Repository: AliAmini93/MRI-MS-Plaques-Segmentation
Branch: main
Commit: 9ac8863a87a8
Files: 3
Total size: 523.4 KB

Directory structure:
gitextract_wx3lw4ny/

├── LICENSE
├── MS_lesion_segmentation_3DAttUNet.ipynb
└── README.md

================================================
FILE CONTENTS
================================================

================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2023 Ali Amini

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: MS_lesion_segmentation_3DAttUNet.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zpPXSgJJP8vc",
    "outputId": "f0f5f0f4-c317-438a-da27-9923e613c99e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Apr 11 07:53:37 2021       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.67       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   41C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ArwHJBuNQn-L",
    "outputId": "36dd4ace-dab6-4908-e4fc-3f1dafbf6fac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HpExRSMvQ0yQ"
   },
   "outputs": [],
   "source": [
    "!pip install numpy\n",
    "!pip install scikit-learn\n",
    "!pip install  tensorflow-gpu\n",
    "!pip install scipy\n",
    "!pip install simpleitk\n",
    "!pip install grpcio\n",
    "!pip install git+https://github.com/aleju/imgaug.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GigYbU1wQ2KP",
    "outputId": "7a25b562-0051-43fc-a3d9-07672153b01e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import nibabel as nib\n",
    "import tensorflow as tf\n",
    "import datetime\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "import numpy as np\n",
    "import h5py\n",
    "import os\n",
    "import random\n",
    "from sklearn.utils import shuffle\n",
    "from tqdm import tqdm\n",
    "import math\n",
    "import SimpleITK as sitk  # For loading the dataset\n",
    "import numpy as np  # For data manipulation\n",
    "import glob  # For populating the list of files\n",
    "import re  # For parsing the filenames (to know their modality)\n",
    "from skimage.transform import resize # For resizing\n",
    "from scipy.ndimage import zoom  # For resizing but not used for now\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import imgaug.augmenters as iaa\n",
    "from sklearn.model_selection import KFold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CWY33UqLQ9Vh"
   },
   "outputs": [],
   "source": [
    "def read_img(img_path):\n",
    "    \"\"\"\n",
    "    Reads a .nii.gz image and returns as a numpy array.\n",
    "    \"\"\"\n",
    "    \n",
    "    return sitk.GetArrayFromImage(sitk.ReadImage(img_path))\n",
    "  \n",
    "def itensity_normalize_one_volume(volume):\n",
    "    \"\"\"\n",
    "    normalize the itensity of an nd volume based on the mean and std of nonzeor region.\n",
    "    inputs:\n",
    "        volume: the input nd volume.\n",
    "    outputs:\n",
    "        out: the normalized nd volume.\n",
    "    \"\"\"\n",
    "    pixels = volume[volume > 0]\n",
    "    mean = pixels.mean()\n",
    "    std  = pixels.std()\n",
    "    out  = (volume - mean)/std\n",
    "    out_random = np.zeros(volume.shape)\n",
    "    out[volume == 0] = out_random[volume == 0]\n",
    "    return out\n",
    "  \n",
    "def get_none_zero_region(im, margin):\n",
    "    \"\"\"\n",
    "    get the bounding box of the non-zero region of an ND volume.\n",
    "    \"\"\"\n",
    "    input_shape = im.shape\n",
    "    if(type(margin) is int ):\n",
    "        margin = [margin]*len(input_shape)\n",
    "    assert(len(input_shape) == len(margin))\n",
    "    indxes = np.nonzero(im)\n",
    "    idx_min = []\n",
    "    idx_max = []\n",
    "    for i in range(len(input_shape)):\n",
    "        idx_min.append(indxes[i].min())\n",
    "        idx_max.append(indxes[i].max())\n",
    "\n",
    "    for i in range(len(input_shape)):\n",
    "        idx_min[i] = max(idx_min[i] - margin[i], 0)\n",
    "        idx_max[i] = min(idx_max[i] + margin[i], input_shape[i] - 1)\n",
    "    return idx_min, idx_max\n",
    "  \n",
    "def crop_ND_volume_with_bounding_box(volume, min_idx, max_idx):\n",
    "    \"\"\"\n",
    "    crop/extract a subregion form an nd image.\n",
    "    \"\"\"\n",
    "    dim = len(volume.shape)\n",
    "    assert(dim >= 2 and dim <= 5)\n",
    "    if(dim == 2):\n",
    "      output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),\n",
    "                               range(min_idx[1], max_idx[1] + 1))]\n",
    "    elif(dim == 3):\n",
    "        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),\n",
    "                               range(min_idx[1], max_idx[1] + 1),\n",
    "                               range(min_idx[2], max_idx[2] + 1))]\n",
    "    elif(dim == 4):\n",
    "        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),\n",
    "                               range(min_idx[1], max_idx[1] + 1),\n",
    "                               range(min_idx[2], max_idx[2] + 1),\n",
    "                               range(min_idx[3], max_idx[3] + 1))]\n",
    "    elif(dim == 5):\n",
    "        output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),\n",
    "                               range(min_idx[1], max_idx[1] + 1),\n",
    "                               range(min_idx[2], max_idx[2] + 1),\n",
    "                               range(min_idx[3], max_idx[3] + 1),\n",
    "                               range(min_idx[4], max_idx[4] + 1))]\n",
    "    else:\n",
    "        raise ValueError(\"the dimension number shoud be 2 to 5\")\n",
    "    return output\n",
    "\n",
    "def my_resize(img, shape, mode='constant', orig_shape=(155, 240, 240)):\n",
    "    \"\"\"\n",
    "    Wrapper for scipy.ndimage.zoom suited for MRI images.\n",
    "    \"\"\"\n",
    "    assert len(shape) == 3, \"Can not have more than 3 dimensions\"\n",
    "    factors = (\n",
    "        shape[0]/orig_shape[0],\n",
    "        shape[1]/orig_shape[1], \n",
    "        shape[2]/orig_shape[2]\n",
    "    )\n",
    "    \n",
    "    # Resize to the given shape\n",
    "    return zoom(img, factors, mode=mode)\n",
    "\n",
    "def get_random_roi_sampling_center(input_shape, output_shape, sample_mode='full', bounding_box = None):\n",
    "    \"\"\"\n",
    "    get a random coordinate representing the center of a roi for sampling\n",
    "    inputs:\n",
    "        input_shape: the shape of sampled volume\n",
    "        output_shape: the desired roi shape\n",
    "        sample_mode: 'valid': the entire roi should be inside the input volume\n",
    "                     'full': only the roi centre should be inside the input volume\n",
    "        bounding_box: the bounding box which the roi center should be limited to\n",
    "    outputs:\n",
    "        center: the output center coordinate of a roi\n",
    "    \"\"\"\n",
    "    center = []\n",
    "    for i in range(len(input_shape)):\n",
    "        if(sample_mode[i] == 'full'):\n",
    "            if(bounding_box):\n",
    "                x0 = bounding_box[i*2]; x1 = bounding_box[i*2 + 1]\n",
    "            else:\n",
    "                x0 = 0; x1 = input_shape[i]\n",
    "        else:\n",
    "            if(bounding_box):\n",
    "                x0 = bounding_box[i*2] + int(output_shape[i]/2)   \n",
    "                x1 = bounding_box[i*2+1] - int(output_shape[i]/2)   \n",
    "            else:\n",
    "                x0 = int(output_shape[i]/2)   \n",
    "                x1 = input_shape[i] - x0\n",
    "        if(x1 <= x0):\n",
    "            centeri = int((x0 + x1)/2)\n",
    "        else:\n",
    "            centeri = np.random.randint(x0, x1)\n",
    "        center.append(centeri)\n",
    "    return center\n",
    "def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\n",
    "    \"\"\"\n",
    "    extract a roi from a 3d volume\n",
    "    inputs:\n",
    "        volume: the input 3D volume\n",
    "        in_center: the center of the roi\n",
    "        output_shape: the size of the roi\n",
    "        fill: 'random' or 'zero', the mode to fill roi region where is outside of the input volume\n",
    "    outputs:\n",
    "        output: the roi volume\n",
    "    \"\"\"\n",
    "    input_shape = volume.shape   \n",
    "    if(fill == 'random'):\n",
    "        output = np.random.normal(0, 1, size = output_shape)\n",
    "    else:\n",
    "        output = np.zeros(output_shape)\n",
    "    r0max = [int(x/2) for x in output_shape]\n",
    "    r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\n",
    "    r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\n",
    "    r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\n",
    "    out_center = r0max\n",
    "\n",
    "    output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\n",
    "                  range(out_center[1] - r0[1], out_center[1] + r1[1]),\n",
    "                  range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\n",
    "        volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\n",
    "                      range(in_center[1] - r0[1], in_center[1] + r1[1]),\n",
    "                      range(in_center[2] - r0[2], in_center[2] + r1[2]))]\n",
    "    return output\n",
    "\n",
    "def zero_padding_3d(volume,size=((29, 30),(11,12),(29,30))):\n",
    "    x= np.pad(volume,pad_width=size,mode='constant', constant_values=0)\n",
    "    return x  \n",
    "\n",
    "def preprocess_label(img):\n",
    "\n",
    "    \"\"\"\n",
    "    pad\n",
    "    \"\"\"\n",
    "    # img = zero_padding_3d(img)\n",
    "        \n",
    "    \"\"\"\n",
    "    Separates out the 1 labels from the segmentation\n",
    "    \"\"\"\n",
    "    ms_plk = img == 1\n",
    "        \n",
    "    return np.array([ms_plk], dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kLXOwRbmRBJY"
   },
   "outputs": [],
   "source": [
    "# Get a list of files for all modalities individually for train\n",
    "mprage = glob.glob('/gdrive/My Drive/main dataset ISBI 2015/training_final_v4/training/*/preprocessed/*mprage_pp.nii')\n",
    "mprage.sort()\n",
    "T2 = glob.glob('/gdrive/My Drive/main dataset ISBI 2015/training_final_v4/training/*/preprocessed/*t2_pp.nii')\n",
    "T2.sort()\n",
    "FLAIR= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/training_final_v4/training/*/preprocessed/*flair_pp.nii')\n",
    "FLAIR.sort()\n",
    "PD= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/training_final_v4/training/*/preprocessed/*pd_pp.nii')\n",
    "PD.sort()\n",
    "mask_1= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/training_final_v4/training/*/masks/*mask1_pp.nii')  # Ground Truth rater1\n",
    "mask_1.sort()\n",
    "mask_2= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/training_final_v4/training/*/masks/*mask2_pp.nii')  # Ground Truth rater2\n",
    "mask_2.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "egvAraTaRExZ"
   },
   "outputs": [],
   "source": [
    "print(np.shape(mprage))\n",
    "print(np.shape(T2))\n",
    "print(np.shape(FLAIR))\n",
    "print(np.shape(PD))\n",
    "print(np.shape(mask_1))\n",
    "print(np.shape(mask_2))\n",
    "print(mprage)\n",
    "print(T2)\n",
    "print(FLAIR)\n",
    "print(PD)\n",
    "print(mask_1)\n",
    "print(mask_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vPNdiq6ERHbZ"
   },
   "outputs": [],
   "source": [
    "# Get a list of files for all modalities individually or test\n",
    "Test_mprage = glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*mprage_pp.nii')\n",
    "Test_mprage.sort()\n",
    "Test_T2 = glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*t2_pp.nii')\n",
    "Test_T2.sort()\n",
    "Test_FLAIR= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*flair_pp.nii')\n",
    "Test_FLAIR.sort()\n",
    "Test_PD= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*pd_pp.nii')\n",
    "Test_PD.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ldpHC_g5RKhR"
   },
   "outputs": [],
   "source": [
    "print(np.shape(Test_mprage))\n",
    "print(np.shape(Test_T2))\n",
    "print(np.shape(Test_FLAIR))\n",
    "print(np.shape(Test_PD))\n",
    "print(Test_mprage)\n",
    "print(Test_T2)\n",
    "print(Test_FLAIR)\n",
    "print(Test_PD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9XZqfiTpRNT-"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Parse all the training filenames and create a dictionary for each patient with structure:\n",
    "\"\"\"\n",
    "pat = re.compile('.*_(\\w*)\\_pp\\.nii')\n",
    "\n",
    "data_paths = [{\n",
    "    pat.findall(item)[0]:item\n",
    "    for item in items\n",
    "}\n",
    "for items in list(zip(mprage, FLAIR, T2, PD, mask_1, mask_2))]\n",
    "print(data_paths)\n",
    "print(type(data_paths))\n",
    "print(np.shape(data_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kb5jMQg0RTBD"
   },
   "outputs": [],
   "source": [
    "n_d = 21\n",
    "input_shape= (2,240,240)\n",
    "input_shape_data = (2,181,240,240)\n",
    "output_channels = 1\n",
    "dataa = np.empty((1392,) + input_shape, dtype=np.float32)\n",
    "labels = np.empty((1392, output_channels) + input_shape[1:], dtype=np.float32)\n",
    "\n",
    "print(type(input_shape))\n",
    "print(np.shape(dataa))\n",
    "print(type(dataa))\n",
    "print(np.shape(labels))\n",
    "print(type((len(data_paths[:n_d]),)))\n",
    "print(type(labels))\n",
    "print(type(data_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fRaZOVdkRWbz"
   },
   "outputs": [],
   "source": [
    "# Parameters for the progress bar\n",
    "total = len(data_paths[:n_d])\n",
    "step = 25 / total\n",
    "k = 0\n",
    "count = 0\n",
    "m = 0\n",
    "counting = 0\n",
    "z=0\n",
    "for i, imgs in enumerate(data_paths[:n_d]):\n",
    "    try:\n",
    "        data=np.empty((1,) + input_shape_data, dtype=np.float32)\n",
    "        label=np.empty((1, output_channels) + input_shape_data[1:], dtype=np.float32)\n",
    "        data[0]= np.array([preprocess(read_img(imgs[m])) for m in ['flair','mprage']], dtype=np.float32)\n",
    "        label[0]= preprocess_label(read_img(imgs['mask1']))[None, ...]\n",
    "        for j in range(181):\n",
    "            if not np.all(label[0][:,j,:,:] == np.zeros((1,240,240))):\n",
    "               dataa[k]= data[0][:,j,:,:]\n",
    "               labels[k]= label[0][:,j,:,:]\n",
    "               k+=1   \n",
    "        # Print the progress bar\n",
    "        print('\\r' + f'Progress: '\n",
    "            f\"[{'=' * int((i+1) * step) + ' ' * (24 - int((i+1) * step))}]\"\n",
    "            f\"({math.ceil((i+1) * 100 / (total))} %)\",\n",
    "            end='')\n",
    "    except Exception as e:\n",
    "        print(f'Something went wrong with {imgs[\"T1\"]}, skipping...\\n Exception:\\n{str(e)}')\n",
    "        continue\n",
    "data=np.empty((1,) + input_shape_data, dtype=np.float32)\n",
    "label=np.empty((1, output_channels) + input_shape_data[1:], dtype=np.uint8)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "a190HzH9RZGj"
   },
   "outputs": [],
   "source": [
    "dataaa = np.einsum('iCHW->iHWC', dataa)\n",
    "labelss = np.einsum('iCHW->iHWC', labels)\n",
    "print(np.shape(dataaa))\n",
    "print(np.shape(labelss))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UaCpJ4xRRb2Y"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_val,y_train,y_val=train_test_split(dataaa,labelss,test_size=0.2,random_state=73)\n",
    "print(np.shape(x_train))\n",
    "print(np.shape(y_train))\n",
    "print(np.shape(x_val))\n",
    "print(np.shape(y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "occ5s4DyRe4p"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10.125,6.75))\n",
    "index =600\n",
    "ax1.imshow(x_train[index,:,:,1], cmap='gray')\n",
    "ax2.imshow(y_train[index,:,:,0], cmap='gray')\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10.125,6.75))\n",
    "index =200\n",
    "ax1.imshow(x_val[index,:,:,1], cmap='gray')\n",
    "ax2.imshow(y_val[index,:,:,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S2O9qef7RhXt"
   },
   "outputs": [],
   "source": [
    "hf = h5py.File(\"/gdrive/My Drive/data_train_ISBI_just_ms_slice.hdf5\", \"w\")\n",
    "dset = hf.create_dataset(\"data_train_ISBI_just_ms_slice\", data=x_train)\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VCcGS7JoRkuR"
   },
   "outputs": [],
   "source": [
    "hf = h5py.File(\"/gdrive/My Drive/label_train_ISBI_just_ms_slice.hdf5\", \"w\")\n",
    "dset = hf.create_dataset(\"label_train_ISBI_just_ms_slice\", data=y_train)\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Hhx9V_GcRnD5"
   },
   "outputs": [],
   "source": [
    "hf = h5py.File(\"/gdrive/My Drive/data_val_ISBI_just_ms_slice.hdf5\", \"w\")\n",
    "dset = hf.create_dataset(\"data_val_ISBI_just_ms_slice\", data=x_val)\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "03xFseJDRpht"
   },
   "outputs": [],
   "source": [
    "hf = h5py.File(\"/gdrive/My Drive/label_val_ISBI_just_ms_slice.hdf5\", \"w\")\n",
    "dset = hf.create_dataset(\"label_val_ISBI_just_ms_slice\", data=y_val)\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uV43ciLgR1Hi"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_just_ms_slice.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_just_ms_slice.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_just_ms_slice'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    for w in [0,1]:\n",
    "                        norm_modal= itensity_normalize_one_volume(hf_x['data_train_just_ms_slice'][i,:,:,w])\n",
    "                        norm_input.append(norm_modal)\n",
    "                    norm_input = np.einsum('CHW->HWC', norm_input)\n",
    "                    data_x.append(norm_input)\n",
    "                    data_y.append(hf_y['label_train_just_ms_slice'][i, :])\n",
    "                    # gt_train = [np.array(data_y),np.array(data_y)]\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f0yjnInIR4Gd"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def valset_generator(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_val_just_ms_slice.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_val_just_ms_slice.hdf5', 'r')\n",
    "        data_len = hf_x['data_val_just_ms_slice'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            for x in batch(index, batch_size):\n",
    "                data_x_val=[]\n",
    "                data_y_val=[]\n",
    "                for i in x:\n",
    "                    norm_input_val=[]\n",
    "                    for w in [0,1]:\n",
    "                        norm_modal_val= itensity_normalize_one_volume(hf_x['data_val_just_ms_slice'][i,:,:,w])\n",
    "                        norm_input_val.append(norm_modal_val)\n",
    "                    norm_input_val = np.einsum('CHW->HWC', norm_input_val)\n",
    "                    data_x_val.append(norm_input_val)\n",
    "                    data_y_val.append(hf_y['label_val_just_ms_slice'][i, :])\n",
    "                    # gt_val = [np.array(data_y_val),np.array(data_y_val)]\n",
    "                yield(np.array(data_x_val),np.array(data_y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JsMZX6TQR69m"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "\n",
    "seq = iaa.Sequential([iaa.Fliplr(0.5),\n",
    "                      iaa.Flipud(0.5)\n",
    "                      # sometimes2(iaa.Rot90(1,True))\n",
    "                ])\n",
    "def trainset_generator_augmentation(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_ISBI_just_ms_slice.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_ISBI_just_ms_slice.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_ISBI_just_ms_slice'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    for w in [0,1]:\n",
    "                        norm_modal= itensity_normalize_one_volume(hf_x['data_train_ISBI_just_ms_slice'][i,:,:,w])\n",
    "                        norm_input.append(norm_modal)\n",
    "                    norm_input = np.einsum('CHW->HWC', norm_input)\n",
    "                    data_x.append(norm_input)\n",
    "                    data_y.append(hf_y['label_train_ISBI_just_ms_slice'][i, :])\n",
    "                images_for_aug = np.array(data_x)\n",
    "                labels_for_aug= np.array(data_y).astype(np.int32)\n",
    "                images_aug, segmaps_aug = seq(images=images_for_aug, segmentation_maps=labels_for_aug)\n",
    "                yield(images_aug,segmaps_aug.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pvYKwZW4SxTh"
   },
   "outputs": [],
   "source": [
    "#####for testing train generator 3D ISBI\n",
    "%matplotlib inline\n",
    "hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "data_x=[]\n",
    "data_y=[]\n",
    "data_y1=[]\n",
    "data_y2=[]\n",
    "for i in range(0,3):\n",
    "    norm_input=[]\n",
    "    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "    croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "    roi_sampled= get_random_roi_sampling_center(np.shape(croped_zero), (128,128,128), sample_mode='valid',bounding_box=None)\n",
    "    for w in [0,1,2,3]:\n",
    "        croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,w], idx_min,idx_max)\n",
    "        extracted_roi=extract_roi_from_volume(croped_zero,roi_sampled, (128,128,128), fill = 'zero')\n",
    "        norm_modal=itensity_normalize_one_volume(extracted_roi)\n",
    "        norm_input.append(norm_modal)   \n",
    "    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "    croped_mask1=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "    extract_roi_mask1= extract_roi_from_volume(croped_mask1,roi_sampled, (128,128,128), fill = 'zero')\n",
    "    croped_mask2=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,1], idx_min,idx_max)\n",
    "    extract_roi_mask2= extract_roi_from_volume(croped_mask2,roi_sampled, (128,128,128), fill = 'zero')\n",
    "    extract_roi_mask= extract_roi_mask1 + extract_roi_mask2\n",
    "    extract_roi_mask[extract_roi_mask<=1]=0\n",
    "    extract_roi_mask[extract_roi_mask>1]= 1\n",
    "    extract_roi_mask= np.expand_dims(extract_roi_mask,axis=0)\n",
    "    extract_roi_mask= np.einsum('CDHW->HWDC', extract_roi_mask)\n",
    "    extract_roi_mask1= np.expand_dims(extract_roi_mask1,axis=0)\n",
    "    extract_roi_mask1= np.einsum('CDHW->HWDC', extract_roi_mask1)\n",
    "    extract_roi_mask2= np.expand_dims(extract_roi_mask2,axis=0)\n",
    "    extract_roi_mask2= np.einsum('CDHW->HWDC', extract_roi_mask2)\n",
    "    data_x.append(norm_input)\n",
    "    data_y.append(extract_roi_mask)\n",
    "    data_y1.append(extract_roi_mask1)\n",
    "    data_y2.append(extract_roi_mask2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "GONAtLk_TSJX",
    "outputId": "e497f1cd-7dd8-465d-e63b-48e64e18ad2a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 128, 128, 128, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f896a51f710>"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1458x972 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.shape(data_x))\n",
    "fig, (ax1, ax2, ax3, ax4)= plt.subplots(1,4,figsize=(2*10.125,2*6.75))\n",
    "u=64\n",
    "ax1.imshow(np.array(data_x)[2,:,:,u,0], cmap='gray')\n",
    "ax2.imshow(np.array(data_y)[2,:,:,u,0], cmap='gray')\n",
    "ax3.imshow(np.array(data_y1)[2,:,:,u,0], cmap='gray')\n",
    "ax4.imshow(np.array(data_y2)[2,:,:,u,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wTZKhb7XJgMe"
   },
   "outputs": [],
   "source": [
    "\n",
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "                    croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    roi_sampled= get_random_roi_sampling_center(np.shape(croped_zero), (128,128,128), sample_mode='valid',bounding_box=None)\n",
    "                    for w in range(0,4):\n",
    "                        croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,w], idx_min,idx_max)\n",
    "                        extracted_roi=extract_roi_from_volume(croped_zero,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                        norm_modal=itensity_normalize_one_volume(extracted_roi)\n",
    "                        norm_input.append(norm_modal)   \n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    croped=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    extract_roi_mask= extract_roi_from_volume(croped,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    extract_roi_mask= np.expand_dims(extract_roi_mask,axis=0)\n",
    "                    extract_roi_mask= np.einsum('CDHW->HWDC', extract_roi_mask)\n",
    "                    data_x.append(norm_input)\n",
    "                    data_y.append(extract_roi_mask)\n",
    "                    # gt_train = [np.array(data_y),np.array(data_y),np.array(data_y),np.array(data_y)]\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qrAoFn2fXF0v"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI_maj(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "                    croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    roi_sampled= get_random_roi_sampling_center(np.shape(croped_zero), (128,128,128), sample_mode='valid',bounding_box=None)\n",
    "                    for w in range(0,4):\n",
    "                        croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,w], idx_min,idx_max)\n",
    "                        extracted_roi=extract_roi_from_volume(croped_zero,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                        norm_modal=itensity_normalize_one_volume(extracted_roi)\n",
    "                        norm_input.append(norm_modal)   \n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    croped_mask1=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    extract_roi_mask1= extract_roi_from_volume(croped_mask1,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    croped_mask2=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,1], idx_min,idx_max)\n",
    "                    extract_roi_mask2= extract_roi_from_volume(croped_mask2,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    extract_roi_mask= extract_roi_mask1 + extract_roi_mask2\n",
    "                    extract_roi_mask[extract_roi_mask<=1]=0\n",
    "                    extract_roi_mask[extract_roi_mask>1]= 1\n",
    "                    extract_roi_mask= np.expand_dims(extract_roi_mask,axis=0)\n",
    "                    extract_roi_mask= np.einsum('CDHW->HWDC', extract_roi_mask)\n",
    "                    data_x.append(norm_input)\n",
    "                    data_y.append(extract_roi_mask)\n",
    "                    # gt_train = [np.array(data_y),np.array(data_y),np.array(data_y),np.array(data_y)]\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 526
    },
    "id": "4w1aCbarRtPy",
    "outputId": "f62c3600-e830-47a2-b4e3-6fa8cbb67f16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "false\n",
      "here2\n",
      "false\n",
      "here2\n",
      "true\n",
      "here1\n",
      "true\n",
      "here1\n",
      "true\n",
      "here1\n",
      "true\n",
      "here1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc944c67320>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1458x972 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####for extract train patch 3D ISBI\n",
    "%matplotlib inline\n",
    "def crop_ND_volume_with_bounding_box(volume, min_idx, max_idx):\n",
    "    \"\"\"\n",
    "    crop/extract a subregion form an nd image.\n",
    "    \"\"\"\n",
    "    dim = len(volume.shape)\n",
    "    channel_dim = volume.shape[-1]\n",
    "    output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),\n",
    "                           range(min_idx[1], max_idx[1] + 1),\n",
    "                           range(min_idx[2], max_idx[2] + 1),\n",
    "                           range(channel_dim))]\n",
    "    return output\n",
    "def get_random_roi_sampling_center(input_shape, output_shape, sample_mode='full', bounding_box = None):\n",
    "    \"\"\"\n",
    "    get a random coordinate representing the center of a roi for sampling\n",
    "    inputs:\n",
    "        input_shape: the shape of sampled volume\n",
    "        output_shape: the desired roi shape\n",
    "        sample_mode: 'valid': the entire roi should be inside the input volume\n",
    "                     'full': only the roi centre should be inside the input volume\n",
    "        bounding_box: the bounding box which the roi center should be limited to\n",
    "    outputs:\n",
    "        center: the output center coordinate of a roi\n",
    "    \"\"\"\n",
    "    center = []\n",
    "    for i in range(len(input_shape)):\n",
    "        if(sample_mode == 'full'):\n",
    "            if(bounding_box):\n",
    "                x0 = bounding_box[i*2]; x1 = bounding_box[i*2 + 1]\n",
    "            else:\n",
    "                x0 = 0; x1 = input_shape[i]\n",
    "        else:\n",
    "            if(bounding_box):\n",
    "                x0 = bounding_box[i*2] + int(output_shape[i]/2)   \n",
    "                x1 = bounding_box[i*2+1] - int(output_shape[i]/2)   \n",
    "            else:\n",
    "                x0 = int(output_shape[i]/2)   \n",
    "                x1 = input_shape[i] - x0\n",
    "        if(x1 <= x0):\n",
    "            centeri = int((x0 + x1)/2)\n",
    "        else:\n",
    "            centeri = np.random.randint(x0, x1)\n",
    "        center.append(centeri)\n",
    "    return center\n",
    "def get_random_roi_sampling_center_v2(croped_zero,croped_zero_mask,nonzero_center='true',mask_num=0):\n",
    "    \"\"\"\n",
    "    get a random coordinate representing the center of a roi for sampling\n",
    "    two option is available:\n",
    "    1) get roi center that centered on nonzero voxels of mask.\n",
    "    2) get roi center that centered on nonzero voxels of brain region with a\n",
    "    margin of border to include more brain region\n",
    "    mask_num => 0=mask_1   and   1=mask_2\n",
    "    \"\"\"\n",
    "    if nonzero_center=='both':\n",
    "       nonzero_center = random.choice(['true','false'])\n",
    "    if nonzero_center=='true':\n",
    "       mask_nonzero = np.array(croped_zero_mask[:,:,:,mask_num].nonzero())\n",
    "       random_select = np.random.randint(0,mask_nonzero.shape[1])\n",
    "       roi_index = mask_nonzero[:,random_select]\n",
    "    if nonzero_center=='false':\n",
    "        nonzero = np.array(croped_zero[:,:,:, 0].nonzero())\n",
    "        random_select = np.random.randint(0,nonzero.shape[1])\n",
    "        roi_index = nonzero[:,random_select]\n",
    "    return roi_index\n",
    "def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\n",
    "    \"\"\"\n",
    "    extract a roi from a 3d volume\n",
    "    inputs:\n",
    "        volume: the input 3D volume\n",
    "        in_center: the center of the roi\n",
    "        output_shape: the size of the roi\n",
    "        fill: 'random' or 'zero', the mode to fill roi region where is outside of the input volume\n",
    "    outputs:\n",
    "        output: the roi volume\n",
    "    \"\"\"\n",
    "    input_shape = volume.shape   \n",
    "    if(fill == 'random'):\n",
    "        output = np.random.normal(0, 1, size = output_shape)\n",
    "    else:\n",
    "        output = np.zeros(output_shape)\n",
    "    r0max = [int(x/2) for x in output_shape]\n",
    "    r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\n",
    "    r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\n",
    "    r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\n",
    "    out_center = r0max\n",
    "\n",
    "    output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\n",
    "                  range(out_center[1] - r0[1], out_center[1] + r1[1]),\n",
    "                  range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\n",
    "        volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\n",
    "                      range(in_center[1] - r0[1], in_center[1] + r1[1]),\n",
    "                      range(in_center[2] - r0[2], in_center[2] + r1[2]))]\n",
    "    return output\n",
    "hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "data_x=[]\n",
    "data_y=[]\n",
    "data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "for i in range(2):\n",
    "    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "    croped_zero = crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,:], idx_min,idx_max) \n",
    "    croped_zero_mask =  crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,:], idx_min,idx_max) \n",
    "    idx_min_mask, idx_max_mask = get_none_zero_region(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "    for j in range(3):\n",
    "        # roi_sampled = get_random_roi_sampling_center(np.shape(croped_zero[:,:,:,0]), (64,64,64), sample_mode='full',bounding_box=[idx_min_mask[0],idx_max_mask[0],idx_min_mask[1],idx_max_mask[1],idx_min_mask[2],idx_max_mask[2]])\n",
    "        roi_sampled = get_random_roi_sampling_center_v2(croped_zero,croped_zero_mask,nonzero_center='both',mask_num=0)\n",
    "        extracted_roi=[]\n",
    "        extracted_roi_mask=[]\n",
    "        for k in range(4):\n",
    "            extracted_roi.append(extract_roi_from_volume(croped_zero[:,:,:,k],roi_sampled, (64,64,64), fill = 'zero'))\n",
    "        for kk in range(2):\n",
    "            extracted_roi_mask.append(extract_roi_from_volume(croped_zero_mask[:,:,:,kk],roi_sampled, (64,64,64), fill = 'zero'))\n",
    "        extracted_roi = np.einsum('CDHW->DHWC', extracted_roi)\n",
    "        extracted_roi_mask = np.einsum('CDHW->DHWC', extracted_roi_mask)\n",
    "        data_x.append(extracted_roi)\n",
    "        data_y.append(extracted_roi_mask)\n",
    "fig, (ax1, ax2, ax3, ax4)= plt.subplots(1,4,figsize=(2*10.125,2*6.75))\n",
    "n=0\n",
    "u=32\n",
    "ch=0\n",
    "ax1.imshow(np.array(data_x)[n,u,:,:,ch], cmap='gray')\n",
    "ax2.imshow(np.array(data_y)[n,u,:,:,0], cmap='gray')\n",
    "ax3.imshow(np.array(data_x)[n,u,:,:,ch], cmap='gray')\n",
    "ax4.imshow(np.array(data_y)[n,u,:,:,1], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AlIU9IydKpBG"
   },
   "outputs": [],
   "source": [
    "def crop_ND_volume_with_bounding_box(volume, min_idx, max_idx):\n",
    "    \"\"\"\n",
    "    crop/extract a subregion form an nd image.\n",
    "    \"\"\"\n",
    "    dim = len(volume.shape)\n",
    "    channel_dim = volume.shape[-1]\n",
    "    output = volume[np.ix_(range(min_idx[0], max_idx[0] + 1),\n",
    "                           range(min_idx[1], max_idx[1] + 1),\n",
    "                           range(min_idx[2], max_idx[2] + 1),\n",
    "                           range(channel_dim))]\n",
    "    return output\n",
    "def get_random_roi_sampling_center_v2(croped_zero,croped_zero_mask,nonzero_center='both',mask_num=1):\n",
    "    \"\"\"\n",
    "    get a random coordinate representing the center of a roi for sampling\n",
    "    two option is available:\n",
    "    1) get roi center that centered on nonzero voxels of mask.\n",
    "    2) get roi center that centered on nonzero voxels of brain region with a\n",
    "    margin of border to include more brain region\n",
    "    \"\"\"\n",
    "    if nonzero_center=='both':\n",
    "       nonzero_center = random.choice(['true','false'])\n",
    "    if nonzero_center=='true':\n",
    "       mask_nonzero = np.array(croped_zero_mask[:,:,:,mask_num].nonzero())\n",
    "       random_select = np.random.randint(0,mask_nonzero.shape[1])\n",
    "       roi_index = mask_nonzero[:,random_select]\n",
    "    if nonzero_center=='false':\n",
    "        nonzero = np.array(croped_zero[:,:,:, 0].nonzero())\n",
    "        random_select = np.random.randint(0,nonzero.shape[1])\n",
    "        roi_index = nonzero[:,random_select]\n",
    "    return roi_index\n",
    "def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\n",
    "    \"\"\"\n",
    "    extract a roi from a 3d volume\n",
    "    inputs:\n",
    "        volume: the input 3D volume\n",
    "        in_center: the center of the roi\n",
    "        output_shape: the size of the roi\n",
    "        fill: 'random' or 'zero', the mode to fill roi region where is outside of the input volume\n",
    "    outputs:\n",
    "        output: the roi volume\n",
    "    \"\"\"\n",
    "    input_shape = volume.shape   \n",
    "    if(fill == 'random'):\n",
    "        output = np.random.normal(0, 1, size = output_shape)\n",
    "    else:\n",
    "        output = np.zeros(output_shape)\n",
    "    r0max = [int(x/2) for x in output_shape]\n",
    "    r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\n",
    "    r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\n",
    "    r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\n",
    "    out_center = r0max\n",
    "\n",
    "    output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\n",
    "                  range(out_center[1] - r0[1], out_center[1] + r1[1]),\n",
    "                  range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\n",
    "        volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\n",
    "                      range(in_center[1] - r0[1], in_center[1] + r1[1]),\n",
    "                      range(in_center[2] - r0[2], in_center[2] + r1[2]))]\n",
    "    return output\n",
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI_aug_random_patch_equal_prob(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "                    croped_zero = crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,:], idx_min,idx_max) \n",
    "                    croped_zero_mask =  crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,:], idx_min,idx_max)\n",
    "                    roi_sampled = get_random_roi_sampling_center_v2(croped_zero,croped_zero_mask,nonzero_center='both',mask_num=1)\n",
    "                    norm_input=[]\n",
    "                    extracted_roi_mask=[] \n",
    "                    for w in range(4):\n",
    "                        extracted_roi = extract_roi_from_volume(croped_zero[:,:,:,w],roi_sampled, (64,64,64), fill = 'zero')\n",
    "                        norm_modal = itensity_normalize_one_volume(extracted_roi)\n",
    "                        norm_input.append(norm_modal)  \n",
    "                    for ww in range(2):\n",
    "                        extracted_roi_mask.append(extract_roi_from_volume(croped_zero_mask[:,:,:,ww],roi_sampled, (64,64,64), fill = 'zero'))\n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    extract_roi_mask= np.einsum('CDHW->HWDC', extracted_roi_mask)\n",
    "                    extract_roi_mask= np.expand_dims(extract_roi_mask[:,:,:,1],axis=-1)\n",
    "                    xy = tf.concat([norm_input, extract_roi_mask], axis=-1)\n",
    "                    ###fliplr\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[1]),lambda: xy)\n",
    "                    ###flipud\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[0]),lambda: xy)\n",
    "                    # ###rot90 counter-clockwise\n",
    "                    # xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: np.rot90(xy,axes=(0,1)),lambda: xy)\n",
    "                    # ###rot90 clockwise\n",
    "                    # xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: np.rot90(xy,axes=(1,0)),lambda: xy)\n",
    "                    images_aug, segmaps_aug = tf.split(xy, [4, 1], axis=-1)\n",
    "                    data_x.append(images_aug.numpy())\n",
    "                    data_y.append(segmaps_aug.numpy())\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j1BPJA3bubZe"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI_aug(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "                    croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    roi_sampled= get_random_roi_sampling_center(np.shape(croped_zero), (128,128,128), sample_mode='valid',bounding_box=None)\n",
    "                    for w in range(0,4):\n",
    "                        croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,w], idx_min,idx_max)\n",
    "                        extracted_roi=extract_roi_from_volume(croped_zero,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                        norm_modal=itensity_normalize_one_volume(extracted_roi)\n",
    "                        norm_input.append(norm_modal)   \n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    croped=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,1], idx_min,idx_max)\n",
    "                    extract_roi_mask= extract_roi_from_volume(croped,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    extract_roi_mask= np.expand_dims(extract_roi_mask,axis=0)\n",
    "                    extract_roi_mask= np.einsum('CDHW->HWDC', extract_roi_mask)\n",
    "                    xy = tf.concat([norm_input, extract_roi_mask], axis=-1)\n",
    "                    ###fliplr\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[1]),lambda: xy)\n",
    "                    ###flipud\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[0]),lambda: xy)\n",
    "                    images_aug, segmaps_aug = tf.split(xy, [4, 1], axis=-1)\n",
    "                    data_x.append(images_aug.numpy())\n",
    "                    data_y.append(segmaps_aug.numpy())\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "n9iqKjIJvRI_"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI_aug_patch_wise(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_420patch.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_420patch.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_420patch.hdf5'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    for w in range(0,4):\n",
    "                        norm_modal=itensity_normalize_one_volume(hf_x['data_train_3D_ISBI_420patch.hdf5'][i,:,:,:,w])\n",
    "                        norm_input.append(norm_modal)   \n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    expanded_mask= np.expand_dims(hf_y['label_train_3D_ISBI_420patch.hdf5'][i,:,:,:,1],axis=0)\n",
    "                    expanded_mask= np.einsum('CDHW->HWDC', expanded_mask)\n",
    "                    xy = tf.concat([norm_input, expanded_mask], axis=-1)\n",
    "                    ###fliplr\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[1]),lambda: xy)\n",
    "                    ###flipud\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[0]),lambda: xy)\n",
    "                    # ###rot90 counter-clockwise\n",
    "                    # xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: np.rot90(xy,axes=(0,1)),lambda: xy)\n",
    "                    # ###rot90 clockwise\n",
    "                    # xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: np.rot90(xy,axes=(1,0)),lambda: xy)\n",
    "                    images_aug, segmaps_aug = tf.split(xy, [4, 1], axis=-1)\n",
    "                    data_x.append(images_aug.numpy())\n",
    "                    data_y.append(segmaps_aug.numpy())\n",
    "                    # gt_train = [np.array(data_y),np.array(data_y),np.array(data_y),np.array(data_y)]\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4CN78XEOXGby"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI_maj_aug_kfold(batch_size,num_fold):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "        index = list(range(data_len))\n",
    "        train_indices=[None]*5\n",
    "        val_indices=[None]*5\n",
    "        kf = KFold(n_splits=5,random_state=0,shuffle=True)\n",
    "        for i, [train_index, val_index] in enumerate(kf.split(index)):\n",
    "            train_indices[i], val_indices[i] = train_index, val_index\n",
    "        while True:\n",
    "            ind = shuffle(train_indices[num_fold])\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "                    croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    roi_sampled= get_random_roi_sampling_center(np.shape(croped_zero), (128,128,128), sample_mode='valid',bounding_box=None)\n",
    "                    for w in range(0,4):\n",
    "                        croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,w], idx_min,idx_max)\n",
    "                        extracted_roi=extract_roi_from_volume(croped_zero,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                        norm_modal=itensity_normalize_one_volume(extracted_roi)\n",
