MagicBathyNet_pixelclass.ipynb

{
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   "source": [
    "# Pixel-based classification on aerial/satellite images of shallow waters\n",
    "\n",
    "# Initial Pytorch Implementation: Panagiotis Agrafiotis (https://github.com/pagraf)\n",
    "# Email: agrafiotis.panagiotis@gmail.com\n",
    "\n",
    "# Description: For the pixel-based classification problem we used U-Net, a well known FCN, and \n",
    "# SegFormer (B5-sized),which is an hierarchical Transformer encoder with an all-MLP decode head. \n",
    "# Both models proved already their high performance on similar tasks. \n",
    "\n",
    "# If you use this code please cite our paper: \"Agrafiotis, P., Janowski, L., Skarlatos, D. & Demir, B. (2024) \n",
    "# MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification \n",
    "# in Shallow Waters, arXiv preprint arXiv:2405.15477, 2024.\"\n",
    "\n",
    "# This .ipynb was structured inspired by the [DeepNetsForEO] (https://github.com/nshaud/DeepNetsForEO) repository.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Attribution-NonCommercial-ShareAlike 4.0 International License\n",
    "\n",
    "# Copyright (c) 2024 Panagiotis Agrafiotis\n",
    "\n",
    "# This license requires that reusers give credit to the creator. It allows reusers \n",
    "# to distribute, remix, adapt, and build upon the material in any medium or format,\n",
    "# for noncommercial purposes only. If others modify or adapt the material, they \n",
    "# must license the modified material under identical terms.\n",
    "\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "# SOFTWARE.\n",
    "\n",
    "\n",
    "# This work is part of MagicBathy project funded by the European Union’s HORIZON Europe research and innovation \n",
    "# programme under the Marie Skłodowska-Curie GA 101063294. Work has been carried out at the Remote Sensing Image \n",
    "# Analysis group. For more information about the project visit https://www.magicbathy.eu/."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "## GPU\n",
    "\n",
    "#Enable GPU with `Runtime->Change runtime type->Hardware Accelerator->GPU` in the top menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZMHN1FoRuUfH"
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   "outputs": [],
   "source": [
    "# Core imports\n",
    "import sys\n",
    "import time\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from tqdm.notebook import tqdm  # Updated for modern `tqdm`\n",
    "import random\n",
    "import itertools\n",
    "\n",
    "# Image and plotting\n",
    "from skimage import io\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Torch imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader  # Use specific imports if needed\n",
    "from torch.optim import Adam, lr_scheduler  # Import specific optimizers/schedulers\n",
    "from torch.autograd import Variable\n",
    "\n",
    "# Geospatial and other libraries\n",
    "import rasterio\n",
    "import gdal\n",
    "import scipy  # Consider importing only required submodules\n",
    "\n",
    "# Semantic Segmentation Models\n",
    "from semanticsegmentation.models import *\n",
    "from semanticsegmentation.models.backbones import MiT\n",
    "from semanticsegmentation.models.segformer import SegFormer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ZScpjpH7uda",
    "outputId": "d6ab5abf-5964-4039-ae2f-f5c4bd91fe6b"
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   "outputs": [],
   "source": [
    "\n",
    "# data are assumed to be in Vaihingen/ directory, otherwise modify accordingly\n",
    "MAIN_FOLDER = '.../MagicBathyNet/'\n",
    "sys.path.append(MAIN_FOLDER)\n",
    "\n",
    "#RGB images and gts folders\n",
    "DATA_FOLDER = MAIN_FOLDER + 'agia_napa/img/aerial/img_{}.tif'\n",
    "LABEL_FOLDER = MAIN_FOLDER + 'agia_napa/gts/aerial/gts_{}.tif'\n",
    "ERODED_FOLDER = MAIN_FOLDER + 'agia_napa/gts/aerial/gts_{}.tif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YEWsRUno-5Ba"
   },
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fterOFMwueZc"
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   "outputs": [],
   "source": [
    "# Parameters\n",
    "\n",
    "#dataset = \"S2\"\n",
    "#dataset = \"SPOT6\"\n",
    "dataset = \"UAV\"\n",
    "\n",
    "#area = \"puck_lagoon\"\n",
    "area = \"agia_napa\"\n",
    "\n",
    "if dataset == \"UAV\":\n",
    "\n",
    "    WINDOW_SIZE = (384, 384)\n",
    "    STRIDE = 32\n",
    "    BATCH_SIZE = 15\n",
    "    norm_param = np.load('norm_param_aerial.npy')\n",
