Multi_pre_processing_data_generator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob, os, random\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from datetime import datetime\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive\n",
    "\n",
    "from scipy import ndimage, misc\n",
    "\n",
    "# directory: ./data/*/*.nii.gz\n",
    "# there are different modalities that should be taken care of\n",
    "\n",
    "\n",
    "class Data:\n",
    "    def __init__(self, moda):\n",
    "        self.moda = moda\n",
    "        # [\"FLAIR_preprocessed\", \"T1_preprocessed\", \"T2_preprocessed\"]\n",
    "        self.input = {m : defaultdict(list) for m in self.moda}\n",
    "        self.target = defaultdict(list)\n",
    "        self.kfold = None\n",
    "        self.batch_size = None\n",
    "        self.patch_size = None\n",
    "        self.patch_gap = None\n",
    "        self.patch_index = defaultdict(list)\n",
    "        self.valid_index = {}\n",
    "        # pre-set seed so that reconstruct can retrieve valid_index for final result\n",
    "        random.seed(3000)\n",
    "        \n",
    "    def fetch_raw_data_from_file(self, modality, file_name):\n",
    "        def fetch_file(modality):\n",
    "            # could be \"Consensus\", \"FLAIR_preprocessed\"\n",
    "            data = []\n",
    "            root, sub_dir, _ = next(os.walk(os.getcwd() + '/data/'))\n",
    "            for sub in sub_dir:\n",
    "                data.append(os.path.join(root, sub + '/' + modality + '.nii.gz'))\n",
    "            return data\n",
    "        file = fetch_file(modality)\n",
    "        raw_data = defaultdict(list)\n",
    "        for i in range(len(file)):\n",
    "            image = nib.load(file[i])\n",
    "            raw_data[image.shape].append(image.get_fdata())\n",
    "        with h5py.File(file_name, 'w') as f:\n",
    "            for i in raw_data:\n",
    "                f.create_dataset(str(i), data=raw_data[i])\n",
    "        return raw_data\n",
    "    \n",
    "    def zero_pad(self, image, div=(32, 32, 32)):\n",
    "        pad_size = [0, 0, 0]\n",
    "        pad = False\n",
    "        for i in range(len(image.shape)):\n",
    "            remain = image.shape[i] % div[i]\n",
    "            if remain != 0:\n",
    "                pad = True\n",
    "                pad_size[i] = (image.shape[i] // div[i] + 1) * div[i] - image.shape[i]\n",
    "        if pad:\n",
    "            # deal with odd number of padding\n",
    "            pad0 = (pad_size[0]//2, pad_size[0] - pad_size[0]//2)\n",
    "            pad1 = (pad_size[1]//2, pad_size[1] - pad_size[1]//2)\n",
    "            pad2 = (pad_size[2]//2, pad_size[2] - pad_size[2]//2)\n",
    "            # https://stackoverflow.com/questions/50008587/zero-padding-a-3d-numpy-array\n",
    "            return np.pad(image, (pad0, pad1, pad2), 'constant')\n",
    "        else:\n",
    "            return image\n",
    "    \n",
    "    def check_file_exist(self, path, pattern):\n",
    "        for m in self.moda:\n",
    "            if not os.path.isfile(path + pattern + m + '.h5'):\n",
    "                return False\n",
    "        return True\n",
    "    \n",
    "    \n",
    "    def fetch_target_data(self, target_path):\n",
    "        self.fetch_raw_data_from_file('Consensus', target_path + 'target_data.h5')\n",
    "        return self.load_target_data(target_path)\n",
    "    \n",
    "    def fetch_raw_data(self, raw_path):\n",
    "        for m in self.moda:\n",