    "                        norm_input.append(norm_modal)   \n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    croped_mask1=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    extract_roi_mask1= extract_roi_from_volume(croped_mask1,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    croped_mask2=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,1], idx_min,idx_max)\n",
    "                    extract_roi_mask2= extract_roi_from_volume(croped_mask2,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    extract_roi_mask= extract_roi_mask1 + extract_roi_mask2\n",
    "                    extract_roi_mask[extract_roi_mask<=1]=0\n",
    "                    extract_roi_mask[extract_roi_mask>1]= 1\n",
    "                    extract_roi_mask= np.expand_dims(extract_roi_mask,axis=0)\n",
    "                    extract_roi_mask= np.einsum('CDHW->HWDC', extract_roi_mask)\n",
    "                    xy = tf.concat([norm_input, extract_roi_mask], axis=-1)\n",
    "                    ###fliplr\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[1]),lambda: xy)\n",
    "                    ###flipud\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[0]),lambda: xy)\n",
    "                    images_aug, segmaps_aug = tf.split(xy, [4, 1], axis=-1)\n",
    "                    data_x.append(images_aug.numpy())\n",
    "                    data_y.append(segmaps_aug.numpy())\n",
    "                    # gt_train = [np.array(data_y),np.array(data_y),np.array(data_y),np.array(data_y)]\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_Ks-TLpTToRq"
   },
   "outputs": [],
   "source": [
    "def batch(iterable, n=1):\n",
    "            l = len(iterable)\n",
    "            for ndx in range(0, l, n):\n",
    "               yield iterable[ndx:min(ndx + n, l)]\n",
    "def trainset_generator_3D_ISBI_maj_aug(batch_size):\n",
    "        hf_x = h5py.File('/gdrive/My Drive/data_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        hf_y = h5py.File('/gdrive/My Drive/label_train_3D_ISBI_21sample.hdf5', 'r')\n",
    "        data_len = hf_x['data_train_3D_ISBI_21sample.hdf5'].shape[0]\n",
    "        while True:\n",
    "            index = list(range(data_len))\n",
    "            ind = shuffle(index)\n",
    "            for x in batch(ind, batch_size):\n",
    "                data_x=[]\n",
    "                data_y=[]\n",
    "                for i in x:\n",
    "                    norm_input=[]\n",
    "                    idx_min, idx_max=get_none_zero_region(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0],0)\n",
    "                    croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    roi_sampled= get_random_roi_sampling_center(np.shape(croped_zero), (128,128,128), sample_mode='valid',bounding_box=None)\n",
    "                    for w in range(0,4):\n",
    "                        croped_zero=crop_ND_volume_with_bounding_box(hf_x['data_train_3D_ISBI_21sample.hdf5'][i,:,:,:,w], idx_min,idx_max)\n",
    "                        extracted_roi=extract_roi_from_volume(croped_zero,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                        norm_modal=itensity_normalize_one_volume(extracted_roi)\n",
    "                        norm_input.append(norm_modal)   \n",
    "                    norm_input = np.einsum('CDHW->HWDC', norm_input)\n",
    "                    croped_mask1=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,0], idx_min,idx_max)\n",
    "                    extract_roi_mask1= extract_roi_from_volume(croped_mask1,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    croped_mask2=crop_ND_volume_with_bounding_box(hf_y['label_train_3D_ISBI_21sample.hdf5'][i,:,:,:,1], idx_min,idx_max)\n",
    "                    extract_roi_mask2= extract_roi_from_volume(croped_mask2,roi_sampled, (128,128,128), fill = 'zero')\n",
    "                    extract_roi_mask= extract_roi_mask1 + extract_roi_mask2\n",
    "                    extract_roi_mask[extract_roi_mask<=0.5]=0\n",
    "                    extract_roi_mask[extract_roi_mask>0.5]= 1\n",
    "                    extract_roi_mask= np.expand_dims(extract_roi_mask,axis=0)\n",
    "                    extract_roi_mask= np.einsum('CDHW->HWDC', extract_roi_mask)\n",
    "                    xy = tf.concat([norm_input, extract_roi_mask], axis=-1)\n",
    "                    ###fliplr\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[1]),lambda: xy)\n",
    "                    ###flipud\n",
    "                    xy = tf.cond(tf.random.uniform(()) > 0.5,lambda: tf.reverse(xy, axis=[0]),lambda: xy)\n",
    "                    images_aug, segmaps_aug = tf.split(xy, [4, 1], axis=-1)\n",
    "                    data_x.append(images_aug.numpy())\n",
    "                    data_y.append(segmaps_aug.numpy())\n",
    "                yield(np.array(data_x),np.array(data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9lk8A9T2R9RD",
    "outputId": "e8a67595-94a2-4030-c602-c3d526536016"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, None, None, 4)]   0         \n",
      "_________________________________________________________________\n",
      "group_normalization (GroupNo (None, None, None, 4)     8         \n",
      "=================================================================\n",
      "Total params: 8\n",
      "Trainable params: 8\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "### GroupNormalization code from https://github.com/titu1994/keras-global-context-networks.git repository\n",
    "from keras.engine import Layer, InputSpec\n",
    "from keras import initializers\n",
    "from keras import regularizers\n",
    "from keras import constraints\n",
    "from keras import backend as K\n",
    "\n",
    "from keras.utils.generic_utils import get_custom_objects\n",
    "\n",
    "\n",
    "class GroupNormalization(Layer):\n",
    "    \"\"\"Group normalization layer\n",
    "    Group Normalization divides the channels into groups and computes within each group\n",
    "    the mean and variance for normalization. GN's computation is independent of batch sizes,\n",
    "    and its accuracy is stable in a wide range of batch sizes\n",
    "    # Arguments\n",
    "        groups: Integer, the number of groups for Group Normalization.\n",
    "        axis: Integer, the axis that should be normalized\n",
    "            (typically the features axis).\n",
    "            For instance, after a `Conv2D` layer with\n",
    "            `data_format=\"channels_first\"`,\n",
    "            set `axis=1` in `BatchNormalization`.\n",
    "        epsilon: Small float added to variance to avoid dividing by zero.\n",
    "        center: If True, add offset of `beta` to normalized tensor.\n",
    "            If False, `beta` is ignored.\n",
    "        scale: If True, multiply by `gamma`.\n",
    "            If False, `gamma` is not used.\n",
    "            When the next layer is linear (also e.g. `nn.relu`),\n",
    "            this can be disabled since the scaling\n",
    "            will be done by the next layer.\n",
    "        beta_initializer: Initializer for the beta weight.\n",
    "        gamma_initializer: Initializer for the gamma weight.\n",
    "        beta_regularizer: Optional regularizer for the beta weight.\n",
    "        gamma_regularizer: Optional regularizer for the gamma weight.\n",
    "        beta_constraint: Optional constraint for the beta weight.\n",
    "        gamma_constraint: Optional constraint for the gamma weight.\n",
    "    # Input shape\n",
    "        Arbitrary. Use the keyword argument `input_shape`\n",
    "        (tuple of integers, does not include the samples axis)\n",
    "        when using this layer as the first layer in a model.\n",
    "    # Output shape\n",
    "        Same shape as input.\n",
    "    # References\n",
    "        - [Group Normalization](https://arxiv.org/abs/1803.08494)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 groups=32,\n",
    "                 axis=-1,\n",
    "                 epsilon=1e-5,\n",
    "                 center=True,\n",
    "                 scale=True,\n",
    "                 beta_initializer='zeros',\n",
    "                 gamma_initializer='ones',\n",
    "                 beta_regularizer=None,\n",
    "                 gamma_regularizer=None,\n",
    "                 beta_constraint=None,\n",
    "                 gamma_constraint=None,\n",
    "                 **kwargs):\n",
    "        super(GroupNormalization, self).__init__(**kwargs)\n",
    "        self.supports_masking = True\n",
    "        self.groups = groups\n",
    "        self.axis = axis\n",
    "        self.epsilon = epsilon\n",
    "        self.center = center\n",
    "        self.scale = scale\n",
    "        self.beta_initializer = initializers.get(beta_initializer)\n",
    "        self.gamma_initializer = initializers.get(gamma_initializer)\n",
    "        self.beta_regularizer = regularizers.get(beta_regularizer)\n",
    "        self.gamma_regularizer = regularizers.get(gamma_regularizer)\n",
    "        self.beta_constraint = constraints.get(beta_constraint)\n",
    "        self.gamma_constraint = constraints.get(gamma_constraint)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        dim = input_shape[self.axis]\n",
    "\n",
    "        if dim is None:\n",
    "            raise ValueError('Axis ' + str(self.axis) + ' of '\n",
    "                             'input tensor should have a defined dimension '\n",
    "                             'but the layer received an input with shape ' +\n",
    "                             str(input_shape) + '.')\n",
    "\n",
    "        if dim < self.groups:\n",
    "            raise ValueError('Number of groups (' + str(self.groups) + ') cannot be '\n",
    "                             'more than the number of channels (' +\n",
    "                             str(dim) + ').')\n",
    "\n",
    "        if dim % self.groups != 0:\n",
    "            raise ValueError('Number of groups (' + str(self.groups) + ') must be a '\n",
    "                             'multiple of the number of channels (' +\n",
    "                             str(dim) + ').')\n",
    "\n",
    "        self.input_spec = InputSpec(ndim=len(input_shape),\n",
    "                                    axes={self.axis: dim})\n",
    "        shape = (dim,)\n",
    "\n",
    "        if self.scale:\n",
    "            self.gamma = self.add_weight(shape=shape,\n",
    "                                         name='gamma',\n",
    "                                         initializer=self.gamma_initializer,\n",
    "                                         regularizer=self.gamma_regularizer,\n",
    "                                         constraint=self.gamma_constraint)\n",
    "        else:\n",
    "            self.gamma = None\n",
    "        if self.center:\n",
    "            self.beta = self.add_weight(shape=shape,\n",
    "                                        name='beta',\n",
    "                                        initializer=self.beta_initializer,\n",
    "                                        regularizer=self.beta_regularizer,\n",
    "                                        constraint=self.beta_constraint)\n",
    "        else:\n",
    "            self.beta = None\n",
    "        self.built = True\n",
    "\n",
    "    def call(self, inputs, **kwargs):\n",
    "        input_shape = K.int_shape(inputs)\n",
    "        tensor_input_shape = K.shape(inputs)\n",
    "\n",
    "        # Prepare broadcasting shape.\n",
    "        reduction_axes = list(range(len(input_shape)))\n",
    "        del reduction_axes[self.axis]\n",
    "        broadcast_shape = [1] * len(input_shape)\n",
    "        broadcast_shape[self.axis] = input_shape[self.axis] // self.groups\n",
    "        broadcast_shape.insert(1, self.groups)\n",
    "\n",
    "        reshape_group_shape = K.shape(inputs)\n",
    "        group_axes = [reshape_group_shape[i] for i in range(len(input_shape))]\n",
    "        group_axes[self.axis] = input_shape[self.axis] // self.groups\n",
    "        group_axes.insert(1, self.groups)\n",
    "\n",
    "        # reshape inputs to new group shape\n",
    "        group_shape = [group_axes[0], self.groups] + group_axes[2:]\n",
    "        group_shape = K.stack(group_shape)\n",
    "        inputs = K.reshape(inputs, group_shape)\n",
    "\n",
    "        group_reduction_axes = list(range(len(group_axes)))\n",
    "        group_reduction_axes = group_reduction_axes[2:]\n",
    "\n",
    "        mean = K.mean(inputs, axis=group_reduction_axes, keepdims=True)\n",
    "        variance = K.var(inputs, axis=group_reduction_axes, keepdims=True)\n",
    "\n",
    "        inputs = (inputs - mean) / (K.sqrt(variance + self.epsilon))\n",
    "\n",
    "        # prepare broadcast shape\n",
    "        inputs = K.reshape(inputs, group_shape)\n",
    "        outputs = inputs\n",
    "\n",
    "        # In this case we must explicitly broadcast all parameters.\n",
    "        if self.scale:\n",
    "            broadcast_gamma = K.reshape(self.gamma, broadcast_shape)\n",
    "            outputs = outputs * broadcast_gamma\n",
    "\n",
    "        if self.center:\n",
    "            broadcast_beta = K.reshape(self.beta, broadcast_shape)\n",
    "            outputs = outputs + broadcast_beta\n",
    "\n",
    "        outputs = K.reshape(outputs, tensor_input_shape)\n",
    "\n",
    "        return outputs\n",
    "\n",
    "    def get_config(self):\n",
    "        config = {\n",
    "            'groups': self.groups,\n",
    "            'axis': self.axis,\n",
    "            'epsilon': self.epsilon,\n",
    "            'center': self.center,\n",
    "            'scale': self.scale,\n",
    "            'beta_initializer': initializers.serialize(self.beta_initializer),\n",
    "            'gamma_initializer': initializers.serialize(self.gamma_initializer),\n",
    "            'beta_regularizer': regularizers.serialize(self.beta_regularizer),\n",
    "            'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),\n",
    "            'beta_constraint': constraints.serialize(self.beta_constraint),\n",
    "            'gamma_constraint': constraints.serialize(self.gamma_constraint)\n",
    "        }\n",
    "        base_config = super(GroupNormalization, self).get_config()\n",
    "        return dict(list(base_config.items()) + list(config.items()))\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return input_shape\n",
    "\n",
    "\n",
    "get_custom_objects().update({'GroupNormalization': GroupNormalization})\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    from keras.layers import Input\n",
    "    from keras.models import Model\n",
    "    ip = Input(shape=(None, None, 4))\n",
    "    x = GroupNormalization(groups=2, axis=-1, epsilon=0.1)(ip)\n",
    "    model = Model(ip, x)\n",
    "    model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MJSvzY30R_0o",
    "outputId": "5363847b-8048-4b92-8831-e8c84a275ecb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 64, 64, 32)] 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 64, 64, 1)    32          input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 4096, 1)      0           conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 4096, 32)     0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Softmax)               (None, 4096, 1)      0           reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dot (Dot)                       (None, 32, 1)        0           reshape[0][0]                    \n",
      "                                                                 softmax[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 1, 1, 32)     0           dot[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 1, 1, 2)      64          reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "group_normalization_1 (GroupNor (None, 1, 1, 2)      4           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 1, 1, 2)      0           group_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 1, 1, 32)     64          activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 1, 1, 32)     0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 64, 64, 32)   0           input_2[0][0]                    \n",
      "                                                                 activation_1[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 164\n",
      "Trainable params: 164\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "### global_context_block code from https://github.com/titu1994/keras-global-context-networks.git repository\n",
    "from keras.layers import Conv1D, Conv2D, Conv3D\n",
    "from keras.layers import Reshape\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Softmax\n",
    "from keras.layers import Permute\n",
    "from keras.layers import add, dot\n",
    "\n",
    "from keras import backend as K\n",
    "\n",
    "def global_context_block(ip, reduction_ratio=16, transform_activation='linear'):\n",
    "    \"\"\"\n",
    "    Adds a Global Context attention block for self attention to the input tensor.\n",
    "    Input tensor can be or rank 3 (temporal), 4 (spatial) or 5 (spatio-temporal).\n",
    "\n",
    "    # Arguments:\n",
    "        ip: input tensor\n",
    "        intermediate_dim: The dimension of the intermediate representation. Can be\n",
    "            `None` or a positive integer greater than 0. If `None`, computes the\n",
    "            intermediate dimension as half of the input channel dimension.\n",
    "        reduction_ratio: Reduces the input filters by this factor for the\n",
    "            bottleneck block of the transform submodule. Node: the reduction\n",
    "            ratio must be set such that it divides the input number of channels,\n",
    "        transform_activation: activation function to apply to the output\n",
    "            of the transform block. Can be any string activation function availahle\n",
    "            to Keras.\n",
    "\n",
    "    # Returns:\n",
    "        a tensor of same shape as input\n",
    "    \"\"\"\n",
    "    channel_dim = 1 if K.image_data_format() == 'channels_first' else -1\n",
    "    ip_shape = K.int_shape(ip)\n",
    "\n",
    "    # check rank and calculate the input shape\n",
    "    if len(ip_shape) == 3:  # temporal / time series data\n",
    "        rank = 3\n",
    "        batchsize, dim1, channels = ip_shape\n",
    "\n",
    "    elif len(ip_shape) == 4:  # spatial / image data\n",
    "        rank = 4\n",
    "\n",
    "        if channel_dim == 1:\n",
    "            batchsize, channels, dim1, dim2 = ip_shape\n",
    "        else:\n",
    "            batchsize, dim1, dim2, channels = ip_shape\n",
    "\n",
    "    elif len(ip_shape) == 5:  # spatio-temporal / Video or Voxel data\n",
    "        rank = 5\n",
    "\n",
    "        if channel_dim == 1:\n",
    "            batchsize, channels, dim1, dim2, dim3 = ip_shape\n",
    "        else:\n",
    "            batchsize, dim1, dim2, dim3, channels = ip_shape\n",
    "\n",
    "    else:\n",
    "        raise ValueError('Input dimension has to be either 3 (temporal), 4 (spatial) or 5 (spatio-temporal)')\n",
    "\n",
    "    if rank > 3:\n",
    "        flat_spatial_dim = -1 if K.image_data_format() == 'channels_first' else 1\n",
    "    else:\n",
    "        flat_spatial_dim = 1\n",
    "\n",
    "    \"\"\" Context Modelling Block \"\"\"\n",
    "    # [B, ***, C] or [B, C, ***]\n",
    "    input_flat = _spatial_flattenND(ip, rank)\n",
    "    # [B, ..., C] or [B, C, ...]\n",
    "    context = _convND(ip, rank, channels=1, kernel=1)\n",
    "    # [B, ..., 1] or [B, 1, ...]\n",
    "    context = _spatial_flattenND(context, rank)\n",
    "    # [B, ***, 1] or [B, 1, ***]\n",
    "    context = Softmax(axis=flat_spatial_dim)(context)\n",
    "\n",
    "    # Compute context block outputs\n",
    "    context = dot([input_flat, context], axes=flat_spatial_dim)\n",
    "    # [B, C, 1]\n",
    "    context = _spatial_expandND(context, rank)\n",
    "    # [B, C, 1...] or [B, 1..., C]\n",
    "\n",
    "    \"\"\" Transform block \"\"\"\n",
    "    # Transform bottleneck\n",
    "    # [B, C // R, 1...] or [B, 1..., C // R]\n",
    "    transform = _convND(context, rank, channels // reduction_ratio, kernel=1)\n",
    "    # Group normalization acts as Layer Normalization when groups = 1\n",
    "    transform = GroupNormalization(groups=1, axis=channel_dim)(transform)\n",
    "    transform = Activation('relu')(transform)\n",
    "\n",
    "    # Transform output block\n",
    "    # [B, C, 1...] or [B, 1..., C]\n",
    "    transform = _convND(transform, rank, channels, kernel=1)\n",
    "    transform = Activation(transform_activation)(transform)\n",
    "\n",
    "    # apply context transform\n",
    "    out = add([ip, transform])\n",
    "\n",
    "    return out\n",
    "\n",
    "\n",
    "def _convND(ip, rank, channels, kernel=1):\n",
    "    assert rank in [3, 4, 5], \"Rank of input must be 3, 4 or 5\"\n",
    "\n",
    "    if rank == 3:\n",
    "        x = Conv1D(channels, kernel, padding='same', use_bias=False, kernel_initializer='he_normal')(ip)\n",
    "    elif rank == 4:\n",
    "        x = Conv2D(channels, (kernel, kernel), padding='same', use_bias=False, kernel_initializer='he_normal')(ip)\n",
    "    else:\n",
    "        x = Conv3D(channels, (kernel, kernel, kernel), padding='same', use_bias=False, kernel_initializer='he_normal')(ip)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def _spatial_flattenND(ip, rank):\n",
    "    assert rank in [3, 4, 5], \"Rank of input must be 3, 4 or 5\"\n",
    "\n",
    "    ip_shape = K.int_shape(ip)\n",
    "    channel_dim = 1 if K.image_data_format() == 'channels_first' else -1\n",
    "\n",
    "    if rank == 3:\n",
    "        x = ip  # identity op for rank 3\n",
    "\n",
    "    elif rank == 4:\n",
    "        if channel_dim == 1:\n",
    "            # [C, D1, D2] -> [C, D1 * D2]\n",
    "            shape = [ip_shape[1], ip_shape[2] * ip_shape[3]]\n",
    "        else:\n",
    "            # [D1, D2, C] -> [D1 * D2, C]\n",
    "            shape = [ip_shape[1] * ip_shape[2], ip_shape[3]]\n",
    "\n",
    "        x = Reshape(shape)(ip)\n",
    "\n",
    "    else:\n",
    "        if channel_dim == 1:\n",
    "            # [C, D1, D2, D3] -> [C, D1 * D2 * D3]\n",
    "            shape = [ip_shape[1], ip_shape[2] * ip_shape[3] * ip_shape[4]]\n",
    "        else:\n",
    "            # [D1, D2, D3, C] -> [D1 * D2 * D3, C]\n",
    "            shape = [ip_shape[1] * ip_shape[2] * ip_shape[3], ip_shape[4]]\n",
    "\n",
    "        x = Reshape(shape)(ip)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def _spatial_expandND(ip, rank):\n",
    "    assert rank in [3, 4, 5], \"Rank of input must be 3, 4 or 5\"\n",
    "\n",
    "    channel_dim = 1 if K.image_data_format() == 'channels_first' else -1\n",
    "\n",
    "    if rank == 3:\n",
    "        x = Permute((2, 1))(ip)  # identity op for rank 3\n",
    "\n",
    "    elif rank == 4:\n",
    "        if channel_dim == 1:\n",
    "            # [C, D1, D2] -> [C, D1 * D2]\n",
    "            shape = [-1, 1, 1]\n",
    "        else:\n",
    "            # [D1, D2, C] -> [D1 * D2, C]\n",
    "            shape = [1, 1, -1]\n",
    "\n",
    "        x = Reshape(shape)(ip)\n",
    "\n",
    "    else:\n",
    "        if channel_dim == 1:\n",
    "            # [C, D1, D2, D3] -> [C, D1 * D2 * D3]\n",
    "            shape = [-1, 1, 1, 1]\n",
    "        else:\n",
    "            # [D1, D2, D3, C] -> [D1 * D2 * D3, C]\n",
    "            shape = [1, 1, 1, -1]\n",
    "\n",
    "        x = Reshape(shape)(ip)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    from keras.layers import Input\n",
    "    from keras.models import Model\n",
    "\n",
    "    ip = Input(shape=(64, 64, 32))\n",
    "    x = global_context_block(ip, reduction_ratio=16)\n",
    "\n",
    "    model = Model(ip, x)\n",
    "\n",
    "    model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GzJec_bLSCrq",
    "outputId": "2be2b188-e030-4a4f-836f-3e2e2cc60279"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 192, 192, 19 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_1 (Conv3D)                (None, 192, 192, 192 3488        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_1_bn (BatchNormalization) (None, 192, 192, 192 128         conv1_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_1_act (Activation)        (None, 192, 192, 192 0           conv1_1_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_2 (Conv3D)                (None, 192, 192, 192 27680       conv1_1_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1_2_bn (BatchNormalization) (None, 192, 192, 192 128         conv1_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_2_act (Activation)        (None, 192, 192, 192 0           conv1_2_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d (MaxPooling3D)    (None, 96, 96, 96, 3 0           conv1_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1 (Conv3D)                (None, 96, 96, 96, 6 55360       max_pooling3d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_bn (BatchNormalization) (None, 96, 96, 96, 6 256         conv2_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_act (Activation)        (None, 96, 96, 96, 6 0           conv2_1_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2 (Conv3D)                (None, 96, 96, 96, 6 110656      conv2_1_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_bn (BatchNormalization) (None, 96, 96, 96, 6 256         conv2_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_act (Activation)        (None, 96, 96, 96, 6 0           conv2_2_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_1 (MaxPooling3D)  (None, 48, 48, 48, 6 0           conv2_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1 (Conv3D)                (None, 48, 48, 48, 1 221312      max_pooling3d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_bn (BatchNormalization) (None, 48, 48, 48, 1 512         conv3_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_act (Activation)        (None, 48, 48, 48, 1 0           conv3_1_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2 (Conv3D)                (None, 48, 48, 48, 1 442496      conv3_1_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_bn (BatchNormalization) (None, 48, 48, 48, 1 512         conv3_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_act (Activation)        (None, 48, 48, 48, 1 0           conv3_2_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_2 (MaxPooling3D)  (None, 24, 24, 24, 1 0           conv3_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1 (Conv3D)                (None, 24, 24, 24, 2 884992      max_pooling3d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_bn (BatchNormalization) (None, 24, 24, 24, 2 1024        conv4_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_act (Activation)        (None, 24, 24, 24, 2 0           conv4_1_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2 (Conv3D)                (None, 24, 24, 24, 2 1769728     conv4_1_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_bn (BatchNormalization) (None, 24, 24, 24, 2 1024        conv4_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_act (Activation)        (None, 24, 24, 24, 2 0           conv4_2_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_3 (MaxPooling3D)  (None, 12, 12, 12, 2 0           conv4_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "center_1 (Conv3D)               (None, 12, 12, 12, 5 3539456     max_pooling3d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "center_1_bn (BatchNormalization (None, 12, 12, 12, 5 2048        center_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "center_1_act (Activation)       (None, 12, 12, 12, 5 0           center_1_bn[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "center_2 (Conv3D)               (None, 12, 12, 12, 5 7078400     center_1_act[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "center_2_bn (BatchNormalization (None, 12, 12, 12, 5 2048        center_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "center_2_act (Activation)       (None, 12, 12, 12, 5 0           center_2_bn[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "g1_conv (Conv3D)                (None, 12, 12, 12, 5 262656      center_2_act[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "g1_bn (BatchNormalization)      (None, 12, 12, 12, 5 2048        g1_conv[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "g1_act (Activation)             (None, 12, 12, 12, 5 0           g1_bn[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d (Conv3D)                 (None, 12, 12, 12, 2 131328      g1_act[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "g_up_1 (Conv3DTranspose)        (None, 12, 12, 12, 2 1769728     conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "xl_1 (Conv3D)                   (None, 12, 12, 12, 2 524544      conv4_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 12, 12, 12, 2 0           g_up_1[0][0]                     \n",
      "                                                                 xl_1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 12, 12, 12, 2 0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "psi_1 (Conv3D)                  (None, 12, 12, 12, 1 257         activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 12, 12, 12, 1 0           psi_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling3d (UpSampling3D)    (None, 24, 24, 24, 1 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "psi_up_1 (Lambda)               (None, 24, 24, 24, 2 0           up_sampling3d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_1 (Multiply)             (None, 24, 24, 24, 2 0           psi_up_1[0][0]                   \n",
      "                                                                 conv4_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_conv_1 (Conv3D)          (None, 24, 24, 24, 2 65792       q_attn_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose (Conv3DTranspo (None, 24, 24, 24, 2 3539200     center_2_act[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_bn_1 (BatchNormalization (None, 24, 24, 24, 2 1024        q_attn_conv_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up1 (Concatenate)               (None, 24, 24, 24, 5 0           conv3d_transpose[0][0]           \n",
      "                                                                 q_attn_bn_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1_d_1 (Conv3D)              (None, 24, 24, 24, 2 3539200     up1[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv1_d_1_bn (BatchNormalizatio (None, 24, 24, 24, 2 1024        conv1_d_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_d_1_act (Activation)      (None, 24, 24, 24, 2 0           conv1_d_1_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1_d_2 (Conv3D)              (None, 24, 24, 24, 2 1769728     conv1_d_1_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1_d_2_bn (BatchNormalizatio (None, 24, 24, 24, 2 1024        conv1_d_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_d_2_act (Activation)      (None, 24, 24, 24, 2 0           conv1_d_2_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "g2_conv (Conv3D)                (None, 24, 24, 24, 2 65792       conv1_d_2_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "g2_bn (BatchNormalization)      (None, 24, 24, 24, 2 1024        g2_conv[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "g2_act (Activation)             (None, 24, 24, 24, 2 0           g2_bn[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_1 (Conv3D)               (None, 24, 24, 24, 1 32896       g2_act[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "g_up_2 (Conv3DTranspose)        (None, 24, 24, 24, 1 442496      conv3d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "xl_2 (Conv3D)                   (None, 24, 24, 24, 1 131200      conv3_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 24, 24, 24, 1 0           g_up_2[0][0]                     \n",
      "                                                                 xl_2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 24, 24, 24, 1 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "psi_2 (Conv3D)                  (None, 24, 24, 24, 1 129         activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 24, 24, 24, 1 0           psi_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling3d_1 (UpSampling3D)  (None, 48, 48, 48, 1 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "psi_up_2 (Lambda)               (None, 48, 48, 48, 1 0           up_sampling3d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_2 (Multiply)             (None, 48, 48, 48, 1 0           psi_up_2[0][0]                   \n",
      "                                                                 conv3_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_conv_2 (Conv3D)          (None, 48, 48, 48, 1 16512       q_attn_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_1 (Conv3DTrans (None, 48, 48, 48, 1 884864      conv1_d_2_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_bn_2 (BatchNormalization (None, 48, 48, 48, 1 512         q_attn_conv_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up2 (Concatenate)               (None, 48, 48, 48, 2 0           conv3d_transpose_1[0][0]         \n",
      "                                                                 q_attn_bn_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2_d_1 (Conv3D)              (None, 48, 48, 48, 1 884864      up2[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_d_1_bn (BatchNormalizatio (None, 48, 48, 48, 1 512         conv2_d_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_d_1_act (Activation)      (None, 48, 48, 48, 1 0           conv2_d_1_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_d_2 (Conv3D)              (None, 48, 48, 48, 1 442496      conv2_d_1_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2_d_2_bn (BatchNormalizatio (None, 48, 48, 48, 1 512         conv2_d_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_d_2_act (Activation)      (None, 48, 48, 48, 1 0           conv2_d_2_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "g3_conv (Conv3D)                (None, 48, 48, 48, 1 16512       conv2_d_2_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "g3_bn (BatchNormalization)      (None, 48, 48, 48, 1 512         g3_conv[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "g3_act (Activation)             (None, 48, 48, 48, 1 0           g3_bn[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_2 (Conv3D)               (None, 48, 48, 48, 6 8256        g3_act[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "g_up_3 (Conv3DTranspose)        (None, 48, 48, 48, 6 110656      conv3d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "xl_3 (Conv3D)                   (None, 48, 48, 48, 6 32832       conv2_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 48, 48, 48, 6 0           g_up_3[0][0]                     \n",
      "                                                                 xl_3[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 48, 48, 48, 6 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "psi_3 (Conv3D)                  (None, 48, 48, 48, 1 65          activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 48, 48, 48, 1 0           psi_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling3d_2 (UpSampling3D)  (None, 96, 96, 96, 1 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "psi_up_3 (Lambda)               (None, 96, 96, 96, 6 0           up_sampling3d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_3 (Multiply)             (None, 96, 96, 96, 6 0           psi_up_3[0][0]                   \n",
      "                                                                 conv2_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_conv_3 (Conv3D)          (None, 96, 96, 96, 6 4160        q_attn_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_2 (Conv3DTrans (None, 96, 96, 96, 6 221248      conv2_d_2_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "q_attn_bn_3 (BatchNormalization (None, 96, 96, 96, 6 256         q_attn_conv_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up3 (Concatenate)               (None, 96, 96, 96, 1 0           conv3d_transpose_2[0][0]         \n",
      "                                                                 q_attn_bn_3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv3_d_1 (Conv3D)              (None, 96, 96, 96, 6 221248      up3[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_d_1_bn (BatchNormalizatio (None, 96, 96, 96, 6 256         conv3_d_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3_d_1_act (Activation)      (None, 96, 96, 96, 6 0           conv3_d_1_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_d_2 (Conv3D)              (None, 96, 96, 96, 6 110656      conv3_d_1_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_d_2_bn (BatchNormalizatio (None, 96, 96, 96, 6 256         conv3_d_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv3_d_2_act (Activation)      (None, 96, 96, 96, 6 0           conv3_d_2_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_transpose_3 (Conv3DTrans (None, 192, 192, 192 55328       conv3_d_2_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up4 (Concatenate)               (None, 192, 192, 192 0           conv3d_transpose_3[0][0]         \n",
      "                                                                 conv1_2_act[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv4_d_1 (Conv3D)              (None, 192, 192, 192 55328       up4[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_d_1_bn (BatchNormalizatio (None, 192, 192, 192 128         conv4_d_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv4_d_1_act (Activation)      (None, 192, 192, 192 0           conv4_d_1_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv4_d_2 (Conv3D)              (None, 192, 192, 192 27680       conv4_d_1_act[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_d_2_bn (BatchNormalizatio (None, 192, 192, 192 128         conv4_d_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv4_d_2_act (Activation)      (None, 192, 192, 192 0           conv4_d_2_bn[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "final (Conv3D)                  (None, 192, 192, 192 33          conv4_d_2_act[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 29,518,404\n",
      "Trainable params: 29,509,828\n",
      "Non-trainable params: 8,576\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Conv2DTranspose, concatenate, BatchNormalization, Activation, add, AveragePooling2D, UpSampling2D, SeparableConvolution2D, Dropout, multiply, GlobalAveragePooling2D, Conv3D, Conv3DTranspose, MaxPooling3D, GlobalAveragePooling3D, UpSampling3D\n",
    "from tensorflow.keras.models import Model, model_from_json\n",
    "from tensorflow.keras.optimizers import Adam, SGD, Nadam, Adagrad, RMSprop\n",
    "from tensorflow.keras.activations import elu, sigmoid\n",
    "from tensorflow.keras.layers import LeakyReLU, Reshape, Dense, Multiply\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import TensorBoard, CSVLogger\n",
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from tensorflow.keras.metrics import AUC\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.layers import Lambda\n",
    "input_shape = (64,64,64,4)\n",
    "input_shape_test= (192,192,192,4)\n",
    "seed_number = 1234\n",
    "l2 = l2(0.0001)\n",
    "ki = glorot_uniform(seed=seed_number)\n",
    "def recall_m(y_true, y_pred):\n",
    "        y_true_f = K.flatten(y_true)\n",
    "        y_pred_f = K.flatten(y_pred)\n",
    "        true_positives = K.sum(K.round(K.clip(y_true_f* y_pred_f, 0, 1)))\n",
    "        possible_positives = K.sum(K.round(K.clip(y_true_f, 0, 1)))\n",
    "        recall = true_positives / (possible_positives + K.epsilon())\n",
    "        return recall\n",
    "\n",
    "def precision_m(y_true, y_pred):\n",
    "        y_true_f = K.flatten(y_true)\n",
    "        y_pred_f = K.flatten(y_pred)\n",
    "        true_positives = K.sum(K.round(K.clip(y_true_f * y_pred_f, 0, 1)))\n",
    "        predicted_positives = K.sum(K.round(K.clip(y_pred_f, 0, 1)))\n",
    "        precision = true_positives / (predicted_positives + K.epsilon())\n",
    "        return precision\n",
    "\n",
    "def f1_m(y_true, y_pred):\n",
    "    precision = precision_m(y_true, y_pred)\n",
    "    recall = recall_m(y_true, y_pred)\n",
    "    return 2*((precision*recall)/(precision+recall+K.epsilon()))\n",
    "\n",
    "\n",
    "def jaccard_distance(y_true, y_pred, smooth=1):\n",
    "    \"\"\"Jaccard distance for semantic segmentation.\n",
    "    Also known as the intersection-over-union loss.\n",
    "    This loss is useful when you have unbalanced numbers of pixels within an image\n",
    "    because it gives all classes equal weight. However, it is not the defacto\n",
    "    standard for image segmentation.\n",
    "    For example, assume you are trying to predict if\n",
    "    each pixel is cat, dog, or background.\n",
    "    You have 80% background pixels, 10% dog, and 10% cat.\n",
    "    If the model predicts 100% background\n",
    "    should it be be 80% right (as with categorical cross entropy)\n",
    "    or 30% (with this loss)?\n",
    "    The loss has been modified to have a smooth gradient as it converges on zero.\n",
    "    This has been shifted so it converges on 0 and is smoothed to avoid exploding\n",
    "    or disappearing gradient.\n",
    "    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)\n",
    "            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))\n",
    "    # Arguments\n",
    "        y_true: The ground truth tensor.\n",
    "        y_pred: The predicted tensor\n",
    "        smooth: Smoothing factor. Default is 100.\n",
    "    # Returns\n",
    "        The Jaccard distance between the two tensors.\n",
    "    # References\n",
    "        - [What is a good evaluation measure for semantic segmentation?](\n",
    "           http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf)\n",
    "    \"\"\"\n",
    "    threshold = 0.5\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    y_pred_f = K.cast(K.greater_equal(K.clip(y_pred_f, 0, 1), threshold),'float32')\n",
    "    intersection = K.sum(K.abs(y_true_f * y_pred_f), axis=-1)\n",
    "    sum_ = K.sum(K.abs(y_true_f) + K.abs(y_pred_f), axis=-1)\n",
    "    jac = (intersection + smooth) / (sum_ - intersection + smooth)\n",
    "    return jac\n",
    "def dice_coef(y_true, y_pred):\n",
    "    threshold = 0.5\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    y_pred_f = K.cast(K.greater_equal(K.clip(y_pred_f, 0, 1), threshold),'float32')\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())\n",
    "def dice_coefficient(y_true, y_pred):\n",
    "    smooth=1\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(K.abs(y_true_f * y_pred_f), axis=-1)\n",
    "    return (2. * intersection + K.epsilon()) / (\n",
    "        K.sum(K.square(y_true_f), -1) + K.sum(K.square(y_pred_f), -1) + K.epsilon())\n",
    "def dice_coefficient_correct(y_true, y_pred):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(K.abs(y_true_f * y_pred_f), axis=-1)\n",
    "    return (2. * intersection) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())\n",
    "def confusion(y_true, y_pred):\n",
    "    smooth=1\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    y_pred_pos = K.clip(y_pred_f, 0, 1)\n",
    "    y_pred_neg = 1 - y_pred_pos\n",
    "    y_pos = K.clip(y_true, 0, 1)\n",
    "    y_neg = 1 - y_pos\n",
    "    tp = K.sum(y_pos * y_pred_pos)\n",
    "    fp = K.sum(y_neg * y_pred_pos)\n",
    "    fn = K.sum(y_pos * y_pred_neg) \n",
    "    prec = (tp + smooth)/(tp+fp+smooth)\n",
    "    recall = (tp+smooth)/(tp+fn+smooth)\n",
    "    return prec, recall\n",
    "\n",
    "def tp(y_true, y_pred):\n",
    "    smooth = 1\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    y_pred_pos = K.round(K.clip(y_pred_f, 0, 1))\n",
    "    y_pos = K.round(K.clip(y_true_f, 0, 1))\n",
    "    tp = (K.sum(y_pos * y_pred_pos) + smooth)/ (K.sum(y_pos) + smooth) \n",
    "    return tp \n",
    "\n",
    "def tn(y_true, y_pred):\n",
    "    smooth = 1\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    y_pred_pos = K.round(K.clip(y_pred_f, 0, 1))\n",
