FCN32-combined.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Loaded\n",
      "conv1_1 Value Loaded\n",
      "Layer name: conv1_1\n",
      "Layer shape: (3, 3, 3, 64)\n",
      "conv1_1 Frozen\n",
      "conv1_1 (64,) Value Loaded\n",
      "\n",
      "\n",
      "conv1_2 Value Loaded\n",
      "Layer name: conv1_2\n",
      "Layer shape: (3, 3, 64, 64)\n",
      "L2 Loss Added\n",
      "conv1_2 (64,) Value Loaded\n",
      "\n",
      "\n",
      "conv2_1 Value Loaded\n",
      "Layer name: conv2_1\n",
      "Layer shape: (3, 3, 64, 128)\n",
      "L2 Loss Added\n",
      "conv2_1 (128,) Value Loaded\n",
      "\n",
      "\n",
      "conv2_2 Value Loaded\n",
      "Layer name: conv2_2\n",
      "Layer shape: (3, 3, 128, 128)\n",
      "L2 Loss Added\n",
      "conv2_2 (128,) Value Loaded\n",
      "\n",
      "\n",
      "conv3_1 Value Loaded\n",
      "Layer name: conv3_1\n",
      "Layer shape: (3, 3, 128, 256)\n",
      "L2 Loss Added\n",
      "conv3_1 (256,) Value Loaded\n",
      "\n",
      "\n",
      "conv3_2 Value Loaded\n",
      "Layer name: conv3_2\n",
      "Layer shape: (3, 3, 256, 256)\n",
      "L2 Loss Added\n",
      "conv3_2 (256,) Value Loaded\n",
      "\n",
      "\n",
      "conv3_3 Value Loaded\n",
      "Layer name: conv3_3\n",
      "Layer shape: (3, 3, 256, 256)\n",
      "L2 Loss Added\n",
      "conv3_3 (256,) Value Loaded\n",
      "\n",
      "\n",
      "conv4_1 Value Loaded\n",
      "Layer name: conv4_1\n",
      "Layer shape: (3, 3, 256, 512)\n",
      "L2 Loss Added\n",
      "conv4_1 (512,) Value Loaded\n",
      "\n",
      "\n",
      "conv4_2 Value Loaded\n",
      "Layer name: conv4_2\n",
      "Layer shape: (3, 3, 512, 512)\n",
      "L2 Loss Added\n",
      "conv4_2 (512,) Value Loaded\n",
      "\n",
      "\n",
      "conv4_3 Value Loaded\n",
      "Layer name: conv4_3\n",
      "Layer shape: (3, 3, 512, 512)\n",
      "L2 Loss Added\n",
      "conv4_3 (512,) Value Loaded\n",
      "\n",
      "\n",
      "conv5_1 Value Loaded\n",
      "Layer name: conv5_1\n",
      "Layer shape: (3, 3, 512, 512)\n",
      "L2 Loss Added\n",
      "conv5_1 (512,) Value Loaded\n",
      "\n",
      "\n",
      "conv5_2 Value Loaded\n",
      "Layer name: conv5_2\n",
      "Layer shape: (3, 3, 512, 512)\n",
      "L2 Loss Added\n",
      "conv5_2 (512,) Value Loaded\n",
      "\n",
      "\n",
      "conv5_3 Value Loaded\n",
      "Layer name: conv5_3\n",
      "Layer shape: (3, 3, 512, 512)\n",
      "L2 Loss Added\n",
      "conv5_3 (512,) Value Loaded\n",
      "\n",
      "\n",
      "Layer name: fc6\n",
      "Layer shape: [7, 7, 512, 4096]\n",
      "fc6 Value Loaded\n",
      "fc6 (4096,) Value Loaded\n",
      "\n",
      "\n",
      "fc6 dropout_added\n",
      "Layer name: fc7\n",
      "Layer shape: [1, 1, 4096, 4096]\n",
      "fc7 Value Loaded\n",
      "fc7 (4096,) Value Loaded\n",
      "\n",
      "\n",
      "fc7 dropout_added\n",
      "fc8 Value Randomly initialized\n",
      "L2 Loss Added\n",
      "Layer name: score_fr\n",
      "Layer shape: [1, 1, 4096, 19]\n",
      "fc8_biases Value Initialized 0\n",
      "\n",
      "\n",
      "Finished building Network.\n",
      "using Pretrained model\n",
      "model_checkpoint_path: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-10\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-1\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-2\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-3\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-4\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-5\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-6\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-7\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-8\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-9\"\n",
      "all_model_checkpoint_paths: \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt-10\"\n",
      "\n",
      "finished\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import copy\n",
    "import tensorflow as tf\n",
    "\n",
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "import sys\n",
    "import csv\n",
    "import math\n",
    "from matplotlib import colors\n",
    "import fcn32_new_wval as fcn32_vgg\n",
    "import csv\n",
    "\n",
    "\n",
    "ckpt_dir = \"/mnt/data3/combined/ckpt_dir/fcn32_combined.ckpt\"\n",
    "\n",
    "PRETRAINED_MODEL_PATH= \"/mnt/data3/combined/ckpt_dir/\"\n",
    "\n",
    "WEIGHT_PATH = \"/mnt/data1/imagenet-data/npy/vgg-epoch-78.npy\"\n",
