FCN-LiuMing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (7.0.0)\n",
      "Requirement already satisfied: cython in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (0.29.15)\n",
      "Requirement already satisfied: imgaug in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (0.4.0)\n",
      "Requirement already satisfied: opencv-python in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (4.2.0.32)\n",
      "Requirement already satisfied: Shapely in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.7.1)\n",
      "Requirement already satisfied: scikit-image>=0.14.2 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (0.16.2)\n",
      "Requirement already satisfied: six in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.15.0)\n",
      "Requirement already satisfied: matplotlib in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (3.1.3)\n",
      "Requirement already satisfied: imageio in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (2.6.1)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.19.4)\n",
      "Requirement already satisfied: scipy in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.4.1)\n",
      "Requirement already satisfied: pillow in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (7.0.0)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.19.4)\n",
      "Requirement already satisfied: pillow in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (7.0.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from matplotlib->imgaug) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from matplotlib->imgaug) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from matplotlib->imgaug) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from matplotlib->imgaug) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.19.4)\n",
      "Requirement already satisfied: six in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.15.0)\n",
      "Requirement already satisfied: setuptools in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->imgaug) (45.2.0.post20200210)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.19.4)\n",
      "Requirement already satisfied: six in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.15.0)\n",
      "Requirement already satisfied: matplotlib in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (3.1.3)\n",
      "Requirement already satisfied: networkx>=2.0 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from scikit-image>=0.14.2->imgaug) (2.4)\n",
      "Requirement already satisfied: imageio in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (2.6.1)\n",
      "Requirement already satisfied: scipy in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.4.1)\n",
      "Requirement already satisfied: PyWavelets>=0.4.0 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from scikit-image>=0.14.2->imgaug) (1.1.1)\n",
      "Requirement already satisfied: pillow in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (7.0.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from networkx>=2.0->scikit-image>=0.14.2->imgaug) (4.4.1)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.19.4)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/lib/python3.7/site-packages (from imgaug) (1.19.4)\n",
      "\u001b[33mWARNING: You are using pip version 20.3; however, version 20.3.3 is available.\n",
      "You should consider upgrading via the '/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting git+https://github.com/lucasb-eyer/pydensecrf.git\n",
      "  Cloning https://github.com/lucasb-eyer/pydensecrf.git to /tmp/pip-req-build-wnu_waj3\n",
      "\u001b[33mWARNING: You are using pip version 20.3; however, version 20.3.3 is available.\n",
      "You should consider upgrading via the '/home/ubuntu/anaconda3/envs/tensorflow2_latest_p37/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#! git clone https://github.com/seth814/Semantic-Shapes\n",
    "! pip install pillow cython imgaug\n",
    "! pip install git+https://github.com/lucasb-eyer/pydensecrf.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'FCN-dataset' already exists and is not an empty directory.\r\n"
     ]
    }
   ],
   "source": [
    "! git clone git@github.com:ElijahMingLiu/FCN-dataset.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import cv2\n",
    "import json\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    # Generates data for Keras\n",
    "    def __init__(self, image_paths, annot_paths, batch_size=5,\n",
    "                 shuffle=True, augment=False):\n",
    "        # 图片路径\n",
    "        self.image_paths = image_paths\n",
    "        # annotation路径\n",
    "        self.annot_paths = annot_paths\n",
    "        # batch大小\n",
    "        self.batch_size = batch_size\n",
    "        # 是否在一个epoch之后打乱数据集\n",
    "        self.shuffle = shuffle\n",
    "        # 是否数据增强\n",
    "        self.augment = augment\n",