    "    y_pred_neg = 1 - y_pred_pos\n",
    "    y_pos = K.round(K.clip(y_true_f, 0, 1))\n",
    "    y_neg = 1 - y_pos \n",
    "    tn = (K.sum(y_neg * y_pred_neg) + smooth) / (K.sum(y_neg) + smooth )\n",
    "    return tn \n",
    "\n",
    "def tversky(y_true, y_pred):\n",
    "    smooth = 1\n",
    "    y_true_pos = K.flatten(y_true)\n",
    "    y_pred_pos = K.flatten(y_pred)\n",
    "    true_pos = K.sum(y_true_pos * y_pred_pos)\n",
    "    false_neg = K.sum(y_true_pos * (1-y_pred_pos))\n",
    "    false_pos = K.sum((1-y_true_pos)*y_pred_pos)\n",
    "    alpha = 0.7\n",
    "    return (true_pos + smooth)/(true_pos + alpha*false_neg + (1-alpha)*false_pos + smooth)\n",
    "\n",
    "def tversky_loss(y_true, y_pred):\n",
    "    return 1 - tversky(y_true,y_pred)\n",
    "\n",
    "def focal_tversky(y_true,y_pred):\n",
    "    pt_1 = tversky(y_true, y_pred)\n",
    "    gamma = 0.75\n",
    "    return K.pow((1-pt_1), gamma)\n",
    "def focal_l2(inputs,output, e=1e-8, weight_L2=0.1):\n",
    "    \"\"\"\n",
    "    loss(input_shape, inp, e=1e-8, weight_L2=0.1)\n",
    "    ------------------------------------------------------\n",
    "    Since keras does not allow custom loss functions to have arguments\n",
    "    other than the true and predicted labels, this function acts as a wrapper\n",
    "    that allows us to implement the custom loss used in the paper, involving\n",
    "    outputs from multiple layers.\n",
    "    L = - L<dice> + weight_L2 ∗ L<L2>\n",
    "    - L<dice> is the dice loss between input and segmentation output.\n",
    "    - L<L2> is the L2 loss between the output and the input.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    `input_shape`: A 4-tuple, required\n",
    "        The shape of an image as the tuple (c, H, W, D), where c is\n",
    "        the no. of channels; H, W and D is the height, width and depth of the\n",
    "        input image, respectively.\n",
    "    `inp`: An keras.layers.Layer instance, required\n",
    "    `e`: Float, optional\n",
    "        A small epsilon term to add in the denominator to avoid dividing by\n",
    "        zero and possible gradient explosion.\n",
    "    `weight_L2`: A real number, optional\n",
    "        The weight to be given to the L2 loss term in the loss function. Adjust to get best\n",
    "        results for your task.\n",
    "\n",
    "    -------\n",
    "    loss_(y_true, y_pred): A custom keras loss function\n",
    "        This function takes as input the predicted and ground labels, uses them\n",
    "        to calculate the dice loss. Combined with the L<L2 computed\n",
    "        earlier, it returns the total loss.\n",
    "    \"\"\"\n",
    "\n",
    "    loss_L2 = K.mean(K.square(inputs - output), axis=(1, 2, 3))\n",
    "    \n",
    "    def loss_(y_true,y_pred):\n",
    "        pt_1 = tversky(y_true, y_pred)\n",
    "        gamma = 0.75\n",
    "        return K.pow((1-pt_1), gamma) + weight_L2 * loss_L2 \n",
    "\n",
    "    return loss_\n",
    "def dice_bce_l2(inputs,output, e=1e-8, weight_L2=0.1):\n",
    "    \"\"\"\n",
    "    loss(input_shape, inp, e=1e-8, weight_L2=0.1)\n",
    "    ------------------------------------------------------\n",
    "    Since keras does not allow custom loss functions to have arguments\n",
    "    other than the true and predicted labels, this function acts as a wrapper\n",
    "    that allows us to implement the custom loss used in the paper, involving\n",
    "    outputs from multiple layers.\n",
    "    L = - L<dice> + weight_L2 ∗ L<L2>\n",
    "    - L<dice> is the dice loss between input and segmentation output.\n",
    "    - L<L2> is the L2 loss between the output and the input.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    `input_shape`: A 4-tuple, required\n",
    "        The shape of an image as the tuple (c, H, W, D), where c is\n",
    "        the no. of channels; H, W and D is the height, width and depth of the\n",
    "        input image, respectively.\n",
    "    `inp`: An keras.layers.Layer instance, required\n",
    "    `e`: Float, optional\n",
    "        A small epsilon term to add in the denominator to avoid dividing by\n",
    "        zero and possible gradient explosion.\n",
    "    `weight_L2`: A real number, optional\n",
    "        The weight to be given to the L2 loss term in the loss function. Adjust to get best\n",
    "        results for your task.\n",
    "\n",
    "    -------\n",
    "    loss_(y_true, y_pred): A custom keras loss function\n",
    "        This function takes as input the predicted and ground labels, uses them\n",
    "        to calculate the dice loss. Combined with the L<L2 computed\n",
    "        earlier, it returns the total loss.\n",
    "    \"\"\"\n",
    "\n",
    "    loss_L2 = K.mean(K.square(inputs - output), axis=(1, 2, 3))\n",
    "    \n",
    "    def loss(y_true, y_pred):\n",
    "        y_true_f = K.flatten(y_true)\n",
    "        y_pred_f = K.flatten(y_pred)\n",
    "        intersection = K.sum(K.abs(y_true_f * y_pred_f), axis=-1)\n",
    "        loss_dice = (2. * intersection) / (\n",
    "            K.sum(K.square(y_true_f), -1) + K.sum(K.square(y_pred_f), -1) + e)\n",
    "\n",
    "        return 1- loss_dice + weight_L2 * loss_L2 + binary_crossentropy(y_true_f, y_pred_f)\n",
    "\n",
    "    return loss\n",
    "def dice_l2(inputs,output, e=1e-8, weight_L2=0.1):\n",
    "    loss_L2 = K.mean(K.square(inputs - output), axis=(1, 2, 3))\n",
    "    \n",
    "    def loss(y_true, y_pred):\n",
    "        y_true_f = K.flatten(y_true)\n",
    "        y_pred_f = K.flatten(y_pred)\n",
    "        intersection = K.sum(K.abs(y_true_f * y_pred_f), axis=-1)\n",
    "        loss_dice = (2. * intersection) / (\n",
    "        K.sum(K.square(y_true_f), -1) + K.sum(K.square(y_pred_f), -1) + e)\n",
    "\n",
    "        return 1- loss_dice + weight_L2 * loss_L2\n",
    "\n",
    "    return loss\n",
    "################################################################################\n",
    "######model_1: MultiRes_Unet\n",
    "\n",
    "\n",
    "def conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(1, 1), activation='relu', name=None):\n",
    "    output_channels=1\n",
    "    input_shape=(120,120,2)\n",
    "    '''\n",
    "    2D Convolutional layers\n",
    "    \n",
    "    Arguments:\n",
    "        x {keras layer} -- input layer \n",
    "        filters {int} -- number of filters\n",
    "        num_row {int} -- number of rows in filters\n",
    "        num_col {int} -- number of columns in filters\n",
    "    \n",
    "    Keyword Arguments:\n",
    "        padding {str} -- mode of padding (default: {'same'})\n",
    "        strides {tuple} -- stride of convolution operation (default: {(1, 1)})\n",
    "        activation {str} -- activation function (default: {'relu'})\n",
    "        name {str} -- name of the layer (default: {None})\n",
    "    \n",
    "    Returns:\n",
    "        [keras layer] -- [output layer]\n",
    "    '''\n",
    "\n",
    "    x = Conv2D(filters, (num_row, num_col), strides=strides, padding=padding, use_bias=False)(x)\n",
    "    x = BatchNormalization(axis=3, scale=False)(x)\n",
    "\n",
    "    if(activation == None):\n",
    "        return x\n",
    "\n",
    "    x = Activation(activation, name=name)(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def trans_conv2d_bn(x, filters, num_row, num_col, padding='same', strides=(2, 2), name=None):\n",
    "    '''\n",
    "    2D Transposed Convolutional layers\n",
    "    \n",
    "    Arguments:\n",
    "        x {keras layer} -- input layer \n",
    "        filters {int} -- number of filters\n",
    "        num_row {int} -- number of rows in filters\n",
    "        num_col {int} -- number of columns in filters\n",
    "    \n",
    "    Keyword Arguments:\n",
    "        padding {str} -- mode of padding (default: {'same'})\n",
    "        strides {tuple} -- stride of convolution operation (default: {(2, 2)})\n",
    "        name {str} -- name of the layer (default: {None})\n",
    "    \n",
    "    Returns:\n",
    "        [keras layer] -- [output layer]\n",
    "    '''\n",
    "\n",
    "    x = Conv2DTranspose(filters, (num_row, num_col), strides=strides, padding=padding)(x)\n",
    "    x = BatchNormalization(axis=3, scale=False)(x)\n",
    "    \n",
    "    return x\n",
    "\n",
    "\n",
    "def MultiResBlock(U, inp, alpha = 1.67):\n",
    "    '''\n",
    "    MultiRes Block\n",
    "    \n",
    "    Arguments:\n",
    "        U {int} -- Number of filters in a corrsponding UNet stage\n",
    "        inp {keras layer} -- input layer \n",
    "    \n",
    "    Returns:\n",
    "        [keras layer] -- [output layer]\n",
    "    '''\n",
    "\n",
    "    W = alpha * U\n",
    "\n",
    "    shortcut = inp\n",
    "\n",
    "    shortcut = conv2d_bn(shortcut, int(W*0.167) + int(W*0.333) +\n",
    "                         int(W*0.5), 1, 1, activation=None, padding='same')\n",
    "\n",
    "    conv3x3 = conv2d_bn(inp, int(W*0.167), 3, 3,\n",
    "                        activation='relu', padding='same')\n",
    "\n",
    "    conv5x5 = conv2d_bn(conv3x3, int(W*0.333), 3, 3,\n",
    "                        activation='relu', padding='same')\n",
    "\n",
    "    conv7x7 = conv2d_bn(conv5x5, int(W*0.5), 3, 3,\n",
    "                        activation='relu', padding='same')\n",
    "\n",
    "    out = concatenate([conv3x3, conv5x5, conv7x7], axis=3)\n",
    "    out = BatchNormalization(axis=3)(out)\n",
    "\n",
    "    out = add([shortcut, out])\n",
    "    out = Activation('relu')(out)\n",
    "    out = BatchNormalization(axis=3)(out)\n",
    "\n",
    "    return out\n",
    "\n",
    "\n",
    "def ResPath(filters, length, inp):\n",
    "    '''\n",
    "    ResPath\n",
    "    \n",
    "    Arguments:\n",
    "        filters {int} -- [description]\n",
    "        length {int} -- length of ResPath\n",
    "        inp {keras layer} -- input layer \n",
    "    \n",
    "    Returns:\n",
    "        [keras layer] -- [output layer]\n",
    "    '''\n",
    "\n",
    "\n",
    "    shortcut = inp\n",
    "    shortcut = conv2d_bn(shortcut, filters, 1, 1,\n",
    "                         activation=None, padding='same')\n",
    "\n",
    "    out = conv2d_bn(inp, filters, 3, 3, activation='relu', padding='same')\n",
    "\n",
    "    out = add([shortcut, out])\n",
    "    out = Activation('relu')(out)\n",
    "    out = BatchNormalization(axis=3)(out)\n",
    "\n",
    "    for i in range(length-1):\n",
    "\n",
    "        shortcut = out\n",
    "        shortcut = conv2d_bn(shortcut, filters, 1, 1,\n",
    "                             activation=None, padding='same')\n",
    "\n",
    "        out = conv2d_bn(out, filters, 3, 3, activation='relu', padding='same')\n",
    "\n",
    "        out = add([shortcut, out])\n",
    "        out = Activation('relu')(out)\n",
    "        out = BatchNormalization(axis=3)(out)\n",
    "\n",
    "    return out\n",
    "\n",
    "\n",
    "def MultiResUnet(height, width, n_channels, output_channels=1, lr=0.0001):\n",
    "    '''\n",
    "    MultiResUNet\n",
    "    \n",
    "    Arguments:\n",
    "        height {int} -- height of image \n",
    "        width {int} -- width of image \n",
    "        n_channels {int} -- number of channels in image\n",
    "    \n",
    "    Returns:\n",
    "        [keras model] -- MultiResUNet model\n",
    "    '''\n",
    "\n",
    "\n",
    "    inputs = Input((height, width, n_channels))\n",
    "\n",
    "    mresblock1 = MultiResBlock(16, inputs)\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(mresblock1)\n",
    "    mresblock1 = ResPath(16, 4, mresblock1)\n",
    "\n",
    "    mresblock2 = MultiResBlock(16*2, pool1)\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(mresblock2)\n",
    "    mresblock2 = ResPath(16*2, 3, mresblock2)\n",
    "\n",
    "    mresblock3 = MultiResBlock(16*4, pool2)\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(mresblock3)\n",
    "    mresblock3 = ResPath(16*4, 2, mresblock3)\n",
    "\n",
    "    mresblock4 = MultiResBlock(16*8, pool3)\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(mresblock4)\n",
    "    mresblock4 = ResPath(16*8, 1, mresblock4)\n",
    "\n",
    "    mresblock5 = MultiResBlock(16*16, pool4)\n",
    "\n",
    "    up6 = concatenate([Conv2DTranspose(\n",
    "        16*8, (2, 2), strides=(2, 2), padding='same')(mresblock5), mresblock4], axis=3)\n",
    "    mresblock6 = MultiResBlock(16*8, up6)\n",
    "\n",
    "    up7 = concatenate([Conv2DTranspose(\n",
    "        16*4, (2, 2), strides=(2, 2), padding='same')(mresblock6), mresblock3], axis=3)\n",
    "    mresblock7 = MultiResBlock(16*4, up7)\n",
    "\n",
    "    up8 = concatenate([Conv2DTranspose(\n",
    "        16*2, (2, 2), strides=(2, 2), padding='same')(mresblock7), mresblock2], axis=3)\n",
    "    mresblock8 = MultiResBlock(16*2, up8)\n",
    "\n",
    "    up9 = concatenate([Conv2DTranspose(16, (2, 2), strides=(\n",
    "        2, 2), padding='same')(mresblock8), mresblock1], axis=3)\n",
    "    mresblock9 = MultiResBlock(16, up9)\n",
    "\n",
    "    conv10 = conv2d_bn(mresblock9, 1, 1, 1, activation='relu')\n",
    "    conv11 =Conv2D(filters=output_channels,kernel_size=(1, 1),strides=1,activation='sigmoid')(conv10)\n",
    "    \n",
    "    model = Model(inputs=[inputs], outputs=[conv11])\n",
    "    \n",
    "    output=conv10\n",
    "    model.compile(Adam(lr=lr),loss=focal_tversky,\n",
    "    metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "\n",
    "    return model\n",
    "################################################################################\n",
    "######model_2: mini_2D_Unet\n",
    "\n",
    "def mini_2D_Unet(input_shape, cout=1, final_activ='sigmoid',opt='SGD', lr=0.0001, weight_L2=0.001):\n",
    "    input_img =Input(shape=input_shape)\n",
    "    x = Conv2D(16, (3,3), activation='relu', padding='same', name='c1_1',data_format='channels_last')(input_img)  \n",
    "    x = Conv2D(16, (3,3), activation='relu', padding='same', name='c1_2',data_format='channels_last')(x)   \n",
    "    c1= Conv2D(32, (3,3), activation='relu', padding='same', name='c1_3',data_format='channels_last')(x)   \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c1_4', strides=2,data_format='channels_last')(c1)     \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c2_1',data_format='channels_last')(x)   \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c2_2',data_format='channels_last')(x)    \n",
    "    c2= Conv2D(64, (3,3), activation='relu', padding='same', name='c2_3',data_format='channels_last')(x) \n",
    "    x = Conv2D(64, (3,3), activation='relu', padding='same', name='c2_4', strides=2,data_format='channels_last')(c2)    \n",
    "    x = Conv2D(64, (3,3), activation='relu', padding='same', name='c3_1',data_format='channels_last')(x)  \n",
    "    x = Conv2D(64, (3,3), activation='relu', padding='same', name='c3_2',data_format='channels_last')(x)   \n",
    "    x = Conv2D(64, (3,3), activation='relu', padding='same', name='c3_3',data_format='channels_last')(x)   \n",
    "    x = Conv2DTranspose(64, (3,3), activation='relu', padding='same', name='d1', strides=2,data_format='channels_last')(x)   \n",
    "    x = concatenate([x, c2], axis=-1, name='m1') \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c4_1',data_format='channels_last')(x)   \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c4_2',data_format='channels_last')(x)     \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c4_3',data_format='channels_last')(x)     \n",
    "    x = Conv2DTranspose(32, (3,3), activation='relu', padding='same', name='d2', strides=2,data_format='channels_last')(x)    \n",
    "    x = concatenate([x, c1],axis=-1, name='m2')\n",
    "    x = Conv2D(16, (3,3), activation='relu', padding='same', name='c5_1',data_format='channels_last')(x)   \n",
    "    x = Conv2D(16, (3,3), activation='relu', padding='same', name='c5_2',data_format='channels_last')(x)    \n",
    "    x = Conv2D(16, (3,3), activation='relu', padding='same', name='c5_3',data_format='channels_last')(x)      \n",
    "    x = Conv2D(cout, (3,3), activation=final_activ, padding='same', name='cout',data_format='channels_last')(x)\n",
    "\n",
    "\n",
    "    model = Model(inputs=[input_img], outputs=[x], name='mini_unet')\n",
    "    output= x\n",
    "    model.compile(adam(lr=lr),loss=loss(input_shape, input_img, output, weight_L2=weight_L2),\n",
    "    metrics=[dice_coefficient, jaccard_distance])\n",
    "\n",
    "    return model\n",
    "################################################################################\n",
    "######model_3: NoDownSample_net\n",
    "def NoDownSample_net(input_shape, cout=1, final_activ='sigmoid', opt='Adam', lr=1., weight_L2=0.001):\n",
    "    def residual_unit(x, filters, size, name=''):  \n",
    "        f1, f2 = filters  \n",
    "        c = Conv2D(f1, (size,size), padding='same', name=name+'_c1')(x)  \n",
    "        c = BatchNormalization(name=name+'_bn1')(c)   \n",
    "        c = Activation('relu', name=name+'_r1')(c)\n",
    "\n",
    "        c = Conv2D(f2, (1,1), padding='same', name=name+'_c2')(x)  \n",
    "        c = BatchNormalization(name=name+'_bn2')(c)   \n",
    "        c = Activation('relu', name=name+'_r2')(c) \n",
    "\n",
    "        c = Add(name=name+'_ad')([c, x])  \n",
    "        c = Activation('relu', name=name+'_r3')(c)\n",
    "        return c\n",
    "\n",
    "    input_img = Input(shape=input_shape, name='input')    \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c1')(input_img)    \n",
    "    x = residual_unit(x, (64, 32), 3, name='c1_1')       \n",
    "    x = Conv2D(64, (3,3), activation='relu', padding='same', name='c2')(x)   \n",
    "    x = residual_unit(x, (128, 64), 3, name='c2_1')     \n",
    "    x = residual_unit(x, (128, 64), 3, name='c2_2')   \n",
    "    x = Conv2D(32, (3,3), activation='relu', padding='same', name='c3')(x) \n",
    "    x = residual_unit(x, (64, 32), 3, name='c3_1')   \n",
    "    x = Conv2D(cout, (3,3), activation=final_activ, padding='same', name='cout')(x) \n",
    "    model = Model(input_img, x, name='NoDownSample_net')\n",
    "    model.compile(Adam(lr=lr),loss=focal_tversky,\n",
    "    metrics=[dice_coef, jaccard_distance])\n",
    "    return model\n",
    "################################################################################\n",
    "######model_4:densenet\n",
    "def denseNet(input_shape, final_activ='sigmoid', opt='Adam', cout=1, lr=0.0001, nb_dense_block=2, growth_rate=2, nb_filter=8, reduction=0.5,\n",
    "             dropout_rate=0.0, weight_decay=1e-4, weights_path=None, weight_L2=0):\n",
    "    '''Instantiate the DenseNet 121 architecture,\n",
    "        # Arguments\n",
    "            nb_dense_block: number of dense blocks to add to end\n",
    "            growth_rate: number of filters to add per dense block\n",
    "            nb_filter: initial number of filters\n",
    "            reduction: reduction factor of transition blocks.\n",
    "            dropout_rate: dropout rate\n",
    "            weight_decay: weight decay factor\n",
    "            classes: optional number of classes to classify images\n",
    "            weights_path: path to pre-trained weights\n",
    "        # Returns\n",
    "            A Keras model instance.\n",
    "    '''\n",
    "    eps = 1.1e-5\n",
    "\n",
    "    def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):\n",
    "        '''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout\n",
    "            # Arguments\n",
    "                x: input tensor \n",
    "                stage: index for dense block\n",
    "                branch: layer index within each dense block\n",
    "                nb_filter: number of filters\n",
    "                dropout_rate: dropout rate\n",
    "                weight_decay: weight decay factor\n",
    "        '''\n",
    "        conv_name_base = 'conv' + str(stage) + '_' + str(branch)\n",
    "        relu_name_base = 'relu' + str(stage) + '_' + str(branch)\n",
    "        # 1x1 Convolution (Bottleneck layer)\n",
    "        inter_channel = nb_filter * 4  \n",
    "        x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x) \n",
    "        x = Activation('relu', name=relu_name_base+'_x1')(x)\n",
    "        x = Conv2D(inter_channel, 1, padding='same', name=conv_name_base+'_x1', use_bias=False)(x)\n",
    "        if dropout_rate:\n",
    "            x = Dropout(dropout_rate)(x)\n",
    "        # 3x3 Convolution\n",
    "        x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x) \n",
    "        x = Activation('relu', name=relu_name_base+'_x2')(x) \n",
    "        x = Conv2D(nb_filter, 3, padding='same', name=conv_name_base+'_x2', use_bias=False)(x)\n",
    "        if dropout_rate:\n",
    "            x = Dropout(dropout_rate)(x)\n",
    "        return x\n",
    "    \n",
    "    def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):\n",
    "        ''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout \n",
    "            # Arguments\n",
    "                x: input tensor\n",
    "                stage: index for dense block\n",
    "                nb_filter: number of filters\n",
    "                compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.\n",
    "                dropout_rate: dropout rate\n",
    "                weight_decay: weight decay factor\n",
    "        '''\n",
    "        conv_name_base = 'conv' + str(stage) + '_blk'\n",
    "        relu_name_base = 'relu' + str(stage) + '_blk'\n",
    "        pool_name_base = 'pool' + str(stage) \n",
    "        x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x) \n",
    "        x = Activation('relu', name=relu_name_base)(x)\n",
    "        x = Conv2D(int(nb_filter * compression), 1, padding='same', name=conv_name_base, use_bias=False)(x)\n",
    "        if dropout_rate:\n",
    "            x = Dropout(dropout_rate)(x)\n",
    "        x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)\n",
    "        return x\n",
    "    \n",
    "    def up_transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):\n",
    "        ''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout \n",
    "            # Arguments\n",
    "                x: input tensor\n",
    "                stage: index for dense block\n",
    "                nb_filter: number of filters\n",
    "                compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.\n",
    "                dropout_rate: dropout rate\n",
    "                weight_decay: weight decay factor\n",
    "        '''\n",
    "        conv_name_base = 'conv' + str(stage) + '_blk'\n",
    "        relu_name_base = 'relu' + str(stage) + '_blk'\n",
    "        pool_name_base = 'ups' + str(stage) \n",
    "        x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x) \n",
    "        x = Activation('relu', name=relu_name_base)(x)\n",
    "        x = Conv2D(int(nb_filter * compression), 1, padding='same', name=conv_name_base, use_bias=False)(x)\n",
    "        if dropout_rate:\n",
    "            x = Dropout(dropout_rate)(x)  \n",
    "        x = UpSampling2D(size=(2,2), name=pool_name_base)(x) \n",
    "        return x\n",
    "\n",
    "\n",
    "    def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):\n",
    "        ''' Build a dense_block where the output of each conv_block is fed to subsequent ones\n",
    "            # Arguments\n",
    "                x: input tensor\n",
    "                stage: index for dense block\n",
    "                nb_layers: the number of layers of conv_block to append to the model.\n",
    "                nb_filter: number of filters\n",
    "                growth_rate: growth rate\n",
    "                dropout_rate: dropout rate\n",
    "                weight_decay: weight decay factor\n",
    "                grow_nb_filters: flag to decide to allow number of filters to grow\n",
    "        '''\n",
    "        concat_feat = x\n",
    "        for i in range(nb_layers):\n",
    "            branch = i+1\n",
    "            x = conv_block(concat_feat, stage, branch, nb_filter, dropout_rate, weight_decay)\n",
    "            concat_feat = concatenate([concat_feat, x], axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))\n",
    "            if grow_nb_filters:\n",
    "                nb_filter += growth_rate\n",
    "        return concat_feat, nb_filter\n",
    "    \n",
    "\n",
    "    # compute compression factor\n",
    "    compression = 1.0 - reduction\n",
    "\n",
    "    # Handle Dimension Ordering for different backends\n",
    "    concat_axis = 3\n",
    "    img_input = Input(shape=input_shape, name='data') \n",
    "    \n",
    "    # From architecture for ImageNet (Table 1 in the paper)\n",
    "    nb_layers = [6,12,6] # For DenseNet-121\n",
    "\n",
    "    # Initial convolution \n",
    "    x = Conv2D(nb_filter, 7, padding='same',  name='conv1', use_bias=False)(img_input)\n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x) \n",
    "    x = Activation('relu', name='relu1')(x)\n",
    "    \n",
    "    # Add dense blocks\n",
    "    for block_idx in range(nb_dense_block):\n",
    "        stage = block_idx+2\n",
    "        x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, \n",
    "                                   growth_rate, dropout_rate=dropout_rate, \n",
    "                                   weight_decay=weight_decay) \n",
    "        # Add transition_block\n",
    "        x = transition_block(x, stage, nb_filter, compression=compression, \n",
    "                             dropout_rate=dropout_rate, weight_decay=weight_decay) \n",
    "        nb_filter = int(nb_filter * compression)\n",
    "\n",
    "     \n",
    "    for block_idx in range(nb_dense_block ):    \n",
    "        up_stage = block_idx + stage + 2\n",
    "        x, nb_filter = dense_block(x, up_stage, nb_layers[block_idx], nb_filter, \n",
    "                                   growth_rate, dropout_rate=dropout_rate, \n",
    "                                   weight_decay=weight_decay) \n",
    "        # Add up_transition_block\n",
    "        x = up_transition_block(x, up_stage, nb_filter, compression=compression, \n",
    "                                dropout_rate=dropout_rate, weight_decay=weight_decay) \n",
    "        nb_filter = int(nb_filter * compression)\n",
    " \n",
    "    final_stage = up_stage + 1\n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv'+str(final_stage)+'_blk_bn')(x) \n",
    "    x = Activation('relu', name='relu'+str(final_stage)+'_blk')(x)\n",
    "    x = Conv2D(cout, 3, padding=\"same\")(x) \n",
    "    x = Activation('sigmoid')(x)\n",
    "    model = Model(img_input, x, name='densenet')\n",
    "    model.compile(Adam(lr=lr),loss=focal_tversky,\n",
    "    metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "\n",
    "################################################################################\n",
    "######model_5: ResUnet  \n",
    "def ResUnet(input_shape, cout=1, final_activ='sigmoid', opt='Adam', lr=0.0001, weight_L2=0):\n",
    "    def conv_block(input_tensor, num_filters):\n",
    "        encoder = BatchNormalization()(input_tensor)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        encoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder)\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        encoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder)\n",
    "        encoder = add([input_tensor,encoder])\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        return encoder \n",
    "    def decoder_conv_block(input_tensor, num_filters):\n",
    "        encoder = BatchNormalization()(input_tensor)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        encoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder)\n",
    "        encoder = add([input_tensor,encoder])\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        return encoder\n",
    "        \n",
    "        \n",
    "\n",
    "    def encoder_block(input_tensor, num_filters):\n",
    "        encoder = conv_block(input_tensor, num_filters)\n",
    "        encoder_pool = MaxPooling2D((2, 2), strides=(2, 2))(encoder)\n",
    "\n",
    "        return encoder_pool, encoder\n",
    "\n",
    "    def decoder_block(input_tensor, concat_tensor, num_filters):\n",
    "        decoder = Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(input_tensor)\n",
    "        decoder = concatenate([concat_tensor, decoder], axis=-1)\n",
    "        decoder = Conv2D(num_filters, (1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(decoder)\n",
    "        decoder = decoder_conv_block(decoder, num_filters)\n",
    "        return decoder \n",
    "    inputs = Input(shape=(240,240,2))\n",
    "    first_conv = Conv2D(32,(3,3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(inputs)\n",
    "    encoder0_pool, encoder0 = encoder_block(first_conv, 32) \n",
    "    encoder0_pool_conv_2D = Conv2D(64,(1,1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder0_pool)\n",
    "    encoder1_pool, encoder1 = encoder_block(encoder0_pool_conv_2D, 64)  \n",
    "    encoder1_pool_conv_2D = Conv2D(128,(1,1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder1_pool)\n",
    "    encoder2_pool, encoder2 = encoder_block(encoder1_pool_conv_2D, 128) \n",
    "    encoder2_pool_conv_2D = Conv2D(256,(1,1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder2_pool)\n",
    "    encoder3_pool, encoder3 = encoder_block(encoder2_pool_conv_2D, 256) \n",
    "    encoder3_pool_conv_2D = Conv2D(512,(1,1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder3_pool)\n",
    "    center = conv_block(encoder3_pool_conv_2D, 512)\n",
    "    decoder3 = decoder_block(center, encoder3, 256) \n",
    "    decoder2 = decoder_block(decoder3, encoder2, 128) \n",
    "    decoder1 = decoder_block(decoder2, encoder1, 64) \n",
    "    decoder0 = decoder_block(decoder1, encoder0, 32) \n",
    "    outputs = Conv2D(cout, (1, 1), activation=final_activ, kernel_initializer=ki, kernel_regularizer=l2)(decoder0) \n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[outputs], name='ResUnet')\n",
    "    output= outputs\n",
    "    model.compile(Adam(lr=lr),loss=focal_tversky,\n",
    "    metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "######model_6: Unet  \n",
    "def Unet(input_shape, cout=1, final_activ='sigmoid', opt='Adam', lr=0.0001, weight_L2=0):\n",
    "    def conv_block(input_tensor, num_filters):\n",
    "        encoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(input_tensor)\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        encoder = Dropout(0.2)(encoder)\n",
    "        encoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder)\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        return encoder\n",
    "\n",
    "    def encoder_block(input_tensor, num_filters):\n",
    "        encoder = conv_block(input_tensor, num_filters)\n",
    "        encoder_pool = MaxPooling2D((2, 2), strides=(2, 2))(encoder)\n",
    "\n",
    "        return encoder_pool, encoder\n",
    "\n",
    "    def decoder_block(input_tensor, concat_tensor, num_filters):\n",
    "        decoder = Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(input_tensor)\n",
    "        decoder = concatenate([concat_tensor, decoder], axis=-1)\n",
    "        decoder = BatchNormalization()(decoder)\n",
    "        decoder = Activation('relu')(decoder)\n",
    "        decoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(decoder)\n",
    "        decoder = BatchNormalization()(decoder)\n",
    "        decoder = Activation('relu')(decoder)\n",
    "        decoder = Conv2D(num_filters, (3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(decoder)\n",
    "        decoder = BatchNormalization()(decoder)\n",
    "        decoder = Activation('relu')(decoder)\n",
    "        return decoder \n",
    "    inputs = Input(shape=(240,240,2)) \n",
    "    encoder0_pool, encoder0 = encoder_block(inputs, 32) \n",
    "    encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64)  \n",
    "    encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) \n",
    "    encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) \n",
    "    center = conv_block(encoder3_pool, 512) \n",
    "    decoder3 = decoder_block(center, encoder3, 256) \n",
    "    decoder2 = decoder_block(decoder3, encoder2, 128) \n",
    "    decoder1 = decoder_block(decoder2, encoder1, 64) \n",
    "    decoder0 = decoder_block(decoder1, encoder0, 32) \n",
    "    outputs = Conv2D(cout, (1, 1), activation=final_activ, kernel_initializer=ki, kernel_regularizer=l2)(decoder0) \n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[outputs], name='Unet')\n",
    "    output= outputs\n",
    "    model.compile(optimizer=Nadam(lr=0.0001), loss=[focal_tversky], metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "#####model_7: Attention_Unet_version\n",
    "def expend_as(tensor, rep,name):\n",
    "\tmy_repeat = Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=3), arguments={'repnum': rep},  name='psi_up'+name)(tensor)\n",
    "\treturn my_repeat\n",
    "\n",
    "\n",
    "def AttnGatingBlock(x, g, inter_shape, name):\n",
    "    ''' take g which is the spatially smaller signal, do a conv to get the same\n",
    "    number of feature channels as x (bigger spatially)\n",
    "    do a conv on x to also get same geature channels (theta_x)\n",
    "    then, upsample g to be same size as x \n",
    "    add x and g (concat_xg)\n",
    "    relu, 1x1 conv, then sigmoid then upsample the final - this gives us attn coefficients'''\n",
    "    \n",
    "    shape_x = K.int_shape(x)  # 32\n",
    "    shape_g = K.int_shape(g)  # 16\n",
    "\n",
    "    theta_x = Conv2D(inter_shape, (2, 2), strides=(2, 2), padding='same', name='xl'+name, kernel_initializer=ki, kernel_regularizer=l2)(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "    phi_g = Conv2D(inter_shape, (1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(g)\n",
    "    upsample_g = Conv2DTranspose(inter_shape, (3, 3),strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2]),padding='same', name='g_up'+name)(phi_g)  # 16\n",
    "\n",
    "    concat_xg = add([upsample_g, theta_x])\n",
    "    act_xg = Activation('relu')(concat_xg)\n",
    "    psi = Conv2D(1, (1, 1), padding='same', name='psi'+name, kernel_initializer=ki, kernel_regularizer=l2)(act_xg)\n",
    "    sigmoid_xg = Activation('sigmoid')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = UpSampling2D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = expend_as(upsample_psi, shape_x[3],  name)\n",
    "    y = multiply([upsample_psi, x], name='q_attn'+name)\n",
    "\n",
    "    result = Conv2D(shape_x[3], (1, 1), padding='same',name='q_attn_conv'+name, kernel_initializer=ki, kernel_regularizer=l2)(y)\n",
    "    result_bn = BatchNormalization(name='q_attn_bn'+name)(result)\n",
    "    return result_bn\n",
    "\n",
    "def UnetConv2D(input, outdim, is_batchnorm, name):\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_1')(input)\n",
    "\tif is_batchnorm:\n",
    "\t\tx =BatchNormalization(name=name + '_1_bn')(x)\n",
    "\tx = Activation('relu',name=name + '_1_act')(x)\n",
    "\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_2')(x)\n",
    "\tif is_batchnorm:\n",
    "\t\tx = BatchNormalization(name=name + '_2_bn')(x)\n",
    "\tx = Activation('relu', name=name + '_2_act')(x)\n",
    "\treturn x\n",
    "\t\n",
    "\n",
    "def UnetGatingSignal(input, is_batchnorm, name):\n",
    "    ''' this is simply 1x1 convolution, bn, activation '''\n",
    "    shape = K.int_shape(input)\n",
    "    x = Conv2D(shape[3] * 1, (1, 1), strides=(1, 1), padding=\"same\",  kernel_initializer=ki, kernel_regularizer=l2, name=name + '_conv')(input)\n",
    "    if is_batchnorm:\n",
    "        x = BatchNormalization(name=name + '_bn')(x)\n",
    "    x = Activation('relu', name = name + '_act')(x)\n",
    "    return x\n",
    "\n",
    "# plain old attention gates in u-net, NO multi-input, NO deep supervision\n",
    "def attn_unet(input_size):   \n",
    "    inputs = Input(shape=input_size)\n",
    "    conv1 = UnetConv2D(inputs, 32, is_batchnorm=True, name='conv1')\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    \n",
    "    conv2 = UnetConv2D(pool1, 64, is_batchnorm=True, name='conv2')\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    conv3 = UnetConv2D(pool2, 128, is_batchnorm=True, name='conv3')\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "\n",
    "    conv4 = UnetConv2D(pool3, 256, is_batchnorm=True, name='conv4')\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
    "    \n",
    "    center = UnetConv2D(pool4, 512, is_batchnorm=True, name='center')\n",
    "    \n",
    "    g1 = UnetGatingSignal(center, is_batchnorm=True, name='g1')\n",
    "    attn1 = AttnGatingBlock(conv4, g1, 256, '_1')\n",
    "    up1 = concatenate([Conv2DTranspose(256, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(center), attn1], name='up1')\n",
    "    \n",
    "    g2 = UnetGatingSignal(up1, is_batchnorm=True, name='g2')\n",
    "    attn2 = AttnGatingBlock(conv3, g2, 128, '_2')\n",
    "    up2 = concatenate([Conv2DTranspose(128, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up1), attn2], name='up2')\n",
    "\n",
    "    g3 = UnetGatingSignal(up1, is_batchnorm=True, name='g3')\n",
    "    attn3 = AttnGatingBlock(conv2, g3, 64, '_3')\n",
    "    up3 = concatenate([Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up2), attn3], name='up3')\n",
    "\n",
    "    up4 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up3), conv1], name='up4')\n",
    "    out = Conv2D(1, (1, 1), activation='sigmoid',  kernel_initializer=ki, name='final')(up4)\n",
    "    \n",
    "    model = Model(inputs=[inputs], outputs=[out])\n",
    "    model.compile(optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True), loss=[focal_tversky], metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "#####model_8: Attention_Unet_deep supervision_version\n",
    "def expend_as(tensor, rep,name):\n",
    "\tmy_repeat = Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=3), arguments={'repnum': rep},  name='psi_up'+name)(tensor)\n",
    "\treturn my_repeat\n",
    "\n",
    "\n",
    "def AttnGatingBlock(x, g, inter_shape, name):\n",
    "    ''' take g which is the spatially smaller signal, do a conv to get the same\n",
    "    number of feature channels as x (bigger spatially)\n",
    "    do a conv on x to also get same geature channels (theta_x)\n",
    "    then, upsample g to be same size as x \n",
    "    add x and g (concat_xg)\n",
    "    relu, 1x1 conv, then sigmoid then upsample the final - this gives us attn coefficients'''\n",
    "    \n",
    "    shape_x = K.int_shape(x)  # 32\n",
    "    shape_g = K.int_shape(g)  # 16\n",
    "\n",
    "    theta_x = Conv2D(inter_shape, (2, 2), strides=(2, 2), padding='same', name='xl'+name, kernel_initializer=ki, kernel_regularizer=l2)(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "    phi_g = Conv2D(inter_shape, (1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(g)\n",
    "    upsample_g = Conv2DTranspose(inter_shape, (3, 3),strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2]),padding='same', name='g_up'+name)(phi_g)  # 16\n",
    "\n",
    "    concat_xg = add([upsample_g, theta_x])\n",
    "    act_xg = Activation('relu')(concat_xg)\n",
    "    psi = Conv2D(1, (1, 1), padding='same', name='psi'+name, kernel_initializer=ki, kernel_regularizer=l2)(act_xg)\n",
    "    sigmoid_xg = Activation('sigmoid')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = UpSampling2D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = expend_as(upsample_psi, shape_x[3],  name)\n",
    "    y = multiply([upsample_psi, x], name='q_attn'+name)\n",
    "\n",
    "    result = Conv2D(shape_x[3], (1, 1), padding='same',name='q_attn_conv'+name, kernel_initializer=ki, kernel_regularizer=l2)(y)\n",
    "    result_bn = BatchNormalization(name='q_attn_bn'+name)(result)\n",
    "    return result_bn\n",
    "\n",
    "def UnetConv2D(input, outdim, is_batchnorm, name):\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_1')(input)\n",
    "\tif is_batchnorm:\n",
    "\t\tx =BatchNormalization(name=name + '_1_bn')(x)\n",
    "\tx = Activation('relu',name=name + '_1_act')(x)\n",
    "\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_2')(x)\n",
    "\tif is_batchnorm:\n",
    "\t\tx = BatchNormalization(name=name + '_2_bn')(x)\n",
    "\tx = Activation('relu', name=name + '_2_act')(x)\n",
    "\treturn x\n",
    "\t\n",
    "\n",
    "def UnetGatingSignal(input, is_batchnorm, name):\n",
    "    ''' this is simply 1x1 convolution, bn, activation '''\n",
    "    shape = K.int_shape(input)\n",
    "    x = Conv2D(shape[3] * 1, (1, 1), strides=(1, 1), padding=\"same\",  kernel_initializer=ki, kernel_regularizer=l2, name=name + '_conv')(input)\n",
    "    if is_batchnorm:\n",
    "        x = BatchNormalization(name=name + '_bn')(x)\n",
    "    x = Activation('relu', name = name + '_act')(x)\n",
    "    return x\n",
    "#regular attention unet with  deep supervision\n",
    "def attn_unet_ds(input_size):   \n",
    "    inputs = Input(shape=input_size)\n",
    "    conv1 = UnetConv2D(inputs, 32, is_batchnorm=True, name='conv1')\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    \n",
    "    conv2 = UnetConv2D(pool1, 64, is_batchnorm=True, name='conv2')\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    conv3 = UnetConv2D(pool2, 128, is_batchnorm=True, name='conv3')\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "\n",
    "    conv4 = UnetConv2D(pool3, 64, is_batchnorm=True, name='conv4')\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
    "    \n",
    "    center = UnetConv2D(pool4, 512, is_batchnorm=True, name='center')\n",
    "    \n",
    "    g1 = UnetGatingSignal(center, is_batchnorm=True, name='g1')\n",
    "    attn1 = AttnGatingBlock(conv4, g1, 128, '_1')\n",
    "    up1 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(center), attn1], name='up1')\n",
    "    \n",
    "    g2 = UnetGatingSignal(up1, is_batchnorm=True, name='g2')\n",
    "    attn2 = AttnGatingBlock(conv3, g2, 64, '_2')\n",
    "    up2 = concatenate([Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up1), attn2], name='up2')\n",
    "\n",
    "    g3 = UnetGatingSignal(up1, is_batchnorm=True, name='g3')\n",
    "    attn3 = AttnGatingBlock(conv2, g3, 32, '_3')\n",
    "    up3 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up2), attn3], name='up3')\n",
    "\n",
    "    up4 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up3), conv1], name='up4')\n",
    "\n",
    "\n",
    "    conv6 = UnetConv2D(up1, 256, is_batchnorm=True, name='conv6')\n",
    "    conv7 = UnetConv2D(up2, 128, is_batchnorm=True, name='conv7')\n",
    "    conv8 = UnetConv2D(up3, 64, is_batchnorm=True, name='conv8')\n",
    "    conv9 = UnetConv2D(up4, 32, is_batchnorm=True, name='conv9')\n",
    "\n",
    "\n",
    "    out6 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred1')(conv6)\n",
    "    out7 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred2')(conv7)\n",
    "    out8 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred3')(conv8)\n",
    "    out9 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='final')(conv9)\n",
    "\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[out6, out7, out8, out9])\n",
    "    loss = {'pred1':focal_tversky,\n",
    "            'pred2':focal_tversky,\n",
    "            'pred3':focal_tversky,\n",
    "            'final':focal_tversky}\n",
    "    \n",
    "    loss_weights = {'pred1':1,\n",
    "                    'pred2':1,\n",
    "                    'pred3':1,\n",
    "                    'final':1}\n",
    "    model.compile(optimizer=SGD(lr=0.1, momentum=0.8, nesterov=True), loss=loss, metrics=[dice_coef,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "#####model_9: Attention_Unet_deep supervision_input pyramid_version\n",
    "def expend_as(tensor, rep,name):\n",
    "\tmy_repeat = Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=3), arguments={'repnum': rep},  name='psi_up'+name)(tensor)\n",
    "\treturn my_repeat\n",
    "\n",
    "\n",
    "def AttnGatingBlock(x, g, inter_shape, name):\n",
