segmentation_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b70b02ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.executing_eagerly()\n",
    "import segmentation_models as sm\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from tensorflow.keras.utils import normalize\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "d8cca26a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7a9a837d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resizing images, if needed\n",
    "SIZE_X = 64\n",
    "SIZE_Y = 64\n",
    "n_classes = 5  # Number of classes for segmentation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc2cc017",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Capture training image info as a list\n",
    "train_images = []\n",
    "for directory_path in glob.glob(\"/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Input/\"):\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.jpg\")):\n",
    "        img = cv2.imread(img_path, 1)\n",
    "        # img = cv2.resize(img, (SIZE_Y, SIZE_X))\n",
    "        train_images.append(img)\n",
    "# Convert list to array for machine learning processing\n",
    "train_images = np.array(train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce0ef92b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Capture mask/label info as a list\n",
    "train_masks = []\n",
    "for directory_path in glob.glob(\"/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Target/\"):\n",
    "    for mask_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        mask = cv2.imread(mask_path, 0)\n",
    "        # mask = cv2.resize(mask, (SIZE_Y, SIZE_X), interpolation = cv2.INTER_NEAREST)  #Otherwise ground truth changes due to interpolation\n",
    "        train_masks.append(mask)\n",
    "# Convert list to array for machine learning processing\n",
    "train_masks = np.array(train_masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "82249199",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ganievanvar/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Encode labels... but multi dim array so need to flatten, encode and reshape\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "n, h, w = train_masks.shape\n",
    "train_masks_reshaped = train_masks.reshape(-1, 1)\n",
    "train_masks_reshaped_encoded = labelencoder.fit_transform(train_masks_reshaped)\n",
    "train_masks_encoded_original_shape = train_masks_reshaped_encoded.reshape(n, h, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4e188f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(train_masks_encoded_original_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e1e80db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# увеличиваем размерность на 1  \n",
    "# train_images = np.expand_dims(train_images, axis=3)\n",
    "# train_images = normalize(train_images, axis=1)\n",
    "\n",
    "train_masks_input = np.expand_dims(train_masks_encoded_original_shape, axis=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91dc0065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_images.shape = (52310, 64, 64, 3) \n",
      "train_masks_input.shape = (52310, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "print('train_images.shape = {}'.format(train_images.shape), \n",
    "      '\\ntrain_masks_input.shape = {}'.format(train_masks_input.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5e4b5a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class values in the dataset are   [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# Create a subset of data for quick testing\n",
    "# Picking 10% for testing and remaining for training\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X1, X_test, y1, y_test = train_test_split(train_images, train_masks_input, test_size=0.10, random_state=0)\n",
    "\n",
    "# Further split training data to a smaller subset for quick testing of models\n",
    "X_train, X_do_not_use, y_train, y_do_not_use = train_test_split(X1, y1, test_size=0.2, random_state=0)\n",
    "\n",
    "print(\"Class values in the dataset are  \", np.unique(y_train))  # 0 is the background/few unlabeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "70262f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# так как классов больше 2, то нужно использовать categorical (one-hot-encoding)\n",
    "from tensorflow.keras.utils import to_categorical \n",
    "\n",
    "train_masks_cat = to_categorical(y_train, num_classes=n_classes)\n",
    "y_train_cat = train_masks_cat.reshape((y_train.shape[0], y_train.shape[1], y_train.shape[2], n_classes))\n",
    "\n",
    "test_masks_cat = to_categorical(y_test, num_classes=n_classes)\n",
    "y_test_cat = test_masks_cat.reshape((y_test.shape[0], y_test.shape[1], y_test.shape[2], n_classes))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "276d4056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_test_cat shape = (5231, 64, 64, 5) \n"
     ]
    }
   ],
   "source": [
    "# последнее число должно совпадать с кол-вом классов с учетом бэкграунда\n",
    "print('y_test_cat shape = {} '.format(y_test_cat.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e189fc",
   "metadata": {},
   "source": [
    "# Задаем общие параметры"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7e189198",
   "metadata": {},
   "outputs": [],
   "source": [
    "activation='softmax'\n",
    "\n",
    "LR = 0.0001\n",
    "optim = tf.keras.optimizers.Adam(LR)\n",
    "\n",
    "\n",
    "dice_loss = sm.losses.DiceLoss(class_weights=np.array([0.25/140, 0.25, 0.25, 0.25, 0.25])) \n",
    "focal_loss = sm.losses.CategoricalFocalLoss()\n",
    "total_loss = dice_loss + (1 * focal_loss)\n",
    "\n",
    "\n",
    "metrics = [sm.metrics.IOUScore(threshold=0.5), sm.metrics.FScore(threshold=0.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fc0c16e1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ganievanvar/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/keras_applications/mobilenet_v2.py:294: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "  warnings.warn('`input_shape` is undefined or non-square, '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