    "        # 执行一遍epoch结束后的操作\n",
    "        # 这里是为了建立索引\n",
    "        self.on_epoch_end()\n",
    "    \n",
    "    \n",
    "    def __len__(self):\n",
    "        # 每个epoch多少batch\n",
    "        return int(np.floor(len(self.image_paths) / self.batch_size))\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        # 返回对应的第index个batch\n",
    "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
    "\n",
    "        image_paths = [self.image_paths[k] for k in indexes]\n",
    "        annot_paths = [self.annot_paths[k] for k in indexes]\n",
    "        \n",
    "        # 根据路径生成numpy数据，y的格式为(batch_size,height, width, classes)\n",
    "        # 相当于，第i个class都对应第i个channel\n",
    "        X, y = self.__data_generation(image_paths, annot_paths)\n",
    "\n",
    "        return X, y\n",
    "    \n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        # 每个epoch之后打乱索引\n",
    "        self.indexes = np.arange(len(self.image_paths))\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.indexes)\n",
    "    \n",
    "    \n",
    "    def get_poly(self, annot_path):\n",
    "        # r阅读annotation文件\n",
    "        with open(annot_path) as handle:\n",
    "            data = json.load(handle)\n",
    "        \n",
    "        shape_dicts = data['shapes']\n",
    "\n",
    "        return shape_dicts\n",
    "    \n",
    "    \n",
    "    def create_multi_masks(self, im, shape_dicts):\n",
    "        #每个channel是一个分类的mask\n",
    "        channels = []\n",
    "        # 根据上面的格式，可以看到label就是'star','rectangle等等'分类\n",
    "        cls = [x['label'] for x in shape_dicts]\n",
    "        # points就是组成这些的点的坐标\n",
    "        poly = [np.array(x['points'], dtype=np.int32) for x in shape_dicts]\n",
    "        # label2poly的格式类似\n",
    "        # {'star': array([[ x1, y1],\n",
    "        # [ x2,  y2],\n",
    "        # ...)\n",
    "        # 'square': array([[x1, y1],\n",
    "        # [x2,  y2],\n",
    "        # ...)\n",
    "        # }\n",
    "        label2poly = dict(zip(cls, poly))\n",
    "        # 背景\n",
    "        \n",
    "        background = np.zeros(shape=(im.shape[0], im.shape[1]), dtype=np.float32)\n",
    "        \n",
    "        # 迭代跑每个object\n",
    "        for i, label in enumerate(labels):\n",
    "            # blank是object的mask\n",
    "            blank = np.zeros(shape=(im.shape[0], im.shape[1]), dtype=np.float32)\n",
    "            \n",
    "            if label in cls:\n",
    "                #cv2.fillPoly会根据points来画出对应的poly\n",
    "                #分别填充blank和background\n",
    "                cv2.fillPoly(blank, [label2poly[label]], 255)\n",
    "                cv2.fillPoly(background, [label2poly[label]], 255)\n",
    "            # 把blank加入channel\n",
    "            channels.append(blank)\n",
    "\n",
    "        # 如果有background对象，那么就也填充\n",
    "        # 如果没有background对象，那么就翻转一下，有其他对象mask的地方为0，否则为255\n",
    "        if 'background' in cls:\n",
    "            background = np.zeros(shape=(im.shape[0], im.shape[1]), dtype=np.float32)\n",
    "            cv2.fillPoly(background, [label2poly['background']], 255)\n",
    "        else:\n",
    "            _, background = cv2.threshold(background, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "        #把background也加入channel\n",
    "        channels.append(background)\n",
    "        # Y为[height, width, n_classes]，然后再除上255，转换为[0,1]区间\n",
    "        Y = np.stack(channels, axis=2) / 255.0\n",
    "\n",
    "        return Y\n",
    "    \n",
    "    def __data_generation(self, image_paths, annot_paths):\n",
    "        \n",
    "        # 建立训练用的numpy数组\n",
    "        X = np.empty((self.batch_size, imshape[0], imshape[1], imshape[2]), dtype=np.float32)\n",
    "        Y = np.empty((self.batch_size, imshape[0], imshape[1], n_classes),  dtype=np.float32)\n",
    "        \n",
    "        for i, (im_path, annot_path) in enumerate(zip(image_paths, annot_paths)):\n",
    "            \n",
    "            # 如果图片\n",
    "            if imshape[2] == 1:\n",
    "                im = cv2.imread(im_path, 0)\n",
    "                im = np.expand_dims(im, axis=2)\n",
    "            elif imshape[2] == 3:\n",
    "                im = cv2.imread(im_path, 1)\n",
    "                im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
    "            shape_dicts = self.get_poly(annot_path)\n",
    "            \n",
    "            # check for augmentation\n",
    "            if self.augment:\n",
    "                im, shape_dicts = self.augment_poly(im, shape_dicts)\n",
    "            \n",
    "            # create target masks\n",
    "            if n_classes == 1:\n",
    "                mask = self.create_binary_masks(im, shape_dicts)\n",
    "            elif n_classes > 1:\n",
    "                mask = self.create_multi_masks(im, shape_dicts)\n",
    "            \n",
    "            X[i,] = im\n",
