{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PpWen5PWk9Ak"
   },
   "source": [
    "# Image Segmentation Tutorial using Segmentation Model Library\n",
    "**Author:** Nattapon Jaroenchai, University of Illinois Uraban-Champaign\n",
    "\n",
    "Welcome to this tutorial on using the Segmentation Models library in Python. This library is a fantastic resource for anyone looking to build models for image segmentation tasks. It provides a simple, consistent interface for constructing models with a range of different architectures and pre-trained weights. This tutorial is designed to get you up and running with the Segmentation Models library and illustrate its power and flexibility.\n",
    "\n",
    "In the world of computer vision, image segmentation plays a vital role. It is the process of partitioning a digital image into multiple segments to simplify and/or change the representation of an image into something more meaningful and easier to analyze. The Segmentation Models library simplifies the process of building and training state-of-the-art segmentation models, allowing you to focus on the more creative aspects of your project.\n",
    "\n",
    "The tutorial will guide you through the following steps:\n",
    "\n",
    "1. Importing the necessary libraries\n",
    "2. Loading the dataset\n",
    "3. Defining the model using the Unet architecture with an ImageNet backbone\n",
    "4. Compiling and training the model\n",
    "5. Plotting the training history\n",
    "\n",
    "By the end of this tutorial, you should have a solid understanding of how to use the Segmentation Models library to construct your own segmentation models. Let's get started!\n",
    "\n",
    "**Reqirements**\n",
    "\n",
    "    keras >= 2.2.0 or tensorflow >= 1.13\n",
    "    segmenation-models==1.0.*\n",
    "    albumentations==0.3.0\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tfDddy-wHGGu"
   },
   "source": [
    "## Create Folders Structures\n",
    "\n",
    "We download all sample dataset and the neccessary libraries from github "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PPNWbvW9qO1e",
    "outputId": "4be17273-8874-4530-f257-93c239870bae",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "input_data = './samples/'\n",
    "model_path = './models/'\n",
    "prediction_path = './predicts/'\n",
    "log_path = './logs/'\n",
    "\n",
    "# Create the folder if it does not exist\n",
    "os.makedirs(input_data, exist_ok=True)\n",
    "os.makedirs(model_path, exist_ok=True)\n",
    "os.makedirs(prediction_path, exist_ok=True)\n",
    "os.makedirs(log_path, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WwsuOBM4uSgh"
   },
   "source": [
    "## Install segmentation models and neccesary libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Requirement already satisfied: decorator>=4.3.0 in /cvmfs/cybergis.illinois.edu/software/conda/cybergisx/geoai-0.8.0/lib/python3.6/site-packages (from networkx>=2.0->scikit-image->efficientnet==1.0.0->segmentation-models) (4.4.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install keras tensorflow matplotlib segmentation-models scikit-learn scipy matplotlib &> /dev/null\n",
    "\n",
    "## Imports libs\n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "os.environ[\"SM_FRAMEWORK\"] = \"tf.keras\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JdBrOWork1xu",
    "outputId": "08daf242-42e7-493e-ef01-547475508ae4",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "import segmentation_models as sm\n",
    "from segmentation_models import Unet\n",
    "from segmentation_models import get_preprocessing\n",
    "from segmentation_models.losses import bce_jaccard_loss\n",
    "from segmentation_models.metrics import iou_score\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Conv2D\n",
    "from tensorflow.keras.optimizers import Adam, SGD, RMSprop\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, TensorBoard\n",
