notebooks/1_Entrenamiento.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flood area detection\n",
    "\n",
    "# **1_Trainning Notebook**\n",
    "\n",
    "In this notebook is made the model training. Is a convolutional neural network CNN with U-Net architecture. It needs a dataset with images and masks to train. These last ones are binary images that delimited the interest object with pixel values equal to 1 where the interest object is and 0 where it isn't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unetseg.train import TrainConfig, train\n",
    "from unetseg.evaluate import plot_data_generator\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Variables statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZONE = \"TEGU\" \n",
    "\n",
    "if ZONE==\"SULA\":\n",
    "    #YEAR = \"2022\"\n",
    "    EVENT=\"moderado\"\n",
    "    RESO='planet'\n",
    "       \n",
    "    #GeneralP\n",
    "    BASE_PATH = \"./../\"\n",
    "    PATH_TRAIN_IMAGE = f'{BASE_PATH}images/{ZONE}/{RESO}/{EVENT}/'\n",
    "    GT_FILE = 'SULA_moderate_flood_event.geojson' \n",
    "    GT_AOI=f'aoi_sula_local_completo_4326.geojson'\n",
    "    PATH_TO_GT=f'{BASE_PATH}data/shp/gt/{ZONE}/{EVENT}/'\n",
    "    \n",
    "    #satproc\n",
    "    SIZE =160\n",
    "    STEP_SIZE = 60\n",
    "    CLASSES = 'M' # 'E' if the event is 'moderado'\n",
    "    \n",
    "    #unet\n",
    "    INPUT_SIZE=256\n",
    "    CLASS_WEIGHT = [1]\n",
    "    STEP_PER_EPOCH = 110\n",
    "    N_CLASSES = 1\n",
    "    VERSION='v1'\n",
    "    N_CHANNELS = 8\n",
    "    MODEL_NAME = f'UNet_{ZONE}_{INPUT_SIZE}x{INPUT_SIZE}_{EVENT}_{RESO}_{str(SIZE)}_{str(STEP_SIZE)}_spe{STEP_PER_EPOCH}_{VERSION}.h5'\n",
    "    \n",
    "if ZONE=='TEGU':\n",
    "    #YEAR = \"2022\"\n",
    "    EVENT='moderado'\n",
    "    RESO='low'\n",
    "   \n",
    "    #GeneralP\n",
    "    BASE_PATH = \"./../\"\n",
    "    PATH_TRAIN_IMAGE = f'{BASE_PATH}images/{ZONE}/{RESO}/{EVENT}/'\n",
    "    GT_FILE = f'{ZONE}CIGALPA_moderate_flood_event.geojson' #revisar 21/6 \n",
    "    GT_AOI=f'aoi_TEGU_planet_floods.geojson'\n",
    "    PATH_TO_GT=f'{BASE_PATH}data/shp/gt/{ZONE}/{EVENT}/'\n",
    "    \n",
    "    #satproc\n",
    "    SIZE =160 \n",
    "    STEP_SIZE = 30 \n",
    "    CLASSES = 'M'\n",
    "\n",
    "    #unet\n",
    "    N_CHANNELS = 8\n",
    "    INPUT_SIZE=160\n",
    "    CLASS_WEIGHT = [1]\n",
    "    STEP_PER_EPOCH = 70\n",
    "    N_CLASSES = 1\n",
    "    VERSION='v1'\n",
    "     MODEL_NAME = f'UNet_{ZONE}_{INPUT_SIZE}x{INPUT_SIZE}_{EVENT}_{RESO}_{str(SIZE)}_{str(STEP_SIZE)}_spe{STEP_PER_EPOCH}_{VERSION}.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Download from buckets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Images download**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sula \n",
    "BUCKET_S2_SULA = f\"gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/SULA/V2/GLOBAL/images/dataset_v2_sula_epsg4326_compress.tif\"\n",
    "BUCKET_PLANET_SULA=f'gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/SULA/V2/LOCAL/images/dataset_sula_local_epsg4326_compress.tif'\n",
    "#Tegucigalpa\n",
    "BUCKET_S2_TEGU= f'gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/TEGU/V2/GLOBAL/images/dataset_v2_tegu_epsg4326.tif' \n",
    "BUCKET_PLANET_TEGU=f'gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/images/TEGUCIGALPA/planet/dataset_local_tegucigalpa_epsg4326.tif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gsutil -m cp -r $BUCKET_S2_SULA $PATH_TRAIN_IMAGE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aoi download**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/SULA/V2/LOCAL/aoi/aoi_sula_local_completo_4326.geojson...\n",
      "/ [1/1 files][  518.0 B/  518.0 B] 100% Done                                    \n",
      "Operation completed over 1 objects/518.0 B.                                      \n"
     ]
    }
   ],
   "source": [
    "!gsutil -m cp -r gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/SULA/V2/LOCAL/aoi/aoi_sula_local_completo_4326.geojson ./../data/shp/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ground truth download**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sula \n",
    "BUCKET_GT_SULA_EXTREME= f'gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/data/shp/gt/SULA_extreme_flood_event.geojson'\n",
    "BUCKET_GT_SULA_MODERATE=f'gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/data/shp/gt/SULA_moderate_flood_event.geojson'\n",
    "#Tegucigalpa\n",
    "BUCKET_GT_TEGU= f'gs://dym-quilmes-trucks-temp/I2UD/flood-area-detection/data/shp/gt/TEGUCIGALPA_moderate_flood_event.geojson'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!gsutil -m cp -r $BUCKET_GT_SULA_EXTREME $PATH_TO_GT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Training dataset generation\n",