    "            Y[i,] = mask\n",
    "            \n",
    "        return X, Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sorted_fns(dir):\n",
    "    return sorted(os.listdir(dir), key=lambda x: int(x.split('.')[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$\n",
    "intersection=y_{true}\\cdot y_{pred}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\frac{2\\times intersection+smooth}{\\sum{y_{true}}+\\sum{y_{pred}}+smooth}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拆开fcn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Conv2D, Input, MaxPooling2D, concatenate, Dropout,\\\n",
    "                                    Lambda, Conv2DTranspose, Add\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "\n",
    "\n",
    "def preprocess_input(x):\n",
    "    x /= 255.\n",
    "    x -= 0.5\n",
    "    x *= 2.\n",
    "    return x\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice(y_true, y_pred, smooth=1.):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型名称\n",
    "model_name = 'fcn_8'\n",
    "# 是否加载预训练的模型\n",
    "pretrained=False\n",
    "\n",
    "# 图片尺寸设置\n",
    "imshape = (256, 256, 3)\n",
    "\n",
    "# 色域\n",
    "hues = {'star': 30,\n",
    "        'square': 0,\n",
    "        'circle': 90,\n",
    "        'triangle': 60}\n",
    "\n",
    "# 标签\n",
    "labels = sorted(hues.keys())\n",
    "\n",
    "#分类数\n",
    "n_classes = len(labels) + 1\n",
    "\n",
    "#图片和标注的路径\n",
    "image_paths = [os.path.join('FCN-dataset/images', x) for x in sorted_fns('images')]\n",
    "annot_paths = [os.path.join('FCN-dataset/annotated', x) for x in sorted_fns('annotated')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = 4\n",
    "i = Input(shape=imshape)\n",
    "s = Lambda(lambda x: preprocess_input(x)) (i)\n",
    "## Block 1\n",
    "x = Conv2D(2**b, (3, 3), activation='elu', padding='same', name='block1_conv1')(s)\n",
    "x = Conv2D(2**b, (3, 3), activation='elu', padding='same', name='block1_conv2')(x)\n",
    "x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
    "f1 = x\n",
    "# <tf.Tensor 'block1_pool/MaxPool_3:0' shape=(None, 128, 128, 16) dtype=float32>\n",
    "\n",
    "# Block 2\n",
    "x = Conv2D(2**(b+1), (3, 3), activation='elu', padding='same', name='block2_conv1')(x)\n",
    "x = Conv2D(2**(b+1), (3, 3), activation='elu', padding='same', name='block2_conv2')(x)\n",
    "x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
    "f2 = x\n",
    "# <tf.Tensor 'block2_pool/MaxPool_3:0' shape=(None, 64, 64, 32) dtype=float32>\n",
    "\n",
    "# Block 3\n",
    "x = Conv2D(2**(b+2), (3, 3), activation='elu', padding='same', name='block3_conv1')(x)\n",
    "x = Conv2D(2**(b+2), (3, 3), activation='elu', padding='same', name='block3_conv2')(x)\n",
    "x = Conv2D(2**(b+2), (3, 3), activation='elu', padding='same', name='block3_conv3')(x)\n",
    "x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
    "pool3 = x\n",
    "# <tf.Tensor 'block3_pool/MaxPool_3:0' shape=(None, 32, 32, 64) dtype=float32>\n",
    "\n",
    "# Block 4\n",
    "x = Conv2D(2**(b+3), (3, 3), activation='elu', padding='same', name='block4_conv1')(x)\n",
    "x = Conv2D(2**(b+3), (3, 3), activation='elu', padding='same', name='block4_conv2')(x)\n",
    "x = Conv2D(2**(b+3), (3, 3), activation='elu', padding='same', name='block4_conv3')(x)\n",
    "pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
    "# <tf.Tensor 'block4_pool/MaxPool_3:0' shape=(None, 16, 16, 128) dtype=float32>\n",
    "\n",
    "# Block 5\n",
    "x = Conv2D(2**(b+3), (3, 3), activation='elu', padding='same', name='block5_conv1')(pool4)\n",
    "x = Conv2D(2**(b+3), (3, 3), activation='elu', padding='same', name='block5_conv2')(x)\n",
    "x = Conv2D(2**(b+3), (3, 3), activation='elu', padding='same', name='block5_conv3')(x)\n",
    "pool5 = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
    "# <tf.Tensor 'block5_pool/MaxPool_3:0' shape=(None, 8, 8, 128) dtype=float32>\n",
    "\n",
    "# 和pool5\n",
    "conv6 = Conv2D(2048 , (7, 7) , activation='elu' , padding='same', name=\"conv6\")(pool5)\n",
    "conv6 = Dropout(0.5)(conv6)\n",
    "conv7 = Conv2D(2048 , (1, 1) , activation='elu' , padding='same', name=\"conv7\")(conv6)\n",
    "conv7 = Dropout(0.5)(conv7)\n",
    "# <tf.Tensor 'dropout_7/cond/Identity:0' shape=(None, 8, 8, 2048) dtype=float32>\n",
    "\n",
    "# pool4的卷积 16*16\n",
    "pool4_n = Conv2D(n_classes, (1, 1), activation='elu', padding='same')(pool4)\n",
    "# <tf.Tensor 'conv2d_6/Elu:0' shape=(None, 16, 16, 5) dtype=float32>\n",
    "\n",
    "# # conv7 upsampling成16*16*5 + poo4\n",
    "u2 = Conv2DTranspose(n_classes, kernel_size=(2, 2), strides=(2, 2), padding='same')(conv7)\n",
    "u2_skip = Add()([pool4_n, u2])\n",
    "# <tf.Tensor 'add_6/add:0' shape=(None, 16, 16, 5) dtype=float32>\n",
    "\n",
    "\n",
    "pool3_n = Conv2D(n_classes, (1, 1), activation='elu', padding='same')(pool3)\n",
