3DGrad-CAM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libaries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T02:42:28.709213Z",
     "start_time": "2020-01-18T02:42:25.793371Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import datetime\n",
    "import numpy as np\n",
    "from deploy_config import*  #Import Paths and Model Config file\n",
    "from loss_funnction_And_matrics import*  #Import Loss functions\n",
    "from Resnet_3D import Resnet3D #import the model\n",
    "import pandas as pd\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configure Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T02:42:30.626315Z",
     "start_time": "2020-01-18T02:42:28.710171Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading The Model from this path--/Local/new_3d_classification_test/Liver_Deploy/mymodel_6.92_28.h5\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "CT (InputLayer)                 [(None, 128, 128, 12 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv3d (Conv3D)                 (None, 128, 128, 128 432         CT[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 128, 128, 128 64          conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6 (TensorFlowOp [(None, 128, 128, 12 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_1 (Conv3D)               (None, 64, 64, 64, 3 32768       tf_op_layer_Relu6[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 64, 64, 64, 3 128         conv3d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_1 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d (MaxPooling3D)    (None, 64, 64, 64, 1 0           conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_2 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Pad (TensorFlowOpLa [(None, 64, 64, 64,  0           max_pooling3d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add (TensorFlowOpLa [(None, 64, 64, 64,  0           conv3d_2[0][0]                   \n",
      "                                                                 tf_op_layer_Pad[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 64, 64, 64, 3 128         tf_op_layer_add[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_2 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_3 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64, 64, 64, 3 128         conv3d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_3 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_4 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_1 (TensorFlowOp [(None, 64, 64, 64,  0           conv3d_4[0][0]                   \n",
      "                                                                 tf_op_layer_add[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 64, 64, 64, 3 128         tf_op_layer_add_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_4 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_5 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 64, 64, 64, 3 128         conv3d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_5 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_6 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_2 (TensorFlowOp [(None, 64, 64, 64,  0           conv3d_6[0][0]                   \n",
      "                                                                 tf_op_layer_add_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 64, 64, 64, 3 128         tf_op_layer_add_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_6 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_7 (Conv3D)               (None, 32, 32, 32, 6 131072      tf_op_layer_Relu6_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 32, 32, 32, 6 256         conv3d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_7 (TensorFlow [(None, 32, 32, 32,  0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_1 (MaxPooling3D)  (None, 32, 32, 32, 3 0           tf_op_layer_add_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_8 (Conv3D)               (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Pad_1 (TensorFlowOp [(None, 32, 32, 32,  0           max_pooling3d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_3 (TensorFlowOp [(None, 32, 32, 32,  0           conv3d_8[0][0]                   \n",
      "                                                                 tf_op_layer_Pad_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 32, 32, 32, 6 256         tf_op_layer_add_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_8 (TensorFlow [(None, 32, 32, 32,  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_9 (Conv3D)               (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 32, 32, 32, 6 256         conv3d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_9 (TensorFlow [(None, 32, 32, 32,  0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_10 (Conv3D)              (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_4 (TensorFlowOp [(None, 32, 32, 32,  0           conv3d_10[0][0]                  \n",
      "                                                                 tf_op_layer_add_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 32, 32, 32, 6 256         tf_op_layer_add_4[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_10 (TensorFlo [(None, 32, 32, 32,  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_11 (Conv3D)              (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_10[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 32, 32, 32, 6 256         conv3d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_11 (TensorFlo [(None, 32, 32, 32,  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_12 (Conv3D)              (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_11[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_5 (TensorFlowOp [(None, 32, 32, 32,  0           conv3d_12[0][0]                  \n",