    "In this stage the training dataset is generated using Satproc. This tool generates the dataset with characteristics needed for the model: size, number of images, value range, etc.\n",
    "\n",
    "### Satproc\n",
    "\n",
    "\n",
    "Satproc_extract_chips tool takes the image and the vector file with the annotations and generates a directory with two folders: \"images\" and \"extent\". Files in the first one are Tiff files with 8-bands. Also, files from the second folder are Tiff type but with 1-band, because we want to detect one type of flooded areas. Each mask is a binary image.\n",
    "\n",
    "Observation: images and the vector file (gt) should have the same georeference, for instance, EPSG:4326"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./../images/TEGU/low/moderado/*.tif ./..//dataset/data_train/TEGU/low/moderado/160_30/ ./../data/shp/gt/TEGU/moderado/TEGUCIGALPA_moderate_flood_event.geojson ./../data/shp/gt/TEGU/moderado/TEGUCIGALPA_moderate_flood_event.geojson\n"
     ]
    }
   ],
   "source": [
    "path_to_files         = f'{PATH_TRAIN_IMAGE}*.tif' #image path\n",
    "train_output_folder   = f'{BASE_PATH}dataset/data_train/{ZONE}/{RESO}/{EVENT}/'+str(SIZE)+'_'+str(STEP_SIZE)+'/' #training dataset folder\n",
    "vector_file           = f'{PATH_TO_GT}{GT_FILE}' # gt file path\n",
    "vector_file_aoi       = vector_file\n",
    "print(path_to_files,train_output_folder, vector_file, vector_file_aoi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: satproc_extract_chips [-h] [--size SIZE] [--step-size STEP_SIZE]\n",
      "                             [--sliding-windows-mode {exact,whole,whole_overlap}]\n",
      "                             [--labels LABELS]\n",
      "                             [--label-property LABEL_PROPERTY]\n",
      "                             [--classes CLASSES [CLASSES ...]]\n",
      "                             [--masks {extent,boundary,distance} [{extent,boundary,distance} ...]]\n",
      "                             [--mask-type {single,class,instance}] [--aoi AOI]\n",
      "                             [--within] [--no-within] [-o OUTPUT_DIR]\n",
      "                             [--rescale] [--no-rescale]\n",
      "                             [--rescale-mode {percentiles,values,s2_rgb_extra}]\n",
      "                             [--lower-cut LOWER_CUT] [--upper-cut UPPER_CUT]\n",
      "                             [--min MIN] [--max MAX] [-b BANDS [BANDS ...]]\n",
      "                             [-t {jpg,tif}] [--write-footprints]\n",
      "                             [--no-write-footprints] [--crs CRS]\n",
      "                             [--skip-existing] [--no-skip-existing]\n",
      "                             [--skip-low-contrast] [--no-skip-low-contrast]\n",
      "                             [--skip-with-empty-mask]\n",
      "                             [--no-skip-with-empty-mask] [--extent-no-border]\n",
      "                             [--version] [-v] [-vv]\n",
      "                             raster [raster ...]\n",
      "\n",
      "Extract chips from a raster file, and optionally generate mask chips\n",
      "\n",
      "positional arguments:\n",
      "  raster                input raster file(s)\n",
      "\n",
      "optional arguments:\n",
      "  -h, --help            show this help message and exit\n",
      "  --size SIZE           size of image tiles, in pixels (default: 256)\n",
      "  --step-size STEP_SIZE\n",
      "                        step size (i.e. stride), in pixels (if None, same as\n",
      "                        size i.e. no overlap) (default: None)\n",
      "  --sliding-windows-mode {exact,whole,whole_overlap}\n",
      "                        mode of sliding windows (default: whole_overlap)\n",
      "  --labels LABELS       inpul label shapefile (default: None)\n",
      "  --label-property LABEL_PROPERTY\n",
      "                        label property to separate in classes (default: class)\n",
      "  --classes CLASSES [CLASSES ...]\n",
      "                        specify classes order in result mask. (default: None)\n",
      "  --masks {extent,boundary,distance} [{extent,boundary,distance} ...], -m {extent,boundary,distance} [{extent,boundary,distance} ...]\n",
      "  --mask-type {single,class,instance}\n",
      "  --aoi AOI             Filter by AOI vector file (default: None)\n",
      "  --within              only create chip is it is within AOI (if provided)\n",
      "                        (default: False)\n",
      "  --no-within           create chip if it intersects with AOI (if provided)\n",
      "                        (default: False)\n",
