LATEST-vgg16-deeplabv3-SYNTHIA-VIPER-DENSE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install umap-learn\n",
    "# !pip uninstall umap -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/37893755/tensorflow-set-cuda-visible-devices-within-jupyter\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"   # see issue #152\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "# https://stackoverflow.com/questions/56008683/could-not-create-cudnn-handle-cudnn-status-internal-error\n",
    "import tensorflow as tf\n",
    "gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.95)\n",
    "config = tf.compat.v1.ConfigProto(gpu_options=gpu_options)\n",
    "config.gpu_options.allow_growth = True\n",
    "session = tf.compat.v1.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()\n",
    "\n",
    "#tensorflow 2.6 kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "\n",
    "import wasserstein_utils\n",
    "import data_utils\n",
    "import losses\n",
    "import networks\n",
    "import deeplabv3 as dlv3\n",
    "import utils\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "import data_utils\n",
    "import data_utils_new\n",
    "import losses\n",
    "\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import umap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "backbone = 'vgg16'\n",
    "dataset = \"SYNTHIA_VIPER_DENSE_13_1\"\n",
    "\n",
    "# H x W x C\n",
    "img_shape = (512,1024,3)\n",
    "\n",
    "# 13 classes + void\n",
    "num_classes = 14\n",
    "\n",
    "batch_size=4\n",
    "\n",
    "do_training = True\n",
    "\n",
    "epochs=100000\n",
    "epoch_step=250\n",
    "\n",
    "num_projections=100\n",
    "\n",
    "\n",
    "import math \n",
    "fig, ax=plt.subplots(2, figsize=(15,10))\n",
    "\n",
    "source_data_dir = '/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/processed-data/1024x512/13_classes/SYNTHIA/train/'\n",
    "cityscapes_data_dir = '/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/processed-data/1024x512/13_classes/VIPER_DENSE_SEQ/Sequence001/'\n",
    "\n",
    "fn_w_dlv3 = \"/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/\" + dataset + \"/\" + backbone + \"_deeplabv3.h5\"\n",
    "fn_w_cls = \"/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/\" + dataset + \"/\" + backbone + \"_deeplabv3_classifier.h5\"\n",
    "\n",
    "fn_w_adapted_dlv3 = \"/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/\" + dataset + \"/\" + backbone +\"_deeplabv3_adapted.h5\"\n",
    "fn_w_adapted_cls = \"/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/\" + dataset + \"/\" + backbone + \"_deeplabv3_classifier_adapted.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_1/vgg16_deeplabv3.h5\n",
      "/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_1/vgg16_deeplabv3_adapted.h5\n"
     ]
    }
   ],
   "source": [
    "print(fn_w_dlv3)\n",
    "print(fn_w_adapted_dlv3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /global/cscratch1/sd/saksman/accda_conda/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1666: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(losses)\n",
    "\n",
    "deeplabv3 = dlv3.deeplabv3(activation=None, \\\n",
    "                           backbone=backbone, \\\n",
    "                           num_classes=num_classes)\n",
    "\n",
    "X = deeplabv3.input\n",
    "Y = tf.keras.layers.Input(shape=(img_shape[0], img_shape[1], num_classes,), dtype='float32', name='label_input')\n",
    "\n",
    "C_in = tf.keras.layers.Input(shape=deeplabv3.layers[-1].output_shape[1:], dtype='float32', name='classifier_input')\n",
    "classifier = tf.keras.Model(C_in, networks.classifier_layers(C_in, num_classes = num_classes, activation='softmax'))\n",
    "\n",