    "\n",
    "TRAINSET_PATH1 = '/mnt/data1/city/csv_files/cityscapes_train_wmask.csv'\n",
    "TRAINSET_PATH2 = '/mnt/data3/Game_data/csv_files/gamedata_train2975.csv'\n",
    "VALSET_PATH1 = '/mnt/data1/city/csv_files/cityscapes_val_wmask.csv'\n",
    "#VALSET_PATH2 = '/mnt/data3/Game_data/csv_files/gamedata_val2.csv'\n",
    "\n",
    "data_csv_path = 'fcn32_combined.csv'\n",
    "\n",
    "\n",
    "NUM_OF_CLASSESS = 19\n",
    "BATCH_SIZE = 1\n",
    "IMAGE_HEIGHT1 = 1024\n",
    "IMAGE_HEIGHT2 = 1046\n",
    "IMAGE_WIDTH1 = 2048\n",
    "IMAGE_WIDTH2 = 1914\n",
    "NUM_CHANNELS = 3\n",
    "N_EPOCHS = 300\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "train_csv_file=open(TRAINSET_PATH1,\"r\")\n",
    "train_filepaths=[]\n",
    "train_labels=[]\n",
    "train_masks=[]\n",
    "for line in train_csv_file:\n",
    "    filepath, label, mask= line.split(\",\")\n",
    "    train_filepaths.append(filepath)\n",
    "    train_labels.append(label)\n",
    "    train_masks.append(mask.split(\"\\r\")[0])\n",
    "    \n",
    "train_image_path1, train_label_path1, train_mask_path1 = tf.train.slice_input_producer([train_filepaths,train_labels, train_masks]\n",
    "                                           ,capacity = 2975)\n",
    "#csv_path = tf.train.string_input_producer([TRAINSET_PATH])\n",
    "#textReader = tf.TextLineReader()\n",
    "#_, content = textReader.read(csv_path)\n",
    "#one_ex = tf.decode_csv(content, record_defaults=[[\"\"], [\"\"], [\"\"]])\n",
    "\n",
    "train_image_content1 = tf.read_file(train_image_path1)\n",
    "train_image1 = tf.image.decode_png(train_image_content1, channels=3)\n",
    "train_image1.set_shape([IMAGE_HEIGHT1, IMAGE_WIDTH1, NUM_CHANNELS])\n",
    "train_image1 = tf.cast(train_image1, tf.float32)\n",
    "\n",
    "train_label_content1 = tf.read_file(train_label_path1)\n",
    "train_label1 = tf.image.decode_png(train_label_content1, channels=1)\n",
    "train_label1.set_shape([IMAGE_HEIGHT1, IMAGE_WIDTH1, 1])\n",
    "train_label1=tf.squeeze(train_label1, squeeze_dims=[2])\n",
    "\n",
    "#record_bytes=2097152\n",
    "#reader=tf.FixedLengthRecordReader(record_bytes)\n",
    "train_mask_content1 =tf.read_file(train_mask_path1)\n",
    "train_mask1=tf.decode_raw(train_mask_content1,tf.uint8)\n",
    "train_mask1.set_shape([2097152])\n",
    "#mask=tf.cast(mask, tf.float32)\n",
    "\n",
    "\n",
    "val_csv_file=open(VALSET_PATH1,\"r\")\n",
    "val_filepaths=[]\n",
    "val_labels=[]\n",
    "val_masks=[]\n",
    "for line in val_csv_file:\n",
    "    filepath, label, mask= line.split(\",\")\n",
    "    val_filepaths.append(filepath)\n",
    "    val_labels.append(label)\n",
    "    val_masks.append(mask.split(\"\\r\")[0])\n",
    "    \n",
    "val_image_path1, val_label_path1, val_mask_path1 = tf.train.slice_input_producer([val_filepaths,val_labels, val_masks]\n",
    "                                           ,capacity = 500)\n",
    "#csv_path = tf.train.string_input_producer([TRAINSET_PATH])\n",
    "#textReader = tf.TextLineReader()\n",
    "#_, content = textReader.read(csv_path)\n",
    "#one_ex = tf.decode_csv(content, record_defaults=[[\"\"], [\"\"], [\"\"]])\n",
    "\n",
    "val_image_content1 = tf.read_file(val_image_path1)\n",
    "val_image1 = tf.image.decode_png(val_image_content1, channels=3)\n",
    "val_image1.set_shape([IMAGE_HEIGHT1, IMAGE_WIDTH1, NUM_CHANNELS])\n",
    "val_image1 = tf.cast(val_image1, tf.float32)\n",
    "\n",
    "val_label_content1 = tf.read_file(val_label_path1)\n",
    "val_label1 = tf.image.decode_png(val_label_content1, channels=1)\n",
    "val_label1.set_shape([IMAGE_HEIGHT1, IMAGE_WIDTH1, 1])\n",
    "val_label1=tf.squeeze(val_label1, squeeze_dims=[2])\n",
    "\n",
    "#record_bytes=2097152\n",
    "#reader=tf.FixedLengthRecordReader(record_bytes)\n",
    "val_mask_content1 =tf.read_file(val_mask_path1)\n",
    "val_mask1=tf.decode_raw(val_mask_content1,tf.uint8)\n",
    "val_mask1.set_shape([2097152])\n",
    "#mask=tf.cast(mask, tf.float32)\n",
    "\n",
    "\n",
    "\n",
    "train_image_batch1, train_label_batch1, train_mask_batch1= tf.train.batch([train_image1, train_label1, train_mask1], batch_size=BATCH_SIZE,\n",
    "                                                               capacity=100 + 3 * BATCH_SIZE,\n",
    "                                                               num_threads=7)\n",
    "\n",
    "val_image_batch1, val_label_batch1, val_mask_batch1= tf.train.batch([val_image1, val_label1, val_mask1], batch_size=BATCH_SIZE,\n",
