Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.style.use(\"ggplot\") # 這行程式碼設置了 matplotlib 的繪圖風格。\n",
    "\n",
    "# 這是 Jupyter Notebook 的魔法命令，它用於指定 matplotlib 繪圖的輸出方式。\n",
    "# 可以直接在 Notebook 中看到圖形，而不需要使用 plt.show()。\n",
    "%matplotlib inline \n",
    "import gc\n",
    "\n",
    "from tqdm.notebook import trange, tqdm\n",
    "\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from keras.layers import Conv2D, Input, MaxPooling2D, Dropout, concatenate, UpSampling2D\n",
    "from keras.models import load_model, Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, TensorBoard\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.layers import *\n",
    "from keras.optimizers import *\n",
    "from keras.models import *\n",
    "from keras.preprocessing.image import *\n",
    "from keras.callbacks import *\n",
    "\n",
    "from keras.utils import plot_model\n",
    "\n",
    "import random \n",
    "\n",
    "## Seeding \n",
    "seed = 2019\n",
    "random.seed = seed\n",
    "np.random.seed = seed\n",
    "tf.seed = seed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGen(keras.utils.Sequence):\n",
    "    def __init__(self, path, ids, batch_size=8, image_size=640):\n",
    "        self.path = path\n",
    "        self.ids = ids\n",
    "        self.batch_size = batch_size\n",
    "        self.image_size = image_size\n",
    "        self.on_epoch_end()\n",
    "\n",
    "    # 回傳 steps_per_epoch , 也就是一個 epoch 需要幾個 batch \n",
    "    def __len__(self): \n",
    "        return int(np.ceil(len(self.ids) / self.batch_size))\n",
    "        \n",
    "    def __load__(self, id_name):\n",
    "        image_path = os.path.join(self.path, \"Images\", id_name)\n",
    "        mask_path = os.path.join(self.path, \"Mask\", id_name)\n",
    "\n",
    "        image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "        image = np.expand_dims(image, axis=-1)\n",
    "        mask = np.expand_dims(mask, axis=-1)\n",
    "\n",
    "        # Normalizaing \n",
    "        image = image / 255.0\n",
    "        mask = mask / 255.0\n",
    "\n",
    "        return image, mask\n",
    "\n",
    "    # 獲取當次 batch 要訓練的照片\n",
    "    def __getitem__(self, index):\n",
    "        if (index + 1) * self.batch_size > len(self.ids):\n",
    "            ids_batch = self.ids[index * self.batch_size : len(self.ids)]\n",
    "        else:\n",
    "            ids_batch = self.ids[index * self.batch_size : (index+1) * self.batch_size]\n",
    "        \n",
    "        images = []\n",
    "        masks = []\n",
    "        \n",
    "        for id_name in ids_batch:\n",
    "            _img, _mask = self.__load__(id_name)\n",
    "            images.append(_img)\n",
    "            masks.append(_mask)\n",
    "        \n",
    "        images = np.array(images)\n",
    "        masks = np.array(masks)\n",
    "\n",
    "        return images, masks\n",
    "    \n",
    "    def get_id(self, i):\n",
    "        return self.ids[i]\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 ['2_jpg.rf.60369561fc1520a37e9145f6ecd47916.jpg', '4_jpg.rf.38e454f25bf0b15ff1cf507920b872bd.jpg', '8_jpg.rf.b0df4a218edbe5fb7e0e023f2428d25f.jpg']\n",
      "1 ['11_jpg.rf.2e71343ba8845ca7db0b208ae1a6b064.jpg']\n",
      "(1, 640, 640, 1) (1, 640, 640, 1)\n",
      "(1, 640, 640, 1) (1, 640, 640, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 參數設定\n",
    "train_path = \"dataset_copy_3_imgs/train/\"\n",
    "train_ids = os.listdir(train_path + \"Images\") # list\n",
    "valid_path = \"dataset_copy_3_imgs/valid/\"\n",
    "valid_ids = os.listdir(valid_path + \"Images\") # list\n",
    "batch_size = 1\n",
    "image_size = 640\n",
    "\n",
    "# 建立 DataGen 物件\n",
    "train_gen = DataGen(train_path, train_ids, batch_size, image_size)\n",
    "valid_gen = DataGen(valid_path, valid_ids, batch_size, image_size)\n",
    "\n",
    "# 使用 DataGen 類別的 __len__ 函式\n",
    "print(train_gen.__len__(), train_ids)\n",
    "print(valid_gen.__len__(), valid_ids)\n",
    "\n",
    "# 使用 DataGen 類別的 __getitem__ 函式\n",
    "x, y = train_gen.__getitem__(0)\n",
    "print(x.shape, y.shape)\n",
    "x, y = train_gen.__getitem__(1)\n",
    "print(x.shape, y.shape)\n",
    "\n",
    "# 從 batch 中選擇其中一張圖片\n",
    "image_to_display = x[0]\n",
    "\n",
    "# 將圖片轉換為 0 到 255 的範圍\n",
    "image_to_display = (image_to_display * 255).astype(np.uint8)\n",
    "\n",
    "# 顯示圖片\n",
    "plt.imshow(image_to_display[:, :, 0], cmap='gray')  # 若是彩色圖片，請將 [:, :, 0] 移除\n",
    "plt.title('Sample Image from train_gen')\n",
    "plt.axis('off')\n",
    "\n",
    "gc.collect() #  用於清理不再使用的對象，釋放內存。（Garbage Collection）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DenseBlock(channels,inputs):\n",
    "\n",
    "    conv1_1 = Conv2D(channels, (1, 1),activation=None, padding='same')(inputs)\n",
    "    conv1_1 = BatchActivate(conv1_1)\n",
    "    conv1_2 = Conv2D(channels//4, (3, 3), activation=None, padding='same')(conv1_1)\n",
    "    conv1_2 = BatchActivate(conv1_2)\n",
    "\n",
    "    conv2=concatenate([inputs,conv1_2])\n",
    "    conv2_1 = Conv2D(channels, (1, 1), activation=None, padding='same')(conv2)\n",
    "    conv2_1 = BatchActivate(conv2_1)\n",
    "    conv2_2 = Conv2D(channels // 4, (3, 3), activation=None, padding='same')(conv2_1)\n",
    "    conv2_2 = BatchActivate(conv2_2)\n",
    "\n",
    "    conv3 = concatenate([inputs, conv1_2,conv2_2])\n",
    "    conv3_1 = Conv2D(channels, (1, 1), activation=None, padding='same')(conv3)\n",
    "    conv3_1 = BatchActivate(conv3_1)\n",
    "    conv3_2 = Conv2D(channels // 4, (3, 3), activation=None, padding='same')(conv3_1)\n",
    "    conv3_2 = BatchActivate(conv3_2)\n",
    "\n",
    "    conv4 = concatenate([inputs, conv1_2, conv2_2,conv3_2])\n",
    "    conv4_1 = Conv2D(channels, (1, 1), activation=None, padding='same')(conv4)\n",
    "    conv4_1 = BatchActivate(conv4_1)\n",
    "    conv4_2 = Conv2D(channels // 4, (3, 3), activation=None, padding='same')(conv4_1)\n",
    "    conv4_2 = BatchActivate(conv4_2)\n",
    "    result=concatenate([inputs,conv1_2, conv2_2,conv3_2,conv4_2])\n",
    "    return result\n",
    "\n",
    "def BatchActivate(x):\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def DenseUNet(image_size=304):\n",
    "\n",
    "    filters=16\n",
    "    keep_prob=0.9\n",
    "    block_size=7\n",
    "\n",
    "    inputs = Input(shape=(image_size, image_size, 1)) # change \n",
    "\n",
    "    conv1 = Conv2D(filters * 1, (3, 3), activation=None, padding=\"same\")(inputs)\n",
    "    conv1 = BatchActivate(conv1)\n",
    "    conv1 = DenseBlock(filters * 1, conv1)\n",