    "    ''' take g which is the spatially smaller signal, do a conv to get the same\n",
    "    number of feature channels as x (bigger spatially)\n",
    "    do a conv on x to also get same geature channels (theta_x)\n",
    "    then, upsample g to be same size as x \n",
    "    add x and g (concat_xg)\n",
    "    relu, 1x1 conv, then sigmoid then upsample the final - this gives us attn coefficients'''\n",
    "    \n",
    "    shape_x = K.int_shape(x)  # 32\n",
    "    shape_g = K.int_shape(g)  # 16\n",
    "\n",
    "    theta_x = Conv2D(inter_shape, (2, 2), strides=(2, 2), padding='same', name='xl'+name, kernel_initializer=ki, kernel_regularizer=l2)(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "    phi_g = Conv2D(inter_shape, (1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(g)\n",
    "    upsample_g = Conv2DTranspose(inter_shape, (3, 3),strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2]),padding='same', name='g_up'+name)(phi_g)  # 16\n",
    "\n",
    "    concat_xg = add([upsample_g, theta_x])\n",
    "    act_xg = Activation('relu')(concat_xg)\n",
    "    psi = Conv2D(1, (1, 1), padding='same', name='psi'+name, kernel_initializer=ki, kernel_regularizer=l2)(act_xg)\n",
    "    sigmoid_xg = Activation('sigmoid')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = UpSampling2D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = expend_as(upsample_psi, shape_x[3],  name)\n",
    "    y = multiply([upsample_psi, x], name='q_attn'+name)\n",
    "\n",
    "    result = Conv2D(shape_x[3], (1, 1), padding='same',name='q_attn_conv'+name, kernel_initializer=ki, kernel_regularizer=l2)(y)\n",
    "    result_bn = BatchNormalization(name='q_attn_bn'+name)(result)\n",
    "    return result_bn\n",
    "\n",
    "def UnetConv2D(input, outdim, is_batchnorm, name):\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_1')(input)\n",
    "\tif is_batchnorm:\n",
    "\t\tx =BatchNormalization(name=name + '_1_bn')(x)\n",
    "\tx = Activation('relu',name=name + '_1_act')(x)\n",
    "\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_2')(x)\n",
    "\tif is_batchnorm:\n",
    "\t\tx = BatchNormalization(name=name + '_2_bn')(x)\n",
    "\tx = Activation('relu', name=name + '_2_act')(x)\n",
    "\treturn x\n",
    "\t\n",
    "\n",
    "def UnetGatingSignal(input, is_batchnorm, name):\n",
    "    ''' this is simply 1x1 convolution, bn, activation '''\n",
    "    shape = K.int_shape(input)\n",
    "    x = Conv2D(shape[3] * 1, (1, 1), strides=(1, 1), padding=\"same\",  kernel_initializer=ki, kernel_regularizer=l2, name=name + '_conv')(input)\n",
    "    if is_batchnorm:\n",
    "        x = BatchNormalization(name=name + '_bn')(x)\n",
    "    x = Activation('relu', name = name + '_act')(x)\n",
    "    return x\n",
    "#regular attention unet with  deep supervision\n",
    "def attn_unet_ds_ip(input_size):   \n",
    "    inputs = Input(shape=input_size)\n",
    "    scale_img_2 = AveragePooling2D(pool_size=(2, 2), name='input_scale2')(inputs)\n",
    "    scale_img_3 = AveragePooling2D(pool_size=(2, 2), name='input_scale3')(scale_img_2)\n",
    "    scale_img_4 = AveragePooling2D(pool_size=(2, 2), name='input_scale4')(scale_img_3)\n",
    "\n",
    "\n",
    "    conv1 = UnetConv2D(inputs, 32, is_batchnorm=True, name='conv1')\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    \n",
    "    input2 = Conv2D(64, (3, 3), padding='same', activation='relu', name='conv_scale2', kernel_initializer=ki, kernel_regularizer=l2)(scale_img_2)\n",
    "    input2 = concatenate([input2, pool1], axis=3)\n",
    "    conv2 = UnetConv2D(pool1, 64, is_batchnorm=True, name='conv2')\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    input3 = Conv2D(128, (3, 3), padding='same', activation='relu', name='conv_scale3', kernel_initializer=ki, kernel_regularizer=l2)(scale_img_3)\n",
    "    input3 = concatenate([input3, pool2], axis=3)\n",
    "    conv3 = UnetConv2D(pool2, 128, is_batchnorm=True, name='conv3')\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "\n",
    "    input4 = Conv2D(256, (3, 3), padding='same', activation='relu', name='conv_scale4', kernel_initializer=ki, kernel_regularizer=l2)(scale_img_4)\n",
    "    input4 = concatenate([input4, pool3], axis=3)\n",
    "    conv4 = UnetConv2D(pool3, 64, is_batchnorm=True, name='conv4')\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
    "    \n",
    "    center = UnetConv2D(pool4, 512, is_batchnorm=True, name='center')\n",
    "    \n",
    "    g1 = UnetGatingSignal(center, is_batchnorm=True, name='g1')\n",
    "    attn1 = AttnGatingBlock(conv4, g1, 128, '_1')\n",
    "    up1 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(center), attn1], name='up1')\n",
    "    \n",
    "    g2 = UnetGatingSignal(up1, is_batchnorm=True, name='g2')\n",
    "    attn2 = AttnGatingBlock(conv3, g2, 64, '_2')\n",
    "    up2 = concatenate([Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up1), attn2], name='up2')\n",
    "\n",
    "    g3 = UnetGatingSignal(up1, is_batchnorm=True, name='g3')\n",
    "    attn3 = AttnGatingBlock(conv2, g3, 32, '_3')\n",
    "    up3 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up2), attn3], name='up3')\n",
    "\n",
    "    up4 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up3), conv1], name='up4')\n",
    "\n",
    "    conv6 = UnetConv2D(up1, 256, is_batchnorm=True, name='conv6')\n",
    "    conv7 = UnetConv2D(up2, 128, is_batchnorm=True, name='conv7')\n",
    "    conv8 = UnetConv2D(up3, 64, is_batchnorm=True, name='conv8')\n",
    "    conv9 = UnetConv2D(up4, 32, is_batchnorm=True, name='conv9')\n",
    "\n",
    "\n",
    "    out6 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred1')(conv6)\n",
    "    out7 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred2')(conv7)\n",
    "    out8 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred3')(conv8)\n",
    "    out9 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='final')(conv9)\n",
    "\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[out6, out7, out8, out9])\n",
    "    loss = {'pred1':focal_tversky,\n",
    "            'pred2':focal_tversky,\n",
    "            'pred3':focal_tversky,\n",
    "            'final':focal_tversky}\n",
    "    \n",
    "    loss_weights = {'pred1':1,\n",
    "                    'pred2':1,\n",
    "                    'pred3':1,\n",
    "                    'final':1}\n",
    "    model.compile(optimizer=SGD(lr=0.1, momentum=0.9, nesterov=True), loss=loss, metrics=[dice_coef,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "#####model_10: Attention_Unet_deep supervision_psp_version\n",
    "def expend_as(tensor, rep,name):\n",
    "\tmy_repeat = Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=3), arguments={'repnum': rep},  name='psi_up'+name)(tensor)\n",
    "\treturn my_repeat\n",
    "\n",
    "\n",
    "def AttnGatingBlock(x, g, inter_shape, name):\n",
    "    ''' take g which is the spatially smaller signal, do a conv to get the same\n",
    "    number of feature channels as x (bigger spatially)\n",
    "    do a conv on x to also get same geature channels (theta_x)\n",
    "    then, upsample g to be same size as x \n",
    "    add x and g (concat_xg)\n",
    "    relu, 1x1 conv, then sigmoid then upsample the final - this gives us attn coefficients'''\n",
    "    \n",
    "    shape_x = K.int_shape(x)  # 32\n",
    "    shape_g = K.int_shape(g)  # 16\n",
    "\n",
    "    theta_x = Conv2D(inter_shape, (2, 2), strides=(2, 2), padding='same', name='xl'+name, kernel_initializer=ki, kernel_regularizer=l2)(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "    phi_g = Conv2D(inter_shape, (1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(g)\n",
    "    upsample_g = Conv2DTranspose(inter_shape, (3, 3),strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2]),padding='same', name='g_up'+name)(phi_g)  # 16\n",
    "\n",
    "    concat_xg = add([upsample_g, theta_x])\n",
    "    act_xg = Activation('relu')(concat_xg)\n",
    "    psi = Conv2D(1, (1, 1), padding='same', name='psi'+name, kernel_initializer=ki, kernel_regularizer=l2)(act_xg)\n",
    "    sigmoid_xg = Activation('sigmoid')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = UpSampling2D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = expend_as(upsample_psi, shape_x[3],  name)\n",
    "    y = multiply([upsample_psi, x], name='q_attn'+name)\n",
    "\n",
    "    result = Conv2D(shape_x[3], (1, 1), padding='same',name='q_attn_conv'+name, kernel_initializer=ki, kernel_regularizer=l2)(y)\n",
    "    result_bn = BatchNormalization(name='q_attn_bn'+name)(result)\n",
    "    return result_bn\n",
    "\n",
    "def UnetConv2D(input, outdim, is_batchnorm, name):\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_1')(input)\n",
    "\tif is_batchnorm:\n",
    "\t\tx =BatchNormalization(name=name + '_1_bn')(x)\n",
    "\tx = Activation('relu',name=name + '_1_act')(x)\n",
    "\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_2')(x)\n",
    "\tif is_batchnorm:\n",
    "\t\tx = BatchNormalization(name=name + '_2_bn')(x)\n",
    "\tx = Activation('relu', name=name + '_2_act')(x)\n",
    "\treturn x\n",
    "\t\n",
    "\n",
    "def UnetGatingSignal(input, is_batchnorm, name):\n",
    "    ''' this is simply 1x1 convolution, bn, activation '''\n",
    "    shape = K.int_shape(input)\n",
    "    x = Conv2D(shape[3] * 1, (1, 1), strides=(1, 1), padding=\"same\",  kernel_initializer=ki, kernel_regularizer=l2, name=name + '_conv')(input)\n",
    "    if is_batchnorm:\n",
    "        x = BatchNormalization(name=name + '_bn')(x)\n",
    "    x = Activation('relu', name = name + '_act')(x)\n",
    "    return x\n",
    "#regular attention unet with  deep supervision\n",
    "def attn_unet_ds_psp(input_size):   \n",
    "    inputs = Input(shape=input_size)\n",
    "    conv1 = UnetConv2D(inputs, 32, is_batchnorm=True, name='conv1')\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    \n",
    "    conv2 = UnetConv2D(pool1, 64, is_batchnorm=True, name='conv2')\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    conv3 = UnetConv2D(pool2, 128, is_batchnorm=True, name='conv3')\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "\n",
    "    conv4 = UnetConv2D(pool3, 64, is_batchnorm=True, name='conv4')\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
    "    \n",
    "    center = UnetConv2D(pool4, 512, is_batchnorm=True, name='center')\n",
    "    \n",
    "    g1 = UnetGatingSignal(center, is_batchnorm=True, name='g1')\n",
    "    attn1 = AttnGatingBlock(conv4, g1, 128, '_1')\n",
    "    up1 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(center), attn1], name='up1')\n",
    "    \n",
    "    g2 = UnetGatingSignal(up1, is_batchnorm=True, name='g2')\n",
    "    attn2 = AttnGatingBlock(conv3, g2, 64, '_2')\n",
    "    up2 = concatenate([Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up1), attn2], name='up2')\n",
    "\n",
    "    g3 = UnetGatingSignal(up1, is_batchnorm=True, name='g3')\n",
    "    attn3 = AttnGatingBlock(conv2, g3, 32, '_3')\n",
    "    up3 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up2), attn3], name='up3')\n",
    "\n",
    "    up4 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up3), conv1], name='up4')\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "    psp1  = Conv2DTranspose(256, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2, name='psp1')(center)\n",
    "    conv6 = UnetConv2D(up1, 256, is_batchnorm=True, name='conv6')\n",
    "    add1  = add([psp1, conv6])\n",
    "    psp2  = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2, name='psp2')(add1)\n",
    "    conv7 = UnetConv2D(up2, 128, is_batchnorm=True, name='conv7')\n",
    "    add2  = add([psp2, conv7])\n",
    "    psp3  = Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2, name='psp3')(add2)\n",
    "    conv8 = UnetConv2D(up3, 64, is_batchnorm=True, name='conv8')\n",
    "    add3  = add([psp3, conv8])\n",
    "    psp4  = Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2, name='psp4')(add3)\n",
    "    conv9 = UnetConv2D(up4, 32, is_batchnorm=True, name='conv9')\n",
    "    add4  = add([psp4, conv9])\n",
    "\n",
    "    out6 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred1')(add1)\n",
    "    out7 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred2')(add2)\n",
    "    out8 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='pred3')(add3)\n",
    "    out9 = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='final')(add4)\n",
    "\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[out6, out7, out8, out9])\n",
    "    loss = {'pred1':focal_tversky,\n",
    "            'pred2':focal_tversky,\n",
    "            'pred3':focal_tversky,\n",
    "            'final':focal_tversky}\n",
    "    \n",
    "    loss_weights = {'pred1':1,\n",
    "                    'pred2':1,\n",
    "                    'pred3':1,\n",
    "                    'final':1}\n",
    "    model.compile(optimizer=SGD(lr=0.1, momentum=0.9, nesterov=True,), loss=loss, metrics=[dice_coef,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "#####model_11: Attention_Unet_input pyramid_version\n",
    "def expend_as(tensor, rep,name):\n",
    "\tmy_repeat = Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=3), arguments={'repnum': rep},  name='psi_up'+name)(tensor)\n",
    "\treturn my_repeat\n",
    "\n",
    "\n",
    "def AttnGatingBlock(x, g, inter_shape, name):\n",
    "    ''' take g which is the spatially smaller signal, do a conv to get the same\n",
    "    number of feature channels as x (bigger spatially)\n",
    "    do a conv on x to also get same geature channels (theta_x)\n",
    "    then, upsample g to be same size as x \n",
    "    add x and g (concat_xg)\n",
    "    relu, 1x1 conv, then sigmoid then upsample the final - this gives us attn coefficients'''\n",
    "    \n",
    "    shape_x = K.int_shape(x)  # 32\n",
    "    shape_g = K.int_shape(g)  # 16\n",
    "\n",
    "    theta_x = Conv2D(inter_shape, (2, 2), strides=(2, 2), padding='same', name='xl'+name, kernel_initializer=ki, kernel_regularizer=l2)(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "    phi_g = Conv2D(inter_shape, (1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(g)\n",
    "    upsample_g = Conv2DTranspose(inter_shape, (3, 3),strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2]),padding='same', name='g_up'+name)(phi_g)  # 16\n",
    "\n",
    "    concat_xg = add([upsample_g, theta_x])\n",
    "    act_xg = Activation('relu')(concat_xg)\n",
    "    psi = Conv2D(1, (1, 1), padding='same', name='psi'+name, kernel_initializer=ki, kernel_regularizer=l2)(act_xg)\n",
    "    sigmoid_xg = Activation('sigmoid')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = UpSampling2D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = expend_as(upsample_psi, shape_x[3],  name)\n",
    "    y = multiply([upsample_psi, x], name='q_attn'+name)\n",
    "\n",
    "    result = Conv2D(shape_x[3], (1, 1), padding='same',name='q_attn_conv'+name, kernel_initializer=ki, kernel_regularizer=l2)(y)\n",
    "    result_bn = BatchNormalization(name='q_attn_bn'+name)(result)\n",
    "    return result_bn\n",
    "\n",
    "def UnetConv2D(input, outdim, is_batchnorm, name):\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_1')(input)\n",
    "\tif is_batchnorm:\n",
    "\t\tx =BatchNormalization(name=name + '_1_bn')(x)\n",
    "\tx = Activation('relu',name=name + '_1_act')(x)\n",
    "\n",
    "\tx = Conv2D(outdim, (3, 3), strides=(1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_2')(x)\n",
    "\tif is_batchnorm:\n",
    "\t\tx = BatchNormalization(name=name + '_2_bn')(x)\n",
    "\tx = Activation('relu', name=name + '_2_act')(x)\n",
    "\treturn x\n",
    "\t\n",
    "\n",
    "def UnetGatingSignal(input, is_batchnorm, name):\n",
    "    ''' this is simply 1x1 convolution, bn, activation '''\n",
    "    shape = K.int_shape(input)\n",
    "    x = Conv2D(shape[3] * 1, (1, 1), strides=(1, 1), padding=\"same\",  kernel_initializer=ki, kernel_regularizer=l2, name=name + '_conv')(input)\n",
    "    if is_batchnorm:\n",
    "        x = BatchNormalization(name=name + '_bn')(x)\n",
    "    x = Activation('relu', name = name + '_act')(x)\n",
    "    return x\n",
    "\n",
    "def attn_unet_ip(input_size):   \n",
    "    inputs = Input(shape=input_size)\n",
    "    scale_img_2 = AveragePooling2D(pool_size=(2, 2), name='input_scale2')(inputs)\n",
    "    scale_img_3 = AveragePooling2D(pool_size=(2, 2), name='input_scale3')(scale_img_2)\n",
    "    scale_img_4 = AveragePooling2D(pool_size=(2, 2), name='input_scale4')(scale_img_3)\n",
    "\n",
    "\n",
    "    conv1 = UnetConv2D(inputs, 32, is_batchnorm=True, name='conv1')\n",
    "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "    \n",
    "    input2 = Conv2D(64, (3, 3), padding='same', activation='relu', name='conv_scale2', kernel_initializer=ki, kernel_regularizer=l2)(scale_img_2)\n",
    "    input2 = concatenate([input2, pool1], axis=3)\n",
    "    conv2 = UnetConv2D(pool1, 64, is_batchnorm=True, name='conv2')\n",
    "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "\n",
    "    input3 = Conv2D(128, (3, 3), padding='same', activation='relu', name='conv_scale3', kernel_initializer=ki, kernel_regularizer=l2)(scale_img_3)\n",
    "    input3 = concatenate([input3, pool2], axis=3)\n",
    "    conv3 = UnetConv2D(pool2, 128, is_batchnorm=True, name='conv3')\n",
    "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
    "\n",
    "    input4 = Conv2D(64, (3, 3), padding='same', activation='relu', name='conv_scale4', kernel_initializer=ki, kernel_regularizer=l2)(scale_img_4)\n",
    "    input4 = concatenate([input4, pool3], axis=3)\n",
    "    conv4 = UnetConv2D(pool3, 256, is_batchnorm=True, name='conv4')\n",
    "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
    "    \n",
    "    center = UnetConv2D(pool4, 512, is_batchnorm=True, name='center')\n",
    "    \n",
    "    g1 = UnetGatingSignal(center, is_batchnorm=True, name='g1')\n",
    "    attn1 = AttnGatingBlock(conv4, g1, 128, '_1')\n",
    "    up1 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(center), attn1], name='up1')\n",
    "    \n",
    "    g2 = UnetGatingSignal(up1, is_batchnorm=True, name='g2')\n",
    "    attn2 = AttnGatingBlock(conv3, g2, 64, '_2')\n",
    "    up2 = concatenate([Conv2DTranspose(64, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up1), attn2], name='up2')\n",
    "\n",
    "    g3 = UnetGatingSignal(up1, is_batchnorm=True, name='g3')\n",
    "    attn3 = AttnGatingBlock(conv2, g3, 32, '_3')\n",
    "    up3 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up2), attn3], name='up3')\n",
    "\n",
    "    up4 = concatenate([Conv2DTranspose(32, (3,3), strides=(2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(up3), conv1], name='up4')\n",
    "\n",
    "    out = Conv2D(1, (1, 1), activation='sigmoid', kernel_initializer=ki, name='final')(up4)\n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[out])\n",
    "    loss =[focal_tversky]\n",
    "    model.compile(optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True), loss=loss, metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "# from tensorflow.keras.applications.vgg19 import VGG19\n",
    "def squeeze_excite_block(input, ratio=8, activation='relu'):\n",
    "    init = input\n",
    "    channel_axis = -1\n",
    "    filters = init.shape[channel_axis]\n",
    "    se_shape = (1, 1, filters)\n",
    "\n",
    "    se = GlobalAveragePooling2D()(init)\n",
    "    se = Reshape(se_shape)(se)\n",
    "    se = Dense(filters // ratio, activation=activation, kernel_initializer=ki, use_bias=False)(se)\n",
    "    se = Dense(filters, activation='sigmoid', kernel_initializer=ki, use_bias=False)(se)\n",
    "\n",
    "    x = Multiply()([init, se])\n",
    "    return x\n",
    "\n",
    "def convolution_block(input, filters):\n",
    "    x = input\n",
    "    x = Conv2D(filters, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters, (3, 3), padding=\"same\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = squeeze_excite_block(x, ratio=8, activation='relu')\n",
    "    x = global_context_block(x, reduction_ratio=16, transform_activation='linear')\n",
    "    return x\n",
    "\n",
    "def encoder1(input):\n",
    "    num_filters = [32, 64, 128, 256]\n",
    "    skip_connections = []\n",
    "    x = input\n",
    "\n",
    "    for i, f in enumerate(num_filters):\n",
    "        x = convolution_block(x, f)\n",
    "        skip_connections.append(x)\n",
    "        x = MaxPooling2D((2, 2))(x)\n",
    "    return x,skip_connections\n",
    "\n",
    "def decoder1(input, skip_connections):\n",
    "    num_filters = [256, 128, 64, 32]\n",
    "    skip_connections.reverse()\n",
    "    x = input\n",
    "\n",
    "    for i, f in enumerate(num_filters):\n",
    "        x = UpSampling2D((2, 2), interpolation='bilinear')(x)\n",
    "        x = concatenate([x, skip_connections[i]])\n",
    "        x = convolution_block(x, f)\n",
    "\n",
    "    return x\n",
    "\n",
    "def encoder2(input):\n",
    "    num_filters = [32, 64, 128, 256]\n",
    "    skip_connections = []\n",
    "    x = input\n",
    "\n",
    "    for i, f in enumerate(num_filters):\n",
    "        x = convolution_block(x, f)\n",
    "        skip_connections.append(x)\n",
    "        x = MaxPooling2D((2, 2))(x)\n",
    "\n",
    "    return x, skip_connections\n",
    "\n",
    "def decoder2(input, skip_1, skip_2):\n",
    "    num_filters = [256, 128, 64, 32]\n",
    "    skip_2.reverse()\n",
    "    x = input\n",
    "\n",
    "    for i, f in enumerate(num_filters):\n",
    "        x = UpSampling2D((2, 2), interpolation='bilinear')(x)\n",
    "        x = concatenate([x, skip_1[i], skip_2[i]])\n",
    "        x = convolution_block(x, f)\n",
    "\n",
    "    return x\n",
    "\n",
    "def output_block(input,name):\n",
    "    x = Conv2D(1, (1, 1), padding=\"same\")(input)\n",
    "    x = Activation('sigmoid', name=name)(x)\n",
    "    return x\n",
    "\n",
    "def double_unet(shape):\n",
    "    input = Input(shape)\n",
    "    pre_out1, skip_1= encoder1(input)\n",
    "    out_enc1 = convolution_block(pre_out1, 256)\n",
    "    x = decoder1(out_enc1, skip_1)\n",
    "    outputs1 = output_block(x,'out_unet1')\n",
    "\n",
    "    x = input * outputs1\n",
    "    x = concatenate([input, x],axis=-1)\n",
    "\n",
    "    pre_out2, skip_2 = encoder2(x)\n",
    "    out_enc2 = convolution_block(pre_out2, 256)\n",
    "    x = decoder2(out_enc2, skip_1, skip_2)\n",
    "    outputs2 = output_block(x,'final')\n",
    "\n",
    "    model = Model(input, outputs=[outputs1,outputs2])\n",
    "    loss = {'out_unet1':focal_tversky,\n",
    "            'final':focal_tversky}\n",
    "    \n",
    "    loss_weights = {'out_unet1':1,\n",
    "                    'final':1}\n",
    "    model.compile(optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True), loss=loss, metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    return model\n",
    "################################################################################\n",
    "\"\"\"\n",
    "Standard UNet++ [Zhou et.al, 2018]\n",
    "Total params: 9,041,601\n",
    "\"\"\"\n",
    "def standard_unit(input_tensor, stage, nb_filter, kernel_size=3,dropout_rate=0):\n",
    "\n",
    "    act = 'relu'\n",
    "\n",
    "    x = Conv2D(nb_filter, (kernel_size, kernel_size), name='conv'+stage+'_1', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(input_tensor)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(act)(x)\n",
    "    x = Dropout(dropout_rate, name='dp'+stage+'_1')(x)\n",
    "    x = Conv2D(nb_filter, (kernel_size, kernel_size), name='conv'+stage+'_2', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(act)(x)\n",
    "    x = Dropout(dropout_rate, name='dp'+stage+'_2')(x)\n",
    "    x = squeeze_excite_block(x, ratio=8, activation=act)\n",
    "    x = global_context_block(x, reduction_ratio=16, transform_activation='linear')\n",
    "    return x\n",
    "def nest_unet(shape, num_class=1, deep_supervision=False):\n",
    "\n",
    "    nb_filter = [32,64,128,256,512]\n",
    "    act = 'relu'\n",
    "\n",
    "    img_input = Input(shape)\n",
    "\n",
    "    conv1_1 = standard_unit(img_input, stage='11', nb_filter=nb_filter[0],dropout_rate=0.2)\n",
    "    pool1 = MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(conv1_1)\n",
    "\n",
    "    conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1],dropout_rate=0.2)\n",
    "    pool2 = MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(conv2_1)\n",
    "\n",
    "    up1_2 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up12', padding='same')(conv2_1)\n",
    "    conv1_2 = concatenate([up1_2, conv1_1], name='merge12')\n",
    "    conv1_2 = standard_unit(conv1_2, stage='12', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2],dropout_rate=0.2)\n",
    "    pool3 = MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(conv3_1)\n",
    "\n",
    "    up2_2 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up22', padding='same')(conv3_1)\n",
    "    conv2_2 = concatenate([up2_2, conv2_1], name='merge22')\n",
    "    conv2_2 = standard_unit(conv2_2, stage='22', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_3 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up13', padding='same')(conv2_2)\n",
    "    conv1_3 = concatenate([up1_3, conv1_1, conv1_2], name='merge13')\n",
    "    conv1_3 = standard_unit(conv1_3, stage='13', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3],dropout_rate=0.2)\n",
    "    pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(conv4_1)\n",
    "\n",
    "    up3_2 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up32', padding='same')(conv4_1)\n",
    "    conv3_2 = concatenate([up3_2, conv3_1], name='merge32')\n",
    "    conv3_2 = standard_unit(conv3_2, stage='32', nb_filter=nb_filter[2])\n",
    "\n",
    "    up2_3 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up23', padding='same')(conv3_2)\n",
    "    conv2_3 = concatenate([up2_3, conv2_1, conv2_2], name='merge23')\n",
    "    conv2_3 = standard_unit(conv2_3, stage='23', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_4 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up14', padding='same')(conv2_3)\n",
    "    conv1_4 = concatenate([up1_4, conv1_1, conv1_2, conv1_3], name='merge14')\n",
    "    conv1_4 = standard_unit(conv1_4, stage='14', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4])\n",
    "\n",
    "    up4_2 = Conv2DTranspose(nb_filter[3], (2, 2), strides=(2, 2), name='up42', padding='same')(conv5_1)\n",
    "    conv4_2 = concatenate([up4_2, conv4_1], name='merge42')\n",
    "    conv4_2 = standard_unit(conv4_2, stage='42', nb_filter=nb_filter[3])\n",
    "\n",
    "    up3_3 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up33', padding='same')(conv4_2)\n",
    "    conv3_3 = concatenate([up3_3, conv3_1, conv3_2], name='merge33')\n",
    "    conv3_3 = standard_unit(conv3_3, stage='33', nb_filter=nb_filter[2])\n",
    "\n",
    "    up2_4 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up24', padding='same')(conv3_3)\n",
    "    conv2_4 = concatenate([up2_4, conv2_1, conv2_2, conv2_3], name='merge24')\n",
    "    conv2_4 = standard_unit(conv2_4, stage='24', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_5 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up15', padding='same')(conv2_4)\n",
    "    conv1_5 = concatenate([up1_5, conv1_1, conv1_2, conv1_3, conv1_4], name='merge15')\n",
    "    conv1_5 = standard_unit(conv1_5, stage='15', nb_filter=nb_filter[0])\n",
    "    nestunet_output_4 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_4', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(conv1_5)\n",
    "\n",
    "    if deep_supervision:\n",
    "        model = Model(img_input, outputs=[nestunet_output_1,\n",
    "                                         nestunet_output_2,\n",
    "                                         nestunet_output_3,\n",
    "                                         nestunet_output_4])\n",
    "        loss = {'output_1':focal_tversky,\n",
    "                'output_1':focal_tversky,\n",
    "                'output_1':focal_tversky,\n",
    "                'output_1':focal_tversky}\n",
    "    \n",
    "        loss_weights = {'output_1':1,\n",
    "                        'output_1':1,\n",
    "                        'output_1':1,\n",
    "                        'output_1':1}\n",
    "        model.compile(optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True), loss=loss, metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    else:\n",
    "        model = Model(img_input, outputs=[nestunet_output_4])\n",
    "        loss =[focal_tversky]\n",
    "        model.compile(optimizer=Nadam(lr=0.000001), loss=loss, metrics=[dice_coefficient])\n",
    "    return model\n",
    "################################################################################\n",
    "######model: Unet_3D  \n",
    "def Unet_3D(input_shape, cout=1, final_activ='sigmoid'):\n",
    "    def conv_block(input_tensor, num_filters):\n",
    "        encoder = Conv3D(num_filters, (3, 3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(input_tensor)\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        encoder = Conv3D(num_filters, (3, 3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(encoder)\n",
    "        encoder = BatchNormalization()(encoder)\n",
    "        encoder = Activation('relu')(encoder)\n",
    "        return encoder\n",
    "    def encoder_block(input_tensor, num_filters):\n",
    "        encoder = conv_block(input_tensor, num_filters)\n",
    "        encoder_pool = MaxPooling3D((2, 2, 2), strides=(2, 2, 2))(encoder)\n",
    "\n",
    "        return encoder_pool, encoder\n",
    "\n",
    "    def decoder_block(input_tensor, concat_tensor, num_filters):\n",
    "        decoder = Conv3DTranspose(num_filters, (2, 2, 2), strides=(2, 2, 2), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(input_tensor)\n",
    "        decoder = concatenate([concat_tensor, decoder], axis=-1)\n",
    "        decoder = BatchNormalization()(decoder)\n",
    "        decoder = Activation('relu')(decoder)\n",
    "        decoder = Conv3D(num_filters, (3, 3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(decoder)\n",
    "        decoder = BatchNormalization()(decoder)\n",
    "        decoder = Activation('relu')(decoder)\n",
    "        decoder = Conv3D(num_filters, (3, 3, 3), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(decoder)\n",
    "        decoder = BatchNormalization()(decoder)\n",
    "        decoder = Activation('relu')(decoder)\n",
    "        return decoder \n",
    "    inputs = Input(shape=input_shape) \n",
    "    encoder0_pool, encoder0 = encoder_block(inputs, 32) \n",
    "    encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64)  \n",
    "    encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) \n",
    "    encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) \n",
    "    center = conv_block(encoder3_pool, 512) \n",
    "    decoder3 = decoder_block(center, encoder3, 256) \n",
    "    decoder2 = decoder_block(decoder3, encoder2, 128) \n",
    "    decoder1 = decoder_block(decoder2, encoder1, 64) \n",
    "    decoder0 = decoder_block(decoder1, encoder0, 32) \n",
    "    outputs = Conv3D(cout, (1, 1, 1), activation=final_activ, kernel_initializer=ki, kernel_regularizer=l2)(decoder0) \n",
    "\n",
    "    model = Model(inputs=[inputs], outputs=[outputs], name='Unet_3D')\n",
    "    output= outputs\n",
    "    model.compile(optimizer=SGD(learning_rate=0.001,momentum=0.9, nesterov=True), loss=[focal_tversky], metrics=[dice_coefficient_correct])\n",
    "    return model\n",
    "##############################################################################\n",
    "\"\"\"\n",
    "Standard UNet++ 3D version[Zhou et.al, 2018]\n",
    "\n",
    "\"\"\"\n",
    "def squeeze_excite_block(input, ratio=16, activation='relu'):\n",
    "    init = input\n",
    "    channel_axis = -1\n",
    "    filters = init.shape[channel_axis]\n",
    "    se_shape = (1, 1, 1, filters)\n",
    "\n",
    "    se = GlobalAveragePooling3D()(init)\n",
    "    se = Reshape(se_shape)(se)\n",
    "    se = Dense(filters // ratio, activation=activation, kernel_initializer=ki, use_bias=False)(se)\n",
    "    se = Dense(filters, activation='sigmoid', kernel_initializer=ki, use_bias=False)(se)\n",
    "\n",
    "    x = Multiply()([init, se])\n",
    "    return x\n",
    "def standard_unit(input_tensor, stage, nb_filter, kernel_size=3,dropout_rate=0):\n",
    "\n",
    "    act = 'elu'\n",
    "\n",
    "    x = Conv3D(nb_filter, (kernel_size, kernel_size, kernel_size), name='conv'+stage+'_1', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(input_tensor)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(act)(x)\n",
    "    x = Dropout(dropout_rate, name='dp'+stage+'_1')(x)\n",
    "    x = Conv3D(nb_filter, (kernel_size, kernel_size, kernel_size), name='conv'+stage+'_2', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(act)(x)\n",
    "    x = Dropout(dropout_rate, name='dp'+stage+'_2')(x)\n",
    "    x = squeeze_excite_block(x)\n",
    "    return x\n",
    "def Nested_Unet_3D(shape, num_class=1, deep_supervision=False):\n",
    "\n",
    "    nb_filter = [16,32,64,128,256]\n",
    "    act = 'elu'\n",
    "\n",
    "    img_input = Input(shape)\n",
    "\n",
    "    conv1_1 = standard_unit(img_input, stage='11', nb_filter=nb_filter[0])\n",
    "    pool1 = MaxPooling3D((2, 2, 2), strides=(2, 2, 2), name='pool1')(conv1_1)\n",
    "\n",
    "    conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1])\n",
    "    pool2 = MaxPooling3D((2, 2, 2), strides=(2, 2, 2), name='pool2')(conv2_1)\n",
    "\n",
    "    up1_2 = Conv3DTranspose(nb_filter[0], (2, 2, 2), strides=(2, 2, 2), name='up12', padding='same')(conv2_1)\n",
    "    conv1_2 = concatenate([up1_2, conv1_1], name='merge12')\n",
    "    conv1_2 = standard_unit(conv1_2, stage='12', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2])\n",
    "    pool3 = MaxPooling3D((2, 2, 2), strides=(2, 2, 2), name='pool3')(conv3_1)\n",
    "\n",
    "    up2_2 = Conv3DTranspose(nb_filter[1], (2, 2, 2), strides=(2, 2, 2), name='up22', padding='same')(conv3_1)\n",
    "    conv2_2 = concatenate([up2_2, conv2_1], name='merge22')\n",
    "    conv2_2 = standard_unit(conv2_2, stage='22', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_3 = Conv3DTranspose(nb_filter[0], (2, 2, 2), strides=(2, 2, 2), name='up13', padding='same')(conv2_2)\n",
    "    conv1_3 = concatenate([up1_3, conv1_1, conv1_2], name='merge13')\n",
    "    conv1_3 = standard_unit(conv1_3, stage='13', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3])\n",
    "    pool4 = MaxPooling3D((2, 2, 2), strides=(2, 2, 2), name='pool4')(conv4_1)\n",
    "\n",
    "    up3_2 = Conv3DTranspose(nb_filter[2], (2, 2, 2), strides=(2, 2, 2), name='up32', padding='same')(conv4_1)\n",
    "    conv3_2 = concatenate([up3_2, conv3_1], name='merge32')\n",
    "    conv3_2 = standard_unit(conv3_2, stage='32', nb_filter=nb_filter[2])\n",
    "\n",
    "    up2_3 = Conv3DTranspose(nb_filter[1], (2, 2, 2), strides=(2, 2, 2), name='up23', padding='same')(conv3_2)\n",
    "    conv2_3 = concatenate([up2_3, conv2_1, conv2_2], name='merge23')\n",
    "    conv2_3 = standard_unit(conv2_3, stage='23', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_4 = Conv3DTranspose(nb_filter[0], (2, 2, 2), strides=(2, 2, 2), name='up14', padding='same')(conv2_3)\n",
    "    conv1_4 = concatenate([up1_4, conv1_1, conv1_2, conv1_3], name='merge14')\n",
    "    conv1_4 = standard_unit(conv1_4, stage='14', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4])\n",
    "\n",
    "    up4_2 = Conv3DTranspose(nb_filter[3], (2, 2, 2), strides=(2, 2, 2), name='up42', padding='same')(conv5_1)\n",
    "    conv4_2 = concatenate([up4_2, conv4_1], name='merge42')\n",
    "    conv4_2 = standard_unit(conv4_2, stage='42', nb_filter=nb_filter[3])\n",
    "\n",
    "    up3_3 = Conv3DTranspose(nb_filter[2], (2, 2, 2), strides=(2, 2, 2), name='up33', padding='same')(conv4_2)\n",
    "    conv3_3 = concatenate([up3_3, conv3_1, conv3_2], name='merge33')\n",
    "    conv3_3 = standard_unit(conv3_3, stage='33', nb_filter=nb_filter[2])\n",
    "\n",
    "    up2_4 = Conv3DTranspose(nb_filter[1], (2, 2, 2), strides=(2, 2, 2), name='up24', padding='same')(conv3_3)\n",
    "    conv2_4 = concatenate([up2_4, conv2_1, conv2_2, conv2_3], name='merge24')\n",
    "    conv2_4 = standard_unit(conv2_4, stage='24', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_5 = Conv3DTranspose(nb_filter[0], (2, 2, 2), strides=(2, 2, 2), name='up15', padding='same')(conv2_4)\n",
    "    conv1_5 = concatenate([up1_5, conv1_1, conv1_2, conv1_3, conv1_4], name='merge15')\n",
    "    conv1_5 = standard_unit(conv1_5, stage='15', nb_filter=nb_filter[0])\n",
    "\n",
    "    # nestunet_output_1 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_1', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(conv1_2)\n",
    "    # nestunet_output_2 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_2', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(conv1_3)\n",
    "    # nestunet_output_3 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_3', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(conv1_4)\n",
    "    nestunet_output_final= Conv3D(num_class, (1, 1, 1), activation='sigmoid', name='output_final', kernel_initializer = ki, padding='same', kernel_regularizer=l2)(conv1_5)\n",
    "\n",
    "    if deep_supervision:\n",
    "        model = Model(img_input, outputs=[nestunet_output_1,\n",
    "                                         nestunet_output_2,\n",
    "                                         nestunet_output_3,\n",
    "                                         nestunet_output_final])\n",
    "        loss = {'output_1':focal_tversky,\n",
    "                'output_2':focal_tversky,\n",
    "                'output_3':focal_tversky,\n",
    "                'output_final':focal_tversky}\n",
    "    \n",
    "        loss_weights = {'output_1':1,\n",
    "                        'output_2':1,\n",
    "                        'output_3':1,\n",
    "                        'output_final':1}\n",
    "        model.compile(optimizer=Nadam(lr=0.0001), loss=loss, metrics=[dice_coef,dice_coefficient,jaccard_distance,AUC(num_thresholds=500)])\n",
    "    else:\n",
    "        model = Model(img_input, outputs=[nestunet_output_final])\n",
    "        loss =[focal_tversky]\n",
    "        model.compile(optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True), loss=loss, metrics=[dice_coefficient_correct])\n",
    "    return model\n",
    "###############################################################################\n",
    "##model: Attention_Unet_version_3D\n",
    "def squeeze_excite_block(input, ratio=16, activation='relu'):\n",
    "    init = input\n",
    "    channel_axis = -1\n",
    "    filters = init.shape[channel_axis]\n",
    "    se_shape = (1, 1, 1, filters)\n",
    "\n",
    "    se = GlobalAveragePooling3D()(init)\n",
    "    se = Reshape(se_shape)(se)\n",
    "    se = Dense(filters // ratio, activation=activation, kernel_initializer=ki, use_bias=False)(se)\n",
    "    se = Dense(filters, activation='sigmoid', kernel_initializer=ki, use_bias=False)(se)\n",
    "\n",
    "    x = Multiply()([init, se])\n",
    "    return x\n",
    "def expend_as_3D(tensor, rep,name):\n",
    "\tmy_repeat = Lambda(lambda x, repnum: K.repeat_elements(x, repnum, axis=-1), arguments={'repnum': rep},  name='psi_up'+name)(tensor)\n",
    "\treturn my_repeat\n",
    "\n",
    "\n",
    "def AttnGatingBlock_3D(x, g, inter_shape, name):\n",
    "    ''' take g which is the spatially smaller signal, do a conv to get the same\n",
    "    number of feature channels as x (bigger spatially)\n",
    "    do a conv on x to also get same geature channels (theta_x)\n",
    "    then, upsample g to be same size as x \n",
    "    add x and g (concat_xg)\n",
    "    relu, 1x1 conv, then sigmoid then upsample the final - this gives us attn coefficients'''\n",
    "    \n",
    "    shape_x = K.int_shape(x)  # 32\n",
    "    shape_g = K.int_shape(g)  # 16\n",
    "\n",
    "    theta_x = Conv3D(inter_shape, (2, 2, 2), strides=(2, 2, 2), padding='same', name='xl'+name, kernel_initializer=ki, kernel_regularizer=l2)(x)  # 16\n",
    "    shape_theta_x = K.int_shape(theta_x)\n",
    "\n",
    "    phi_g = Conv3D(inter_shape, (1, 1, 1), padding='same', kernel_initializer=ki, kernel_regularizer=l2)(g)\n",
    "    upsample_g = Conv3DTranspose(inter_shape, (3, 3, 3),strides=(shape_theta_x[1] // shape_g[1], shape_theta_x[2] // shape_g[2], shape_theta_x[3] // shape_g[3]),padding='same', name='g_up'+name)(phi_g)  # 16\n",
    "\n",
    "    concat_xg = add([upsample_g, theta_x])\n",
    "    act_xg = Activation('relu')(concat_xg)\n",
    "    psi = Conv3D(1, (1, 1, 1), padding='same', name='psi'+name, kernel_initializer=ki, kernel_regularizer=l2)(act_xg)\n",
    "    sigmoid_xg = Activation('sigmoid')(psi)\n",
    "    shape_sigmoid = K.int_shape(sigmoid_xg)\n",
    "    upsample_psi = UpSampling3D(size=(shape_x[1] // shape_sigmoid[1], shape_x[2] // shape_sigmoid[2], shape_x[3] // shape_sigmoid[3]))(sigmoid_xg)  # 32\n",
    "\n",
    "    upsample_psi = expend_as_3D(upsample_psi, shape_x[-1],  name)\n",
    "    y = multiply([upsample_psi, x], name='q_attn'+name)\n",
    "\n",
    "    result = Conv3D(shape_x[-1], (1, 1, 1), padding='same',name='q_attn_conv'+name, kernel_initializer=ki, kernel_regularizer=l2)(y)\n",
    "    result_bn = BatchNormalization(name='q_attn_bn'+name)(result)\n",
    "    return result_bn\n",
    "\n",
    "def UnetConv3D(input, outdim, is_batchnorm, name):\n",
    "    x = Conv3D(outdim, (3, 3, 3), strides=(1, 1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_1')(input)\n",
    "    if is_batchnorm:\n",
    "       x =BatchNormalization(name=name + '_1_bn')(x)\n",
    "    x = Activation('relu',name=name + '_1_act')(x)\n",
    "    x = Conv3D(outdim, (3, 3, 3), strides=(1, 1, 1), kernel_initializer=ki, kernel_regularizer=l2, padding=\"same\", name=name+'_2')(x)\n",
    "    if is_batchnorm:\n",
    "       x = BatchNormalization(name=name + '_2_bn')(x)\n",
    "    x = Activation('relu', name=name + '_2_act')(x)\n",
    "    return x\n",
    "\t\n",
    "\n",
    "def UnetGatingSignal_3D(input, is_batchnorm, name):\n",
    "    ''' this is simply 1x1 convolution, bn, activation '''\n",
    "    shape = K.int_shape(input)\n",
    "    x = Conv3D(shape[-1] * 1, (1, 1, 1), strides=(1, 1, 1), padding=\"same\",  kernel_initializer=ki, kernel_regularizer=l2, name=name + '_conv')(input)\n",
    "    if is_batchnorm:\n",
    "        x = BatchNormalization(name=name + '_bn')(x)\n",
    "    x = Activation('relu', name = name + '_act')(x)\n",
    "    return x\n",
    "\n",
    "# plain old attention gates in u-net, NO multi-input, NO deep supervision\n",
    "def attn_unet_3D(input_size):   \n",
    "    inputs = Input(shape=input_size)\n",
    "\n",
    "    conv1 = UnetConv3D(inputs, 32, is_batchnorm=True, name='conv1')\n",
    "    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)\n",
    "\n",
    "    conv2 = UnetConv3D(pool1, 64, is_batchnorm=True, name='conv2')\n",
    "    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)\n",
    "\n",
    "    conv3 = UnetConv3D(pool2, 128, is_batchnorm=True, name='conv3')\n",
    "    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)\n",
    "\n",
    "    conv4 = UnetConv3D(pool3, 256, is_batchnorm=True, name='conv4')\n",
    "    pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)\n",
    "    \n",
    "    center = UnetConv3D(pool4, 512, is_batchnorm=True, name='center')\n",
    "\n",
    "    g1 = UnetGatingSignal_3D(center, is_batchnorm=True, name='g1')\n",
    "    attn1 = AttnGatingBlock_3D(conv4, g1, 256, '_1')\n",
    "    up1 = concatenate([Conv3DTranspose(256, (3,3,3), strides=(2,2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(center), attn1], name='up1')\n",
    "    conv1_d = UnetConv3D(up1, 256, is_batchnorm=True, name='conv1_d')\n",
    "\n",
    "    g2 = UnetGatingSignal_3D(conv1_d, is_batchnorm=True, name='g2')\n",
    "    attn2 = AttnGatingBlock_3D(conv3, g2, 128, '_2')\n",