    "                                                               capacity=50 + 3 * BATCH_SIZE,\n",
    "                                                               num_threads=7)\n",
    "\n",
    "\n",
    "train_csv_file=open(TRAINSET_PATH2,\"r\")\n",
    "train_filepaths=[]\n",
    "train_labels=[]\n",
    "train_masks=[]\n",
    "for line in train_csv_file:\n",
    "    filepath, label, mask= line.split(\",\")\n",
    "    train_filepaths.append(filepath)\n",
    "    train_labels.append(label)\n",
    "    train_masks.append(mask.split(\"\\r\")[0])\n",
    "\n",
    "\n",
    "train_image_path2, train_label_path2, train_mask_path2 = tf.train.slice_input_producer([train_filepaths,train_labels, train_masks]\n",
    "                                           ,capacity = 2975)\n",
    "#csv_path = tf.train.string_input_producer([TRAINSET_PATH])\n",
    "#textReader = tf.TextLineReader()\n",
    "#_, content = textReader.read(csv_path)\n",
    "#one_ex = tf.decode_csv(content, record_defaults=[[\"\"], [\"\"], [\"\"]])\n",
    "\n",
    "train_image_content2 = tf.read_file(train_image_path2)\n",
    "train_image2 = tf.image.decode_png(train_image_content2, channels=3)\n",
    "train_image2.set_shape([IMAGE_HEIGHT2, IMAGE_WIDTH2, NUM_CHANNELS])\n",
    "train_image2 = tf.cast(train_image2, tf.float32)\n",
    "\n",
    "train_label_content2 = tf.read_file(train_label_path2)\n",
    "train_label2 = tf.image.decode_png(train_label_content2, channels=1)\n",
    "train_label2.set_shape([IMAGE_HEIGHT2, IMAGE_WIDTH2, 1])\n",
    "train_label2=tf.squeeze(train_label2, squeeze_dims=[2])\n",
    "\n",
    "#record_bytes=2097152\n",
    "#reader=tf.FixedLengthRecordReader(record_bytes)\n",
    "train_mask_content2 =tf.read_file(train_mask_path2)\n",
    "train_mask2=tf.decode_raw(train_mask_content2,tf.uint8)\n",
    "train_mask2.set_shape([2002044])\n",
    "#mask=tf.cast(mask, tf.float32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "train_image_batch2, train_label_batch2, train_mask_batch2= tf.train.batch([train_image2, train_label2, train_mask2], batch_size=BATCH_SIZE,\n",
    "                                                               capacity=100 + 3 * BATCH_SIZE,\n",
    "                                                               num_threads=7)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "with tf.device('/gpu:0'):\n",
    "    sess = tf.Session()\n",
    "    #images_tf = tf.placeholder(tf.float32,[None, 1024, 2048, 3])\n",
    "    #labels_tf = tf.placeholder(tf.int32,[None, 1024, 2048])\n",
    "    #masks_tf = tf.placeholder(tf.float32,[None,2097152])\n",
    "    train_mode = tf.placeholder(tf.bool)\n",
    "    selection = tf.placeholder(tf.bool)\n",
    "    \n",
    "    vgg_fcn = fcn32_vgg.FCN32VGG(WEIGHT_PATH)#('./vgg16.npy')\n",
    "    \n",
    "    if selection is not None:\n",
    "        train_image_batch=tf.cond(selection, lambda:train_image_batch1, lambda:train_image_batch2)\n",
    "        val_image_batch=val_image_batch1\n",
    "        \n",
    "        train_label_batch=tf.cond(selection, lambda:train_label_batch1, lambda:train_label_batch2)\n",
    "        val_label_batch=val_label_batch1\n",
    "        \n",
    "        train_mask_batch=tf.cond(selection, lambda:train_mask_batch1, lambda:train_mask_batch2)\n",
    "        val_mask_batch=val_mask_batch1\n",
    "        \n",
    "        \n",
    "    if train_mode is not None:\n",
    "        images_tf=tf.cond(train_mode, lambda:train_image_batch, lambda:val_image_batch)\n",
    "        labels_tf=tf.cond(train_mode, lambda:train_label_batch, lambda:val_label_batch)\n",
    "        masks_tf=tf.cond(train_mode, lambda:train_mask_batch, lambda:val_mask_batch)\n",
    "                          \n",
    "    with tf.name_scope(\"content_vgg\"):\n",
    "        vgg_fcn.build(images_tf, train=train_mode, num_classes=19, random_init_fc8=True, debug=False)\n",
    "\n",
    "\n",
    "    #head=[]\n",
    "    #sum_of_weights=tf.reduce_sum(weights)\n",
    "    #labels_oh= tf.one_hot(labels_tf,19)\n",
    "    \n",
    "    labels_tf_flat=tf.reshape(tf.mul(tf.reshape(labels_tf,[-1]),masks_tf),[-1])\n",
    "    #mask_reshped=tf.reshape(masks_tf,[1024,2048])\n",
    "    #labels_tf_flat2= tf.gather(labels_tf_flat,tf.where(tf.not_equal(labels_tf_flat,19))[1])\n",
    "    logits = tf.reshape(vgg_fcn.upscore, (-1, NUM_OF_CLASSESS))\n",
    "    #logits = tf.gather(logits,tf.where(tf.not_equal(labels_tf_flat,19)))\n",