    "    pool1 = MaxPooling2D((2, 2))(conv1)\n",
    "\n",
    "    conv2 = DenseBlock(filters * 2, pool1)\n",
    "    pool2 = MaxPooling2D((2, 2))(conv2)\n",
    "\n",
    "    conv3 = DenseBlock(filters * 4, pool2)\n",
    "    pool3 = MaxPooling2D((2, 2))(conv3)\n",
    "\n",
    "    convm = DenseBlock(filters * 8, pool3)\n",
    "\n",
    "    deconv3 = Conv2DTranspose(filters * 4, (3, 3), strides=(2, 2), padding=\"same\")(convm)\n",
    "    uconv3 = concatenate([deconv3, conv3])\n",
    "    uconv3 = Conv2D(filters * 4, (1, 1), activation=None, padding=\"same\")(uconv3)\n",
    "    uconv3 = BatchActivate(uconv3)\n",
    "    uconv3 = DenseBlock(filters * 4, uconv3)\n",
    "\n",
    "\n",
    "    deconv2 = Conv2DTranspose(filters * 2, (3, 3), strides=(2, 2), padding=\"same\")(uconv3)\n",
    "    uconv2 = concatenate([deconv2, conv2])\n",
    "    uconv2 = Conv2D(filters * 2, (1, 1), activation=None, padding=\"same\")(uconv2)\n",
    "    uconv2 = BatchActivate(uconv2)\n",
    "    uconv2 = DenseBlock(filters * 2, uconv2)\n",
    "\n",
    "    deconv1 = Conv2DTranspose(filters * 1, (3, 3), strides=(2, 2), padding=\"same\")(uconv2)\n",
    "    uconv1 = concatenate([deconv1, conv1])\n",
    "    uconv1 = Conv2D(filters * 1, (1, 1), activation=None, padding=\"same\")(uconv1)\n",
    "    uconv1 = BatchActivate(uconv1)\n",
    "    uconv1 = DenseBlock(filters * 1, uconv1)\n",
    "\n",
    "    outputs = Conv2D(1, (1, 1), padding=\"same\", activation=None)(uconv1)\n",
    "    outputs = Activation('sigmoid')(outputs)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\layers\\normalization\\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = DenseUNet(image_size=640) # change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 640, 640, 1)]        0         []                            \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)             (None, 640, 640, 16)         160       ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization (Batch  (None, 640, 640, 16)         64        ['conv2d[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation (Activation)     (None, 640, 640, 16)         0         ['batch_normalization[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)           (None, 640, 640, 16)         272       ['activation[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_1 (Bat  (None, 640, 640, 16)         64        ['conv2d_1[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_1 (Activation)   (None, 640, 640, 16)         0         ['batch_normalization_1[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)           (None, 640, 640, 4)          580       ['activation_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_2 (Bat  (None, 640, 640, 4)          16        ['conv2d_2[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_2 (Activation)   (None, 640, 640, 4)          0         ['batch_normalization_2[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)   (None, 640, 640, 20)         0         ['activation[0][0]',          \n",
      "                                                                     'activation_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)           (None, 640, 640, 16)         336       ['concatenate[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_3 (Bat  (None, 640, 640, 16)         64        ['conv2d_3[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_3 (Activation)   (None, 640, 640, 16)         0         ['batch_normalization_3[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)           (None, 640, 640, 4)          580       ['activation_3[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_4 (Bat  (None, 640, 640, 4)          16        ['conv2d_4[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_4 (Activation)   (None, 640, 640, 4)          0         ['batch_normalization_4[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate  (None, 640, 640, 24)         0         ['activation[0][0]',          \n",
      " )                                                                   'activation_2[0][0]',        \n",
      "                                                                     'activation_4[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)           (None, 640, 640, 16)         400       ['concatenate_1[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_5 (Bat  (None, 640, 640, 16)         64        ['conv2d_5[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_5 (Activation)   (None, 640, 640, 16)         0         ['batch_normalization_5[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)           (None, 640, 640, 4)          580       ['activation_5[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_6 (Bat  (None, 640, 640, 4)          16        ['conv2d_6[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_6 (Activation)   (None, 640, 640, 4)          0         ['batch_normalization_6[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate  (None, 640, 640, 28)         0         ['activation[0][0]',          \n",
      " )                                                                   'activation_2[0][0]',        \n",
      "                                                                     'activation_4[0][0]',        \n",
      "                                                                     'activation_6[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)           (None, 640, 640, 16)         464       ['concatenate_2[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_7 (Bat  (None, 640, 640, 16)         64        ['conv2d_7[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_7 (Activation)   (None, 640, 640, 16)         0         ['batch_normalization_7[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)           (None, 640, 640, 4)          580       ['activation_7[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_8 (Bat  (None, 640, 640, 4)          16        ['conv2d_8[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_8 (Activation)   (None, 640, 640, 4)          0         ['batch_normalization_8[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate  (None, 640, 640, 32)         0         ['activation[0][0]',          \n",
      " )                                                                   'activation_2[0][0]',        \n",
      "                                                                     'activation_4[0][0]',        \n",
      "                                                                     'activation_6[0][0]',        \n",