    "# A combined model, giving the output of classifier(deeplabv3(X))\n",
    "combined = tf.keras.Model(X, classifier(deeplabv3(X)))\n",
    "combined.compile(loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False))\n",
    "\n",
    "# A model outputting hxwx1 labels for each image. Also useful to verify the\n",
    "# mIoU with Keras' built-in function. Will however also consider the 'ignore' class. \n",
    "combined_ = tf.keras.Model(X, tf.cast(tf.keras.backend.argmax(combined(X), axis=-1), 'float32'))\n",
    "combined_.compile(metrics=[tf.keras.metrics.MeanIoU(num_classes=num_classes)], loss=tf.keras.losses.CategoricalCrossentropy(from_logits=False))\n",
    "\n",
    "# Set up training\n",
    "opt = tf.keras.optimizers.Adam(lr=1e-4, epsilon=1e-8, decay=1e-6)\n",
    "# opt = tf.keras.optimizers.SGD(lr=1e-4, momentum=.9)\n",
    "\n",
    "loss_function = losses.weighted_ce_loss(num_classes, 0)\n",
    "wce_loss = loss_function(Y, classifier(deeplabv3(X)), from_logits=False)\n",
    "\n",
    "# https://stackoverflow.com/questions/55434653/batch-normalization-doesnt-have-gradient-in-tensorflow-2-0\n",
    "params = deeplabv3.trainable_weights + classifier.trainable_weights\n",
    "\n",
    "updates = opt.get_updates(wce_loss, params)\n",
    "\n",
    "train = tf.keras.backend.function(inputs=[X,Y], outputs=[wce_loss], updates=updates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deeplabv3.load_weights(fn_w_dlv3)\n",
    "# classifier.load_weights(fn_w_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt \n",
    "# importlib.reload(data_utils)\n",
    "\n",
    "# do_training = True\n",
    "\n",
    "# # Training on source domain\n",
    "# if do_training == True:\n",
    "#     try:\n",
    "#         deeplabv3.load_weights(fn_w_dlv3)\n",
    "#         classifier.load_weights(fn_w_cls)\n",
    "\n",
    "#         print(\"Successfully loaded model. Continuing training.\")\n",
    "#     except:\n",
    "#         print(\"Could not load previous model weights. Is a new model present?\")\n",
    "        \n",
    "#     start_time = time.time()\n",
    "\n",
    "#     fig,ax = plt.subplots(1,figsize=(10,7))\n",
    "\n",
    "#     loss_history = []\n",
    "\n",
    "#     for itr in range(epochs):\n",
    "#     # for itr in range(1):\n",
    "#         source_train_data, source_train_labels = utils.sample_batch(source_data_dir, batch_size=batch_size, seed=itr)\n",
    "#         source_train_labels = tf.keras.utils.to_categorical(source_train_labels, num_classes=num_classes)\n",
    "\n",
    "#         loss_history.append(train(inputs=[source_train_data, source_train_labels ]))\n",
    "\n",
    "#         if np.isnan(np.sum(loss_history[-1])):\n",
    "#             print('ERROR. ENCOUNTERED NAN')\n",
    "#             break\n",
    "\n",
    "#         if itr%epoch_step == 0:\n",
    "#             if itr != 0:\n",
    "#                 ax.clear()\n",
    "#                 ax.plot(np.asarray(loss_history))\n",
    "\n",
    "#             ax.set_title(\"Training loss on source domain\")\n",
    "#             ax.set_xlabel(\"Epoch\")\n",
    "#             ax.set_ylabel(\"Loss\")\n",
    "\n",
    "#             display.clear_output(wait=True)\n",
    "#             display.display(plt.gcf())\n",
    "#             time.sleep(1e-3)\n",
    "            \n",
    "#         if itr % (epochs // 10) == 0 or itr == epochs - 1:\n",
    "#             deeplabv3.save_weights(fn_w_dlv3)\n",
    "#             classifier.save_weights(fn_w_cls)\n",
    "\n",
    "#     training_time = time.time() - start_time\n",
    "# else:\n",
    "#     deeplabv3.load_weights(fn_w_dlv3)\n",
    "#     classifier.load_weights(fn_w_cls)\n",
    "#     print(\"Loaded model weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3_classifier.h5')\n",
