MagicBathyNet_bathymetry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bathymetry prediction on aerial/satellite images using bathy-U-Net\n",
    "# Initial Pytorch Implementation: Panagiotis Agrafiotis (https://github.com/pagraf)\n",
    "# Email: agrafiotis.panagiotis@gmail.com\n",
    "\n",
    "# Description: magicbathy_unet.py is a simplified U-Net model modified for estimating water depth from RGB images. \n",
    "# The model retains the encoder-decoder structure with reduced layers and channels, using skip connections to \n",
    "# maintain spatial information during depth prediction. It outputs continuous values, suitable for depth estimation,\n",
    "# even with limited annotated data.\n",
    "\n",
    "# If you use this code please cite our paper: \n",
    "# P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, \"MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry \n",
    "# Prediction and Pixel-Based Classification in Shallow Waters,\" IGARSS 2024 - 2024 IEEE International Geoscience and Remote \n",
    "# Sensing Symposium, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.\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/.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## GPU\n",
    "#Enable GPU with `Runtime->Change runtime type->Hardware Accelerator->GPU` in the top menu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZMHN1FoRuUfH"
   },
   "outputs": [],
   "source": [
    "# Standard Library\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import random\n",
    "import itertools\n",
    "\n",
    "# Numerical Libraries\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "from scipy import ndimage\n",
    "\n",
    "# Image Processing Libraries\n",
    "from skimage import io\n",
    "from skimage.transform import resize, rotate, resize_local_mean\n",
    "\n",
    "# Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Machine Learning Libraries\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, mean_squared_error\n",
    "\n",
    "# PyTorch Libraries\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as data\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "import torch.optim.lr_scheduler\n",
    "import torch.nn.init\n",
    "\n",
    "# Raster and Remote Sensing Libraries\n",
    "import rasterio\n",
    "import gdal\n",
    "\n",
    "# Deep Learning Model\n",
    "from bathymetry.magicbathy_unet import *\n",
    "\n",
    "# Utility Libraries\n",
    "from glob import glob\n",
    "from IPython.display import clear_output\n",
    "from torchvision.transforms import RandomCrop, Resize\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ZScpjpH7uda",
    "outputId": "d6ab5abf-5964-4039-ae2f-f5c4bd91fe6b"
   },
   "outputs": [],
   "source": [
    "# insert your data folder path here\n",
    "FOLDER = '/.../'\n",
    "sys.path.append(FOLDER)\n",
    "\n",
    "MAIN_FOLDER = FOLDER\n",
    "\n",
    "#RGB images and depth folders\n",
    "DATA_FOLDER = MAIN_FOLDER + '.../img_{}.tif'\n",
    "LABEL_FOLDER = MAIN_FOLDER + '.../depth_{}.tif'\n",
    "ERODED_FOLDER = MAIN_FOLDER + '.../depth_{}.tif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YEWsRUno-5Ba"
   },
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fterOFMwueZc"
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "\n",
    "#set the dataset modality here\n",
    "\n",
    "#dataset = \"S2\"\n",
    "dataset = \"SPOT6\"\n",
    "#dataset = \"UAV\"\n",
    "\n",
    "if dataset == \"UAV\":\n",
    "    norm_param = np.load('norm_param_aerial.npy')\n",
    "    norm_param_depth = -30.443   #-30.443 FOR AGIA NAPA, -11 FOR PUCK LAGOON\n",
    "    WINDOW_SIZE = (720, 720)\n",
    "    STRIDE = 16\n",
    "    BATCH_SIZE = 1\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",
    "elif dataset == \"SPOT6\":\n",
    "    norm_param = np.load('norm_param_spot6_an.npy')\n",
    "    norm_param_depth = -30.443   #-30.443 FOR AGIA NAPA, -11 FOR PUCK LAGOON\n",
    "    WINDOW_SIZE = (30, 30)\n",
    "    STRIDE = 2\n",
    "    BATCH_SIZE = 1\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",