      "                                                                     'activation_8[0][0]']        \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2  (None, 320, 320, 32)         0         ['concatenate_3[0][0]']       \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)           (None, 320, 320, 32)         1056      ['max_pooling2d[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_9 (Bat  (None, 320, 320, 32)         128       ['conv2d_9[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " activation_9 (Activation)   (None, 320, 320, 32)         0         ['batch_normalization_9[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_9[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_10 (Ba  (None, 320, 320, 8)          32        ['conv2d_10[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_10 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_10[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_4 (Concatenate  (None, 320, 320, 40)         0         ['max_pooling2d[0][0]',       \n",
      " )                                                                   'activation_10[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)          (None, 320, 320, 32)         1312      ['concatenate_4[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_11 (Ba  (None, 320, 320, 32)         128       ['conv2d_11[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_11 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_11[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_11[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_12 (Ba  (None, 320, 320, 8)          32        ['conv2d_12[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_12 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_12[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_5 (Concatenate  (None, 320, 320, 48)         0         ['max_pooling2d[0][0]',       \n",
      " )                                                                   'activation_10[0][0]',       \n",
      "                                                                     'activation_12[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)          (None, 320, 320, 32)         1568      ['concatenate_5[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_13 (Ba  (None, 320, 320, 32)         128       ['conv2d_13[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_13 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_13[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_13[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_14 (Ba  (None, 320, 320, 8)          32        ['conv2d_14[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_14 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_14[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_6 (Concatenate  (None, 320, 320, 56)         0         ['max_pooling2d[0][0]',       \n",
      " )                                                                   'activation_10[0][0]',       \n",
      "                                                                     'activation_12[0][0]',       \n",
      "                                                                     'activation_14[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)          (None, 320, 320, 32)         1824      ['concatenate_6[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_15 (Ba  (None, 320, 320, 32)         128       ['conv2d_15[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_15 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_15[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_15[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_16 (Ba  (None, 320, 320, 8)          32        ['conv2d_16[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_16 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_16[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_7 (Concatenate  (None, 320, 320, 64)         0         ['max_pooling2d[0][0]',       \n",
      " )                                                                   'activation_10[0][0]',       \n",
      "                                                                     'activation_12[0][0]',       \n",
      "                                                                     'activation_14[0][0]',       \n",
      "                                                                     'activation_16[0][0]']       \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPoolin  (None, 160, 160, 64)         0         ['concatenate_7[0][0]']       \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)          (None, 160, 160, 64)         4160      ['max_pooling2d_1[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization_17 (Ba  (None, 160, 160, 64)         256       ['conv2d_17[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_17 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_17[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_17[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_18 (Ba  (None, 160, 160, 16)         64        ['conv2d_18[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_18 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_18[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_8 (Concatenate  (None, 160, 160, 80)         0         ['max_pooling2d_1[0][0]',     \n",
      " )                                                                   'activation_18[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)          (None, 160, 160, 64)         5184      ['concatenate_8[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_19 (Ba  (None, 160, 160, 64)         256       ['conv2d_19[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_19 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_19[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_19[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_20 (Ba  (None, 160, 160, 16)         64        ['conv2d_20[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_20 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_20[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_9 (Concatenate  (None, 160, 160, 96)         0         ['max_pooling2d_1[0][0]',     \n",
      " )                                                                   'activation_18[0][0]',       \n",
      "                                                                     'activation_20[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)          (None, 160, 160, 64)         6208      ['concatenate_9[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_21 (Ba  (None, 160, 160, 64)         256       ['conv2d_21[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_21 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_21[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_21[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_22 (Ba  (None, 160, 160, 16)         64        ['conv2d_22[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_22 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_22[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_10 (Concatenat  (None, 160, 160, 112)        0         ['max_pooling2d_1[0][0]',     \n",