    "    labels_tf_flat = tf.cast(labels_tf_flat, tf.int32)\n",
    "\n",
    "    #epsilon = tf.constant(value=1e-4)\n",
    "    #logits = logits + epsilon\n",
    "    #labels_flat_sparse = tf.reshape(labels_oh, (-1, NUM_OF_CLASSESS))\n",
    "    #labels_flat_sparse=tf.cast(labels_flat_sparse, tf.float64)\n",
    "    #ww_flat=tf.reshape(weights,[-1])\n",
    "    #ww_flat = tf.cast(ww_flat, tf.float64)\n",
    "    #softmax = tf.nn.softmax(logits)\n",
    "    #softmax = tf.cast(softmax, tf.float64)\n",
    "    #test1=-tf.reduce_sum(labels_flat_sparse * tf.log(softmax), reduction_indices=[1])\n",
    "    #test2=tf.reduce_sum(test1)\n",
    "    #cross_entropy = tf.mul(-tf.reduce_sum(labels_flat_sparse * tf.log(softmax), reduction_indices=[1]),ww_flat)\n",
    "    #cross_entropy = -tf.reduce_sum(labels_flat_sparse * tf.log(softmax), reduction_indices=[1])\n",
    "    #mask_flat=tf.reshape(train_mask_batch,[-1])\n",
    "    masks_tf=tf.cast(masks_tf, tf.float32)\n",
    "    cross_entropy =tf.mul(tf.nn.sparse_softmax_cross_entropy_with_logits(logits,labels_tf_flat),masks_tf)\n",
    "    #cross_entropy_sum= tf.reduce_sum(cross_entropy,\n",
    "    #                                name='xentropy_mean')\n",
    "    #cross_entropy_mean = tf.reduce_mean(cross_entropy,name='xentropy_mean')\n",
    "    #cross_entropy_mean = cross_entropy_sum/tf.cast(sum_of_weights, tf.float32)\n",
    "    cross_entropy_mean = tf.reduce_mean(cross_entropy,\n",
    "                                        name='xentropy_mean')\n",
    "    l2_loss=tf.reduce_sum(tf.get_collection(\"losses\"))\n",
    "    total_loss=cross_entropy_mean +l2_loss\n",
    "    #loss_tf = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(vgg_fcn.upscore,\n",
    "     #                                                                     tf.squeeze(labels_tf, squeeze_dims=[3]),\n",
    "      #                                                                    name=\"entropy\")))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #train_op = tf.train.MomentumOptimizer(0.0001, 0.99).minimize(cross_entropy_mean)\n",
    "    train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(cross_entropy_mean)\n",
    "    predictions=tf.argmax(logits, 1)\n",
    "    #labels_tf_flat=tf.reshape(labels_tf_flat,[-1])\n",
    "    #labels_flat=tf.cast(labels_tf_flat, tf.int64)\n",
    "    pred_flat=tf.reshape(predictions,[-1])\n",
    "with tf.device('/cpu:0'):\n",
    "    miou,update_cm=tf.contrib.metrics.streaming_mean_iou(pred_flat,labels_tf_flat,19,weights=masks_tf)\n",
    "    if not os.path.exists(ckpt_dir):\n",
    "        os.makedirs(ckpt_dir)\n",
    "    saver = tf.train.Saver(max_to_keep=150)\n",
    "with tf.device('/gpu:0'):\n",
    "    print('Finished building Network.')\n",
    "    \n",
    "    init_op = tf.group(tf.initialize_all_variables(),\n",
    "                       tf.initialize_local_variables())\n",
    "    sess.run(init_op)\n",
    "        \n",
    "    if PRETRAINED_MODEL_PATH:\n",
    "        print \"using Pretrained model\"\n",
    "        ckpt = tf.train.get_checkpoint_state(PRETRAINED_MODEL_PATH)\n",
    "        print(ckpt)\n",
    "        saver.restore(sess,ckpt.model_checkpoint_path)\n",
    "        \n",
    "    # print(csv_path)\n",
    "    # For populating queues with batches, very important!\n",
    "    coord = tf.train.Coordinator()\n",
    "    threads = tf.train.start_queue_runners(sess=sess,coord=coord)\n",
    "    \n",
    "    print(\"finished\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epoch = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kv7IuVIAChWS/13q9w/yyDwBossQvrpVt4wkam6Egjk7Kv39Q0nkjSdi8xm2X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\nIiIi4gmFChEREfGEQoWIiIh44v8B9XVMzTBgSFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd84c43be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vPUPLNmv6XG+mUFIhae3yQ/xf5lok0/xp+MGdtk0pje3eGFt2/A7bL/kba0Pe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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd84c4197d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5QM772W/mB3PqVE/m/tlzuf6ijjb7h740B30oj36TnM+rUTE0ADzW1z9Thoej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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6183bfc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VVUd1tvd23Z/MW9zuugOqw/cxiDUlDZJqSufPU62DpJy0SRxCRZIwNHNZTbx+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\nQURERDz7/1hsvvgMflN9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd61853e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "999"
