notebooks/imagesegmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image segmentation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_In the [quickstart section](./quickstart.ipynb), you saw a short example of how to train an image segmentation model. In this tutorial, we'll recreate that using the mid-level APIs._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "using FastAI, Metalhead\n",
    "import CairoMakie; CairoMakie.activate!(type=\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In image segmentation, instead of assigning a class to a whole image as in [image classification](../methods/imageclassification.md), we want to classify each pixel of an image. We'll use the CamVid dataset which contains street images with every pixel annotated as one of 32 classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inside the dataset folder, we find `images/`, a subfolder with all the input images, `labels/`, a subfolder containing the segmentation masks (saved as images) and `codes.txt` which contains the class names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Vector{String}:\n",
       " \"codes.txt\"\n",
       " \"images\"\n",
       " \"labels\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir = datasetpath(\"camvid_tiny\")\n",
    "readdir(dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every line in `codes.txt` corresponds to one class, so let's load it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32-element Vector{String}:\n",
       " \"Animal\"\n",
       " \"Archway\"\n",
       " \"Bicyclist\"\n",
       " \"Bridge\"\n",
       " \"Building\"\n",
       " \"Car\"\n",
       " \"CartLuggagePram\"\n",
       " \"Child\"\n",
       " \"Column_Pole\"\n",
       " \"Fence\"\n",
       " \"LaneMkgsDriv\"\n",
       " \"LaneMkgsNonDriv\"\n",
       " \"Misc_Text\"\n",
       " ⋮\n",
       " \"SignSymbol\"\n",
       " \"Sky\"\n",
       " \"SUVPickupTruck\"\n",
       " \"TrafficCone\"\n",
       " \"TrafficLight\"\n",
       " \"Train\"\n",
       " \"Tree\"\n",
       " \"Truck_Bus\"\n",
       " \"Tunnel\"\n",
       " \"VegetationMisc\"\n",
       " \"Void\"\n",
       " \"Wall\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = readlines(open(joinpath(dir, \"codes.txt\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create our data containers we'll use [`loadfolderdata`](#), which creates data containers from folder contents. The function `loadfn` is applied to the file paths and we use `loadfile` to `loadmask` to get images and masks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(mapobs(loadfile, [\"/home/lorenz/.julia/datadeps/fastai-camvid_tiny/camvid_tiny/images/0001TP_0067…]), mapobs(#1, [\"/home/lorenz/.julia/datadeps/fastai-camvid_tiny/camvid_tiny/labels/0001TP_0067…]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images = Datasets.loadfolderdata(\n",
    "    joinpath(dir, \"images\"),\n",
    "    filterfn=isimagefile,\n",
    "    loadfn=loadfile)\n",
    "\n",
    "masks = Datasets.loadfolderdata(\n",
    "    joinpath(dir, \"labels\"),\n",
    "    filterfn=isimagefile,\n",
    "    loadfn=f -> loadmask(f, classes))\n",
    "\n",
    "data = (images, masks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can get an observation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "image, mask = sample = getobs(data, 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "96×128 PermutedDimsArray(::Array{RGB{N0f8},2}, (2, 1)) with eltype ColorTypes.RGB{FixedPointNumbers.N0f8}:\n",
       "\u001b[0m\u001b[38;2;4;4;4;48;2;8;8;8m▀\u001b[38;2;7;7;7;48;2;15;15;15m▀\u001b[38;2;7;7;7;48;2;14;14;14m▀\u001b[38;2;8;8;8;48;2;16;16;16m▀\u001b[38;2;7;7;7;48;2;16;16;16m▀\u001b[38;2;7;7;7;48;2;15;15;15m▀\u001b[38;2;6;6;6;48;2;14;14;14m▀\u001b[38;2;12;12;12;48;2;16;17;16m▀\u001b[38;2;12;13;13;48;2;27;29;28m▀\u001b[38;2;19;21;21;48;2;39;44;44m▀\u001b[38;2;35;44;42;48;2;64;81;79m▀\u001b[38;2;9;11;10;48;2;18;20;20m▀\u001b[38;2;8;8;8;48;2;17;17;17m▀\u001b[38;2;9;9;9;48;2;18;19;19m▀\u001b[38;2;11;12;12;48;2;22;25;24m▀\u001b[38;2;25;28;27;48;2;48;54;52m▀\u001b[38;2;40;44;42;48;2;66;72;69m▀\u001b[38;2;43;47;46;48;2;86;94;91m▀\u001b[38;2;36;40;39;48;2;73;80;78m▀\u001b[38;2;40;45;43;48;2;83;91;88m▀\u001b[38;2;39;43;43;48;2;81;89;87m▀\u001b[38;2;37;41;41;48;2;74;81;81m▀\u001b[38;2;50;55;53;48;2;88;96;93m▀\u001b[38;2;76;89;92;48;2;143;165;170m▀\u001b[38;2;89;104;110;48;2;185;214;225m▀\u001b[38;2;90;104;108;48;2;190;218;226m▀\u001b[38;2;87;100;104;48;2;188;214;220m▀\u001b[38;2;81;94;99;48;2;174;199;207m▀\u001b[38;2;76;89;94;48;2;162;187;195m▀\u001b[38;2;77;89;94;48;2;159;184;192m▀\u001b[38;2;77;89;94;48;2;161;185;193m▀\u001b[38;2;79;91;95;48;2;165;188;195m▀\u001b[38;2;81;94;97;48;2;169;194;200m▀\u001b[38;2;82;94;98;48;2;171;195;203m▀\u001b[38;2;84;96;101;48;2;177;201;210m▀\u001b[38;2;87;100;104;48;2;184;209;218m▀\u001b[38;2;89;102;106;48;2;184;210;218m▀\u001b[38;2;90;102;107;48