      " e)                                                                  'activation_18[0][0]',       \n",
      "                                                                     'activation_20[0][0]',       \n",
      "                                                                     'activation_22[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)          (None, 160, 160, 64)         7232      ['concatenate_10[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_23 (Ba  (None, 160, 160, 64)         256       ['conv2d_23[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_23 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_23[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_23[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_24 (Ba  (None, 160, 160, 16)         64        ['conv2d_24[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_24 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_24[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_11 (Concatenat  (None, 160, 160, 128)        0         ['max_pooling2d_1[0][0]',     \n",
      " e)                                                                  'activation_18[0][0]',       \n",
      "                                                                     'activation_20[0][0]',       \n",
      "                                                                     'activation_22[0][0]',       \n",
      "                                                                     'activation_24[0][0]']       \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPoolin  (None, 80, 80, 128)          0         ['concatenate_11[0][0]']      \n",
      " g2D)                                                                                             \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)          (None, 80, 80, 128)          16512     ['max_pooling2d_2[0][0]']     \n",
      "                                                                                                  \n",
      " batch_normalization_25 (Ba  (None, 80, 80, 128)          512       ['conv2d_25[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_25 (Activation)  (None, 80, 80, 128)          0         ['batch_normalization_25[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)          (None, 80, 80, 32)           36896     ['activation_25[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_26 (Ba  (None, 80, 80, 32)           128       ['conv2d_26[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_26 (Activation)  (None, 80, 80, 32)           0         ['batch_normalization_26[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_12 (Concatenat  (None, 80, 80, 160)          0         ['max_pooling2d_2[0][0]',     \n",
      " e)                                                                  'activation_26[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)          (None, 80, 80, 128)          20608     ['concatenate_12[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_27 (Ba  (None, 80, 80, 128)          512       ['conv2d_27[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_27 (Activation)  (None, 80, 80, 128)          0         ['batch_normalization_27[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)          (None, 80, 80, 32)           36896     ['activation_27[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_28 (Ba  (None, 80, 80, 32)           128       ['conv2d_28[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_28 (Activation)  (None, 80, 80, 32)           0         ['batch_normalization_28[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_13 (Concatenat  (None, 80, 80, 192)          0         ['max_pooling2d_2[0][0]',     \n",
      " e)                                                                  'activation_26[0][0]',       \n",
      "                                                                     'activation_28[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)          (None, 80, 80, 128)          24704     ['concatenate_13[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_29 (Ba  (None, 80, 80, 128)          512       ['conv2d_29[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_29 (Activation)  (None, 80, 80, 128)          0         ['batch_normalization_29[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)          (None, 80, 80, 32)           36896     ['activation_29[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_30 (Ba  (None, 80, 80, 32)           128       ['conv2d_30[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_30 (Activation)  (None, 80, 80, 32)           0         ['batch_normalization_30[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_14 (Concatenat  (None, 80, 80, 224)          0         ['max_pooling2d_2[0][0]',     \n",
      " e)                                                                  'activation_26[0][0]',       \n",
      "                                                                     'activation_28[0][0]',       \n",
      "                                                                     'activation_30[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)          (None, 80, 80, 128)          28800     ['concatenate_14[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_31 (Ba  (None, 80, 80, 128)          512       ['conv2d_31[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_31 (Activation)  (None, 80, 80, 128)          0         ['batch_normalization_31[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)          (None, 80, 80, 32)           36896     ['activation_31[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_32 (Ba  (None, 80, 80, 32)           128       ['conv2d_32[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_32 (Activation)  (None, 80, 80, 32)           0         ['batch_normalization_32[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_15 (Concatenat  (None, 80, 80, 256)          0         ['max_pooling2d_2[0][0]',     \n",
      " e)                                                                  'activation_26[0][0]',       \n",
      "                                                                     'activation_28[0][0]',       \n",
      "                                                                     'activation_30[0][0]',       \n",