    "    train_images = ['409', '418', '350', '399', '361', '430', '380', '359', '371', '377', '379', '360', '368', '419', '389', '420', '401', '408', '352', '388', '362', '421', '412', '351', '349', '390', '400', '378']\n",
    "    test_images = ['411', '387', '410', '398', '370', '369', '397']\n",
    "    if WINDOW_SIZE[0] < 128:\n",
    "        scale = 128/WINDOW_SIZE[0]\n",
    "        test_window = 128\n",
    "    else:\n",
    "        scale = 1\n",
    "        test_window = WINDOW_SIZE[0]\n",
    "    inv_scale = 1/scale\n",
    "elif dataset == \"SPOT6\":\n",
    "    norm_param = np.load('norm_param_spot6_an2.npy')\n",
    "    WINDOW_SIZE = (16, 16)\n",
    "    STRIDE = 1\n",
    "    BATCH_SIZE = 20\n",
    "    MAIN_FOLDER = FOLDER \n",
    "    train_images = ['409', '418', '350', '399', '361', '430', '380', '359', '371', '377', '379', '360', '368', '419', '389', '420', '401', '408', '352', '388', '362', '421', '412', '351', '349', '390', '400', '378']\n",
    "    test_images = ['411', '387', '410', '398', '370', '369', '397']\n",
    "    \n",
    "    if WINDOW_SIZE[0] < 256:\n",
    "        scale = 256/WINDOW_SIZE[0]\n",
    "        test_window = 256\n",
    "    else:\n",
    "        scale = 1\n",
    "        test_window = WINDOW_SIZE[0]\n",
    "    inv_scale = 1/scale\n",
    "elif dataset == \"S2\":\n",
    "    norm_param = np.load('norm_param_s2_an.npy')\n",
    "    WINDOW_SIZE = (16, 16)\n",
    "    STRIDE = 2\n",
    "    BATCH_SIZE = 20\n",
    "    MAIN_FOLDER = FOLDER \n",
    "    train_images = ['409', '418', '350', '399', '361', '430', '380', '359', '371', '377', '379', '360', '368', '419', '389', '420', '401', '408', '352', '388', '362', '421', '412', '351', '349', '390', '400', '378']\n",
    "    test_images = ['411', '387', '410', '398', '370', '369', '397']\n",
    "    if WINDOW_SIZE[0] < 256:\n",
    "        scale = 256/WINDOW_SIZE[0]\n",
    "        test_window = 256\n",
    "    else:\n",
    "        scale = 1\n",
    "        test_window = WINDOW_SIZE[0]\n",
    "    inv_scale = 1/scale\n",
    "\n",
    "print(norm_param)\n",
    "\n",
    "if area == \"agia_napa\":\n",
    "    #net = SegFormer('MiT-B5', 5)\n",
    "    net = UNet(3, 5)\n",
    "    LABELS = [\"poseidonia\", \"rock\", \"macroalgae\", \"sand\", \"ignored\"] # Label names\n",
    "    N_CLASSES = len(LABELS) # Number of classes\n",
    "    print(N_CLASSES)\n",
    "    WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing\n",
    "    l =1.\n",
    "    WEIGHTS[4] = 0.\n",
    "    WEIGHTS[0] = l\n",
    "    WEIGHTS[1] = l\n",
    "    WEIGHTS[2] = l\n",
    "    WEIGHTS[3] = l\n",
    "    print(WEIGHTS)\n",
    "\n",
    "if area == \"puck_lagoon\":\n",
    "    net = SegFormer('MiT-B5', 3)\n",
    "    #net = UNet(3, 3)\n",
    "    LABELS = [\"sand\", \"poseidonia\", \"ignored\"] # Label names\n",
    "    N_CLASSES = len(LABELS) # Number of classes\n",
    "    print(N_CLASSES)\n",
    "    WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing\n",
    "    l =1.\n",
    "    WEIGHTS[0] = l\n",
    "    WEIGHTS[1] = l\n",
    "    WEIGHTS[2] = 0.\n",
    "\n",
    "print(WEIGHTS)\n",
    "\n",
    "\n",
    "base_lr = 0.000001\n",
    "\n",
    "\n",
    "CACHE = True # Store the dataset in-memory\n",
    "epsilon = 1e-20  # Small positive value\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IRs2xwrU_TrG"
   },
   "source": [
    "\n",
    "# Visualizing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 276
    },
    "id": "T2fTWftA_aWy",
    "outputId": "9e153a11-3629-4b92-f71e-55bec8013a8a"
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   "source": [
    "# color palette\n",
    "\n",
    "if area == \"agia_napa\":\n",
    "    palette = {0 : (0, 128, 0),     #poseidonia\n",
    "               1 : (0, 0, 255),     #rock\n",
    "               2 : (255, 0, 0),     #macroalgae\n",
    "               3 : (255, 128, 0),   #sand\n",
    "               4 : (0, 0, 0)}       #Undefined (black)\n",
    "\n",
    "if area == \"puck_lagoon\":\n",
    "    palette = {0 : (255, 128, 0),   #sand\n",
    "               1 : (0, 128, 0),     #poseidonia\n",
    "               2 : (0, 0, 0)}       #Undefined (black)\n",
    "\n",
    "\n",
    "invert_palette = {v: k for k, v in palette.items()}\n",
    "\n",
    "def convert_to_color(arr_2d, palette=palette):\n",
    "    \"\"\" Numeric labels to RGB-color encoding \"\"\"\n",
    "    arr_3d = np.zeros((arr_2d.shape[0], arr_2d.shape[1], 3), dtype=np.uint8)\n",
    "\n",
    "    for c, i in palette.items():\n",
    "        m = arr_2d == c\n",