    "elif dataset == \"S2\":\n",
    "    norm_param = np.load('norm_param_s2_an.npy')\n",
    "    norm_param_depth = -30.443   #-30.443 FOR AGIA NAPA, -11 FOR PUCK LAGOON\n",
    "    WINDOW_SIZE = (18, 18)\n",
    "    STRIDE = 2\n",
    "    BATCH_SIZE = 1\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",
    "print(norm_param)\n",
    "print(norm_param.shape)\n",
    "\n",
    "net = UNet_bathy(3, 1)\n",
    "base_lr = 0.0001\n",
    "\n",
    "CACHE = True # Store the dataset in-memory"
   ]
  },
  {
   "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",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# We load one tile from the dataset and we display it\n",
    "img = io.imread(MAIN_FOLDER+'.../img_409.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+'.../depth_409.tif')\n",
    "fig.add_subplot(122)\n",
    "plt.imshow(gt/norm_param_depth)\n",
    "print(gt/norm_param_depth)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e6xMCqbYANIb"
   },
   "source": [
    "We need to define some utils functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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)\n",
    "    x2 = x1 + w\n",
    "    y1 = random.randint(0, H - h)\n",
    "    y2 = y1 + h\n",
    "    return x1, x2, y1, y2\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):\n",
    "    # Exclude 0 values from calculation\n",
    "    non_zero_mask = predictions != 0\n",
    "    \n",
    "    # Calculate RMSE, MAE, and collect predictions and targets\n",
    "    rmse = np.sqrt(np.mean(((predictions - gts) ** 2)[non_zero_mask]))\n",
    "    mae = np.mean(np.abs((predictions - gts)[non_zero_mask]))\n",
    "    std_dev = np.std((predictions - gts)[non_zero_mask])\n",
    "    \n",
    "    print(\"RMSE : {:.3f}m\".format(rmse*-norm_param_depth))\n",
    "    print(\"MAE : {:.3f}m\".format(mae*-norm_param_depth))\n",
    "    print(\"Std_Dev : {:.3f}m\".format(std_dev*-norm_param_depth))\n",
    "    print(\"---\")\n",
    "    \n",
    "    return rmse\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",
    "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",
    "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": [
    "random.seed(1)\n",
    "\n",
    "# Dataset class     \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",
    "        # 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",
    "        # 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",
    "            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",
    "            \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 = 1/norm_param_depth * np.asarray(io.imread(self.label_files[random_idx]), dtype='float32')\n",
    "            if self.cache:\n",
    "                self.label_cache_[random_idx] = label\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",
    "\n",
    "        data_p, label_p = self.data_augmentation(data_p, label_p)\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": "markdown",
   "metadata": {
    "id": "QLgB8FGQC3XW"
   },
   "source": [
    "We can now instantiate the network using the specified parameters."
   ]
  },
  {
   "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)"
   ]
  },
  {
   "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": [
    "# 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",
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "dvEqRB6wI6pX"
   },
   "outputs": [],
   "source": [
    "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.Adam(net.parameters(), lr=base_lr)\n",
    "\n",
    "# We define the scheduler\n",
    "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [10], gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xcxL0DQIJVtg"
   },
   "outputs": [],
   "source": [
    "crop_size = 256\n",
    "pad_size = 32  # Define pad size here\n",
    "\n",
    "def test(net, test_ids, all=True):\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",
    "\n",
    "    test_labels = [1 / norm_param_depth * np.asarray(io.imread(LABEL_FOLDER.format(id)), dtype='float32') for id in test_ids]\n",