    "\n",
    "# conv7 + pool4 upsampling + pool3\n",
    "u4 = Conv2DTranspose(n_classes, kernel_size=(2, 2), strides=(2, 2), padding='same')(u2_skip)\n",
    "u4_skip = Add()([pool3_n, u4])\n",
    "# <tf.Tensor 'add_7/add:0' shape=(None, 32, 32, 5) dtype=float32>\n",
    "\n",
    "# upsampling成256*256\n",
    "o = Conv2DTranspose(n_classes, kernel_size=(8, 8), strides=(8, 8), padding='same',\n",
    "                    activation='softmax')(u4_skip)\n",
    "\n",
    "\n",
    "model = Model(inputs=i, outputs=o, name='fcn_multi')\n",
    "model.compile(optimizer=Adam(1e-4),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=[dice])\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_1/add:0' shape=(None, 32, 32, 5) dtype=float32>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u4_skip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'conv2d_transpose_2/truediv:0' shape=(None, 256, 256, 5) dtype=float32>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-10-b5c8bc69414c>:4: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.fit, which supports generators.\n",
      "Epoch 1/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 1.5777 - dice: 0.2123\n",
      "Epoch 2/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 1.4580 - dice: 0.2675\n",
      "Epoch 3/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 1.2873 - dice: 0.3513\n",
      "Epoch 4/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 1.1126 - dice: 0.4394\n",
      "Epoch 5/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.9529 - dice: 0.5251\n",
      "Epoch 6/500\n",
      "40/40 [==============================] - 21s 515ms/step - loss: 0.8187 - dice: 0.5929\n",
      "Epoch 7/500\n",
      "40/40 [==============================] - 22s 545ms/step - loss: 0.7105 - dice: 0.6559\n",
      "Epoch 8/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.6354 - dice: 0.7024\n",
      "Epoch 9/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.5726 - dice: 0.7339\n",
      "Epoch 10/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.5265 - dice: 0.7623\n",
      "Epoch 11/500\n",
      "40/40 [==============================] - 21s 515ms/step - loss: 0.4814 - dice: 0.7779\n",
      "Epoch 12/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.4427 - dice: 0.8019\n",
      "Epoch 13/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.4025 - dice: 0.8115\n",
      "Epoch 14/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.3709 - dice: 0.8312\n",
      "Epoch 15/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.3464 - dice: 0.8393\n",
      "Epoch 16/500\n",
      "40/40 [==============================] - 22s 545ms/step - loss: 0.3233 - dice: 0.8462\n",
      "Epoch 17/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.3094 - dice: 0.8533\n",
      "Epoch 18/500\n",
      "40/40 [==============================] - 21s 515ms/step - loss: 0.2952 - dice: 0.8579\n",
      "Epoch 19/500\n",
      "40/40 [==============================] - 21s 516ms/step - loss: 0.2856 - dice: 0.8618\n",
      "Epoch 20/500\n",
      "40/40 [==============================] - 21s 516ms/step - loss: 0.2777 - dice: 0.8639\n",
      "Epoch 21/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.2733 - dice: 0.8662\n",
      "Epoch 22/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.2687 - dice: 0.8680\n",
      "Epoch 23/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.2672 - dice: 0.8685\n",
      "Epoch 24/500\n",
      "40/40 [==============================] - 22s 544ms/step - loss: 0.2598 - dice: 0.8720\n",
      "Epoch 25/500\n",
      "40/40 [==============================] - 20s 512ms/step - loss: 0.2537 - dice: 0.8738\n",
      "Epoch 26/500\n",
      "40/40 [==============================] - 21s 515ms/step - loss: 0.2491 - dice: 0.8763\n",
      "Epoch 27/500\n",
      "40/40 [==============================] - 21s 515ms/step - loss: 0.2440 - dice: 0.8784\n",
      "Epoch 28/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.2399 - dice: 0.8810\n",
      "Epoch 29/500\n",
      "40/40 [==============================] - 20s 512ms/step - loss: 0.2348 - dice: 0.8834\n",
      "Epoch 30/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.2328 - dice: 0.8850\n",
      "Epoch 31/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.2327 - dice: 0.8852\n",
      "Epoch 32/500\n",
      "40/40 [==============================] - 21s 515ms/step - loss: 0.2273 - dice: 0.8870\n",
      "Epoch 33/500\n",
      "40/40 [==============================] - 22s 545ms/step - loss: 0.2246 - dice: 0.8887\n",
      "Epoch 34/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.2230 - dice: 0.8894\n",
      "Epoch 35/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.2198 - dice: 0.8902\n",
      "Epoch 36/500\n",
      "40/40 [==============================] - 21s 513ms/step - loss: 0.2168 - dice: 0.8917\n",