    "        arr_3d[m] = i\n",
    "\n",
    "    return arr_3d\n",
    "\n",
    "def convert_from_color(arr_3d, palette=invert_palette):\n",
    "    \"\"\" RGB-color encoding to grayscale labels \"\"\"\n",
    "    arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)\n",
    "\n",
    "    for c, i in palette.items():\n",
    "        m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)\n",
    "        arr_2d[m] = i\n",
    "\n",
    "    return arr_2d\n",
    "\n",
    "# We load one tile from the dataset and we display it\n",
    "img = io.imread(MAIN_FOLDER+'agia_napa/img/aerial/img_410.tif')\n",
    "fig = plt.figure()\n",
    "fig.add_subplot(121)\n",
    "norm_img = (img - norm_param[0]) / (norm_param[1] - norm_param[0]) \n",
    "plt.imshow(norm_img)\n",
    "\n",
    "# We load the ground truth\n",
    "gt = io.imread(MAIN_FOLDER+'agia_napa/gts/aerial/gts_410.tif')\n",
    "fig.add_subplot(122)\n",
    "plt.imshow(gt)\n",
    "plt.show()\n",
    "\n",
    "# We also check that we can convert the ground truth into an array format\n",
    "array_gt = convert_from_color(gt)\n",
    "print(\"Ground truth in numerical format has shape ({},{}) : \\n\".format(*array_gt.shape[:2]), array_gt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e6xMCqbYANIb"
   },
   "source": [
    "We need to define a bunch of utils functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "aBs-fgNZAJc2"
   },
   "outputs": [],
   "source": [
    "# Utils\n",
    "\n",
    "def get_random_pos(img, window_shape):\n",
    "    \"\"\" Extract of 2D random patch of shape window_shape in the image \"\"\"\n",
    "    w, h = window_shape\n",
    "    W, H = img.shape[-2:]\n",
    "    x1 = random.randint(0, W - w - 0)\n",
    "    x2 = x1 + w\n",
    "    y1 = random.randint(0, H - h - 0)\n",
    "    y2 = y1 + h\n",
    "    return x1, x2, y1, y2\n",
    "\n",
    "def CrossEntropy2d(input, target, weight=None, reduction=True):\n",
    "    \"\"\" 2D version of the cross entropy loss \"\"\"\n",
    "    dim = input.dim()\n",
    "    if dim == 2:\n",
    "        return F.cross_entropy(input, target, weight, reduction='mean')\n",
    "##        return nn.CrossEntropyLoss(output, target,weight)\n",
    "    elif dim == 4:\n",
    "        output = input.view(input.size(0),input.size(1), -1)\n",
    "        output = torch.transpose(output,1,2).contiguous()\n",
    "        output = output.view(-1,output.size(2))\n",
    "        target = target.view(-1)\n",
    "        return F.cross_entropy(output, target,weight, reduction='mean')\n",
    "##        return nn.CrossEntropyLoss(output, target,weight)\n",
    "    else:\n",
    "        raise ValueError('Expected 2 or 4 dimensions (got {})'.format(dim))\n",
    "\n",
    "#def accuracy(input, target):\n",
    "#    return 100 * float(np.count_nonzero(input == target)) / target.size\n",
    "\n",
    "def sliding_window(top, step=10, window_size=(20,20)):\n",
    "    \"\"\" Slide a window_shape window across the image with a stride of step \"\"\"\n",
    "    for x in range(0, top.shape[0], step):\n",
    "        if x + window_size[0] > top.shape[0]:\n",
    "            x = top.shape[0] - window_size[0]\n",
    "        for y in range(0, top.shape[1], step):\n",
    "            if y + window_size[1] > top.shape[1]:\n",
    "                y = top.shape[1] - window_size[1]\n",
    "            yield x, y, window_size[0], window_size[1]\n",
    "            \n",
    "def count_sliding_window(top, step=10, window_size=(20,20)):\n",
    "    \"\"\" Count the number of windows in an image \"\"\"\n",
    "    c = 0\n",
    "    for x in range(0, top.shape[0], step):\n",
    "        if x + window_size[0] > top.shape[0]:\n",
    "            x = top.shape[0] - window_size[0]\n",
    "        for y in range(0, top.shape[1], step):\n",
    "            if y + window_size[1] > top.shape[1]:\n",
    "                y = top.shape[1] - window_size[1]\n",
    "            c += 1\n",
    "    return c\n",
    "\n",
    "def grouper(n, iterable):\n",
    "    \"\"\" Browse an iterator by chunk of n elements \"\"\"\n",
    "    it = iter(iterable)\n",
    "    while True:\n",
    "        chunk = tuple(itertools.islice(it, n))\n",
    "        if not chunk:\n",
    "            return\n",
    "        yield chunk\n",
    "\n",
    "def metrics(predictions, gts, label_values=LABELS[:-1]):\n",
    "    cm = confusion_matrix(\n",
    "            gts,\n",
    "            predictions)\n",
    "    \n",
    "    cols_rows_to_use = N_CLASSES-1\n",
    "    cm = cm[:cols_rows_to_use,:cols_rows_to_use]\n",
