test_calgary_campinas_cart_5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "E:\\anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import sys\n",
    "import random\n",
    "import time\n",
    "import scipy\n",
    "from keras.optimizers import Adam\n",
    "import sys\n",
    "MY_UTILS_PATH = \"../Modules/\"\n",
    "if not MY_UTILS_PATH in sys.path:\n",
    "    sys.path.append(MY_UTILS_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(256, 256)\n",
      "13056\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 255.5, 255.5, -0.5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#20%1d mask\n",
    "res=256\n",
    "K=[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    " 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    " 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
    " 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,0, 0, 0,\n",
    " 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
    " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
    " 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
    " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
    " 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
    " 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
    " 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n",
    "D=np.tile(K,[res,1])\n",
    "D=D.T\n",
    "imgplot=plt.imshow(D)\n",
    "print(D.shape)\n",
    "x_pdf2=D\n",
    "print((x_pdf2).sum())\n",
    "imgplot=plt.imshow(D,cmap = \"gray\")\n",
    "plt.axis(\"off\")\n",
    "#51slice，20%rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use no fftshift\n",
    "var_sampling_mask=1-x_pdf2\n",
    "var_sampling_mask=np.fft.fftshift(var_sampling_mask)\n",
    "var_sampling_mask=var_sampling_mask.astype(bool)\n",
    "plt.figure()\n",
    "plt.imshow(var_sampling_mask,cmap = \"gray\")\n",
    "under_rate = '20'\n",
    "imshape = (256,256)\n",
    "norm = np.sqrt(imshape[0]*imshape[1])\n",
    "nchannels = 2 #complex data real + imag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./Test\\e14498s5_P60928.7.npy\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.imshow(var_sampling_mask, cmap = \"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n",
    "\n",
    "out_path = \"../Recs\"\n",
    "test_path = \"./Test/*.npy\"\n",
    "kspace_files = np.asarray(glob.glob(test_path))\n",
    "print(kspace_files[-1])\n",
    "print(len(kspace_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\pythonenv\\env3.6\\lib\\site-packages\\keras\\activations.py:211: UserWarning: Do not pass a layer instance (such as LeakyReLU) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n",
      "  identifier=identifier.__class__.__name__))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cn1 (?, 256, 256, 2)\n",
      "ifft2 (?, 256, 256, 2)\n",
      "o2 (?, 256, 256, 2)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 256, 256, 2)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda_125 (Lambda)             (None, 256, 256, 2)  0           input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_126 (Lambda)             (None, 256, 256, 2)  0           lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_131 (Lambda)             (None, 256, 256, 2)  0           lambda_126[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 256, 256, 64) 1216        lambda_131[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 256, 256, 2)  130         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 256, 256, 2)  0           conv2d_66[0][0]                  \n",
      "                                                                 lambda_131[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_132 (Lambda)             (None, 256, 256, 2)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_137 (Lambda)             (None, 256, 256, 2)  0           lambda_132[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, 256, 256, 2)  0           lambda_137[0][0]                 \n",
      "                                                                 lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_138 (Lambda)             (None, 256, 256, 2)  0           add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 256, 256, 64) 1216        lambda_138[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 256, 256, 2)  130         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, 256, 256, 2)  0           conv2d_72[0][0]                  \n",
      "                                                                 lambda_138[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_143 (Lambda)             (None, 256, 256, 2)  0           add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_148 (Lambda)             (None, 256, 256, 2)  0           lambda_143[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_24 (Add)                    (None, 256, 256, 2)  0           lambda_148[0][0]                 \n",
      "                                                                 lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_149 (Lambda)             (None, 256, 256, 2)  0           add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 256, 256, 64) 1216        lambda_149[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 256, 256, 2)  130         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_25 (Add)                    (None, 256, 256, 2)  0           conv2d_78[0][0]                  \n",
      "                                                                 lambda_149[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_154 (Lambda)             (None, 256, 256, 2)  0           add_25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_159 (Lambda)             (None, 256, 256, 2)  0           lambda_154[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_26 (Add)                    (None, 256, 256, 2)  0           lambda_159[0][0]                 \n",
      "                                                                 lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_160 (Lambda)             (None, 256, 256, 2)  0           add_26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 256, 256, 64) 1216        lambda_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 256, 256, 2)  130         conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_27 (Add)                    (None, 256, 256, 2)  0           conv2d_84[0][0]                  \n",
      "                                                                 lambda_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_165 (Lambda)             (None, 256, 256, 2)  0           add_27[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_170 (Lambda)             (None, 256, 256, 2)  0           lambda_165[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_28 (Add)                    (None, 256, 256, 2)  0           lambda_170[0][0]                 \n",
      "                                                                 lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_171 (Lambda)             (None, 256, 256, 2)  0           add_28[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 256, 256, 64) 1216        lambda_171[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 256, 256, 2)  130         conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_29 (Add)                    (None, 256, 256, 2)  0           conv2d_90[0][0]                  \n",
      "                                                                 lambda_171[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_176 (Lambda)             (None, 256, 256, 2)  0           add_29[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_181 (Lambda)             (None, 256, 256, 2)  0           lambda_176[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_30 (Add)                    (None, 256, 256, 2)  0           lambda_181[0][0]                 \n",
      "                                                                 lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_182 (Lambda)             (None, 256, 256, 2)  0           add_30[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 745,290\n",
      "Trainable params: 745,290\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "## wnet_c5d5_64，DC-CNN，“A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction”\n",
    "under_rate = '20'\n",
    "import dcwn_1 as dcwn\n",
    "mask = np.concatenate((var_sampling_mask[:,:,np.newaxis],var_sampling_mask[:,:,np.newaxis]),axis = -1) > 0\n",
    "mask = mask.astype(np.float32)\n",
    "stats = np.load(\"../Data/stats_fs_unet_norm_2c_20\" + under_rate + \".npy\")\n",
    "\n",
    "model = dcwn.wnet_dccnn_5_64(stats[0],stats[1],stats[2],stats[3],mask,\n",
    "                                   kshape = (5,5),kshape2=(3,3))\n",
    "\n",
    "opt = Adam()\n",
    "model.compile(loss = [dcwn.nrmse,dcwn.nrmse],optimizer=opt, loss_weights=[0.01, 0.99])\n",
    "\n",
    "model_name = \"../Models/wnet_dc\" + under_rate +\"wnet_c5d5_64_1d20_11_17_1.hdf5\"\n",
    "model.load_weights(model_name)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#zero-fill\n",
    "imshape = (256,256)\n",
    "norm = np.sqrt(imshape[0]*imshape[1])\n",
    "\n",
    "for jj in range(len(kspace_files)):\n",
    "    kspace = np.load(kspace_files[jj])/norm\n",
    "    kspace2 = kspace.copy()\n",
    "    kspace[:,var_sampling_mask,:] = 0\n",
    "    kspace = (kspace-stats[0])/stats[1]\n",
    "    rec = np.abs(np.fft.ifft2(kspace2[:,:,:,0]+1j*kspace2[:,:,:,1])).astype(np.float64)\n",
    "    zero_fill=np.abs(np.fft.ifft2(kspace[:,:,:,0]+1j*kspace[:,:,:,1])).astype(np.float64)   \n",
    "    fig = plt.figure(dpi = 100)\n",
    "    plt.imshow(zero_fill[100], cmap = \"gray\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshape = (256,256)\n",
    "norm = np.sqrt(imshape[0]*imshape[1])\n",
    "\n",
    "for jj in range(len(kspace_files)):\n",
    "    kspace = np.load(kspace_files[jj])/norm\n",
    "    kspace2 = kspace.copy()\n",
    "    kspace[:,var_sampling_mask,:] = 0\n",
    "    kspace = (kspace-stats[0])/stats[1]\n",
    "    rec = np.abs(np.fft.ifft2(kspace2[:,:,:,0]+1j*kspace2[:,:,:,1])).astype(np.float64)\n",
    "    pred0 = model.predict(kspace)[1].astype(np.float64)\n",
    "    pred=np.abs(pred0[:,:,:,0]+1j*pred0[:,:,:,1])\n",
    "    fig = plt.figure(dpi = 100)\n",
    "    plt.imshow(pred[100] , cmap = \"gray\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 89.5, 54.5, -0.5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# import PIL.Image as img\n",
    "x1, x2, y1, y2 = 140, 230, 130, 185\n",
    "fig, ax = plt.subplots(dpi = 100)\n",
    "ax.imshow(pred[100] , cmap = \"gray\")\n",
    "ax.add_patch(patches.Rectangle((x1,y1),90,55,linewidth=1,edgecolor='red',fill=False))\n",
    "ax.axis(\"off\")\n",
    "axins = zoomed_inset_axes(ax, 1.5, loc=2) \n",
    "axins.imshow(pred[100,y1:y2,x1:x2] , cmap = \"gray\")\n",
    "axins.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.919165007728452\n",
      "28.58245272874974\n",
      "0.07967612736810449\n",
      "35.18219444995515\n",
      "[46.97471605 46.90077618 46.90556601 46.96463281 46.90780668 46.90444402\n",
      " 46.80316259 46.82395538 46.50566397 45.58023455 44.83317472 43.53534149\n",
      " 42.48333877 41.99300319 41.48538618 41.80381112 41.33119862 40.85135408\n",
      " 40.25779434 39.75700901 39.80877445 39.16712542 39.08765219 38.30036011\n",
      " 38.24905696 37.97645442 37.42682539 37.00859351 37.05094945 36.850978\n",
      " 36.63028381 36.41412241 36.45461466 35.83084594 36.20736707 36.12459631\n",
      " 36.02906621 35.62580405 35.48428317 35.46556126 35.10131673 34.65993067\n",
      " 34.62118056 34.09326432 33.80860337 33.7178835  33.6583781  33.7761369\n",
      " 33.7331006  33.714407   33.8947864  33.99039342 33.99053549 34.23673965\n",
      " 33.87207919 34.02204084 33.93830355 33.98731993 33.7458214  33.6843714\n",
      " 33.46095771 33.28126509 33.20729373 33.43625859 33.52919442 33.58217514\n",
      " 33.34324577 33.42386622 33.58611123 33.89362377 33.58281389 33.21235303\n",
      " 33.53058121 33.55913932 33.41269379 33.50587009 33.47329252 33.48222606\n",
      " 33.65910811 33.4699014  33.62936327 33.64362302 33.73179589 33.43309531\n",
      " 33.50707243 33.68823262 33.96092    33.92184206 34.19280579 34.01720113\n",
      " 33.78879434 33.58158668 33.41476303 33.36471739 33.72950229 34.06253114\n",
      " 33.71440309 33.84547425 33.98908873 33.57896703 33.82567991 33.84106593\n",
      " 34.11594909 34.36679575 34.4770912  34.46222502 34.41294654 34.37348285\n",
      " 34.55268173 34.74011793 34.65916604 34.35066561 34.56829373 34.61891223\n",
      " 34.71471731 34.82810312 34.68544938 34.63184434 34.35391099 34.4155336\n",
      " 34.40001531 34.55136331 34.81088349 34.8631578  34.96464405 35.29360743\n",
      " 35.50131808 35.91333539 36.2766714  36.62722581 36.67264814 37.09685943\n",
      " 37.05909218 36.75149569 37.05316286 37.37647817 37.2315022  37.82258255\n",
      " 37.98804609 37.93271462 37.97857204 38.26999256 38.68735751 38.80066889\n",
      " 38.74564716 39.25650562 39.48655146 40.02319292 40.34296622 41.04472943\n",
      " 41.41904193 41.6753182  41.80662628 42.01878472 42.39914616 42.21830824\n",
      " 41.60357293 42.45171082 44.09546467 45.60973863 46.35787553 46.74539836\n",
      " 46.91981284 46.94487934 47.00557437 46.97766794 46.89992867 46.94725621\n",
      " 46.93982852 46.92959266]\n",
      "0.01785342261702143\n",
      "[0.00449238 0.00453079 0.00452829 0.0044976  0.00452712 0.00452887\n",
      " 0.00458199 0.00457104 0.00474165 0.00527473 0.00574849 0.00667491\n",