      "Epoch 37/500\n",
      "40/40 [==============================] - 20s 512ms/step - loss: 0.2117 - dice: 0.8935\n",
      "Epoch 38/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.2070 - dice: 0.8951\n",
      "Epoch 39/500\n",
      "40/40 [==============================] - 21s 514ms/step - loss: 0.2039 - dice: 0.8963\n",
      "Epoch 40/500\n",
      " 1/40 [..............................] - ETA: 0s - loss: 0.1135 - dice: 0.9443"
     ]
    },
    {
     "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-10-b5c8bc69414c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m model.fit_generator(generator=dataGenerator,\n\u001b[1;32m      3\u001b[0m                     \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataGenerator\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----> 4\u001b[0;31m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m      5\u001b[0m                     )\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m               \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\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    323\u001b[0m               instructions)\n\u001b[0;32m--> 324\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\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    325\u001b[0m     return tf_decorator.make_decorator(\n\u001b[1;32m    326\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'deprecated'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1878\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1880\u001b[0;31m         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1882\u001b[0m   @deprecation.deprecated(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    107\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0mkwargs\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    108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0mkwargs\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    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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   1122\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m   1123\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-> 1124\u001b[0;31m               \u001b[0mtmp_logs\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   1125\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   1126\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~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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    778\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\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    781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_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~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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    805\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    806\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--> 807\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    808\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    809\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~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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   2827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2829\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2831\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1846\u001b[0m                            resource_variable_ops.BaseResourceVariable))],\n\u001b[1;32m   1847\u001b[0m         \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m         cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1850\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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   1922\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[1;32m   1923\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1924\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1926\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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    548\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow2_latest_p37/cpu/lib/python3.7/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     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[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\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[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\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[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "dataGenerator = DataGenerator(image_paths, annot_paths)\n",
    "model.fit_generator(generator=dataGenerator,\n",
    "                    steps_per_epoch=len(dataGenerator),\n",
    "                    epochs=500, verbose=1,\n",
    "                    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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