test_and_explore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test stuff and explore FCN with VGG16 example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    div#notebook-container    { width: 75%; }\n",
       "    div#menubar-container     { width: 75%; }\n",
       "    div#maintoolbar-container { width: 75%; }\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "import tensorflow as tf\n",
    "import helper\n",
    "import cv2\n",
    "import numpy as np\n",
    "import scipy.misc\n",
    "import warnings\n",
    "from distutils.version import LooseVersion\n",
    "import project_tests as tests\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "display(HTML(data=\"\"\"\n",
    "<style>\n",
    "    div#notebook-container    { width: 75%; }\n",
    "    div#menubar-container     { width: 75%; }\n",
    "    div#maintoolbar-container { width: 75%; }\n",
    "</style>\"\"\"))\n",
    "\n",
    "# Check for a GPU\n",
    "if not tf.test.gpu_device_name():\n",
    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\n",
    "\n",
    "\n",
    "data_dir = './data'\n",
    "runs_dir = './runs'\n",
    "\n",
    "# Download pretrained vgg model\n",
    "helper.maybe_download_pretrained_vgg(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to vgg model\n",
    "vgg_tag = 'vgg16'\n",
    "vgg_path = os.path.join(data_dir, 'vgg')\n",
    "\n",
    "def getGraph(sess):\n",
    "    tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)\n",
    "    graph = tf.get_default_graph()\n",
    "    return graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print all variables / layer names in graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./data/vgg/variables/variables\n",
      "INFO:tensorflow:Restoring parameters from ./runs/semantic_segmentation_model.ckpt\n",
      "image_input\n",
      "\t(<tf.Tensor 'image_input:0' shape=(?, ?, ?, 3) dtype=float32>,)\n",
      "keep_prob\n",
      "\t(<tf.Tensor 'keep_prob:0' shape=<unknown> dtype=float32>,)\n",
      "Processing/split/split_dim\n",
      "\t(<tf.Tensor 'Processing/split/split_dim:0' shape=() dtype=int32>,)\n",
      "Processing/split\n",
      "\t(<tf.Tensor 'Processing/split:0' shape=(?, ?, ?, 1) dtype=float32>, <tf.Tensor 'Processing/split:1' shape=(?, ?, ?, 1) dtype=float32>, <tf.Tensor 'Processing/split:2' shape=(?, ?, ?, 1) dtype=float32>)\n",
      "Processing/sub/y\n",
      "\t(<tf.Tensor 'Processing/sub/y:0' shape=() dtype=float32>,)\n",
      "Processing/sub\n",
      "\t(<tf.Tensor 'Processing/sub:0' shape=(?, ?, ?, 1) dtype=float32>,)\n",
      "Processing/sub_1/y\n",
      "\t(<tf.Tensor 'Processing/sub_1/y:0' shape=() dtype=float32>,)\n",
      "Processing/sub_1\n",
      "\t(<tf.Tensor 'Processing/sub_1:0' shape=(?, ?, ?, 1) dtype=float32>,)\n",
      "Processing/sub_2/y\n",
      "\t(<tf.Tensor 'Processing/sub_2/y:0' shape=() dtype=float32>,)\n",
      "Processing/sub_2\n",
      "\t(<tf.Tensor 'Processing/sub_2:0' shape=(?, ?, ?, 1) dtype=float32>,)\n",
      "Processing/concat/axis\n",
      "\t(<tf.Tensor 'Processing/concat/axis:0' shape=() dtype=int32>,)\n",
      "Processing/concat\n",
      "\t(<tf.Tensor 'Processing/concat:0' shape=(?, ?, ?, 3) dtype=float32>,)\n",
      "conv1_1/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv1_1/filter/Initializer/Const:0' shape=(3, 3, 3, 64) dtype=float32>,)\n",
      "conv1_1/filter\n",
      "\t(<tf.Tensor 'conv1_1/filter:0' shape=(3, 3, 3, 64) dtype=float32_ref>,)\n",
      "conv1_1/filter/Assign\n",
      "\t(<tf.Tensor 'conv1_1/filter/Assign:0' shape=(3, 3, 3, 64) dtype=float32_ref>,)\n",
      "conv1_1/filter/read\n",
      "\t(<tf.Tensor 'conv1_1/filter/read:0' shape=(3, 3, 3, 64) dtype=float32>,)\n",
      "conv1_1/L2Loss\n",
      "\t(<tf.Tensor 'conv1_1/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv1_1/weight_loss/y\n",
      "\t(<tf.Tensor 'conv1_1/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv1_1/weight_loss\n",
      "\t(<tf.Tensor 'conv1_1/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv1_1/Conv2D\n",
      "\t(<tf.Tensor 'conv1_1/Conv2D:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "conv1_1/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv1_1/biases/Initializer/Const:0' shape=(64,) dtype=float32>,)\n",
      "conv1_1/biases\n",
      "\t(<tf.Tensor 'conv1_1/biases:0' shape=(64,) dtype=float32_ref>,)\n",
      "conv1_1/biases/Assign\n",
      "\t(<tf.Tensor 'conv1_1/biases/Assign:0' shape=(64,) dtype=float32_ref>,)\n",
      "conv1_1/biases/read\n",
      "\t(<tf.Tensor 'conv1_1/biases/read:0' shape=(64,) dtype=float32>,)\n",
      "conv1_1/BiasAdd\n",
      "\t(<tf.Tensor 'conv1_1/BiasAdd:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "conv1_1/Relu\n",
      "\t(<tf.Tensor 'conv1_1/Relu:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "conv1_2/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv1_2/filter/Initializer/Const:0' shape=(3, 3, 64, 64) dtype=float32>,)\n",
      "conv1_2/filter\n",
      "\t(<tf.Tensor 'conv1_2/filter:0' shape=(3, 3, 64, 64) dtype=float32_ref>,)\n",
      "conv1_2/filter/Assign\n",
      "\t(<tf.Tensor 'conv1_2/filter/Assign:0' shape=(3, 3, 64, 64) dtype=float32_ref>,)\n",
      "conv1_2/filter/read\n",
      "\t(<tf.Tensor 'conv1_2/filter/read:0' shape=(3, 3, 64, 64) dtype=float32>,)\n",
      "conv1_2/L2Loss\n",
      "\t(<tf.Tensor 'conv1_2/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv1_2/weight_loss/y\n",
      "\t(<tf.Tensor 'conv1_2/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv1_2/weight_loss\n",
      "\t(<tf.Tensor 'conv1_2/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv1_2/Conv2D\n",
      "\t(<tf.Tensor 'conv1_2/Conv2D:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "conv1_2/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv1_2/biases/Initializer/Const:0' shape=(64,) dtype=float32>,)\n",
      "conv1_2/biases\n",
      "\t(<tf.Tensor 'conv1_2/biases:0' shape=(64,) dtype=float32_ref>,)\n",
      "conv1_2/biases/Assign\n",
      "\t(<tf.Tensor 'conv1_2/biases/Assign:0' shape=(64,) dtype=float32_ref>,)\n",
      "conv1_2/biases/read\n",