    "print(\"Loaded model weights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance before adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "importlib.reload(utils)\n",
    "\n",
    "import data_utils_new\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "source_cat_iou,source_miou = utils.compute_miou(source_data_dir, combined_, data_utils_new.label_ids_13, 0)\n",
    "\n",
    "for k in source_cat_iou:\n",
    "    print(k, source_cat_iou[k])\n",
    "print(source_miou)\n",
    "\n",
    "print('Computed ' + dataset + ' mIoU in', time.time() - start_time)\n",
    "\n",
    "\n",
    "# road 0.8615078545158885\n",
    "# sidewalk 0.9000774695735323\n",
    "# building 0.9430844244367906\n",
    "# traffic light 0.5906543878603067\n",
    "# traffic sign 0.47512503948229323\n",
    "# vegetation 0.8432760653705371\n",
    "# sky 0.9643722032928421\n",
    "# person 0.7238042430944608\n",
    "# rider 0.6191497998404211\n",
    "# car 0.882677391642491\n",
    "# bus 0.911323854999263\n",
    "# motorcycle 0.6885880663029611\n",
    "# bicycle 0.34777010181426876\n",
    "# 0.7501085309404659\n",
    "# Computed SYNTHIA_VIPER_DENSE_13_1 mIoU in 3522.347924232483"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(utils)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "cit_cat_iou,cit_miou = utils.compute_miou(cityscapes_data_dir, combined_, data_utils_new.label_ids_13, 0)\n",
    "\n",
    "for k in cit_cat_iou:\n",
    "    print(k, cit_cat_iou[k])\n",
    "print(cit_miou)\n",
    "\n",
    "print('Computed VIPER mIoU in', time.time() - start_time)\n",
    "\n",
    "# road 0.46610276620252705\n",
    "# sidewalk 0.08673575303732681\n",
    "# building 0.47584591747660204\n",
    "# traffic light 0.09605701550329426\n",
    "# traffic sign 0.014402255800938481\n",
    "# vegetation 0.13339936275003222\n",
    "# sky 0.7302335987742893\n",
    "# person 0.19711547556738282\n",
    "# rider 0.0\n",
    "# car 0.8627417161587038\n",
    "# bus 0.01936740214450999\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.237077020262739\n",
    "# Computed VIPER mIoU in 166.7798900604248"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pretrained weights for SYNTHIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model weights\n"
     ]
    }
   ],
   "source": [
    "deeplabv3.load_weights(\"/pscratch/sd/c/chubakov/code/mas3-continual/weights/SYNTHIA_VIPER_DENSE_13_3/vgg16_deeplabv3_adapted.h5\")\n",
    "classifier.load_weights(\"/pscratch/sd/c/chubakov/code/mas3-continual/weights/SYNTHIA_VIPER_DENSE_13_3/vgg16_deeplabv3_classifier_adapted.h5\")\n",
    "print(\"Loaded model weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import data_utils_new\n",
    "importlib.reload(utils)\n",
    "\n",
    "start_time = time.time()\n",
    "means, _, ct = utils.learn_gaussians(source_data_dir, deeplabv3, combined, batch_size, data_utils_new.label_ids_13)\n",
    "print(\"computed means in\", time.time() - start_time)\n",
    "\n",
    "start_time = time.time()\n",
    "means, covs, ct = utils.learn_gaussians(source_data_dir, deeplabv3, combined, batch_size, data_utils_new.label_ids_13, \\\n",
    "                                  initial_means=means)\n",
    "print(\"finished training gaussians in\", time.time() - start_time)\n",
    "\n",
    "np.save(\"./extras/means_1024x512_\" + backbone + \"deeplabv3_\" + dataset + \".npy\", means)\n",
    "np.save(\"./extras/covs_1024x512_\" + backbone + \"deeplabv3_\" + dataset + \".npy\", covs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(means.shape[0]):\n",
    "    print('=====================================================')\n",
    "    print(means[i])\n",
