vgg_3d_grad_cam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10).  Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
      " https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
      "\n",
      "Using gpu device 0: TITAN X (Pascal) (CNMeM is disabled, cuDNN 5110)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import gc\n",
    "import sys\n",
    "import time\n",
    "import datetime\n",
    "import traceback\n",
    "from collections import OrderedDict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nibabel as nib\n",
    "from tqdm import *\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "\n",
    "import theano\n",
    "import theano.tensor as T\n",
    "import lasagne\n",
    "from lasagne.layers import InputLayer\n",
    "from lasagne.layers.dnn import Conv3DDNNLayer\n",
    "from lasagne.layers.dnn import Pool3DDNNLayer\n",
    "from lasagne.layers import BatchNormLayer\n",
    "from lasagne.layers import NonlinearityLayer\n",
    "from lasagne.layers import ElemwiseSumLayer\n",
    "from lasagne.layers import DenseLayer\n",
    "from lasagne.layers import GlobalPoolLayer\n",
    "from lasagne.nonlinearities import rectify, softmax, identity\n",
    "from lasagne.layers import set_all_param_values\n",
    "from lasagne.layers import DropoutLayer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from skimage.transform import resize\n",
    "from skimage.transform import rotate\n",
    "import nibabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_var = T.tensor5(name='input', dtype='float32')\n",
    "target_var = T.ivector()\n",
    "inp_shape = (None, 1, 110, 110, 110)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "modelname = 'models/pretrained_vgg.npz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mx = nib.load('image/example.nii').get_data().max(axis=0).max(axis=0).max(axis=0)\n",
    "mri = np.array(nib.load('image/example.nii').get_data()) / mx\n",
    "mri_rz = resize(mri,(256,256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_net():\n",
    "    \"\"\"Method for VGG like net Building.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    nn : lasagne.layer\n",
    "        Network.\n",
    "    \"\"\"\n",
    "    nn = {}\n",
    "    nn['input'] = InputLayer(inp_shape, input_var=input_var)\n",
    "\n",
    "    nn['conv1a'] = Conv3DDNNLayer(nn['input'], 8, 3)\n",
    "    nn['conv1b'] = Conv3DDNNLayer(nn['conv1a'], 8, 3, nonlinearity=identity)\n",
    "    nn['nl1'] = NonlinearityLayer(nn['conv1b'])\n",
    "    nn['pool1'] = Pool3DDNNLayer(nn['nl1'], 2)\n",
    "\n",
    "    nn['conv2a'] = Conv3DDNNLayer(nn['pool1'], 16, 3)\n",
    "    nn['conv2b'] = Conv3DDNNLayer(nn['conv2a'], 16, 3, nonlinearity=identity)\n",
    "    nn['nl2'] = NonlinearityLayer(nn['conv2b'])\n",
    "    nn['pool2'] = Pool3DDNNLayer(nn['nl2'], 2)\n",
    "\n",
    "    nn['conv3a'] = Conv3DDNNLayer(nn['pool2'], 32, 3)\n",
    "    nn['conv3b'] = Conv3DDNNLayer(nn['conv3a'], 32, 3)\n",
    "    nn['conv3c'] = Conv3DDNNLayer(nn['conv3b'], 32, 3, nonlinearity=identity)\n",
    "    nn['nl3'] = NonlinearityLayer(nn['conv3c'])\n",
    "    nn['pool3'] = Pool3DDNNLayer(nn['nl3'], 2)\n",
    "\n",
    "    nn['conv4a'] = Conv3DDNNLayer(nn['pool3'], 64, 3)\n",
    "    nn['conv4b'] = Conv3DDNNLayer(nn['conv4a'], 64, 3)\n",
    "    nn['conv4c'] = Conv3DDNNLayer(nn['conv4b'], 64, 3, nonlinearity=identity)\n",
    "    nn['nl4'] = NonlinearityLayer(nn['conv4c'])\n",
    "    nn['pool4'] = Pool3DDNNLayer(nn['nl4'], 2)\n",
    "\n",
    "    nn['dense1'] = DenseLayer(nn['pool4'], num_units=128)\n",
    "    nn['bn'] = BatchNormLayer(nn['dense1'])\n",
    "    nn['dropout'] = DropoutLayer(nn['bn'], p=0.7)\n",
    "\n",
    "    nn['dense2'] = DenseLayer(nn['dropout'], num_units=64)\n",
    "    \n",
    "    #nn['pool4'] = GlobalPoolLayer(nn['nl4'])\n",
    "\n",
    "    nn['prob'] = DenseLayer(nn['dense2'], num_units=2,\n",
    "                    nonlinearity=lasagne.nonlinearities.softmax)\n",
    "    return nn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "net = build_net()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#you can change to the layer that you want to visualize\n",
    "outlayer, activation = lasagne.layers.get_output([net['prob'],net['conv4c']],deterministic=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#prediction = lasagne.layers.get_output(net['prob'], deterministic=False)\n",
    "loss = lasagne.objectives.categorical_crossentropy(outlayer,\n",
    "                                                   target_var)\n",
