index.html 26.34 KiB
<!doctype html>
<html>
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
  <meta name="apple-mobile-web-app-capable" content="yes">
  <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
  <title>OTBTF, The Orfeo ToolBox extension for deep learning</title>
  <link rel="stylesheet" href="revealjs/dist/reset.css">
  <link rel="stylesheet" href="revealjs/dist/reveal.css">
  <link rel="stylesheet" href="a11y-light.css">
  <link rel="stylesheet" href="otb.css" id="theme">
  <style type="text/css">
    .reveal pre {
      width: 512px
    .slide-number {
      opacity:0
  </style>
</head>
<body>
  <div class="reveal">
    <div class="slides">
      <!------------------------------------------------------------------------
        PREMIER SLIDE
        ------------------------------------------------------------------------->
      <section data-background="illustrations/blank.png" background-size="contain">
        <h1> Status of OTBTF </h1>
        <h2> The Orfeo ToolBox extension for deep learning </h2>
        </br>
        <p> Rémi Cresson<sup>1</sup>, Nicolas Narçon<sup>1</sup>, Vincent Delbar<sup>2</sup></p>
        <small>(1) French National Research Institute for Agriculture, Food and the Environment (INRAE),
          <br>
           (2)
          LaTeleScop</small>
        <br>
        <br>
        <br>
        <br>
        <img width="30%" data-src="illustrations/foss4g_logo.png">
      </section>
      <!------------------------------------------------------------------------
        WHAT IS OTBTF
        ------------------------------------------------------------------------->
      <section>
        <section>
          <h1>What is OTBTF?</h1>
        </section>
        <section>
          <h2>In short</h2>
          <ul>
            <li><h>Generic</h> framework for deep learning on rasters</li>
            <li>Developped at INRAE for <h>research</h>, <h>education</h> and <h>production</h>
            </li>
            <li>Use <h>deep learning</h> techniques on geospatial images</li>
            <ul>
              <li>Create datasets (samples selection, patches extraction)</li>
              <li>Train models (CLI, Python)</li>
              <li>Apply models in OTB applications</li>
            </ul>
          </ul>
          <br>
7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
<br> <img width="15%" data-src="illustrations/logo.png"> </section> <section> <h2>Improvements over the years</h2> <ol> <li><h>Documentation</h></li> <li><h>Docker</h> builds for CPU/GPU</li> <li><h>CI/CD</h></li> <li><h>TensorFlow 2</h> support</li> <li><h>Python classes</h> to build/train models</li> </ol> <br> <br> <img width="45%" data-src="illustrations/ci.jpg"> <p><small>https://gitlab.irstea.fr/remi.cresson/otbtf</small></p> </section> <section> <h2>Repository and docker images</h2> <img width="5%" data-src="illustrations/repos_git.jpg" style="float:left;padding-left:20%"> <pre style="width:800px"><code data-trim class="bash"> git clone https://github.com/remicres/otbtf.git </pre></code> <img width="4%" data-src="illustrations/repos_docker.jpg" style="float:left;padding-left:20%"> <pre style="width:800px"><code data-trim class="bash" > docker pull mdl4eo/otbtf:3.3.0-cpu docker pull mdl4eo/otbtf:3.3.0-gpu # GPU enabled </pre></code> </section> </section> <!------------------------------------------------------------------------ WHAT FOR -------------------------------------------------------------------------> <section> <section> <h1>What for</h1> </section> <section> <img width="40%" data-src="illustrations/ensembles.png"> </section> <section> <h2>Tensorflow computational graphs</h2> <img width="17.5%" data-src="illustrations/computational_graph.gif"> <p><small>Source: the TensorFlow website</small></p> </section> <section> <h3>Example: scalar product</h3> <br> <img width="20%" data-src="illustrations/graph_1.png"> <br> <img width="48px" data-src="illustrations/python.png" style="float:left;padding-left:15%;margin:30px"> <pre style="width:1050px"><code data-trim class="python"> import tensorflow as tf x1 = tf.keras.Input(shape=[None, None, None], name="x1") x2 = tf.keras.Input(shape=[None, None, None], name="x2") # Scalar product