      " 0.00753434 0.00797191 0.00845168 0.00814745 0.00860305 0.00909169\n",
      " 0.0097347  0.01031245 0.01025117 0.01103713 0.01113858 0.01219535\n",
      " 0.0122676  0.01265871 0.01348562 0.01415085 0.01408202 0.01440998\n",
      " 0.01478081 0.01515326 0.01508279 0.01620578 0.01551829 0.01566688\n",
      " 0.01584014 0.01659289 0.01686546 0.01690185 0.0176257  0.01854453\n",
      " 0.01862745 0.01979471 0.02045418 0.02066894 0.02081102 0.02053078\n",
      " 0.02063276 0.02067721 0.02025224 0.02003054 0.02003021 0.01947042\n",
      " 0.02030525 0.01995769 0.02015103 0.02003763 0.02060256 0.02074884\n",
      " 0.02128945 0.02173447 0.02192036 0.02135007 0.02112285 0.0209944\n",
      " 0.02157993 0.02138056 0.02098489 0.02025495 0.02099286 0.02190759\n",
      " 0.02111948 0.02105016 0.02140808 0.02117965 0.02125924 0.02123738\n",
      " 0.02080927 0.02126754 0.02088066 0.02084641 0.02063586 0.02135785\n",
      " 0.02117672 0.02073962 0.02009862 0.02018925 0.01956915 0.01996881\n",
      " 0.02050088 0.02099583 0.02140298 0.02152665 0.02064131 0.01986487\n",
      " 0.02067722 0.02036754 0.02003355 0.02100216 0.02041401 0.02037788\n",
      " 0.01974308 0.01918106 0.01893903 0.01897147 0.01907941 0.0191663\n",
      " 0.01877493 0.01837411 0.01854616 0.01921671 0.01874121 0.01863231\n",
      " 0.01842793 0.01818893 0.01849013 0.01860459 0.01920953 0.01907373\n",
      " 0.01910784 0.01877778 0.01822503 0.01811567 0.01790524 0.01723979\n",
      " 0.01683241 0.01605261 0.01539497 0.01478601 0.01470889 0.01400778\n",
      " 0.01406882 0.01457597 0.01407843 0.01356402 0.01379232 0.01288496\n",
      " 0.01264183 0.01272262 0.01265563 0.01223806 0.01166392 0.01151274\n",
      " 0.01158591 0.01092413 0.01063861 0.01000121 0.00963971 0.00889152\n",
      " 0.00851648 0.00826887 0.00814481 0.00794828 0.00760773 0.00776778\n",
      " 0.00833746 0.00756183 0.00625805 0.00525685 0.00482301 0.00461256\n",
      " 0.00452087 0.00450784 0.00447645 0.00449085 0.00453123 0.00450661\n",
      " 0.00451046 0.00451578]\n",
      "0.9349123255893578\n",
      "[0.97798306 0.97764371 0.97767872 0.97793904 0.97767093 0.97763666\n",
      " 0.97719037 0.97785655 0.97718323 0.97659618 0.97542894 0.97312745\n",
      " 0.97108746 0.97095847 0.96973155 0.97156761 0.9713965  0.97004211\n",
      " 0.96971573 0.96922843 0.96829084 0.96660886 0.96594051 0.96257288\n",
      " 0.9620227  0.96097539 0.95961923 0.95973765 0.95936113 0.9578661\n",
      " 0.95714453 0.95496425 0.95607847 0.95163297 0.95335493 0.95295753\n",
      " 0.95228341 0.94783794 0.94517629 0.94630387 0.94465937 0.94144546\n",
      " 0.94258319 0.93851245 0.93470692 0.93370808 0.93051955 0.93170526\n",
      " 0.93191456 0.92912593 0.93176278 0.93145727 0.92994893 0.92771796\n",
      " 0.92297007 0.92320318 0.92505534 0.9235023  0.91989287 0.92042952\n",
      " 0.91784863 0.91790825 0.91810791 0.91806309 0.91842818 0.9209793\n",
      " 0.91961274 0.91744746 0.91789399 0.92287589 0.91944715 0.91418726\n",
      " 0.91925396 0.9181198  0.91575385 0.91491547 0.91472837 0.91651318\n",
      " 0.91982703 0.91557402 0.91594057 0.91562154 0.91410326 0.91115505\n",
      " 0.91196218 0.91222957 0.91270557 0.91014335 0.91399654 0.91198405\n",
      " 0.91060229 0.90950365 0.90686403 0.90948726 0.91462411 0.91910726\n",
      " 0.91201117 0.91453517 0.91893724 0.91507885 0.91893383 0.92010965\n",
      " 0.92235678 0.92377399 0.92633164 0.92661449 0.92644442 0.92817437\n",
      " 0.92904059 0.92969879 0.92754978 0.9285786  0.92812589 0.93023252\n",
      " 0.93131325 0.93240657 0.9309389  0.93262616 0.93099099 0.93065955\n",
      " 0.92851834 0.93115551 0.93405508 0.93479044 0.93648572 0.94275203\n",
      " 0.94384492 0.94829807 0.94942657 0.95154051 0.95175146 0.95447482\n",
      " 0.95320252 0.95181732 0.95555971 0.95730475 0.95688204 0.95935294\n",
      " 0.96164977 0.96111951 0.96062675 0.96133074 0.96335259 0.96346744\n",
      " 0.96239295 0.9642486  0.96481235 0.96740406 0.96837055 0.97008478\n",
      " 0.97097993 0.971175   0.97259291 0.9729953  0.97311535 0.97275314\n",
      " 0.971647   0.9723801  0.97375064 0.97603598 0.97699314 0.97720979\n",
      " 0.97778651 0.97781214 0.97816214 0.97801418 0.97766625 0.97784711\n",
      " 0.9778313  0.97780473]\n"
     ]
    }
   ],
   "source": [
    "#compare psnr and ssim\n",
    "import math\n",
    "from skimage.measure import compare_ssim\n",
    "def psnr2(target, ref,ma,mi):\n",
    "    # assume RGB image\n",
    "    target_data = np.array(target, dtype=float)\n",
    "    ref_data = np.array(ref, dtype=float)\n",
    "\n",
    "    diff = ref_data - target_data\n",
    "    diff = diff.flatten('C')\n",
    "\n",
    "    rmse = math.sqrt(np.mean(diff ** 2.))\n",
    "    nrmse=rmse/(ma-mi)\n",
    "\n",
    "    return 20 * math.log10(ma/ rmse),nrmse \n",
    "\n",
    "from skimage.measure import compare_ssim\n",
    "psnr_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "nrmse_all_nonzero= np.zeros(shape=(len(pred)))\n",
    "ssim_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "rate_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "ma=np.max(rec)\n",
    "mi=np.min(rec)\n",
    "print(np.max(pred))\n",
    "print(np.max(rec))\n",
    "print(np.min(rec))\n",
    "for i in range(len(pred)):\n",
    "\n",
    "    psnr_all_nonzero[i],nrmse_all_nonzero[i]=psnr2(pred[i,:,:],rec[i,:,:],ma,mi)\n",
    "    ssim_all_nonzero[i],diff_all=compare_ssim(pred[i,:,:],rec[i,:,:],full=True,data_range=ma-mi)\n",
    "print(np.mean(psnr_all_nonzero[20:-20]))\n",
    "print(psnr_all_nonzero)\n",
    "print(np.mean(nrmse_all_nonzero[20:-20]))\n",
    "print(nrmse_all_nonzero)\n",
    "print(np.mean(ssim_all_nonzero[20:-20]))\n",
    "print(ssim_all_nonzero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\pythonenv\\env3.6\\lib\\site-packages\\keras\\activations.py:211: UserWarning: Do not pass a layer instance (such as LeakyReLU) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n",
      "  identifier=identifier.__class__.__name__))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "se1 shepe (?, 1, 1, 128)\n",
      "se2 shepe (?, 1, 1, 16)\n",
      "se3 shepe (?, 1, 1, 128)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "cn1 (?, 256, 256, 2)\n",
      "ifft2 (?, 256, 256, 2)\n",
      "se1 shepe (?, 1, 1, 128)\n",
      "se2 shepe (?, 1, 1, 16)\n",
      "se3 shepe (?, 1, 1, 128)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 128)\n",
      "se2 shepe (?, 1, 1, 16)\n",
      "se3 shepe (?, 1, 1, 128)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 128)\n",
      "se2 shepe (?, 1, 1, 16)\n",
      "se3 shepe (?, 1, 1, 128)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 128)\n",
      "se2 shepe (?, 1, 1, 16)\n",
      "se3 shepe (?, 1, 1, 128)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "se1 shepe (?, 1, 1, 32)\n",
      "se2 shepe (?, 1, 1, 4)\n",
      "se3 shepe (?, 1, 1, 32)\n",
      "o2 (?, 256, 256, 2)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 256, 256, 2)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 256, 256, 2)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_2 (Lambda)               (None, 256, 256, 2)  0           lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_7 (Lambda)               (None, 256, 256, 2)  0           lambda_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 256, 256, 32) 608         lambda_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 256, 256, 32) 9248        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 256, 256, 32) 9248        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 128, 128, 32) 0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 128, 64) 18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 128, 64) 36928       conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 128, 128, 64) 36928       conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 64, 64, 64)   0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 64, 64, 128)  73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 64, 128)  147584      conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 64, 64, 128)  147584      conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 128)          0           conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 1, 1, 128)    0           global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 1, 1, 16)     2064        reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 1, 1, 128)    2176        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 64, 64, 128)  0           conv2d_9[0][0]                   \n",
      "                                                                 conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 128, 128, 128 0           multiply_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 128, 128, 64) 73792       up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 128, 128, 128 0           conv2d_12[0][0]                  \n",
      "                                                                 conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 128, 128, 32) 36896       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_2 (Glo (None, 32)           0           conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 1, 1, 32)     0           global_average_pooling2d_2[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 1, 1, 4)      132         reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 1, 1, 32)     160         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_2 (Multiply)           (None, 128, 128, 32) 0           conv2d_15[0][0]                  \n",
      "                                                                 conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 256, 256, 32) 0           multiply_2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 256, 256, 32) 9248        up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 256, 256, 64) 0           conv2d_18[0][0]                  \n",
      "                                                                 conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 256, 256, 32) 18464       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_3 (Glo (None, 32)           0           conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_3 (Reshape)             (None, 1, 1, 32)     0           global_average_pooling2d_3[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 1, 1, 4)      132         reshape_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 1, 1, 32)     160         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_3 (Multiply)           (None, 256, 256, 32) 0           conv2d_21[0][0]                  \n",
      "                                                                 conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 256, 256, 2)  66          multiply_3[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 256, 256, 2)  0           conv2d_24[0][0]                  \n",
      "                                                                 lambda_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_8 (Lambda)               (None, 256, 256, 2)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_13 (Lambda)              (None, 256, 256, 2)  0           lambda_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 256, 256, 2)  0           lambda_13[0][0]                  \n",
      "                                                                 lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_14 (Lambda)              (None, 256, 256, 2)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 256, 256, 32) 608         lambda_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 128, 128, 32) 0           conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 128, 128, 64) 18496       max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 128, 128, 64) 36928       conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 128, 128, 64) 36928       conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 64, 64, 64)   0           conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 64, 64, 128)  73856       max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 64, 64, 128)  147584      conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 64, 64, 128)  147584      conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_4 (Glo (None, 128)          0           conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_4 (Reshape)             (None, 1, 1, 128)    0           global_average_pooling2d_4[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 1, 1, 16)     2064        reshape_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 1, 1, 128)    2176        conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_4 (Multiply)           (None, 64, 64, 128)  0           conv2d_33[0][0]                  \n",
      "                                                                 conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 128, 128, 128 0           multiply_4[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 128, 128, 64) 73792       up_sampling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 128, 128, 128 0           conv2d_36[0][0]                  \n",
      "                                                                 conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 128, 128, 32) 36896       concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_5 (Glo (None, 32)           0           conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_5 (Reshape)             (None, 1, 1, 32)     0           global_average_pooling2d_5[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 1, 1, 4)      132         reshape_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 1, 1, 32)     160         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_5 (Multiply)           (None, 128, 128, 32) 0           conv2d_39[0][0]                  \n",