    "from unet_util import dice_coef_loss, dice_coef, jacard_coef, dice_coef_loss, Residual_CNN_block, multiplication, attention_up_and_concatenate, multiplication2, attention_up_and_concatenate2, UNET_224, evaluate_prediction_result\n",
    "\n",
    "\n",
    "sm.set_framework('tf.keras')\n",
    "sm.framework()\n",
    "tf.random.set_seed(180431)\n",
    "\n",
    "# https://stackoverflow.com/questions/75433717/module-keras-utils-generic-utils-has-no-attribute-get-custom-objects-when-im\n",
    "# open the file keras.py, change all the 'init_keras_custom_objects' to 'init_tfkeras_custom_objects'.\n",
    "# the location of the keras.py is in the error message. In your case, it should be in /usr/local/lib/python3.8/dist-packages/efficientnet/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CqQMBCzWyX6-"
   },
   "source": [
    "## Load the sample dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ZnkDEWzTyXGh",
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = np.load(input_data+'/train_data.npy').astype(np.float32) #50 samples\n",
    "Y_train = np.load(input_data+'/train_label.npy').astype(np.float32)\n",
    "X_validation = np.load(input_data+'/vali_data.npy').astype(np.float32) #20 samples\n",
    "Y_validation = np.load(input_data+'/vali_label.npy').astype(np.float32)\n",
    "X_test = np.load(input_data+'/test_data.npy').astype(np.float32) # 50 samples\n",
    "Y_test = np.load(input_data+'/test_label.npy').astype(np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pgmCLEbPJEjk"
   },
   "source": [
    "### Explore the data\n",
    "\n",
    "The study utilizes eight raster layers for analysis and modeling. These layers include \n",
    "\n",
    "\n",
    "1. the digital elevation model (DEM)\n",
    "2. geometric curvature \n",
    "3. slope\n",
    "4. positive openness\n",
    "5. TPI with a 9-cell by 9-cell window\n",
    "6. return intensity\n",
    "7. Geomorphons type\n",
    "8. TPI with a 3-cell by 3-cell window\n",
    "\n",
    "All these data layers have a resolution of 1 meter, providing detailed information about the study area. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "jtKyRXllImAa",
    "outputId": "8faafafd-0473-4bb2-b856-06bde9b360a5",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 4,figsize=(8,4))\n",
    "fig.tight_layout()\n",
    "labels = [\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\"]\n",
    "count = 0\n",
    "img_order = [0,1,2,3,4,5,6,7]\n",
    "for ax in axs.flat:\n",
    "  image = X_train[3,:,:,img_order[count]]\n",
    "  im = ax.imshow(image)\n",
    "  ax.set_xlabel(\"(\"+labels[count]+\")\", fontsize = 10) # X label\n",
    "  count = count + 1\n",
    "\n",
    "fig.colorbar(im, ax=axs.ravel().tolist() , shrink=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HpHkqpE-xjc6"
   },
   "source": [
    "## Create U-net Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "vROSSm2MpvsX",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Avaiable backbones for U-net architechture\n",
    "# 'vgg16' 'vgg19' 'resnet18' 'resnet34' 'resnet50' 'resnet101' 'resnet152' 'inceptionv3' \n",
    "# 'inceptionresnetv2' 'densenet121' 'densenet169' 'densenet201' 'seresnet18' 'seresnet34' \n",
    "# 'seresnet50' 'seresnet101' 'seresnet152'\n",
    "backend = 'vgg19'\n",
    "\n",
    "# Fine-tuning flag\n",
    "finetune = True\n",
    "\n",
    "# TensorBoard log\n",
    "logdir = log_path+backend\n",
    "os.makedirs(logdir, exist_ok=True)\n",
    "\n",
    "# define hyperparameters and callback modules\n",
    "patience = 10\n",
    "maxepoch = 10\n",
    "callbacks = [ReduceLROnPlateau(monitor='val_loss', factor=0.7, patience=patience, min_lr=1e-9, verbose=1, mode='min'),\n",
    "             EarlyStopping(monitor='val_loss', patience=patience, verbose=0),\n",
    "             ModelCheckpoint(model_path+backend+'.h5', monitor='val_loss', save_best_only=True, verbose=0),\n",
    "             TensorBoard(log_dir=logdir)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yYZNyJfAwt84"