    "    \n",
    "    print(\"Confusion matrix :\")\n",
    "    print(cm)\n",
    "    \n",
    "    print(\"---\")\n",
    "    \n",
    "    # Compute global accuracy\n",
    "    total = sum(sum(cm))\n",
    "    accuracy = sum([cm[x][x] for x in range(len(cm))])\n",
    "\n",
    "    accuracy *= 100 / (float(total)+ epsilon)\n",
    "\n",
    "\n",
    "    #accuracy *= 100 / float(total)\n",
    "    print(\"{} pixels processed\".format(total))\n",
    "    print(\"Total accuracy : {}%\".format(accuracy))\n",
    "    \n",
    "    print(\"---\")\n",
    "    \n",
    "    # Compute F1 score\n",
    "    F1Score = np.zeros(len(label_values))\n",
    "    for i in range(len(label_values)):\n",
    "        try:\n",
    "            F1Score[i] = 2. * cm[i,i] / (np.sum(cm[i,:]) + np.sum(cm[:,i]) + epsilon) \n",
    "        except:\n",
    "            # Ignore exception if there is no element in class i for test set\n",
    "            pass\n",
    "    print(\"F1Score :\")\n",
    "    for l_id, score in enumerate(F1Score):\n",
    "        print(\"{}: {}\".format(label_values[l_id], score))\n",
    "\n",
    "    print(\"---\")\n",
    "        \n",
    "    # Compute kappa coefficient\n",
    "    total = np.sum(cm)\n",
    "    pa = np.trace(cm) / float(total)\n",
    "    pe = np.sum(np.sum(cm, axis=0) * np.sum(cm, axis=1)) / float(total*total)\n",
    "    kappa = (pa - pe) / (1 - pe);\n",
    "    print(\"Kappa: \" + str(kappa))\n",
    "    return accuracy\n",
    "\n",
    "\n",
    "def metrics_2(predictions, gts, label_values=LABELS[:-1]):\n",
    "    cm = confusion_matrix(\n",
    "            gts,\n",
    "            predictions)\n",
    "    \n",
    "    cols_rows_to_use = N_CLASSES-1\n",
    "    cm = cm[:cols_rows_to_use,:cols_rows_to_use]\n",
    "       \n",
    "    # Compute global accuracy\n",
    "    total = sum(sum(cm))\n",
    "    accuracy = sum([cm[x][x] for x in range(len(cm))])\n",
    "    \n",
    "    accuracy *= 100 / (float(total)+ epsilon)\n",
    "\n",
    "    \n",
    "    # Compute F1 score\n",
    "    F1Score = np.zeros(len(label_values))\n",
    "    for i in range(len(label_values)):\n",
    "        try:\n",
    "            F1Score[i] = 2. * cm[i,i] / (np.sum(cm[i,:]) + np.sum(cm[:,i]) + epsilon)\n",
    "        except:\n",
    "            # Ignore exception if there is no element in class i for test set\n",
    "            pass\n",
    "    \n",
    "    return accuracy\n",
    "\n",
    "def read_geotiff(filename, b):\n",
    "    ds = gdal.Open(filename)\n",
    "    band = ds.GetRasterBand(b)\n",
    "    arr = band.ReadAsArray()\n",
    "    return arr, ds\n",
    "\n",
    "def write_geotiff(filename, arr, in_ds):\n",
    "    if arr.dtype == np.float32:\n",
    "        arr_type = gdal.GDT_Float32\n",
    "    else:\n",
    "        arr_type = gdal.GDT_Int32\n",
    "\n",
    "    driver = gdal.GetDriverByName(\"GTiff\")\n",
    "    out_ds = driver.Create(filename, arr.shape[1], arr.shape[0], 1, arr_type)\n",
    "    out_ds.SetProjection(in_ds.GetProjection())\n",
    "    out_ds.SetGeoTransform(in_ds.GetGeoTransform())\n",
    "    band = out_ds.GetRasterBand(1)\n",
    "    band.WriteArray(arr)\n",
    "    band.FlushCache()\n",
    "    band.ComputeStatistics(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v84piuYZAnRb"
   },
   "source": [
    "\n",
    "# Loading the dataset\n",
    "\n",
    "We define a PyTorch dataset (`torch.utils.data.Dataset)` that loads all the tiles in memory and performs random sampling. Tiles are stored in memory on the fly.\n",
    "\n",
    "The dataset also performs random data augmentation (horizontal and vertical flips) and normalizes the data in [0, 1]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "28W_ISbr_aaG"
   },
   "outputs": [],
   "source": [
    "# Dataset class\n",
    "from torchvision.transforms import Resize\n",
    "\n",
    "random.seed(1)\n",
    "        \n",
    "class dataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, ids, data_files=DATA_FOLDER, label_files=LABEL_FOLDER,\n",
    "                            cache=False, augmentation=True):\n",
    "        super(dataset, self).__init__()\n",
    "        \n",
    "        self.augmentation = augmentation\n",
    "        self.cache = cache\n",
    "        \n",
    "        # List of files\n",
    "        self.data_files = [DATA_FOLDER.format(id) for id in ids]\n",
    "        self.label_files = [LABEL_FOLDER.format(id) for id in ids]\n",
    "        \n",
    "        \n",
    "\n",
    "        # Sanity check : raise an error if some files do not exist\n",