;2;181;207;216m▀\u001b[38;2;88;102;108;48;2;179;207;219m▀\u001b[38;2;84;97;103;48;2;177;204;214m▀\u001b[38;2;68;78;82;48;2;139;158;166m▀\u001b[38;2;21;26;28;48;2;41;50;52m▀\u001b[38;2;13;16;17;48;2;26;32;34m▀\u001b[38;2;14;17;17;48;2;28;34;36m▀\u001b[38;2;25;29;29;48;2;40;48;48m▀\u001b[38;2;97;102;101;48;2;154;166;163m▀\u001b[38;2;122;127;127;48;2;237;251;250m▀\u001b[38;2;121;128;127;48;2;237;255;253m▀\u001b[38;2;121;128;127;48;2;241;255;254m▀\u001b[38;2;119;128;127;48;2;241;255;254m▀\u001b[38;2;120;128;127;48;2;238;253;251m▀\u001b[38;2;125;128;127;48;2;236;251;248m▀\u001b[38;2;126;128;127;48;2;236;251;248m▀\u001b[38;2;124;128;127;48;2;231;248;246m▀\u001b[38;2;123;127;127;48;2;227;246;244m▀\u001b[38;2;122;127;127;48;2;225;245;243m▀\u001b[38;2;87;93;93;48;2;135;147;151m▀\u001b[38;2;22;27;26;48;2;30;35;38m▀\u001b[38;2;17;22;20;48;2;23;27;31m▀\u001b[38;2;21;27;23;48;2;24;29;31m▀\u001b[38;2;27;33;28;48;2;26;30;32m▀\u001b[38;2;34;39;32;48;2;28;32;32m▀\u001b[38;2;21;25;22;48;2;26;31;31m▀\u001b[38;2;13;16;16;48;2;27;35;35m▀\u001b[38;2;8;10;10;48;2;17;21;20m▀\u001b[0m\n",
       "\u001b[0m\u001b[38;2;9;10;10;48;2;26;29;29m▀\u001b[38;2;15;15;16;48;2;31;35;35m▀\u001b[38;2;15;14;14;48;2;17;17;17m▀\u001b[38;2;16;16;16;48;2;16;16;16m▀\u001b[38;2;16;16;16;48;2;15;15;15m▀\u001b[38;2;15;15;15;48;2;16;15;15m▀\u001b[38;2;15;14;14;48;2;15;15;15m▀\u001b[38;2;14;14;13;48;2;15;15;15m▀\u001b[38;2;18;18;17;48;2;16;16;16m▀\u001b[38;2;37;42;42;48;2;27;30;29m▀\u001b[38;2;58;74;73;48;2;43;55;54m▀\u001b[38;2;20;26;28;48;2;21;30;34m▀\u001b[38;2;19;23;25;48;2;15;20;23m▀\u001b[38;2;19;22;23;48;2;14;16;17m▀\u001b[38;2;23;26;27;48;2;16;19;21m▀\u001b[38;2;42;49;50;48;2;22;28;33m▀\u001b[38;2;52;58;57;48;2;47;55;57m▀\u001b[38;2;82;90;87;48;2;69;77;74m▀\u001b[38;2;71;78;76;48;2;72;79;77m▀\u001b[38;2;93;100;97;48;2;92;99;96m▀\u001b[38;2;88;96;94;48;2;84;92;90m▀\u001b[38;2;73;80;79;48;2;74;81;79m▀\u001b[38;2;76;83;80;48;2;72;79;77m▀\u001b[38;2;128;145;148;48;2;115;128;128m▀\u001b[38;2;182;210;222;48;2;180;206;216m▀\u001b[38;2;195;222;230;48;2;201;227;233m▀\u001b[38;2;201;227;233;48;2;216;240;240m▀\u001b[38;2;199;223;227;48;2;222;244;242m▀\u001b[38;2;188;212;216;48;2;219;240;238m▀\u001b[38;2;179;203;207;48;2;211;233;234m▀\u001b[38;2;176;199;205;48;2;200;225;229m▀\u001b[38;2;175;199;205;48;2;190;216;223m▀\u001b[38;2;178;203;209;48;2;183;210;220m▀\u001b[38;2;179;204;213;48;2;181;209;222m▀\u001b[38;2;188;214;223;48;2;182;211;224m▀\u001b[38;2;193;221;231;48;2;186;215;226m▀\u001b[38;2;188;216;227;48;2;188;216;227m▀\u001b[38;2;182;210;223;48;2;193;220;231m▀\u001b[38;2;193;220;231;48;2;208;234;240m▀\u001b[38;2;202;227;232;48;2;212;234;236m▀\u001b[38;2;142;161;165;48;2;109;122;124m▀\u001b[38;2;41;48;50;48;2;34;40;42m▀\u001b[38;2;27;32;34;48;2;29;35;37m▀\u001b[38;2;30;38;40;48;2;32;40;41m▀\u001b[38;2;33;41;43;48;2;33;42;44m▀\u001b[38;2;116;130;128;48;2;112;128;127m▀\u001b[38;2;220;242;238;48;2;211;234;231m▀\u001b[38;2;233;252;249;48;2;220;242;238m▀\u001b[38;2;230;248;245;48;2;207;229;227m▀\u001b[38;2;224;243;239;48;2;197;219;218m▀\u001b[38;2;212;233;230;48;2;191;214;213m▀\u001b[38;2;206;228;226;48;2;183;206;207m▀\u001b[38;2;203;226;224;48;2;178;202;205m▀\u001b[38;2;198;221;221;48;2;175;198;204m▀\u001b[38;2;194;219;220;48;2;172;196;203m▀\u001b[38;2;197;221;223;48;2;171;197;206m▀\u001b[38;2;117;129;138;48;2;102;116;128m▀\u001b[38;2;25;29;33;48;2;22;25;28m▀\u001b[38;2;20;24;27;48;2;17;20;22m▀\u001b[38;2;19;22;25;48;2;16;18;20m▀\u001b[38;2;18;21;24;48;2;19;24;26m▀\u001b[38;2;22;26;28;48;2;25;32;33m▀\u001b[38;2;27;34;35;48;2;30;38;39m▀\u001b[38;2;30;37;38;48;2;34;43;43m▀\u001b[38;2;18;21;21;48;2;18;22;22m▀\u001b[0m\n",
       "\u001b[0m\u001b[38;2;22;25;24;48;2;9;10;9m▀\u001b[38;2;51;59;56;48;2;33;37;35m▀\u001b[38;2;34;37;37;48;2;48;53;52m▀\u001b[38;2;16;16;16;48;2;22;24;24m▀\u001b[38;2;15;14;14;48;2;15;14;14m▀\u001b[38;2;16;16;16;48;2;16;16;16m▀\u001b[38;2;16;16;15;48;2;16;16;16m▀\u001b[38;2;15;15;15;48;2;15;15;15m▀\u001b[38;2;16;16;16;48;2;16;16;16m▀\u001b[38;2;17;17;17;48;2;16;15;15m▀\u001b[38;2;26;30;30;48;2;16;17;18m▀\u001b[38;2;22;28;33;48;2;19;24;29m▀\u001b[38;2;9;11;14;48;2;8;9;11m▀\u001b[38;2;7;8;8;48;2;7;7;7m▀\u001b[38;2;9;10;11;48;2;9;9;9m▀\u001b[38;2;15;18;23;48;2;15;18;22m▀\u001b[38;2;46;54;57;48;2;46;55;59m▀\u001b[38;2;49;55;54;48;2;39;45;45m▀\u001b[38;2;68;76;73;48;2;63;68;67m▀\u001b[38;2;89;97;94;48;2;90;99;96m▀\u001b[38;2;83;91;89;48;2;84;92;90m▀\u001b[38;2;73;81;80;48;2;67;75;74m▀\u001b[38;2;71;79;77;48;2;74;82;82m▀\u001b[38;2;102;111;109;48;2;92;100;98m▀\u001b[38;2;178;199;203;48;2;155;172;172m▀\u001b[38;2;207;230;231;48;2;194;216;217m▀\u001b[38;2;221;242;239;48;2;209;230;228m▀\u001b[38;2;229;250;245;48;2;217;239;234m▀\u001b[38;2;231;251;247;48;2;218;239;234m▀\u001b[38;2;233;252;249;48;2;224;242;239m▀\u001b[38;2;226;248;248;48;2;223;243;241m▀\u001b[38;2;203;230;236;48;2;195;220;224m▀\u001b[38;2;183;212;225;48;2;176;204;214m▀\u001b[38;2;179;209;225;48;2;177;204;215m▀\