    "    print(covs[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# means = np.load(\"./extras/means_1024x512_\" + backbone + \"deeplabv3_\" + dataset + \".npy\")\n",
    "# covs = np.load(\"./extras/covs_1024x512_\" + backbone + \"deeplabv3_\" + dataset + \".npy\")\n",
    "\n",
    "# means = np.load(\"./extras/means_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_COMBINED_13.npy\")\n",
    "# covs = np.load(\"./extras/covs_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_COMBINED_13.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SYNTHIA -> VIPER1\n",
    "means = np.load(\"./extras/means_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_DENSE_13_1.npy\")\n",
    "covs = np.load(\"./extras/covs_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_DENSE_13_1.npy\")\n",
    "\n",
    "means = np.nan_to_num(means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = np.load(\"./extras/means_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_DENSE_13_1.npy\")\n",
    "covs = np.load(\"./extras/covs_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_DENSE_13_1.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# means = np.load(\"./extras/means_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_COMBINED_13.npy\")\n",
    "# covs = np.load(\"./extras/covs_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_COMBINED_13.npy\")\n",
    "means = np.nan_to_num(means)\n",
    "# means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.559155464172363\n"
     ]
    }
   ],
   "source": [
    "import data_utils_new\n",
    "import matplotlib.pyplot as plt \n",
    "import umap.umap_ as umap\n",
    "# Generate data from the gmm model and plot it\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "n_samples = np.ones(num_classes, dtype=int)\n",
    "n_samples[0] = 0\n",
    "n_samples *= 2000\n",
    "\n",
    "xx, yy = utils.sample_from_gaussians(means, covs, n_samples=n_samples)\n",
    "\n",
    "NUM_COLORS = num_classes\n",
    "\n",
    "reducer = umap.UMAP()\n",
    "\n",
    "umap_embedding = reducer.fit_transform(xx)\n",
    "\n",
    "plt.figure(figsize=(16,14))\n",
    "cm = plt.get_cmap('gist_rainbow')\n",
    "\n",
    "shift = 1 / len(data_utils_new.label_ids_13.keys())\n",
    "idx = 0\n",
    "for label in data_utils_new.label_ids_13:\n",
    "    ind = yy == data_utils_new.label_ids_13[label]\n",
    "    \n",
    "    plt.scatter(umap_embedding[:,0][ind], umap_embedding[:,1][ind], label=label, \\\n",
    "                color=cm(1.*idx/NUM_COLORS))\n",
    "    idx += 1\n",
    "\n",
    "plt.title(\"Embedding scatter-plot\")\n",
    "plt.legend()\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "print(time.time() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/cscratch1/sd/saksman/accda_conda/lib/python3.6/site-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(wasserstein_utils)\n",
    "\n",
    "Z_s = tf.keras.layers.Input(shape=(img_shape[0], img_shape[1], num_classes,) )\n",
    "Y_s = tf.keras.backend.placeholder(shape=(None, img_shape[0], img_shape[1], num_classes), dtype='float32') #labels of input images oneHot\n",
    "lambda2 = 5e-1\n",
    "\n",
    "loss_function = losses.weighted_ce_loss(num_classes, 0)\n",
    "wce_loss = loss_function(Y_s, classifier(Z_s), from_logits=False)\n",
    "\n",
    "# Wasserstein matcing loss\n",
    "theta = tf.keras.backend.placeholder(shape = (num_projections, num_classes), dtype='float32')\n",
    "matching_loss = wasserstein_utils.sWasserstein_hd(deeplabv3(X), Z_s, theta, nclass=num_classes, Cp=None, Cq=None,)\n",
    "\n",
    "# Overall loss is a weighted combination of the two losses\n",
    "total_loss = wce_loss + lambda2*matching_loss\n",
    "\n",
    "params = deeplabv3.trainable_weights + classifier.trainable_weights\n",
    "\n",
    "# Optimizer and training setup\n",
