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remi cresson authorededc29d06
/*=========================================================================
Copyright (c) Remi Cresson (IRSTEA). All rights reserved.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#include "itkFixedArray.h"
#include "itkObjectFactory.h"
#include "otbWrapperApplicationFactory.h"
// Application engine
#include "otbStandardFilterWatcher.h"
#include "itkFixedArray.h"
// Tensorflow stuff
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
// Tensorflow model filter
#include "otbTensorflowMultisourceModelFilter.h"
// Tensorflow graph load
#include "otbTensorflowGraphOperations.h"
// Layerstack
#include "otbTensorflowSource.h"
// Streaming
#include "otbTensorflowStreamerFilter.h"
namespace otb
{
namespace Wrapper
{
class TensorflowModelServe : public Application
{
public:
/** Standard class typedefs. */
typedef TensorflowModelServe Self;
typedef Application Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Standard macro */
itkNewMacro(Self);
itkTypeMacro(TensorflowModelServe, Application);
/** Typedefs for tensorflow */
typedef otb::TensorflowMultisourceModelFilter<FloatVectorImageType, FloatVectorImageType> TFModelFilterType;
typedef otb::TensorflowSource<FloatVectorImageType> InputImageSource;
/** Typedef for streaming */
typedef otb::ImageRegionSquareTileSplitter<FloatVectorImageType::ImageDimension> TileSplitterType;
typedef otb::TensorflowStreamerFilter<FloatVectorImageType, FloatVectorImageType> StreamingFilterType;
/** Typedefs for images */
typedef FloatVectorImageType::SizeType SizeType;
void DoUpdateParameters()
{
}
//
// Store stuff related to one source
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//
struct ProcessObjectsBundle
{
InputImageSource m_ImageSource;
SizeType m_PatchSize;
std::string m_Placeholder;
// Parameters keys
std::string m_KeyIn; // Key of input image list
std::string m_KeyPszX; // Key for samples sizes X
std::string m_KeyPszY; // Key for samples sizes Y
std::string m_KeyPHName; // Key for placeholder name in the tensorflow model
};
//
// Add an input source, which includes:
// -an input image list
// -an input patchsize (dimensions of samples)
//
void AddAnInputImage()
{
// Number of source
unsigned int inputNumber = m_Bundles.size() + 1;
// Create keys and descriptions
std::stringstream ss_key_group, ss_desc_group,
ss_key_in, ss_desc_in,
ss_key_dims_x, ss_desc_dims_x,
ss_key_dims_y, ss_desc_dims_y,
ss_key_ph, ss_desc_ph;
// Parameter group key/description
ss_key_group << "source" << inputNumber;
ss_desc_group << "Parameters for source #" << inputNumber;
// Parameter group keys
ss_key_in << ss_key_group.str() << ".il";
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 << "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
AddParameter(ParameterType_Group, ss_key_group.str(), ss_desc_group.str());
AddParameter(ParameterType_InputImageList, ss_key_in.str(), ss_desc_in.str() );
AddParameter(ParameterType_Int, ss_key_dims_x.str(), ss_desc_dims_x.str());
SetMinimumParameterIntValue (ss_key_dims_x.str(), 1);
AddParameter(ParameterType_Int, ss_key_dims_y.str(), ss_desc_dims_y.str());
SetMinimumParameterIntValue (ss_key_dims_y.str(), 1);
AddParameter(ParameterType_String, ss_key_ph.str(), ss_desc_ph.str());
// Add a new bundle
ProcessObjectsBundle bundle;
bundle.m_KeyIn = ss_key_in.str();
bundle.m_KeyPszX = ss_key_dims_x.str();
bundle.m_KeyPszY = ss_key_dims_y.str();
bundle.m_KeyPHName = ss_key_ph.str();
m_Bundles.push_back(bundle);
}
void DoInit()
{
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// Documentation
SetName("TensorflowModelServe");
SetDescription("Multisource deep learning classifier using TensorFlow. Change the "
+ tf::ENV_VAR_NAME_NSOURCES + " environment variable to set the number of sources.");
SetDocLongDescription("The application run a TensorFlow model over multiple data sources. "
"The number of input sources can be changed at runtime by setting the system "
"environment variable " + tf::ENV_VAR_NAME_NSOURCES + ". For each source, you have to "
"set (1) the placeholder name, as named in the TensorFlow model, (2) the receptive "
"field and (3) the image(s) source. The output is a multiband image, stacking all "
"outputs tensors together: you have to specify (1) the names of the output tensors, as "
"named in the TensorFlow model (typically, an operator's output) and (2) the expression "
"field of each output tensor. The output tensors values will be stacked in the same "
"order as they appear in the \"model.output\" parameter (you can use a space separator "
"between names). You might consider to use extended filename to bypass the automatic "
"memory footprint calculator of the otb application engine, and set a good splitting "
"strategy (Square tiles is good for convolutional networks) or use the \"optim\" "
"parameter group to impose your squared tiles sizes");
SetDocAuthors("Remi Cresson");
// Input/output images
AddAnInputImage();
for (int i = 1; i < tf::GetNumberOfSources() ; i++)
AddAnInputImage();
// Input model
AddParameter(ParameterType_Group, "model", "model parameters");
AddParameter(ParameterType_Directory, "model.dir", "TensorFlow model_save directory");
MandatoryOn ("model.dir");
SetParameterDescription ("model.dir", "The model directory should contains the model Google Protobuf (.pb) and variables");
AddParameter(ParameterType_StringList, "model.userplaceholders", "Additional single-valued placeholders. Supported types: int, float, bool.");
MandatoryOff ("model.userplaceholders");
SetParameterDescription ("model.userplaceholders", "Syntax to use is \"placeholder_1=value_1 ... placeholder_N=value_N\"");
AddParameter(ParameterType_Bool, "model.fullyconv", "Fully convolutional");
MandatoryOff ("model.fullyconv");
// Output tensors parameters
AddParameter(ParameterType_Group, "output", "Output tensors parameters");
AddParameter(ParameterType_Float, "output.spcscale", "The output spacing scale, related to the first input");
SetDefaultParameterFloat ("output.spcscale", 1.0);
SetParameterDescription ("output.spcscale", "The output image size/scale and spacing*scale where size and spacing corresponds to the first input");
AddParameter(ParameterType_StringList, "output.names", "Names of the output tensors");
MandatoryOn ("output.names");
// Output Field of Expression
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, "optim" , "This group of parameters allows optimization of processing time");
AddParameter(ParameterType_Bool, "optim.disabletiling", "Disable tiling");
MandatoryOff ("optim.disabletiling");
SetParameterDescription ("optim.disabletiling", "Tiling avoids to process a too large subset of image, but sometimes it can be useful to disable it");
AddParameter(ParameterType_Int, "optim.tilesizex", "Tile width used to stream the filter output");
SetMinimumParameterIntValue ("optim.tilesizex", 1);
SetDefaultParameterInt ("optim.tilesizex", 16);
AddParameter(ParameterType_Int, "optim.tilesizey", "Tile height used to stream the filter output");
SetMinimumParameterIntValue ("optim.tilesizey", 1);
SetDefaultParameterInt ("optim.tilesizey", 16);
// Output image
AddParameter(ParameterType_OutputImage, "out", "output image");
// Example