      "9412608/9406464 [==============================] - 1s 0us/step\n",
      "Model: \"model_14\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, None, None, 3 864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, None, None, 3 128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, None, None, 3 0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, None, None, 3 288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, None, None, 3 128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, None, None, 3 0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, None, None, 1 512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, None, None, 1 64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, None, None, 9 1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, None, None, 9 384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, None, None, 9 0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, None, None, 9 0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, None, None, 9 864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, None, None, 9 384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, None, None, 9 0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, None, None, 2 2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, None, None, 2 96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, None, None, 1 3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, None, None, 1 576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, None, None, 1 0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, None, None, 1 1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, None, None, 1 576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, None, None, 1 0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, None, None, 2 3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, None, None, 2 96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, None, None, 2 0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, None, None, 1 3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, None, None, 1 576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, None, None, 1 0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, None, None, 1 0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, None, None, 1 1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, None, None, 1 576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, None, None, 1 0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, None, None, 3 4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, None, None, 3 128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, None, None, 1 6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, None, None, 1 768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, None, None, 1 0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, None, None, 1 1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, None, None, 1 768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, None, None, 1 0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, None, None, 3 6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, None, None, 3 128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, None, None, 3 0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, None, None, 1 6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, None, None, 1 768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, None, None, 1 0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, None, None, 1 1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, None, None, 1 768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, None, None, 1 0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, None, None, 3 6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, None, None, 3 128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, None, None, 3 0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, None, None, 1 6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, None, None, 1 768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, None, None, 1 0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, None, None, 1 0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, None, None, 1 1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, None, None, 1 768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, None, None, 1 0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, None, None, 6 12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, None, None, 6 256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, None, None, 3 24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, None, None, 3 1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, None, None, 3 0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, None, None, 3 3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, None, None, 3 1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, None, None, 3 0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, None, None, 6 24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, None, None, 6 256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, None, None, 6 0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, None, None, 3 24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, None, None, 3 1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, None, None, 3 0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, None, None, 3 3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, None, None, 3 1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, None, None, 3 0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, None, None, 6 24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, None, None, 6 256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, None, None, 