    "    eroded_labels = [1 / norm_param_depth * np.asarray(io.imread(LABEL_FOLDER.format(id)), dtype='float32') for id in test_ids]\n",
    "    all_preds = []\n",
    "    all_gts = []\n",
    "    all_masked_preds = []\n",
    "    all_masked_gts = []\n",
    "\n",
    "    # Switch the network to inference mode\n",
    "    net.eval()\n",
    "    \n",
    "    mse = None\n",
    "    \n",
    "    ratio = crop_size / WINDOW_SIZE[0]\n",
    "    \n",
    "    for img, gt, gt_e in tqdm(zip(test_images, test_labels, eroded_labels), total=len(test_ids), leave=False):\n",
    "        img = scipy.ndimage.zoom(img, (ratio, ratio, 1), order=1)\n",
    "        gt = scipy.ndimage.zoom(gt, (ratio, ratio), order=1)\n",
    "        gt_e = scipy.ndimage.zoom(gt_e, (ratio, ratio), order=1)\n",
    "        \n",
    "        # Pad the image, ground truth, and eroded ground truth with reflection\n",
    "        img = np.pad(img, ((pad_size, pad_size), (pad_size, pad_size), (0, 0)), mode='reflect')\n",
    "        gt = np.pad(gt, ((pad_size, pad_size), (pad_size, pad_size)), mode='reflect')\n",
    "        gt_e = np.pad(gt_e, ((pad_size, pad_size), (pad_size, pad_size)), mode='reflect')\n",
    "\n",
    "        # Convert image to tensor\n",
    "        img_tensor = np.copy(img).transpose((2, 0, 1))\n",
    "        img_tensor = np.expand_dims(img_tensor, axis=0)\n",
    "        img_tensor = torch.from_numpy(img_tensor).cuda()\n",
    "\n",
    "        # Do the inference on the whole image\n",
    "        with torch.no_grad():\n",
    "            outs = net(img_tensor.float())\n",
    "            pred = outs.data.cpu().numpy().squeeze()\n",
    "            \n",
    "        # Remove padding from prediction\n",
    "        pred = pred[pad_size:-pad_size, pad_size:-pad_size]\n",
    "        img = img[pad_size:-pad_size, pad_size:-pad_size]\n",
    "        gt = gt[pad_size:-pad_size, pad_size:-pad_size]\n",
    "        gt_e = gt_e[pad_size:-pad_size, pad_size:-pad_size]\n",
    "\n",
    "        # Display the result\n",
    "        clear_output()\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(pred)  \n",
    "        fig.add_subplot(1, 3, 3)\n",
    "        plt.imshow(gt)\n",
    "        plt.show()\n",
    "        \n",
    "        # Generate mask for non-annotated pixels in depth data \n",
    "        gt_mask = (gt_e != 0).astype(np.float32) \n",
    "        gt_mask = torch.from_numpy(gt_mask) \n",
    "        gt_mask = gt_mask.unsqueeze(0)\n",
    "        gt_mask = gt_mask.reshape(crop_size, crop_size)\n",
    "        gt_mask = gt_mask.to(device) \n",
    "\n",
    "        img_mask = (img != 0).astype(np.float32) \n",
    "        img_mask = np.mean(img_mask, axis=2)\n",
    "        img_mask = torch.from_numpy(img_mask)  \n",
    "        #img_mask = img_mask.reshape(crop_size, crop_size)\n",
    "        img_mask = img_mask.to(device) \n",
    "        \n",
    "        print(gt_mask.shape)\n",
    "        print(img_mask.shape)\n",
    "        \n",
    "        combined_mask = img_mask*gt_mask\n",
    "      \n",
    "        masked_pred = pred * combined_mask.cpu().numpy()\n",
    "        masked_gt_e = gt_e * combined_mask.cpu().numpy()\n",
    "        all_preds.append(pred)\n",
    "        all_gts.append(gt_e)\n",
    "        \n",
    "        all_masked_preds.append(masked_pred)\n",
    "        all_masked_gts.append(masked_gt_e)\n",
    "        \n",
    "\n",
    "        clear_output()\n",
    "\n",
    "        metrics(masked_pred.ravel(), masked_gt_e.ravel())\n",
    "        # print(f\"RSME: {rmse*-norm_param_depth}\")\n",
    "        rmse = metrics(np.concatenate([p.ravel() for p in all_masked_preds]), np.concatenate([p.ravel() for p in all_masked_gts]).ravel())\n",
    "\n",
    "    # Returning all predictions and ground truths if 'all' is set to True\n",
    "    if all:\n",
    "        return rmse, all_preds, all_gts\n",
    "    else:\n",
    "        return rmse  # Returning the final MSE for the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pl_9f-KgJPFk"
   },
   "source": [