141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210
y = tf.reduce_sum(tf.multiply(x1, x2), axis=-1) # Create model model = tf.keras.Model(inputs={"x1": x1, "x2": x2}, outputs={"y": y}) model.save("/tmp/my_savedmodel") </code></pre> <img width="48px" data-src="illustrations/cli.png" style="float:left;padding-left:15%;margin:30px"> <pre style="width:1050px"><code data-trim class="bash"> export OTB_TF_NSOURCES=2 otbcli_TensorflowModelServe \ -source1.il "input_img_1.tif" -source2.il "input_img_2.tif" \ -model.dir "/tmp/my_savedmodel" -model.fullyconv on \ -out "output.tif" </code></pre> </section> <section> <h2>Deep learning</h2> <h3><strike>Bridging the gap between deep learning and EO</strike></h3> <h3>Bridging the gap between litterature and real life </h3> <img width="40%" data-src="illustrations/listof.jpg"> <p><small>Made with imgflip.com</small></p> </section> </section> <!------------------------------------------------------------------------ FEATURES -------------------------------------------------------------------------> <section> <section> <h1>Features</h1> </section> <section> <h2>Out of the box</h2> <ul> <li> Additional <h>applications</h> for the Orfeo ToolBox <ul> <li>For users (GIS, teaching)</li> <li>For production</li> </ul> </li> <li> <h>Python API</h> (<y>NEW!</y>) <ul> <li>Dedicated to model training</li> <li>For data scientists</li> <li>To go large scale with distributed training</li> </ul> </li> </ul> <br><br> </section> <section> <h2>OTB Applications</h2> <ul> <li> <h>TensorflowModelServe</h>: Inference on real world remote sensing products </li> <li> <g>PatchesExtraction</g>: extract patches in images </li> <li> <h>PatchesSelection</h>: for patches selection from rasters
211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280
</li> <li> <g>TrainClassifierFromDeepFeatures</g>: train traditionnal classifiers that use features from deep nets </li> <li> <g>ImageClassifierFromDeepFeatures</g>: use traditionnal classifiers with features from deep nets </li> <li> <g>LabelImageSampleSelection</g>: select patches from a label image </li> <li> <g>DensePolygonClassStatistics</g>: fast terrain truth polygons statistics </li> <li> <g>TensorflowModelTrain</g>: training/validation (educational purpose) </li> </ul> <br><br> </section> <section> <h3>TensorflowModelServe</h3> <h4>Streamable inference: a key feature to go large scale</h4> <img width="60%" data-src="illustrations/pipeline.png"> <p><small>Typical pipeline for inference in production</small></p> </section> <section> <h3>Patches extraction</h3> <h4>CLI example</h4> <img width="48px" data-src="illustrations/cli.png" style="float:left;padding-left:22%;margin:30px"> <pre style="width:800px"><code data-trim class="bash"> export OTB_TF_NSOURCES=3 # Number of sources otbcli_PatchesExtraction -vec "myvec.gpkg" \ -source1.patchsizex 16 -source1.patchsizey 16 \ -source1.il "raster_x.tif" -source1.out "x1.tif" \ -source2.patchsizex 64 -source2.patchsizey 64 \ -source2.il "raster_y.tif" -source2.out "y1.tif" \ -source3.patchsizex 64 -source3.patchsizey 64 \ -source3.il "raster_z.tif" -source3.out "z1.tif" </code></pre> <img width="60%" data-src="illustrations/patches2.png"> <p><small>Patches are stacked in rows and stored in well known raster formats</small></p> </section> <section> <h2>Python API</h2> <p>Say that you have generated some patches images with PatchesExtraction:</p> <img width="40%" data-src="illustrations/patches2.png"> <br> <p>The <g>otbtf.DatasetFromPatchesImages</g> creates a dataset ready to use in TF/Keras:</p> <img width="48px" data-src="illustrations/python.png" style="float:left;padding-left:20%;margin:30px"> <pre style="width:900px"><code data-trim class="python"> import otbtf files = {"x": ["x1.tif", ..., "xN.tif"], "y": ["y1.tif", ..., "yN.tif"], "z": ["z1.tif", ..., "zN.tif"]} ds = otbtf.DatasetFromPatchesImages(filenames_dict=files) # This is a TensorFlow dataset tf_ds = ds.get_tf_dataset(batch_size=8) </code></pre> </section> <section> <h3>TFRecords</h3> <p>Any <g>otbtf.Dataset</g> can be exported in the <g>TFRecords</g> format:</p> <pre style="width:800px"><code data-trim class="python">