6 0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, None, None, 3 24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, None, None, 3 1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, None, None, 3 0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, None, None, 3 3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, None, None, 3 1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, None, None, 3 0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, None, None, 6 24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, None, None, 6 256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, None, None, 6 0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, None, None, 3 24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, None, None, 3 1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, None, None, 3 0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, None, None, 3 3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, None, None, 3 1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, None, None, 3 0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, None, None, 9 36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, None, None, 9 384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, None, None, 5 55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, None, None, 5 2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, None, None, 5 0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, None, None, 5 5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, None, None, 5 2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, None, None, 5 0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, None, None, 9 55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, None, None, 9 384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, None, None, 9 0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, None, None, 5 55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, None, None, 5 2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, None, None, 5 0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, None, None, 5 5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, None, None, 5 2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, None, None, 5 0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, None, None, 9 55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, None, None, 9 384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, None, None, 9 0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, None, None, 5 55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, None, None, 5 2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, None, None, 5 0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, None, None, 5 0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, None, None, 5 5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, None, None, 5 2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, None, None, 5 0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, None, None, 1 92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, None, None, 1 640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, None, None, 9 153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, None, None, 9 3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, None, None, 9 0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, None, None, 9 8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, None, None, 9 3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, None, None, 9 0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, None, None, 1 153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, None, None, 1 640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, None, None, 1 0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, None, None, 9 153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, None, None, 9 3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, None, None, 9 0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, None, None, 9 8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, None, None, 9 3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, None, None, 9 0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, None, None, 1 153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, None, None, 1 640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, None, None, 1 0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, None, None, 9 153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, None, None, 9 3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, None, None, 9 0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, None, None, 9 8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, None, None, 9 3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, None, None, 9 0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, None, None, 3 307200      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, None, None, 3 1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, None, None, 1 409600      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, None, None, 1 5120        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, None, None, 1 0           Conv_1_bn[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0_upsampling (UpSa (None, None, None, 1 0           out_relu[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0_concat (Concaten (None, None, None, 1 0           decoder_stage0_upsampling[0][0]  \n",