    "        for f in self.data_files + self.label_files:\n",
    "            if not os.path.isfile(f):\n",
    "                raise KeyError('{} is not a file !'.format(f))\n",
    "        \n",
    "        # Initialize cache dicts\n",
    "        self.data_cache_ = {}\n",
    "        self.label_cache_ = {}\n",
    "            \n",
    "    \n",
    "    def __len__(self):\n",
    "        # Default epoch size is 10 000 samples\n",
    "        return 10000\n",
    "    \n",
    "    @classmethod\n",
    "    def data_augmentation(cls, *arrays, flip=True, mirror=True):\n",
    "        will_flip, will_mirror = False, False\n",
    "        if flip and random.random() < 0.5:\n",
    "            will_flip = True\n",
    "        if mirror and random.random() < 0.5:\n",
    "            will_mirror = True\n",
    "        \n",
    "        results = []\n",
    "        for array in arrays:\n",
    "            if will_flip:\n",
    "                if len(array.shape) == 2:\n",
    "                    array = array[::-1, :]\n",
    "                else:\n",
    "                    array = array[:, ::-1, :]\n",
    "            if will_mirror:\n",
    "                if len(array.shape) == 2:\n",
    "                    array = array[:, ::-1]\n",
    "                else:\n",
    "                    array = array[:, :, ::-1]\n",
    "            results.append(np.copy(array))\n",
    "            \n",
    "        return tuple(results)\n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "        \n",
    "        # Pick a random image\n",
    "        random_idx = random.randint(0, len(self.data_files) - 1)\n",
    "        \n",
    "        \n",
    "        # If the tile hasn't been loaded yet, put in cache\n",
    "        if random_idx in self.data_cache_.keys():\n",
    "            data = self.data_cache_[random_idx]\n",
    "        else:\n",
    "            # Data is normalized in [0, 1]\n",
    "            #if dataset == \"S2\":\n",
    "            data = np.asarray(io.imread(self.data_files[random_idx]).transpose((2,0,1)), dtype='float32')\n",
    "            data = (data - norm_param[0][:, np.newaxis, np.newaxis]) / (norm_param[1][:, np.newaxis, np.newaxis] - norm_param[0][:, np.newaxis, np.newaxis]) \n",
    "            #else:\n",
    "             #   data = np.asarray(io.imread(self.data_files[random_idx]).transpose((2,0,1)), dtype='float32') / norm_param          \n",
    "                       \n",
    "  \n",
    "            if self.cache:\n",
    "                self.data_cache_[random_idx] = data\n",
    "            \n",
    "        if random_idx in self.label_cache_.keys():\n",
    "            label = self.label_cache_[random_idx]\n",
    "        else: \n",
    "            # Labels are converted from RGB to their numeric values\n",
    "            label = np.asarray(convert_from_color(io.imread(self.label_files[random_idx])), dtype='int64')\n",
    "            if self.cache:\n",
    "                self.label_cache_[random_idx] = label\n",
    "        \n",
    "        if dataset == \"UAV\":\n",
    "            data[data == 0] = 0.5   \n",
    "        if dataset == \"SPOT6\":\n",
    "            data[data == 0] = 0.5\n",
    "        if dataset == \"S2\":\n",
    "            data[data == 0] = 0.5\n",
    "\n",
    "\n",
    "        x1, x2, y1, y2 = get_random_pos(data, WINDOW_SIZE)\n",
    "        data_p = data[:, x1:x2,y1:y2]\n",
    "        label_p = label[x1:x2,y1:y2]\n",
    "\n",
    "        data_p, label_p = self.data_augmentation(data_p, label_p)\n",
    "   \n",
    "        data_p = scipy.ndimage.zoom(data_p, (1,scale,scale), order=0)\n",
    "        label_p = scipy.ndimage.zoom(label_p, scale, order=0)\n",
    "   \n",
    "        \n",
    "        return (torch.from_numpy(data_p),\n",
    "                torch.from_numpy(label_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X2KmX78VA8yR"
   },
   "source": [
    "# Load the Network on GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pnQ-_dF-ILje",
    "outputId": "f368ee0b-ec5f-4b4b-bdf5-21b81a191d7e",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "torch.cuda.init()\n",
    "net = net.to(device)\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WhuWCc2cI43p"
   },
   "source": [
    "\n",
    "# Loading the data\n",
    "\n",
    "We now create a train/test split. In our case, we specify a fixed train/test split for benchmarking MagicBathyNet.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jAKhuI8t9MWC",