      "  -o OUTPUT_DIR, --output-dir OUTPUT_DIR\n",
      "                        output dir (default: .)\n",
      "  --rescale             rescale intensity using percentiles (lower/upper cuts)\n",
      "                        (default: False)\n",
      "  --no-rescale          do not rescale intensity (default: True)\n",
      "  --rescale-mode {percentiles,values,s2_rgb_extra}\n",
      "                        choose mode of intensity rescaling (default:\n",
      "                        percentiles)\n",
      "  --lower-cut LOWER_CUT\n",
      "                        (for 'percentiles' mode) lower cut of percentiles for\n",
      "                        cumulative count in intensity rescaling (default: 2)\n",
      "  --upper-cut UPPER_CUT\n",
      "                        (for 'percentiles' mode) upper cut of percentiles for\n",
      "                        cumulative count in intensity rescaling (default: 98)\n",
      "  --min MIN             (for 'values' mode) minimum value in intensity\n",
      "                        rescaling (default: None)\n",
      "  --max MAX             (for 'values' mode) maximum value in intensity\n",
      "                        rescaling (default: None)\n",
      "  -b BANDS [BANDS ...], --bands BANDS [BANDS ...]\n",
      "                        specify band indexes. If type is 'jpg', defaults to\n",
      "                        (1, 2, 3). If type is 'tif', defaults to the total\n",
      "                        band count. (default: None)\n",
      "  -t {jpg,tif}, --type {jpg,tif}\n",
      "                        output chip format (default: tif)\n",
      "  --write-footprints    write a GeoJSON file of chip footprints (default:\n",
      "                        False)\n",
      "  --no-write-footprints\n",
      "                        do not write a GeoJSON file of chip footprints\n",
      "                        (default: True)\n",
      "  --crs CRS             force CRS of input files (default: None)\n",
      "  --skip-existing       skip already existing chips (and masks) (default:\n",
      "                        True)\n",
      "  --no-skip-existing    do not skip already existing chips (and masks)\n",
      "                        (default: True)\n",
      "  --skip-low-contrast   skip image chips with low contrast (default: False)\n",
      "  --no-skip-low-contrast\n",
      "                        do not skip image chips with low contrast (default:\n",
      "                        True)\n",
      "  --skip-with-empty-mask\n",
      "                        skip chips with empty mask (default: True)\n",
      "  --no-skip-with-empty-mask\n",
      "                        do not skip chips with empty mask (default: True)\n",
      "  --extent-no-border    do not include border in extent mask (default: False)\n",
      "  --version             show program's version number and exit\n",
      "  -v, --verbose         set loglevel to INFO (default: None)\n",
      "  -vv, --very-verbose   set loglevel to DEBUG (default: None)\n"
     ]
    }
   ],
   "source": [
    "#!satproc_extract_chips --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Satproc execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!satproc_extract_chips \\\n",
    "                $path_to_files \\\n",
    "                -o  $train_output_folder \\\n",
    "                --size $SIZE \\\n",
    "                --step-size $STEP_SIZE \\\n",
    "                --aoi $vector_file_aoi\\\n",
    "                --labels $vector_file \\\n",
    "                --label-property 'class' \\\n",
    "                --classes $CLASSES \\\n",
    "                --rescale \\\n",
    "                --rescale-mode percentiles --lower-cut 0 --upper-cut 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arguments:\n",
    "* **path_to_files:** Images path\n",
    "\n",
    "* **o:** Destination path\n",
    "\n",
    "We recommend use a descriptive path. For instances: \"./../data_train/120_40/\" describes: data_train → data use for train; 120_40 → < image size >_< step size >.\n",
    "\n",
    "* **size:** Chips size (chips are square images)\n",
    "* **step-size:** Process step size. It should be less or equal to 'size'. If it's equal to size, there isn't an overlap in the resulting images\n",
    "\n",
    "Sometimes, it's useful generating overlaping images for the training, because the overlaping increase the number of images in the training dataset.\n",
    "\n",
    "* **label-property:** Field name where the classes are defined (it's only used for training dataset).\n",
    "\n",
    "* **classes:** Classes names (as they appear in the geojson file), separated by spaces\n",
    "\n",