u001b[38;2;180;208;222;48;2;182;206;215m▀\u001b[38;2;184;209;218;48;2;185;208;214m▀\u001b[38;2;192;216;222;48;2;192;215;218m▀\u001b[38;2;208;232;236;48;2;204;228;232m▀\u001b[38;2;218;242;244;48;2;205;231;235m▀\u001b[38;2;216;237;237;48;2;192;216;218m▀\u001b[38;2;105;115;117;48;2;90;102;105m▀\u001b[38;2;30;36;37;48;2;32;38;40m▀\u001b[38;2;27;33;35;48;2;30;36;39m▀\u001b[38;2;28;35;35;48;2;30;36;38m▀\u001b[38;2;29;37;38;48;2;30;37;38m▀\u001b[38;2;93;107;105;48;2;73;84;85m▀\u001b[38;2;194;216;213;48;2;167;188;188m▀\u001b[38;2;206;229;227;48;2;192;214;215m▀\u001b[38;2;193;215;215;48;2;179;201;204m▀\u001b[38;2;181;203;205;48;2;166;188;192m▀\u001b[38;2;174;195;198;48;2;159;181;187m▀\u001b[38;2;168;190;195;48;2;156;179;187m▀\u001b[38;2;164;186;194;48;2;153;175;186m▀\u001b[38;2;158;182;192;48;2;150;173;185m▀\u001b[38;2;156;181;192;48;2;149;173;186m▀\u001b[38;2;157;181;193;48;2;153;176;188m▀\u001b[38;2;96;110;122;48;2;93;107;119m▀\u001b[38;2;22;25;29;48;2;21;25;29m▀\u001b[38;2;18;21;24;48;2;24;28;31m▀\u001b[38;2;21;25;28;48;2;27;34;36m▀\u001b[38;2;24;31;32;48;2;30;37;38m▀\u001b[38;2;28;36;36;48;2;37;46;48m▀\u001b[38;2;35;44;44;48;2;39;50;53m▀\u001b[38;2;35;45;45;48;2;36;46;46m▀\u001b[38;2;17;22;21;48;2;17;21;22m▀\u001b[0m\n",
       "\u001b[0m\u001b[38;2;4;4;4;48;2;3;3;3m▀\u001b[38;2;12;12;12;48;2;6;6;6m▀\u001b[38;2;22;24;23;48;2;8;8;8m▀\u001b[38;2;17;18;17;48;2;12;12;12m▀\u001b[38;2;15;15;15;48;2;16;16;16m▀\u001b[38;2;15;15;15;48;2;23;24;25m▀\u001b[38;2;16;16;16;48;2;17;18;18m▀\u001b[38;2;16;16;16;48;2;16;16;16m▀\u001b[38;2;16;16;16;48;2;16;16;16m▀\u001b[38;2;16;16;16;48;2;16;16;15m▀\u001b[38;2;16;17;18;48;2;17;18;18m▀\u001b[38;2;16;20;25;48;2;16;20;24m▀\u001b[38;2;8;9;10;48;2;8;9;9m▀\u001b[38;2;8;8;8;48;2;7;7;7m▀\u001b[38;2;8;8;9;48;2;8;8;9m▀\u001b[38;2;14;18;21;48;2;14;17;20m▀\u001b[38;2;39;47;50;48;2;18;24;27m▀\u001b[38;2;26;30;30;48;2;15;16;18m▀\u001b[38;2;45;49;47;48;2;34;38;38m▀\u001b[38;2;88;97;93;48;2;79;87;84m▀\u001b[38;2;83;91;89;48;2;84;92;89m▀\u001b[38;2;72;79;78;48;2;81;88;86m▀\u001b[38;2;75;83;82;48;2;72;81;81m▀\u001b[38;2;78;86;84;48;2;67;73;73m▀\u001b[38;2;121;135;136;48;2;105;116;115m▀\u001b[38;2;159;181;187;48;2;146;167;173m▀\u001b[38;2;174;196;199;48;2;152;174;181m▀\u001b[38;2;182;204;205;48;2;153;175;182m▀\u001b[38;2;181;204;205;48;2;154;175;182m▀\u001b[38;2;179;201;203;48;2;154;175;183m▀\u001b[38;2;180;203;205;48;2;153;175;183m▀\u001b[38;2;171;195;201;48;2;153;176;183m▀\u001b[38;2;165;189;197;48;2;154;177;184m▀\u001b[38;2;167;190;198;48;2;159;181;188m▀\u001b[38;2;176;198;205;48;2;177;199;203m▀\u001b[38;2;186;208;213;48;2;185;208;212m▀\u001b[38;2;185;209;214;48;2;171;194;200m▀\u001b[38;2;184;210;217;48;2;169;192;198m▀\u001b[38;2;184;209;217;48;2;168;191;197m▀\u001b[38;2;160;181;186;48;2;140;159;165m▀\u001b[38;2;65;76;79;48;2;52;61;65m▀\u001b[38;2;29;36;37;48;2;30;36;38m▀\u001b[38;2;30;34;36;48;2;29;35;36m▀\u001b[38;2;29;34;35;48;2;30;37;38m▀\u001b[38;2;29;35;35;48;2;30;36;37m▀\u001b[38;2;63;73;73;48;2;47;56;55m▀\u001b[38;2;157;177;178;48;2;137;155;157m▀\u001b[38;2;178;201;205;48;2;173;196;201m▀\u001b[38;2;168;190;195;48;2;164;186;193m▀\u001b[38;2;156;178;185;48;2;157;179;186m▀\u001b[38;2;152;174;182;48;2;156;177;185m▀\u001b[38;2;153;175;184;48;2;159;181;188m▀\u001b[38;2;152;175;186;48;2;155;178;187m▀\u001b[38;2;151;174;185;48;2;149;170;181m▀\u001b[38;2;148;172;184;48;2;128;148;158m▀\u001b[38;2;139;161;171;48;2;69;81;86m▀\u001b[38;2;69;80;87;48;2;27;33;36m▀\u001b[38;2;24;30;33;48;2;29;37;39m▀\u001b[38;2;29;37;39;48;2;34;43;44m▀\u001b[38;2;32;40;41;48;2;38;48;49m▀\u001b[38;2;37;47;48;48;2;39;50;51m▀\u001b[38;2;44;58;66;48;2;46;61;71m▀\u001b[38;2;42;56;63;48;2;41;55;61m▀\u001b[38;2;36;46;45;48;2;36;46;46m▀\u001b[38;2;17;22;22;48;2;17;22;22m▀\u001b[0m\n",
       "\u001b[0m\u001b[38;2;3;3;3;48;2;3;3;3m▀\u001b[38;2;6;6;6;48;2;7;7;7m▀\u001b[38;2;8;8;8;48;2;8;8;8m▀\u001b[38;2;11;11;11;48;2;12;12;12m▀\u001b[38;2;14;14;14;48;2;13;13;13m▀\u001b[38;2;38;41;41;48;2;23;24;24m▀\u001b[38;2;37;41;41;48;2;45;49;47m▀\u001b[38;2;18;18;18;48;2;38;42;41m▀\u001b[38;2;16;16;16;48;2;18;19;19m▀\u001b[38;2;17;17;16;48;2;17;17;17m▀\u001b[38;2;18;18;20;48;2;19;19;20m▀\u001b[38;2;16;20;24;48;2;15;19;22m▀\u001b[38;2;7;8;9;48;2;7;7;8m▀\u001b[38;2;7;7;7;48;2;8;8;8m▀\u001b[38;2;9;9;10;48;2;9;9;10m▀\u001b[38;2;14;17;21;48;2;15;18;22m▀\u001b[38;2;15;19;24;48;2;14;18;22m▀\u001b[38;2;10;11;13;48;2;10;11;11m▀\u001b[38;2;26;29;29;48;2;25;27;28m▀\u001b[38;2;65;71;69;48;2;55;61;61m▀\u001b[38;2;80;88;86;48;2;71;77;76m▀\u001b[38;2;84;92;90;48;2;84;92;89m▀\u001b[38;2;74;82;81;48;2;77;85;84m▀\u001b[38;2;60;67;68;48;2;60;68;69m▀\u001b[38;2;93;101;100;48;2;85;93;93m▀\u001b[38;2;117;134;138;48;2;118;134;140m▀\u001b[38;2;134;154;160;48;2;137;156;164m▀\u001b[38;2;150;172;179;48;2;143;164;172m▀\u001b[38;2;153;174;181;48;2;144;165;173m▀\u001b[38;2;150;171;179;48;2;142;163;170m▀\u001b[38;2;143;164;173;48;2;138;159;167m▀\u001b[38;2;144;165;172;48;2;140;161;168m▀\u001b[38;2;148