    "outputId": "bb577297-e49a-4604-91e1-b2526c132d50"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Load the datasets\n",
    "all_files = sorted(glob(LABEL_FOLDER.replace('{}', '*')))\n",
    "all_ids = [f.split('area')[-1].split('.')[0] for f in all_files]\n",
    "\n",
    "train_ids = train_images\n",
    "test_ids = test_images\n",
    "\n",
    "print(\"Tiles for training : \", train_ids)\n",
    "print(\"Tiles for testing : \", test_ids)\n",
    "\n",
    "\n",
    "train_set = dataset(train_ids, cache=CACHE)\n",
    "train_loader = torch.utils.data.DataLoader(train_set,batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yODkcni5JC1o"
   },
   "source": [
    "\n",
    "# Designing the optimizer\n",
    "We use the `torch.optim.lr_scheduler` to reduce the learning rate by 10 after 10 or so epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "dvEqRB6wI6pX"
   },
   "outputs": [],
   "source": [
    "\n",
    "params_dict = dict(net.named_parameters())\n",
    "params = []\n",
    "for key, value in params_dict.items():\n",
    "    if '_D' in key:\n",
    "        params += [{'params':[value],'lr': base_lr}]\n",
    "    else:\n",
    "        params += [{'params':[value],'lr': base_lr}]  \n",
    "\n",
    "optimizer = optim.SGD(net.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0005)\n",
    "# We define the scheduler\n",
    "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [200], gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "xcxL0DQIJVtg"
   },
   "outputs": [],
   "source": [
    "def test(net, test_ids, all=False, stride=STRIDE, batch_size=BATCH_SIZE, window_size=(test_window,test_window)):\n",
    "    # Use the network on the test set\n",
    "    test_images = ((np.asarray(io.imread(DATA_FOLDER.format(id)), dtype='float32') - norm_param[0]) / (norm_param[1] - norm_param[0]) for id in test_ids)\n",
    "    test_labels = (np.asarray(io.imread(LABEL_FOLDER.format(id)), dtype='uint8') for id in test_ids)\n",
    "    eroded_labels = (convert_from_color(io.imread(ERODED_FOLDER.format(id))) for id in test_ids)\n",
    "    all_preds = []\n",
    "    all_gts = []\n",
    "    \n",
    "    # Switch the network to inference mode\n",
    "    net.eval()\n",
    "\n",
    "    for img, gt, gt_e in tqdm(zip(test_images, test_labels, eroded_labels), total=len(test_ids), leave=False):\n",
    "        pred = np.zeros(img.shape[:2] + (N_CLASSES,))\n",
    "        \n",
    "        print(gt.shape)\n",
    "\n",
    "        gt = scipy.ndimage.zoom(gt, (scale,scale,1), order=0)\n",
    "        #gt_e = scipy.ndimage.zoom(gt_e, (scale,scale,1), order=0)\n",
    "        img = scipy.ndimage.zoom(img, (scale,scale,1), order=0)\n",
    "        img = np.clip(img, 0, 1)\n",
    "        pred = scipy.ndimage.zoom(pred, (scale,scale,1), order=0)\n",
    "\n",
    "        total = count_sliding_window(img, step=stride, window_size=window_size) // batch_size\n",
    "        for i, coords in enumerate(tqdm(grouper(batch_size, sliding_window(img, step=stride, window_size=window_size)), total=total, leave=False)):\n",
    "            # Display in progress results\n",
    "            if i > 0 and total > 10 and i % int(10 * total / 100) == 0:\n",
    "                    _pred = np.argmax(pred, axis=-1)\n",
    "                    fig = plt.figure()\n",
    "                    fig.add_subplot(1,3,1)\n",
    "                    plt.imshow(np.asarray(255 * img, dtype='uint8'))\n",
    "                    fig.add_subplot(1,3,2)\n",
    "                    plt.imshow(convert_to_color(_pred))\n",
    "                    fig.add_subplot(1,3,3)\n",
    "                    plt.imshow(gt)\n",
    "                    clear_output()\n",
    "                    plt.show()\n",
    "                    \n",
    "            # Build the tensor\n",
    "            image_patches = [np.copy(img[x:x+w, y:y+h]).transpose((2,0,1)) for x,y,w,h in coords]\n",
    "            image_patches = np.asarray(image_patches)\n",
    "            image_patches = Variable(torch.from_numpy(image_patches).cuda(), volatile=True)\n",
    "            \n",
    "            # Do the inference\n",
    "            outs = net(image_patches.float())\n",
    "            #outs = net(image_patches)['out'] # Use for ResNet and pytorch ready models\n",
    "            outs = outs.data.cpu().numpy()\n",
    "                      \n",
    "            # Fill in the results array\n",
    "            for out, (x, y, w, h) in zip(outs, coords):\n",
    "                out = out.transpose((1,2,0))\n",
    "                pred[x:x+w, y:y+h] += out\n",
    "            del(outs)\n",