      "                                                                 block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0a_conv (Conv2D)   (None, None, None, 2 4276224     decoder_stage0_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0a_bn (BatchNormal (None, None, None, 2 1024        decoder_stage0a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0a_relu (Activatio (None, None, None, 2 0           decoder_stage0a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0b_conv (Conv2D)   (None, None, None, 2 589824      decoder_stage0a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0b_bn (BatchNormal (None, None, None, 2 1024        decoder_stage0b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage0b_relu (Activatio (None, None, None, 2 0           decoder_stage0b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1_upsampling (UpSa (None, None, None, 2 0           decoder_stage0b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1_concat (Concaten (None, None, None, 4 0           decoder_stage1_upsampling[0][0]  \n",
      "                                                                 block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1a_conv (Conv2D)   (None, None, None, 1 516096      decoder_stage1_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1a_bn (BatchNormal (None, None, None, 1 512         decoder_stage1a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1a_relu (Activatio (None, None, None, 1 0           decoder_stage1a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1b_conv (Conv2D)   (None, None, None, 1 147456      decoder_stage1a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1b_bn (BatchNormal (None, None, None, 1 512         decoder_stage1b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage1b_relu (Activatio (None, None, None, 1 0           decoder_stage1b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2_upsampling (UpSa (None, None, None, 1 0           decoder_stage1b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2_concat (Concaten (None, None, None, 2 0           decoder_stage2_upsampling[0][0]  \n",
      "                                                                 block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2a_conv (Conv2D)   (None, None, None, 6 156672      decoder_stage2_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2a_bn (BatchNormal (None, None, None, 6 256         decoder_stage2a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2a_relu (Activatio (None, None, None, 6 0           decoder_stage2a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2b_conv (Conv2D)   (None, None, None, 6 36864       decoder_stage2a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2b_bn (BatchNormal (None, None, None, 6 256         decoder_stage2b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage2b_relu (Activatio (None, None, None, 6 0           decoder_stage2b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3_upsampling (UpSa (None, None, None, 6 0           decoder_stage2b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3_concat (Concaten (None, None, None, 1 0           decoder_stage3_upsampling[0][0]  \n",
      "                                                                 block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3a_conv (Conv2D)   (None, None, None, 3 46080       decoder_stage3_concat[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3a_bn (BatchNormal (None, None, None, 3 128         decoder_stage3a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3a_relu (Activatio (None, None, None, 3 0           decoder_stage3a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3b_conv (Conv2D)   (None, None, None, 3 9216        decoder_stage3a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3b_bn (BatchNormal (None, None, None, 3 128         decoder_stage3b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage3b_relu (Activatio (None, None, None, 3 0           decoder_stage3b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4_upsampling (UpSa (None, None, None, 3 0           decoder_stage3b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4a_conv (Conv2D)   (None, None, None, 1 4608        decoder_stage4_upsampling[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4a_bn (BatchNormal (None, None, None, 1 64          decoder_stage4a_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4a_relu (Activatio (None, None, None, 1 0           decoder_stage4a_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4b_conv (Conv2D)   (None, None, None, 1 2304        decoder_stage4a_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4b_bn (BatchNormal (None, None, None, 1 64          decoder_stage4b_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "decoder_stage4b_relu (Activatio (None, None, None, 1 0           decoder_stage4b_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "final_conv (Conv2D)             (None, None, None, 5 725         decoder_stage4b_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, None, None, 5 0           final_conv[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 8,048,021\n",
      "Trainable params: 8,011,925\n",
      "Non-trainable params: 36,096\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "BACKBONE3 = 'mobilenetv2'\n",
    "preprocess_input3 = sm.get_preprocessing(BACKBONE3)\n",
    "\n",
    "\n",
    "# preprocess input\n",
    "X_train3 = preprocess_input3(X_train)\n",
    "X_test3 = preprocess_input3(X_test)\n",
    "\n",
    "\n",
    "# define model\n",
    "model3 = sm.Unet(BACKBONE3, encoder_weights='imagenet', classes=n_classes, activation=activation)\n",
    "\n",
    "# compile keras model with defined optimozer, loss and metrics\n",
    "model3.compile(optim, total_loss, metrics)\n",
    "#model3.compile(optimizer='adam', loss='categorical_crossentropy', metrics=metrics)\n",
    "\n",
    "\n",