      "                                                                 tf_op_layer_add_4[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 32, 32, 32, 6 256         tf_op_layer_add_5[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_12 (TensorFlo [(None, 32, 32, 32,  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_13 (Conv3D)              (None, 16, 16, 16, 1 524288      tf_op_layer_Relu6_12[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 16, 1 512         conv3d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_13 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_2 (MaxPooling3D)  (None, 16, 16, 16, 6 0           tf_op_layer_add_5[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_14 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_13[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Pad_2 (TensorFlowOp [(None, 16, 16, 16,  0           max_pooling3d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_6 (TensorFlowOp [(None, 16, 16, 16,  0           conv3d_14[0][0]                  \n",
      "                                                                 tf_op_layer_Pad_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 16, 1 512         tf_op_layer_add_6[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_14 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_15 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_14[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 16, 1 512         conv3d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_15 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_16 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_15[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_7 (TensorFlowOp [(None, 16, 16, 16,  0           conv3d_16[0][0]                  \n",
      "                                                                 tf_op_layer_add_6[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 16, 1 512         tf_op_layer_add_7[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_16 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_17 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_16[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 16, 16, 16, 1 512         conv3d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_17 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_18 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_17[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_8 (TensorFlowOp [(None, 16, 16, 16,  0           conv3d_18[0][0]                  \n",
      "                                                                 tf_op_layer_add_7[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 16, 16, 16, 1 512         tf_op_layer_add_8[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_18 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling3d (Globa (None, 128)          0           tf_op_layer_Relu6_18[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 5)            645         global_average_pooling3d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 3,597,685\n",
      "Trainable params: 3,594,965\n",
      "Non-trainable params: 2,720\n",
      "__________________________________________________________________________________________________\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "CT (InputLayer)                 [(None, 128, 128, 12 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv3d (Conv3D)                 (None, 128, 128, 128 432         CT[0][0]                         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 128, 128, 128 64          conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6 (TensorFlowOp [(None, 128, 128, 12 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_1 (Conv3D)               (None, 64, 64, 64, 3 32768       tf_op_layer_Relu6[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 64, 64, 64, 3 128         conv3d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_1 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d (MaxPooling3D)    (None, 64, 64, 64, 1 0           conv3d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_2 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Pad (TensorFlowOpLa [(None, 64, 64, 64,  0           max_pooling3d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add (TensorFlowOpLa [(None, 64, 64, 64,  0           conv3d_2[0][0]                   \n",
      "                                                                 tf_op_layer_Pad[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 64, 64, 64, 3 128         tf_op_layer_add[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_2 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_3 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64, 64, 64, 3 128         conv3d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_3 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_4 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_1 (TensorFlowOp [(None, 64, 64, 64,  0           conv3d_4[0][0]                   \n",
      "                                                                 tf_op_layer_add[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 64, 64, 64, 3 128         tf_op_layer_add_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_4 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_5 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 64, 64, 64, 3 128         conv3d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_5 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_6 (Conv3D)               (None, 64, 64, 64, 3 27648       tf_op_layer_Relu6_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_2 (TensorFlowOp [(None, 64, 64, 64,  0           conv3d_6[0][0]                   \n",
      "                                                                 tf_op_layer_add_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 64, 64, 64, 3 128         tf_op_layer_add_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_6 (TensorFlow [(None, 64, 64, 64,  0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_7 (Conv3D)               (None, 32, 32, 32, 6 131072      tf_op_layer_Relu6_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 32, 32, 32, 6 256         conv3d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_7 (TensorFlow [(None, 32, 32, 32,  0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_1 (MaxPooling3D)  (None, 32, 32, 32, 3 0           tf_op_layer_add_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_8 (Conv3D)               (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Pad_1 (TensorFlowOp [(None, 32, 32, 32,  0           max_pooling3d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_3 (TensorFlowOp [(None, 32, 32, 32,  0           conv3d_8[0][0]                   \n",