    "        \n",
    "        \n",
    "        pred = scipy.ndimage.zoom(pred, (inv_scale,inv_scale,1), order=0)\n",
    "        pred = np.argmax(pred, axis=-1)\n",
    "\n",
    "        # Display the result\n",
    "        clear_output()\n",
    "        fig = plt.figure(figsize=(14.0, 8.0))\n",
    "        fig.add_subplot(1,3,1)\n",
    "        plt.imshow(np.asarray(255* img, dtype='uint8'))\n",
    "        plt.title('RGB Image')\n",
    "        fig.add_subplot(1,3,2)\n",
    "        plt.imshow(convert_to_color(pred))\n",
    "        plt.title('Prediction')\n",
    "        fig.add_subplot(1,3,3)\n",
    "        plt.imshow(gt)\n",
    "        plt.title('Ground Truth')\n",
    "        plt.show()\n",
    "        fig.savefig(DATA_FOLDER + \"/test_image_result\")\n",
    "\n",
    "        all_preds.append(pred)\n",
    "        all_gts.append(gt_e)\n",
    "\n",
    "        clear_output()\n",
    "        # Compute some metrics\n",
    "        \n",
    "        metrics(pred.ravel(), gt_e.ravel())\n",
    "        accuracy = metrics(np.concatenate([p.ravel() for p in all_preds]), np.concatenate([p.ravel() for p in all_gts]).ravel())\n",
    "    if all:\n",
    "        return accuracy, all_preds, all_gts\n",
    "    else:\n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pl_9f-KgJPFk"
   },
   "source": [
    "# Training the network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "XhILeXKGJV4s"
   },
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "import numpy.ma as ma\n",
    "\n",
    "\n",
    "def train(net, optimizer, epochs, scheduler=None, weights=WEIGHTS, save_epoch = 200):\n",
    "    global epoch_folder\n",
    "    global data_folder\n",
    "    losses = np.zeros(10000000)\n",
    "    mean_losses = np.zeros(100000000)\n",
    "    weights = weights.to(device)\n",
    "    accuracies_plot = np.zeros(100000000)\n",
    "    mean_accuracies_plot = np.zeros(1000000)\n",
    "    epoch_folder = '/home/pagraf/Desktop/magicbathy/'\n",
    "\n",
    "    #criterion = nn.CrossEntropyLoss(weight=weights)\n",
    "    iter_ = 0\n",
    "    \n",
    "    for e in range(1, epochs + 1):\n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "        net.train()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            data, target = Variable(data.to(device)), Variable(target.to(device))\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            \n",
    "            data = torch.clamp(data, min=0, max=1)\n",
    "            output = net(data.float())\n",
    "\n",
    "            loss = CrossEntropy2d(output, target, weight=weights)\n",
    "            \n",
    "            loss.backward()\n",
    "            \n",
    "            optimizer.step()\n",
    "            \n",
    "            losses[iter_] = loss.item() ##loss.data[0]\n",
    "            mean_losses[iter_] = np.mean(losses[max(0,iter_-100):iter_])\n",
    "            \n",
    "            pred = np.argmax(output.data.cpu().numpy()[0], axis=0)\n",
    "            gt = target.data.cpu().numpy()[0]\n",
    "\n",
    "            accuracies_plot[iter_] = metrics_2(np.concatenate([p.ravel() for p in pred]), np.concatenate([p.ravel() for p in gt]).ravel())/100\n",
    "            mean_accuracies_plot[iter_] = np.mean(accuracies_plot[max(0,iter_-100):iter_])\n",
    "        \n",
    "            if iter_ % 100 == 0:\n",
    "                if iter_ % 1000 == 0 and iter_ != 0:\n",
    "                    try:\n",
    "                        os.mkdir(DATA_FOLDER)\n",
    "                    except FileExistsError:\n",
    "                        pass\n",
    "                clear_output()\n",
    "                rgb = np.asarray(255 * np.transpose(data.data.cpu().numpy()[0],(1,2,0)), dtype='uint8')\n",
    "    \n",
    "                pred = np.argmax(output.data.cpu().numpy()[0], axis=0)\n",
    "                gt = target.data.cpu().numpy()[0]\n",
    "                c1 = N_CLASSES - 1\n",
    "                print(c1)\n",
    "                gt_sparse = np.delete(gt, np.where(gt == c1))      ##delete background\n",
    "                pred_sparse = np.delete(pred, np.where(gt == c1))  ##delete background\n",
    "                print('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\tAccuracy: {}'.format(\n",
    "                    e, epochs, batch_idx, len(train_loader),\n",
    "                    100. * batch_idx / len(train_loader), loss.item(), metrics_2(np.concatenate([p.ravel() for p in pred]), np.concatenate([p.ravel() for p in gt]).ravel()))) ##loss.data[0]\n",
    "\n",