    "print(model3.summary())\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "354d8dcc",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " 267/1177 [=====>........................] - ETA: 12:22 - loss: 1.0179 - iou_score: 0.0868 - f1-score: 0.1226"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-18e0009d9ab8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m history3=model3.fit(X_train3, \n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0my_train_cat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    853\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    856\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2940\u001b[0m       (graph_function,\n\u001b[1;32m   2941\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2942\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   2943\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1916\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1917\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1919\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1920\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    553\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    556\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    557\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/road_signs/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history3=model3.fit(X_train3, \n",
    "          y_train_cat,\n",
    "          batch_size=32, \n",
    "          epochs=10,\n",
    "          verbose=1,\n",
    "          validation_data=(X_test3, y_test_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27a7e509",
   "metadata": {},
   "outputs": [],
   "source": [
    "model3.save('mobilenetv2_backbone_10epochs.hdf5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8578eb18",
   "metadata": {},
   "source": [
    "# Графики потерь и точности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aff1015",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#plot the training and validation accuracy and loss at each epoch\n",
    "loss = history1.history['loss']\n",
    "val_loss = history1.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'y', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "acc = history1.history['iou_score']\n",
    "val_acc = history1.history['val_iou_score']\n",
    "\n",
    "plt.plot(epochs, acc, 'y', label='Training IOU')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation IOU')\n",
    "plt.title('Training and validation IOU')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('IOU')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e72e048e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = '/Users/ganievanvar/RoadAR/4points_segmentation/models/mobilenetv2_backbone_50epochs.hdf5'\n",
    "model_mobnet = tf.keras.models.load_model(model_path, compile=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ffe4d4cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_mobnet = model_mobnet.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "919c7d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5231, 64, 64, 5)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_mobnet.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dc9ed963",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_mobnet_argmax= np.argmax(pred_mobnet, axis=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "84f4fa26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5231, 64, 64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_mobnet.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "808fb6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "IOU_mobnet = MeanIoU(num_classes=n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "55eb00ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'UnreadVariable' shape=(5, 5) dtype=float64, numpy=\n",
       "array([[2.000065e+07, 2.202300e+05, 1.841910e+05, 2.474490e+05,\n",
       "        1.669900e+05],\n",
       "       [8.507900e+04, 5.528400e+04, 0.000000e+00, 1.133600e+04,\n",
       "        0.000000e+00],\n",
       "       [8.323400e+04, 0.000000e+00, 5.316600e+04, 1.920000e+02,\n",
       "        1.510700e+04],\n",
       "       [1.086330e+05, 1.925400e+04, 0.000000e+00, 2.368200e+04,\n",
       "        0.000000e+00],\n",
       "       [8.536600e+04, 0.000000e+00, 1.641600e+04, 1.710000e+02,\n",
       "        4.974600e+04]])>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_test[:,:,:,0] - значения настоящие\n",
    "# y_pred_mobnet_argmax - предикты\n",
    "IOU_mobnet.update_state(y_test[:,:,:,0], y_pred_mobnet_argmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "af9336b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IOU Score for model_mobnet =  0.2886323\n"
     ]
    }
   ],
   "source": [
    "print('IOU Score for model_mobnet = ', IOU_mobnet.result().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "39fde298",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "test_img_number = random.randint(0, len(X_test))\n",
    "test_img = X_test[test_img_number]\n",
    "ground_truth=y_test[test_img_number]\n",
    "test_img_norm=test_img[:,:,:]\n",
    "test_img_input=np.expand_dims(test_img_norm, 0)\n",
    "\n",
    "#Weighted average ensemble\n",
    "\n",
    "\n",
    "test_img_input_mobnet = preprocess_input2(test_img_input)\n",
    "\n",
    "test_pred_mobnet = model_mobnet.predict(test_img_input_mobnet)\n",
    "test_prediction = np.argmax(test_pred_mobnet, axis=3)[0,:,:]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(231)\n",
    "plt.title('Testing Image')\n",
    "plt.imshow(test_img[:,:,0], cmap='gray')\n",
    "plt.subplot(232)\n",
    "plt.title('Testing Label')\n",
    "plt.imshow(ground_truth[:,:,0], cmap='jet')\n",
    "plt.subplot(233)\n",
    "plt.title('Prediction on test image')\n",
    "plt.imshow(test_prediction, cmap='jet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f847cb5b",
   "metadata": {},
   "source": [
    "# Нахождение координат с помощью сегментации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "412314c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_centers(pred_tensor):\n",
    "    img_centers = []\n",
    "    for i in range(pred_tensor.shape[0]):\n",
    "        temp_centers = []\n",
    "        for point_class in range(1, 5):\n",
    "            \n",
    "            x_max = np.where(pred_tensor[i] == point_class)[0].max()\n",
    "            x_min = np.where(pred_tensor[i] == point_class)[0].min()\n",
    "\n",