;169;176;48;2;146;166;173m▀\u001b[38;2;154;175;182;48;2;150;171;178m▀\u001b[38;2;166;188;194;48;2;151;173;181m▀\u001b[38;2;169;192;198;48;2;150;171;179m▀\u001b[38;2;157;179;187;48;2;143;164;174m▀\u001b[38;2;154;176;184;48;2;143;164;172m▀\u001b[38;2;154;176;183;48;2;144;165;174m▀\u001b[38;2;110;127;134;48;2;93;108;115m▀\u001b[38;2;45;54;55;48;2;35;42;44m▀\u001b[38;2;31;36;38;48;2;30;37;37m▀\u001b[38;2;22;26;28;48;2;24;29;30m▀\u001b[38;2;26;31;32;48;2;27;32;34m▀\u001b[38;2;26;30;32;48;2;26;31;33m▀\u001b[38;2;41;48;48;48;2;41;49;49m▀\u001b[38;2;133;151;153;48;2;122;139;142m▀\u001b[38;2;171;194;200;48;2;163;185;191m▀\u001b[38;2;169;190;195;48;2;173;195;198m▀\u001b[38;2;170;191;196;48;2;178;198;201m▀\u001b[38;2;162;184;191;48;2;165;186;193m▀\u001b[38;2;157;178;186;48;2;119;136;144m▀\u001b[38;2;148;170;178;48;2;75;88;90m▀\u001b[38;2;123;142;150;48;2;48;56;58m▀\u001b[38;2;55;65;69;48;2;22;28;32m▀\u001b[38;2;22;27;30;48;2;19;22;25m▀\u001b[38;2;21;26;29;48;2;20;24;27m▀\u001b[38;2;33;41;43;48;2;34;43;44m▀\u001b[38;2;39;49;54;48;2;43;56;62m▀\u001b[38;2;35;45;48;48;2;36;45;51m▀\u001b[38;2;38;49;50;48;2;39;49;50m▀\u001b[38;2;44;59;69;48;2;46;62;71m▀\u001b[38;2;41;53;60;48;2;41;53;57m▀\u001b[38;2;35;45;46;48;2;35;45;45m▀\u001b[38;2;16;21;21;48;2;16;21;21m▀\u001b[0m\n",
       "\u001b[0m\u001b[38;2;3;3;3;48;2;4;4;4m▀\u001b[38;2;8;8;8;48;2;8;8;8m▀\u001b[38;2;9;9;9;48;2;8;8;8m▀\u001b[38;2;13;13;13;48;2;12;12;12m▀\u001b[38;2;14;14;14;48;2;13;13;13m▀\u001b[38;2;13;13;14;48;2;11;11;12m▀\u001b[38;2;22;23;23;48;2;12;12;12m▀\u001b[38;2;43;47;46;48;2;19;21;20m▀\u001b[38;2;38;42;42;48;2;40;45;43m▀\u001b[38;2;19;20;20;48;2;40;45;44m▀\u001b[38;2;18;20;21;48;2;21;24;25m▀\u001b[38;2;15;19;22;48;2;14;19;21m▀\u001b[38;2;8;8;8;48;2;8;10;9m▀\u001b[38;2;8;8;8;48;2;8;9;8m▀\u001b[38;2;8;8;9;48;2;8;9;9m▀\u001b[38;2;14;19;23;48;2;14;19;23m▀\u001b[38;2;14;18;22;48;2;13;17;21m▀\u001b[38;2;10;11;12;48;2;10;10;11m▀\u001b[38;2;26;28;29;48;2;27;29;30m▀\u001b[38;2;51;58;58;48;2;53;60;60m▀\u001b[38;2;56;62;62;48;2;49;55;56m▀\u001b[38;2;82;90;87;48;2;73;80;78m▀\u001b[38;2;77;86;84;48;2;80;89;87m▀\u001b[38;2;63;70;71;48;2;70;78;77m▀\u001b[38;2;77;86;88;48;2;81;91;93m▀\u001b[38;2;117;133;138;48;2;117;134;139m▀\u001b[38;2;136;156;163;48;2;133;153;158m▀\u001b[38;2;139;159;166;48;2;135;155;161m▀\u001b[38;2;138;158;166;48;2;137;156;163m▀\u001b[38;2;137;157;165;48;2;137;157;163m▀\u001b[38;2;137;158;165;48;2;137;158;165m▀\u001b[38;2;140;161;168;48;2;137;157;165m▀\u001b[38;2;142;163;170;48;2;137;157;164m▀\u001b[38;2;142;163;171;48;2;136;156;164m▀\u001b[38;2;142;162;170;48;2;134;154;163m▀\u001b[38;2;140;160;169;48;2;130;151;160m▀\u001b[38;2;135;155;165;48;2;127;148;158m▀\u001b[38;2;133;154;163;48;2;127;148;157m▀\u001b[38;2;135;155;165;48;2;128;148;158m▀\u001b[38;2;89;105;112;48;2;84;100;108m▀\u001b[38;2;38;46;48;48;2;37;47;48m▀\u001b[38;2;32;40;41;48;2;31;38;39m▀\u001b[38;2;26;32;33;48;2;22;26;28m▀\u001b[38;2;26;32;34;48;2;23;27;29m▀\u001b[38;2;26;32;34;48;2;24;30;31m▀\u001b[38;2;43;51;51;48;2;44;53;53m▀\u001b[38;2;118;135;138;48;2;114;131;134m▀\u001b[38;2;160;181;185;48;2;134;153;157m▀\u001b[38;2;168;189;191;48;2;131;149;152m▀\u001b[38;2;167;188;192;48;2;150;171;176m▀\u001b[38;2;154;175;182;48;2;127;144;150m▀\u001b[38;2;88;100;108;48;2;52;59;63m▀\u001b[38;2;20;23;26;48;2;23;29;32m▀\u001b[38;2;26;32;36;48;2;35;46;53m▀\u001b[38;2;26;33;39;48;2;26;34;39m▀\u001b[38;2;18;21;24;48;2;17;20;22m▀\u001b[38;2;20;23;25;48;2;19;23;25m▀\u001b[38;2;34;43;43;48;2;33;42;42m▀\u001b[38;2;43;58;63;48;2;43;57;62m▀\u001b[38;2;37;48;54;48;2;38;51;57m▀\u001b[38;2;37;47;48;48;2;35;45;46m▀\u001b[38;2;38;49;51;48;2;36;45;46m▀\u001b[38;2;36;46;47;48;2;34;43;44m▀\u001b[38;2;33;43;43;48;2;32;42;43m▀\u001b[38;2;16;20;21;48;2;15;20;20m▀\u001b[0m\n",
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       "\u001b[0m\u001b[38;2;4;4;4;48;2;4;4;4m▀\u001b[38;2;8;8;8;48;2;8;8;8m▀\u001b[38;2;9;9;9;48;2;9;9;9m▀\u001b[38;2;13;13;13;48;2;12;12;12m▀\u001b[38;2;14;14;14;48;2;14;14;14m▀\u001b[38;2;11;11;11;48;2;14;15;15m▀\u001b[38;2;7;7;7;48;2;11;11;11m▀\u001b[38;2;6;6;6;48;2;7;7;7m▀\u001b[38;2;7;7;7;48;2;6;6;6m▀\u001b[38;2;7;7;7;48;2;7;7;7m▀\u001b[38;2;13;16;18;48;2;13;15;18m▀\u001b[38;2;14;18;20;48;2;15;19;23m▀\u001b[38;2;27;98;68;48;2;11;25;19m▀\u001b[38;2;47;196;151;48;2;14;46;31m▀\u001b[38;2;16;62;43;48;2;10;19;16m▀\u001b[38;2;16;20;25;48;2;16;22;27m▀\u001b[38;2;12;15;17;48;2;11;14;16m▀\u001b[38;2;10;11;11;48;2;13;14;15m▀\u001b[38;2;30;33;34;48;2;44;48;48m▀\u001b[38;2;51;58;59;48;2;52;59;59m▀\u001b[38;2;49;57;56;48;2;47;55;54m▀\u001b[38;2;45;51;52;48;2;44;50;50m▀\u001b[38;2;59;65;65;48;2;50;55;55m▀\u001b[38;2;81;88;86;48;2;75;82;80m▀\u001b[38;2;92;103;104;48;2;90;101;101m▀\u001b[38;2;103;121;126;48;2;98;116;122m▀\u001b[38;2;115;136;143;48;2;108;129;138m▀\u001b[38;2;114;136;143;48;2;107;128;137m▀\u001b[38;2;114;135;144;48;2;106;128;137m▀\u001b[38;2;112;134;143;48;2;106;128;138m▀\u001b[38;2;113;135;144;48;2;109;131;141m▀\u