      "                                                                     'activation_32[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTr  (None, 160, 160, 64)         147520    ['concatenate_15[0][0]']      \n",
      " anspose)                                                                                         \n",
      "                                                                                                  \n",
      " concatenate_16 (Concatenat  (None, 160, 160, 192)        0         ['conv2d_transpose[0][0]',    \n",
      " e)                                                                  'concatenate_11[0][0]']      \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)          (None, 160, 160, 64)         12352     ['concatenate_16[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_33 (Ba  (None, 160, 160, 64)         256       ['conv2d_33[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_33 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_33[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)          (None, 160, 160, 64)         4160      ['activation_33[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_34 (Ba  (None, 160, 160, 64)         256       ['conv2d_34[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_34 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_34[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_34[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_35 (Ba  (None, 160, 160, 16)         64        ['conv2d_35[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_35 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_35[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_17 (Concatenat  (None, 160, 160, 80)         0         ['activation_33[0][0]',       \n",
      " e)                                                                  'activation_35[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)          (None, 160, 160, 64)         5184      ['concatenate_17[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_36 (Ba  (None, 160, 160, 64)         256       ['conv2d_36[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_36 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_36[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_36[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_37 (Ba  (None, 160, 160, 16)         64        ['conv2d_37[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_37 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_37[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_18 (Concatenat  (None, 160, 160, 96)         0         ['activation_33[0][0]',       \n",
      " e)                                                                  'activation_35[0][0]',       \n",
      "                                                                     'activation_37[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)          (None, 160, 160, 64)         6208      ['concatenate_18[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_38 (Ba  (None, 160, 160, 64)         256       ['conv2d_38[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_38 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_38[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_38[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_39 (Ba  (None, 160, 160, 16)         64        ['conv2d_39[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_39 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_39[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_19 (Concatenat  (None, 160, 160, 112)        0         ['activation_33[0][0]',       \n",
      " e)                                                                  'activation_35[0][0]',       \n",
      "                                                                     'activation_37[0][0]',       \n",
      "                                                                     'activation_39[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)          (None, 160, 160, 64)         7232      ['concatenate_19[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_40 (Ba  (None, 160, 160, 64)         256       ['conv2d_40[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_40 (Activation)  (None, 160, 160, 64)         0         ['batch_normalization_40[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)          (None, 160, 160, 16)         9232      ['activation_40[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_41 (Ba  (None, 160, 160, 16)         64        ['conv2d_41[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_41 (Activation)  (None, 160, 160, 16)         0         ['batch_normalization_41[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_20 (Concatenat  (None, 160, 160, 128)        0         ['activation_33[0][0]',       \n",
      " e)                                                                  'activation_35[0][0]',       \n",
      "                                                                     'activation_37[0][0]',       \n",
      "                                                                     'activation_39[0][0]',       \n",
      "                                                                     'activation_41[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_transpose_1 (Conv2D  (None, 320, 320, 32)         36896     ['concatenate_20[0][0]']      \n",
      " Transpose)                                                                                       \n",
      "                                                                                                  \n",
      " concatenate_21 (Concatenat  (None, 320, 320, 96)         0         ['conv2d_transpose_1[0][0]',  \n",
      " e)                                                                  'concatenate_7[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)          (None, 320, 320, 32)         3104      ['concatenate_21[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_42 (Ba  (None, 320, 320, 32)         128       ['conv2d_42[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_42 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_42[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)          (None, 320, 320, 32)         1056      ['activation_42[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_43 (Ba  (None, 320, 320, 32)         128       ['conv2d_43[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_43 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_43[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_43[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_44 (Ba  (None, 320, 320, 8)          32        ['conv2d_44[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_44 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_44[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_22 (Concatenat  (None, 320, 320, 40)         0         ['activation_42[0][0]',       \n",