      "                                                                 conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 256, 256, 32) 0           multiply_5[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 256, 256, 32) 9248        up_sampling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 256, 256, 64) 0           conv2d_42[0][0]                  \n",
      "                                                                 conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 256, 256, 32) 18464       concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_6 (Glo (None, 32)           0           conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_6 (Reshape)             (None, 1, 1, 32)     0           global_average_pooling2d_6[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 1, 1, 4)      132         reshape_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 1, 1, 32)     160         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_6 (Multiply)           (None, 256, 256, 32) 0           conv2d_45[0][0]                  \n",
      "                                                                 conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 256, 256, 2)  66          multiply_6[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 256, 256, 2)  0           conv2d_48[0][0]                  \n",
      "                                                                 lambda_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_19 (Lambda)              (None, 256, 256, 2)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_24 (Lambda)              (None, 256, 256, 2)  0           lambda_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 256, 256, 2)  0           lambda_24[0][0]                  \n",
      "                                                                 lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_25 (Lambda)              (None, 256, 256, 2)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 256, 256, 32) 608         lambda_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 128, 128, 32) 0           conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 128, 128, 64) 18496       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 128, 128, 64) 36928       conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 128, 128, 64) 36928       conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2D)  (None, 64, 64, 64)   0           conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 64, 64, 128)  73856       max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 64, 64, 128)  147584      conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 64, 64, 128)  147584      conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_7 (Glo (None, 128)          0           conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_7 (Reshape)             (None, 1, 1, 128)    0           global_average_pooling2d_7[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 1, 1, 16)     2064        reshape_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 1, 1, 128)    2176        conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_7 (Multiply)           (None, 64, 64, 128)  0           conv2d_57[0][0]                  \n",
      "                                                                 conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2D)  (None, 128, 128, 128 0           multiply_7[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 128, 128, 64) 73792       up_sampling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 128, 128, 128 0           conv2d_60[0][0]                  \n",
      "                                                                 conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 128, 128, 32) 36896       concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_8 (Glo (None, 32)           0           conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_8 (Reshape)             (None, 1, 1, 32)     0           global_average_pooling2d_8[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 1, 1, 4)      132         reshape_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 1, 1, 32)     160         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_8 (Multiply)           (None, 128, 128, 32) 0           conv2d_63[0][0]                  \n",
      "                                                                 conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_6 (UpSampling2D)  (None, 256, 256, 32) 0           multiply_8[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 256, 256, 32) 9248        up_sampling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 256, 256, 64) 0           conv2d_66[0][0]                  \n",
      "                                                                 conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 256, 256, 32) 18464       concatenate_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_9 (Glo (None, 32)           0           conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_9 (Reshape)             (None, 1, 1, 32)     0           global_average_pooling2d_9[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 1, 1, 4)      132         reshape_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 1, 1, 32)     160         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_9 (Multiply)           (None, 256, 256, 32) 0           conv2d_69[0][0]                  \n",
      "                                                                 conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 256, 256, 2)  66          multiply_9[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 256, 256, 2)  0           conv2d_72[0][0]                  \n",
      "                                                                 lambda_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_30 (Lambda)              (None, 256, 256, 2)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_35 (Lambda)              (None, 256, 256, 2)  0           lambda_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 256, 256, 2)  0           lambda_35[0][0]                  \n",
      "                                                                 lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_36 (Lambda)              (None, 256, 256, 2)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 256, 256, 32) 608         lambda_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2D)  (None, 128, 128, 32) 0           conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 128, 128, 64) 18496       max_pooling2d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 128, 128, 64) 36928       conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 128, 128, 64) 36928       conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2D)  (None, 64, 64, 64)   0           conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 64, 64, 128)  73856       max_pooling2d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 64, 64, 128)  147584      conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 64, 64, 128)  147584      conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_10 (Gl (None, 128)          0           conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_10 (Reshape)            (None, 1, 1, 128)    0           global_average_pooling2d_10[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 1, 1, 16)     2064        reshape_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 1, 1, 128)    2176        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_10 (Multiply)          (None, 64, 64, 128)  0           conv2d_81[0][0]                  \n",
      "                                                                 conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_7 (UpSampling2D)  (None, 128, 128, 128 0           multiply_10[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 128, 128, 64) 73792       up_sampling2d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 128, 128, 128 0           conv2d_84[0][0]                  \n",
      "                                                                 conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 128, 128, 32) 36896       concatenate_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 128, 128, 32) 9248        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_11 (Gl (None, 32)           0           conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_11 (Reshape)            (None, 1, 1, 32)     0           global_average_pooling2d_11[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 1, 1, 4)      132         reshape_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 1, 1, 32)     160         conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_11 (Multiply)          (None, 128, 128, 32) 0           conv2d_87[0][0]                  \n",
      "                                                                 conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_8 (UpSampling2D)  (None, 256, 256, 32) 0           multiply_11[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 256, 256, 32) 9248        up_sampling2d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_8 (Concatenate)     (None, 256, 256, 64) 0           conv2d_90[0][0]                  \n",
      "                                                                 conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 256, 256, 32) 18464       concatenate_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_12 (Gl (None, 32)           0           conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_12 (Reshape)            (None, 1, 1, 32)     0           global_average_pooling2d_12[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, 1, 1, 4)      132         reshape_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_95 (Conv2D)              (None, 1, 1, 32)     160         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_12 (Multiply)          (None, 256, 256, 32) 0           conv2d_93[0][0]                  \n",
      "                                                                 conv2d_95[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_96 (Conv2D)              (None, 256, 256, 2)  66          multiply_12[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 256, 256, 2)  0           conv2d_96[0][0]                  \n",
      "                                                                 lambda_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_41 (Lambda)              (None, 256, 256, 2)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_46 (Lambda)              (None, 256, 256, 2)  0           lambda_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 256, 256, 2)  0           lambda_46[0][0]                  \n",
      "                                                                 lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_47 (Lambda)              (None, 256, 256, 2)  0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_97 (Conv2D)              (None, 256, 256, 32) 608         lambda_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_98 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_97[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_99 (Conv2D)              (None, 256, 256, 32) 9248        conv2d_98[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2D)  (None, 128, 128, 32) 0           conv2d_99[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_100 (Conv2D)             (None, 128, 128, 64) 18496       max_pooling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_101 (Conv2D)             (None, 128, 128, 64) 36928       conv2d_100[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_102 (Conv2D)             (None, 128, 128, 64) 36928       conv2d_101[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D) (None, 64, 64, 64)   0           conv2d_102[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_103 (Conv2D)             (None, 64, 64, 128)  73856       max_pooling2d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_104 (Conv2D)             (None, 64, 64, 128)  147584      conv2d_103[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_105 (Conv2D)             (None, 64, 64, 128)  147584      conv2d_104[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_13 (Gl (None, 128)          0           conv2d_105[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_13 (Reshape)            (None, 1, 1, 128)    0           global_average_pooling2d_13[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv2d_106 (Conv2D)             (None, 1, 1, 16)     2064        reshape_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_107 (Conv2D)             (None, 1, 1, 128)    2176        conv2d_106[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_13 (Multiply)          (None, 64, 64, 128)  0           conv2d_105[0][0]                 \n",
      "                                                                 conv2d_107[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_9 (UpSampling2D)  (None, 128, 128, 128 0           multiply_13[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_108 (Conv2D)             (None, 128, 128, 64) 73792       up_sampling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_9 (Concatenate)     (None, 128, 128, 128 0           conv2d_108[0][0]                 \n",