001b[38;2;114;136;144;48;2;112;134;142m▀\u001b[38;2;115;137;145;48;2;113;135;143m▀\u001b[38;2;115;137;145;48;2;114;136;144m▀\u001b[38;2;114;135;144;48;2;114;135;143m▀\u001b[38;2;112;133;142;48;2;112;133;141m▀\u001b[38;2;110;131;141;48;2;110;131;139m▀\u001b[38;2;110;131;141;48;2;109;129;139m▀\u001b[38;2;108;129;138;48;2;103;124;132m▀\u001b[38;2;59;70;76;48;2;54;65;71m▀\u001b[38;2;31;35;39;48;2;36;43;46m▀\u001b[38;2;25;29;30;48;2;24;28;29m▀\u001b[38;2;20;23;23;48;2;18;20;20m▀\u001b[38;2;21;25;25;48;2;19;20;21m▀\u001b[38;2;22;26;27;48;2;20;22;23m▀\u001b[38;2;30;35;35;48;2;32;38;37m▀\u001b[38;2;34;39;39;48;2;31;36;36m▀\u001b[38;2;30;34;35;48;2;21;24;25m▀\u001b[38;2;24;28;29;48;2;26;33;36m▀\u001b[38;2;28;33;37;48;2;34;44;49m▀\u001b[38;2;35;44;50;48;2;35;45;50m▀\u001b[38;2;32;42;47;48;2;32;41;46m▀\u001b[38;2;32;42;47;48;2;30;40;44m▀\u001b[38;2;34;45;51;48;2;24;31;35m▀\u001b[38;2;23;30;35;48;2;21;27;31m▀\u001b[38;2;17;19;21;48;2;16;18;20m▀\u001b[38;2;20;23;25;48;2;18;22;23m▀\u001b[38;2;31;40;39;48;2;29;37;38m▀\u001b[38;2;35;44;45;48;2;33;44;47m▀\u001b[38;2;32;41;42;48;2;29;37;39m▀\u001b[38;2;32;41;42;48;2;32;42;42m▀\u001b[38;2;39;52;60;48;2;40;54;61m▀\u001b[38;2;34;44;48;48;2;35;44;48m▀\u001b[38;2;29;37;37;48;2;29;37;37m▀\u001b[38;2;14;18;18;48;2;14;18;19m▀\u001b[0m\n",
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       "\u001b[0m\u001b[38;2;8;8;9;48;2;8;9;10m▀\u001b[38;2;16;17;18;48;2;16;18;19m▀\u001b[38;2;16;17;19;48;2;17;17;19m▀\u001b[38;2;16;18;20;48;2;17;18;20m▀\u001b[38;2;17;18;20;48;2;16;18;20m▀\u001b[38;2;17;18;20;48;2;16;19;20m▀\u001b[38;2;17;18;21;48;2;16;19;20m▀\u001b[38;2;17;18;20;48;2;17;18;20m▀\u001b[38;2;17;18;20;48;2;17;18;20m▀\u001b[38;2;17;18;20;48;2;17;18;20m▀\u001b[38;2;17;18;21;48;2;16;19;21m▀\u001b[38;2;17;19;21;48;2;16;19;20m▀\u001b[38;2;18;19;21;48;2;17;19;20m▀\u001b[38;2;17;19;21;48;2;18;19;21m▀\u001b[38;2;18;20;22;48;2;18;20;21m▀\u001b[38;2;19;20;22;48;2;17;18;19m▀\u001b[38;2;18;18;20;48;2;15;16;17m▀\u001b[38;2;17;18;19;48;2;17;19;19m▀\u001b[38;2;20;22;24;48;2;16;19;20m▀\u001b[38;2;19;21;24;48;2;15;18;19m▀\u001b[38;2;17;19;21;48;2;16;19;20m▀\u001b[38;2;16;19;21;48;2;15;19;21m▀\u001b[38;2;17;19;21;48;2;16;19;22m▀\u001b[38;2;16;19;22;48;2;16;20;24m▀\u001b[38;2;16;20;23;48;2;17;21;25m▀\u001b[38;2;18;22;25;48;2;20;25;30m▀\u001b[38;2;20;24;28;48;2;21;27;32m▀\u001b[38;2;19;23;26;48;2;19;25;31m▀\u001b[38;2;17;20;23;48;2;20;26;32m▀\u001b[38;2;16;19;23;48;2;24;31;37m▀\u001b[38;2;15;18;22;48;2;28;38;47m▀\u001b[38;2;17;21;25;48;2;35;48;61m▀\u001b[38;2;20;25;29;48;2;42;57;70m▀\u001b[38;2;19;24;28;48;2;46;63;78m▀\u001b[38;2;20;25;30;48;2;51;71;88m▀\u001b[38;2;21;28;32;48;2;56;78;99m▀\u001b[38;2;23;31;37;48;2;60;84;108m▀\u001b[38;2;27;37;44;48;2;63;88;113m▀\u001b[38;2;31;42;51;48;2;64;90;117m▀\u001b[38;2;35;47;57;48;2;64;91;118m▀\u001b[38;2;37;52;63;48;2;64;91;118m▀\u001b[38;2;39;54;67;48;2;63;90;119m▀\u001b[38;2;41;57;72;48;2;61;88;118m▀\u001b[38;2;43;60;75;48;2;59;85;114m▀\u001b[38;2;43;62;77;48;2;56;82;111m▀\u001b[38;2;43;61;76;48;2;54;78;108m▀\u001b[38;2;43;59;74;48;2;52;74;102m▀\u001b[38;2;38;53;65;48;2;47;66;93m▀\u001b[38;2;31;43;52;48;2;36;50;69m▀\u001b[38;2;22;30;37;48;2;16;21;29m▀\u001b[38;2;19;25;31;48;2;10;11;16m▀\u001b[38;2;16;21;26;48;2;8;9;11m▀\u001b[38;2;14;17;22;48;2;7;8;9m▀\u001b[38;2;12;14;18;48;2;7;7;8m▀\u001b[38;2;11;12;16;48;2;7;8;8m▀\u001b[38;2;10;11;14;48;2;8;8;9m▀\u001b[38;2;8;9;11;48;2;9;10;9m▀\u001b[38;2;8;8;10;48;2;11;13;12m▀\u001b[38;2;7;8;9;48;2;13;16;15m▀\u001b[38;2;7;7;8;48;2;16;19;19m▀\u001b[38;2;7;7;8;48;2;19;24;23m▀\u001b[38;2;7;8;8;48;2;23;29;29m▀\u001b[38;2;8;9;9;48;2;26;33;34m▀\u001b[38;2;10;11;10;48;2;28;37;37m▀\u001b[38;2;5;6;5;48;2;14;19;19m▀\u001b[0m\n",
       "\u001b[0m\u001b[38;2;4;4;5m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;9;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;9;10;11m▀\u001b[38;2;9;10;10m▀\u001b[38;2;7;8;8m▀\u001b[38;2;7;8;8m▀\u001b[38;2;8;10;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;8;9m▀\u001b[38;2;8;10;10m▀\u001b[38;2;8;9;10m▀\u001b[38;2;8;10;12m▀\u001b[38;2;9;11;14m▀\u001b[38;2;10;13;16m▀\u001b[38;2;12;16;19m▀\u001b[38;2;13;18;22m▀\u001b[38;2;15;21;26m▀\u001b[38;2;19;27;33m▀\u001b[38;2;25;34;43m▀\u001b[38;2;29;40;52m▀\u001b[38;2;32;44;57m▀\u001b[38;2;33;46;59m▀\u001b[38;2;34;46;60m▀\u001b[38;2;33;47;61m▀\u001b[38;2;33;47;63m▀\u001b[38;2;33;47;63m▀\u001b[38;2;33;46;63m▀\u001b[38;2;32;46;62m▀\u001b[38;2;31;45;61m▀\u001b[38;2;30;43;59m▀\u001b[38;2;28;41;57m▀\u001b[38;2;25;36;52m▀\u001b[38;2;21;30;45m▀\u001b[38;2;16;23;37m▀\u001b[38;2;12;18;29m▀\u001b[38;2;9;13;22m▀\u001b[38;2;7;9;16m▀\u001b[38;2;5;6;9m▀\u001b[38;2;3;3;3m▀\u001b[38;2;3;3;3m▀\u001b[38;2;2;2;2m▀\u001b[38;2;2;2;3m▀\u001b[38;2;4;4;3m▀\u001b[38;2;6;7;6m▀\u001b[38;2;8;10;9m▀\u001b[38;2;11;14;13m▀\u001b[38;2;13;17;16m▀\u001b[38;2;14;19;19m▀\u001b[38;2;14;19;20m▀\u001b[38;2;15;20;21m▀\u001b[38;2;16;21;22m▀\u001b[38;2;15;20;23m▀\u001b[38;2;14;20;22m▀\u001b[38;2;7;10;11m▀\u001b[0m"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each mask is an array with elements the same type as our classes (here `String`), but efficiently stored using integers under the hood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6×6 view(::IndirectArrays.IndirectArray{String, 2, Base.ReinterpretArray{UInt8, 2, ColorTypes.Gray{FixedPointNumbers.N0f8}, PermutedDimsArray{ColorTypes.Gray{FixedPointNumbers.N0f8}, 2, (2, 1), (2, 1), Matrix{ColorTypes.Gray{FixedPointNumbers.N0f8}}}, false}, Vector{String}}, 50:55, 50:55) with eltype String:\n",