    "            # different modality: same order of images\n",
    "            self.fetch_raw_data_from_file(m, raw_path + 'raw_data_' + m + '.h5')\n",
    "        return self.load_raw_data(raw_path)\n",
    "    \n",
    "    def load_target_data(self, target_path):\n",
    "        target_file = h5py.File(target_path + 'target_data.h5', 'r')\n",
    "        target_data = defaultdict(list)\n",
    "        for i in target_file.keys():\n",
    "            target_data[i] = target_file[i]\n",
    "        return target_data, target_file\n",
    "    \n",
    "    def load_raw_data(self, raw_path):\n",
    "        raw_file = []\n",
    "        raw_data = {m : defaultdict(list) for m in self.moda}\n",
    "        for m in self.moda:\n",
    "            data = defaultdict(list)\n",
    "            file = h5py.File(raw_path + 'raw_data_' + m + '.h5', 'r') \n",
    "            raw_file.append(file)\n",
    "            for i in file.keys():\n",
    "                # to get the matrix: self.data[i][:]\n",
    "                # d.data[i][j][0], d.data[i][j][1]\n",
    "                data[i] = file[i]\n",
    "            raw_data[m] = data\n",
    "        return raw_data, raw_file\n",
    "    \n",
    "        # raw_data:\n",
    "        # {'FLAIR_preprocessed': defaultdict(list,\n",
    "        #               {'(128, 224, 256)': <HDF5 dataset \"(128, 224, 256)\": shape (5, 128, 224, 256), type \"<f8\">,\n",
    "        #                '(144, 512, 512)': <HDF5 dataset \"(144, 512, 512)\": shape (5, 144, 512, 512), type \"<f8\">,\n",
    "        #                '(261, 336, 336)': <HDF5 dataset \"(261, 336, 336)\": shape (5, 261, 336, 336), type \"<f8\">}),\n",
    "        #   'T1_preprocessed': defaultdict(list,\n",
    "        #               {'(128, 224, 256)': <HDF5 dataset \"(128, 224, 256)\": shape (5, 128, 224, 256), type \"<f8\">,\n",
    "        #                '(144, 512, 512)': <HDF5 dataset \"(144, 512, 512)\": shape (5, 144, 512, 512), type \"<f8\">,\n",
    "        #                '(261, 336, 336)': <HDF5 dataset \"(261, 336, 336)\": shape (5, 261, 336, 336), type \"<f8\">}),\n",
    "        #   'T2_preprocessed': defaultdict(list,\n",
    "        #               {'(128, 224, 256)': <HDF5 dataset \"(128, 224, 256)\": shape (5, 128, 224, 256), type \"<f8\">,\n",
    "        #                '(144, 512, 512)': <HDF5 dataset \"(144, 512, 512)\": shape (5, 144, 512, 512), type \"<f8\">,\n",
    "        #                '(261, 336, 336)': <HDF5 dataset \"(261, 336, 336)\": shape (5, 261, 336, 336), type \"<f8\">})}\n",
    "        # raw_file:\n",
    "        #  [<HDF5 file \"raw_data_FLAIR_preprocessed.h5\" (mode r)>,\n",
    "        #   <HDF5 file \"raw_data_T1_preprocessed.h5\" (mode r)>,\n",
    "        #   <HDF5 file \"raw_data_T2_preprocessed.h5\" (mode r)>]\n",
    "\n",
    "    def pad_target_data(self, patch_size, pad_path, target_path):\n",
    "        # input\n",
    "        target_data = None\n",
    "        target_file = None\n",
    "        if os.path.isfile(target_path + \"target_data.h5\"):\n",
    "            target_data, target_file = self.load_target_data(target_path)\n",
    "        else:\n",
    "            target_data, target_file = self.fetch_target_data(target_path)\n",
    "        \n",
    "        pad_target_data = defaultdict(list)\n",
    "        for i in target_data.keys():\n",
    "            for j in range(target_data[i].shape[0]):\n",
    "                img = self.zero_pad(target_data[i][j], patch_size)\n",
    "                pad_target_data[img.shape].append(img)\n",
    "\n",