     ]
    },
    {
     "data": {
      "image/png": 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lBlrrBqXUV8AxwARcrRdpfmU2K6WWuMuEnFQkwkJStz/aLSZN86faW//g/TAt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\njKi8eU/LmTATcaKujX22ANB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6185d3490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hL3gV67DwOPvoTlo3bXyE00uPjnl8PYl1EoxLU9fj8tvEWgjruKJDuz32mdlw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\nk5wGPtCLgLUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd618330a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lS0N9Y6TASvv6ddk5o2j+8X+L3ubXA4epN7So4Ev7R6eR0Mjz77UycjEzi8Ub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\n1OsZGlSNBvQfwOUnXt5pv88f//nKFyOVcOe2JEnKzOAgSZI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6182d3d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ujfqnceiHHVPHO356vD9hoitALP57OCh0zX3//LoC7n+7Z/K4Ze8U8/HJbRy5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\neHAws3wz+7mZvWtmzWb2XzO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd61819da50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1100out of range\n"
     ]
    },
    {
     "ename": "NotFoundError",
     "evalue": "/mnt/data1/city/all/train/images/weimar_000087_000019_leftImg8bit.png\n\t [[Node: ReadFile = ReadFile[_device=\"/job:localhost/replica:0/task:0/cpu:0\"](input_producer/Gather)]]\n\t [[Node: DecodePng_1/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_18_DecodePng_1\", tensor_type=DT_UINT8, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\nCaused by op u'ReadFile', defined at:\n  File \"/usr/lib/python2.7/runpy.py\", line 162, in _run_module_as_main\n    \"__main__\", fname, loader, pkg_name)\n  File \"/usr/lib/python2.7/runpy.py\", line 72, in _run_code\n    exec code in run_globals\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py\", line 474, in start\n    ioloop.IOLoop.instance().start()\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py\", line 887, in start\n    handler_func(fd_obj, events)\n  File \"/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py\", line 390, in execute_request\n    user_expressions, allow_stdin)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py\", line 501, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py\", line 2717, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py\", line 2821, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py\", line 2881, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-1-eb13c91fbb33>\", line 61, in <module>\n    train_image_content1 = tf.read_file(train_image_path1)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py\", line 107, in read_file\n    result = _op_def_lib.apply_op(\"ReadFile\", filename=filename, name=name)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py\", line 749, in apply_op\n    op_def=op_def)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py\", line 2380, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py\", line 1298, in __init__\n    self._traceback = _extract_stack()\n\nNotFoundError (see above for traceback): /mnt/data1/city/all/train/images/weimar_000087_000019_leftImg8bit.png\n\t [[Node: ReadFile = ReadFile[_device=\"/job:localhost/replica:0/task:0/cpu:0\"](input_producer/Gather)]]\n\t [[Node: DecodePng_1/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_18_DecodePng_1\", tensor_type=DT_UINT8, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-f3bb6e5c6091>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0mcoord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_stop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m     \u001b[0mcoord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthreads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"requesting stop\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;31m#print(iou[1])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/coordinator.pyc\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, threads, stop_grace_period_secs)\u001b[0m\n\u001b[1;32m    384\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_registered_threads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info_to_raise\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m         \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info_to_raise\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m       \u001b[0;32melif\u001b[0m \u001b[0mstragglers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         raise RuntimeError(\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/queue_runner.