      "                                                                 conv2d_102[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_109 (Conv2D)             (None, 128, 128, 32) 36896       concatenate_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_110 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_109[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_111 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_110[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_14 (Gl (None, 32)           0           conv2d_111[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_14 (Reshape)            (None, 1, 1, 32)     0           global_average_pooling2d_14[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv2d_112 (Conv2D)             (None, 1, 1, 4)      132         reshape_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_113 (Conv2D)             (None, 1, 1, 32)     160         conv2d_112[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_14 (Multiply)          (None, 128, 128, 32) 0           conv2d_111[0][0]                 \n",
      "                                                                 conv2d_113[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_10 (UpSampling2D) (None, 256, 256, 32) 0           multiply_14[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_114 (Conv2D)             (None, 256, 256, 32) 9248        up_sampling2d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)    (None, 256, 256, 64) 0           conv2d_114[0][0]                 \n",
      "                                                                 conv2d_99[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_115 (Conv2D)             (None, 256, 256, 32) 18464       concatenate_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_116 (Conv2D)             (None, 256, 256, 32) 9248        conv2d_115[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_117 (Conv2D)             (None, 256, 256, 32) 9248        conv2d_116[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_15 (Gl (None, 32)           0           conv2d_117[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_15 (Reshape)            (None, 1, 1, 32)     0           global_average_pooling2d_15[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv2d_118 (Conv2D)             (None, 1, 1, 4)      132         reshape_15[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_119 (Conv2D)             (None, 1, 1, 32)     160         conv2d_118[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_15 (Multiply)          (None, 256, 256, 32) 0           conv2d_117[0][0]                 \n",
      "                                                                 conv2d_119[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_120 (Conv2D)             (None, 256, 256, 2)  66          multiply_15[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 256, 256, 2)  0           conv2d_120[0][0]                 \n",
      "                                                                 lambda_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 256, 256, 2)  0           add_9[0][0]                      \n",
      "                                                                 lambda_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_52 (Lambda)              (None, 256, 256, 2)  0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_57 (Lambda)              (None, 256, 256, 2)  0           lambda_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 256, 256, 2)  0           lambda_57[0][0]                  \n",
      "                                                                 lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_58 (Lambda)              (None, 256, 256, 2)  0           add_11[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 3,303,810\n",
      "Trainable params: 3,303,810\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# wnet_c5d5_uca_cscasde_5_32，uca,“MRI reconstruction via cascaded channel-wise attention network,”\n",
    "under_rate = '20'\n",
    "import dcwn_1 as dcwn\n",
    "mask = np.concatenate((var_sampling_mask[:,:,np.newaxis],var_sampling_mask[:,:,np.newaxis]),axis = -1) > 0\n",
    "mask = mask.astype(np.float32)\n",
    "stats = np.load(\"../Data/stats_fs_unet_norm_2c_20\" + under_rate + \".npy\")\n",
    "\n",
    "model = dcwn.wnet_cca_cscasde_5_32(stats[0],stats[1],stats[2],stats[3],mask,\n",
    "                                   kshape = (5,5),kshape2=(3,3))\n",
    "\n",
    "opt = Adam()\n",
    "model.compile(loss = [dcwn.nrmse,dcwn.nrmse],optimizer=opt, loss_weights=[0.01, 0.99])\n",
    "model_name = \"../Models/wnet_dc\" + under_rate +\"wnet_c5d5_cca_cscasde_5_32_1d20_12_9_2.hdf5\"\n",
    "# model.load_weights(model_name)\n",
    "model.load_weights(model_name)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshape = (256,256)\n",
    "norm = np.sqrt(imshape[0]*imshape[1])\n",
    "\n",
    "for jj in range(len(kspace_files)):\n",
    "    kspace = np.load(kspace_files[jj])/norm\n",
    "    kspace2 = kspace.copy()\n",
    "    kspace[:,var_sampling_mask,:] = 0\n",
    "    kspace = (kspace-stats[0])/stats[1]\n",
    "    rec = np.abs(np.fft.ifft2(kspace2[:,:,:,0]+1j*kspace2[:,:,:,1])).astype(np.float64)\n",
    "    pred0 = model.predict(kspace)[1].astype(np.float64)\n",
    "    pred=np.abs(pred0[:,:,:,0]+1j*pred0[:,:,:,1])\n",
    "    fig = plt.figure(dpi = 100)\n",
    "    plt.imshow(pred[100] , cmap = \"gray\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 89.5, 54.5, -0.5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# import PIL.Image as img\n",
    "x1, x2, y1, y2 = 140, 230, 130, 185\n",
    "fig, ax = plt.subplots(dpi = 100)\n",
    "ax.imshow(pred[100] , cmap = \"gray\")\n",
    "ax.add_patch(patches.Rectangle((x1,y1),90,55,linewidth=1,edgecolor='red',fill=False))\n",
    "ax.axis(\"off\")\n",
    "axins = zoomed_inset_axes(ax, 1.5, loc=2) \n",
    "axins.imshow(pred[100,y1:y2,x1:x2] , cmap = \"gray\")\n",
    "axins.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.169474394260746\n",
      "28.58245272874974\n",
      "0.07967612736810449\n",
      "35.40846487570485\n",
      "[47.00384288 46.93792838 46.93420098 47.00064862 46.94679153 46.93494267\n",
      " 46.84406597 46.89578115 46.54255968 45.88969061 45.0827042  43.78080087\n",
      " 42.72969621 42.09738507 41.87538859 41.83139132 41.3902631  40.87324348\n",
      " 40.60837335 40.21411428 39.91977685 39.2602462  39.27732235 38.62896131\n",
      " 38.13036008 38.10172929 37.54504295 37.19205746 37.12093181 36.74444518\n",
      " 36.73099964 36.48008658 36.53034799 35.9375699  36.27901851 36.24707741\n",
      " 36.05014504 35.70168791 35.83448624 35.58922664 35.33227156 34.97752485\n",
      " 34.8292235  34.33369771 34.09491818 33.88363876 33.70888596 33.80381596\n",
      " 33.8827031  33.89379201 34.21412836 34.16279061 34.24183072 34.55141265\n",
      " 34.27999231 34.26796102 34.01351328 34.22290401 34.0478621  33.8571692\n",
      " 33.53659058 33.35635178 33.28667979 33.63934792 33.74036527 33.67557014\n",
      " 33.66322179 33.6964564  33.94575763 34.18455062 33.95694577 33.49756828\n",
      " 33.75790647 33.6664046  33.74500392 33.92012693 34.04400758 33.93236533\n",
      " 33.91561573 33.80432416 33.90607293 33.77133352 33.84480251 33.53341877\n",
      " 33.36016063 33.77027628 34.06272986 34.03000958 34.20716692 34.22317544\n",
      " 34.03675738 33.8825761  33.94519259 33.84822459 34.08169782 34.19430497\n",
      " 34.14411873 34.09785518 34.07459115 33.71910824 34.0436096  33.96130478\n",
      " 34.18406917 34.4390593  34.59032214 34.64616493 34.71770902 34.65709298\n",
      " 34.71274326 34.86945579 34.80067681 34.41909323 34.64601812 34.82640144\n",
      " 35.00723673 35.20593972 35.06707136 35.05232262 34.77314143 34.8177319\n",
      " 34.98796029 35.11216038 35.27704932 35.31408297 35.43543273 35.66446426\n",
      " 35.92205071 36.34764822 36.58572749 36.89285493 36.89560983 37.22336554\n",
      " 37.16392196 37.26516845 37.50433305 37.70994579 37.82877442 38.01528869\n",
      " 38.25497875 38.20657583 38.23153107 38.42647248 38.73768406 39.03951268\n",
      " 39.00946374 39.44778295 39.65382094 40.25779955 40.62005325 41.03146606\n",
      " 41.61418305 41.82088582 41.88023374 42.11138367 42.64202822 42.2096182\n",
      " 41.67594254 42.36023651 44.21792582 45.74400884 46.39917313 46.81316279\n",
      " 46.95888975 46.97777485 47.04898581 47.00567425 46.94196383 46.98508225\n",
      " 46.97732827 46.96465266]\n",
      "0.017388926555906837\n",
      "[0.00447734 0.00451145 0.00451338 0.00447899 0.00450685 0.004513\n",
      " 0.00456046 0.00453339 0.00472155 0.00509012 0.00558569 0.00648892\n",
      " 0.00732365 0.00787668 0.00808059 0.00812162 0.00854475 0.00906881\n",
      " 0.00934961 0.00978378 0.010121   0.01091943 0.01089799 0.0117426\n",
      " 0.01243639 0.01247745 0.01330332 0.01385509 0.01396901 0.01458781\n",
      " 0.01461041 0.01503862 0.01495185 0.01600788 0.01539081 0.01544751\n",
      " 0.01580175 0.01644856 0.01619899 0.01666292 0.01716322 0.01787871\n",
      " 0.01818658 0.01925429 0.01979094 0.02027825 0.02069036 0.02046546\n",
      " 0.02028043 0.02025456 0.01952117 0.01963689 0.01945901 0.01877767\n",
      " 0.01937371 0.01940056 0.01997729 0.01950146 0.01989845 0.02034014\n",
      " 0.02110488 0.02154739 0.02172093 0.02085667 0.02061551 0.02076987\n",
      " 0.02079942 0.02071999 0.02013374 0.01958776 0.02010782 0.0211999\n",
      " 0.02057392 0.0207918  0.0206045  0.02019324 0.01990728 0.02016481\n",
      " 0.02020373 0.02046426 0.02022594 0.02054214 0.02036912 0.02111258\n",
      " 0.02153795 0.02054464 0.01986442 0.01993939 0.01953682 0.01950085\n",
      " 0.0199239  0.02028073 0.02013505 0.02036109 0.01982109 0.01956578\n",
      " 0.01967915 0.01978425 0.01983731 0.02066602 0.01990819 0.02009773\n",
      " 0.01958885 0.01902214 0.01869374 0.01857394 0.01842158 0.01855059\n",
      " 0.01843211 0.01810254 0.01824645 0.01906592 0.01857426 0.01819249\n",
      " 0.01781765 0.01741467 0.01769533 0.01772541 0.01830439 0.01821066\n",
      " 0.01785724 0.01760371 0.01727269 0.0171992  0.01696058 0.0165192\n",
      " 0.01603651 0.01526968 0.01485682 0.01434067 0.01433613 0.01380524\n",
      " 0.01390005 0.01373896 0.01336582 0.01305314 0.01287578 0.01260224\n",
      " 0.01225924 0.01232774 0.01229237 0.01201956 0.01159653 0.01120048\n",
      " 0.0112393  0.0106862  0.01043569 0.00973469 0.00933705 0.0089051\n",
      " 0.00832728 0.00813145 0.00807608 0.00786399 0.00739794 0.00777556\n",
      " 0.00826828 0.00764189 0.00617044 0.00517621 0.00480014 0.00457672\n",
      " 0.00450057 0.0044908  0.00445413 0.0044764  0.00450935 0.00448702\n",
      " 0.00449103 0.00449759]\n",
      "0.9386083742384292\n",
      "[0.97817891 0.97788638 0.9778529  0.97813072 0.97791299 0.97781154\n",
      " 0.97741917 0.97817565 0.97739588 0.97711023 0.97601367 0.97380876\n",
      " 0.97167289 0.97131077 0.97124929 0.97187782 0.97171054 0.97040533\n",
      " 0.97060124 0.97022468 0.96886002 0.96739963 0.96670114 0.96386212\n",
      " 0.9611405  0.96153921 0.96095571 0.96107425 0.96021456 0.95745687\n",
      " 0.95850185 0.95585357 0.95689042 0.95267188 0.95502181 0.95499522\n",
      " 0.95286891 0.94972306 0.9496624  0.94894721 0.94818303 0.94654918\n",
      " 0.94652202 0.94252628 0.93961623 0.9371696  0.93246691 0.93342163\n",
      " 0.93460082 0.93306661 0.93556421 0.93365131 0.93449037 0.93324057\n",
      " 0.92907945 0.92831874 0.92676489 0.92787832 0.92687704 0.9235636\n",
      " 0.91976874 0.92028171 0.92193438 0.92234176 0.92252981 0.92455467\n",
      " 0.92524444 0.92309174 0.92504247 0.92794426 0.92463264 0.91960437\n",
      " 0.92297724 0.92144066 0.92175748 0.92364181 0.92456962 0.92572533\n",
      " 0.92576848 0.92337749 0.92255142 0.92036586 0.91839869 0.91335963\n",
      " 0.91141071 0.91641721 0.91628777 0.91271148 0.91459769 0.91604351\n",
      " 0.91680688 0.91690002 0.91883166 0.91902609 0.92278366 0.92390202\n",
      " 0.92118176 0.91932755 0.92154829 0.91845402 0.92299399 0.92242178\n",
      " 0.92467424 0.92652613 0.92842604 0.92982973 0.93132232 0.93256593\n",
      " 0.93261767 0.93303359 0.9321117  0.9301801  0.93017455 0.93424616\n",
      " 0.93599608 0.93807019 0.93655034 0.93798568 0.93593133 0.93645224\n",
      " 0.93682476 0.93937587 0.94021059 0.94110229 0.94299835 0.94731739\n",
      " 0.94927935 0.95302898 0.95348359 0.95489588 0.95431113 0.95659406\n",
      " 0.95483262 0.95598613 0.95886889 0.95973773 0.9606294  0.96125874\n",
      " 0.96341243 0.96293682 0.962246   0.96234625 0.96387925 0.96456754\n",
      " 0.96369218 0.96512525 0.96570984 0.96794489 0.96911277 0.97024371\n",
      " 0.97173517 0.97191184 0.97264375 0.97335634 0.97373237 0.97287791\n",
      " 0.97189328 0.97241453 0.97415904 0.97649877 0.97720209 0.97756573\n",
      " 0.97800962 0.97799509 0.97839432 0.97819358 0.97789442 0.97805416\n",
      " 0.97807379 0.97801506]\n"
     ]
    }
   ],
   "source": [
    "#compare psnr and ssim\n",
    "import math\n",
    "from skimage.measure import compare_ssim\n",
    "def psnr2(target, ref,ma,mi):\n",
    "    # assume RGB image\n",
    "    target_data = np.array(target, dtype=float)\n",
    "    ref_data = np.array(ref, dtype=float)\n",
    "\n",
    "    diff = ref_data - target_data\n",
    "    diff = diff.flatten('C')\n",
    "\n",
    "    rmse = math.sqrt(np.mean(diff ** 2.))\n",
    "    nrmse=rmse/(ma-mi)\n",
    "\n",
    "    return 20 * math.log10(ma/ rmse),nrmse \n",
    "\n",
    "from skimage.measure import compare_ssim\n",