    "* **labels:** Vector fil with ground truth annotations\n",
    "\n",
    "* **aoi:** aoi file path. When an area of interest is stablished, Satproc only process that area of the image and the annotations inside it\n",
    "\n",
    "* **rescale:** Rescale band values to 0-255\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "The model is a convolutional neural network (CNN) with U-Net architecture. In the training process, it uses the dataset (images and masks) created with Satproc to learn about the patterns that defines the object of interest.\n",
    "\n",
    "In this stage, we define the model configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = TrainConfig(width=INPUT_SIZE,  #  tamaño de la imagen procesada por la UNet (debe ser multiplos de 16 , por ej 160, 320,etc; y no menor a 80)\n",
    "                     height=INPUT_SIZE,\n",
    "                     n_channels=N_CHANNELS,  #  número de canales de la imagen, rgb -> 3\n",
    "                     n_classes=N_CLASSES, # número de clases a clasificar\n",
    "                     apply_image_augmentation=True, #  si es True , amplia el dataset generando imagenes nuevas a partir de pequeñas variaciones de las ya existentes (rotación,)\n",
    "                     seed=42,\n",
    "                     epochs=20, # Cantidad de veces que el dataset entero puede pasar por el proceso de entrenamiento\n",
    "                     batch_size=16, #cantidad de datos que se procesan por vez, puede ser limitado por la memoria de gpu disponible (debe ser multiplo de 16)\n",
    "                     steps_per_epoch=STEP_PER_EPOCH, #  típicamente debe ser igual al numero de imágenes / el batch_size, si es mayor incrementara el número de imágenes generadas con image augmentation\n",
    "                     early_stopping_patience=8, # a medida que entrena se guardan los resultados del entrenamiento despues de cada epoch, si el error no varió luego de ¿¿10 ?? iteraciones , se corta el proceso porque se entiende que el error ya disminuyó significativamente \n",
    "                     validation_split=0.1, # se divide la muestra en training (80% de los datos) y validation (20% de los datos) para calcular el error durante el proceso de entrenamiento\n",
    "                     test_split=0.1, # Cantidad de imágenes del dataset\n",
    "                     images_path=train_output_folder,#ruta a las imágenes generadas con Satproc\n",
    "                     model_path=os.path.join(f'{BASE_PATH}data/weights/', MODEL_NAME),#  ruta del modelo entrenado\n",
    "                     model_architecture='unet',\n",
    "                     evaluate=True  ,\n",
    "                     class_weights= CLASS_WEIGHT) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's useful to name the model using information about training parameters. For instance: < model >< proyect >< images_dimension >< size >< step_size >_< step_per_epoch >.h5 or similar.\n",
    "\n",
    "We can visualize some images and mask from the training dataset. (Left: image, Right: mask.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_generator(num_samples=3, fig_size=(20, 20), train_config=config,img_ch =3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-01 12:35:02.310274: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:02.322406: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:02.323346: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:02.325177: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-08-01 12:35:02.325691: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:02.326586: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:02.327554: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:03.001373: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:03.002221: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:03.003013: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 12:35:03.003740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10794 MB memory:  -> device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0, compute capability: 3.7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 160, 160, 8  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 160, 160, 32  2336        ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 160, 160, 32  9248        ['conv2d[0][0]']                 \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 80, 80, 32)   0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 80, 80, 32)  128         ['max_pooling2d[0][0]']          \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 80, 80, 64)   18496       ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 80, 80, 64)   36928       ['conv2d_2[0][0]']               \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 40, 40, 64)  