   },
   "source": [
    "### **Scenario 1:**\n",
    "\n",
    "Create a U-net model **without** ImageNet pre-trained weights. The model input has 8 raster layers. \n",
    "\n",
    "**Note:** All backbones have weights trained on 2012 ILSVRC ImageNet datase (encoder_weights='imagenet')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "vQYrKMPWrSpu"
   },
   "outputs": [],
   "source": [
    "# Unet with ImageNet backends\n",
    "# base_model = sm.Unet(backend, classes = 1, encoder_weights=None, input_shape=(None, None, 8), encoder_freeze = False )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R55cPIsjIKQc"
   },
   "source": [
    "### **Scenario 2:**\n",
    "\n",
    "Create a U-net model **with** ImageNet pre-trained weights. The model input has 8 raster layers. \n",
    "\n",
    "In this case, the backbones are trained with RGB (ImageNet) so we need to add new input wiht 8 channels. A Conv2D layer will convert 8 channels input to 3 channels input for the pretrained backbones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "5bujGpzaIWAq",
    "tags": []
   },
   "outputs": [],
   "source": [
    "pretrained_model = sm.Unet(backend, classes = 1, encoder_weights = 'imagenet', encoder_freeze = finetune)\n",
    "\n",
    "input = Input(shape=(None, None, 8))\n",
    "l1 = Conv2D(3, (1, 1))(input) # map N channels data to 3 channels\n",
    "out = pretrained_model(l1)\n",
    "base_model = Model(input, out, name = pretrained_model.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "JGdJ9-KMJJW0",
    "tags": []
   },
   "outputs": [],
   "source": [
    "base_model.compile(optimizer = Adam(), \n",
    "                loss = dice_coef_loss, \n",
    "                metrics=[dice_coef,'accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "COHqbcuSJ0o4"
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VdWfrtVJJztg",
    "outputId": "d9759446-1256-476a-ed4e-8e34bae67792",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "13/13 [==============================] - 36s 3s/step - loss: -0.1836 - dice_coef: 0.1826 - accuracy: 0.7206 - val_loss: -0.1220 - val_dice_coef: 0.1229 - val_accuracy: 0.2213 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "13/13 [==============================] - 35s 3s/step - loss: -0.2809 - dice_coef: 0.2792 - accuracy: 0.8002 - val_loss: -0.1448 - val_dice_coef: 0.1457 - val_accuracy: 0.3616 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "13/13 [==============================] - 35s 3s/step - loss: -0.3841 - dice_coef: 0.3812 - accuracy: 0.8789 - val_loss: -0.2377 - val_dice_coef: 0.2376 - val_accuracy: 0.7139 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "13/13 [==============================] - 34s 3s/step - loss: -0.4939 - dice_coef: 0.4896 - accuracy: 0.9224 - val_loss: -0.1642 - val_dice_coef: 0.1647 - val_accuracy: 0.4743 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "13/13 [==============================] - 35s 3s/step - loss: -0.5763 - dice_coef: 0.5723 - accuracy: 0.9402 - val_loss: -0.2857 - val_dice_coef: 0.2840 - val_accuracy: 0.8104 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "13/13 [==============================] - 34s 3s/step - loss: -0.6174 - dice_coef: 0.6190 - accuracy: 0.9457 - val_loss: -0.2395 - val_dice_coef: 0.2395 - val_accuracy: 0.7374 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "13/13 [==============================] - 35s 3s/step - loss: -0.6550 - dice_coef: 0.6528 - accuracy: 0.9487 - val_loss: -0.2885 - val_dice_coef: 0.2869 - val_accuracy: 0.8011 - lr: 0.0010\n",
      "Epoch 8/10\n",
      "13/13 [==============================] - 35s 3s/step - loss: -0.6603 - dice_coef: 0.6596 - accuracy: 0.9490 - val_loss: -0.3222 - val_dice_coef: 0.3201 - val_accuracy: 0.8186 - lr: 0.0010\n",
      "Epoch 9/10\n",
      "13/13 [==============================] - 34s 3s/step - loss: -0.6747 - dice_coef: 0.6705 - accuracy: 0.9508 - val_loss: -0.2500 - val_dice_coef: 0.2491 - val_accuracy: 0.7133 - lr: 0.0010\n",