      "\t(<tf.Tensor 'conv1_2/biases/read:0' shape=(64,) dtype=float32>,)\n",
      "conv1_2/BiasAdd\n",
      "\t(<tf.Tensor 'conv1_2/BiasAdd:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "conv1_2/Relu\n",
      "\t(<tf.Tensor 'conv1_2/Relu:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "pool1\n",
      "\t(<tf.Tensor 'pool1:0' shape=(?, ?, ?, 64) dtype=float32>,)\n",
      "conv2_1/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv2_1/filter/Initializer/Const:0' shape=(3, 3, 64, 128) dtype=float32>,)\n",
      "conv2_1/filter\n",
      "\t(<tf.Tensor 'conv2_1/filter:0' shape=(3, 3, 64, 128) dtype=float32_ref>,)\n",
      "conv2_1/filter/Assign\n",
      "\t(<tf.Tensor 'conv2_1/filter/Assign:0' shape=(3, 3, 64, 128) dtype=float32_ref>,)\n",
      "conv2_1/filter/read\n",
      "\t(<tf.Tensor 'conv2_1/filter/read:0' shape=(3, 3, 64, 128) dtype=float32>,)\n",
      "conv2_1/L2Loss\n",
      "\t(<tf.Tensor 'conv2_1/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv2_1/weight_loss/y\n",
      "\t(<tf.Tensor 'conv2_1/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv2_1/weight_loss\n",
      "\t(<tf.Tensor 'conv2_1/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv2_1/Conv2D\n",
      "\t(<tf.Tensor 'conv2_1/Conv2D:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "conv2_1/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv2_1/biases/Initializer/Const:0' shape=(128,) dtype=float32>,)\n",
      "conv2_1/biases\n",
      "\t(<tf.Tensor 'conv2_1/biases:0' shape=(128,) dtype=float32_ref>,)\n",
      "conv2_1/biases/Assign\n",
      "\t(<tf.Tensor 'conv2_1/biases/Assign:0' shape=(128,) dtype=float32_ref>,)\n",
      "conv2_1/biases/read\n",
      "\t(<tf.Tensor 'conv2_1/biases/read:0' shape=(128,) dtype=float32>,)\n",
      "conv2_1/BiasAdd\n",
      "\t(<tf.Tensor 'conv2_1/BiasAdd:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "conv2_1/Relu\n",
      "\t(<tf.Tensor 'conv2_1/Relu:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "conv2_2/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv2_2/filter/Initializer/Const:0' shape=(3, 3, 128, 128) dtype=float32>,)\n",
      "conv2_2/filter\n",
      "\t(<tf.Tensor 'conv2_2/filter:0' shape=(3, 3, 128, 128) dtype=float32_ref>,)\n",
      "conv2_2/filter/Assign\n",
      "\t(<tf.Tensor 'conv2_2/filter/Assign:0' shape=(3, 3, 128, 128) dtype=float32_ref>,)\n",
      "conv2_2/filter/read\n",
      "\t(<tf.Tensor 'conv2_2/filter/read:0' shape=(3, 3, 128, 128) dtype=float32>,)\n",
      "conv2_2/L2Loss\n",
      "\t(<tf.Tensor 'conv2_2/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv2_2/weight_loss/y\n",
      "\t(<tf.Tensor 'conv2_2/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv2_2/weight_loss\n",
      "\t(<tf.Tensor 'conv2_2/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv2_2/Conv2D\n",
      "\t(<tf.Tensor 'conv2_2/Conv2D:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "conv2_2/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv2_2/biases/Initializer/Const:0' shape=(128,) dtype=float32>,)\n",
      "conv2_2/biases\n",
      "\t(<tf.Tensor 'conv2_2/biases:0' shape=(128,) dtype=float32_ref>,)\n",
      "conv2_2/biases/Assign\n",
      "\t(<tf.Tensor 'conv2_2/biases/Assign:0' shape=(128,) dtype=float32_ref>,)\n",
      "conv2_2/biases/read\n",
      "\t(<tf.Tensor 'conv2_2/biases/read:0' shape=(128,) dtype=float32>,)\n",
      "conv2_2/BiasAdd\n",
      "\t(<tf.Tensor 'conv2_2/BiasAdd:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "conv2_2/Relu\n",
      "\t(<tf.Tensor 'conv2_2/Relu:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "pool2\n",
      "\t(<tf.Tensor 'pool2:0' shape=(?, ?, ?, 128) dtype=float32>,)\n",
      "conv3_1/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv3_1/filter/Initializer/Const:0' shape=(3, 3, 128, 256) dtype=float32>,)\n",
      "conv3_1/filter\n",
      "\t(<tf.Tensor 'conv3_1/filter:0' shape=(3, 3, 128, 256) dtype=float32_ref>,)\n",
      "conv3_1/filter/Assign\n",
      "\t(<tf.Tensor 'conv3_1/filter/Assign:0' shape=(3, 3, 128, 256) dtype=float32_ref>,)\n",
      "conv3_1/filter/read\n",
      "\t(<tf.Tensor 'conv3_1/filter/read:0' shape=(3, 3, 128, 256) dtype=float32>,)\n",
      "conv3_1/L2Loss\n",
      "\t(<tf.Tensor 'conv3_1/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv3_1/weight_loss/y\n",
      "\t(<tf.Tensor 'conv3_1/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv3_1/weight_loss\n",
      "\t(<tf.Tensor 'conv3_1/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv3_1/Conv2D\n",
      "\t(<tf.Tensor 'conv3_1/Conv2D:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_1/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv3_1/biases/Initializer/Const:0' shape=(256,) dtype=float32>,)\n",
      "conv3_1/biases\n",
      "\t(<tf.Tensor 'conv3_1/biases:0' shape=(256,) dtype=float32_ref>,)\n",
      "conv3_1/biases/Assign\n",
      "\t(<tf.Tensor 'conv3_1/biases/Assign:0' shape=(256,) dtype=float32_ref>,)\n",
      "conv3_1/biases/read\n",
      "\t(<tf.Tensor 'conv3_1/biases/read:0' shape=(256,) dtype=float32>,)\n",
      "conv3_1/BiasAdd\n",
      "\t(<tf.Tensor 'conv3_1/BiasAdd:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_1/Relu\n",
      "\t(<tf.Tensor 'conv3_1/Relu:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_2/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv3_2/filter/Initializer/Const:0' shape=(3, 3, 256, 256) dtype=float32>,)\n",
      "conv3_2/filter\n",
      "\t(<tf.Tensor 'conv3_2/filter:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "conv3_2/filter/Assign\n",
      "\t(<tf.Tensor 'conv3_2/filter/Assign:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "conv3_2/filter/read\n",
      "\t(<tf.Tensor 'conv3_2/filter/read:0' shape=(3, 3, 256, 256) dtype=float32>,)\n",
      "conv3_2/L2Loss\n",
      "\t(<tf.Tensor 'conv3_2/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv3_2/weight_loss/y\n",
      "\t(<tf.Tensor 'conv3_2/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv3_2/weight_loss\n",
      "\t(<tf.Tensor 'conv3_2/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv3_2/Conv2D\n",