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, sess, enqueue_op, coord)\u001b[0m\n\u001b[1;32m    223\u001b[0m           \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m           \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menqueue_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_queue_closed_exception_types\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=catching-non-exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m           \u001b[0;31m# This exception indicates that a queue was closed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    715\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 717\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    718\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    719\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/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 915\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    916\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\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/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\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    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 965\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 985\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\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[0m\u001b[1;32m    986\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFoundError\u001b[0m: /mnt/data1/city/all/train/images/weimar_000087_000019_leftImg8bit.png\n\t [[Node: ReadFile = ReadFile[_device=\"/job:localhost/replica:0/task:0/cpu:0\"](input_producer/Gather)]]\n\t [[Node: DecodePng_1/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_18_DecodePng_1\", tensor_type=DT_UINT8, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n\nCaused by op u'ReadFile', defined at:\n  File \"/usr/lib/python2.7/runpy.py\", line 162, in _run_module_as_main\n    \"__main__\", fname, loader, pkg_name)\n  File \"/usr/lib/python2.7/runpy.py\", line 72, in _run_code\n    exec code in run_globals\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py\", line 3, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py\", line 474, in start\n    ioloop.IOLoop.instance().start()\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py\", line 177, in start\n    super(ZMQIOLoop, self).start()\n  File \"/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py\", line 887, in start\n    handler_func(fd_obj, events)\n  File \"/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\n    self._handle_recv()\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\n    callback(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py\", line 275, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py\", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py\", line 228, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py\", line 390, in execute_request\n    user_expressions, allow_stdin)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py\", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py\", line 501, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py\", line 2717, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py\", line 2821, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py\", line 2881, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-1-eb13c91fbb33>\", line 61, in <module>\n    train_image_content1 = tf.read_file(train_image_path1)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py\", line 107, in read_file\n    result = _op_def_lib.apply_op(\"ReadFile\", filename=filename, name=name)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py\", line 749, in apply_op\n    op_def=op_def)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py\", line 2380, in create_op\n    original_op=self._default_original_op, op_def=op_def)\n  File \"/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py\", line 1298, in __init__\n    self._traceback = _extract_stack()\n\nNotFoundError (see above for traceback): /mnt/data1/city/all/train/images/weimar_000087_000019_leftImg8bit.png\n\t [[Node: ReadFile = ReadFile[_device=\"/job:localhost/replica:0/task:0/cpu:0\"](input_producer/Gather)]]\n\t [[Node: DecodePng_1/_203 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/gpu:0\", send_device=\"/job:localhost/replica:0/task:0/cpu:0\", send_device_incarnation=1, tensor_name=\"edge_18_DecodePng_1\", tensor_type=DT_UINT8, _device=\"/job:localhost/replica:0/task:0/gpu:0\"]()]]\n"
