fed the TensorFlow model. For each input, the name of the
* placeholder (InputPlaceholders, a std::vector of std::string) and the
* receptive field (InputReceptiveFields, a std::vector of SizeType) i.e. the
* input space that the model will "see", must be provided. Hence the number of
* input images, and the size of InputPlaceholders and InputReceptiveFields must
* be the same. If not, an exception will be thrown during the method
* GenerateOutputInformation().
*
* The TensorFlow graph and session must be set using the SetGraph() and
* SetSession() methods.
*
* Target nodes names of the TensorFlow graph that must be triggered can be set
* with the SetTargetNodesNames.
*
* The OutputTensorNames consists in a strd::vector of std::string, and
* corresponds to the names of tensors that will be computed during the session.
* As for input placeholders, output tensors field of expression
* (OutputExpressionFields, a std::vector of SizeType), i.e. the output
* space that the TensorFlow model will "generate", must be provided.
*
* Finally, a list of scalar placeholders can be fed in the form of std::vector
* of std::string, each one expressing the assigment of a signle valued
* placeholder, e.g. "drop_rate=0.5 learning_rate=0.002 toto=true".
* See otb::tf::ExpressionToTensor() to know more about syntax.
*
* \ingroup OTBTensorflow
*/
......@@ -69,10 +93,10 @@ public:
typedef typename TInputImage::RegionType RegionType;
/** Typedefs for parameters */
typedef std::pair<std::string, tensorflow::Tensor> DictType;
typedef std::pair<std::string, tensorflow::Tensor> DictElementType;
typedef std::vector<std::string> StringList;
typedef std::vector<SizeType> SizeListType;
typedef std::vector<DictType> DictListType;
typedef std::vector<DictElementType> DictType;
typedef std::vector<tensorflow::DataType> DataTypeListType;
typedef std::vector<tensorflow::TensorShapeProto> TensorShapeProtoList;
typedef std::vector<tensorflow::Tensor> TensorListType;
......@@ -84,15 +108,30 @@ public:
tensorflow::Session * GetSession() { return m_Session; }
/** Model parameters */
void PushBackInputBundle(std::string placeholder, SizeType fieldOfView, ImagePointerType image);
itkSetMacro(InputPlaceholdersNames, StringList);
itkGetMacro(InputPlaceholdersNames, StringList);
itkSetMacro(InputFOVSizes, SizeListType);
itkGetMacro(InputFOVSizes, SizeListType);
void SetUserPlaceholders(DictListType dict) { m_UserPlaceholders = dict; }
DictListType GetUserPlaceholders() { return m_UserPlaceholders; }
itkSetMacro(OutputTensorsNames, StringList);
itkGetMacro(OutputTensorsNames, StringList);
void PushBackInputTensorBundle(std::string name, SizeType receptiveField, ImagePointerType image);
void PushBackOuputTensorBundle(std::string name, SizeType expressionField);
/** Input placeholders names */
itkSetMacro(InputPlaceholders, StringList);
itkGetMacro(InputPlaceholders, StringList);
/** Receptive field */
itkSetMacro(InputReceptiveFields, SizeListType);
itkGetMacro(InputReceptiveFields, SizeListType);
/** Output tensors names */
itkSetMacro(OutputTensors, StringList);
itkGetMacro(OutputTensors, StringList);
/** Expression field */
itkSetMacro(OutputExpressionFields, SizeListType);
itkGetMacro(OutputExpressionFields, SizeListType);
/** User placeholders */
void SetUserPlaceholders(DictType dict) { m_UserPlaceholders = dict; }
DictType GetUserPlaceholders() { return m_UserPlaceholders; }
/** Target nodes names */
itkSetMacro(TargetNodesNames, StringList);
itkGetMacro(TargetNodesNames, StringList);
......@@ -108,24 +147,27 @@ protected:
TensorflowMultisourceModelBase();
virtual ~TensorflowMultisourceModelBase() {};
virtual void RunSession(DictListType & inputs, TensorListType & outputs);
virtual std::stringstream GenerateDebugReport(DictType & inputs);
virtual void RunSession(DictType & inputs, TensorListType & outputs);
private:
TensorflowMultisourceModelBase(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
// Tensorflow graph and session
tensorflow::GraphDef m_Graph; // The tensorflow graph
tensorflow::Session * m_Session; // The tensorflow session
tensorflow::GraphDef m_Graph; // The TensorFlow graph
tensorflow::Session * m_Session; // The TensorFlow session
// Model parameters
StringList m_InputPlaceholdersNames; // Input placeholders names
SizeListType m_InputFOVSizes; // Input tensors field of view (FOV) sizes
DictListType m_UserPlaceholders; // User placeholders
StringList m_OutputTensorsNames; // User tensors
StringList m_TargetNodesNames; // User target tensors
// Read-only
StringList m_InputPlaceholders; // Input placeholders names
SizeListType m_InputReceptiveFields; // Input receptive fields
StringList m_OutputTensors; // Output tensors names
SizeListType m_OutputExpressionFields; // Output expression fields
DictType m_UserPlaceholders; // User placeholders
StringList m_TargetNodesNames; // User nodes target
// Internal, read-only
DataTypeListType m_InputTensorsDataTypes; // Input tensors datatype
DataTypeListType m_OutputTensorsDataTypes; // Output tensors datatype
TensorShapeProtoList m_InputTensorsShapes; // Input tensors shapes
......
include/otbTensorflowMultisourceModelBase.hxx
View file @ 70004b8e
......@@ -20,22 +20,57 @@ template <class TInputImage, class TOutputImage>
TensorflowMultisourceModelBase<TInputImage, TOutputImage>
::TensorflowMultisourceModelBase()
{
m_Session = nullptr;
}
template <class TInputImage, class TOutputImage>
void
TensorflowMultisourceModelBase<TInputImage, TOutputImage>
::PushBackInputBundle(std::string placeholder, SizeType fieldOfView, ImagePointerType image)