      " e)                                                                  'activation_44[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)          (None, 320, 320, 32)         1312      ['concatenate_22[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_45 (Ba  (None, 320, 320, 32)         128       ['conv2d_45[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_45 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_45[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_45[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_46 (Ba  (None, 320, 320, 8)          32        ['conv2d_46[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_46 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_46[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_23 (Concatenat  (None, 320, 320, 48)         0         ['activation_42[0][0]',       \n",
      " e)                                                                  'activation_44[0][0]',       \n",
      "                                                                     'activation_46[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)          (None, 320, 320, 32)         1568      ['concatenate_23[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_47 (Ba  (None, 320, 320, 32)         128       ['conv2d_47[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_47 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_47[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_47[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_48 (Ba  (None, 320, 320, 8)          32        ['conv2d_48[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_48 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_48[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_24 (Concatenat  (None, 320, 320, 56)         0         ['activation_42[0][0]',       \n",
      " e)                                                                  'activation_44[0][0]',       \n",
      "                                                                     'activation_46[0][0]',       \n",
      "                                                                     'activation_48[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)          (None, 320, 320, 32)         1824      ['concatenate_24[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_49 (Ba  (None, 320, 320, 32)         128       ['conv2d_49[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_49 (Activation)  (None, 320, 320, 32)         0         ['batch_normalization_49[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)          (None, 320, 320, 8)          2312      ['activation_49[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_50 (Ba  (None, 320, 320, 8)          32        ['conv2d_50[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_50 (Activation)  (None, 320, 320, 8)          0         ['batch_normalization_50[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_25 (Concatenat  (None, 320, 320, 64)         0         ['activation_42[0][0]',       \n",
      " e)                                                                  'activation_44[0][0]',       \n",
      "                                                                     'activation_46[0][0]',       \n",
      "                                                                     'activation_48[0][0]',       \n",
      "                                                                     'activation_50[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_transpose_2 (Conv2D  (None, 640, 640, 16)         9232      ['concatenate_25[0][0]']      \n",
      " Transpose)                                                                                       \n",
      "                                                                                                  \n",
      " concatenate_26 (Concatenat  (None, 640, 640, 48)         0         ['conv2d_transpose_2[0][0]',  \n",
      " e)                                                                  'concatenate_3[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)          (None, 640, 640, 16)         784       ['concatenate_26[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_51 (Ba  (None, 640, 640, 16)         64        ['conv2d_51[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_51 (Activation)  (None, 640, 640, 16)         0         ['batch_normalization_51[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)          (None, 640, 640, 16)         272       ['activation_51[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_52 (Ba  (None, 640, 640, 16)         64        ['conv2d_52[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_52 (Activation)  (None, 640, 640, 16)         0         ['batch_normalization_52[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)          (None, 640, 640, 4)          580       ['activation_52[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_53 (Ba  (None, 640, 640, 4)          16        ['conv2d_53[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_53 (Activation)  (None, 640, 640, 4)          0         ['batch_normalization_53[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_27 (Concatenat  (None, 640, 640, 20)         0         ['activation_51[0][0]',       \n",
      " e)                                                                  'activation_53[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)          (None, 640, 640, 16)         336       ['concatenate_27[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_54 (Ba  (None, 640, 640, 16)         64        ['conv2d_54[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_54 (Activation)  (None, 640, 640, 16)         0         ['batch_normalization_54[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)          (None, 640, 640, 4)          580       ['activation_54[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_55 (Ba  (None, 640, 640, 4)          16        ['conv2d_55[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_55 (Activation)  (None, 640, 640, 4)          0         ['batch_normalization_55[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_28 (Concatenat  (None, 640, 640, 24)         0         ['activation_51[0][0]',       \n",
      " e)                                                                  'activation_53[0][0]',       \n",