       " \"TrafficLight\"  \"TrafficLight\"  …  \"TrafficLight\"  \"Column_Pole\"\n",
       " \"TrafficLight\"  \"TrafficLight\"     \"TrafficLight\"  \"TrafficLight\"\n",
       " \"TrafficLight\"  \"TrafficLight\"     \"TrafficLight\"  \"Column_Pole\"\n",
       " \"OtherMoving\"   \"TrafficLight\"     \"TrafficLight\"  \"TrafficLight\"\n",
       " \"OtherMoving\"   \"TrafficLight\"     \"TrafficLight\"  \"Column_Pole\"\n",
       " \"OtherMoving\"   \"OtherMoving\"   …  \"TrafficLight\"  \"TrafficLight\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view(mask, 50:55, 50:55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to create a learning method for image segmentation. This means using images to predict masks, so we'll use the [`Image`](#) and [`Mask`](#) blocks as input and target. Since the dataset is 2D, we'll use 2-dimensional blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BlockMethod(Image{2} -> Mask{2, String})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "method = BlockMethod(\n",
    "    (Image{2}(), Mask{2}(classes)),\n",
    "    (\n",
    "        ProjectiveTransforms((128, 128)),\n",
    "        ImagePreprocessing(),\n",
    "        OneHot()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The encodings passed in transform samples into formats suitable as inputs and outputs for a model, and also allow decoding model outputs to get back to our target format: an array of class labels for every pixel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that samples from the created data container conform to the blocks of the learning method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkblock(method.blocks, sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can ascertain the encodings work as expected by creating a batch of encoded data and visualizing it. Here the segmentation masks are color-coded and overlayed on top of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs, ys = FastAI.makebatch(method, data, 1:3)\n",
    "showbatch(method, (xs, ys))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use [`describemethod`](#) to get more information about learning methods created through the data block API. We see which representations our data goes through and which encodings transform which parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\paragraph{\\texttt{LearningMethod} summary}\n",
       "\\begin{itemize}\n",
       "\\item Task: \\texttt{Image\\{2\\} -> Mask\\{2, String\\}}\n",
       "\n",
       "\n",
       "\\item Model blocks: \\texttt{FastAI.Bounded\\{2, FastAI.ImageTensor\\{2\\}\\} -> FastAI.Bounded\\{2, FastAI.OneHotTensor\\{2, String\\}\\}}\n",
       "\n",
       "\\end{itemize}\n",
       "Encoding a sample (\\texttt{encode(method, context, sample)})\n",
       "\n",
       "\\begin{tabular}\n",
       "{r | r | r | r}\n",
       "Encoding & Name & \\texttt{method.blocks[1]} & \\texttt{method.blocks[2]} \\\\\n",
       "\\hline\n",
       " & \\texttt{(input, target)} & \\texttt{Image\\{2\\}} & \\texttt{Mask\\{2, String\\}} \\\\\n",
       "\\texttt{ProjectiveTransforms} &  & \\textbf{\\texttt{FastAI.Bounded\\{2, Image\\{2\\}\\}}} & \\textbf{\\texttt{FastAI.Bounded\\{2, Mask\\{2, String\\}\\}}} \\\\\n",
       "\\texttt{ImagePreprocessing} &  & \\textbf{\\texttt{FastAI.Bounded\\{2, FastAI.ImageTensor\\{2\\}\\}}} & \\texttt{FastAI.Bounded\\{2, Mask\\{2, String\\}\\}} \\\\\n",
       "\\texttt{OneHot} & \\texttt{(x, y)} & \\texttt{FastAI.Bounded\\{2, FastAI.ImageTensor\\{2\\}\\}} & \\textbf{\\texttt{FastAI.Bounded\\{2, FastAI.OneHotTensor\\{2, String\\}\\}}} \\\\\n",
       "\\end{tabular}\n",
       "Decoding a model output (\\texttt{decode(method, context, ŷ)})\n",
       "\n",
       "\\begin{tabular}\n",
       "{r | r | r}\n",
       "Decoding & Name & \\texttt{method.outputblock} \\\\\n",
       "\\hline\n",
       " & \\texttt{ŷ} & \\texttt{FastAI.Bounded\\{2, FastAI.OneHotTensor\\{2, String\\}\\}} \\\\\n",
       "\\texttt{OneHot} &  & \\textbf{\\texttt{FastAI.Bounded\\{2, Mask\\{2, String\\}\\}}} \\\\\n",
       "\\texttt{ImagePreprocessing} &  & \\texttt{FastAI.Bounded\\{2, Mask\\{2, String\\}\\}} \\\\\n",
       "\\texttt{ProjectiveTransforms} & \\texttt{target\\_pred} & \\texttt{FastAI.Bounded\\{2, Mask\\{2, String\\}\\}} \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "#### `LearningMethod` summary\n",