    "psnr_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "nrmse_all_nonzero= np.zeros(shape=(len(pred)))\n",
    "ssim_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "rate_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "ma=np.max(rec)\n",
    "mi=np.min(rec)\n",
    "print(np.max(pred))\n",
    "print(np.max(rec))\n",
    "print(np.min(rec))\n",
    "for i in range(len(pred)):\n",
    "    psnr_all_nonzero[i],nrmse_all_nonzero[i]=psnr2(pred[i,:,:],rec[i,:,:],ma,mi)\n",
    "    ssim_all_nonzero[i],diff_all=compare_ssim(pred[i,:,:],rec[i,:,:],full=True,data_range=ma-mi)\n",
    "print(np.mean(psnr_all_nonzero[20:-20]))\n",
    "print(psnr_all_nonzero)\n",
    "print(np.mean(nrmse_all_nonzero[20:-20]))\n",
    "print(nrmse_all_nonzero)\n",
    "print(np.mean(ssim_all_nonzero[20:-20]))\n",
    "print(ssim_all_nonzero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\pythonenv\\env3.6\\lib\\site-packages\\keras\\activations.py:211: UserWarning: Do not pass a layer instance (such as LeakyReLU) as the activation argument of another layer. Instead, advanced activation layers should be used just like any other layer in a model.\n",
      "  identifier=identifier.__class__.__name__))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn1 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn2 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn3 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn4 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn5 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn6 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn7 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn8 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn9 (?, 256, 256, 2)\n",
      "se shape (?, 32)\n",
      "se 2 shape (?, 1, 1, 32)\n",
      "cn10 (?, 256, 256, 2)\n",
      "o2 (?, 256, 256, 2)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 256, 256, 2)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda_63 (Lambda)              (None, 256, 256, 2)  0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_64 (Lambda)              (None, 256, 256, 2)  0           lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_69 (Lambda)              (None, 256, 256, 2)  0           lambda_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dwt_1 (DWT)                     (None, 128, 128, 8)  0           lambda_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_121 (Conv2D)             (None, 128, 128, 32) 2336        dwt_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_122 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_121[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_11 (Concatenate)    (None, 128, 128, 64) 0           conv2d_121[0][0]                 \n",
      "                                                                 conv2d_122[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_123 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_124 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_123[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_12 (Concatenate)    (None, 128, 128, 96) 0           conv2d_121[0][0]                 \n",
      "                                                                 conv2d_122[0][0]                 \n",
      "                                                                 conv2d_124[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_125 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_126 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_13 (Concatenate)    (None, 128, 128, 128 0           conv2d_121[0][0]                 \n",
      "                                                                 conv2d_122[0][0]                 \n",
      "                                                                 conv2d_124[0][0]                 \n",
      "                                                                 conv2d_126[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_127 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_128 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_127[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_14 (Concatenate)    (None, 128, 128, 160 0           conv2d_121[0][0]                 \n",
      "                                                                 conv2d_122[0][0]                 \n",
      "                                                                 conv2d_124[0][0]                 \n",
      "                                                                 conv2d_126[0][0]                 \n",
      "                                                                 conv2d_128[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_129 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_16 (Gl (None, 32)           0           conv2d_129[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_16 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_16[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 1, 32)        5152        reshape_16[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_17 (Reshape)            (None, 1, 1, 32)     0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_16 (Multiply)          (None, 128, 128, 32) 0           conv2d_129[0][0]                 \n",
      "                                                                 reshape_17[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_130 (Conv2D)             (None, 128, 128, 8)  2312        multiply_16[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_1 (IWT)                     (None, 256, 256, 2)  0           conv2d_130[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_131 (Conv2D)             (None, 256, 256, 2)  6           iwt_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_70 (Lambda)              (None, 256, 256, 2)  0           conv2d_131[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_75 (Lambda)              (None, 256, 256, 2)  0           lambda_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 256, 256, 2)  0           lambda_75[0][0]                  \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_76 (Lambda)              (None, 256, 256, 2)  0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_15 (Concatenate)    (None, 256, 256, 4)  0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_132 (Conv2D)             (None, 256, 256, 8)  40          concatenate_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_2 (DWT)                     (None, 128, 128, 32) 0           conv2d_132[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_133 (Conv2D)             (None, 128, 128, 32) 9248        dwt_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_134 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_133[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_16 (Concatenate)    (None, 128, 128, 64) 0           conv2d_133[0][0]                 \n",
      "                                                                 conv2d_134[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_135 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_136 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_135[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_17 (Concatenate)    (None, 128, 128, 96) 0           conv2d_133[0][0]                 \n",
      "                                                                 conv2d_134[0][0]                 \n",
      "                                                                 conv2d_136[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_137 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_138 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_137[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_18 (Concatenate)    (None, 128, 128, 128 0           conv2d_133[0][0]                 \n",
      "                                                                 conv2d_134[0][0]                 \n",
      "                                                                 conv2d_136[0][0]                 \n",
      "                                                                 conv2d_138[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_139 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_140 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_139[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_19 (Concatenate)    (None, 128, 128, 160 0           conv2d_133[0][0]                 \n",
      "                                                                 conv2d_134[0][0]                 \n",
      "                                                                 conv2d_136[0][0]                 \n",
      "                                                                 conv2d_138[0][0]                 \n",
      "                                                                 conv2d_140[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_141 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_17 (Gl (None, 32)           0           conv2d_141[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_18 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_17[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 1, 32)        3104        reshape_18[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_19 (Reshape)            (None, 1, 1, 32)     0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_17 (Multiply)          (None, 128, 128, 32) 0           conv2d_141[0][0]                 \n",
      "                                                                 reshape_19[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_142 (Conv2D)             (None, 128, 128, 8)  2312        multiply_17[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_2 (IWT)                     (None, 256, 256, 2)  0           conv2d_142[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_81 (Lambda)              (None, 256, 256, 2)  0           iwt_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_86 (Lambda)              (None, 256, 256, 2)  0           lambda_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 256, 256, 2)  0           lambda_86[0][0]                  \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_87 (Lambda)              (None, 256, 256, 2)  0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_20 (Concatenate)    (None, 256, 256, 6)  0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_143 (Conv2D)             (None, 256, 256, 8)  56          concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_3 (DWT)                     (None, 128, 128, 32) 0           conv2d_143[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_144 (Conv2D)             (None, 128, 128, 32) 9248        dwt_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_145 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_144[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_21 (Concatenate)    (None, 128, 128, 64) 0           conv2d_144[0][0]                 \n",
      "                                                                 conv2d_145[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_146 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_147 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_146[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_22 (Concatenate)    (None, 128, 128, 96) 0           conv2d_144[0][0]                 \n",
      "                                                                 conv2d_145[0][0]                 \n",
      "                                                                 conv2d_147[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_148 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_22[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_149 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_148[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_23 (Concatenate)    (None, 128, 128, 128 0           conv2d_144[0][0]                 \n",
      "                                                                 conv2d_145[0][0]                 \n",
      "                                                                 conv2d_147[0][0]                 \n",
      "                                                                 conv2d_149[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_150 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_23[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_151 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_150[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_24 (Concatenate)    (None, 128, 128, 160 0           conv2d_144[0][0]                 \n",
      "                                                                 conv2d_145[0][0]                 \n",
      "                                                                 conv2d_147[0][0]                 \n",
      "                                                                 conv2d_149[0][0]                 \n",
      "                                                                 conv2d_151[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_152 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_24[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_18 (Gl (None, 32)           0           conv2d_152[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_20 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_18[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 1, 32)        3104        reshape_20[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_21 (Reshape)            (None, 1, 1, 32)     0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_18 (Multiply)          (None, 128, 128, 32) 0           conv2d_152[0][0]                 \n",