     ]
    }
   ],
   "source": [
    "total_train_loss_list,train_loss_list,plot_train_loss,plot_total_train_loss,plot_train_miou= [],[],[],[],[]\n",
    "val_loss_list,plot_val_loss,plot_val_miou= [],[],[]\n",
    "#for epoch in xrange(N_EPOCHS):\n",
    "cmap = colors.ListedColormap([[0.4667,0.0431,0.1254],[0.902,0.5882,0.5490],[0,0.2353,0.3921]\n",
    "                              ,[0,0,0.5569],[0.7451,0.6,0.6],[0,0,0.902]\n",
    "                              ,[0.8627,0.0784,0.2352],[0.6,0.6,0.6],[1,0,0]\n",
    "                              ,[0.502,0.251,0.502],[0.9569,0.1372,0.9098],[0.2745,0.5098,0.7059]\n",
    "                              ,[0.5961,0.9843,0.5961],[0.9804,0.6667,0.1176],[0.8627,0.8627,0]\n",
    "                              ,[0,0.3137,0.3922],[0,0,0.2745],[0.4196,0.5569,0.1372]\n",
    "                              ,[0.4,0.4,0.6118],[0,0,0]])\n",
    "norm=colors.NoNorm(0,19)\n",
    "try:\n",
    "    while not coord.should_stop() and epoch< N_EPOCHS:\n",
    "        epoch+=1\n",
    "        epoch_start_time = time.time()\n",
    "        for iteration in xrange(2975/BATCH_SIZE): \n",
    "            if (iteration%1000==0):\n",
    "                _,train_loss,total_train_loss,_,train_pred_pic, train_label_pic = sess.run([train_op,cross_entropy_mean,total_loss,\n",
    "                                                            update_cm,vgg_fcn.pred_up,labels_tf],feed_dict={train_mode: True,selection:True})\n",
    "                plt.figure(1)\n",
    "                plt.imshow(train_label_pic[0],cmap=cmap,norm=norm)\n",
    "                plt.figure(2)\n",
    "                plt.imshow(train_pred_pic[0],cmap=cmap,norm=norm)\n",
    "                #plt.figure(3)\n",
    "                #plt.imshow(output2[0],cmap=cmap,norm=norm)\n",
    "                plt.show()\n",
    "            else:\n",
    "                _,train_loss,total_train_loss,_ = sess.run([train_op,cross_entropy_mean,total_loss,\n",
    "                                                            update_cm],feed_dict={train_mode: True,selection:True})\n",
    "            \n",
    "            train_loss_list.append(train_loss)\n",
    "            total_train_loss_list.append(total_train_loss)\n",
    "            \n",
    "            if (iteration%1000==0):       \n",
    "                _,train_loss,total_train_loss,_,train_pred_pic, train_label_pic = sess.run([train_op,cross_entropy_mean,total_loss,\n",
    "                                                            update_cm,vgg_fcn.pred_up,labels_tf],feed_dict={train_mode: True,selection:False})\n",
    "                plt.figure(3)\n",
    "                plt.imshow(train_label_pic[0],cmap=cmap,norm=norm)\n",
    "                plt.figure(4)\n",
    "                plt.imshow(train_pred_pic[0],cmap=cmap,norm=norm)\n",
    "                #plt.figure(3)\n",
    "                #plt.imshow(output2[0],cmap=cmap,norm=norm)\n",
    "                plt.show()\n",
    "            else:\n",
    "                _,train_loss,total_train_loss,_ = sess.run([train_op,cross_entropy_mean,total_loss,\n",
    "                                                            update_cm],feed_dict={train_mode: True,selection:False})\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "            train_loss_list.append(train_loss)\n",
    "            total_train_loss_list.append(total_train_loss)\n",
    "            sys.stdout.write('\\r\\r'+ str(iteration))\n",
    "            sys.stdout.flush()\n",
    "           \n",
    "        train_iou=sess.run(miou)\n",
    "        train_loss_mean = np.mean(train_loss_list)\n",
    "        total_train_loss_mean=np.mean(total_train_loss_list)\n",
    "        plot_train_loss.append(train_loss_mean)\n",
    "        plot_total_train_loss.append(total_train_loss_mean)\n",
    "\n",
    "        train_loss_list = []\n",
    "        total_train_loss_list = []\n",
    "        plot_train_miou.append(train_iou)\n",
    "        \n",
    "        var_miou = filter(lambda x: x.name.endswith('total_confusion_matrix:0'), tf.local_variables())\n",
    "        init_miou=tf.initialize_variables(var_miou)\n",
    "        sess.run(init_miou)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        for iteration in xrange(500/BATCH_SIZE): \n",