    "        target_file.close()\n",
    "\n",
    "        with h5py.File(pad_path + \"pad_target_data.h5\" , 'w') as f:\n",
    "            f.create_dataset(\"patch_size\", data=patch_size)\n",
    "            for i in pad_target_data:\n",
    "                f.create_dataset(str(i), data=pad_target_data[i])\n",
    "        \n",
    "        pad_file = h5py.File(pad_path + \"pad_target_data.h5\" , 'r')\n",
    "        for i in pad_file.keys():\n",
    "            if i == \"patch_size\":\n",
    "                continue\n",
    "            self.target[i] = pad_file[i][:]\n",
    "        \n",
    "    def pad_raw_data(self, patch_size, pad_path, raw_path):\n",
    "        # input\n",
    "        raw_data = None\n",
    "        raw_file = None\n",
    "        if self.check_file_exist(raw_path, \"raw_data_\"):\n",
    "            raw_data, raw_file = self.load_raw_data(raw_path)\n",
    "        else:\n",
    "            raw_data, raw_file = self.fetch_raw_data(raw_path)\n",
    "        \n",
    "        pad_data = {m : defaultdict(list) for m in self.moda}\n",
    "        for m in raw_data.keys():\n",
    "            data = defaultdict(list)\n",
    "            for i in raw_data[m].keys():\n",
    "                for j in range(raw_data[m][i].shape[0]):\n",
    "                    img = self.zero_pad(raw_data[m][i][j], patch_size)\n",
    "                    data[img.shape].append(img)\n",
    "            pad_data[m] = data\n",
    "            \n",
    "        for f in raw_file:\n",
    "            f.close()\n",
    "\n",
    "        for m in pad_data.keys():\n",
    "            with h5py.File(pad_path + \"pad_data_\" + m + \".h5\" , 'w') as f:\n",
    "                f.create_dataset(\"patch_size\", data=patch_size)\n",
    "                for i in pad_data[m]:\n",
    "                    f.create_dataset(str(i), data=pad_data[m][i])\n",
    "        \n",
    "        for m in pad_data.keys():\n",
    "            pad_file = h5py.File(pad_path + \"pad_data_\" + m + \".h5\" , 'r')\n",
    "            for i in pad_file.keys():\n",
    "                if i == \"patch_size\":\n",
    "                    continue\n",
    "                self.input[m][i] = pad_file[i][:]\n",
    "        \n",
    "    \n",
    "    def load_data(self, patch_size=(32, 32, 32), \n",
    "                  pad_path=\"./model/h5df_data/\", raw_path=\"./model/h5df_data/\", target_path=\"./model/h5df_data/\"):\n",
    "                  # pad_path=\"./model/h5df_data/pad_data.h5\", raw_path=\"./model/h5df_data/raw_data.h5\"):\n",
    "        padded = True\n",
    "        if self.check_file_exist(raw_path, \"pad_data_\"):\n",
    "            for m in self.moda:\n",
    "                pad_file = h5py.File(pad_path + \"pad_data_\" + m + \".h5\", 'r')\n",
    "                if np.all(pad_file[\"patch_size\"][:] == list(patch_size)):\n",
    "                    # self.data = pad_file[\"pad_data\"]\n",
    "                    for i in pad_file.keys():\n",
    "                        self.input[m][i] = pad_file[i][:]\n",
    "                else:\n",
    "                    padded = False\n",
    "                    break\n",
    "        else:\n",
    "            padded = False\n",
    "        if padded == False:\n",
    "            self.pad_raw_data(patch_size, pad_path, raw_path)\n",
    "        \n",
    "        if os.path.isfile(target_path + \"pad_target_data.h5\"):\n",
    "            pad_file = h5py.File(target_path + \"pad_target_data.h5\", 'r')\n",
    "            if np.all(pad_file[\"patch_size\"][:] == list(patch_size)):\n",