      "                                                                 reshape_21[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_153 (Conv2D)             (None, 128, 128, 8)  2312        multiply_18[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_3 (IWT)                     (None, 256, 256, 2)  0           conv2d_153[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_92 (Lambda)              (None, 256, 256, 2)  0           iwt_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_97 (Lambda)              (None, 256, 256, 2)  0           lambda_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 256, 256, 2)  0           lambda_97[0][0]                  \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_98 (Lambda)              (None, 256, 256, 2)  0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_25 (Concatenate)    (None, 256, 256, 8)  0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "                                                                 lambda_98[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_154 (Conv2D)             (None, 256, 256, 8)  72          concatenate_25[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_4 (DWT)                     (None, 128, 128, 32) 0           conv2d_154[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_155 (Conv2D)             (None, 128, 128, 32) 9248        dwt_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_156 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_155[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_26 (Concatenate)    (None, 128, 128, 64) 0           conv2d_155[0][0]                 \n",
      "                                                                 conv2d_156[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_157 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_26[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_158 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_157[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_27 (Concatenate)    (None, 128, 128, 96) 0           conv2d_155[0][0]                 \n",
      "                                                                 conv2d_156[0][0]                 \n",
      "                                                                 conv2d_158[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_159 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_27[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_160 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_159[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_28 (Concatenate)    (None, 128, 128, 128 0           conv2d_155[0][0]                 \n",
      "                                                                 conv2d_156[0][0]                 \n",
      "                                                                 conv2d_158[0][0]                 \n",
      "                                                                 conv2d_160[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_161 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_28[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_162 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_161[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_29 (Concatenate)    (None, 128, 128, 160 0           conv2d_155[0][0]                 \n",
      "                                                                 conv2d_156[0][0]                 \n",
      "                                                                 conv2d_158[0][0]                 \n",
      "                                                                 conv2d_160[0][0]                 \n",
      "                                                                 conv2d_162[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_163 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_29[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_19 (Gl (None, 32)           0           conv2d_163[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_22 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_19[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 1, 32)        3104        reshape_22[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_23 (Reshape)            (None, 1, 1, 32)     0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_19 (Multiply)          (None, 128, 128, 32) 0           conv2d_163[0][0]                 \n",
      "                                                                 reshape_23[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_164 (Conv2D)             (None, 128, 128, 8)  2312        multiply_19[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_4 (IWT)                     (None, 256, 256, 2)  0           conv2d_164[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_103 (Lambda)             (None, 256, 256, 2)  0           iwt_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_108 (Lambda)             (None, 256, 256, 2)  0           lambda_103[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 256, 256, 2)  0           lambda_108[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_109 (Lambda)             (None, 256, 256, 2)  0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_30 (Concatenate)    (None, 256, 256, 10) 0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "                                                                 lambda_98[0][0]                  \n",
      "                                                                 lambda_109[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_165 (Conv2D)             (None, 256, 256, 8)  88          concatenate_30[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_5 (DWT)                     (None, 128, 128, 32) 0           conv2d_165[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_166 (Conv2D)             (None, 128, 128, 32) 9248        dwt_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_167 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_166[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_31 (Concatenate)    (None, 128, 128, 64) 0           conv2d_166[0][0]                 \n",
      "                                                                 conv2d_167[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_168 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_31[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_169 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_168[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_32 (Concatenate)    (None, 128, 128, 96) 0           conv2d_166[0][0]                 \n",
      "                                                                 conv2d_167[0][0]                 \n",
      "                                                                 conv2d_169[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_170 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_32[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_171 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_170[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_33 (Concatenate)    (None, 128, 128, 128 0           conv2d_166[0][0]                 \n",
      "                                                                 conv2d_167[0][0]                 \n",
      "                                                                 conv2d_169[0][0]                 \n",
      "                                                                 conv2d_171[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_172 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_33[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_173 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_172[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_34 (Concatenate)    (None, 128, 128, 160 0           conv2d_166[0][0]                 \n",
      "                                                                 conv2d_167[0][0]                 \n",
      "                                                                 conv2d_169[0][0]                 \n",
      "                                                                 conv2d_171[0][0]                 \n",
      "                                                                 conv2d_173[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_174 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_34[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_20 (Gl (None, 32)           0           conv2d_174[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_24 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_20[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 1, 32)        3104        reshape_24[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_25 (Reshape)            (None, 1, 1, 32)     0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_20 (Multiply)          (None, 128, 128, 32) 0           conv2d_174[0][0]                 \n",
      "                                                                 reshape_25[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_175 (Conv2D)             (None, 128, 128, 8)  2312        multiply_20[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_5 (IWT)                     (None, 256, 256, 2)  0           conv2d_175[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_114 (Lambda)             (None, 256, 256, 2)  0           iwt_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_119 (Lambda)             (None, 256, 256, 2)  0           lambda_114[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 256, 256, 2)  0           lambda_119[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_120 (Lambda)             (None, 256, 256, 2)  0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_35 (Concatenate)    (None, 256, 256, 12) 0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "                                                                 lambda_98[0][0]                  \n",
      "                                                                 lambda_109[0][0]                 \n",
      "                                                                 lambda_120[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_176 (Conv2D)             (None, 256, 256, 8)  104         concatenate_35[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_6 (DWT)                     (None, 128, 128, 32) 0           conv2d_176[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_177 (Conv2D)             (None, 128, 128, 32) 9248        dwt_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_178 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_177[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_36 (Concatenate)    (None, 128, 128, 64) 0           conv2d_177[0][0]                 \n",
      "                                                                 conv2d_178[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_179 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_36[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_180 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_179[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_37 (Concatenate)    (None, 128, 128, 96) 0           conv2d_177[0][0]                 \n",
      "                                                                 conv2d_178[0][0]                 \n",
      "                                                                 conv2d_180[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_181 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_37[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_182 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_181[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_38 (Concatenate)    (None, 128, 128, 128 0           conv2d_177[0][0]                 \n",
      "                                                                 conv2d_178[0][0]                 \n",
      "                                                                 conv2d_180[0][0]                 \n",
      "                                                                 conv2d_182[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_183 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_38[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_184 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_183[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_39 (Concatenate)    (None, 128, 128, 160 0           conv2d_177[0][0]                 \n",
      "                                                                 conv2d_178[0][0]                 \n",
      "                                                                 conv2d_180[0][0]                 \n",
      "                                                                 conv2d_182[0][0]                 \n",
      "                                                                 conv2d_184[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_185 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_39[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_21 (Gl (None, 32)           0           conv2d_185[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_26 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_21[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 1, 32)        3104        reshape_26[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_27 (Reshape)            (None, 1, 1, 32)     0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_21 (Multiply)          (None, 128, 128, 32) 0           conv2d_185[0][0]                 \n",