      "\t(<tf.Tensor 'conv3_2/Conv2D:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_2/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv3_2/biases/Initializer/Const:0' shape=(256,) dtype=float32>,)\n",
      "conv3_2/biases\n",
      "\t(<tf.Tensor 'conv3_2/biases:0' shape=(256,) dtype=float32_ref>,)\n",
      "conv3_2/biases/Assign\n",
      "\t(<tf.Tensor 'conv3_2/biases/Assign:0' shape=(256,) dtype=float32_ref>,)\n",
      "conv3_2/biases/read\n",
      "\t(<tf.Tensor 'conv3_2/biases/read:0' shape=(256,) dtype=float32>,)\n",
      "conv3_2/BiasAdd\n",
      "\t(<tf.Tensor 'conv3_2/BiasAdd:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_2/Relu\n",
      "\t(<tf.Tensor 'conv3_2/Relu:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_3/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv3_3/filter/Initializer/Const:0' shape=(3, 3, 256, 256) dtype=float32>,)\n",
      "conv3_3/filter\n",
      "\t(<tf.Tensor 'conv3_3/filter:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "conv3_3/filter/Assign\n",
      "\t(<tf.Tensor 'conv3_3/filter/Assign:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "conv3_3/filter/read\n",
      "\t(<tf.Tensor 'conv3_3/filter/read:0' shape=(3, 3, 256, 256) dtype=float32>,)\n",
      "conv3_3/L2Loss\n",
      "\t(<tf.Tensor 'conv3_3/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv3_3/weight_loss/y\n",
      "\t(<tf.Tensor 'conv3_3/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv3_3/weight_loss\n",
      "\t(<tf.Tensor 'conv3_3/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv3_3/Conv2D\n",
      "\t(<tf.Tensor 'conv3_3/Conv2D:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_3/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv3_3/biases/Initializer/Const:0' shape=(256,) dtype=float32>,)\n",
      "conv3_3/biases\n",
      "\t(<tf.Tensor 'conv3_3/biases:0' shape=(256,) dtype=float32_ref>,)\n",
      "conv3_3/biases/Assign\n",
      "\t(<tf.Tensor 'conv3_3/biases/Assign:0' shape=(256,) dtype=float32_ref>,)\n",
      "conv3_3/biases/read\n",
      "\t(<tf.Tensor 'conv3_3/biases/read:0' shape=(256,) dtype=float32>,)\n",
      "conv3_3/BiasAdd\n",
      "\t(<tf.Tensor 'conv3_3/BiasAdd:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv3_3/Relu\n",
      "\t(<tf.Tensor 'conv3_3/Relu:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "pool3\n",
      "\t(<tf.Tensor 'pool3:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "layer3_out\n",
      "\t(<tf.Tensor 'layer3_out:0' shape=(?, ?, ?, 256) dtype=float32>,)\n",
      "conv4_1/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv4_1/filter/Initializer/Const:0' shape=(3, 3, 256, 512) dtype=float32>,)\n",
      "conv4_1/filter\n",
      "\t(<tf.Tensor 'conv4_1/filter:0' shape=(3, 3, 256, 512) dtype=float32_ref>,)\n",
      "conv4_1/filter/Assign\n",
      "\t(<tf.Tensor 'conv4_1/filter/Assign:0' shape=(3, 3, 256, 512) dtype=float32_ref>,)\n",
      "conv4_1/filter/read\n",
      "\t(<tf.Tensor 'conv4_1/filter/read:0' shape=(3, 3, 256, 512) dtype=float32>,)\n",
      "conv4_1/L2Loss\n",
      "\t(<tf.Tensor 'conv4_1/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv4_1/weight_loss/y\n",
      "\t(<tf.Tensor 'conv4_1/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv4_1/weight_loss\n",
      "\t(<tf.Tensor 'conv4_1/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv4_1/Conv2D\n",
      "\t(<tf.Tensor 'conv4_1/Conv2D:0' shape=(?, ?, ?, 512) dtype=float32>,)\n",
      "conv4_1/biases/Initializer/Const\n",
      "\t(<tf.Tensor 'conv4_1/biases/Initializer/Const:0' shape=(512,) dtype=float32>,)\n",
      "conv4_1/biases\n",
      "\t(<tf.Tensor 'conv4_1/biases:0' shape=(512,) dtype=float32_ref>,)\n",
      "conv4_1/biases/Assign\n",
      "\t(<tf.Tensor 'conv4_1/biases/Assign:0' shape=(512,) dtype=float32_ref>,)\n",
      "conv4_1/biases/read\n",
      "\t(<tf.Tensor 'conv4_1/biases/read:0' shape=(512,) dtype=float32>,)\n",
      "conv4_1/BiasAdd\n",
      "\t(<tf.Tensor 'conv4_1/BiasAdd:0' shape=(?, ?, ?, 512) dtype=float32>,)\n",
      "conv4_1/Relu\n",
      "\t(<tf.Tensor 'conv4_1/Relu:0' shape=(?, ?, ?, 512) dtype=float32>,)\n",
      "conv4_2/filter/Initializer/Const\n",
      "\t(<tf.Tensor 'conv4_2/filter/Initializer/Const:0' shape=(3, 3, 512, 512) dtype=float32>,)\n",
      "conv4_2/filter\n",
      "\t(<tf.Tensor 'conv4_2/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "conv4_2/filter/Assign\n",
      "\t(<tf.Tensor 'conv4_2/filter/Assign:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "conv4_2/filter/read\n",
      "\t(<tf.Tensor 'conv4_2/filter/read:0' shape=(3, 3, 512, 512) dtype=float32>,)\n",
      "conv4_2/L2Loss\n",
      "\t(<tf.Tensor 'conv4_2/L2Loss:0' shape=() dtype=float32>,)\n",
      "conv4_2/weight_loss/y\n",
      "\t(<tf.Tensor 'conv4_2/weight_loss/y:0' shape=() dtype=float32>,)\n",
      "conv4_2/weight_loss\n",
      "\t(<tf.Tensor 'conv4_2/weight_loss:0' shape=() dtype=float32>,)\n",
      "conv4_2/Conv2D\n",
      "\t(<tf.Tensor 'conv4_2/Conv2D:0' shape=(?, ?, ?, 512) dtype=float32>,)\n",
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      "\t(<tf.Tensor 'save/RestoreV2_8/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_8/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_8/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_8\n",
      "\t(<tf.Tensor 'save/RestoreV2_8:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_8\n",
      "\t(<tf.Tensor 'save/Assign_8:0' shape=(256,) dtype=float32_ref>,)\n",
      "save/RestoreV2_9/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_9/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_9/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_9/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_9\n",
      "\t(<tf.Tensor 'save/RestoreV2_9:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_9\n",
      "\t(<tf.Tensor 'save/Assign_9:0' shape=(3, 3, 128, 256) dtype=float32_ref>,)\n",
      "save/RestoreV2_10/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_10/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_10/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_10/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_10\n",