    "    up2 = concatenate([Conv3DTranspose(128, (3,3,3), strides=(2,2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(conv1_d), attn2], name='up2')\n",
    "    conv2_d= UnetConv3D(up2, 128, is_batchnorm=True, name='conv2_d')\n",
    "\n",
    "    g3 = UnetGatingSignal_3D(conv2_d, is_batchnorm=True, name='g3')\n",
    "    attn3 = AttnGatingBlock_3D(conv2, g3, 64, '_3')\n",
    "    up3 = concatenate([Conv3DTranspose(64, (3,3,3), strides=(2,2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(conv2_d), attn3], name='up3')\n",
    "    conv3_d= UnetConv3D(up3, 64, is_batchnorm=True, name='conv3_d')\n",
    "\n",
    "    up4 = concatenate([Conv3DTranspose(32, (3,3,3), strides=(2,2,2), padding='same', activation='relu', kernel_initializer=ki, kernel_regularizer=l2)(conv3_d), conv1], name='up4')\n",
    "    conv4_d= UnetConv3D(up4, 32, is_batchnorm=True, name='conv4_d')\n",
    "\n",
    "    out = Conv3D(1, (1, 1, 1), activation='sigmoid',  kernel_initializer=ki, name='final')(conv4_d)\n",
    "    \n",
    "    model = Model(inputs=[inputs], outputs=[out])\n",
    "    model.compile(optimizer=SGD(lr=0.01, momentum=0.9, nesterov=True), loss=[focal_tversky], metrics=[dice_coefficient_correct])\n",
    "    return model\n",
    "################################################################################\n",
    "def step_decay(epoch):\n",
    "   initial_lrate = 0.0001\n",
    "   drop = 0.8\n",
    "   epochs_drop = 40.0\n",
    "   lrate = initial_lrate * math.pow(drop,  \n",
    "           math.floor((1+epoch)/epochs_drop))\n",
    "   return lrate\n",
    "lrate = LearningRateScheduler(step_decay)\n",
    "csv = CSVLogger(\"/gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/1.log\", separator=',', append=True)\n",
    "# rlr=ReduceLROnPlateau(monitor='dice_coefficient_correct', factor=0.9, patience=10, min_delta=0.0001, min_lr=0.0001, verbose=1, mode='max')\n",
    "# ES=EarlyStopping(monitor='val_dice_coefficient',min_delta=0.0001 ,patience=250,verbose=1,mode='max')\n",
    "filepath = \"/gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-{epoch:02d}-{loss:.4f}-{dice_coefficient_correct:.4f}.hdf5\"\n",
    "checkpoint =ModelCheckpoint(filepath,monitor='dice_coefficient_correct', verbose=1, save_weights_only=True,save_best_only=False, mode='max',period=25)\n",
    "callbacks=[checkpoint, csv]\n",
    "\n",
    "model_1 = attn_unet_3D(input_shape_test)\n",
    "model_2 = attn_unet_3D(input_shape_test)\n",
    "\n",
    "print(model_1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kKKY5PrxSfix",
    "outputId": "248c2f80-3cb7-4f0d-81f4-ba89f4e3d3a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "3/3 [==============================] - 79s 19s/step - loss: 1.5531 - dice_coefficient_correct: 0.0218\n",
      "Epoch 2/1000\n",
      "3/3 [==============================] - 24s 9s/step - loss: 1.5545 - dice_coefficient_correct: 0.0207\n",
      "Epoch 3/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5527 - dice_coefficient_correct: 0.0220\n",
      "Epoch 4/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5444 - dice_coefficient_correct: 0.0288\n",
      "Epoch 5/1000\n",
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      "Epoch 6/1000\n",
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      "Epoch 7/1000\n",
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      "Epoch 8/1000\n",
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      "Epoch 9/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5501 - dice_coefficient_correct: 0.0238\n",
      "Epoch 10/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5505 - dice_coefficient_correct: 0.0236\n",
      "Epoch 11/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5430 - dice_coefficient_correct: 0.0296\n",
      "Epoch 12/1000\n",
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      "Epoch 13/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5318 - dice_coefficient_correct: 0.0388\n",
      "Epoch 14/1000\n",
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      "Epoch 15/1000\n",
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      "Epoch 16/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5440 - dice_coefficient_correct: 0.0285\n",
      "Epoch 17/1000\n",
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      "Epoch 18/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5253 - dice_coefficient_correct: 0.0439\n",
      "Epoch 19/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5501 - dice_coefficient_correct: 0.0233\n",
      "Epoch 20/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5439 - dice_coefficient_correct: 0.0284\n",
      "Epoch 21/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5282 - dice_coefficient_correct: 0.0414\n",
      "Epoch 22/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5416 - dice_coefficient_correct: 0.0301\n",
      "Epoch 23/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5247 - dice_coefficient_correct: 0.0440\n",
      "Epoch 24/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5470 - dice_coefficient_correct: 0.0256\n",
      "Epoch 25/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5236 - dice_coefficient_correct: 0.0449\n",
      "\n",
      "Epoch 00025: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-25-1.5270-0.0420.hdf5\n",
      "Epoch 26/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5318 - dice_coefficient_correct: 0.0380\n",
      "Epoch 27/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5368 - dice_coefficient_correct: 0.0338\n",
      "Epoch 28/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5381 - dice_coefficient_correct: 0.0327\n",
      "Epoch 29/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5184 - dice_coefficient_correct: 0.0489\n",
      "Epoch 30/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5264 - dice_coefficient_correct: 0.0423\n",
      "Epoch 31/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5464 - dice_coefficient_correct: 0.0257\n",
      "Epoch 32/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5095 - dice_coefficient_correct: 0.0564\n",
      "Epoch 33/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5190 - dice_coefficient_correct: 0.0484\n",
      "Epoch 34/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5103 - dice_coefficient_correct: 0.0556\n",
      "Epoch 35/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5442 - dice_coefficient_correct: 0.0272\n",
      "Epoch 36/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5169 - dice_coefficient_correct: 0.0499\n",
      "Epoch 37/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5279 - dice_coefficient_correct: 0.0406\n",
      "Epoch 38/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5257 - dice_coefficient_correct: 0.0427\n",
      "Epoch 39/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5376 - dice_coefficient_correct: 0.0325\n",
      "Epoch 40/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4989 - dice_coefficient_correct: 0.0650\n",
      "Epoch 41/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5287 - dice_coefficient_correct: 0.0397\n",
      "Epoch 42/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5214 - dice_coefficient_correct: 0.0457\n",
      "Epoch 43/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5184 - dice_coefficient_correct: 0.0482\n",
      "Epoch 44/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4831 - dice_coefficient_correct: 0.0786\n",
      "Epoch 45/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4720 - dice_coefficient_correct: 0.0884\n",
      "Epoch 46/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5201 - dice_coefficient_correct: 0.0467\n",
      "Epoch 47/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4993 - dice_coefficient_correct: 0.0642\n",
      "Epoch 48/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5160 - dice_coefficient_correct: 0.0500\n",
      "Epoch 49/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5188 - dice_coefficient_correct: 0.0475\n",
      "Epoch 50/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5201 - dice_coefficient_correct: 0.0463\n",
      "\n",
      "Epoch 00050: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-50-1.5208-0.0458.hdf5\n",
      "Epoch 51/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5213 - dice_coefficient_correct: 0.0454\n",
      "Epoch 52/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5313 - dice_coefficient_correct: 0.0372\n",
      "Epoch 53/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5193 - dice_coefficient_correct: 0.0469\n",
      "Epoch 54/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4993 - dice_coefficient_correct: 0.0637\n",
      "Epoch 55/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5167 - dice_coefficient_correct: 0.0490\n",
      "Epoch 56/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5074 - dice_coefficient_correct: 0.0568\n",
      "Epoch 57/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5095 - dice_coefficient_correct: 0.0550\n",
      "Epoch 58/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5125 - dice_coefficient_correct: 0.0524\n",
      "Epoch 59/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5018 - dice_coefficient_correct: 0.0613\n",
      "Epoch 60/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4915 - dice_coefficient_correct: 0.0701\n",
      "Epoch 61/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4968 - dice_coefficient_correct: 0.0656\n",
      "Epoch 62/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4882 - dice_coefficient_correct: 0.0729\n",
      "Epoch 63/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5340 - dice_coefficient_correct: 0.0346\n",
      "Epoch 64/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4968 - dice_coefficient_correct: 0.0654\n",
      "Epoch 65/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5042 - dice_coefficient_correct: 0.0590\n",
      "Epoch 66/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5009 - dice_coefficient_correct: 0.0616\n",
      "Epoch 67/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5123 - dice_coefficient_correct: 0.0520\n",
      "Epoch 68/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4889 - dice_coefficient_correct: 0.0723\n",
      "Epoch 69/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4905 - dice_coefficient_correct: 0.0703\n",
      "Epoch 70/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4978 - dice_coefficient_correct: 0.0641\n",
      "Epoch 71/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4886 - dice_coefficient_correct: 0.0723\n",
      "Epoch 72/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 1.4807 - dice_coefficient_correct: 0.0787\n",
      "Epoch 73/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4938 - dice_coefficient_correct: 0.0678\n",
      "Epoch 74/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4711 - dice_coefficient_correct: 0.0870\n",
      "Epoch 75/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4873 - dice_coefficient_correct: 0.0727\n",
      "\n",
      "Epoch 00075: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-75-1.4826-0.0768.hdf5\n",
      "Epoch 76/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4442 - dice_coefficient_correct: 0.1107\n",
      "Epoch 77/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.5033 - dice_coefficient_correct: 0.0591\n",
      "Epoch 78/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4794 - dice_coefficient_correct: 0.0795\n",
      "Epoch 79/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4533 - dice_coefficient_correct: 0.1027\n",
      "Epoch 80/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4736 - dice_coefficient_correct: 0.0853\n",
      "Epoch 81/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4521 - dice_coefficient_correct: 0.1036\n",
      "Epoch 82/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4612 - dice_coefficient_correct: 0.0956\n",
      "Epoch 83/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4526 - dice_coefficient_correct: 0.1041\n",
      "Epoch 84/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4760 - dice_coefficient_correct: 0.0827\n",
      "Epoch 85/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4640 - dice_coefficient_correct: 0.0929\n",
      "Epoch 86/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4558 - dice_coefficient_correct: 0.0995\n",
      "Epoch 87/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4157 - dice_coefficient_correct: 0.1352\n",
      "Epoch 88/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4235 - dice_coefficient_correct: 0.1327\n",
      "Epoch 89/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4340 - dice_coefficient_correct: 0.1193\n",
      "Epoch 90/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4651 - dice_coefficient_correct: 0.0917\n",
      "Epoch 91/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4141 - dice_coefficient_correct: 0.1383\n",
      "Epoch 92/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4093 - dice_coefficient_correct: 0.1415\n",
      "Epoch 93/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4105 - dice_coefficient_correct: 0.1403\n",
      "Epoch 94/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.3761 - dice_coefficient_correct: 0.1734\n",
      "Epoch 95/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4256 - dice_coefficient_correct: 0.1280\n",
      "Epoch 96/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4030 - dice_coefficient_correct: 0.1486\n",
      "Epoch 97/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4674 - dice_coefficient_correct: 0.0891\n",
      "Epoch 98/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4146 - dice_coefficient_correct: 0.1359\n",
      "Epoch 99/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.3947 - dice_coefficient_correct: 0.1540\n",
      "Epoch 100/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.4252 - dice_coefficient_correct: 0.1303\n",
      "\n",
      "Epoch 00100: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-100-1.3797-0.1744.hdf5\n",
      "Epoch 101/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 1.3346 - dice_coefficient_correct: 0.2129\n",
      "Epoch 102/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.3630 - dice_coefficient_correct: 0.1841\n",
      "Epoch 103/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2531 - dice_coefficient_correct: 0.3014\n",
      "Epoch 104/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.3174 - dice_coefficient_correct: 0.2345\n",
      "Epoch 105/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2858 - dice_coefficient_correct: 0.2671\n",
      "Epoch 106/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2397 - dice_coefficient_correct: 0.3128\n",
      "Epoch 107/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2681 - dice_coefficient_correct: 0.2964\n",
      "Epoch 108/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1968 - dice_coefficient_correct: 0.3693\n",
      "Epoch 109/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2240 - dice_coefficient_correct: 0.3332\n",
      "Epoch 110/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2510 - dice_coefficient_correct: 0.3350\n",
      "Epoch 111/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1362 - dice_coefficient_correct: 0.4565\n",
      "Epoch 112/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1340 - dice_coefficient_correct: 0.4446\n",
      "Epoch 113/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1307 - dice_coefficient_correct: 0.4738\n",
      "Epoch 114/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1517 - dice_coefficient_correct: 0.4201\n",
      "Epoch 115/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1848 - dice_coefficient_correct: 0.4334\n",
      "Epoch 116/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.2748 - dice_coefficient_correct: 0.3094\n",
      "Epoch 117/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0337 - dice_coefficient_correct: 0.5972\n",
      "Epoch 118/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0372 - dice_coefficient_correct: 0.5793\n",
      "Epoch 119/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9787 - dice_coefficient_correct: 0.6341\n",
      "Epoch 120/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0245 - dice_coefficient_correct: 0.6141\n",
      "Epoch 121/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0653 - dice_coefficient_correct: 0.5652\n",
      "Epoch 122/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0771 - dice_coefficient_correct: 0.5314\n",
      "Epoch 123/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9832 - dice_coefficient_correct: 0.6228\n",
      "Epoch 124/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.1656 - dice_coefficient_correct: 0.4855\n",
      "Epoch 125/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0564 - dice_coefficient_correct: 0.5570\n",
      "\n",
      "Epoch 00125: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-125-1.0463-0.5643.hdf5\n",
      "Epoch 126/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9937 - dice_coefficient_correct: 0.6503\n",
      "Epoch 127/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0099 - dice_coefficient_correct: 0.6494\n",
      "Epoch 128/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9680 - dice_coefficient_correct: 0.6904\n",
      "Epoch 129/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0632 - dice_coefficient_correct: 0.5393\n",
      "Epoch 130/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0517 - dice_coefficient_correct: 0.6002\n",
      "Epoch 131/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9859 - dice_coefficient_correct: 0.6420\n",
      "Epoch 132/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9837 - dice_coefficient_correct: 0.6639\n",
      "Epoch 133/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9371 - dice_coefficient_correct: 0.6722\n",
      "Epoch 134/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0130 - dice_coefficient_correct: 0.6435\n",
      "Epoch 135/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 1.0195 - dice_coefficient_correct: 0.5711\n",
      "Epoch 136/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9185 - dice_coefficient_correct: 0.7366\n",
      "Epoch 137/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9898 - dice_coefficient_correct: 0.6477\n",
      "Epoch 138/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.9673 - dice_coefficient_correct: 0.6799\n",
      "Epoch 139/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9855 - dice_coefficient_correct: 0.6322\n",
      "Epoch 140/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9352 - dice_coefficient_correct: 0.6881\n",
      "Epoch 141/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9441 - dice_coefficient_correct: 0.7065\n",
      "Epoch 142/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9136 - dice_coefficient_correct: 0.7303\n",
      "Epoch 143/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9490 - dice_coefficient_correct: 0.6824\n",
      "Epoch 144/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9043 - dice_coefficient_correct: 0.7329\n",
      "Epoch 145/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8650 - dice_coefficient_correct: 0.7920\n",
      "Epoch 146/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9089 - dice_coefficient_correct: 0.7222\n",
      "Epoch 147/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9156 - dice_coefficient_correct: 0.7304\n",
      "Epoch 148/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9078 - dice_coefficient_correct: 0.7371\n",
      "Epoch 149/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0179 - dice_coefficient_correct: 0.6281\n",
      "Epoch 150/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9811 - dice_coefficient_correct: 0.6391\n",
      "\n",
      "Epoch 00150: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-150-0.9604-0.6804.hdf5\n",
      "Epoch 151/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8909 - dice_coefficient_correct: 0.7512\n",
      "Epoch 152/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8538 - dice_coefficient_correct: 0.7871\n",
      "Epoch 153/1000\n",
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      "Epoch 154/1000\n",
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      "Epoch 155/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8553 - dice_coefficient_correct: 0.7808\n",
      "Epoch 156/1000\n",
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      "Epoch 157/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9001 - dice_coefficient_correct: 0.7326\n",
      "Epoch 158/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8648 - dice_coefficient_correct: 0.7770\n",
      "Epoch 159/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8628 - dice_coefficient_correct: 0.7992\n",
      "Epoch 160/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8733 - dice_coefficient_correct: 0.7723\n",
      "Epoch 161/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0591 - dice_coefficient_correct: 0.5602\n",
      "Epoch 162/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8893 - dice_coefficient_correct: 0.7373\n",
      "Epoch 163/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9184 - dice_coefficient_correct: 0.7472\n",
      "Epoch 164/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9149 - dice_coefficient_correct: 0.7205\n",
      "Epoch 165/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8556 - dice_coefficient_correct: 0.8019\n",
      "Epoch 166/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8764 - dice_coefficient_correct: 0.7681\n",
      "Epoch 167/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9045 - dice_coefficient_correct: 0.7493\n",
      "Epoch 168/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9068 - dice_coefficient_correct: 0.7259\n",
      "Epoch 169/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8847 - dice_coefficient_correct: 0.7871\n",
      "Epoch 170/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8931 - dice_coefficient_correct: 0.7359\n",
      "Epoch 171/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9366 - dice_coefficient_correct: 0.7217\n",
      "Epoch 172/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8957 - dice_coefficient_correct: 0.7188\n",
      "Epoch 173/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9437 - dice_coefficient_correct: 0.7180\n",
      "Epoch 174/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8701 - dice_coefficient_correct: 0.7755\n",
      "Epoch 175/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8990 - dice_coefficient_correct: 0.7386\n",
      "\n",
      "Epoch 00175: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-175-0.8988-0.7475.hdf5\n",
      "Epoch 176/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8767 - dice_coefficient_correct: 0.7708\n",
      "Epoch 177/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8641 - dice_coefficient_correct: 0.7766\n",
      "Epoch 178/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8557 - dice_coefficient_correct: 0.7773\n",
      "Epoch 179/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8477 - dice_coefficient_correct: 0.7857\n",
      "Epoch 180/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8503 - dice_coefficient_correct: 0.7982\n",
      "Epoch 181/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8879 - dice_coefficient_correct: 0.7628\n",
      "Epoch 182/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8565 - dice_coefficient_correct: 0.7979\n",
      "Epoch 183/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8536 - dice_coefficient_correct: 0.7947\n",
      "Epoch 184/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 1.0171 - dice_coefficient_correct: 0.6056\n",
      "Epoch 185/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8739 - dice_coefficient_correct: 0.7644\n",
      "Epoch 186/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8884 - dice_coefficient_correct: 0.7472\n",
      "Epoch 187/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8721 - dice_coefficient_correct: 0.7782\n",
      "Epoch 188/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8614 - dice_coefficient_correct: 0.8001\n",
      "Epoch 189/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8332 - dice_coefficient_correct: 0.8026\n",
      "Epoch 190/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8330 - dice_coefficient_correct: 0.7950\n",
      "Epoch 191/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9128 - dice_coefficient_correct: 0.7307\n",
      "Epoch 192/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9308 - dice_coefficient_correct: 0.7130\n",
      "Epoch 193/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9178 - dice_coefficient_correct: 0.7143\n",
      "Epoch 194/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8618 - dice_coefficient_correct: 0.7846\n",
      "Epoch 195/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8783 - dice_coefficient_correct: 0.7639\n",
      "Epoch 196/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9374 - dice_coefficient_correct: 0.6853\n",
      "Epoch 197/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8288 - dice_coefficient_correct: 0.8117\n",
      "Epoch 198/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8224 - dice_coefficient_correct: 0.7995\n",
      "Epoch 199/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8372 - dice_coefficient_correct: 0.8041\n",
      "Epoch 200/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8798 - dice_coefficient_correct: 0.7627\n",
      "\n",
      "Epoch 00200: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-200-0.8955-0.7503.hdf5\n",
      "Epoch 201/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.8501 - dice_coefficient_correct: 0.8019\n",
      "Epoch 202/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8202 - dice_coefficient_correct: 0.8028\n",
      "Epoch 203/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8065 - dice_coefficient_correct: 0.8277\n",
      "Epoch 204/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8188 - dice_coefficient_correct: 0.8189\n",
      "Epoch 205/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8945 - dice_coefficient_correct: 0.7412\n",
      "Epoch 206/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8704 - dice_coefficient_correct: 0.7675\n",
      "Epoch 207/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8536 - dice_coefficient_correct: 0.7867\n",
      "Epoch 208/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8272 - dice_coefficient_correct: 0.8271\n",
      "Epoch 209/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8653 - dice_coefficient_correct: 0.7569\n",
      "Epoch 210/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8415 - dice_coefficient_correct: 0.8087\n",
      "Epoch 211/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8426 - dice_coefficient_correct: 0.7991\n",
      "Epoch 212/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8415 - dice_coefficient_correct: 0.7975\n",
      "Epoch 213/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8284 - dice_coefficient_correct: 0.8228\n",
      "Epoch 214/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8537 - dice_coefficient_correct: 0.7832\n",
      "Epoch 215/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8948 - dice_coefficient_correct: 0.7478\n",
      "Epoch 216/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8230 - dice_coefficient_correct: 0.8151\n",
      "Epoch 217/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8122 - dice_coefficient_correct: 0.8211\n",
      "Epoch 218/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8512 - dice_coefficient_correct: 0.7850\n",
      "Epoch 219/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8204 - dice_coefficient_correct: 0.8218\n",
      "Epoch 220/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8900 - dice_coefficient_correct: 0.7319\n",
      "Epoch 221/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8338 - dice_coefficient_correct: 0.8126\n",
      "Epoch 222/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8364 - dice_coefficient_correct: 0.8023\n",
      "Epoch 223/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8304 - dice_coefficient_correct: 0.8039\n",
      "Epoch 224/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8362 - dice_coefficient_correct: 0.8205\n",
      "Epoch 225/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8404 - dice_coefficient_correct: 0.7837\n",
      "\n",
      "Epoch 00225: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-225-0.8281-0.8029.hdf5\n",
      "Epoch 226/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8445 - dice_coefficient_correct: 0.7887\n",
      "Epoch 227/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8507 - dice_coefficient_correct: 0.7930\n",
      "Epoch 228/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8228 - dice_coefficient_correct: 0.8070\n",
      "Epoch 229/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8381 - dice_coefficient_correct: 0.8086\n",
      "Epoch 230/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8116 - dice_coefficient_correct: 0.8158\n",
      "Epoch 231/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8273 - dice_coefficient_correct: 0.8116\n",
      "Epoch 232/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8284 - dice_coefficient_correct: 0.8122\n",
      "Epoch 233/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8961 - dice_coefficient_correct: 0.7379\n",
      "Epoch 234/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7920 - dice_coefficient_correct: 0.8506\n",
      "Epoch 235/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8287 - dice_coefficient_correct: 0.7932\n",
      "Epoch 236/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8476 - dice_coefficient_correct: 0.7760\n",
      "Epoch 237/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9305 - dice_coefficient_correct: 0.6853\n",
      "Epoch 238/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8089 - dice_coefficient_correct: 0.8404\n",
      "Epoch 239/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8075 - dice_coefficient_correct: 0.8164\n",
      "Epoch 240/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7912 - dice_coefficient_correct: 0.8299\n",
      "Epoch 241/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.9064 - dice_coefficient_correct: 0.7162\n",
      "Epoch 242/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.8612 - dice_coefficient_correct: 0.8030\n",
      "Epoch 243/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8300 - dice_coefficient_correct: 0.7795\n",
      "Epoch 244/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8050 - dice_coefficient_correct: 0.8247\n",
      "Epoch 245/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8414 - dice_coefficient_correct: 0.7946\n",
      "Epoch 246/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8073 - dice_coefficient_correct: 0.8280\n",
      "Epoch 247/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8077 - dice_coefficient_correct: 0.8233\n",
      "Epoch 248/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.8686 - dice_coefficient_correct: 0.7634\n",
      "Epoch 249/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7901 - dice_coefficient_correct: 0.8327\n",
      "Epoch 250/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7975 - dice_coefficient_correct: 0.8245\n",
      "\n",
      "Epoch 00250: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-250-0.8054-0.8175.hdf5\n",
      "Epoch 251/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.8253 - dice_coefficient_correct: 0.8193\n",
      "Epoch 252/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8090 - dice_coefficient_correct: 0.8238\n",
      "Epoch 253/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8034 - dice_coefficient_correct: 0.8441\n",
      "Epoch 254/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8627 - dice_coefficient_correct: 0.7670\n",
      "Epoch 255/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8014 - dice_coefficient_correct: 0.8337\n",
      "Epoch 256/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8381 - dice_coefficient_correct: 0.7972\n",
      "Epoch 257/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8224 - dice_coefficient_correct: 0.8108\n",
      "Epoch 258/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7902 - dice_coefficient_correct: 0.8440\n",
      "Epoch 259/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8255 - dice_coefficient_correct: 0.7974\n",
      "Epoch 260/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8032 - dice_coefficient_correct: 0.8309\n",
      "Epoch 261/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7978 - dice_coefficient_correct: 0.8341\n",
      "Epoch 262/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8310 - dice_coefficient_correct: 0.7995\n",
      "Epoch 263/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8523 - dice_coefficient_correct: 0.7747\n",
      "Epoch 264/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7853 - dice_coefficient_correct: 0.8401\n",
      "Epoch 265/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8759 - dice_coefficient_correct: 0.7445\n",
      "Epoch 266/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8127 - dice_coefficient_correct: 0.8134\n",
      "Epoch 267/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7851 - dice_coefficient_correct: 0.8471\n",
      "Epoch 268/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8761 - dice_coefficient_correct: 0.7509\n",
      "Epoch 269/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7887 - dice_coefficient_correct: 0.8445\n",
      "Epoch 270/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7927 - dice_coefficient_correct: 0.8383\n",
      "Epoch 271/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8519 - dice_coefficient_correct: 0.7867\n",
      "Epoch 272/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8410 - dice_coefficient_correct: 0.7826\n",
      "Epoch 273/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7888 - dice_coefficient_correct: 0.8445\n",
      "Epoch 274/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8060 - dice_coefficient_correct: 0.8269\n",
      "Epoch 275/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8689 - dice_coefficient_correct: 0.7556\n",
      "\n",
      "Epoch 00275: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-275-0.8661-0.7608.hdf5\n",
      "Epoch 276/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8179 - dice_coefficient_correct: 0.8197\n",
      "Epoch 277/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8026 - dice_coefficient_correct: 0.8244\n",
      "Epoch 278/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7779 - dice_coefficient_correct: 0.8504\n",
      "Epoch 279/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8048 - dice_coefficient_correct: 0.8331\n",
      "Epoch 280/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8071 - dice_coefficient_correct: 0.8110\n",
      "Epoch 281/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8171 - dice_coefficient_correct: 0.8176\n",
      "Epoch 282/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8017 - dice_coefficient_correct: 0.8445\n",
      "Epoch 283/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7957 - dice_coefficient_correct: 0.8261\n",
      "Epoch 284/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8022 - dice_coefficient_correct: 0.8333\n",
      "Epoch 285/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8268 - dice_coefficient_correct: 0.8140\n",
      "Epoch 286/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8355 - dice_coefficient_correct: 0.7968\n",
      "Epoch 287/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7867 - dice_coefficient_correct: 0.8444\n",
      "Epoch 288/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8291 - dice_coefficient_correct: 0.8017\n",
      "Epoch 289/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8376 - dice_coefficient_correct: 0.7961\n",
      "Epoch 290/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8062 - dice_coefficient_correct: 0.8323\n",
      "Epoch 291/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7932 - dice_coefficient_correct: 0.8290\n",
      "Epoch 292/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8281 - dice_coefficient_correct: 0.7938\n",
      "Epoch 293/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7822 - dice_coefficient_correct: 0.8553\n",
      "Epoch 294/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7817 - dice_coefficient_correct: 0.8443\n",
      "Epoch 295/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7905 - dice_coefficient_correct: 0.8325\n",
      "Epoch 296/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8092 - dice_coefficient_correct: 0.8276\n",
      "Epoch 297/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8127 - dice_coefficient_correct: 0.8078\n",
      "Epoch 298/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7958 - dice_coefficient_correct: 0.8474\n",
      "Epoch 299/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7878 - dice_coefficient_correct: 0.8378\n",
      "Epoch 300/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8059 - dice_coefficient_correct: 0.8185\n",
      "\n",
      "Epoch 00300: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-300-0.8232-0.8058.hdf5\n",
      "Epoch 301/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8178 - dice_coefficient_correct: 0.8056\n",
      "Epoch 302/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8087 - dice_coefficient_correct: 0.8342\n",
      "Epoch 303/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7957 - dice_coefficient_correct: 0.8318\n",
      "Epoch 304/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7820 - dice_coefficient_correct: 0.8409\n",
      "Epoch 305/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8070 - dice_coefficient_correct: 0.8171\n",
      "Epoch 306/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8018 - dice_coefficient_correct: 0.8261\n",
      "Epoch 307/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7758 - dice_coefficient_correct: 0.8504\n",
      "Epoch 308/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7916 - dice_coefficient_correct: 0.8357\n",
      "Epoch 309/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8063 - dice_coefficient_correct: 0.8205\n",
      "Epoch 310/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7831 - dice_coefficient_correct: 0.8518\n",
      "Epoch 311/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8022 - dice_coefficient_correct: 0.8233\n",
      "Epoch 312/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8095 - dice_coefficient_correct: 0.8245\n",
      "Epoch 313/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7825 - dice_coefficient_correct: 0.8481\n",
      "Epoch 314/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7949 - dice_coefficient_correct: 0.8341\n",
      "Epoch 315/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7985 - dice_coefficient_correct: 0.8246\n",
      "Epoch 316/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8115 - dice_coefficient_correct: 0.8232\n",
      "Epoch 317/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7924 - dice_coefficient_correct: 0.8377\n",
      "Epoch 318/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8349 - dice_coefficient_correct: 0.8037\n",
      "Epoch 319/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7813 - dice_coefficient_correct: 0.8435\n",
      "Epoch 320/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8429 - dice_coefficient_correct: 0.7629\n",
      "Epoch 321/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8082 - dice_coefficient_correct: 0.8080\n",
      "Epoch 322/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7821 - dice_coefficient_correct: 0.8343\n",
      "Epoch 323/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7769 - dice_coefficient_correct: 0.8576\n",
      "Epoch 324/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7879 - dice_coefficient_correct: 0.8252\n",
      "Epoch 325/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7826 - dice_coefficient_correct: 0.8472\n",
      "\n",
      "Epoch 00325: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-325-0.7837-0.8374.hdf5\n",
      "Epoch 326/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7808 - dice_coefficient_correct: 0.8519\n",
      "Epoch 327/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7958 - dice_coefficient_correct: 0.8271\n",
      "Epoch 328/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7972 - dice_coefficient_correct: 0.8363\n",
      "Epoch 329/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8013 - dice_coefficient_correct: 0.8215\n",
      "Epoch 330/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8250 - dice_coefficient_correct: 0.8002\n",
      "Epoch 331/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7781 - dice_coefficient_correct: 0.8371\n",
      "Epoch 332/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8394 - dice_coefficient_correct: 0.7837\n",
      "Epoch 333/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7889 - dice_coefficient_correct: 0.8472\n",
      "Epoch 334/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.8225 - dice_coefficient_correct: 0.8024\n",
      "Epoch 335/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7812 - dice_coefficient_correct: 0.8525\n",
      "Epoch 336/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8673 - dice_coefficient_correct: 0.7591\n",
      "Epoch 337/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8211 - dice_coefficient_correct: 0.8074\n",
      "Epoch 338/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8168 - dice_coefficient_correct: 0.8099\n",
      "Epoch 339/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8273 - dice_coefficient_correct: 0.7856\n",
      "Epoch 340/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7852 - dice_coefficient_correct: 0.8376\n",
      "Epoch 341/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7796 - dice_coefficient_correct: 0.8513\n",
      "Epoch 342/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7760 - dice_coefficient_correct: 0.8413\n",
      "Epoch 343/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7920 - dice_coefficient_correct: 0.8264\n",
      "Epoch 344/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7775 - dice_coefficient_correct: 0.8374\n",
      "Epoch 345/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8030 - dice_coefficient_correct: 0.8136\n",
      "Epoch 346/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7618 - dice_coefficient_correct: 0.8691\n",
      "Epoch 347/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7831 - dice_coefficient_correct: 0.8416\n",
      "Epoch 348/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8659 - dice_coefficient_correct: 0.7676\n",
      "Epoch 349/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7873 - dice_coefficient_correct: 0.8320\n",
      "Epoch 350/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7718 - dice_coefficient_correct: 0.8427\n",
      "\n",
      "Epoch 00350: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-350-0.7736-0.8470.hdf5\n",
      "Epoch 351/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7829 - dice_coefficient_correct: 0.8433\n",
      "Epoch 352/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8024 - dice_coefficient_correct: 0.8249\n",
      "Epoch 353/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7812 - dice_coefficient_correct: 0.8491\n",
      "Epoch 354/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8724 - dice_coefficient_correct: 0.7543\n",
      "Epoch 355/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8193 - dice_coefficient_correct: 0.8035\n",
      "Epoch 356/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7905 - dice_coefficient_correct: 0.8332\n",
      "Epoch 357/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7915 - dice_coefficient_correct: 0.8413\n",
      "Epoch 358/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7984 - dice_coefficient_correct: 0.8293\n",
      "Epoch 359/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7753 - dice_coefficient_correct: 0.8437\n",
      "Epoch 360/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7667 - dice_coefficient_correct: 0.8511\n",