      "                                                                 reshape_27[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_186 (Conv2D)             (None, 128, 128, 8)  2312        multiply_21[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_6 (IWT)                     (None, 256, 256, 2)  0           conv2d_186[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_125 (Lambda)             (None, 256, 256, 2)  0           iwt_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_130 (Lambda)             (None, 256, 256, 2)  0           lambda_125[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 256, 256, 2)  0           lambda_130[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_131 (Lambda)             (None, 256, 256, 2)  0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_40 (Concatenate)    (None, 256, 256, 14) 0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "                                                                 lambda_98[0][0]                  \n",
      "                                                                 lambda_109[0][0]                 \n",
      "                                                                 lambda_120[0][0]                 \n",
      "                                                                 lambda_131[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_187 (Conv2D)             (None, 256, 256, 8)  120         concatenate_40[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_7 (DWT)                     (None, 128, 128, 32) 0           conv2d_187[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_188 (Conv2D)             (None, 128, 128, 32) 9248        dwt_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_189 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_188[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_41 (Concatenate)    (None, 128, 128, 64) 0           conv2d_188[0][0]                 \n",
      "                                                                 conv2d_189[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_190 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_41[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_191 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_190[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_42 (Concatenate)    (None, 128, 128, 96) 0           conv2d_188[0][0]                 \n",
      "                                                                 conv2d_189[0][0]                 \n",
      "                                                                 conv2d_191[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_192 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_42[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_193 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_192[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_43 (Concatenate)    (None, 128, 128, 128 0           conv2d_188[0][0]                 \n",
      "                                                                 conv2d_189[0][0]                 \n",
      "                                                                 conv2d_191[0][0]                 \n",
      "                                                                 conv2d_193[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_194 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_43[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_195 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_194[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_44 (Concatenate)    (None, 128, 128, 160 0           conv2d_188[0][0]                 \n",
      "                                                                 conv2d_189[0][0]                 \n",
      "                                                                 conv2d_191[0][0]                 \n",
      "                                                                 conv2d_193[0][0]                 \n",
      "                                                                 conv2d_195[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_196 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_44[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_22 (Gl (None, 32)           0           conv2d_196[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_28 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_22[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 1, 32)        3104        reshape_28[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_29 (Reshape)            (None, 1, 1, 32)     0           conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_22 (Multiply)          (None, 128, 128, 32) 0           conv2d_196[0][0]                 \n",
      "                                                                 reshape_29[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_197 (Conv2D)             (None, 128, 128, 8)  2312        multiply_22[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_7 (IWT)                     (None, 256, 256, 2)  0           conv2d_197[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_136 (Lambda)             (None, 256, 256, 2)  0           iwt_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_147 (Lambda)             (None, 256, 256, 2)  0           iwt_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_141 (Lambda)             (None, 256, 256, 2)  0           lambda_136[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_152 (Lambda)             (None, 256, 256, 2)  0           lambda_147[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 256, 256, 2)  0           lambda_141[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 256, 256, 2)  0           lambda_152[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_142 (Lambda)             (None, 256, 256, 2)  0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_153 (Lambda)             (None, 256, 256, 2)  0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_50 (Concatenate)    (None, 256, 256, 18) 0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "                                                                 lambda_98[0][0]                  \n",
      "                                                                 lambda_109[0][0]                 \n",
      "                                                                 lambda_120[0][0]                 \n",
      "                                                                 lambda_131[0][0]                 \n",
      "                                                                 lambda_142[0][0]                 \n",
      "                                                                 lambda_153[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_209 (Conv2D)             (None, 256, 256, 8)  152         concatenate_50[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_9 (DWT)                     (None, 128, 128, 32) 0           conv2d_209[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_210 (Conv2D)             (None, 128, 128, 32) 9248        dwt_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_211 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_210[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_51 (Concatenate)    (None, 128, 128, 64) 0           conv2d_210[0][0]                 \n",
      "                                                                 conv2d_211[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_212 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_51[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_213 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_212[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_52 (Concatenate)    (None, 128, 128, 96) 0           conv2d_210[0][0]                 \n",
      "                                                                 conv2d_211[0][0]                 \n",
      "                                                                 conv2d_213[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_214 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_52[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_215 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_214[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_53 (Concatenate)    (None, 128, 128, 128 0           conv2d_210[0][0]                 \n",
      "                                                                 conv2d_211[0][0]                 \n",
      "                                                                 conv2d_213[0][0]                 \n",
      "                                                                 conv2d_215[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_216 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_53[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_217 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_216[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_54 (Concatenate)    (None, 128, 128, 160 0           conv2d_210[0][0]                 \n",
      "                                                                 conv2d_211[0][0]                 \n",
      "                                                                 conv2d_213[0][0]                 \n",
      "                                                                 conv2d_215[0][0]                 \n",
      "                                                                 conv2d_217[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_218 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_54[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_24 (Gl (None, 32)           0           conv2d_218[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_32 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_24[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 1, 32)        3104        reshape_32[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_33 (Reshape)            (None, 1, 1, 32)     0           conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_24 (Multiply)          (None, 128, 128, 32) 0           conv2d_218[0][0]                 \n",
      "                                                                 reshape_33[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_219 (Conv2D)             (None, 128, 128, 8)  2312        multiply_24[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_9 (IWT)                     (None, 256, 256, 2)  0           conv2d_219[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_158 (Lambda)             (None, 256, 256, 2)  0           iwt_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "lambda_163 (Lambda)             (None, 256, 256, 2)  0           lambda_158[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 256, 256, 2)  0           lambda_163[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_164 (Lambda)             (None, 256, 256, 2)  0           add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_55 (Concatenate)    (None, 256, 256, 20) 0           lambda_76[0][0]                  \n",
      "                                                                 lambda_69[0][0]                  \n",
      "                                                                 lambda_87[0][0]                  \n",
      "                                                                 lambda_98[0][0]                  \n",
      "                                                                 lambda_109[0][0]                 \n",
      "                                                                 lambda_120[0][0]                 \n",
      "                                                                 lambda_131[0][0]                 \n",
      "                                                                 lambda_142[0][0]                 \n",
      "                                                                 lambda_153[0][0]                 \n",
      "                                                                 lambda_164[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_220 (Conv2D)             (None, 256, 256, 8)  168         concatenate_55[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dwt_10 (DWT)                    (None, 128, 128, 32) 0           conv2d_220[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_221 (Conv2D)             (None, 128, 128, 32) 9248        dwt_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_222 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_221[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_56 (Concatenate)    (None, 128, 128, 64) 0           conv2d_221[0][0]                 \n",
      "                                                                 conv2d_222[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_223 (Conv2D)             (None, 128, 128, 32) 2080        concatenate_56[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_224 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_223[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_57 (Concatenate)    (None, 128, 128, 96) 0           conv2d_221[0][0]                 \n",
      "                                                                 conv2d_222[0][0]                 \n",
      "                                                                 conv2d_224[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_225 (Conv2D)             (None, 128, 128, 32) 3104        concatenate_57[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_226 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_225[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_58 (Concatenate)    (None, 128, 128, 128 0           conv2d_221[0][0]                 \n",