      "                                                                 tf_op_layer_Pad_1[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 32, 32, 32, 6 256         tf_op_layer_add_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_8 (TensorFlow [(None, 32, 32, 32,  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_9 (Conv3D)               (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 32, 32, 32, 6 256         conv3d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_9 (TensorFlow [(None, 32, 32, 32,  0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_10 (Conv3D)              (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_4 (TensorFlowOp [(None, 32, 32, 32,  0           conv3d_10[0][0]                  \n",
      "                                                                 tf_op_layer_add_3[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 32, 32, 32, 6 256         tf_op_layer_add_4[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_10 (TensorFlo [(None, 32, 32, 32,  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_11 (Conv3D)              (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_10[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 32, 32, 32, 6 256         conv3d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_11 (TensorFlo [(None, 32, 32, 32,  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_12 (Conv3D)              (None, 32, 32, 32, 6 110592      tf_op_layer_Relu6_11[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_5 (TensorFlowOp [(None, 32, 32, 32,  0           conv3d_12[0][0]                  \n",
      "                                                                 tf_op_layer_add_4[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 32, 32, 32, 6 256         tf_op_layer_add_5[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_12 (TensorFlo [(None, 32, 32, 32,  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_13 (Conv3D)              (None, 16, 16, 16, 1 524288      tf_op_layer_Relu6_12[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 16, 1 512         conv3d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_13 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling3d_2 (MaxPooling3D)  (None, 16, 16, 16, 6 0           tf_op_layer_add_5[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_14 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_13[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Pad_2 (TensorFlowOp [(None, 16, 16, 16,  0           max_pooling3d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_6 (TensorFlowOp [(None, 16, 16, 16,  0           conv3d_14[0][0]                  \n",
      "                                                                 tf_op_layer_Pad_2[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 16, 1 512         tf_op_layer_add_6[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_14 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_15 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_14[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 16, 1 512         conv3d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_15 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_16 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_15[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_7 (TensorFlowOp [(None, 16, 16, 16,  0           conv3d_16[0][0]                  \n",
      "                                                                 tf_op_layer_add_6[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 16, 1 512         tf_op_layer_add_7[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_16 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_17 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_16[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 16, 16, 16, 1 512         conv3d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_17 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv3d_18 (Conv3D)              (None, 16, 16, 16, 1 442368      tf_op_layer_Relu6_17[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_add_8 (TensorFlowOp [(None, 16, 16, 16,  0           conv3d_18[0][0]                  \n",
      "                                                                 tf_op_layer_add_7[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 16, 16, 16, 1 512         tf_op_layer_add_8[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Relu6_18 (TensorFlo [(None, 16, 16, 16,  0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling3d (Globa (None, 128)          0           tf_op_layer_Relu6_18[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 5)            645         global_average_pooling3d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 3,597,685\n",
      "Trainable params: 3,594,965\n",
      "Non-trainable params: 2,720\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "###---Load The model----###\n",
    "INPUT_PATCH_SIZE=(128,128,128,1)\n",
    "inputs = tf.keras.Input(shape=INPUT_PATCH_SIZE, name='CT')\n",
    "Model_3D=Resnet3D(inputs,num_classes=NUMBER_OF_CLASSES) # Load Model\n",
    "Model_3D.load_weights('mymodel_21.30_30.h5') #Load Weights\n",
    "print('Loading The Model from this path--{}'.format(MODEL_PATH))\n",
    "Model_3D.summary()\n",
    "###---lAYER-Name--to-visualize--###\n",
    "LAYER_NAME='conv3d_18'\n",
    "# Create a graph that outputs target convolution and output\n",
    "grad_model = tf.keras.models.Model([Model_3D.inputs], [Model_3D.get_layer(LAYER_NAME).output, Model_3D.output])\n",