    "                # Plot loss\n",
    "                fig1, ax1 = plt.subplots(figsize=(14.0, 8.0))\n",
    "                ax1.plot(mean_losses[:iter_], 'blue')\n",
    "                ax1.set_title('Training Loss')\n",
    "                ax1.set_xlabel('Iteration')\n",
    "                ax1.set_ylabel('Loss')\n",
    "                ax1.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "\n",
    "                # Plot accuracy\n",
    "                fig2, ax2 = plt.subplots(figsize=(14.0, 8.0))\n",
    "                ax2.plot(mean_accuracies_plot[:iter_], 'orange')\n",
    "                ax2.set_title('Validation Accuracy')\n",
    "                ax2.set_xlabel('Iteration')\n",
    "                ax2.set_ylabel('Accuracy')\n",
    "                ax2.grid(color='black', linestyle='-', linewidth=0.5)\n",
    "    \n",
    "                plt.show()\n",
    "    \n",
    "                if iter_ % 1000 == 0 and iter_ != 0:\n",
    "                    fig1.savefig(DATA_FOLDER + \"/train_loss_{}_out_of_{}\".format(e, epochs))\n",
    "                    fig2.savefig(DATA_FOLDER + \"/validation_accuracy_{}_out_of_{}\".format(e, epochs))\n",
    "\n",
    "                # Plot images\n",
    "                fig = plt.figure(figsize=(14.0, 8.0))\n",
    "                fig.add_subplot(131)\n",
    "                if dataset == \"S2\":\n",
    "                    rgb = rgb[:, :, ::-1]\n",
    "                plt.imshow(rgb)\n",
    "                plt.title('RGB')\n",
    "                fig.add_subplot(132)\n",
    "                plt.imshow(convert_to_color(gt))\n",
    "                plt.title('Ground truth')\n",
    "                fig.add_subplot(133)\n",
    "                plt.title('Prediction')\n",
    "                plt.imshow(convert_to_color(pred))\n",
    "                plt.suptitle('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\\nLoss: {:.6f}\\nAccuracy: {}'.format(\n",
    "                    e, epochs, batch_idx, len(train_loader),\n",
    "                    100. * batch_idx / len(train_loader), loss.item(), metrics_2(np.concatenate([p.ravel() for p in pred]), np.concatenate([p.ravel() for p in gt]).ravel())))\n",
    "                plt.show()\n",
    "\n",
    "                if iter_ % 1000 == 0 and iter_ != 0:\n",
    "                    fig.savefig(DATA_FOLDER + \"/train_images_{}_out_of_{}\".format(e, epochs))\n",
    "\n",
    "            iter_ += 1\n",
    "\n",
    "            \n",
    "            del(data, target, loss)      \n",
    "            \n",
    "        if e % save_epoch == 0:\n",
    "            try:\n",
    "                os.mkdir(epoch_folder)\n",
    "            except FileExistsError:\n",
    "                pass\n",
    "            \n",
    "            # We validate with the largest possible stride for faster computing\n",
    "            acc = test(net, test_ids, all=False, stride=min(WINDOW_SIZE))\n",
    "            torch.save(net.state_dict(),epoch_folder + 'model_epoch{}'.format(e))\n",
    "    torch.save(net.state_dict(), epoch_folder + 'model_final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "fc434ea1-9fca-4dcd-b964-a9c3f9cf282f",
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   "outputs": [],
   "source": [
    "train(net, optimizer, 100, scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cxf4xSjAePhn"
   },
   "source": [
    "\n",
    "# Testing the network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S2C345YCQ8U7"
   },
   "outputs": [],
   "source": [
    "net.load_state_dict(torch.load('./model_final'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6UqKC5TJXnu2",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "_, all_preds, all_gts = test(net, test_ids, all=True, stride=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R47BdtUjeKpF"
   },
   "source": [
    "\n",
    "# Saving the results\n",
    "\n",
    "We can visualize and save the resulting tiles for qualitative assessment.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q4ICWhJOXwiu",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for p, id_ in zip(all_preds, test_ids):\n",
    "    img = convert_to_color(p)\n",
    "    plt.imshow(img) and plt.show()\n",
    "    io.imsave('/home/pagraf/Desktop/magicbathy/inference_tile_{}.png'.format(id_), img)\n",
    "    #nlcd02_arr_1, nlcd02_ds_1 = read_geotiff(MAIN_FOLDER + 'puck_laggon/img/s2/img_3051.tif', 3)\n",
    "    #write_geotiff('./inference_tile_{}.tif'.format(id_), img[1], nlcd02_ds_1)"
   ]
  }
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