    "opt = tf.keras.optimizers.Adam(lr=1e-4, epsilon=1e-6, decay=1e-6)\n",
    "\n",
    "updates = opt.get_updates(total_loss, params)\n",
    "train = tf.keras.backend.function(inputs=[X,Z_s,Y_s,theta], outputs=[total_loss, wce_loss, matching_loss], updates=updates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = []\n",
    "target_miou = []\n",
    "\n",
    "# deeplabv3.load_weights(fn_w_dlv3)\n",
    "# classifier.load_weights(fn_w_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model weights\n"
     ]
    }
   ],
   "source": [
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3_classifier.h5')\n",
    "print(\"Loaded model weights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial SYNTHIA->VIPER adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mIoU at 7 epochs: 0.04392110384427584\n",
      "Iteration: 8\n",
      "mIoU at 8 epochs: 0.006518378423956724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(2,figsize=(15,10))\n",
    "\n",
    "batch_size = 1\n",
    "\n",
    "thres = .99\n",
    "epochs2 = 50000\n",
    "epochs2 = 2000\n",
    "epochstep = 2\n",
    "\n",
    "# load model weights\n",
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3_classifier.h5')\n",
    "print(\"Loaded model weights\")\n",
    "\n",
    "# load replay buffer params\n",
    "means = np.load(\"/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/extras/means_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_DENSE_13_1.npy\")\n",
    "covs = np.load(\"/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/extras/covs_1024x512_vgg16deeplabv3_SYNTHIA_VIPER_DENSE_13_1.npy\")\n",
    "print(\"Loaded means and covariances for replay buffer\")\n",
    "\n",
    "for itr in range(epochs2):\n",
    "    \n",
    "    if itr%epochstep == 0:\n",
    "        print('Iteration: {}'.format(itr))\n",
    "    \n",
    "    target_train_data, target_train_labels = utils.sample_batch(cityscapes_data_dir, batch_size=batch_size, seed=itr)\n",
    "    # print(target_train_data.shape)\n",
    "\n",
    "    # make sure the #samples from gaussians match the distribution of the labels\n",
    "    n_samples = np.zeros(num_classes, dtype=int)\n",
    "    cls, ns = np.unique(target_train_labels, return_counts=True)\n",
    "    for i in range(len(cls)):\n",
    "        if cls[i] == 0:\n",
    "            continue\n",
    "        n_samples[cls[i]] = ns[i]\n",
    "\n",
    "    if np.sum(n_samples) % np.prod(img_shape) != 0:\n",
    "        remaining = np.prod(img_shape[:-1]) - np.sum(n_samples) % np.prod(img_shape[:-1])\n",
    "\n",
    "        aux = np.copy(n_samples) / np.sum(n_samples)\n",
    "        aux *= remaining\n",
    "        aux = np.floor(aux).astype('int')\n",
    "        \n",
    "        n_samples += aux\n",
    "\n",
    "        # in case there are extra samples left, dump them on the highest represented class\n",
    "        n_samples[np.argmax(n_samples)] += remaining - np.sum(aux)\n",
    "\n",
    "\n",
    "    Yembed,Yembedlabels = utils.sample_from_gaussians(means, covs, n_samples = n_samples)\n",
    "    Yembed = Yembed.reshape(-1, img_shape[0], img_shape[1], num_classes)\n",
    "    Yembedlabels = Yembedlabels.reshape(-1, img_shape[0], img_shape[1])\n",
    "    \n",
    "    # print('source shape: ',Yembed.shape)\n",
    "    \n",
    "\n",
    "    Yembedlabels = tf.keras.utils.to_categorical(Yembedlabels, num_classes=num_classes)\n",
    "    \n",
    "    # print(Yembed.shape)\n",
    "\n",
    "    theta_instance = tf.keras.backend.variable(wasserstein_utils.generateTheta(num_projections,num_classes))\n",
    "    loss.append(train(inputs=[target_train_data, Yembed, Yembedlabels, theta_instance]))\n",