      "\t(<tf.Tensor 'save/RestoreV2_10:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_10\n",
      "\t(<tf.Tensor 'save/Assign_10:0' shape=(256,) dtype=float32_ref>,)\n",
      "save/RestoreV2_11/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_11/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_11/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_11/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_11\n",
      "\t(<tf.Tensor 'save/RestoreV2_11:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_11\n",
      "\t(<tf.Tensor 'save/Assign_11:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "save/RestoreV2_12/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_12/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_12/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_12/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_12\n",
      "\t(<tf.Tensor 'save/RestoreV2_12:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_12\n",
      "\t(<tf.Tensor 'save/Assign_12:0' shape=(256,) dtype=float32_ref>,)\n",
      "save/RestoreV2_13/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_13/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_13/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_13/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_13\n",
      "\t(<tf.Tensor 'save/RestoreV2_13:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_13\n",
      "\t(<tf.Tensor 'save/Assign_13:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "save/RestoreV2_14/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_14/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_14/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_14/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_14\n",
      "\t(<tf.Tensor 'save/RestoreV2_14:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_14\n",
      "\t(<tf.Tensor 'save/Assign_14:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/RestoreV2_15/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_15/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_15/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_15/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_15\n",
      "\t(<tf.Tensor 'save/RestoreV2_15:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_15\n",
      "\t(<tf.Tensor 'save/Assign_15:0' shape=(3, 3, 256, 512) dtype=float32_ref>,)\n",
      "save/RestoreV2_16/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_16/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_16/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_16/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_16\n",
      "\t(<tf.Tensor 'save/RestoreV2_16:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_16\n",
      "\t(<tf.Tensor 'save/Assign_16:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/RestoreV2_17/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_17/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_17/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_17/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_17\n",
      "\t(<tf.Tensor 'save/RestoreV2_17:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_17\n",
      "\t(<tf.Tensor 'save/Assign_17:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/RestoreV2_18/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_18/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_18/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_18/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_18\n",
      "\t(<tf.Tensor 'save/RestoreV2_18:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_18\n",
      "\t(<tf.Tensor 'save/Assign_18:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/RestoreV2_19/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_19/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_19/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_19/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_19\n",
      "\t(<tf.Tensor 'save/RestoreV2_19:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_19\n",
      "\t(<tf.Tensor 'save/Assign_19:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/RestoreV2_20/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_20/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_20/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_20/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_20\n",
      "\t(<tf.Tensor 'save/RestoreV2_20:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_20\n",
      "\t(<tf.Tensor 'save/Assign_20:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/RestoreV2_21/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_21/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_21/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_21/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_21\n",
      "\t(<tf.Tensor 'save/RestoreV2_21:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_21\n",
      "\t(<tf.Tensor 'save/Assign_21:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/RestoreV2_22/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_22/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_22/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_22/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_22\n",
      "\t(<tf.Tensor 'save/RestoreV2_22:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_22\n",
      "\t(<tf.Tensor 'save/Assign_22:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/RestoreV2_23/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_23/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_23/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_23/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_23\n",
      "\t(<tf.Tensor 'save/RestoreV2_23:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_23\n",
      "\t(<tf.Tensor 'save/Assign_23:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/RestoreV2_24/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_24/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_24/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_24/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_24\n",
      "\t(<tf.Tensor 'save/RestoreV2_24:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_24\n",