      "Epoch 10/10\n",
      "13/13 [==============================] - 34s 3s/step - loss: -0.6788 - dice_coef: 0.6778 - accuracy: 0.9518 - val_loss: -0.2610 - val_dice_coef: 0.2602 - val_accuracy: 0.7245 - lr: 0.0010\n"
     ]
    }
   ],
   "source": [
    "train_history = base_model.fit(x = X_train,y = Y_train, \n",
    "                          validation_data = (X_validation, Y_validation), \n",
    "                          batch_size = 4, epochs = maxepoch, verbose=1, callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aTi__p7qJ8p0"
   },
   "source": [
    "## Fine-tuning \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BgwbK0Q8KAao",
    "outputId": "92bfa1d8-793c-4b46-ac18-94417fdf12f5",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/20\n",
      "13/13 [==============================] - 52s 4s/step - loss: -0.7020 - dice_coef: 0.6973 - accuracy: 0.9521 - val_loss: -0.3190 - val_dice_coef: 0.3172 - val_accuracy: 0.7957 - lr: 1.0000e-05\n",
      "Epoch 11/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7132 - dice_coef: 0.7096 - accuracy: 0.9541 - val_loss: -0.3929 - val_dice_coef: 0.3898 - val_accuracy: 0.8567 - lr: 1.0000e-05\n",
      "Epoch 12/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7026 - dice_coef: 0.6992 - accuracy: 0.9541 - val_loss: -0.4615 - val_dice_coef: 0.4573 - val_accuracy: 0.8926 - lr: 1.0000e-05\n",
      "Epoch 13/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7067 - dice_coef: 0.7024 - accuracy: 0.9539 - val_loss: -0.5283 - val_dice_coef: 0.5233 - val_accuracy: 0.9193 - lr: 1.0000e-05\n",
      "Epoch 14/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7012 - dice_coef: 0.6977 - accuracy: 0.9545 - val_loss: -0.5524 - val_dice_coef: 0.5471 - val_accuracy: 0.9269 - lr: 1.0000e-05\n",
      "Epoch 15/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7042 - dice_coef: 0.7054 - accuracy: 0.9547 - val_loss: -0.5714 - val_dice_coef: 0.5656 - val_accuracy: 0.9323 - lr: 1.0000e-05\n",
      "Epoch 16/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7154 - dice_coef: 0.7131 - accuracy: 0.9556 - val_loss: -0.5836 - val_dice_coef: 0.5777 - val_accuracy: 0.9354 - lr: 1.0000e-05\n",
      "Epoch 17/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7091 - dice_coef: 0.7092 - accuracy: 0.9549 - val_loss: -0.5964 - val_dice_coef: 0.5904 - val_accuracy: 0.9392 - lr: 1.0000e-05\n",
      "Epoch 18/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7142 - dice_coef: 0.7110 - accuracy: 0.9557 - val_loss: -0.5972 - val_dice_coef: 0.5908 - val_accuracy: 0.9396 - lr: 1.0000e-05\n",
      "Epoch 19/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7070 - dice_coef: 0.7051 - accuracy: 0.9559 - val_loss: -0.6084 - val_dice_coef: 0.6018 - val_accuracy: 0.9430 - lr: 1.0000e-05\n",
      "Epoch 20/20\n",
      "13/13 [==============================] - 53s 4s/step - loss: -0.7102 - dice_coef: 0.7063 - accuracy: 0.9560 - val_loss: -0.6216 - val_dice_coef: 0.6154 - val_accuracy: 0.9465 - lr: 1.0000e-05\n"
     ]
    }
   ],
   "source": [
    "# In the case that you want to run fine-tuning process, set the fintune flag to True and run this block.\n",
    "if(finetune):\n",
    "\n",
    "  # For fine-tuning we need to set the tranable flag to true for the whole model\n",
    "  base_model.trainable = True\n",
    "\n",
    "  # Recompile the model with the smaller learning rate at the optimizer (Adam(1e-5))\n",
    "  base_model.compile(optimizer = Adam(1e-5), loss = dice_coef_loss, metrics=[dice_coef,'accuracy'])\n",
    "\n",
    "  # train the model again\n",
    "  train_history_2 = base_model.fit(x = X_train, y = Y_train,\n",
    "                              validation_data=(X_validation, Y_validation),\n",
    "                              batch_size=4,epochs=maxepoch+10,\n",
    "                              initial_epoch = len(train_history.history['val_loss'])-1,\n",