0           ['conv2d_3[0][0]']               \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 40, 40, 64)   0           ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 40, 40, 64)  256         ['dropout[0][0]']                \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 40, 40, 128)  73856       ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 40, 40, 128)  147584      ['conv2d_4[0][0]']               \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 20, 20, 128)  0          ['conv2d_5[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 20, 20, 128)  0           ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 20, 20, 128)  512        ['dropout_1[0][0]']              \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 20, 20, 256)  295168      ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 20, 20, 256)  590080      ['conv2d_6[0][0]']               \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 10, 10, 256)  0          ['conv2d_7[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_2 (Dropout)            (None, 10, 10, 256)  0           ['max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 10, 10, 256)  1024       ['dropout_2[0][0]']              \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 10, 10, 512)  1180160     ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 10, 10, 512)  2359808     ['conv2d_8[0][0]']               \n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPooling2D)  (None, 5, 5, 512)   0           ['conv2d_9[0][0]']               \n",
      "                                                                                                  \n",
      " dropout_3 (Dropout)            (None, 5, 5, 512)    0           ['max_pooling2d_4[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 5, 5, 1024)   4719616     ['dropout_3[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 5, 5, 1024)   9438208     ['conv2d_10[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTransp  (None, 10, 10, 512)  2097664    ['conv2d_11[0][0]']              \n",
      " ose)                                                                                             \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 10, 10, 1024  0           ['conv2d_transpose[0][0]',       \n",
      "                                )                                 'conv2d_9[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 10, 10, 1024  4096       ['concatenate[0][0]']            \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 10, 10, 512)  4719104     ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 10, 10, 512)  2359808     ['conv2d_12[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_4 (Dropout)            (None, 10, 10, 512)  0           ['conv2d_13[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_transpose_1 (Conv2DTran  (None, 20, 20, 256)  524544     ['dropout_4[0][0]']              \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 20, 20, 512)  0           ['conv2d_transpose_1[0][0]',     \n",
      "                                                                  'conv2d_7[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 20, 20, 512)  2048       ['concatenate_1[0][0]']          \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 20, 20, 256)  1179904     ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 20, 20, 256)  590080      ['conv2d_14[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_5 (Dropout)            (None, 20, 20, 256)  0           ['conv2d_15[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_transpose_2 (Conv2DTran  (None, 40, 40, 128)  131200     ['dropout_5[0][0]']              \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 40, 40, 256)  0           ['conv2d_transpose_2[0][0]',     \n",
      "                                                                  'conv2d_5[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 40, 40, 256)  1024       ['concatenate_2[0][0]']          \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 40, 40, 128)  295040      ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 40, 40, 128)  147584      ['conv2d_16[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_6 (Dropout)            (None, 40, 40, 128)  0           ['conv2d_17[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_transpose_3 (Conv2DTran  (None, 80, 80, 64)  32832       ['dropout_6[0][0]']              \n",
      " spose)                                                                                           \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, 80, 80, 128)  0           ['conv2d_transpose_3[0][0]',     \n",