      "\t(<tf.Tensor 'save/Assign_24:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/RestoreV2_25/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_25/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_25/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_25/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_25\n",
      "\t(<tf.Tensor 'save/RestoreV2_25:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_25\n",
      "\t(<tf.Tensor 'save/Assign_25:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/RestoreV2_26/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_26/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_26/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_26/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_26\n",
      "\t(<tf.Tensor 'save/RestoreV2_26:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_26\n",
      "\t(<tf.Tensor 'save/Assign_26:0' shape=(4096,) dtype=float32_ref>,)\n",
      "save/RestoreV2_27/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_27/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_27/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_27/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_27\n",
      "\t(<tf.Tensor 'save/RestoreV2_27:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_27\n",
      "\t(<tf.Tensor 'save/Assign_27:0' shape=(7, 7, 512, 4096) dtype=float32_ref>,)\n",
      "save/RestoreV2_28/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_28/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_28/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_28/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_28\n",
      "\t(<tf.Tensor 'save/RestoreV2_28:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_28\n",
      "\t(<tf.Tensor 'save/Assign_28:0' shape=(4096,) dtype=float32_ref>,)\n",
      "save/RestoreV2_29/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_29/tensor_names:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_29/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_29/shape_and_slices:0' shape=(1,) dtype=string>,)\n",
      "save/RestoreV2_29\n",
      "\t(<tf.Tensor 'save/RestoreV2_29:0' shape=<unknown> dtype=float32>,)\n",
      "save/Assign_29\n",
      "\t(<tf.Tensor 'save/Assign_29:0' shape=(1, 1, 4096, 4096) dtype=float32_ref>,)\n",
      "save/restore_shard\n",
      "\t()\n",
      "save/restore_all\n",
      "\t()\n",
      "save/Const_1\n",
      "\t(<tf.Tensor 'save/Const_1:0' shape=() dtype=string>,)\n",
      "save/SaveV2_1/tensor_names\n",
      "\t(<tf.Tensor 'save/SaveV2_1/tensor_names:0' shape=(30,) dtype=string>,)\n",
      "save/SaveV2_1/shape_and_slices\n",
      "\t(<tf.Tensor 'save/SaveV2_1/shape_and_slices:0' shape=(30,) dtype=string>,)\n",
      "save/SaveV2_1\n",
      "\t()\n",
      "save/control_dependency_1\n",
      "\t(<tf.Tensor 'save/control_dependency_1:0' shape=() dtype=string>,)\n",
      "save/RestoreV2_30/tensor_names\n",
      "\t(<tf.Tensor 'save/RestoreV2_30/tensor_names:0' shape=(30,) dtype=string>,)\n",
      "save/RestoreV2_30/shape_and_slices\n",
      "\t(<tf.Tensor 'save/RestoreV2_30/shape_and_slices:0' shape=(30,) dtype=string>,)\n",
      "save/RestoreV2_30\n",
      "\t(<tf.Tensor 'save/RestoreV2_30:0' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:1' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:2' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:3' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:4' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:5' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:6' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:7' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:8' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:9' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:10' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:11' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:12' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:13' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:14' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:15' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:16' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:17' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:18' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:19' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:20' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:21' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:22' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:23' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:24' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:25' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:26' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:27' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:28' shape=<unknown> dtype=float32>, <tf.Tensor 'save/RestoreV2_30:29' shape=<unknown> dtype=float32>)\n",
      "save/Assign_30\n",
      "\t(<tf.Tensor 'save/Assign_30:0' shape=(64,) dtype=float32_ref>,)\n",
      "save/Assign_31\n",
      "\t(<tf.Tensor 'save/Assign_31:0' shape=(3, 3, 3, 64) dtype=float32_ref>,)\n",
      "save/Assign_32\n",
      "\t(<tf.Tensor 'save/Assign_32:0' shape=(64,) dtype=float32_ref>,)\n",
      "save/Assign_33\n",
      "\t(<tf.Tensor 'save/Assign_33:0' shape=(3, 3, 64, 64) dtype=float32_ref>,)\n",
      "save/Assign_34\n",
      "\t(<tf.Tensor 'save/Assign_34:0' shape=(128,) dtype=float32_ref>,)\n",
      "save/Assign_35\n",
      "\t(<tf.Tensor 'save/Assign_35:0' shape=(3, 3, 64, 128) dtype=float32_ref>,)\n",
      "save/Assign_36\n",
      "\t(<tf.Tensor 'save/Assign_36:0' shape=(128,) dtype=float32_ref>,)\n",
      "save/Assign_37\n",
      "\t(<tf.Tensor 'save/Assign_37:0' shape=(3, 3, 128, 128) dtype=float32_ref>,)\n",
      "save/Assign_38\n",
      "\t(<tf.Tensor 'save/Assign_38:0' shape=(256,) dtype=float32_ref>,)\n",
      "save/Assign_39\n",
      "\t(<tf.Tensor 'save/Assign_39:0' shape=(3, 3, 128, 256) dtype=float32_ref>,)\n",
      "save/Assign_40\n",
      "\t(<tf.Tensor 'save/Assign_40:0' shape=(256,) dtype=float32_ref>,)\n",