       "\n",
       "  * Task: `Image{2} -> Mask{2, String}`\n",
       "  * Model blocks: `FastAI.Bounded{2, FastAI.ImageTensor{2}} -> FastAI.Bounded{2, FastAI.OneHotTensor{2, String}}`\n",
       "\n",
       "Encoding a sample (`encode(method, context, sample)`)\n",
       "\n",
       "|               Encoding |              Name |                             `method.blocks[1]` |                                      `method.blocks[2]` |\n",
       "| ----------------------:| -----------------:| ----------------------------------------------:| -------------------------------------------------------:|\n",
       "|                        | `(input, target)` |                                     `Image{2}` |                                       `Mask{2, String}` |\n",
       "| `ProjectiveTransforms` |                   |              **`FastAI.Bounded{2, Image{2}}`** |                **`FastAI.Bounded{2, Mask{2, String}}`** |\n",
       "|   `ImagePreprocessing` |                   | **`FastAI.Bounded{2, FastAI.ImageTensor{2}}`** |                    `FastAI.Bounded{2, Mask{2, String}}` |\n",
       "|               `OneHot` |          `(x, y)` |     `FastAI.Bounded{2, FastAI.ImageTensor{2}}` | **`FastAI.Bounded{2, FastAI.OneHotTensor{2, String}}`** |\n",
       "\n",
       "Decoding a model output (`decode(method, context, ŷ)`)\n",
       "\n",
       "|               Decoding |          Name |                                `method.outputblock` |\n",
       "| ----------------------:| -------------:| ---------------------------------------------------:|\n",
       "|                        |          `ŷ` | `FastAI.Bounded{2, FastAI.OneHotTensor{2, String}}` |\n",
       "|               `OneHot` |               |            **`FastAI.Bounded{2, Mask{2, String}}`** |\n",
       "|   `ImagePreprocessing` |               |                `FastAI.Bounded{2, Mask{2, String}}` |\n",
       "| `ProjectiveTransforms` | `target_pred` |                `FastAI.Bounded{2, Mask{2, String}}` |\n"
      ],
      "text/plain": [
       "\u001b[1m  \u001b[36mLearningMethod\u001b[39m summary\u001b[22m\n",
       "\u001b[1m  ------------------------\u001b[22m\n",
       "\n",
       "    •  Task: \u001b[36mImage{2} -> Mask{2, String}\u001b[39m\n",
       "\n",
       "    •  Model blocks: \u001b[36mFastAI.Bounded{2, FastAI.ImageTensor{2}} ->\n",
       "       FastAI.Bounded{2, FastAI.OneHotTensor{2, String}}\u001b[39m\n",
       "\n",
       "  Encoding a sample (\u001b[36mencode(method, context, sample)\u001b[39m)\n",
       "\n",
       "              Encoding            Name                         \u001b[36mmethod.blocks[1]\u001b[39m                                  \u001b[36mmethod.blocks[2]\u001b[39m\n",
       "  –––––––––––––––––––– ––––––––––––––– –––––––––––––––––––––––––––––––––––––––– –––––––––––––––––––––––––––––––––––––––––––––––––\n",
       "                       \u001b[36m(input, target)\u001b[39m                                 \u001b[36mImage{2}\u001b[39m                                   \u001b[36mMask{2, String}\u001b[39m\n",
       "  \u001b[36mProjectiveTransforms\u001b[39m                              \u001b[1m\u001b[36mFastAI.Bounded{2, Image{2}}\u001b[39m\u001b[22m                \u001b[1m\u001b[36mFastAI.Bounded{2, Mask{2, String}}\u001b[39m\u001b[22m\n",
       "    \u001b[36mImagePreprocessing\u001b[39m                 \u001b[1m\u001b[36mFastAI.Bounded{2, FastAI.ImageTensor{2}}\u001b[39m\u001b[22m                \u001b[36mFastAI.Bounded{2, Mask{2, String}}\u001b[39m\n",
       "                \u001b[36mOneHot\u001b[39m          \u001b[36m(x, y)\u001b[39m \u001b[36mFastAI.Bounded{2, FastAI.ImageTensor{2}}\u001b[39m \u001b[1m\u001b[36mFastAI.Bounded{2, FastAI.OneHotTensor{2, String}}\u001b[39m\u001b[22m\n",
       "\n",
       "  Decoding a model output (\u001b[36mdecode(method, context, ŷ)\u001b[39m)\n",
       "\n",
       "              Decoding        Name                                \u001b[36mmethod.outputblock\u001b[39m\n",
       "  –––––––––––––––––––– ––––––––––– –––––––––––––––––––––––––––––––––––––––––––––––––\n",
       "                                \u001b[36mŷ\u001b[39m \u001b[36mFastAI.Bounded{2, FastAI.OneHotTensor{2, String}}\u001b[39m\n",
       "                \u001b[36mOneHot\u001b[39m                            \u001b[1m\u001b[36mFastAI.Bounded{2, Mask{2, String}}\u001b[39m\u001b[22m\n",
       "    \u001b[36mImagePreprocessing\u001b[39m                            \u001b[36mFastAI.Bounded{2, Mask{2, String}}\u001b[39m\n",
       "  \u001b[36mProjectiveTransforms\u001b[39m \u001b[36mtarget_pred\u001b[39m                \u001b[36mFastAI.Bounded{2, Mask{2, String}}\u001b[39m"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describemethod(method)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a `method` and a matching data container, the only thing we need before we can create a `Learner` is a backbone architecture to build the segmentation model from. We'll use a slightly modified ResNet, but you can use any convolutional architecture.\n",