      "                                                                  'conv2d_3[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 80, 80, 128)  512        ['concatenate_3[0][0]']          \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 80, 80, 64)   73792       ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 80, 80, 64)   36928       ['conv2d_18[0][0]']              \n",
      "                                                                                                  \n",
      " dropout_7 (Dropout)            (None, 80, 80, 64)   0           ['conv2d_19[0][0]']              \n",
      "                                                                                                  \n",
      " conv2d_transpose_4 (Conv2DTran  (None, 160, 160, 32  8224       ['dropout_7[0][0]']              \n",
      " spose)                         )                                                                 \n",
      "                                                                                                  \n",
      " concatenate_4 (Concatenate)    (None, 160, 160, 64  0           ['conv2d_transpose_4[0][0]',     \n",
      "                                )                                 'conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 160, 160, 32  18464       ['concatenate_4[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 160, 160, 32  9248        ['conv2d_20[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 160, 160, 1)  33          ['conv2d_21[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 31,105,537\n",
      "Trainable params: 31,100,737\n",
      "Non-trainable params: 4,800\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "All images: 1035\n",
      "Num. training images: 838\n",
      "Num. validation images: 93\n",
      "Num. test images: 104\n",
      "Compile and fit the UNet model\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-01 12:35:10.885351: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8202\n",
      "2022-08-01 12:35:11.543998: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-01 12:35:11.548566: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-01 12:35:11.548612: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2022-08-01 12:35:11.549208: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-01 12:35:11.549285: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] INTERNAL: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70/70 [==============================] - ETA: 0s - loss: 0.3223 - mean_io_u: 0.4779\n",
      "Epoch 1: val_loss improved from inf to 0.15518, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 90s 1s/step - loss: 0.3223 - mean_io_u: 0.4779 - val_loss: 0.1552 - val_mean_io_u: 0.4769\n",
      "Epoch 2/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0937 - mean_io_u: 0.4780\n",
      "Epoch 2: val_loss improved from 0.15518 to 0.10667, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 60s 865ms/step - loss: 0.0937 - mean_io_u: 0.4780 - val_loss: 0.1067 - val_mean_io_u: 0.4777\n",
      "Epoch 3/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0885 - mean_io_u: 0.4772\n",
      "Epoch 3: val_loss did not improve from 0.10667\n",
      "70/70 [==============================] - 50s 722ms/step - loss: 0.0885 - mean_io_u: 0.4772 - val_loss: 0.1308 - val_mean_io_u: 0.4725\n",
      "Epoch 4/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0811 - mean_io_u: 0.4775\n",
      "Epoch 4: val_loss did not improve from 0.10667\n",
      "70/70 [==============================] - 45s 647ms/step - loss: 0.0811 - mean_io_u: 0.4775 - val_loss: 0.2721 - val_mean_io_u: 0.4774\n",
      "Epoch 5/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0746 - mean_io_u: 0.4791\n",
      "Epoch 5: val_loss did not improve from 0.10667\n",
      "70/70 [==============================] - 47s 678ms/step - loss: 0.0746 - mean_io_u: 0.4791 - val_loss: 0.1358 - val_mean_io_u: 0.4731\n",
      "Epoch 6/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0714 - mean_io_u: 0.4784\n",
      "Epoch 6: val_loss did not improve from 0.10667\n",
      "70/70 [==============================] - 44s 634ms/step - loss: 0.0714 - mean_io_u: 0.4784 - val_loss: 0.1269 - val_mean_io_u: 0.4758\n",
      "Epoch 7/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0717 - mean_io_u: 0.4777\n",
      "Epoch 7: val_loss did not improve from 0.10667\n",
      "70/70 [==============================] - 45s 641ms/step - loss: 0.0717 - mean_io_u: 0.4777 - val_loss: 0.2118 - val_mean_io_u: 0.4732\n",
      "Epoch 8/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0694 - mean_io_u: 0.4780\n",
      "Epoch 8: val_loss improved from 0.10667 to 0.07965, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 52s 749ms/step - loss: 0.0694 - mean_io_u: 0.4780 - val_loss: 0.0797 - val_mean_io_u: 0.4732\n",