      "save/Assign_41\n",
      "\t(<tf.Tensor 'save/Assign_41:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "save/Assign_42\n",
      "\t(<tf.Tensor 'save/Assign_42:0' shape=(256,) dtype=float32_ref>,)\n",
      "save/Assign_43\n",
      "\t(<tf.Tensor 'save/Assign_43:0' shape=(3, 3, 256, 256) dtype=float32_ref>,)\n",
      "save/Assign_44\n",
      "\t(<tf.Tensor 'save/Assign_44:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/Assign_45\n",
      "\t(<tf.Tensor 'save/Assign_45:0' shape=(3, 3, 256, 512) dtype=float32_ref>,)\n",
      "save/Assign_46\n",
      "\t(<tf.Tensor 'save/Assign_46:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/Assign_47\n",
      "\t(<tf.Tensor 'save/Assign_47:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/Assign_48\n",
      "\t(<tf.Tensor 'save/Assign_48:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/Assign_49\n",
      "\t(<tf.Tensor 'save/Assign_49:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/Assign_50\n",
      "\t(<tf.Tensor 'save/Assign_50:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/Assign_51\n",
      "\t(<tf.Tensor 'save/Assign_51:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/Assign_52\n",
      "\t(<tf.Tensor 'save/Assign_52:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/Assign_53\n",
      "\t(<tf.Tensor 'save/Assign_53:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/Assign_54\n",
      "\t(<tf.Tensor 'save/Assign_54:0' shape=(512,) dtype=float32_ref>,)\n",
      "save/Assign_55\n",
      "\t(<tf.Tensor 'save/Assign_55:0' shape=(3, 3, 512, 512) dtype=float32_ref>,)\n",
      "save/Assign_56\n",
      "\t(<tf.Tensor 'save/Assign_56:0' shape=(4096,) dtype=float32_ref>,)\n",
      "save/Assign_57\n",
      "\t(<tf.Tensor 'save/Assign_57:0' shape=(7, 7, 512, 4096) dtype=float32_ref>,)\n",
      "save/Assign_58\n",
      "\t(<tf.Tensor 'save/Assign_58:0' shape=(4096,) dtype=float32_ref>,)\n",
      "save/Assign_59\n",
      "\t(<tf.Tensor 'save/Assign_59:0' shape=(1, 1, 4096, 4096) dtype=float32_ref>,)\n",
      "save/restore_all_1\n",
      "\t()\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "with tf.Session() as sess:\n",
    "    graph = getGraph(sess)\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess, \"./runs/semantic_segmentation_model.ckpt\")\n",
    "    for i in graph.get_operations():\n",
    "        print(\"{}\\n\\t{}\".format(i.name, i.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get trainable variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'conv1_1/filter:0' shape=(3, 3, 3, 64) dtype=float32_ref>\n",
      "<tf.Variable 'conv1_1/biases:0' shape=(64,) dtype=float32_ref>\n",
      "<tf.Variable 'conv1_2/filter:0' shape=(3, 3, 64, 64) dtype=float32_ref>\n",
      "<tf.Variable 'conv1_2/biases:0' shape=(64,) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_1/filter:0' shape=(3, 3, 64, 128) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_1/biases:0' shape=(128,) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_2/filter:0' shape=(3, 3, 128, 128) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_2/biases:0' shape=(128,) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_1/filter:0' shape=(3, 3, 128, 256) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_1/biases:0' shape=(256,) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_2/filter:0' shape=(3, 3, 256, 256) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_2/biases:0' shape=(256,) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_3/filter:0' shape=(3, 3, 256, 256) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_3/biases:0' shape=(256,) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_1/filter:0' shape=(3, 3, 256, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_1/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_2/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_2/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_3/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_3/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_1/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_1/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_2/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_2/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_3/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_3/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'fc6/weights:0' shape=(7, 7, 512, 4096) dtype=float32_ref>\n",
      "<tf.Variable 'fc6/biases:0' shape=(4096,) dtype=float32_ref>\n",
      "<tf.Variable 'fc7/weights:0' shape=(1, 1, 4096, 4096) dtype=float32_ref>\n",
      "<tf.Variable 'fc7/biases:0' shape=(4096,) dtype=float32_ref>\n"
     ]
    }
   ],
   "source": [
    "for var in tf.trainable_variables():\n",
    "    print(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get global variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'conv1_1/filter:0' shape=(3, 3, 3, 64) dtype=float32_ref>\n",
      "<tf.Variable 'conv1_1/biases:0' shape=(64,) dtype=float32_ref>\n",
      "<tf.Variable 'conv1_2/filter:0' shape=(3, 3, 64, 64) dtype=float32_ref>\n",
      "<tf.Variable 'conv1_2/biases:0' shape=(64,) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_1/filter:0' shape=(3, 3, 64, 128) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_1/biases:0' shape=(128,) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_2/filter:0' shape=(3, 3, 128, 128) dtype=float32_ref>\n",
      "<tf.Variable 'conv2_2/biases:0' shape=(128,) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_1/filter:0' shape=(3, 3, 128, 256) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_1/biases:0' shape=(256,) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_2/filter:0' shape=(3, 3, 256, 256) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_2/biases:0' shape=(256,) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_3/filter:0' shape=(3, 3, 256, 256) dtype=float32_ref>\n",