    "loss = loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grad = T.grad(loss, activation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#conv_output = lasagne.layers.get_output(net['voxres9_out'])\n",
    "test_fn = theano.function([input_var], outlayer)\n",
    "#conv_fn = theano.function([input_var], conv_output)\n",
    "grad_fn = theano.function([input_var,target_var], grad)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "activation_fn = theano.function([input_var], activation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with np.load(modelname) as f:\n",
    "    param_values = [f['arr_%d' % i] for i in range(len(f.files))]\n",
    "\n",
    "lasagne.layers.set_all_param_values(net['prob'], param_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:38: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n",
      "/usr/local/lib/python3.5/dist-packages/matplotlib/__init__.py:805: MatplotlibDeprecationWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n",
      "  mplDeprecation)\n",
      "/usr/local/lib/python3.5/dist-packages/matplotlib/rcsetup.py:155: MatplotlibDeprecationWarning: axes.hold is deprecated, will be removed in 3.0\n",
      "  mplDeprecation)\n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:48: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n",
      "/usr/local/lib/python3.5/dist-packages/ipykernel/__main__.py:58: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n",
      "    Future behavior will be consistent with the long-time default:\n",
      "    plot commands add elements without first clearing the\n",
      "    Axes and/or Figure.\n"
     ]
    },
    {
     "data": {
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/HSGCKbuX3gWcAv5DQ/OvAT8F/Jgx5pestVcjQk0AW4Dn/OISgLX2RWPMceA2YASQwLRe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\nCiYAAAAAelEwAQAAANCLggkAAACAXv4BSTKZJcH0VWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff846fd8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grad_val = -grad_fn(np.array(mri).reshape((-1, 1, 110, 110, 110)),np.array(np.int32(1)).reshape((1)))\n",
    "\n",
    "activation_val = activation_fn(np.array(mri).reshape((-1, 1, 110, 110, 110)))[0]\n",
    "\n",
    "class_weights = np.sum(grad_val, axis=(2,3,4)) / np.prod(grad_val.shape[2:5])\n",
    "\n",
    "cam = np.zeros(dtype = np.float32, shape = activation_val.shape[1:4])\n",
    "\n",
    "for j, w in enumerate(class_weights[0,:]):\n",
    "    cam += w * activation_val[j, :, :, :]\n",
    "\n",
    "res = (cam - np.min(cam)) / (np.max(cam) - np.min(cam))\n",
    "\n",
    "seg0_name = 'image/3DUCM/example_level_00.npy'\n",
    "seg0 = np.load(seg0_name)\n",
    "seg0 = np.flipud(seg0)\n",
    "seg0_array = np.unique(seg0)\n",
    "vox_cnt = []\n",
    "\n",
    "for cc in range(len(seg0_array)):\n",
    "    vox_cnt.append(len(np.where(seg0==seg0_array[cc])[0]))\n",
    "bg_seg = seg0_array[np.where(np.array(vox_cnt) == max(vox_cnt))[0]][0]\n",
    "\n",
    "res = resize(res,(110,110,110))\n",
    "res[np.where(seg0==bg_seg)] = 0\n",
    "\n",
    "res = resize(res,(256,256,256))\n",
    "\n",
    "mri_rz = resize(mri,(256,256,256))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(21, 5))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.axis('off')\n",
    "matr = rotate(mri_rz[:, :, 128].reshape(256, 256), 90)\n",
    "plt.imshow(matr, cmap=plt.cm.Greys_r, interpolation=None,\n",
    "           vmax=1., vmin=0.)\n",
    "plt.hold(True)\n",
    "matr = rotate(res[:, :, 128].reshape(256, 256), 90)\n",
    "plt.imshow(matr,\n",
    "           interpolation=None, vmax=1., vmin=.0, alpha=1,\n",
    "           cmap=plt.cm.rainbow)\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.axis('off')\n",
    "matr = rotate(mri_rz[128,:, :].reshape(256, 256), 90)\n",
    "plt.imshow(matr, cmap=plt.cm.Greys_r, interpolation=None,\n",
    "           vmax=1., vmin=0.)\n",
    "plt.hold(True)\n",
    "matr = rotate(res[128,:, :].reshape(256, 256), 90)\n",
    "plt.imshow(matr,\n",
    "           interpolation=None, vmax=1., vmin=.0, alpha=1,\n",
    "           cmap=plt.cm.rainbow)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.axis('off')\n",
    "matr = rotate(mri_rz[:,128, :].reshape(256, 256), 90)\n",
    "plt.imshow(matr, cmap=plt.cm.Greys_r, interpolation=None,\n",
    "           vmax=1., vmin=0.)\n",
    "plt.hold(True)\n",
    "matr = rotate(res[:,128, :].reshape(256, 256), 90)\n",
    "plt.imshow(matr,\n",
    "           interpolation=None, vmax=1., vmin=.0, alpha=1,\n",
    "           cmap=plt.cm.rainbow)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.tick_params(labelsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}