      "Epoch 361/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7760 - dice_coefficient_correct: 0.8442\n",
      "Epoch 362/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7578 - dice_coefficient_correct: 0.8564\n",
      "Epoch 363/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7986 - dice_coefficient_correct: 0.8292\n",
      "Epoch 364/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7816 - dice_coefficient_correct: 0.8363\n",
      "Epoch 365/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7887 - dice_coefficient_correct: 0.8316\n",
      "Epoch 366/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7727 - dice_coefficient_correct: 0.8473\n",
      "Epoch 367/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7801 - dice_coefficient_correct: 0.8373\n",
      "Epoch 368/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7682 - dice_coefficient_correct: 0.8553\n",
      "Epoch 369/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7842 - dice_coefficient_correct: 0.8328\n",
      "Epoch 370/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8078 - dice_coefficient_correct: 0.8204\n",
      "Epoch 371/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7943 - dice_coefficient_correct: 0.8268\n",
      "Epoch 372/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8035 - dice_coefficient_correct: 0.8187\n",
      "Epoch 373/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7664 - dice_coefficient_correct: 0.8652\n",
      "Epoch 374/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7971 - dice_coefficient_correct: 0.8203\n",
      "Epoch 375/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7829 - dice_coefficient_correct: 0.8520\n",
      "\n",
      "Epoch 00375: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-375-0.7713-0.8567.hdf5\n",
      "Epoch 376/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8017 - dice_coefficient_correct: 0.8201\n",
      "Epoch 377/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7693 - dice_coefficient_correct: 0.8562\n",
      "Epoch 378/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7783 - dice_coefficient_correct: 0.8402\n",
      "Epoch 379/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8308 - dice_coefficient_correct: 0.7966\n",
      "Epoch 380/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7641 - dice_coefficient_correct: 0.8461\n",
      "Epoch 381/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7721 - dice_coefficient_correct: 0.8512\n",
      "Epoch 382/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.8189 - dice_coefficient_correct: 0.8051\n",
      "Epoch 383/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7569 - dice_coefficient_correct: 0.8689\n",
      "Epoch 384/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8250 - dice_coefficient_correct: 0.8036\n",
      "Epoch 385/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8387 - dice_coefficient_correct: 0.7793\n",
      "Epoch 386/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7670 - dice_coefficient_correct: 0.8367\n",
      "Epoch 387/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7853 - dice_coefficient_correct: 0.8496\n",
      "Epoch 388/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8162 - dice_coefficient_correct: 0.8077\n",
      "Epoch 389/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8137 - dice_coefficient_correct: 0.8235\n",
      "Epoch 390/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7812 - dice_coefficient_correct: 0.8441\n",
      "Epoch 391/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8289 - dice_coefficient_correct: 0.7785\n",
      "Epoch 392/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8004 - dice_coefficient_correct: 0.8108\n",
      "Epoch 393/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8197 - dice_coefficient_correct: 0.8406\n",
      "Epoch 394/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7899 - dice_coefficient_correct: 0.8292\n",
      "Epoch 395/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7690 - dice_coefficient_correct: 0.8470\n",
      "Epoch 396/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8061 - dice_coefficient_correct: 0.8169\n",
      "Epoch 397/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7751 - dice_coefficient_correct: 0.8580\n",
      "Epoch 398/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8248 - dice_coefficient_correct: 0.8037\n",
      "Epoch 399/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8223 - dice_coefficient_correct: 0.7800\n",
      "Epoch 400/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8043 - dice_coefficient_correct: 0.8166\n",
      "\n",
      "Epoch 00400: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-400-0.7871-0.8389.hdf5\n",
      "Epoch 401/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8136 - dice_coefficient_correct: 0.7974\n",
      "Epoch 402/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8153 - dice_coefficient_correct: 0.8078\n",
      "Epoch 403/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7836 - dice_coefficient_correct: 0.8319\n",
      "Epoch 404/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7885 - dice_coefficient_correct: 0.8313\n",
      "Epoch 405/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7940 - dice_coefficient_correct: 0.8221\n",
      "Epoch 406/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7818 - dice_coefficient_correct: 0.8516\n",
      "Epoch 407/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7823 - dice_coefficient_correct: 0.8321\n",
      "Epoch 408/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7714 - dice_coefficient_correct: 0.8464\n",
      "Epoch 409/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8355 - dice_coefficient_correct: 0.7880\n",
      "Epoch 410/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7630 - dice_coefficient_correct: 0.8720\n",
      "Epoch 411/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7935 - dice_coefficient_correct: 0.8280\n",
      "Epoch 412/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7699 - dice_coefficient_correct: 0.8454\n",
      "Epoch 413/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7814 - dice_coefficient_correct: 0.8480\n",
      "Epoch 414/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7586 - dice_coefficient_correct: 0.8671\n",
      "Epoch 415/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7454 - dice_coefficient_correct: 0.8615\n",
      "Epoch 416/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7392 - dice_coefficient_correct: 0.8768\n",
      "Epoch 417/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7913 - dice_coefficient_correct: 0.8324\n",
      "Epoch 418/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7733 - dice_coefficient_correct: 0.8459\n",
      "Epoch 419/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7878 - dice_coefficient_correct: 0.8243\n",
      "Epoch 420/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7729 - dice_coefficient_correct: 0.8320\n",
      "Epoch 421/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8298 - dice_coefficient_correct: 0.8056\n",
      "Epoch 422/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7691 - dice_coefficient_correct: 0.8425\n",
      "Epoch 423/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7989 - dice_coefficient_correct: 0.8127\n",
      "Epoch 424/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7699 - dice_coefficient_correct: 0.8386\n",
      "Epoch 425/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7639 - dice_coefficient_correct: 0.8539\n",
      "\n",
      "Epoch 00425: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-425-0.7882-0.8304.hdf5\n",
      "Epoch 426/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7632 - dice_coefficient_correct: 0.8620\n",
      "Epoch 427/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7710 - dice_coefficient_correct: 0.8371\n",
      "Epoch 428/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7758 - dice_coefficient_correct: 0.8508\n",
      "Epoch 429/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7757 - dice_coefficient_correct: 0.8319\n",
      "Epoch 430/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7778 - dice_coefficient_correct: 0.8436\n",
      "Epoch 431/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7880 - dice_coefficient_correct: 0.8268\n",
      "Epoch 432/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7858 - dice_coefficient_correct: 0.8364\n",
      "Epoch 433/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7953 - dice_coefficient_correct: 0.8298\n",
      "Epoch 434/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7858 - dice_coefficient_correct: 0.8253\n",
      "Epoch 435/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7547 - dice_coefficient_correct: 0.8585\n",
      "Epoch 436/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7679 - dice_coefficient_correct: 0.8517\n",
      "Epoch 437/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7702 - dice_coefficient_correct: 0.8421\n",
      "Epoch 438/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7666 - dice_coefficient_correct: 0.8513\n",
      "Epoch 439/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7837 - dice_coefficient_correct: 0.8358\n",
      "Epoch 440/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7256 - dice_coefficient_correct: 0.8818\n",
      "Epoch 441/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7896 - dice_coefficient_correct: 0.8233\n",
      "Epoch 442/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7898 - dice_coefficient_correct: 0.8254\n",
      "Epoch 443/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7847 - dice_coefficient_correct: 0.8345\n",
      "Epoch 444/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8083 - dice_coefficient_correct: 0.8095\n",
      "Epoch 445/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7756 - dice_coefficient_correct: 0.8477\n",
      "Epoch 446/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8314 - dice_coefficient_correct: 0.7790\n",
      "Epoch 447/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7457 - dice_coefficient_correct: 0.8586\n",
      "Epoch 448/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7429 - dice_coefficient_correct: 0.8719\n",
      "Epoch 449/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7754 - dice_coefficient_correct: 0.8431\n",
      "Epoch 450/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7486 - dice_coefficient_correct: 0.8641\n",
      "\n",
      "Epoch 00450: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-450-0.7520-0.8653.hdf5\n",
      "Epoch 451/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7972 - dice_coefficient_correct: 0.8127\n",
      "Epoch 452/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7603 - dice_coefficient_correct: 0.8539\n",
      "Epoch 453/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7889 - dice_coefficient_correct: 0.8239\n",
      "Epoch 454/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7988 - dice_coefficient_correct: 0.8119\n",
      "Epoch 455/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7879 - dice_coefficient_correct: 0.8283\n",
      "Epoch 456/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7673 - dice_coefficient_correct: 0.8690\n",
      "Epoch 457/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8196 - dice_coefficient_correct: 0.8015\n",
      "Epoch 458/1000\n",
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      "Epoch 459/1000\n",
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      "Epoch 460/1000\n",
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      "Epoch 461/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7878 - dice_coefficient_correct: 0.8328\n",
      "Epoch 462/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7618 - dice_coefficient_correct: 0.8575\n",
      "Epoch 463/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7433 - dice_coefficient_correct: 0.8666\n",
      "Epoch 464/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7596 - dice_coefficient_correct: 0.8597\n",
      "Epoch 465/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7535 - dice_coefficient_correct: 0.8546\n",
      "Epoch 466/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8017 - dice_coefficient_correct: 0.8229\n",
      "Epoch 467/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7503 - dice_coefficient_correct: 0.8679\n",
      "Epoch 468/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7549 - dice_coefficient_correct: 0.8482\n",
      "Epoch 469/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7980 - dice_coefficient_correct: 0.8295\n",
      "Epoch 470/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7708 - dice_coefficient_correct: 0.8357\n",
      "Epoch 471/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7791 - dice_coefficient_correct: 0.8404\n",
      "Epoch 472/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7599 - dice_coefficient_correct: 0.8458\n",
      "Epoch 473/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7675 - dice_coefficient_correct: 0.8552\n",
      "Epoch 474/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7414 - dice_coefficient_correct: 0.8766\n",
      "Epoch 475/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7556 - dice_coefficient_correct: 0.8525\n",
      "\n",
      "Epoch 00475: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-475-0.7755-0.8289.hdf5\n",
      "Epoch 476/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7600 - dice_coefficient_correct: 0.8618\n",
      "Epoch 477/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7239 - dice_coefficient_correct: 0.8886\n",
      "Epoch 478/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8380 - dice_coefficient_correct: 0.7770\n",
      "Epoch 479/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7607 - dice_coefficient_correct: 0.8469\n",
      "Epoch 480/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7389 - dice_coefficient_correct: 0.8824\n",
      "Epoch 481/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7495 - dice_coefficient_correct: 0.8589\n",
      "Epoch 482/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7679 - dice_coefficient_correct: 0.8418\n",
      "Epoch 483/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7596 - dice_coefficient_correct: 0.8565\n",
      "Epoch 484/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7831 - dice_coefficient_correct: 0.8327\n",
      "Epoch 485/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7919 - dice_coefficient_correct: 0.8239\n",
      "Epoch 486/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7469 - dice_coefficient_correct: 0.8631\n",
      "Epoch 487/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7516 - dice_coefficient_correct: 0.8610\n",
      "Epoch 488/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7851 - dice_coefficient_correct: 0.8385\n",
      "Epoch 489/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8235 - dice_coefficient_correct: 0.7803\n",
      "Epoch 490/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8102 - dice_coefficient_correct: 0.8081\n",
      "Epoch 491/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7577 - dice_coefficient_correct: 0.8640\n",
      "Epoch 492/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7619 - dice_coefficient_correct: 0.8455\n",
      "Epoch 493/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7709 - dice_coefficient_correct: 0.8450\n",
      "Epoch 494/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7657 - dice_coefficient_correct: 0.8527\n",
      "Epoch 495/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7753 - dice_coefficient_correct: 0.8363\n",
      "Epoch 496/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7572 - dice_coefficient_correct: 0.8548\n",
      "Epoch 497/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7499 - dice_coefficient_correct: 0.8632\n",
      "Epoch 498/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7728 - dice_coefficient_correct: 0.8453\n",
      "Epoch 499/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7671 - dice_coefficient_correct: 0.8359\n",
      "Epoch 500/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7414 - dice_coefficient_correct: 0.8739\n",
      "\n",
      "Epoch 00500: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-500-0.7455-0.8694.hdf5\n",
      "Epoch 501/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7611 - dice_coefficient_correct: 0.8439\n",
      "Epoch 502/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7634 - dice_coefficient_correct: 0.8419\n",
      "Epoch 503/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7442 - dice_coefficient_correct: 0.8727\n",
      "Epoch 504/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7735 - dice_coefficient_correct: 0.8290\n",
      "Epoch 505/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8053 - dice_coefficient_correct: 0.8115\n",
      "Epoch 506/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7482 - dice_coefficient_correct: 0.8603\n",
      "Epoch 507/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7592 - dice_coefficient_correct: 0.8646\n",
      "Epoch 508/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7494 - dice_coefficient_correct: 0.8523\n",
      "Epoch 509/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8075 - dice_coefficient_correct: 0.8048\n",
      "Epoch 510/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7503 - dice_coefficient_correct: 0.8611\n",
      "Epoch 511/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7461 - dice_coefficient_correct: 0.8559\n",
      "Epoch 512/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7612 - dice_coefficient_correct: 0.8508\n",
      "Epoch 513/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7643 - dice_coefficient_correct: 0.8502\n",
      "Epoch 514/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8063 - dice_coefficient_correct: 0.8068\n",
      "Epoch 515/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7499 - dice_coefficient_correct: 0.8670\n",
      "Epoch 516/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7529 - dice_coefficient_correct: 0.8569\n",
      "Epoch 517/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7523 - dice_coefficient_correct: 0.8505\n",
      "Epoch 518/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7527 - dice_coefficient_correct: 0.8538\n",
      "Epoch 519/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7559 - dice_coefficient_correct: 0.8676\n",
      "Epoch 520/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7544 - dice_coefficient_correct: 0.8560\n",
      "Epoch 521/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7653 - dice_coefficient_correct: 0.8573\n",
      "Epoch 522/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7510 - dice_coefficient_correct: 0.8714\n",
      "Epoch 523/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7467 - dice_coefficient_correct: 0.8624\n",
      "Epoch 524/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7806 - dice_coefficient_correct: 0.8279\n",
      "Epoch 525/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7435 - dice_coefficient_correct: 0.8699\n",
      "\n",
      "Epoch 00525: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-525-0.7414-0.8648.hdf5\n",
      "Epoch 526/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7770 - dice_coefficient_correct: 0.8319\n",
      "Epoch 527/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7550 - dice_coefficient_correct: 0.8510\n",
      "Epoch 528/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7307 - dice_coefficient_correct: 0.8760\n",
      "Epoch 529/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7459 - dice_coefficient_correct: 0.8650\n",
      "Epoch 530/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7527 - dice_coefficient_correct: 0.8553\n",
      "Epoch 531/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7522 - dice_coefficient_correct: 0.8604\n",
      "Epoch 532/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7363 - dice_coefficient_correct: 0.8682\n",
      "Epoch 533/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7477 - dice_coefficient_correct: 0.8625\n",
      "Epoch 534/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7936 - dice_coefficient_correct: 0.8371\n",
      "Epoch 535/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7578 - dice_coefficient_correct: 0.8415\n",
      "Epoch 536/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7341 - dice_coefficient_correct: 0.8825\n",
      "Epoch 537/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7593 - dice_coefficient_correct: 0.8500\n",
      "Epoch 538/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7337 - dice_coefficient_correct: 0.8677\n",
      "Epoch 539/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7777 - dice_coefficient_correct: 0.8410\n",
      "Epoch 540/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7517 - dice_coefficient_correct: 0.8701\n",
      "Epoch 541/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7888 - dice_coefficient_correct: 0.8115\n",
      "Epoch 542/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8045 - dice_coefficient_correct: 0.8050\n",
      "Epoch 543/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7889 - dice_coefficient_correct: 0.8262\n",
      "Epoch 544/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7525 - dice_coefficient_correct: 0.8484\n",
      "Epoch 545/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7629 - dice_coefficient_correct: 0.8424\n",
      "Epoch 546/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7670 - dice_coefficient_correct: 0.8475\n",
      "Epoch 547/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7797 - dice_coefficient_correct: 0.8266\n",
      "Epoch 548/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7492 - dice_coefficient_correct: 0.8659\n",
      "Epoch 549/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7981 - dice_coefficient_correct: 0.8071\n",
      "Epoch 550/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7453 - dice_coefficient_correct: 0.8621\n",
      "\n",
      "Epoch 00550: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-550-0.7453-0.8639.hdf5\n",
      "Epoch 551/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7372 - dice_coefficient_correct: 0.8651\n",
      "Epoch 552/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7678 - dice_coefficient_correct: 0.8356\n",
      "Epoch 553/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7501 - dice_coefficient_correct: 0.8612\n",
      "Epoch 554/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7660 - dice_coefficient_correct: 0.8408\n",
      "Epoch 555/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7433 - dice_coefficient_correct: 0.8546\n",
      "Epoch 556/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7230 - dice_coefficient_correct: 0.8881\n",
      "Epoch 557/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7511 - dice_coefficient_correct: 0.8580\n",
      "Epoch 558/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7439 - dice_coefficient_correct: 0.8527\n",
      "Epoch 559/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7518 - dice_coefficient_correct: 0.8561\n",
      "Epoch 560/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7416 - dice_coefficient_correct: 0.8698\n",
      "Epoch 561/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7427 - dice_coefficient_correct: 0.8585\n",
      "Epoch 562/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7499 - dice_coefficient_correct: 0.8620\n",
      "Epoch 563/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7408 - dice_coefficient_correct: 0.8676\n",
      "Epoch 564/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7636 - dice_coefficient_correct: 0.8477\n",
      "Epoch 565/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7430 - dice_coefficient_correct: 0.8673\n",
      "Epoch 566/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7727 - dice_coefficient_correct: 0.8345\n",
      "Epoch 567/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7665 - dice_coefficient_correct: 0.8448\n",
      "Epoch 568/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7438 - dice_coefficient_correct: 0.8559\n",
      "Epoch 569/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7559 - dice_coefficient_correct: 0.8539\n",
      "Epoch 570/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7530 - dice_coefficient_correct: 0.8579\n",
      "Epoch 571/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7423 - dice_coefficient_correct: 0.8505\n",
      "Epoch 572/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7626 - dice_coefficient_correct: 0.8583\n",
      "Epoch 573/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7524 - dice_coefficient_correct: 0.8474\n",
      "Epoch 574/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7467 - dice_coefficient_correct: 0.8593\n",
      "Epoch 575/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7241 - dice_coefficient_correct: 0.8801\n",
      "\n",
      "Epoch 00575: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-575-0.7337-0.8680.hdf5\n",
      "Epoch 576/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7566 - dice_coefficient_correct: 0.8510\n",
      "Epoch 577/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7591 - dice_coefficient_correct: 0.8499\n",
      "Epoch 578/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7415 - dice_coefficient_correct: 0.8604\n",
      "Epoch 579/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7752 - dice_coefficient_correct: 0.8495\n",
      "Epoch 580/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7729 - dice_coefficient_correct: 0.8420\n",
      "Epoch 581/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7471 - dice_coefficient_correct: 0.8484\n",
      "Epoch 582/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7370 - dice_coefficient_correct: 0.8784\n",
      "Epoch 583/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7582 - dice_coefficient_correct: 0.8509\n",
      "Epoch 584/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7317 - dice_coefficient_correct: 0.8717\n",
      "Epoch 585/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7494 - dice_coefficient_correct: 0.8605\n",
      "Epoch 586/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7507 - dice_coefficient_correct: 0.8463\n",
      "Epoch 587/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7531 - dice_coefficient_correct: 0.8563\n",
      "Epoch 588/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7490 - dice_coefficient_correct: 0.8560\n",
      "Epoch 589/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7247 - dice_coefficient_correct: 0.8757\n",
      "Epoch 590/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7434 - dice_coefficient_correct: 0.8534\n",
      "Epoch 591/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7652 - dice_coefficient_correct: 0.8478\n",
      "Epoch 592/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7614 - dice_coefficient_correct: 0.8475\n",
      "Epoch 593/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7434 - dice_coefficient_correct: 0.8731\n",
      "Epoch 594/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7983 - dice_coefficient_correct: 0.8193\n",
      "Epoch 595/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7624 - dice_coefficient_correct: 0.8482\n",
      "Epoch 596/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7532 - dice_coefficient_correct: 0.8490\n",
      "Epoch 597/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7545 - dice_coefficient_correct: 0.8491\n",
      "Epoch 598/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7401 - dice_coefficient_correct: 0.8728\n",
      "Epoch 599/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7224 - dice_coefficient_correct: 0.8706\n",
      "Epoch 600/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7381 - dice_coefficient_correct: 0.8738\n",
      "\n",
      "Epoch 00600: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-600-0.7367-0.8711.hdf5\n",
      "Epoch 601/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7516 - dice_coefficient_correct: 0.8588\n",
      "Epoch 602/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7611 - dice_coefficient_correct: 0.8384\n",
      "Epoch 603/1000\n",
      "3/3 [==============================] - 13s 5s/step - loss: 0.7330 - dice_coefficient_correct: 0.8771\n",
      "Epoch 604/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7272 - dice_coefficient_correct: 0.8798\n",
      "Epoch 605/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7418 - dice_coefficient_correct: 0.8556\n",
      "Epoch 606/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7824 - dice_coefficient_correct: 0.8312\n",
      "Epoch 607/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7431 - dice_coefficient_correct: 0.8588\n",
      "Epoch 608/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7473 - dice_coefficient_correct: 0.8643\n",
      "Epoch 609/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7575 - dice_coefficient_correct: 0.8417\n",
      "Epoch 610/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7545 - dice_coefficient_correct: 0.8508\n",
      "Epoch 611/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7268 - dice_coefficient_correct: 0.8747\n",
      "Epoch 612/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7232 - dice_coefficient_correct: 0.8798\n",
      "Epoch 613/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7325 - dice_coefficient_correct: 0.8710\n",
      "Epoch 614/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7607 - dice_coefficient_correct: 0.8394\n",
      "Epoch 615/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7411 - dice_coefficient_correct: 0.8659\n",
      "Epoch 616/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7755 - dice_coefficient_correct: 0.8225\n",
      "Epoch 617/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7213 - dice_coefficient_correct: 0.8871\n",
      "Epoch 618/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7839 - dice_coefficient_correct: 0.8192\n",
      "Epoch 619/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7329 - dice_coefficient_correct: 0.8681\n",
      "Epoch 620/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7468 - dice_coefficient_correct: 0.8576\n",
      "Epoch 621/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7372 - dice_coefficient_correct: 0.8651\n",
      "Epoch 622/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7638 - dice_coefficient_correct: 0.8407\n",
      "Epoch 623/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7423 - dice_coefficient_correct: 0.8610\n",
      "Epoch 624/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7710 - dice_coefficient_correct: 0.8335\n",
      "Epoch 625/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7555 - dice_coefficient_correct: 0.8559\n",
      "\n",
      "Epoch 00625: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-625-0.7480-0.8633.hdf5\n",
      "Epoch 626/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7312 - dice_coefficient_correct: 0.8681\n",
      "Epoch 627/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7614 - dice_coefficient_correct: 0.8437\n",
      "Epoch 628/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7502 - dice_coefficient_correct: 0.8503\n",
      "Epoch 629/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7404 - dice_coefficient_correct: 0.8704\n",
      "Epoch 630/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7563 - dice_coefficient_correct: 0.8459\n",
      "Epoch 631/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7874 - dice_coefficient_correct: 0.8252\n",
      "Epoch 632/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7581 - dice_coefficient_correct: 0.8470\n",
      "Epoch 633/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7714 - dice_coefficient_correct: 0.8302\n",
      "Epoch 634/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7476 - dice_coefficient_correct: 0.8534\n",
      "Epoch 635/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7580 - dice_coefficient_correct: 0.8491\n",
      "Epoch 636/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7766 - dice_coefficient_correct: 0.8256\n",
      "Epoch 637/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7336 - dice_coefficient_correct: 0.8713\n",
      "Epoch 638/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7408 - dice_coefficient_correct: 0.8519\n",
      "Epoch 639/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7675 - dice_coefficient_correct: 0.8297\n",
      "Epoch 640/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7313 - dice_coefficient_correct: 0.8631\n",
      "Epoch 641/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7457 - dice_coefficient_correct: 0.8530\n",
      "Epoch 642/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7617 - dice_coefficient_correct: 0.8535\n",
      "Epoch 643/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7236 - dice_coefficient_correct: 0.8763\n",
      "Epoch 644/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7987 - dice_coefficient_correct: 0.7920\n",
      "Epoch 645/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7490 - dice_coefficient_correct: 0.8650\n",
      "Epoch 646/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7431 - dice_coefficient_correct: 0.8620\n",
      "Epoch 647/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7415 - dice_coefficient_correct: 0.8579\n",
      "Epoch 648/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7521 - dice_coefficient_correct: 0.8521\n",
      "Epoch 649/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7275 - dice_coefficient_correct: 0.8703\n",
      "Epoch 650/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7200 - dice_coefficient_correct: 0.8682\n",
      "\n",
      "Epoch 00650: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-650-0.7229-0.8689.hdf5\n",
      "Epoch 651/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7422 - dice_coefficient_correct: 0.8652\n",
      "Epoch 652/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7325 - dice_coefficient_correct: 0.8653\n",
      "Epoch 653/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7347 - dice_coefficient_correct: 0.8709\n",
      "Epoch 654/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7471 - dice_coefficient_correct: 0.8621\n",
      "Epoch 655/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7633 - dice_coefficient_correct: 0.8366\n",
      "Epoch 656/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7440 - dice_coefficient_correct: 0.8567\n",
      "Epoch 657/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7662 - dice_coefficient_correct: 0.8453\n",
      "Epoch 658/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7278 - dice_coefficient_correct: 0.8619\n",
      "Epoch 659/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7471 - dice_coefficient_correct: 0.8421\n",
      "Epoch 660/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7493 - dice_coefficient_correct: 0.8631\n",
      "Epoch 661/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7230 - dice_coefficient_correct: 0.8791\n",
      "Epoch 662/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7435 - dice_coefficient_correct: 0.8490\n",
      "Epoch 663/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7367 - dice_coefficient_correct: 0.8568\n",
      "Epoch 664/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7195 - dice_coefficient_correct: 0.8790\n",
      "Epoch 665/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7359 - dice_coefficient_correct: 0.8623\n",
      "Epoch 666/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7274 - dice_coefficient_correct: 0.8717\n",
      "Epoch 667/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7662 - dice_coefficient_correct: 0.8419\n",
      "Epoch 668/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7426 - dice_coefficient_correct: 0.8509\n",
      "Epoch 669/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7234 - dice_coefficient_correct: 0.8722\n",
      "Epoch 670/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7374 - dice_coefficient_correct: 0.8592\n",
      "Epoch 671/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7285 - dice_coefficient_correct: 0.8638\n",
      "Epoch 672/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7655 - dice_coefficient_correct: 0.8516\n",
      "Epoch 673/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7249 - dice_coefficient_correct: 0.8669\n",
      "Epoch 674/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7592 - dice_coefficient_correct: 0.8451\n",
      "Epoch 675/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7430 - dice_coefficient_correct: 0.8569\n",
      "\n",
      "Epoch 00675: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-675-0.7307-0.8652.hdf5\n",
      "Epoch 676/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7303 - dice_coefficient_correct: 0.8687\n",
      "Epoch 677/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7398 - dice_coefficient_correct: 0.8650\n",
      "Epoch 678/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7414 - dice_coefficient_correct: 0.8610\n",
      "Epoch 679/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7480 - dice_coefficient_correct: 0.8483\n",
      "Epoch 680/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7414 - dice_coefficient_correct: 0.8718\n",
      "Epoch 681/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7707 - dice_coefficient_correct: 0.8339\n",
      "Epoch 682/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7110 - dice_coefficient_correct: 0.8876\n",
      "Epoch 683/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7437 - dice_coefficient_correct: 0.8525\n",
      "Epoch 684/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7495 - dice_coefficient_correct: 0.8504\n",
      "Epoch 685/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7182 - dice_coefficient_correct: 0.8793\n",
      "Epoch 686/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7104 - dice_coefficient_correct: 0.8853\n",
      "Epoch 687/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7260 - dice_coefficient_correct: 0.8642\n",
      "Epoch 688/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7479 - dice_coefficient_correct: 0.8561\n",
      "Epoch 689/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7069 - dice_coefficient_correct: 0.8847\n",
      "Epoch 690/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7314 - dice_coefficient_correct: 0.8616\n",
      "Epoch 691/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7910 - dice_coefficient_correct: 0.8274\n",
      "Epoch 692/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7207 - dice_coefficient_correct: 0.8710\n",
      "Epoch 693/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7809 - dice_coefficient_correct: 0.8264\n",
      "Epoch 694/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7638 - dice_coefficient_correct: 0.8293\n",
      "Epoch 695/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7556 - dice_coefficient_correct: 0.8444\n",
      "Epoch 696/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7287 - dice_coefficient_correct: 0.8724\n",
      "Epoch 697/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7342 - dice_coefficient_correct: 0.8575\n",
      "Epoch 698/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7222 - dice_coefficient_correct: 0.8810\n",
      "Epoch 699/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7434 - dice_coefficient_correct: 0.8581\n",
      "Epoch 700/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7302 - dice_coefficient_correct: 0.8579\n",
      "\n",
      "Epoch 00700: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-700-0.7302-0.8537.hdf5\n",
      "Epoch 701/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7590 - dice_coefficient_correct: 0.8355\n",
      "Epoch 702/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7319 - dice_coefficient_correct: 0.8663\n",
      "Epoch 703/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7586 - dice_coefficient_correct: 0.8396\n",
      "Epoch 704/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7261 - dice_coefficient_correct: 0.8772\n",
      "Epoch 705/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7111 - dice_coefficient_correct: 0.8751\n",
      "Epoch 706/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7041 - dice_coefficient_correct: 0.8850\n",
      "Epoch 707/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7099 - dice_coefficient_correct: 0.8857\n",
      "Epoch 708/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7333 - dice_coefficient_correct: 0.8683\n",
      "Epoch 709/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7454 - dice_coefficient_correct: 0.8494\n",
      "Epoch 710/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7460 - dice_coefficient_correct: 0.8584\n",
      "Epoch 711/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7318 - dice_coefficient_correct: 0.8624\n",
      "Epoch 712/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7229 - dice_coefficient_correct: 0.8776\n",
      "Epoch 713/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7194 - dice_coefficient_correct: 0.8783\n",
      "Epoch 714/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7568 - dice_coefficient_correct: 0.8390\n",
      "Epoch 715/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7392 - dice_coefficient_correct: 0.8571\n",
      "Epoch 716/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7572 - dice_coefficient_correct: 0.8446\n",
      "Epoch 717/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7167 - dice_coefficient_correct: 0.8791\n",
      "Epoch 718/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7177 - dice_coefficient_correct: 0.8822\n",
      "Epoch 719/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7836 - dice_coefficient_correct: 0.8161\n",
      "Epoch 720/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7294 - dice_coefficient_correct: 0.8625\n",
      "Epoch 721/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7742 - dice_coefficient_correct: 0.8311\n",
      "Epoch 722/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7290 - dice_coefficient_correct: 0.8679\n",
      "Epoch 723/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7289 - dice_coefficient_correct: 0.8648\n",
      "Epoch 724/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7242 - dice_coefficient_correct: 0.8737\n",
      "Epoch 725/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7140 - dice_coefficient_correct: 0.8774\n",
      "\n",
      "Epoch 00725: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-725-0.7181-0.8753.hdf5\n",
      "Epoch 726/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7819 - dice_coefficient_correct: 0.8071\n",
      "Epoch 727/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7965 - dice_coefficient_correct: 0.8034\n",
      "Epoch 728/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7083 - dice_coefficient_correct: 0.8787\n",
      "Epoch 729/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7278 - dice_coefficient_correct: 0.8591\n",
      "Epoch 730/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7479 - dice_coefficient_correct: 0.8664\n",
      "Epoch 731/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7127 - dice_coefficient_correct: 0.8724\n",
      "Epoch 732/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7159 - dice_coefficient_correct: 0.8804\n",
      "Epoch 733/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7365 - dice_coefficient_correct: 0.8619\n",
      "Epoch 734/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7184 - dice_coefficient_correct: 0.8710\n",
      "Epoch 735/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7285 - dice_coefficient_correct: 0.8644\n",
      "Epoch 736/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7120 - dice_coefficient_correct: 0.8833\n",
      "Epoch 737/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7261 - dice_coefficient_correct: 0.8745\n",
      "Epoch 738/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7231 - dice_coefficient_correct: 0.8680\n",
      "Epoch 739/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7475 - dice_coefficient_correct: 0.8567\n",