    "                # self.data = pad_file[\"pad_data\"]\n",
    "                for i in pad_file.keys():\n",
    "                    self.target[i] = pad_file[i][:]\n",
    "            else:\n",
    "                self.pad_target_data(patch_size, pad_path, target_path)\n",
    "        else:\n",
    "            self.pad_target_data(patch_size, pad_path, target_path)\n",
    "    \n",
    "    ################################################################################\n",
    "    \n",
    "    def gen_patch_index(self, patch_size, patch_gap, patch_path):\n",
    "        count = 0\n",
    "        patch_index = defaultdict(list)\n",
    "        # https://arxiv.org/pdf/1710.02316.pdf  at least 0.01% voxels contain lesions\n",
    "        voxel = int(patch_size[0]*patch_size[1]*patch_size[2]*0.0001)\n",
    "        \n",
    "        # patches of segmentation\n",
    "        for i in self.target:\n",
    "            if i == \"patch_size\":\n",
    "                continue\n",
    "            shape = self.target[i][0].shape\n",
    "            patch_num = [int((shape[i]-patch_size[i]) / patch_gap) for i in range(len(shape))]\n",
    "            for j in range(self.target[i].shape[0]):\n",
    "                patch_ind = []\n",
    "                # assume this is a 3d image\n",
    "                for a in range(patch_num[0]):\n",
    "                    for b in range(patch_num[1]):\n",
    "                        for c in range(patch_num[2]):\n",
    "                            patch_iter = [a * patch_gap, b * patch_gap, c * patch_gap, 1]\n",
    "                            if (np.sum(self.target[i][j][\n",
    "                                patch_iter[0]:patch_iter[0] + patch_size[0],\n",
    "                                patch_iter[1]:patch_iter[1] + patch_size[1],\n",
    "                                patch_iter[2]:patch_iter[2] + patch_size[2]]) <= voxel):\n",
    "                                # 0: does not satisfy, need to skip when generating\n",
    "                                patch_iter[3] = 0\n",
    "                            patch_ind.append(patch_iter)\n",
    "                count += len(patch_ind)\n",
    "                patch_index[i].append(patch_ind)\n",
    "            for c in range(len(patch_index[i])):\n",
    "                np.random.shuffle(patch_index[i][c]) # in-place shuffule\n",
    "        \n",
    "        with h5py.File(patch_path + \"pad_patch_index.h5\", 'w') as f:\n",
    "            f.create_dataset(\"count\", data=count)\n",
    "            f.create_dataset(\"patch_size\", data=patch_size)\n",
    "            f.create_dataset(\"patch_gap\", data=patch_gap)\n",
    "            for i in patch_index:\n",
    "                f.create_dataset(str(i), data=patch_index[i])\n",
    "        \n",
    "        index_file = h5py.File(patch_path + \"pad_patch_index.h5\", 'r')\n",
    "        for i in index_file.keys():\n",
    "            if i == \"count\" or i == \"patch_size\" or i == \"patch_gap\":\n",
    "                continue\n",
    "            self.patch_index[i] = index_file[i][:]\n",
    "        # return the total number of patches\n",
    "        return index_file[\"count\"][()]\n",
    "\n",
    "    \n",
    "    def load_patch_index(self, patch_size, patch_gap, patch_path):\n",
    "        if os.path.isfile(patch_path + \"pad_patch_index.h5\"):\n",
    "            index_file = h5py.File(patch_path + \"pad_patch_index.h5\", 'r')\n",
    "            # print(list(pat_ind.keys()))\n",