    "            if (iteration%500==0):\n",
    "                val_loss,_,val_pred_pic, val_label_pic = sess.run([cross_entropy_mean,\n",
    "                                                            update_cm,vgg_fcn.pred_up,labels_tf],feed_dict={train_mode: False,selection:False})\n",
    "                plt.figure(1)\n",
    "                plt.imshow(val_label_pic[0],cmap=cmap,norm=norm)\n",
    "                plt.figure(2)\n",
    "                plt.imshow(val_pred_pic[0],cmap=cmap,norm=norm)\n",
    "                #plt.figure(3)\n",
    "                #plt.imshow(output2[0],cmap=cmap,norm=norm)\n",
    "                plt.show()\n",
    "            else:\n",
    "                    val_loss,_ = sess.run([cross_entropy_mean,update_cm],feed_dict={train_mode: False,selection:False})\n",
    "            val_loss_list.append(val_loss)\n",
    "            sys.stdout.write('\\r\\r' + str(iteration))\n",
    "            sys.stdout.flush()\n",
    "        \n",
    "            \n",
    "        val_iou=sess.run(miou)    \n",
    "        val_loss_mean = np.mean(val_loss_list)\n",
    "        plot_val_loss.append(val_loss_mean)\n",
    "        \n",
    "        val_loss_list = []\n",
    "        plot_val_miou.append(val_iou)\n",
    "        \n",
    "        var_miou = filter(lambda x: x.name.endswith('total_confusion_matrix:0'), tf.local_variables())\n",
    "        init_miou=tf.initialize_variables(var_miou)\n",
    "        sess.run(init_miou)\n",
    "        \n",
    "        clear_output()\n",
    "        print \"Epoch\", epoch\n",
    "        \n",
    "        print \"===========**Training ACCURACY**================\"\n",
    "        print 'Training mIOU: ', train_iou\n",
    "        print \"Training Loss:\", train_loss_mean \n",
    "        print \"Training Loss:\", total_train_loss_mean \n",
    "        \n",
    "        print \"===========**VALIDATION ACCURACY**================\"\n",
    "        print 'Validation mIOU: ', val_iou\n",
    "        print \"Validation Loss:\", val_loss_mean \n",
    "        \n",
    "        print \"=================**TIME**====================\"\n",
    "        print 'Time Elapsed for Epoch:' + str(epoch) + ' is ' + str(\n",
    "                (time.time() - epoch_start_time) / 60.) + ' minutes'\n",
    "        \n",
    "        plt.figure(1) \n",
    "        dd = plt.plot(plot_total_train_loss,'r',label=\"Training\")\n",
    "        plt.title(\"Total LOSS\")\n",
    "        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "        plt.figure(2) \n",
    "        aa = plt.plot(plot_train_loss,'r',label=\"Training\")\n",
    "        aav = plt.plot(plot_val_loss,'g',label=\"Validation\")\n",
    "        plt.title(\"LOSS\")\n",
    "        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "        plt.figure(3)\n",
    "        cc = plt.plot(plot_train_miou,'r',label=\"Training\")\n",
    "        aav = plt.plot(plot_val_miou,'g',label=\"Validation\")\n",
    "        plt.title(\"mIOU\")\n",
    "        plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "        plt.show()\n",
    "        saver.save(sess, ckpt_dir, global_step=epoch)\n",
    "        ofile  = open(data_csv_path, \"a\")\n",
    "        writer = csv.writer(ofile)\n",
    "        timep=((time.time() - epoch_start_time) / 60.)\n",
    "        writer.writerow([train_loss_mean,train_iou,timep,val_loss_mean,val_iou,epoch])\n",
    "        ofile.close()\n",
    "except tf.errors.OutOfRangeError:\n",
    "    print (\"out of range\")\n",
    "finally:\n",
    "    coord.request_stop()\n",
    "    coord.join(threads)\n",
    "    print(\"requesting stop\")\n",
    "        #print(iou[1])\n",
    "            #train_loss= sess.run(vgg_fcn.upscore,\n",
    "             #   feed_dict={ images_tf: train_imbatch})\n",
    "            #res=sess.run(image)\n",
    "            #plt.imshow(train_labatch[0])\n",
    "            #plt.show()\n",
    "    #    aa= tt * np.log10(train_loss)\n",
    "    #    print(len(aa))\n",
    "    #    print(len(aa[0]))\n",
    "    #print(sum)\n",
    "    #print(sum2)\n",
    "    #print(len(ww))\n",
    "    #print(len(ww[0]))\n",
    "    #print(len(ww[0][0]))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vgg_fcn.save_npy(sess, \"/mnt/data3/Game_data/npy/fcn32-combined-epoch-\" + str(epoch) + \".npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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