      "Epoch 740/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8073 - dice_coefficient_correct: 0.7986\n",
      "Epoch 741/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7381 - dice_coefficient_correct: 0.8575\n",
      "Epoch 742/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7460 - dice_coefficient_correct: 0.8516\n",
      "Epoch 743/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7349 - dice_coefficient_correct: 0.8634\n",
      "Epoch 744/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7330 - dice_coefficient_correct: 0.8667\n",
      "Epoch 745/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7483 - dice_coefficient_correct: 0.8449\n",
      "Epoch 746/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7192 - dice_coefficient_correct: 0.8698\n",
      "Epoch 747/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7193 - dice_coefficient_correct: 0.8752\n",
      "Epoch 748/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7675 - dice_coefficient_correct: 0.8266\n",
      "Epoch 749/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7332 - dice_coefficient_correct: 0.8679\n",
      "Epoch 750/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7024 - dice_coefficient_correct: 0.8881\n",
      "\n",
      "Epoch 00750: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-750-0.7080-0.8843.hdf5\n",
      "Epoch 751/1000\n",
      "3/3 [==============================] - 12s 4s/step - loss: 0.7265 - dice_coefficient_correct: 0.8558\n",
      "Epoch 752/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7366 - dice_coefficient_correct: 0.8586\n",
      "Epoch 753/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7540 - dice_coefficient_correct: 0.8322\n",
      "Epoch 754/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7248 - dice_coefficient_correct: 0.8653\n",
      "Epoch 755/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7163 - dice_coefficient_correct: 0.8735\n",
      "Epoch 756/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7140 - dice_coefficient_correct: 0.8771\n",
      "Epoch 757/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7617 - dice_coefficient_correct: 0.8361\n",
      "Epoch 758/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7096 - dice_coefficient_correct: 0.8842\n",
      "Epoch 759/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7315 - dice_coefficient_correct: 0.8598\n",
      "Epoch 760/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7325 - dice_coefficient_correct: 0.8647\n",
      "Epoch 761/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.8062 - dice_coefficient_correct: 0.8161\n",
      "Epoch 762/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7679 - dice_coefficient_correct: 0.8262\n",
      "Epoch 763/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7075 - dice_coefficient_correct: 0.8813\n",
      "Epoch 764/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7205 - dice_coefficient_correct: 0.8646\n",
      "Epoch 765/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7266 - dice_coefficient_correct: 0.8742\n",
      "Epoch 766/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7163 - dice_coefficient_correct: 0.8775\n",
      "Epoch 767/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7199 - dice_coefficient_correct: 0.8671\n",
      "Epoch 768/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7324 - dice_coefficient_correct: 0.8619\n",
      "Epoch 769/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7248 - dice_coefficient_correct: 0.8708\n",
      "Epoch 770/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7365 - dice_coefficient_correct: 0.8602\n",
      "Epoch 771/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7036 - dice_coefficient_correct: 0.8912\n",
      "Epoch 772/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7085 - dice_coefficient_correct: 0.8783\n",
      "Epoch 773/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7172 - dice_coefficient_correct: 0.8715\n",
      "Epoch 774/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7442 - dice_coefficient_correct: 0.8596\n",
      "Epoch 775/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7285 - dice_coefficient_correct: 0.8600\n",
      "\n",
      "Epoch 00775: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-775-0.7213-0.8708.hdf5\n",
      "Epoch 776/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7130 - dice_coefficient_correct: 0.8746\n",
      "Epoch 777/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7109 - dice_coefficient_correct: 0.8842\n",
      "Epoch 778/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7573 - dice_coefficient_correct: 0.8410\n",
      "Epoch 779/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7386 - dice_coefficient_correct: 0.8545\n",
      "Epoch 780/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7135 - dice_coefficient_correct: 0.8695\n",
      "Epoch 781/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7279 - dice_coefficient_correct: 0.8645\n",
      "Epoch 782/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7077 - dice_coefficient_correct: 0.8798\n",
      "Epoch 783/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7279 - dice_coefficient_correct: 0.8679\n",
      "Epoch 784/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7706 - dice_coefficient_correct: 0.8327\n",
      "Epoch 785/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7486 - dice_coefficient_correct: 0.8549\n",
      "Epoch 786/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7260 - dice_coefficient_correct: 0.8582\n",
      "Epoch 787/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7224 - dice_coefficient_correct: 0.8777\n",
      "Epoch 788/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7317 - dice_coefficient_correct: 0.8619\n",
      "Epoch 789/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7216 - dice_coefficient_correct: 0.8672\n",
      "Epoch 790/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7152 - dice_coefficient_correct: 0.8750\n",
      "Epoch 791/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7247 - dice_coefficient_correct: 0.8687\n",
      "Epoch 792/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7077 - dice_coefficient_correct: 0.8819\n",
      "Epoch 793/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7101 - dice_coefficient_correct: 0.8797\n",
      "Epoch 794/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7046 - dice_coefficient_correct: 0.8906\n",
      "Epoch 795/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6950 - dice_coefficient_correct: 0.8844\n",
      "Epoch 796/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7181 - dice_coefficient_correct: 0.8740\n",
      "Epoch 797/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7364 - dice_coefficient_correct: 0.8560\n",
      "Epoch 798/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7034 - dice_coefficient_correct: 0.8868\n",
      "Epoch 799/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7475 - dice_coefficient_correct: 0.8501\n",
      "Epoch 800/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7159 - dice_coefficient_correct: 0.8728\n",
      "\n",
      "Epoch 00800: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-800-0.7186-0.8708.hdf5\n",
      "Epoch 801/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7059 - dice_coefficient_correct: 0.8879\n",
      "Epoch 802/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7252 - dice_coefficient_correct: 0.8571\n",
      "Epoch 803/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7212 - dice_coefficient_correct: 0.8754\n",
      "Epoch 804/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7159 - dice_coefficient_correct: 0.8764\n",
      "Epoch 805/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7231 - dice_coefficient_correct: 0.8715\n",
      "Epoch 806/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7413 - dice_coefficient_correct: 0.8514\n",
      "Epoch 807/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7136 - dice_coefficient_correct: 0.8796\n",
      "Epoch 808/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7070 - dice_coefficient_correct: 0.8850\n",
      "Epoch 809/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7076 - dice_coefficient_correct: 0.8734\n",
      "Epoch 810/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6928 - dice_coefficient_correct: 0.8961\n",
      "Epoch 811/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7203 - dice_coefficient_correct: 0.8637\n",
      "Epoch 812/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7671 - dice_coefficient_correct: 0.8176\n",
      "Epoch 813/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7308 - dice_coefficient_correct: 0.8659\n",
      "Epoch 814/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7345 - dice_coefficient_correct: 0.8518\n",
      "Epoch 815/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7241 - dice_coefficient_correct: 0.8659\n",
      "Epoch 816/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7352 - dice_coefficient_correct: 0.8597\n",
      "Epoch 817/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7151 - dice_coefficient_correct: 0.8839\n",
      "Epoch 818/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7135 - dice_coefficient_correct: 0.8696\n",
      "Epoch 819/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7089 - dice_coefficient_correct: 0.8834\n",
      "Epoch 820/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7206 - dice_coefficient_correct: 0.8729\n",
      "Epoch 821/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6926 - dice_coefficient_correct: 0.8850\n",
      "Epoch 822/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7320 - dice_coefficient_correct: 0.8658\n",
      "Epoch 823/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7549 - dice_coefficient_correct: 0.8303\n",
      "Epoch 824/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7143 - dice_coefficient_correct: 0.8761\n",
      "Epoch 825/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7342 - dice_coefficient_correct: 0.8524\n",
      "\n",
      "Epoch 00825: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-825-0.7222-0.8646.hdf5\n",
      "Epoch 826/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7382 - dice_coefficient_correct: 0.8498\n",
      "Epoch 827/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7109 - dice_coefficient_correct: 0.8776\n",
      "Epoch 828/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7229 - dice_coefficient_correct: 0.8608\n",
      "Epoch 829/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7058 - dice_coefficient_correct: 0.8862\n",
      "Epoch 830/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7076 - dice_coefficient_correct: 0.8786\n",
      "Epoch 831/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7222 - dice_coefficient_correct: 0.8641\n",
      "Epoch 832/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7162 - dice_coefficient_correct: 0.8708\n",
      "Epoch 833/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7226 - dice_coefficient_correct: 0.8625\n",
      "Epoch 834/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7008 - dice_coefficient_correct: 0.8909\n",
      "Epoch 835/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7181 - dice_coefficient_correct: 0.8670\n",
      "Epoch 836/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7215 - dice_coefficient_correct: 0.8658\n",
      "Epoch 837/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7045 - dice_coefficient_correct: 0.8817\n",
      "Epoch 838/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7005 - dice_coefficient_correct: 0.8870\n",
      "Epoch 839/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7141 - dice_coefficient_correct: 0.8737\n",
      "Epoch 840/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7129 - dice_coefficient_correct: 0.8753\n",
      "Epoch 841/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7072 - dice_coefficient_correct: 0.8707\n",
      "Epoch 842/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7057 - dice_coefficient_correct: 0.8869\n",
      "Epoch 843/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7276 - dice_coefficient_correct: 0.8676\n",
      "Epoch 844/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7155 - dice_coefficient_correct: 0.8798\n",
      "Epoch 845/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7011 - dice_coefficient_correct: 0.8826\n",
      "Epoch 846/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7131 - dice_coefficient_correct: 0.8765\n",
      "Epoch 847/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7121 - dice_coefficient_correct: 0.8668\n",
      "Epoch 848/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7443 - dice_coefficient_correct: 0.8500\n",
      "Epoch 849/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7015 - dice_coefficient_correct: 0.8831\n",
      "Epoch 850/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7212 - dice_coefficient_correct: 0.8684\n",
      "\n",
      "Epoch 00850: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-850-0.7187-0.8697.hdf5\n",
      "Epoch 851/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7364 - dice_coefficient_correct: 0.8476\n",
      "Epoch 852/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7147 - dice_coefficient_correct: 0.8645\n",
      "Epoch 853/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7210 - dice_coefficient_correct: 0.8638\n",
      "Epoch 854/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7122 - dice_coefficient_correct: 0.8703\n",
      "Epoch 855/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7174 - dice_coefficient_correct: 0.8696\n",
      "Epoch 856/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7428 - dice_coefficient_correct: 0.8549\n",
      "Epoch 857/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7110 - dice_coefficient_correct: 0.8686\n",
      "Epoch 858/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7417 - dice_coefficient_correct: 0.8519\n",
      "Epoch 859/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7261 - dice_coefficient_correct: 0.8574\n",
      "Epoch 860/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7342 - dice_coefficient_correct: 0.8502\n",
      "Epoch 861/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7104 - dice_coefficient_correct: 0.8796\n",
      "Epoch 862/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7377 - dice_coefficient_correct: 0.8504\n",
      "Epoch 863/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7313 - dice_coefficient_correct: 0.8490\n",
      "Epoch 864/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7159 - dice_coefficient_correct: 0.8805\n",
      "Epoch 865/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7072 - dice_coefficient_correct: 0.8698\n",
      "Epoch 866/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7228 - dice_coefficient_correct: 0.8657\n",
      "Epoch 867/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7407 - dice_coefficient_correct: 0.8529\n",
      "Epoch 868/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7132 - dice_coefficient_correct: 0.8715\n",
      "Epoch 869/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6957 - dice_coefficient_correct: 0.8888\n",
      "Epoch 870/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7257 - dice_coefficient_correct: 0.8610\n",
      "Epoch 871/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7069 - dice_coefficient_correct: 0.8775\n",
      "Epoch 872/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7330 - dice_coefficient_correct: 0.8628\n",
      "Epoch 873/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7019 - dice_coefficient_correct: 0.8784\n",
      "Epoch 874/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7109 - dice_coefficient_correct: 0.8751\n",
      "Epoch 875/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7110 - dice_coefficient_correct: 0.8747\n",
      "\n",
      "Epoch 00875: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-875-0.7113-0.8761.hdf5\n",
      "Epoch 876/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7243 - dice_coefficient_correct: 0.8624\n",
      "Epoch 877/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7208 - dice_coefficient_correct: 0.8597\n",
      "Epoch 878/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7540 - dice_coefficient_correct: 0.8472\n",
      "Epoch 879/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7254 - dice_coefficient_correct: 0.8542\n",
      "Epoch 880/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7159 - dice_coefficient_correct: 0.8687\n",
      "Epoch 881/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7387 - dice_coefficient_correct: 0.8449\n",
      "Epoch 882/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7629 - dice_coefficient_correct: 0.8355\n",
      "Epoch 883/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7108 - dice_coefficient_correct: 0.8707\n",
      "Epoch 884/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7127 - dice_coefficient_correct: 0.8747\n",
      "Epoch 885/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7214 - dice_coefficient_correct: 0.8666\n",
      "Epoch 886/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7432 - dice_coefficient_correct: 0.8386\n",
      "Epoch 887/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7103 - dice_coefficient_correct: 0.8688\n",
      "Epoch 888/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7215 - dice_coefficient_correct: 0.8644\n",
      "Epoch 889/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7265 - dice_coefficient_correct: 0.8578\n",
      "Epoch 890/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7020 - dice_coefficient_correct: 0.8774\n",
      "Epoch 891/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7138 - dice_coefficient_correct: 0.8741\n",
      "Epoch 892/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7008 - dice_coefficient_correct: 0.8817\n",
      "Epoch 893/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6949 - dice_coefficient_correct: 0.8863\n",
      "Epoch 894/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7294 - dice_coefficient_correct: 0.8521\n",
      "Epoch 895/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6947 - dice_coefficient_correct: 0.8876\n",
      "Epoch 896/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7265 - dice_coefficient_correct: 0.8524\n",
      "Epoch 897/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7383 - dice_coefficient_correct: 0.8517\n",
      "Epoch 898/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6929 - dice_coefficient_correct: 0.8913\n",
      "Epoch 899/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7267 - dice_coefficient_correct: 0.8519\n",
      "Epoch 900/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7485 - dice_coefficient_correct: 0.8503\n",
      "\n",
      "Epoch 00900: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-900-0.7339-0.8572.hdf5\n",
      "Epoch 901/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6962 - dice_coefficient_correct: 0.8836\n",
      "Epoch 902/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7005 - dice_coefficient_correct: 0.8781\n",
      "Epoch 903/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7514 - dice_coefficient_correct: 0.8245\n",
      "Epoch 904/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7079 - dice_coefficient_correct: 0.8791\n",
      "Epoch 905/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7079 - dice_coefficient_correct: 0.8712\n",
      "Epoch 906/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7165 - dice_coefficient_correct: 0.8656\n",
      "Epoch 907/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6913 - dice_coefficient_correct: 0.8893\n",
      "Epoch 908/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6996 - dice_coefficient_correct: 0.8819\n",
      "Epoch 909/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6986 - dice_coefficient_correct: 0.8894\n",
      "Epoch 910/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7023 - dice_coefficient_correct: 0.8801\n",
      "Epoch 911/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7258 - dice_coefficient_correct: 0.8563\n",
      "Epoch 912/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7092 - dice_coefficient_correct: 0.8785\n",
      "Epoch 913/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7001 - dice_coefficient_correct: 0.8740\n",
      "Epoch 914/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7433 - dice_coefficient_correct: 0.8538\n",
      "Epoch 915/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6970 - dice_coefficient_correct: 0.8879\n",
      "Epoch 916/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7484 - dice_coefficient_correct: 0.8357\n",
      "Epoch 917/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7005 - dice_coefficient_correct: 0.8873\n",
      "Epoch 918/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7125 - dice_coefficient_correct: 0.8572\n",
      "Epoch 919/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6990 - dice_coefficient_correct: 0.8789\n",
      "Epoch 920/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7125 - dice_coefficient_correct: 0.8691\n",
      "Epoch 921/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7084 - dice_coefficient_correct: 0.8755\n",
      "Epoch 922/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7457 - dice_coefficient_correct: 0.8472\n",
      "Epoch 923/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7124 - dice_coefficient_correct: 0.8651\n",
      "Epoch 924/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7057 - dice_coefficient_correct: 0.8819\n",
      "Epoch 925/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7092 - dice_coefficient_correct: 0.8707\n",
      "\n",
      "Epoch 00925: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-925-0.7068-0.8701.hdf5\n",
      "Epoch 926/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7220 - dice_coefficient_correct: 0.8639\n",
      "Epoch 927/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7430 - dice_coefficient_correct: 0.8477\n",
      "Epoch 928/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6896 - dice_coefficient_correct: 0.8831\n",
      "Epoch 929/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7159 - dice_coefficient_correct: 0.8644\n",
      "Epoch 930/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7428 - dice_coefficient_correct: 0.8450\n",
      "Epoch 931/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7082 - dice_coefficient_correct: 0.8700\n",
      "Epoch 932/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7125 - dice_coefficient_correct: 0.8680\n",
      "Epoch 933/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7058 - dice_coefficient_correct: 0.8748\n",
      "Epoch 934/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7291 - dice_coefficient_correct: 0.8566\n",
      "Epoch 935/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7077 - dice_coefficient_correct: 0.8768\n",
      "Epoch 936/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7118 - dice_coefficient_correct: 0.8689\n",
      "Epoch 937/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7199 - dice_coefficient_correct: 0.8712\n",
      "Epoch 938/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6913 - dice_coefficient_correct: 0.8819\n",
      "Epoch 939/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7209 - dice_coefficient_correct: 0.8594\n",
      "Epoch 940/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7005 - dice_coefficient_correct: 0.8849\n",
      "Epoch 941/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6815 - dice_coefficient_correct: 0.8908\n",
      "Epoch 942/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6994 - dice_coefficient_correct: 0.8823\n",
      "Epoch 943/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6921 - dice_coefficient_correct: 0.8836\n",
      "Epoch 944/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7279 - dice_coefficient_correct: 0.8595\n",
      "Epoch 945/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7023 - dice_coefficient_correct: 0.8706\n",
      "Epoch 946/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7109 - dice_coefficient_correct: 0.8689\n",
      "Epoch 947/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7095 - dice_coefficient_correct: 0.8667\n",
      "Epoch 948/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7157 - dice_coefficient_correct: 0.8636\n",
      "Epoch 949/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7067 - dice_coefficient_correct: 0.8703\n",
      "Epoch 950/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6766 - dice_coefficient_correct: 0.9038\n",
      "\n",
      "Epoch 00950: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-950-0.6773-0.9012.hdf5\n",
      "Epoch 951/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6872 - dice_coefficient_correct: 0.8877\n",
      "Epoch 952/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6892 - dice_coefficient_correct: 0.8840\n",
      "Epoch 953/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7087 - dice_coefficient_correct: 0.8684\n",
      "Epoch 954/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7067 - dice_coefficient_correct: 0.8770\n",
      "Epoch 955/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7299 - dice_coefficient_correct: 0.8622\n",
      "Epoch 956/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7013 - dice_coefficient_correct: 0.8651\n",
      "Epoch 957/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7193 - dice_coefficient_correct: 0.8634\n",
      "Epoch 958/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7093 - dice_coefficient_correct: 0.8778\n",
      "Epoch 959/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7039 - dice_coefficient_correct: 0.8710\n",
      "Epoch 960/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7312 - dice_coefficient_correct: 0.8470\n",
      "Epoch 961/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6958 - dice_coefficient_correct: 0.8781\n",
      "Epoch 962/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7128 - dice_coefficient_correct: 0.8713\n",
      "Epoch 963/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6821 - dice_coefficient_correct: 0.8982\n",
      "Epoch 964/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7051 - dice_coefficient_correct: 0.8647\n",
      "Epoch 965/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7433 - dice_coefficient_correct: 0.8447\n",
      "Epoch 966/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7006 - dice_coefficient_correct: 0.8751\n",
      "Epoch 967/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7103 - dice_coefficient_correct: 0.8687\n",
      "Epoch 968/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7060 - dice_coefficient_correct: 0.8704\n",
      "Epoch 969/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6998 - dice_coefficient_correct: 0.8762\n",
      "Epoch 970/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7080 - dice_coefficient_correct: 0.8695\n",
      "Epoch 971/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7117 - dice_coefficient_correct: 0.8692\n",
      "Epoch 972/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7214 - dice_coefficient_correct: 0.8530\n",
      "Epoch 973/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7058 - dice_coefficient_correct: 0.8767\n",
      "Epoch 974/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6960 - dice_coefficient_correct: 0.8854\n",
      "Epoch 975/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7112 - dice_coefficient_correct: 0.8592\n",
      "\n",
      "Epoch 00975: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-975-0.7219-0.8519.hdf5\n",
      "Epoch 976/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6941 - dice_coefficient_correct: 0.8926\n",
      "Epoch 977/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7144 - dice_coefficient_correct: 0.8656\n",
      "Epoch 978/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7068 - dice_coefficient_correct: 0.8659\n",
      "Epoch 979/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7141 - dice_coefficient_correct: 0.8712\n",
      "Epoch 980/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6974 - dice_coefficient_correct: 0.8884\n",
      "Epoch 981/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7152 - dice_coefficient_correct: 0.8578\n",
      "Epoch 982/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6850 - dice_coefficient_correct: 0.8924\n",
      "Epoch 983/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6923 - dice_coefficient_correct: 0.8840\n",
      "Epoch 984/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6887 - dice_coefficient_correct: 0.8858\n",
      "Epoch 985/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6759 - dice_coefficient_correct: 0.8939\n",
      "Epoch 986/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6861 - dice_coefficient_correct: 0.8904\n",
      "Epoch 987/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7058 - dice_coefficient_correct: 0.8739\n",
      "Epoch 988/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6813 - dice_coefficient_correct: 0.8931\n",
      "Epoch 989/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7000 - dice_coefficient_correct: 0.8762\n",
      "Epoch 990/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6826 - dice_coefficient_correct: 0.8885\n",
      "Epoch 991/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6982 - dice_coefficient_correct: 0.8785\n",
      "Epoch 992/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6874 - dice_coefficient_correct: 0.8850\n",
      "Epoch 993/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7271 - dice_coefficient_correct: 0.8600\n",
      "Epoch 994/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7486 - dice_coefficient_correct: 0.8370\n",
      "Epoch 995/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7292 - dice_coefficient_correct: 0.8549\n",
      "Epoch 996/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7072 - dice_coefficient_correct: 0.8618\n",
      "Epoch 997/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7290 - dice_coefficient_correct: 0.8562\n",
      "Epoch 998/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7368 - dice_coefficient_correct: 0.8411\n",
      "Epoch 999/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.6964 - dice_coefficient_correct: 0.8789\n",
      "Epoch 1000/1000\n",
      "3/3 [==============================] - 13s 4s/step - loss: 0.7381 - dice_coefficient_correct: 0.8282\n",
      "\n",
      "Epoch 01000: saving model to /gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-1000-0.7227-0.8533.hdf5\n"
     ]
    }
   ],
   "source": [
    "my_model= model.fit(trainset_generator_3D_ISBI_aug_random_patch_equal_prob(7),steps_per_epoch=3,epochs=1000,shuffle=False,verbose=1,initial_epoch=0,callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hBiXbybmPhjK"
   },
   "outputs": [],
   "source": [
    "model.save(\"/gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/fullmodel.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "rrNx9Kh1TVqO",
    "outputId": "3f60ecdb-df7c-4129-f210-f74b89a21450"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2f32076c50>]"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(my_model.history['loss'])\n",
    "plt.plot(my_model.history['dice_coefficient_correct'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JdXlhtZaSgl0"
   },
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LrtJzK1RSkkW"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model_1.load_weights('/gdrive/MyDrive/nima_log/new_log_ISBI_data_3D/mozila_mohammadreza acount/Attention_Unet_3D/7/saved-model-3D-MS-250-0.8007-0.8398.hdf5')\n",
    "model_2.load_weights('/gdrive/My Drive/nima_log/new_log_ISBI_data_3D/chrome_mahmoodian acount/Attention_Unet_3D/7/saved-model-3D-MS-250-0.8054-0.8175.hdf5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b-hlMTWCQr3D",
    "outputId": "6947871f-1151-4f34-d135-390812a93356"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "###for testing on ISBI test dataset in 3D models part 1\n",
    "hf_x = h5py.File('/gdrive/My Drive/data_test_3D_ISBI_61sample.hdf5', 'r')\n",
    "data_len = hf_x['data_test_3D_ISBI_61sample.hdf5'].shape[0]\n",
    "print(data_len)\n",
    "num_test=61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Kwx7av2UUUAh"
   },
   "outputs": [],
   "source": [
    "###for testing on ISBI test dataset in 3D models part 2(AttUnet version 192)\n",
    "##################\n",
    "Test_FLAIR= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*flair_pp.nii')\n",
    "Test_FLAIR.sort()\n",
    "list_name=[]\n",
    "for i,name in enumerate(Test_FLAIR):\n",
    "    _,x=name.rsplit('preprocessed/',1)\n",
    "    y,_,_=x.rsplit('_',2)\n",
    "    z=y+'_JHU.nii'\n",
    "    list_name.append(z)\n",
    "##################\n",
    "def zero_padding_3d(volume,size=((29, 30),(11,12),(29,30))):\n",
    "    x= np.pad(volume,pad_width=size,mode='constant', constant_values=0)\n",
    "    return x\n",
    "#################\n",
    "for i in range(num_test):\n",
    "    norm_input=[]\n",
    "    for j in range(0,4):\n",
    "        padding=zero_padding_3d(hf_x['data_test_3D_ISBI_61sample.hdf5'][i,:,:,:,j],size=((5, 6),(0,0),(5,6)))\n",
    "        padding=padding[:,12:-13,:]\n",
    "        norm_test= itensity_normalize_one_volume(padding)\n",
    "        norm_input.append(norm_test)\n",
    "    norm_input= np.einsum('CDHW->HWDC',norm_input)\n",
    "    expanded= np.expand_dims(norm_input, axis=0)\n",
    "    pred_1= model_1.predict(expanded)\n",
    "    pred_2= model_2.predict(expanded)\n",
    "    pred_1[pred_1>=0.8]= 1\n",
    "    pred_1[pred_1<0.8]= 0\n",
    "    pred_2[pred_2>=0.8]= 1\n",
    "    pred_2[pred_2<0.8]= 0\n",
    "    pred_final =pred_1 + pred_2\n",
    "    pred_final[pred_final<=1.5]=0\n",
    "    pred_final[pred_final>0.5]= 1\n",
    "    pred_final= np.squeeze(pred_final)\n",
    "    final_mask= np.einsum('HWD->DHW',pred_final)\n",
    "    final_mask= final_mask[5:-6,:,5:-6]\n",
    "    final_mask= zero_padding_3d(final_mask, size=((0,0),(12,13),(0,0)))\n",
    "    final_mask= np.einsum('DHW->WHD',final_mask)\n",
    "    loaded_img= nib.load(Test_FLAIR[i])\n",
    "    img = nib.Nifti1Image(final_mask,affine=loaded_img.affine,header=loaded_img.header,extra=loaded_img.extra,file_map=loaded_img.file_map)\n",
    "    img.to_filename(os.path.join('/gdrive/My Drive/test_Attunet_for_ISBI_site',list_name[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Zc4mYrCnQs8f"
   },
   "outputs": [],
   "source": [
    "###for testing on ISBI test dataset in 3D models part 2\n",
    "##################\n",
    "Test_FLAIR= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*flair_pp.nii')\n",
    "Test_FLAIR.sort()\n",
    "list_name=[]\n",
    "for i,name in enumerate(Test_FLAIR):\n",
    "    _,x=name.rsplit('preprocessed/',1)\n",
    "    y,_,_=x.rsplit('_',2)\n",
    "    z=y+'_JHU.nii'\n",
    "    list_name.append(z)\n",
    "##################\n",
    "def zero_padding_3d(volume,size=((29, 30),(11,12),(29,30))):\n",
    "    x= np.pad(volume,pad_width=size,mode='constant', constant_values=0)\n",
    "    return x\n",
    "#################\n",
    "for i in range(num_test):\n",
    "    norm_input=[]\n",
    "    for j in range(0,4):\n",
    "        padding=zero_padding_3d(hf_x['data_test_3D_ISBI_61sample.hdf5'][i,:,:,:,j],size=((37, 38),(19,20),(37,38)))\n",
    "        norm_test= itensity_normalize_one_volume(padding)\n",
    "        norm_input.append(norm_test)\n",
    "    norm_input= np.einsum('CDHW->HWDC',norm_input)\n",
    "    expanded= np.expand_dims(norm_input, axis=0)\n",
    "    pred_1= model_1.predict(expanded)\n",
    "    pred_2= model_2.predict(expanded)\n",
    "    pred_1[pred_1>0.5]= 1\n",
    "    pred_1[pred_1<=0.5]= 0\n",
    "    pred_2[pred_2>0.5]= 1\n",
    "    pred_2[pred_2<=0.5]= 0\n",
    "    pred_final =pred_1 + pred_2\n",
    "    pred_final[pred_final<=1]=0\n",
    "    pred_final[pred_final>1]= 1\n",
    "    final_mask= np.squeeze(pred_final)\n",
    "    final_mask= np.einsum('HWD->DHW',final_mask)\n",
    "    final_mask= final_mask[37:-38,19:-20,37:-38]\n",
    "    final_mask= np.einsum('DHW->WHD',final_mask)\n",
    "    loaded_img= nib.load(Test_FLAIR[i])\n",
    "    img = nib.Nifti1Image(final_mask,affine=loaded_img.affine,header=loaded_img.header,extra=loaded_img.extra,file_map=loaded_img.file_map)\n",
    "    img.to_filename(os.path.join('/gdrive/My Drive/test_unet_for_ISBI_site',list_name[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EuxdPXqYMDfS"
   },
   "outputs": [],
   "source": [
    "###for testing on ISBI test dataset in 3D models part 2(Unet 256 version)\n",
    "##################\n",
    "Test_FLAIR= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*flair_pp.nii')\n",
    "Test_FLAIR.sort()\n",
    "list_name=[]\n",
    "for i,name in enumerate(Test_FLAIR):\n",
    "    _,x=name.rsplit('preprocessed/',1)\n",
    "    y,_,_=x.rsplit('_',2)\n",
    "    z=y+'_JHU.nii'\n",
    "    list_name.append(z)\n",
    "##################\n",
    "def zero_padding_3d(volume,size=((29, 30),(11,12),(29,30))):\n",
    "    x= np.pad(volume,pad_width=size,mode='constant', constant_values=0)\n",
    "    return x\n",
    "#################\n",
    "for i in range(num_test):\n",
    "    norm_input=[]\n",
    "    for j in range(0,4):\n",
    "        padding=zero_padding_3d(hf_x['data_test_3D_ISBI_61sample.hdf5'][i,:,:,:,j],size=((37, 38),(19,20),(37,38)))\n",
    "        norm_test= itensity_normalize_one_volume(padding)\n",
    "        norm_input.append(norm_test)\n",
    "    norm_input= np.einsum('CDHW->HWDC',norm_input)\n",
    "    expanded= np.expand_dims(norm_input, axis=0)\n",
    "    pred_1= model_1.predict(expanded)\n",
    "    pred_2= model_2.predict(expanded)\n",
    "    pred_1[pred_1>=0.8]= 1\n",
    "    pred_1[pred_1<0.8]= 0\n",
    "    pred_2[pred_2>=0.8]= 1\n",
    "    pred_2[pred_2<0.8]= 0\n",
    "    pred_final =pred_1 + pred_2\n",
    "    norm_input=[]\n",
    "    expanded=[]\n",
    "    pred_1=[]\n",
    "    pred_2=[]\n",
    "    pred_final[pred_final<=1.5]=0\n",
    "    pred_final[pred_final>0.5]= 1\n",
    "    final_mask= np.squeeze(pred_final)\n",
    "    pred_final=[]\n",
    "    final_mask= np.einsum('HWD->DHW',final_mask)\n",
    "    final_mask= final_mask[37:-38,19:-20,37:-38]\n",
    "    final_mask= np.einsum('DHW->WHD',final_mask)\n",
    "    loaded_img= nib.load(Test_FLAIR[i])\n",
    "    img = nib.Nifti1Image(final_mask,affine=loaded_img.affine,header=loaded_img.header,extra=loaded_img.extra,file_map=loaded_img.file_map)\n",
    "    img.to_filename(os.path.join('/gdrive/My Drive/test_unet_for_ISBI_site',list_name[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ppXhmFIxYwMs"
   },
   "outputs": [],
   "source": [
    "###for testing on ISBI test dataset in 3D models part 2(nestedUnet version 192)\n",
    "##################\n",
    "Test_FLAIR= glob.glob('/gdrive/My Drive/main dataset ISBI 2015/testdata_website/testdata_website/*/preprocessed/*flair_pp.nii')\n",
    "Test_FLAIR.sort()\n",
    "list_name=[]\n",
    "for i,name in enumerate(Test_FLAIR):\n",
    "    _,x=name.rsplit('preprocessed/',1)\n",
    "    y,_,_=x.rsplit('_',2)\n",
    "    z=y+'_JHU.nii'\n",
    "    list_name.append(z)\n",
    "##################\n",
    "def zero_padding_3d(volume,size=((29, 30),(11,12),(29,30))):\n",
    "    x= np.pad(volume,pad_width=size,mode='constant', constant_values=0)\n",
    "    return x\n",
    "#################\n",
    "for i in range(num_test):\n",
    "    norm_input=[]\n",
    "    for j in range(0,4):\n",
    "        padding=zero_padding_3d(hf_x['data_test_3D_ISBI_61sample.hdf5'][i,:,:,:,j],size=((5, 6),(0,0),(5,6)))\n",
    "        padding=padding[:,12:-13,:]\n",
    "        norm_test= itensity_normalize_one_volume(padding)\n",
    "        norm_input.append(norm_test)\n",
    "    norm_input= np.einsum('CDHW->HWDC',norm_input)\n",
    "    expanded= np.expand_dims(norm_input, axis=0)\n",
    "    pred_1= model_1.predict(expanded)\n",
    "    pred_2= model_2.predict(expanded)\n",
    "    pred_1[pred_1>=0.8]= 1\n",
    "    pred_1[pred_1<0.8]= 0\n",
    "    pred_2[pred_2>=0.8]= 1\n",
    "    pred_2[pred_2<0.8]= 0\n",
    "    pred_final =pred_1 + pred_2\n",
    "    norm_input=[]\n",
    "    expanded=[]\n",
    "    pred_1=[]\n",
    "    pred_2=[]\n",
    "    pred_final[pred_final<=1.5]=0\n",
    "    pred_final[pred_final>0.5]= 1\n",
    "    final_mask= np.squeeze(pred_final)\n",
    "    pred_final=[]\n",
    "    final_mask= np.einsum('HWD->DHW',final_mask)\n",
    "    final_mask= final_mask[5:-6,:,5:-6]\n",
    "    final_mask= zero_padding_3d(final_mask, size=((0,0),(12,13),(0,0)))\n",
    "    final_mask= np.einsum('DHW->WHD',final_mask)\n",
    "    loaded_img= nib.load(Test_FLAIR[i])\n",
    "    img = nib.Nifti1Image(final_mask,affine=loaded_img.affine,header=loaded_img.header,extra=loaded_img.extra,file_map=loaded_img.file_map)\n",
    "    img.to_filename(os.path.join('/gdrive/My Drive/test_Nestunet_for_ISBI_site',list_name[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BQRRpphwSoWC"
   },
   "outputs": [],
   "source": [
    "# K.set_learning_phase(0)\n",
    "model.evaluate_generator(valset_generator(1),steps=279,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "obI0M-Z1SrAA"
   },
   "outputs": [],
   "source": [
    "# K.set_learning_phase(0)\n",
    "model.evaluate_generator(valset_generator(9),steps=31,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qp29muYqSt2r"
   },
   "outputs": [],
   "source": [
    "def check_preds(ypred, ytrue):\n",
    "    smooth = np.finfo(float).eps\n",
    "    pred = np.ndarray.flatten(np.clip(ypred,0,1))\n",
    "    gt = np.ndarray.flatten(np.clip(ytrue,0,1))\n",
    "    intersection = np.sum(pred * gt) \n",
    "    union = np.sum(pred) + np.sum(gt)   \n",
    "    return np.round((2 * intersection + smooth)/(union + smooth),decimals=5)\n",
    "def auc(y_true, y_pred):\n",
    "    smooth = 0.0000001\n",
    "    y_pred_pos = np.round(np.clip(y_pred, 0, 1))\n",
    "    y_pred_neg = 1 - y_pred_pos\n",
    "    y_pos = np.round(np.clip(y_true, 0, 1))\n",
    "    y_neg = 1 - y_pos\n",
    "    tp = np.sum(y_pos * y_pred_pos)\n",
    "    tn = np.sum(y_neg * y_pred_neg)\n",
    "    fp = np.sum(y_neg * y_pred_pos)\n",
    "    fn = np.sum(y_pos * y_pred_neg)\n",
    "    tpr = (tp + smooth) / (tp + fn + smooth)  #recall(sensitivity)\n",
    "    tnr = (tn + smooth) / (tn + fp + smooth)  #specificity\n",
    "    prec = (tp + smooth) / (tp + fp + smooth) #precision\n",
    "    fpr = (fp + smooth) / (fp + tn + smooth)\n",
    "    return [tpr, tnr, prec, fpr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YSGMYx1ASweD"
   },
   "outputs": [],
   "source": [
    "################################################################################\n",
    "###for validation\n",
    "hf_x_val = h5py.File('/gdrive/My Drive/data_val_ISBI_just_ms_slice.hdf5', 'r')\n",
    "hf_y_val = h5py.File('/gdrive/My Drive/label_val_ISBI_just_ms_slice.hdf5', 'r')\n",
    "hf_x_train = h5py.File('/gdrive/My Drive/data_train_ISBI_just_ms_slice.hdf5', 'r')\n",
    "hf_y_train = h5py.File('/gdrive/My Drive/label_train_ISBI_just_ms_slice.hdf5', 'r')\n",
    "print(np.shape(hf_x_val['data_val_ISBI_just_ms_slice'][0,:,:,:]))\n",
    "print(np.shape(hf_y_val['label_val_ISBI_just_ms_slice'][0,:,:,:]))\n",
    "print(np.shape(hf_x_train['data_train_ISBI_just_ms_slice'][0,:,:,:]))\n",
    "print(np.shape(hf_y_train['label_train_ISBI_just_ms_slice'][0,:,:,:]))\n",
    "num_val=279\n",
    "dsc = np.zeros((num_val,1))\n",
    "preds = np.zeros((num_val,240,240,1))\n",
    "gt = np.zeros((num_val,240,240,1))\n",
    "tpr = np.zeros_like(dsc)\n",
    "tn = np.zeros_like(dsc)\n",
    "prec = np.zeros_like(dsc)\n",
    "tnr = np.zeros_like(dsc)\n",
    "fpr = np.zeros_like(dsc)"
   ]
  }
 ],
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================================================
FILE: README.md
================================================
# Segmentation of Multiple Sclerosis Disease Plaques from MRI Images Using 3D Attention U-Net