    "            y_max = np.where(pred_tensor[i] == point_class)[1].max()\n",
    "            y_min = np.where(pred_tensor[i] == point_class)[1].min()\n",
    "\n",
    "            center = ((x_max - (x_max - x_min) / 2) / 64, (y_max - (y_max - y_min) / 2) / 64)\n",
    "\n",
    "            temp_centers.append(center)\n",
    "            \n",
    "        img_centers.append(temp_centers)\n",
    "        \n",
    "    return img_centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "4b25b0b7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.702021_0.372445_0.351691_0.883907_0.288347_0.624686_0.765365_0.631666_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.701799_0.885159_0.293460_0.512951_0.691658_0.702190_0.303601_0.695921_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.701334_0.425043_0.371116_0.871529_0.304915_0.644873_0.767535_0.651699_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.210792_0.468656_0.674846_0.917776_0.266230_0.695504_0.619408_0.690928_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.702224_0.876418_0.292592_0.527700_0.691942_0.704577_0.302873_0.699542_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.605258_0.921212_0.360395_0.650449_0.773198_0.792830_0.192455_0.778831_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.702114_0.510096_0.339665_0.915621_0.289372_0.707964_0.752407_0.717753_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.702344_0.906538_0.256057_0.499584_0.671855_0.706043_0.286545_0.700078_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.576261_0.760337_0.445435_0.918734_0.163596_0.832553_0.858100_0.846518_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.701051_0.460921_0.339395_0.880898_0.286825_0.666179_0.753621_0.675640_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.701122_0.918501_0.307081_0.556997_0.702235_0.740802_0.305968_0.734697_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.530298_0.771868_0.466908_0.908027_0.116281_0.832068_0.880925_0.847826_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.417455_0.690135_0.578499_0.899300_0.166604_0.802269_0.829348_0.787166_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.703018_0.911731_0.291102_0.505090_0.688787_0.711764_0.305333_0.705057_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.702114_0.420179_0.349644_0.919755_0.289703_0.666544_0.762055_0.673390_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.702268_0.907941_0.288580_0.505691_0.689784_0.709771_0.301065_0.703861_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.700889_0.473720_0.342455_0.883349_0.286198_0.673652_0.757146_0.683417_.jpg\n",
      "/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/0.701177_0.439324_0.336128_0.876615_0.286032_0.652885_0.751274_0.663054_.jpg\n"
     ]
    }
   ],
   "source": [
    "test_images = []\n",
    "for directory_path in glob.glob(\"/Users/ganievanvar/RoadAR/4points_segmentation/cars64x64/Test/\"):\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.jpg\")):\n",
    "        print(img_path)\n",
    "        img = cv2.imread(img_path)\n",
    "        # img = cv2.resize(img, (SIZE_Y, SIZE_X))\n",
    "        test_images.append(img)\n",
    "# Convert list to array for machine learning processing\n",
    "test_images = np.array(test_images)\n",
    "\n",
    "# preprocess input for mobilenetv2\n",
    "X_test = preprocess_input2(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "768d3836",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_mobnet = model_mobnet.predict(X_test)\n",
    "y_pred_mobnet_argmax= np.argmax(pred_mobnet, axis=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "9b4c1480",
   "metadata": {},
   "outputs": [],
   "source": [
    "coodinates_Unet = find_centers(y_pred_mobnet_argmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "d2571149",
   "metadata": {},
   "outputs": [
    {
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       "  (0.7734375, 0.15625),\n",
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       " [(0.90625, 0.5078125),\n",
       "  (0.7734375, 0.1640625),\n",
       "  (0.8046875, 0.4921875),\n",
       "  (0.7734375, 0.828125)],\n",
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       "  (0.7734375, 0.1640625),\n",
       "  (0.8203125, 0.4921875),\n",
       "  (0.765625, 0.8203125)],\n",
       " [(0.8984375, 0.5),\n",
       "  (0.7734375, 0.1640625),\n",
       "  (0.71875, 0.4921875),\n",
       "  (0.78125, 0.8359375)],\n",
       " [(0.8984375, 0.4921875),\n",
       "  (0.7734375, 0.1640625),\n",
       "  (0.6953125, 0.5),\n",
       "  (0.7890625, 0.8359375)],\n",
       " [(0.8984375, 0.5),\n",
       "  (0.7734375, 0.1640625),\n",
       "  (0.78125, 0.4921875),\n",
       "  (0.7734375, 0.828125)],\n",
       " [(0.8984375, 0.4921875),\n",
       "  (0.7734375, 0.15625),\n",
       "  (0.6953125, 0.5),\n",
       "  (0.765625, 0.8203125)],\n",
       " [(0.8984375, 0.4921875),\n",
       "  (0.78125, 0.15625),\n",
       "  (0.6875, 0.4921875),\n",
       "  (0.78125, 0.8359375)],\n",
       " [(0.8984375, 0.5078125),\n",
       "  (0.7890625, 0.15625),\n",
       "  (0.7109375, 0.4921875),\n",
       "  (0.796875, 0.8359375)],\n",
       " [(0.8984375, 0.5),\n",
       "  (0.7734375, 0.15625),\n",
       "  (0.703125, 0.4921875),\n",
       "  (0.7734375, 0.828125)],\n",
       " [(0.8984375, 0.5),\n",
       "  (0.7734375, 0.1640625),\n",
       "  (0.71875, 0.4921875),\n",
       "  (0.7890625, 0.8359375)],\n",
       " [(0.90625, 0.5078125),\n",
       "  (0.7734375, 0.1640625),\n",
       "  (0.7734375, 0.4921875),\n",
       "  (0.7734375, 0.828125)]]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coodinates_Unet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "26f84731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4096"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e21af77f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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