      "                                                                     'activation_55[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)          (None, 640, 640, 16)         400       ['concatenate_28[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_56 (Ba  (None, 640, 640, 16)         64        ['conv2d_56[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_56 (Activation)  (None, 640, 640, 16)         0         ['batch_normalization_56[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)          (None, 640, 640, 4)          580       ['activation_56[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_57 (Ba  (None, 640, 640, 4)          16        ['conv2d_57[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_57 (Activation)  (None, 640, 640, 4)          0         ['batch_normalization_57[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_29 (Concatenat  (None, 640, 640, 28)         0         ['activation_51[0][0]',       \n",
      " e)                                                                  'activation_53[0][0]',       \n",
      "                                                                     'activation_55[0][0]',       \n",
      "                                                                     'activation_57[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)          (None, 640, 640, 16)         464       ['concatenate_29[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_58 (Ba  (None, 640, 640, 16)         64        ['conv2d_58[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_58 (Activation)  (None, 640, 640, 16)         0         ['batch_normalization_58[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)          (None, 640, 640, 4)          580       ['activation_58[0][0]']       \n",
      "                                                                                                  \n",
      " batch_normalization_59 (Ba  (None, 640, 640, 4)          16        ['conv2d_59[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " activation_59 (Activation)  (None, 640, 640, 4)          0         ['batch_normalization_59[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_30 (Concatenat  (None, 640, 640, 32)         0         ['activation_51[0][0]',       \n",
      " e)                                                                  'activation_53[0][0]',       \n",
      "                                                                     'activation_55[0][0]',       \n",
      "                                                                     'activation_57[0][0]',       \n",
      "                                                                     'activation_59[0][0]']       \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)          (None, 640, 640, 1)          33        ['concatenate_30[0][0]']      \n",
      "                                                                                                  \n",
      " activation_60 (Activation)  (None, 640, 640, 1)          0         ['conv2d_60[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 612865 (2.34 MB)\n",
      "Trainable params: 609089 (2.32 MB)\n",
      "Non-trainable params: 3776 (14.75 KB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('model/model-DenseUnet.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "AbortedError",
     "evalue": "Graph execution error:\n\nDetected at node model/conv2d_transpose_1/conv2d_transpose defined at (most recent call last):\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\runpy.py\", line 197, in _run_module_as_main\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\runpy.py\", line 87, in _run_code\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\traitlets\\config\\application.py\", line 1053, in launch_instance\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\asyncio\\base_events.py\", line 601, in run_forever\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\asyncio\\base_events.py\", line 1905, in _run_once\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\asyncio\\events.py\", line 80, in _run\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 529, in dispatch_queue\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 518, in process_one\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 424, in dispatch_shell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 766, in execute_request\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3046, in run_cell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3101, in _run_cell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3306, in run_cell_async\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3488, in run_ast_nodes\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3548, in run_code\n\n  File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_3644\\422988625.py\", line 12, in <module>\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2655, in predict\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2440, in predict_function\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2425, in step_function\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2413, in run_step\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2381, in predict_step\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 590, in __call__\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\base_layer.py\", line 1149, in __call__\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 96, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\functional.py\", line 515, in call\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\functional.py\", line 672, in _run_internal_graph\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\base_layer.py\", line 1149, in __call__\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 96, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\layers\\convolutional\\conv2d_transpose.py\", line 296, in call\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\backend.py\", line 6268, in conv2d_transpose\n\nOperation received an exception:Status: 1, message: could not create a primitive, in file tensorflow/core/kernels/mkl/mkl_conv_grad_input_ops.cc:545\n\t [[{{node model/conv2d_transpose_1/conv2d_transpose}}]] [Op:__inference_predict_function_6092]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAbortedError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\user\\Desktop\\Dense-Unet\\Test.ipynb 儲存格 12\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/Dense-Unet/Test.ipynb#X14sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m x_image\u001b[39m.\u001b[39mappend(x)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/Dense-Unet/Test.ipynb#X14sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m y_mask\u001b[39m.\u001b[39mappend(y)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/Dense-Unet/Test.ipynb#X14sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m result \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mpredict(x)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/Dense-Unet/Test.ipynb#X14sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m result_list\u001b[39m.\u001b[39mappend(result)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/user/Desktop/Dense-Unet/Test.ipynb#X14sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m image_name\u001b[39m.\u001b[39mappend(test_gen\u001b[39m.\u001b[39mget_id(i))\n",