    "    miou = combined_.evaluate(target_train_data, target_train_labels, verbose=False)[-1] * 14/13\n",
    "    target_miou.append(miou)\n",
    "    \n",
    "    print(\"mIoU at {0} epochs: {1}\".format(itr, miou)) \n",
    "\n",
    "    if itr%epochstep==0:\n",
    "        # Debug info. First, the mIoU. Second, the categorical CE loss (ignoring class weights and containing) \n",
    "        # the ignore class\n",
    "        if itr != 0:\n",
    "            ax[0].clear()\n",
    "            \n",
    "            ll = np.asarray(loss)\n",
    "            ax[0].plot(ll[:,0], label='total loss')\n",
    "            ax[0].plot(ll[:,1], label='ce loss')\n",
    "            ax[0].plot(ll[:,2] * lambda2, label='wasserstein loss')\n",
    "            ax[0].legend()\n",
    "            \n",
    "        ax[0].set_title(\"Loss\")\n",
    "        ax[0].set_xlabel(\"Epochs\")\n",
    "        ax[0].set_ylabel(\"Loss\")\n",
    "        \n",
    "        if itr != 0:\n",
    "            ax[1].clear()\n",
    "            ax[1].plot(np.asarray(target_miou))\n",
    "        \n",
    "        ax[1].set_title(\"MIOU on target domain\")\n",
    "        ax[1].set_xlabel(\"Epochs\")\n",
    "        ax[1].set_ylabel(\"Mean IOU\")\n",
    "        \n",
    "        display.display(plt.gcf())\n",
    "        display.clear_output(wait=True)\n",
    "        \n",
    "        deeplabv3.save_weights(fn_w_adapted_dlv3)\n",
    "        classifier.save_weights(fn_w_adapted_cls)\n",
    "        \n",
    "        time.sleep(1e-3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance after adaptation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "importlib.reload(utils)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "source_cat_iou,source_miou = utils.compute_miou(source_data_dir, combined_, data_utils.label_ids_13, 0)\n",
    "\n",
    "for k in source_cat_iou:\n",
    "    print(k, source_cat_iou[k])\n",
    "print(source_miou)\n",
    "\n",
    "print('Computed ' + dataset + ' mIoU in', time.time() - start_time)\n",
    "\n",
    "# VIPER 1\n",
    "# road 0.45548384569118733\n",
    "# sidewalk 0.22908076309616884\n",
    "# building 0.7231414831527551\n",
    "# traffic light 0.13056876105257134\n",
    "# traffic sign 0.10746477632561094\n",
    "# vegetation 0.29910880116931143\n",
    "# sky 0.7702276075574622\n",
    "# person 0.3968236052300994\n",
    "# rider 0.0012631338117709702\n",
    "# car 0.3250214488701445\n",
    "# bus 0.011364789604663108\n",
    "# motorcycle 0.0014075893390935838\n",
    "# bicycle 0.0013295668128586176\n",
    "# 0.26556047474720745\n",
    "# Computed SYNTHIA_VIPER_DENSE_13_1 mIoU in 701.9752759933472\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "cit_cat_iou,cit_miou = utils.compute_miou(cityscapes_data_dir, combined_, data_utils.label_ids_13, 0)\n",
    "\n",
    "for k in cit_cat_iou:\n",
    "    print(k, cit_cat_iou[k])\n",
    "print(cit_miou)\n",
    "\n",
    "print('Computed VIPER mIoU in', time.time() - start_time)\n",
    "\n",
    "# VIPER-1\n",
    "# road 0.8170088043832361\n",
    "# sidewalk 0.33026286655816334\n",
    "# building 0.632127644160952\n",
    "# traffic light 0.4009491872069492\n",
    "# traffic sign 0.006254310466994813\n",
    "# vegetation 0.1306418583589563\n",
    "# sky 0.8650156523569378\n",
    "# person 0.19010653822990375\n",
    "# rider 0.0\n",
    "# car 0.8404013262102543\n",
    "# bus 0.0\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.32405909137941136\n",
    "# Computed VIPER mIoU in 18.68591570854187\n",
    "\n",
    "# VIPER-2\n",
    "# road 0.6166949852470773\n",
    "# sidewalk 0.3787735571503562\n",
    "# building 0.6049256780597033\n",
    "# traffic light 0.186964255325101\n",
    "# traffic sign 0.0008168001712463807\n",