## Overview
This repository contains the implementation of a 3D Attention U-Net model for segmenting Multiple Sclerosis (MS) disease plaques from MRI images. The model is designed to accurately segment lesions and track their evolution over time, addressing the unique challenges presented by MS lesion segmentation in longitudinal MRI datasets.

## Dataset
The model was trained and tested on the dataset from the *Longitudinal MS Lesion Segmentation Challenge*, conducted at the 2015 International Symposium on Biomedical Imaging in New York, NY. The challenge involved applying automatic lesion segmentation algorithms to MR neuroimaging data acquired at multiple time points from MS patients.

### Reference
- Carass A, et al. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. Neuroimage. 2017;148:77-102. [Paper Link](https://doi.org/10.1016/j.neuroimage.2016.12.064)

### Challenge Details
- The challenge focused on evaluating the segmentation accuracy of algorithms against manual segmentations and their ability to track lesion evolution.
- Data included MR neuroimaging from multiple time points, providing a dynamic view of lesion development in MS patients.

## Model Performance
The model achieved a performance metric of 92.871 on the test set, demonstrating its effectiveness in segmenting MS lesions with high accuracy.

## Model Overview
The `attn_unet_3D` function defines an Attention U-Net model for 3D image data, particularly suited for tasks like volumetric image segmentation. This architecture is a variant of the standard U-Net model, augmented with attention gates to focus on salient features, enhancing the model's performance in applications like medical image analysis.

## Architecture Details

### Input Layer
- **Input**: The model starts with an input layer that accepts 3D data.

### Downsampling Path (Encoder)
- **Convolutional Blocks**: Each block in this path consists of two 3D convolutional layers with ReLU activation, followed by batch normalization.
- **Number of Filters**: The number of filters doubles at each level (32, 64, 128, 256), allowing the network to capture more complex features.
- **Pooling**: Each convolutional block is followed by a 3D Max Pooling layer to reduce the spatial dimensions.

### Center Block
- **Convolution**: A central convolutional block with 512 filters processes the data at the lowest resolution.

### Attention Gates
- **Function**: Attention gates are applied before each upsampling step. They aim to enhance relevant features and suppress irrelevant ones for better segmentation accuracy.
- **Components**: These gates involve operations like convolutions, activations, and upsampling to generate a gating signal.

### Upsampling Path (Decoder)
- **Transposed Convolutions**: The model uses 3D transposed convolutions to increase the spatial resolution of feature maps.
- **Concatenation**: The transposed feature maps are concatenated with corresponding cropped feature maps from the downsampling path.
- **Filters**: The number of filter halves with each upsampling step (256, 128, 64, 32).

### Output Layer
- **Final Convolution**: A 3D convolutional layer with a single filter and a sigmoid activation function generates the final output, typically a segmented 3D image.
![image](https://github.com/user-attachments/assets/81436efc-f122-4f89-887d-52712f982802)

## Compilation Details

### Loss Function
- **Focal Tversky Loss**: A custom loss function that is a variant of the Tversky loss, adapted to focus more on learning from difficult examples. It's particularly useful for dealing with class imbalance, which is common in medical image segmentation.

### Metric
- **Dice Coefficient Correct**: This metric is likely a variant of the Dice coefficient, a common metric in image segmentation tasks, especially for evaluating performance in medical imaging.
- 
## Comparative Visualization of MRI Slice and Corresponding Segmentation Outputs
![image](https://github.com/AliAmini93/MRI-MS-Plaques-Segmentation/assets/96921261/a9607f7a-4ee6-4173-90d7-9bfefb0ae7c7)