    "grad_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to Resize the 3D volume "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T02:42:30.697792Z",
     "start_time": "2020-01-18T02:42:30.628309Z"
    }
   },
   "outputs": [],
   "source": [
    "def resize_image_with_crop_or_pad(image, img_size=(64, 64, 64), **kwargs):\n",
    "    \"\"\"Image resizing. Resizes image by cropping or padding dimension\n",
    "     to fit specified size.\n",
    "    Args:\n",
    "        image (np.ndarray): image to be resized\n",
    "        img_size (list or tuple): new image size\n",
    "        kwargs (): additional arguments to be passed to np.pad\n",
    "    Returns:\n",
    "        np.ndarray: resized image\n",
    "    \"\"\"\n",
    "\n",
    "    assert isinstance(image, (np.ndarray, np.generic))\n",
    "    assert (image.ndim - 1 == len(img_size) or image.ndim == len(img_size)), \\\n",
    "        'Example size doesnt fit image size'\n",
    "\n",
    "    # Get the image dimensionality\n",
    "    rank = len(img_size)\n",
    "\n",
    "    # Create placeholders for the new shape\n",
    "    from_indices = [[0, image.shape[dim]] for dim in range(rank)]\n",
    "    to_padding = [[0, 0] for dim in range(rank)]\n",
    "\n",
    "    slicer = [slice(None)] * rank\n",
    "\n",
    "    # For each dimensions find whether it is supposed to be cropped or padded\n",
    "    for i in range(rank):\n",
    "        if image.shape[i] < img_size[i]:\n",
    "            to_padding[i][0] = (img_size[i] - image.shape[i]) // 2\n",
    "            to_padding[i][1] = img_size[i] - image.shape[i] - to_padding[i][0]\n",
    "        else:\n",
    "            from_indices[i][0] = int(np.floor((image.shape[i] - img_size[i]) / 2.))\n",
    "            from_indices[i][1] = from_indices[i][0] + img_size[i]\n",
    "\n",
    "        # Create slicer object to crop or leave each dimension\n",
    "        slicer[i] = slice(from_indices[i][0], from_indices[i][1])\n",
    "\n",
    "    # Pad the cropped image to extend the missing dimension\n",
    "    return np.pad(image[slicer], to_padding, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading volume and compute the gradients "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T02:42:38.366221Z",
     "start_time": "2020-01-18T02:42:30.700783Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tusha\\Anaconda3\\envs\\tf2\\lib\\site-packages\\ipykernel_launcher.py:38: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 128, 1)\n",
      "(1, 128, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "##----Load---Image\n",
    "import SimpleITK as sitk\n",
    "img_path='XXX-IMAG.nii.gz'\n",
    "img_sitk = sitk.ReadImage(img_path, sitk.sitkFloat32)\n",
    "image= sitk.GetArrayFromImage(img_sitk)\n",
    "resized_img=resize_image_with_crop_or_pad(image=image,img_size=(128,128,128),mode='symmetric')\n",
    "#resized_img=image\n",
    "resized_img.shape\n",
    "io_img=tf.expand_dims(resized_img, axis=-1)\n",
    "print(io_img.shape)\n",
    "io_img=tf.expand_dims(io_img, axis=0)\n",
    "print(io_img.shape)\n",
    "###----index of the class\n",
    "CLASS_INDEX=2\n",
    "\n",
    "###--Compute GRADIENT\n",
    "with tf.GradientTape() as tape:\n",
    "    conv_outputs, predictions = grad_model(io_img)\n",
    "    loss = predictions[:, CLASS_INDEX]\n",
    "\n",
    "# Extract filters and gradients\n",
    "output = conv_outputs[0]\n",
    "grads = tape.gradient(loss, conv_outputs)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T02:42:38.423653Z",
     "start_time": "2020-01-18T02:42:38.367219Z"
    }
   },
   "outputs": [],
   "source": [
    "# Average gradients spatially\n",
    "weights = tf.reduce_mean(grads, axis=(0, 1,2))\n",
    "# Build a ponderated map of filters according to gradients importance\n",
    "cam = np.zeros(output.shape[0:3], dtype=np.float32)\n",
    "\n",
    "for index, w in enumerate(weights):\n",
    "    cam += w * output[:, :, :, index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-18T02:44:45.242057Z",
     "start_time": "2020-01-18T02:44:44.574098Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Overlay')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage.transform import resize\n",
    "from matplotlib import pyplot as plt\n",
    "capi=resize(cam,(128,128,128))\n",
    "#print(capi.shape)\n",
    "capi = np.maximum(capi,0)\n",
    "heatmap = (capi - capi.min()) / (capi.max() - capi.min())\n",
    "f, axarr = plt.subplots(2,3,figsize=(15,10));\n",
    "f.suptitle('Grad-CAM')\n",
    "slice_count=127\n",
    "slice_count2=30\n",
    "    \n",
    "axial_ct_img=np.squeeze(resized_img[slice_count, :,:])\n",
    "axial_grad_cmap_img=np.squeeze(heatmap[slice_count,:, :])\n",
    "\n",
    "coronal_ct_img=np.squeeze(resized_img[:,slice_count2,:])\n",
    "coronal_grad_cmap_img=np.squeeze(heatmap[:,slice_count2,:]) \n",
    "\n",
    "img_plot = axarr[0,0].imshow(axial_ct_img, cmap='gray');\n",
    "axarr[0,0].axis('off')\n",
    "axarr[0,0].set_title('CT')\n",
    "    \n",
    "img_plot = axarr[0,1].imshow(axial_grad_cmap_img, cmap='jet');\n",
    "axarr[0,1].axis('off')\n",
    "axarr[0,1].set_title('Grad-CAM')\n",
    "    \n",
    "axial_overlay=cv2.addWeighted(axial_ct_img,0.3,axial_grad_cmap_img, 0.6, 0)\n",
    "    \n",
    "img_plot = axarr[0,2].imshow(axial_overlay,cmap='jet');\n",
    "axarr[0,2].axis('off')\n",
    "axarr[0,2].set_title('Overlay')\n",
    "\n",
    "\n",
    "img_plot = axarr[1,0].imshow(coronal_ct_img, cmap='gray');\n",
    "axarr[1,0].axis('off')\n",
    "axarr[1,0].set_title('CT')\n",
    "    \n",
    "img_plot = axarr[1,1].imshow(coronal_grad_cmap_img, cmap='jet');\n",
    "axarr[1,1].axis('off')\n",
    "axarr[1,1].set_title('Grad-CAM')\n",
    "    \n",
    "Coronal_overlay=cv2.addWeighted(coronal_ct_img,0.3,coronal_grad_cmap_img, 0.6, 0)\n",
    "    \n",
    "img_plot = axarr[1,2].imshow(Coronal_overlay,cmap='jet');\n",
    "axarr[1,2].axis('off')\n",
    "axarr[1,2].set_title('Overlay')"
   ]
  }
 ],
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