    "            if (np.all(index_file[\"patch_size\"][:] == list(patch_size))) and (index_file[\"patch_gap\"][()] == patch_gap):\n",
    "                for i in index_file.keys():\n",
    "                    if i == \"count\" or i == \"patch_size\" or i == \"patch_gap\":\n",
    "                        continue\n",
    "                    self.patch_index[i] = index_file[i][:]\n",
    "                return index_file[\"count\"][()]\n",
    "            else:\n",
    "                index_file.close()\n",
    "                return self.gen_patch_index(patch_size, patch_gap, patch_path)\n",
    "        else:\n",
    "            return self.gen_patch_index(patch_size, patch_gap, patch_path)\n",
    "            \n",
    "    def prekfold(self, patch_size, patch_gap, batch_size, kfold=5, patch_path='./model/h5df_data/'):\n",
    "        self.kfold = kfold\n",
    "        self.patch_size = patch_size\n",
    "        self.patch_gap = patch_gap\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        # initialize validation index for training\n",
    "        # K-fold LOOCV: leave one out cross validation\n",
    "        for i in self.target:\n",
    "            if i == \"patch_size\":\n",
    "                continue\n",
    "            self.valid_index[i] = random.sample(range(self.kfold), self.kfold)\n",
    "\n",
    "        num = self.load_patch_index(patch_size, patch_gap, patch_path)\n",
    "        train_num = num // self.kfold * (self.kfold - 1)\n",
    "        valid_num = num - train_num\n",
    "        \n",
    "        return train_num // batch_size, valid_num // batch_size\n",
    "                    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.multi_generator import *\n",
    "\n",
    "config = {}\n",
    "config[\"patch_size\"] = (64, 64, 64)  # switch to None to train on the whole image\n",
    "config[\"patch_gap\"] = 16\n",
    "config[\"batch_size\"] = 2\n",
    "config[\"kfold\"] = 5\n",
    "\n",
    "# d = Data([\"FLAIR_preprocessed\", \"T1_preprocessed\", \"T2_preprocessed\"])\n",
    "d = Data([\"FLAIR_preprocessed\"])\n",
    "d.load_data(config[\"patch_size\"])\n",
    "train_num, valid_num = d.prekfold(config[\"patch_size\"], config[\"patch_gap\"], config[\"batch_size\"], config[\"kfold\"])\n",
    "\n",
    "train_generator = DataGenerator(d.moda, d.input, d.target, d.patch_index, d.kfold, d.batch_size,\n",
    "                                d.patch_size, d.patch_gap, d.valid_index, True)\n",
    "valid_generator = DataGenerator(d.moda, d.input, d.target, d.patch_index, d.kfold, d.batch_size,\n",
    "                                d.patch_size, d.patch_gap, d.valid_index, False)\n",
    "train_generator.set_index(0)\n",
    "valid_generator.set_index(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26496 6624\n",
      "defaultdict(<class 'list'>, {'(128, 256, 256)': array([[[ 48, 144,  80,   1],\n",
      "        [ 32,   0,  64,   0],\n",
      "        [ 16, 160,  64,   1],\n",
      "        ...,\n",
      "        [  0,   0,   0,   0],\n",
      "        [  0,  80,  96,   1],\n",
      "        [ 16, 160, 176,   1]],\n",
      "\n",
      "       [[ 32, 112, 128,   1],\n",
      "        [ 48,  48, 144,   0],\n",
      "        [  0, 144,   0,   0],\n",
      "        ...,\n",
      "        [ 32,  80, 176,   1],\n",
      "        [ 16,  48, 128,   0],\n",
      "        [ 48, 160,  48,   1]],\n",
      "\n",
      "       [[ 16,  16, 160,   0],\n",
      "        [ 16,  32, 160,   1],\n",
      "        [ 48,  96,  80,   1],\n",
      "        ...,\n",