    "# Training the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "XhILeXKGJV4s"
   },
   "outputs": [],
   "source": [
    "crop_size = 256\n",
    "epoch_folder = '/.../'\n",
    "\n",
    "class CustomLoss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CustomLoss, self).__init__()\n",
    "\n",
    "    def forward(self, output, depth, mask):\n",
    "        # Mask out areas with no annotations\n",
    "        mse_loss = nn.MSELoss(reduction='none')\n",
    "\n",
    "        loss = mse_loss(output, depth)\n",
    "        loss = (loss * mask.float()).sum() # gives \\sigma_euclidean over unmasked elements\n",
    "\n",
    "        non_zero_elements = mask.sum()\n",
    "        rmse_loss_val = torch.sqrt(loss / non_zero_elements)\n",
    "\n",
    "\n",
    "        return rmse_loss_val\n",
    "\n",
    "\n",
    "def train(net, optimizer, epochs, scheduler=None, save_epoch = 15):\n",
    "    global epoch_folder\n",
    "    global data_folder\n",
    "    losses = np.zeros(10000000)\n",
    "    mean_losses = np.zeros(100000000)\n",
    "    mean_rmse_plot = np.zeros(1000000)\n",
    "    mean_mse_plot = np.zeros(1000000)\n",
    "    rmse_plot = np.zeros(1000000)\n",
    "    mse_plot = np.zeros(1000000)\n",
    "    epoch_folder = epoch_folder\n",
    "    criterion = CustomLoss()\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",
    "        \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",
    "            size=(256, 256)\n",
    "            \n",
    "            # Resizing data_p and label_p\n",
    "            data = F.interpolate(data, size=size, mode='nearest')\n",
    "            target = F.interpolate(target.unsqueeze(0), size=size, mode='nearest')\n",
    "            \n",
    "            #target = target.unsqueeze(0) #needed for aerial\n",
    "            \n",
    "            data_size = data.size()[2:]  # Get the original data size\n",
    "\n",
    "            if data_size[0] > crop_size and data_size[1] > crop_size:\n",
    "                    # Use RandomCrop transformation for data and target\n",
    "                data_transform = RandomCrop(size=crop_size)\n",
    "                target_transform = RandomCrop(size=crop_size)\n",
    "    \n",
    "                    # Apply RandomCrop transformation to data and target\n",
    "                data = data_transform(data)\n",
    "                target = target_transform(target)\n",
    "                \n",
    "            # Generate mask for non-annotated pixels in depth data\n",
    "            target_mask = (target.cpu().numpy() != 0).astype(np.float32)  \n",
    "            target_mask = torch.from_numpy(target_mask)  \n",
    "            target_mask = target_mask.reshape(crop_size, crop_size)\n",
    "            target_mask = target_mask.to(device)  \n",
    "            \n",
    "            data_mask = (data.cpu().numpy() != 0).astype(np.float32)  \n",
    "            data_mask = np.mean(data_mask, axis=1)\n",
    "            data_mask = torch.from_numpy(data_mask) \n",
    "            #data_mask = data_mask.reshape(crop_size, crop_size)\n",
    "            data_mask = data_mask.to(device) \n",
    "            \n",
    "            # Combine the masks\n",
    "            combined_mask = target_mask * data_mask\n",
    "            combined_mask = (combined_mask >= 0.5).float()\n",
    "            # Check if combined_mask is 0\n",
    "            if torch.sum(combined_mask) == 0:\n",
    "            # Use another pair of data and target\n",
    "                continue\n",
    "\n",
    "            data = torch.clamp(data, min=0, max=1)\n",
    "            output = net(data.float())\n",
    "\n",
    "            loss = criterion(output, target, combined_mask)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            losses[iter_] = loss.item() ##loss.data[0]\n",
    "            mean_losses[iter_] = np.mean(losses[max(0,iter_-100):iter_])\n",
    "            \n",
    "            pred = output.data.cpu().numpy()[0]\n",
    "            gt = target.data.cpu().numpy()[0]\n",
    "            \n",
    "            # Apply the mask to the predictions and ground truth\n",