    "We'll use [`methodmodel`](#) to construct the model from the backbone. Since we want mask outputs, the intermediate feature representation needs to be scaled back up. Based on the `Block`s we built our method from, `methodmodel` knows that it needs to build a mapping `ImageTensor{2} -> OneHotTensor{2}` and constructs a U-Net model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone = Models.xresnet18()\n",
    "model = methodmodel(method, backbone);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a similar vein, [`methodlossfn`](#) creates a loss function suitable for comparing model outputs and encoded targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "segmentationloss (generic function with 1 method)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lossfn = methodlossfn(method)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we turn our data container into training and validation data loaders that will iterate over batches of encoded data and construct a `Learner`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Learner()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindl, validdl = methoddataloaders(data, method, 16)\n",
    "optimizer = ADAM()\n",
    "learner = Learner(model, (traindl, validdl), optimizer, lossfn, ToGPU())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we could also have used [`methodlearner`](#) which is a shorthand that calls `methoddataloaders` and `methodmodel` for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's finetune the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 1 TrainingPhase(): 100%|██████████████████████████| Time: 0:06:11\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   1.0 │ 3.42341 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   1.0 │ 27.9754 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 2 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   2.0 │ 3.12984 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   2.0 │ 655.577 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 3 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m   Loss \u001b[0m│\n",
      "├───────────────┼───────┼────────┤\n",
      "│ TrainingPhase │   3.0 │ 2.7591 │\n",
      "└───────────────┴───────┴────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   3.0 │ 186.798 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 4 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   4.0 │ 2.43153 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   4.0 │ 11.3832 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 5 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   5.0 │ 2.24048 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   5.0 │ 3.09137 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 6 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   6.0 │ 2.09765 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   6.0 │ 2.35466 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 7 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   7.0 │ 2.02989 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   7.0 │ 2.10237 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 8 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   8.0 │ 1.97969 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   8.0 │ 2.04701 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 9 TrainingPhase(): 100%|██████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │   9.0 │ 1.96196 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │   9.0 │ 1.95681 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mEpoch 10 TrainingPhase(): 100%|█████████████████████████| Time: 0:00:03\u001b[39m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌───────────────┬───────┬─────────┐\n",
      "│\u001b[1m         Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├───────────────┼───────┼─────────┤\n",
      "│ TrainingPhase │  10.0 │ 1.94524 │\n",
      "└───────────────┴───────┴─────────┘\n",
      "┌─────────────────┬───────┬─────────┐\n",
      "│\u001b[1m           Phase \u001b[0m│\u001b[1m Epoch \u001b[0m│\u001b[1m    Loss \u001b[0m│\n",
      "├─────────────────┼───────┼─────────┤\n",
      "│ ValidationPhase │  10.0 │ 1.93706 │\n",
      "└─────────────────┴───────┴─────────┘\n"
     ]
    }
   ],
   "source": [
    "fitonecycle!(learner, 10, 0.033)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And look at the results on a batch of validation data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showoutputs(method, learner; n = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data block API can also be used for 3D segmentation, but the tutorial for that is still in the works."
   ]
  }
 ],
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