      "        [ 16,  64,  80,   0],\n",
      "        [ 16,  80,   0,   0],\n",
      "        [  0, 144,  96,   1]],\n",
      "\n",
      "       [[ 32,  80,  96,   1],\n",
      "        [ 32, 160, 176,   1],\n",
      "        [ 16,  16, 128,   0],\n",
      "        ...,\n",
      "        [ 16, 128,  32,   0],\n",
      "        [  0,   0,  32,   0],\n",
      "        [ 32, 128, 128,   1]],\n",
      "\n",
      "       [[  0, 160,  80,   1],\n",
      "        [ 32,  48, 128,   1],\n",
      "        [ 16,   0,  48,   0],\n",
      "        ...,\n",
      "        [ 16, 160,   0,   0],\n",
      "        [ 16,  96, 128,   1],\n",
      "        [ 48,  32, 112,   1]]]), '(192, 512, 512)': array([[[ 80, 224,  80,   0],\n",
      "        [ 32, 416,  48,   0],\n",
      "        [ 96, 336, 320,   1],\n",
      "        ...,\n",
      "        [ 96, 400,  96,   0],\n",
      "        [ 16, 240, 128,   0],\n",
      "        [  0, 128, 336,   0]],\n",
      "\n",
      "       [[112, 432, 144,   0],\n",
      "        [ 80, 112, 400,   0],\n",
      "        [ 48, 208,  80,   0],\n",
      "        ...,\n",
      "        [ 64, 272,  32,   0],\n",
      "        [ 32, 112, 208,   0],\n",
      "        [ 48, 368,   0,   0]],\n",
      "\n",
      "       [[ 32, 384,  48,   0],\n",
      "        [ 48, 192, 224,   1],\n",
      "        [ 80,  64,   0,   0],\n",
      "        ...,\n",
      "        [ 64,  96, 432,   0],\n",
      "        [ 16, 320,  48,   0],\n",
      "        [ 64, 320,  64,   0]],\n",
      "\n",
      "       [[ 96, 384,  16,   0],\n",
      "        [ 64, 352, 400,   0],\n",
      "        [ 80, 176,  64,   0],\n",
      "        ...,\n",
      "        [ 80, 192, 320,   1],\n",
      "        [ 32, 352, 128,   0],\n",
      "        [112, 352, 160,   0]],\n",
      "\n",
      "       [[ 48, 304,  32,   0],\n",
      "        [ 80, 176, 112,   0],\n",
      "        [ 80, 352, 336,   1],\n",
      "        ...,\n",
      "        [ 80,  16,  80,   0],\n",
      "        [112,  64,  48,   0],\n",
      "        [112, 352, 144,   0]]]), '(320, 384, 384)': array([[[ 48, 272,  96,   0],\n",
      "        [208,  64, 112,   0],\n",
      "        [ 16,   0, 176,   0],\n",
      "        ...,\n",
      "        [ 80, 224, 144,   1],\n",
      "        [112, 304, 160,   0],\n",
      "        [ 96, 304, 128,   0]],\n",
      "\n",
      "       [[  0, 160, 240,   0],\n",
      "        [ 64,  80, 176,   1],\n",
      "        [192, 240,  16,   0],\n",
      "        ...,\n",
      "        [240, 224,  32,   0],\n",
      "        [208,  96,  96,   0],\n",
      "        [160,   0,  32,   0]],\n",
      "\n",
      "       [[224, 304, 128,   0],\n",
      "        [176, 128, 112,   0],\n",
      "        [  0, 176, 144,   0],\n",
      "        ...,\n",
      "        [ 80,  16, 240,   0],\n",
      "        [224, 272, 144,   0],\n",
      "        [ 48, 304, 304,   0]],\n",
      "\n",
      "       [[ 80, 160,  80,   0],\n",
      "        [ 80,  80, 160,   0],\n",
      "        [160, 128,  16,   0],\n",
      "        ...,\n",
      "        [224, 160, 128,   0],\n",
      "        [192, 256,  80,   0],\n",
      "        [128,  32,  96,   0]],\n",
      "\n",
      "       [[208, 160, 256,   0],\n",
      "        [176, 144,  48,   0],\n",
      "        [160,  80, 144,   0],\n",
      "        ...,\n",
      "        [144, 272, 272,   1],\n",
      "        [192, 192,  64,   0],\n",
      "        [240, 304,  48,   0]]])})\n"
     ]
    }
   ],
   "source": [
    "print(train_num, valid_num)\n",