      "Epoch 9/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0686 - mean_io_u: 0.4774\n",
      "Epoch 9: val_loss improved from 0.07965 to 0.07855, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 51s 741ms/step - loss: 0.0686 - mean_io_u: 0.4774 - val_loss: 0.0786 - val_mean_io_u: 0.4733\n",
      "Epoch 10/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0676 - mean_io_u: 0.4772\n",
      "Epoch 10: val_loss improved from 0.07855 to 0.07219, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 53s 762ms/step - loss: 0.0676 - mean_io_u: 0.4772 - val_loss: 0.0722 - val_mean_io_u: 0.4772\n",
      "Epoch 11/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0652 - mean_io_u: 0.4791\n",
      "Epoch 11: val_loss did not improve from 0.07219\n",
      "70/70 [==============================] - 39s 555ms/step - loss: 0.0652 - mean_io_u: 0.4791 - val_loss: 0.0983 - val_mean_io_u: 0.4776\n",
      "Epoch 12/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0648 - mean_io_u: 0.4780\n",
      "Epoch 12: val_loss did not improve from 0.07219\n",
      "70/70 [==============================] - 48s 689ms/step - loss: 0.0648 - mean_io_u: 0.4780 - val_loss: 0.0735 - val_mean_io_u: 0.4742\n",
      "Epoch 13/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0625 - mean_io_u: 0.4783\n",
      "Epoch 13: val_loss improved from 0.07219 to 0.06631, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 51s 729ms/step - loss: 0.0625 - mean_io_u: 0.4783 - val_loss: 0.0663 - val_mean_io_u: 0.4759\n",
      "Epoch 14/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0633 - mean_io_u: 0.4773\n",
      "Epoch 14: val_loss improved from 0.06631 to 0.06301, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 47s 675ms/step - loss: 0.0633 - mean_io_u: 0.4773 - val_loss: 0.0630 - val_mean_io_u: 0.4780\n",
      "Epoch 15/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0601 - mean_io_u: 0.4779\n",
      "Epoch 15: val_loss did not improve from 0.06301\n",
      "70/70 [==============================] - 40s 581ms/step - loss: 0.0601 - mean_io_u: 0.4779 - val_loss: 0.0703 - val_mean_io_u: 0.4714\n",
      "Epoch 16/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0575 - mean_io_u: 0.4782\n",
      "Epoch 16: val_loss did not improve from 0.06301\n",
      "70/70 [==============================] - 43s 625ms/step - loss: 0.0575 - mean_io_u: 0.4782 - val_loss: 0.0697 - val_mean_io_u: 0.4724\n",
      "Epoch 17/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0565 - mean_io_u: 0.4783\n",
      "Epoch 17: val_loss did not improve from 0.06301\n",
      "70/70 [==============================] - 43s 620ms/step - loss: 0.0565 - mean_io_u: 0.4783 - val_loss: 0.0738 - val_mean_io_u: 0.4687\n",
      "Epoch 18/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0585 - mean_io_u: 0.4774\n",
      "Epoch 18: val_loss improved from 0.06301 to 0.06089, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 52s 755ms/step - loss: 0.0585 - mean_io_u: 0.4774 - val_loss: 0.0609 - val_mean_io_u: 0.4753\n",
      "Epoch 19/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0541 - mean_io_u: 0.4784\n",
      "Epoch 19: val_loss improved from 0.06089 to 0.06052, saving model to ./../data/weights/UNet_TEGU_160x160_moderado_low_160_30_spe70_v1.h5\n",
      "70/70 [==============================] - 48s 688ms/step - loss: 0.0541 - mean_io_u: 0.4784 - val_loss: 0.0605 - val_mean_io_u: 0.4741\n",
      "Epoch 20/20\n",
      "70/70 [==============================] - ETA: 0s - loss: 0.0550 - mean_io_u: 0.4777\n",
      "Epoch 20: val_loss did not improve from 0.06052\n",
      "70/70 [==============================] - 46s 666ms/step - loss: 0.0550 - mean_io_u: 0.4777 - val_loss: 0.0648 - val_mean_io_u: 0.4760\n",
      "6/6 [==============================] - 5s 959ms/step - loss: 0.0575 - mean_io_u: 0.4775\n",
      "*** Final  metrics ***\n",
      "Loss: 0.0574849508702755\n",
      "Mean IoU: 0.4774548411369324\n"
     ]
    }
   ],
   "source": [
    "res_config = train(config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(res_config.history['loss'])\n",
    "plt.plot(res_config.history['val_loss'])\n",
    "plt.title('Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Val'], loc='upper left')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(res_config.history['mean_io_u'])\n",
    "plt.plot(res_config.history['val_mean_io_u'])\n",
    "plt.title('mean_iou')\n",
    "plt.ylabel('val_mean_iou')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Val'], loc='upper left')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNKlws8+6PcuvJrSWdSmTxJ",
   "include_colab_link": true,
   "name": "2 - Entrenamiento",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.11"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}