    "            masked_pred = pred * combined_mask.cpu().numpy()\n",
    "            masked_gt = gt * combined_mask.cpu().numpy()\n",
    "\n",
    "            rmse_plot[iter_] = metrics(np.concatenate([p.ravel() for p in masked_pred]), np.concatenate([p.ravel() for p in masked_gt]).ravel())\n",
    "            mean_rmse_plot[iter_] = np.mean(rmse_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(np.transpose(data.data.cpu().numpy()[0],(1,2,0)), dtype='float32')\n",
    "                pred = output.data.cpu().numpy()[0]\n",
    "                gt = target.data.cpu().numpy()[0]\n",
    "                masked_pred = pred * combined_mask.cpu().numpy()\n",
    "                masked_gt = gt * combined_mask.cpu().numpy()\n",
    "                \n",
    "                print('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\Mean RMSE in m: {}'.format(\n",
    "                    e, epochs, batch_idx, len(train_loader),\n",
    "                    100. * batch_idx / len(train_loader), loss.item(), -norm_param_depth * metrics(np.concatenate([p.ravel() for p in masked_pred]), np.concatenate([p.ravel() for p in masked_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(-norm_param_depth * mean_rmse_plot[:iter_], 'red')\n",
    "                ax2.set_title('Mean RMSE in m')\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",
    "        \n",
    "                fig = plt.figure(figsize=(14.0, 8.0))\n",
    "                fig.add_subplot(131)\n",
    "                plt.imshow(rgb)\n",
    "                plt.title('RGB')\n",
    "                fig.add_subplot(132)\n",
    "                plt.imshow(gt[0,:,:], cmap='viridis_r', vmin=0, vmax=1)\n",
    "                plt.title('Ground truth')\n",
    "                fig.add_subplot(133)\n",
    "                plt.title('Prediction')\n",
    "                plt.imshow(masked_pred[0,:,:],  cmap='viridis_r', vmin=0, vmax=1)\n",
    "                plt.suptitle('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\\nLoss: {:.6f}'.format(\n",
    "                    e, epochs, batch_idx, len(train_loader),\n",
    "                    100. * batch_idx / len(train_loader), loss.item()))\n",
    "                plt.show()\n",
    "\n",
    "                if iter_ % 1000 == 0 and iter_ != 0:\n",
    "                    # plt.savefig(MAIN_FOLDER + model_folder +\"output_data_filled_irfanview_no_shades_10/diagram_{}_out_of_{}\".format(e,epochs))\n",
    "                    fig.savefig(DATA_FOLDER + \"/train_images_{}_out_of_{}\".format(e, epochs))\n",
    "                    # plt.savefig(\"Train_epoch_{}/{}_{}/{}_({:.0f}%).png\".format(e, epochs, batch_idx, len(train_loader), 100. * batch_idx / len(train_loader)))\n",
    "            iter_ += 1\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)\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, 10, scheduler)"
   ]
  },
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   "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'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6UqKC5TJXnu2",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "_, all_preds, all_gts= test(net, test_ids, all=True)\n",
    "#print(all_preds)\n",
    "#print(all_gts)"
   ]
  },
  {
   "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
   },
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   "source": [
    "ratio = crop_size / WINDOW_SIZE[0]\n",
    "\n",
    "for p, id_ in zip(all_preds, test_ids):\n",
    "    img = p*norm_param_depth\n",
    "    \n",
    "    img = scipy.ndimage.zoom(img, (1/ratio, 1/ratio), order=1)\n",
    "    \n",
    "    #print(img)\n",
    "    plt.imshow(img, cmap='viridis') and plt.show()\n",
    "\n",
    "    #io.imsave('/home/pagraf/Desktop/magicbathy/inference_tile_{}.png'.format(id_), img)\n",
    "    nlcd02_arr_1, nlcd02_ds_1 = read_geotiff(MAIN_FOLDER + 'agia_napa/img/spot6/img_410.tif', 3)\n",
    "    write_geotiff('./inference_tile_{}.tif'.format(id_), img, nlcd02_ds_1)"
   ]
  }
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