    "print(d.patch_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(d.target[\"(128, 256, 256)\"][0][0:64, 0:64, 32:96]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43a352a38d8b4384a8973c1c5abbd7c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=32, description='id', max=63), Output()), _dom_classes=('widget-interact…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6216.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import h5py\n",
    "import nibabel as nib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive\n",
    "\n",
    "def fetch_file(pattern=\"\"):\n",
    "    path = os.getcwd() + '/model/h5df_data/'\n",
    "    _, _, files = next(os.walk(path))\n",
    "    result = []\n",
    "    for file in files:\n",
    "        if pattern in file:\n",
    "            result.append(h5py.File(path+file, 'r'))\n",
    "    return result\n",
    "\n",
    "\n",
    "def show_image(images):\n",
    "    # show image with [None, None, : ,: ,:] dimension\n",
    "    def show_frame(id):\n",
    "        length = len(images)\n",
    "        for i in range(length):\n",
    "            ax = plt.subplot(1, length, i+1)\n",
    "            plt.imshow(images[i][id, :, :], cmap='gray')\n",
    "    interact(show_frame, \n",
    "             id=widgets.IntSlider(min=0, max=images[0].shape[0]-1, step=1, value=images[0].shape[0]/2))\n",
    "\n",
    "\n",
    "\n",
    "i = 1999\n",
    "inputs = train_generator[i][0]\n",
    "\n",
    "target = train_generator[i][1]\n",
    "print(inputs.shape, target.shape)\n",
    "show_image(inputs[0])\n",
    "print(np.sum(target[0]))\n",
    "# break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n",
      "(2, 1, 64, 64, 64) (2, 1, 64, 64, 64)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-1f9428959bb3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/scratch/yl4217/MS-Lesion-Segmentation/model/multi_generator.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, batch_index)\u001b[0m\n\u001b[1;32m    144\u001b[0m                                         \u001b[0mnum\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m                                         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m                                             \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;31m#                                             return np.expand_dims(cur_image, axis=1), np.expand_dims(cur_target, axis=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m                             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(len(train_generator)):\n",
    "    inputs = train_generator[i][0]\n",
    "    target = train_generator[i][1]\n",
    "    print(inputs.shape, target.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'list'>, {'(128, 256, 256)': array([[[ 16, 128,  32,   0],\n",
      "        [ 32,  16,  32,   0],\n",
      "        [ 32,  96,  48,   0],\n",
      "        ...,\n",
      "        [ 16, 144,  48,   0],\n",
      "        [ 16, 176,  16,   0],\n",
      "        [  0, 160,  64,   0]],\n",
      "\n",
      "       [[ 48,  32,  80,   0],\n",
      "        [ 32, 176, 128,   1],\n",
      "        [  0,   0, 112,   0],\n",
      "        ...,\n",
      "        [ 48,  48, 176,   0],\n",
      "        [ 32,  80,  16,   0],\n",
      "        [ 32,  80,   0,   0]],\n",
      "\n",
      "       [[  0,  80, 176,   1],\n",
      "        [ 16,  32, 128,   1],\n",
      "        [ 32,  32, 160,   1],\n",
      "        ...,\n",
      "        [ 16,  16,  16,   0],\n",
      "        [ 16,  80,  96,   1],\n",