      "                                                                 conv2d_222[0][0]                 \n",
      "                                                                 conv2d_224[0][0]                 \n",
      "                                                                 conv2d_226[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_227 (Conv2D)             (None, 128, 128, 32) 4128        concatenate_58[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_228 (Conv2D)             (None, 128, 128, 32) 9248        conv2d_227[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_59 (Concatenate)    (None, 128, 128, 160 0           conv2d_221[0][0]                 \n",
      "                                                                 conv2d_222[0][0]                 \n",
      "                                                                 conv2d_224[0][0]                 \n",
      "                                                                 conv2d_226[0][0]                 \n",
      "                                                                 conv2d_228[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_229 (Conv2D)             (None, 128, 128, 32) 5152        concatenate_59[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_25 (Gl (None, 32)           0           conv2d_229[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_34 (Reshape)            (None, 1, 32)        0           global_average_pooling2d_25[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 1, 32)        3104        reshape_34[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "reshape_35 (Reshape)            (None, 1, 1, 32)     0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "multiply_25 (Multiply)          (None, 128, 128, 32) 0           conv2d_229[0][0]                 \n",
      "                                                                 reshape_35[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_230 (Conv2D)             (None, 128, 128, 8)  2312        multiply_25[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "iwt_10 (IWT)                    (None, 256, 256, 2)  0           conv2d_230[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_169 (Lambda)             (None, 256, 256, 2)  0           iwt_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_174 (Lambda)             (None, 256, 256, 2)  0           lambda_169[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 256, 256, 2)  0           lambda_174[0][0]                 \n",
      "                                                                 lambda_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_175 (Lambda)             (None, 256, 256, 2)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "lambda_180 (Lambda)             (None, 256, 256, 2)  0           lambda_175[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 591,022\n",
      "Trainable params: 591,022\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# ddd_c10d10_32_dense_block_ca1d_dwt_noshortcut01_cartfirst，DCWN\n",
    "under_rate = '20'\n",
    "import dcwn_1 as dcwn\n",
    "mask = np.concatenate((var_sampling_mask[:,:,np.newaxis],var_sampling_mask[:,:,np.newaxis]),axis = -1) > 0\n",
    "mask = mask.astype(np.float32)\n",
    "stats = np.load(\"../Data/stats_fs_unet_norm_2c_20\" + under_rate + \".npy\")\n",
    "\n",
    "model = dcwn.ddd_c10d10_32_dense_block_ca1d_dwt_noshortcut01_cartfirst(stats[0],stats[1],stats[2],stats[3],mask,\n",
    "                                   kshape = (5,5),kshape2=(3,3))\n",
    "\n",
    "opt = Adam()\n",
    "model.compile(loss = [dcwn.nrmse,dcwn.nrmse],optimizer=opt, loss_weights=[0.01, 0.99])\n",
    "model_name = \"../Models/wnet_dc\" + under_rate +\"ddd_c10d10_32_dense_block_ca1d_dwt_noshortcut01_cartfirst_1dR5_2020_1_9.hdf5\"\n",
    "# model.load_weights(model_name)\n",
    "model.load_weights(model_name)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshape = (256,256)\n",
    "norm = np.sqrt(imshape[0]*imshape[1])\n",
    "\n",
    "for jj in range(len(kspace_files)):\n",
    "    kspace = np.load(kspace_files[jj])/norm\n",
    "    kspace2 = kspace.copy()\n",
    "    kspace[:,var_sampling_mask,:] = 0\n",
    "    kspace = (kspace-stats[0])/stats[1]\n",
    "    rec = np.abs(np.fft.ifft2(kspace2[:,:,:,0]+1j*kspace2[:,:,:,1])).astype(np.float64)\n",
    "    pred0 = model.predict(kspace)[1].astype(np.float64)\n",
    "    pred=np.abs(pred0[:,:,:,0]+1j*pred0[:,:,:,1])\n",
    "    fig = plt.figure(dpi = 100)\n",
    "    plt.imshow(pred[100] , cmap = \"gray\")\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 89.5, 54.5, -0.5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# import PIL.Image as img\n",
    "x1, x2, y1, y2 = 140, 230, 130, 185\n",
    "fig, ax = plt.subplots(dpi = 100)\n",
    "ax.imshow(pred[100] , cmap = \"gray\")\n",
    "ax.add_patch(patches.Rectangle((x1,y1),90,55,linewidth=1,edgecolor='red',fill=False))\n",
    "ax.axis(\"off\")\n",
    "axins = zoomed_inset_axes(ax, 1.5, loc=2) \n",
    "axins.imshow(pred[100,y1:y2,x1:x2] , cmap = \"gray\")\n",
    "axins.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.30888990934704\n",
      "28.58245272874974\n",
      "0.07967612736810449\n",
      "36.35870715901768\n",
      "[47.0097765  46.93048189 46.93489879 46.98853465 46.95862219 46.93128354\n",
      " 46.85580376 46.88665972 46.58920433 46.04506237 45.26056755 44.30343425\n",
      " 43.03949551 42.75439991 42.46701585 42.59587011 42.1045024  41.88999268\n",
      " 41.59227989 40.95702265 41.00190226 40.40125349 40.27341315 39.49392652\n",
      " 39.08511238 39.08580459 38.70084051 38.42755621 38.15126509 37.70317361\n",
      " 37.84869128 37.65522368 37.42973431 37.02607659 37.40908953 37.48285125\n",
      " 37.22071503 36.92255588 36.87694093 36.53341523 36.26205101 35.80321445\n",
      " 35.69614003 35.38617853 35.07528751 34.99137803 34.86279807 34.9374634\n",
      " 34.83348806 34.81156165 35.08066216 35.01154597 35.2276384  35.44423721\n",
      " 35.22425995 35.14730314 35.04312695 35.08357592 34.88134583 34.78158611\n",
      " 34.50717294 34.12016996 34.22044555 34.45339215 34.5322022  34.66151409\n",
      " 34.70845669 34.84884427 34.84708028 35.06730081 34.83046104 34.37710055\n",
      " 34.64983154 34.50168332 34.70730942 34.84334395 34.92117759 34.95357126\n",
      " 34.79195491 34.83215776 35.00242179 34.99150713 34.84524691 34.62218763\n",
      " 34.38094227 34.95489558 35.11048984 34.91718257 35.15546514 35.17775475\n",
      " 34.81620083 34.74621422 34.89440826 34.61172189 34.86751437 35.01767688\n",
      " 35.00445658 34.78835752 34.86957803 34.52278246 35.08098573 34.89184103\n",
      " 34.9460099  35.14699105 35.38616749 35.45820181 35.41999942 35.24317704\n",
      " 35.62574448 35.69419793 35.6657741  35.39668798 35.66852757 35.84267592\n",
      " 36.05852678 36.21182757 35.97142078 35.94401726 35.68499971 35.71122561\n",
      " 35.79056092 35.88610585 36.13245966 36.14537981 36.43352231 36.59214954\n",
      " 36.87669214 37.22255211 37.58752621 37.9602163  38.15532378 38.27704908\n",
      " 38.17907542 38.14099587 38.59792478 38.73353266 39.01353994 39.15010396\n",
      " 39.16219512 39.24864143 39.32203203 39.61417436 39.72522569 39.93453953\n",
      " 39.76485601 40.14885331 40.5684965  41.21097592 41.27162707 41.75004535\n",
      " 42.34831631 42.50214313 42.75035571 42.95113779 43.15064029 42.93919943\n",
      " 42.22280687 43.04373341 44.48576336 45.86454203 46.47695185 46.81961544\n",
      " 46.94255496 46.96186631 47.0225032  47.00644357 46.93107578 46.97715048\n",
      " 46.9817749  46.9625254 ]\n",
      "0.015596430022828189\n",
      "[0.00447428 0.00451532 0.00451302 0.00448524 0.00450071 0.0045149\n",
      " 0.00455431 0.00453816 0.00469626 0.00499988 0.00547248 0.00610999\n",
      " 0.00706704 0.00730285 0.00754852 0.00743736 0.00787023 0.00806701\n",
      " 0.00834831 0.00898175 0.00893547 0.00957524 0.00971721 0.01062958\n",
      " 0.01114183 0.01114095 0.01164583 0.01201806 0.01240649 0.01306332\n",
      " 0.01284629 0.01313564 0.01348111 0.0141224  0.01351319 0.01339892\n",
      " 0.01380946 0.01429172 0.01436697 0.01494657 0.0154209  0.01625742\n",
      " 0.01645907 0.01705703 0.0176786  0.01785021 0.01811642 0.01796136\n",
      " 0.01817766 0.0182236  0.01766767 0.01780882 0.01737122 0.0169434\n",
      " 0.01737798 0.01753263 0.01774418 0.01766174 0.01807778 0.0182866\n",
      " 0.01887355 0.01973349 0.01950698 0.01899078 0.01881925 0.01854115\n",
      " 0.01844121 0.01814555 0.01814923 0.01769487 0.01818399 0.01915831\n",
      " 0.0185661  0.01888549 0.01844365 0.01815704 0.01799507 0.01792808\n",
      " 0.01826479 0.01818044 0.01782753 0.01784995 0.01815307 0.01862529\n",
      " 0.01914984 0.01792535 0.0176071  0.01800334 0.01751617 0.01747127\n",
      " 0.01821387 0.01836122 0.01805061 0.01864774 0.01810659 0.01779625\n",
      " 0.01782336 0.01827235 0.01810229 0.01883967 0.01766701 0.01805595\n",
      " 0.01794369 0.01753326 0.01705705 0.01691618 0.01699074 0.01734017\n",
      " 0.01659301 0.01646275 0.01651671 0.0170364  0.01651148 0.01618373\n",
      " 0.0157865  0.01551033 0.01594562 0.015996   0.01648019 0.01643051\n",
      " 0.01628112 0.01610301 0.0156527  0.01562944 0.01511946 0.01484584\n",
      " 0.01436738 0.01380654 0.01323842 0.0126824  0.0124007  0.01222813\n",
      " 0.01236683 0.01242117 0.01178463 0.01160208 0.01123402 0.01105878\n",
      " 0.01104339 0.01093403 0.01084203 0.01048344 0.01035025 0.01010381\n",
      " 0.01030314 0.00985757 0.00939264 0.00872295 0.00866226 0.00819804\n",
      " 0.00765238 0.00751805 0.00730625 0.0071393  0.00697719 0.00714912\n",
      " 0.00776376 0.00706359 0.00598307 0.00510488 0.00475735 0.00457332\n",
      " 0.00450905 0.00449903 0.00446773 0.004476   0.00451501 0.00449112\n",
      " 0.00448873 0.00449869]\n",
      "0.9479510010744626\n",
      "[0.97812018 0.97778173 0.97778641 0.97801869 0.97788716 0.97774632\n",
      " 0.97740898 0.97804047 0.97740055 0.97716497 0.97614083 0.97467929\n",
      " 0.97247117 0.97266175 0.97254567 0.97352533 0.9734898  0.97319765\n",
      " 0.97318786 0.9725814  0.97183806 0.97121399 0.9704988  0.96860989\n",
      " 0.96644642 0.967067   0.96673387 0.9669904  0.96633731 0.96393879\n",
      " 0.9654912  0.96371683 0.96302869 0.96061499 0.96289826 0.96295748\n",
      " 0.96095797 0.95860112 0.95746335 0.95679997 0.95603555 0.9540538\n",
      " 0.95397501 0.95245287 0.94976686 0.94801548 0.94600556 0.94632602\n",
      " 0.9461945  0.94325205 0.94478078 0.94399943 0.94457495 0.94305992\n",
      " 0.940787   0.93857387 0.93882489 0.93808613 0.93657048 0.93605274\n",
      " 0.93337286 0.93155207 0.93391816 0.93311813 0.93268747 0.93547372\n",
      " 0.93631641 0.93673622 0.93632548 0.938311   0.93534477 0.93105188\n",
      " 0.93465063 0.93319319 0.9351211  0.93603299 0.93681994 0.93915337\n",
      " 0.93666443 0.93675896 0.93713885 0.93706699 0.9322859  0.92935286\n",
      " 0.92706925 0.93263924 0.93132512 0.92649188 0.92921572 0.92966917\n",
      " 0.92876724 0.92896424 0.93142417 0.92990435 0.93246928 0.93333833\n",
      " 0.93227712 0.92903274 0.93274328 0.92941882 0.93561713 0.9341806\n",
      " 0.93438588 0.93574371 0.93860056 0.93987841 0.93905744 0.93912728\n",
      " 0.94202155 0.94139134 0.94054123 0.94068838 0.9419748  0.94486473\n",
      " 0.94562055 0.94782487 0.94620585 0.94739278 0.94579979 0.94674861\n",
      " 0.94648749 0.94780985 0.94891656 0.94991917 0.95227871 0.95571776\n",
      " 0.9574009  0.95979974 0.96036324 0.96226512 0.96291743 0.96316916\n",
      " 0.96179506 0.96238943 0.96537295 0.96621889 0.96734789 0.96736113\n",
      " 0.96799084 0.96819895 0.96804926 0.9684761  0.96893348 0.96911142\n",
      " 0.96770239 0.96863843 0.96905441 0.97141335 0.97167479 0.97240113\n",
      " 0.97353726 0.97321685 0.97437617 0.97491346 0.97477693 0.97441404\n",
      " 0.97312271 0.97373532 0.97481473 0.97670108 0.97728513 0.97752499\n",
      " 0.97786065 0.97786022 0.9782111  0.97813014 0.97778176 0.97794982\n",
      " 0.97801767 0.9779204 ]\n"
     ]
    }
   ],
   "source": [
    "#compare psnr and ssim\n",
    "import math\n",
    "from skimage.measure import compare_ssim\n",
    "def psnr2(target, ref,ma,mi):\n",
    "    # assume RGB image\n",
    "    target_data = np.array(target, dtype=float)\n",
    "    ref_data = np.array(ref, dtype=float)\n",
    "\n",
    "    diff = ref_data - target_data\n",
    "    diff = diff.flatten('C')\n",
    "\n",
    "    rmse = math.sqrt(np.mean(diff ** 2.))\n",
    "    nrmse=rmse/(ma-mi)\n",
    "\n",
    "    return 20 * math.log10(ma/ rmse),nrmse \n",
    "\n",
    "from skimage.measure import compare_ssim\n",
    "psnr_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "nrmse_all_nonzero= np.zeros(shape=(len(pred)))\n",
    "ssim_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "rate_all_nonzero = np.zeros(shape=(len(pred)))\n",
    "ma=np.max(rec)\n",
    "mi=np.min(rec)\n",
    "print(np.max(pred))\n",
    "print(np.max(rec))\n",
    "print(np.min(rec))\n",
    "for i in range(len(pred)):\n",
    "    psnr_all_nonzero[i],nrmse_all_nonzero[i]=psnr2(pred[i,:,:],rec[i,:,:],ma,mi)\n",
    "    ssim_all_nonzero[i],diff_all=compare_ssim(pred[i,:,:],rec[i,:,:],full=True,data_range=ma-mi)\n",
    "print(np.mean(psnr_all_nonzero[20:-20]))\n",
    "print(psnr_all_nonzero)\n",
    "print(np.mean(nrmse_all_nonzero[20:-20]))\n",
    "print(nrmse_all_nonzero)\n",
    "print(np.mean(ssim_all_nonzero[20:-20]))\n",
    "print(ssim_all_nonzero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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