    "# vegetation 0.09151754024216284\n",
    "# sky 0.8323521128681671\n",
    "# person 0.47658649376653717\n",
    "# rider 0.0\n",
    "# car 0.8210425378196808\n",
    "# bus 0.3683831960382732\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.33677362743756195\n",
    "# Computed VIPER 2 mIoU in 12.673284530639648\n",
    "\n",
    "# VIPER-3\n",
    "# road 0.7241212757775349\n",
    "# sidewalk 0.20394133957802926\n",
    "# building 0.4374052246162175\n",
    "# traffic light 0.1886698588396244\n",
    "# traffic sign 0.0013552072158326775\n",
    "# vegetation 0.23934770268178143\n",
    "# sky 0.8418698288550998\n",
    "# person 1.9359460000129065e-05\n",
    "# rider 0.0\n",
    "# car 0.8522794354467441\n",
    "# bus 0.0\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.26838532557468187\n",
    "# Computed VIPER 3 mIoU in 9.569716215133667\n",
    "\n",
    "# VIPER-4\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils\n",
    "importlib.reload(utils)\n",
    "\n",
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_4/vgg16_deeplabv3_adapted.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_4/vgg16_deeplabv3_classifier_adapted.h5')\n",
    "\n",
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3_classifier.h5')\n",
    "\n",
    "start_time = time.time()\n",
    "source_cat_iou,source_miou = utils.compute_miou('./processed-data/1024x512/13_classes/VIPER_DENSE_SEQ/Sequence010/', combined_, data_utils.label_ids_13, 0)\n",
    "for k in source_cat_iou:\n",
    "    print(k, source_cat_iou[k])\n",
    "print(source_miou)\n",
    "\n",
    "print('Computed ' + dataset + ' mIoU in', time.time() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 \t : \t 0.3304527332435952\n",
      "2 \t : \t 0.33019344797055583\n",
      "3 \t : \t 0.2819066622949565\n",
      "4 \t : \t 0.31654615749708054\n",
      "5 \t : \t 0.3073701739865\n",
      "6 \t : \t 0.3069781458418397\n",
      "7 \t : \t 0.2667288525194096\n",
      "8 \t : \t 0.24169105277904077\n",
      "9 \t : \t 0.27019105419790657\n",
      "10 \t : \t 0.24983465619512438\n"
     ]
    }
   ],
   "source": [
    "import utils\n",
    "importlib.reload(utils)\n",
    "\n",
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_1/vgg16_deeplabv3_adapted.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_1/vgg16_deeplabv3_classifier_adapted.h5')\n",
    "\n",
    "\n",
    "# deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3.h5')\n",
    "# classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/fangjun/SYNTHIA_VIPER_COMBINED_13/vgg16_deeplabv3_classifier.h5')\n",
    "\n",
    "for target_i in range(1,11):\n",
    "    target_data_dir = './processed-data/1024x512/13_classes/VIPER_DENSE_SEQ/Sequence' + str(target_i).rjust(3, '0') + '/'\n",
    "    source_cat_iou,source_miou = utils.compute_miou(target_data_dir, combined_, data_utils.label_ids_13, 0)\n",
    "    # for k in source_cat_iou:\n",
    "    #     print(k, source_cat_iou[k])\n",
    "    print(target_i, '\\t : \\t', source_miou)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import data_utils_new\n",
    "importlib.reload(utils)\n",
    "\n",
    "dataset = \"SYNTHIA_VIPER_DENSE_13_\"\n",
    "\n",
    "fig,ax=plt.subplots(2,figsize=(15,10))\n",
    "\n",
    "batch_size = 4\n",
    "\n",
    "thres = .99\n",
    "epochs_target = 500\n",
    "epochstep = 100\n",
    "\n",
    "target_means = []\n",
    "target_covs = []\n",
    "\n",
    "deeplabv3.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_1/vgg16_deeplabv3_adapted.h5')\n",
    "classifier.load_weights('/global/cfs/cdirs/m3691/segmentation_datasets/tilek/mas3-continual_latest/weights/SYNTHIA_VIPER_DENSE_13_1/vgg16_deeplabv3_classifier_adapted.h5')\n",
    "print(\"Loaded model weights\")\n",