      "        [  0, 128,  80,   1]],\n",
      "\n",
      "       [[ 32, 112, 176,   1],\n",
      "        [ 32, 128, 160,   1],\n",
      "        [ 16, 112, 144,   1],\n",
      "        ...,\n",
      "        [ 16, 112, 128,   1],\n",
      "        [ 16,  80,  96,   1],\n",
      "        [ 48,  80, 128,   1]],\n",
      "\n",
      "       [[ 16,   0,  48,   0],\n",
      "        [ 16,  48,  64,   0],\n",
      "        [ 32, 144,  80,   1],\n",
      "        ...,\n",
      "        [  0, 128,  96,   1],\n",
      "        [ 16, 144,  48,   0],\n",
      "        [ 16,   0,  32,   0]]]), '(192, 512, 512)': array([[[  0, 384, 224,   0],\n",
      "        [ 96, 224, 192,   1],\n",
      "        [ 96, 288, 256,   1],\n",
      "        ...,\n",
      "        [ 80, 368, 384,   0],\n",
      "        [112, 144, 384,   0],\n",
      "        [ 32, 240, 224,   1]],\n",
      "\n",
      "       [[  0, 352, 432,   0],\n",
      "        [112, 112, 240,   0],\n",
      "        [ 32, 208,   0,   0],\n",
      "        ...,\n",
      "        [112,  64, 320,   0],\n",
      "        [ 32, 368, 272,   0],\n",
      "        [112, 368, 384,   0]],\n",
      "\n",
      "       [[ 48, 320,  96,   0],\n",
      "        [ 16, 256, 176,   0],\n",
      "        [ 96, 224, 160,   0],\n",
      "        ...,\n",
      "        [ 32,  48,  32,   0],\n",
      "        [  0, 416, 192,   0],\n",
      "        [ 64, 304,  96,   0]],\n",
      "\n",
      "       [[ 32, 272, 176,   0],\n",
      "        [  0,  16, 112,   0],\n",
      "        [ 16, 384, 304,   0],\n",
      "        ...,\n",
      "        [ 96, 432, 160,   0],\n",
      "        [  0, 272, 368,   0],\n",
      "        [ 32, 368, 272,   1]],\n",
      "\n",
      "       [[ 96, 160, 336,   1],\n",
      "        [ 16, 384,   0,   0],\n",
      "        [ 80, 224, 400,   1],\n",
      "        ...,\n",
      "        [ 48,  16, 320,   0],\n",
      "        [ 80, 368, 384,   1],\n",
      "        [ 64,  48,  80,   0]]]), '(320, 384, 384)': array([[[144, 176, 128,   1],\n",
      "        [ 80, 256,  96,   0],\n",
      "        [  0, 192, 224,   0],\n",
      "        ...,\n",
      "        [128,  64,   0,   0],\n",
      "        [192, 272, 176,   1],\n",
      "        [144,  16, 272,   0]],\n",
      "\n",
      "       [[ 48, 160,  64,   0],\n",
      "        [ 48, 304,  32,   0],\n",
      "        [ 64, 288, 176,   0],\n",
      "        ...,\n",
      "        [ 96, 176, 160,   1],\n",
      "        [208,   0, 224,   0],\n",
      "        [144,  16, 112,   0]],\n",
      "\n",
      "       [[ 16, 272, 112,   0],\n",
      "        [128, 288, 128,   0],\n",
      "        [ 96,  48, 176,   0],\n",
      "        ...,\n",
      "        [160, 192,  96,   0],\n",
      "        [112,  48, 304,   0],\n",
      "        [ 80,  80,  16,   0]],\n",
      "\n",
      "       [[ 96, 112,  48,   0],\n",
      "        [224, 144, 160,   0],\n",
      "        [160,  64, 240,   0],\n",
      "        ...,\n",
      "        [208, 240,   0,   0],\n",
      "        [192, 192,  48,   0],\n",
      "        [ 16,  32, 112,   0]],\n",
      "\n",
      "       [[ 32,  64,  16,   0],\n",
      "        [ 64, 240, 144,   0],\n",
      "        [176, 288, 272,   0],\n",
      "        ...,\n",
      "        [ 16, 240, 128,   0],\n",
      "        [176,  16,  16,   0],\n",
      "        [144,  48, 240,   0]]])})\n"
     ]
    }
   ],
   "source": [
    "print(d.patch_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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