    "                              verbose=1 ,callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EJtJvflIW-Pc"
   },
   "source": [
    "## Visualize the training performance\n",
    "\n",
    "We setup the TensorBoard to monitor and record the training process information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 957
    },
    "id": "kOoBQSdOh63u",
    "outputId": "93b0e578-a17c-4cd3-9ffd-796e0cec9e70",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming you have the training history stored in a variable named 'history'\n",
    "loss = train_history.history['loss'] + train_history_2.history['loss']\n",
    "val_loss = train_history.history['val_loss'] + train_history_2.history['val_loss']\n",
    "accuracy = train_history.history['accuracy'] + train_history_2.history['accuracy']\n",
    "val_accuracy = train_history.history['val_accuracy'] + train_history_2.history['val_accuracy']\n",
    "\n",
    "# Plotting loss\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot([10-1,10-1],plt.ylim(), label='Start Fine Tuning')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plotting accuracy\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(accuracy, label='Training Accuracy')\n",
    "plt.plot(val_accuracy, label='Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.plot([10-1,10-1],plt.ylim(), label='Start Fine Tuning')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nva4s5g2Xhvm"
   },
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Tm895AfXrlI",
    "outputId": "8386d2ba-b9be-4a60-c619-6813bc5a63ad",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model path:./models/vgg19\n",
      "F1 score of Stream: 0.6348320818990887\n",
      "Precision of Stream: 0.5810533739046401\n",
      "Recall of Stream: 0.6995809715485185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, precision_score,recall_score\n",
    "\n",
    "# load the test data\n",
    "X_test = np.load(input_data+'test_data.npy').astype(np.float32)\n",
    "\n",
    "# predict the test data using the loaded model\n",
    "test_predicted= base_model.predict(X_test)\n",
    "\n",
    "# convert the prediction probability to true or false with threshold at 0.5\n",
    "test_predicted_threshold = (test_predicted > 0.5).astype(np.uint8)\n",
    "\n",
    "y_true_f = K.flatten(Y_test.astype(np.uint8))\n",
    "y_pred_f = K.flatten(test_predicted_threshold.astype(np.uint8))\n",
    "\n",
    "f1_stream = f1_score(y_true_f, y_pred_f,labels=[1], average = 'micro')\n",
    "precision_stream = precision_score(y_true_f, y_pred_f,labels=[1], average = 'micro')\n",
    "recall_stream = recall_score(y_true_f, y_pred_f,labels=[1], average = 'micro')\n",
    "\n",
    "print('Model path:' + model_path + backend)\n",
    "print('F1 score of Stream: '+str(f1_stream))\n",
    "print('Precision of Stream: '+str(precision_stream))\n",
    "print('Recall of Stream: '+str(recall_stream))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "id": "7KphFHm5GzL4",
    "outputId": "79d07ae2-e5d8-43f8-c104-130ac3504bfe",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2,2,figsize=(8, 5)) \n",
    "axarr[0,0].imshow(test_predicted_threshold[0][:,:,0], cmap = 'coolwarm', interpolation='nearest')\n",
    "axarr[0,0].title.set_text('Prediction')\n",
    "axarr[1,0].imshow(Y_test[0][:,:,0], cmap = 'coolwarm', interpolation='nearest')\n",
    "axarr[1,0].title.set_text('Reference')\n",
    "\n",
    "axarr[0,1].imshow(test_predicted_threshold[4][:,:,0], cmap = 'coolwarm', interpolation='nearest')\n",
    "axarr[0,1].title.set_text('Prediction')\n",
    "axarr[1,1].imshow(Y_test[4][:,:,0], cmap = 'coolwarm', interpolation='nearest')\n",
    "axarr[1,1].title.set_text('Reference')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "A100",
   "machine_shape": "hm",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "geoai-Python3",
   "language": "python",
   "name": "geoai-py3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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