      "File \u001b[1;32mc:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[39m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[39m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32mc:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39mTFE_Py_Execute(ctx\u001b[39m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     55\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[1;31mAbortedError\u001b[0m: Graph execution error:\n\nDetected at node model/conv2d_transpose_1/conv2d_transpose defined at (most recent call last):\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\runpy.py\", line 197, in _run_module_as_main\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\runpy.py\", line 87, in _run_code\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\traitlets\\config\\application.py\", line 1053, in launch_instance\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 195, in start\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\asyncio\\base_events.py\", line 601, in run_forever\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\asyncio\\base_events.py\", line 1905, in _run_once\n\n  File \"C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Python39_64\\lib\\asyncio\\events.py\", line 80, in _run\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 529, in dispatch_queue\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 518, in process_one\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 424, in dispatch_shell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 766, in execute_request\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 429, in do_execute\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3046, in run_cell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3101, in _run_cell\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3306, in run_cell_async\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3488, in run_ast_nodes\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3548, in run_code\n\n  File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_3644\\422988625.py\", line 12, in <module>\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2655, in predict\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2440, in predict_function\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2425, in step_function\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2413, in run_step\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 2381, in predict_step\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\training.py\", line 590, in __call__\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\base_layer.py\", line 1149, in __call__\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 96, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\functional.py\", line 515, in call\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\functional.py\", line 672, in _run_internal_graph\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 65, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\engine\\base_layer.py\", line 1149, in __call__\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 96, in error_handler\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\layers\\convolutional\\conv2d_transpose.py\", line 296, in call\n\n  File \"c:\\Users\\user\\Desktop\\Dense-Unet\\.venv\\lib\\site-packages\\keras\\src\\backend.py\", line 6268, in conv2d_transpose\n\nOperation received an exception:Status: 1, message: could not create a primitive, in file tensorflow/core/kernels/mkl/mkl_conv_grad_input_ops.cc:545\n\t [[{{node model/conv2d_transpose_1/conv2d_transpose}}]] [Op:__inference_predict_function_6092]"
     ]
    }
   ],
   "source": [
    "## Dataset for prediction\n",
    "test_path = \"dataset_copy_3_imgs/valid/\"\n",
    "test_ids = os.listdir(test_path + \"Images\")\n",
    "test_gen = DataGen(test_path, test_ids, image_size=image_size, batch_size=batch_size)\n",
    "\n",
    "# 先預測 2 batch 的照片\n",
    "x_image, y_mask, result_list, image_name = [], [], [], []\n",
    "for i in range(5):\n",
    "    x, y = test_gen.__getitem__(i)\n",
    "    x_image.append(x)\n",
    "    y_mask.append(y)\n",
    "    result = model.predict(x)\n",
    "    result_list.append(result)\n",
    "    image_name.append(test_gen.get_id(i))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['101_jpg.rf.4c511af7a1c76cbaf95c464a520cf88a.jpg', '103_jpg.rf.a37da8d11fb8a2c7330f64f64ce59e1e.jpg', '109_jpg.rf.d1572b8ca404851ee30b60871180073f.jpg', '114_jpg.rf.48671828485d2f4819c15905f3a42f92.jpg', '121_jpg.rf.d38fc94b258d67991fba10115cee43fe.jpg']\n"
     ]
    }
   ],
   "source": [
    "print(image_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 建立圖表物件(Figure object)，hsapce 、 wspace 垂直間距，水平間距\n",
    "fig = plt.figure(figsize=(8, 15))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "# print(result.shape) # (1, 640, 640, 1)\n",
    "\n",
    "\n",
    "for i in range(5):\n",
    "    # AxesSubplot 代表子圖（subplot）的物件。\n",
    "    ax = fig.add_subplot(5, 3, 3*i + 1)\n",
    "    ax.set_title(\"Original\")\n",
    "    ax.imshow(x_image[i][0]*255, cmap=\"gray\")\n",
    "    ax.set_axis_off()\n",
    "\n",
    "    ax = fig.add_subplot(5, 3, 3*i + 2)\n",
    "    ax.set_title(\"Mask\")\n",
    "    ax.imshow(y_mask[i][0]*255, cmap=\"gray\")\n",
    "    ax.set_axis_off()\n",
    "\n",
    "    res = result_list[i][0] > 0.5\n",
    "    ax = fig.add_subplot(5, 3, 3*i + 3)\n",
    "    ax.set_title(\"Predict\")\n",
    "    ax.imshow(res*255, cmap=\"gray\") # 要把第一個 batch 忽略，所以 [0]\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save predict images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 影像數據通常是一個 NumPy 陣列，且數據類型應為 np.uint8。\n",
    "for i in range(len(result_list)):\n",
    "    path = \"result/predict_images/\" + image_name[i] + \".jpg\"\n",
    "    image = (result_list[i][0] * 255).astype(np.uint8)\n",
    "    cv2.imwrite(path, image)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}