      "<tf.Variable 'conv3_3/biases:0' shape=(256,) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_1/filter:0' shape=(3, 3, 256, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_1/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_2/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_2/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_3/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv4_3/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_1/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_1/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_2/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_2/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_3/filter:0' shape=(3, 3, 512, 512) dtype=float32_ref>\n",
      "<tf.Variable 'conv5_3/biases:0' shape=(512,) dtype=float32_ref>\n",
      "<tf.Variable 'fc6/weights:0' shape=(7, 7, 512, 4096) dtype=float32_ref>\n",
      "<tf.Variable 'fc6/biases:0' shape=(4096,) dtype=float32_ref>\n",
      "<tf.Variable 'fc7/weights:0' shape=(1, 1, 4096, 4096) dtype=float32_ref>\n",
      "<tf.Variable 'fc7/biases:0' shape=(4096,) dtype=float32_ref>\n"
     ]
    }
   ],
   "source": [
    "for var in tf.global_variables():\n",
    "    print(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test inference on video\n",
    "Necessary imports and initialization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from moviepy.editor import VideoFileClip\n",
    "from IPython.display import HTML\n",
    "from moviepy.editor import *\n",
    "\n",
    "import helper\n",
    "import project_tests as tests\n",
    "from main import load_vgg, layers, optimize, train_nn, test_nn\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "model_checkpoint = \"./runs/semantic_segmentation_model.ckpt\"\n",
    "num_classes = 2\n",
    "\n",
    "image_shape = (160, 576)\n",
    "\n",
    "video_fps = 30\n",
    "video_output_folder = \"videos_output/\"\n",
    "videos = [\n",
    "   \"data/project_video.mp4\",\n",
    "   \"data/challenge_video.mp4\",\n",
    "   \"data/harder_challenge_video.mp4\"\n",
    "]\n",
    "\n",
    "def process_video_image(sess, logits, keep_prob, image_input_op, image_src, image_shape):\n",
    "    # first crop image to correct aspect of `image_shape`\n",
    "    image_src_shape  = image_src.shape\n",
    "    new_y = (image_shape[0] * image_src_shape[1]) // image_shape[1]\n",
    "    image_crop = image_src[new_y:,:]\n",
    "    image_resized = scipy.misc.imresize(image_crop, image_shape)\n",
    "\n",
    "    feed_dict = {keep_prob: 1.0,\n",
    "                 image_input_op: [image_resized]}\n",
    "    im_softmax = sess.run([tf.nn.softmax(logits)],\n",
    "                          feed_dict=feed_dict)\n",
    "    im_softmax = im_softmax[0][:, 1].reshape(image_shape[0], image_shape[1])\n",
    "    segmentation = (im_softmax > 0.5).reshape(image_shape[0], image_shape[1], 1)\n",
    "    mask = np.dot(segmentation, np.array([[0, 255, 0, 127]]))\n",
    "    mask = scipy.misc.toimage(mask, mode=\"RGBA\")\n",
    "    street_im = scipy.misc.toimage(image_resized)\n",
    "    street_im.paste(mask, box=None, mask=mask)\n",
    "    return np.asarray(street_im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./data/vgg/variables/variables\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-69-361dad811693>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m# Build NN using load_vgg, layers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mimage_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_layer3_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_layer4_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_layer7_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_vgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mlast_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvgg_layer3_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_layer4_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_layer7_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/lutz_ph/SelfDrivingCar-Nanodegree/CarND-Semantic-Segmentation/main.py\u001b[0m in \u001b[0;36mload_vgg\u001b[0;34m(sess, vgg_path)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mvgg_layer4_out_tensor_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'layer4_out:0'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mvgg_layer7_out_tensor_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'layer7_out:0'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mvgg_tag\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvgg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mimage_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tensor_by_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvgg_input_tensor_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/saved_model/loader_impl.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(sess, tags, export_dir, **saver_kwargs)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m       \u001b[0;31m# Restore the variables using the built saver in the provided session.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m       \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m       tf_logging.info(\"The specified SavedModel has no variables; no \"\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, sess, save_path)\u001b[0m\n\u001b[1;32m   1750\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m         sess.run(self.saver_def.restore_op_name,\n\u001b[0;32m-> 1752\u001b[0;31m                  {self.saver_def.filename_tensor_name: save_path})\n\u001b[0m\u001b[1;32m   1753\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNotFoundError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1754\u001b[0m       \u001b[0mexception_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexception_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexception_traceback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    898\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 900\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    901\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1135\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1136\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1314\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1316\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1317\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1320\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1322\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1323\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1324\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1305\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1307\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/volume/USERSTORE/f_xrotor/Software/anaconda3/envs/CarND-TensorFlow-Lab/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1407\u001b[0m       return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1408\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m           run_metadata)\n\u001b[0m\u001b[1;32m   1410\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_exception_on_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "video_fps = 10\n",
    "clip_part = (0.0, 6.0)\n",
    "\n",
    "tf.reset_default_graph()\n",
    "# TF placeholders\n",
    "correct_label = tf.placeholder(tf.int32, [None, None, None, num_classes], name=\"correct_label\")\n",
    "learning_rate = tf.placeholder(tf.float32, name=\"learning_rate\")\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    # Path to vgg model\n",
    "    vgg_path = os.path.join(data_dir, \"vgg\")\n",
    "\n",
    "    # Build NN using load_vgg, layers\n",
    "    image_input, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)\n",
    "    last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)\n",
    "\n",
    "    tvars = tf.trainable_variables()\n",
    "    trainable_vars = [var for var in tvars if \"fc6\" not in var.name and \n",
    "                                              \"fc7\" not in var.name]\n",
    "\n",
    "    # Set-up optimizer\n",
    "    return_list = optimize(last_layer, correct_label, learning_rate, num_classes, trainable_vars)\n",
    "    logits_op, train_op, loss_op, mean_iou_value, mean_iou_update_op = return_list\n",
    "\n",
    "    #graph = tf.get_default_graph()\n",
    "    saver = tf.train.Saver()\n",
    "    try:\n",
    "        saver.restore(sess, model_checkpoint)\n",
    "    except:\n",
    "        print(\"Couldn't load model last checkpoint ({}).\".format(model_checkpoint))\n",
    "        print(\"You need to either provide the required checkpoint files or train the network from scratch!\")\n",
    "  \n",
    "    input_image_op  = graph.get_tensor_by_name(\"image_input:0\")\n",
    "    logits_op       = graph.get_tensor_by_name(\"decoder_logits:0\")\n",
    "    keep_prob       = graph.get_tensor_by_name(\"keep_prob:0\")\n",
    "    \n",
    "    for video in videos:\n",
    "        if not os.path.exists(video_output_folder):\n",
    "            os.makedirs(video_output_folder)\n",
    "        result_path = video_output_folder + os.path.basename(video)\n",
    "        if not os.path.isfile(video):\n",
    "            print(\"Video {} doesn't exist!\".format(video))\n",
    "        else:\n",
    "            clip1 = VideoFileClip(video) #.subclip(*clip_part)\n",
    "            video_slowdown_factor = video_fps / clip1.fps\n",
    "            clip1 = clip1.fx(vfx.speedx, video_slowdown_factor)\n",
    "            white_clip = clip1.fl_image(lambda img: process_video_image(sess, logits_op, keep_prob, input_image_op, img, image_shape))\n",
    "            %time white_clip.write_videofile(result_path, audio=False, fps=video_fps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=\"960\" height=\"540\" controls><source src=\"videos_output/harder_challenge_video.mp4\"></video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"<video width=\"960\" height=\"540\" controls><source src=\"{0}\"></video>\"\"\".format(\"videos_output/harder_challenge_video.mp4\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./data/vgg/variables/variables\n",
      "INFO:tensorflow:Restoring parameters from ./runs/semantic_segmentation_model.ckpt\n",
      "INFO:tensorflow:No assets to save.\n",
      "INFO:tensorflow:No assets to write.\n",
      "INFO:tensorflow:SavedModel written to: ./saved/saved_model.pb\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b'./saved/saved_model.pb'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# TF placeholders\n",
    "correct_label = tf.placeholder(tf.int32, [None, None, None, num_classes], name=\"correct_label\")\n",
    "learning_rate = tf.placeholder(tf.float32, name=\"learning_rate\")\n",
    "\n",
    "# Create a builder\n",
    "builder = tf.saved_model.builder.SavedModelBuilder('./saved/')\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    # Path to vgg model\n",
    "    vgg_path = os.path.join(data_dir, \"vgg\")\n",
    "\n",
    "    # Build NN using load_vgg, layers\n",
    "    image_input, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)\n",
    "    last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)\n",
    "\n",
    "    tvars = tf.trainable_variables()\n",
    "    trainable_vars = [var for var in tvars if \"fc6\" not in var.name and \n",
    "                                              \"fc7\" not in var.name]\n",
    "\n",
    "    # Set-up optimizer\n",
    "    return_list = optimize(last_layer, correct_label, learning_rate, num_classes, trainable_vars)\n",
    "    logits_op, train_op, loss_op, mean_iou_value, mean_iou_update_op = return_list\n",
    "\n",
    "    #graph = tf.get_default_graph()\n",
    "    saver = tf.train.Saver()\n",
    "    try:\n",
    "        saver.restore(sess, model_checkpoint)\n",
    "    except:\n",
    "        print(\"Couldn't load model last checkpoint ({}).\".format(model_checkpoint))\n",
    "        print(\"You need to either provide the required checkpoint files or train the network from scratch!\")\n",
    "  \n",
    "\n",
    "    builder.add_meta_graph_and_variables(sess,\n",
    "                                       [tf.saved_model.tag_constants.TRAINING],\n",
    "                                       signature_def_map=None,\n",
    "                                       assets_collection=None)\n",
    "builder.save()  "
   ]
  }
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