    "\n",
    "log_f = open(\"log.txt\", \"a\")\n",
    "\n",
    "starting_target = 2\n",
    "\n",
    "# if starting_target > 2:\n",
    "\n",
    "#     for target_i in range(2, starting_target):\n",
    "\n",
    "#         print('generating stats for target {}'.format(target_i))\n",
    "\n",
    "#         fn_w_adapted_dlv3 = \"weights/\" + dataset + str(target_i) + \"/\" + backbone +\"_deeplabv3_adapted.h5\"\n",
    "#         fn_w_adapted_dlv3 = \"weights/\" + dataset + str(target_i) + \"/\" + backbone + \"_deeplabv3_classifier_adapted.h5\"\n",
    "\n",
    "#         deeplabv3.load_weights(fn_w_adapted_dlv3)\n",
    "#         classifier.load_weights(fn_w_adapted_dlv3)    \n",
    "\n",
    "#         # learn gaussians for the previous target domain   \n",
    "#         start_time = time.time()\n",
    "#         cur_means, _, cur_ct = utils.learn_gaussians_seq(prev_target_data_dir, deeplabv3, combined, batch_size, data_utils_new.label_ids_13)\n",
    "#         print(\"computed means in for target {}\".format(target_i), time.time() - start_time)\n",
    "\n",
    "#         start_time = time.time()\n",
    "#         cur_means, cur_covs, cur_ct = utils.learn_gaussians_seq(prev_target_data_dir, deeplabv3, combined, batch_size, data_utils_new.label_ids_13, initial_means=cur_means)\n",
    "#         print(\"finished training gaussians in\", time.time() - start_time)\n",
    "\n",
    "#         cur_means = np.nan_to_num(cur_means)\n",
    "\n",
    "#         target_means.append(cur_means)\n",
    "#         target_covs.append(cur_covs)\n",
    "\n",
    "for target_i in range(starting_target, 10):   \n",
    "        \n",
    "    prev_target_data_dir = './processed-data/1024x512/13_classes/VIPER_DENSE_SEQ/Sequence' + str(target_i-1).rjust(3, '0') + '/'\n",
    "    target_data_dir = './processed-data/1024x512/13_classes/VIPER_DENSE_SEQ/Sequence' + str(target_i).rjust(3, '0') + '/'\n",
    "    \n",
    "    print('adapting to target domain {}'.format(target_i), '\\t', prev_target_data_dir, '\\t', target_data_dir)\n",
    "        \n",
    "    # learn gaussians for the previous target domain   \n",
    "    start_time = time.time()\n",
    "    cur_means, _, cur_ct = utils.learn_gaussians_seq(prev_target_data_dir, deeplabv3, combined, batch_size, data_utils_new.label_ids_13)\n",
    "    print(\"computed means in for target {}\".format(target_i), time.time() - start_time)\n",
    "\n",
    "    start_time = time.time()\n",
    "    cur_means, cur_covs, cur_ct = utils.learn_gaussians_seq(prev_target_data_dir, deeplabv3, combined, batch_size, data_utils_new.label_ids_13, initial_means=cur_means)\n",
    "    print(\"finished training gaussians in\", time.time() - start_time)\n",
    "\n",
    "    cur_means = np.nan_to_num(cur_means)\n",
    "    \n",
    "    target_means.append(cur_means)\n",
    "    target_covs.append(cur_covs)\n",
    "    \n",
    "    loss = []\n",
    "    target_miou = []\n",
    "    \n",
    "    cur_epochs = max(epochs_target, epochs_target // (target_i - 1))\n",
    "    print('number of epochs for current iter : ', cur_epochs) \n",
    "         \n",
    "    for itr in range(epochs_target):\n",
    "    \n",
    "        if itr%epochstep == 0:\n",
    "            print('Iteration: {}'.format(itr))\n",
    "            \n",
    "        # target_samples_X = []\n",
    "        # target_samples_Y = []\n",
    "        # rb_samples_X = []\n",
    "        # rb_samples_Y = []\n",
    "\n",
    "        \n",
    "        for prev_target in range(0, target_i-1):            \n",
    "           \n",
    "            # target_train_data, target_train_labels = utils.sample_batch(target_data_dir, batch_size=batch_size, seed=itr)\n",
    "            target_train_data, target_train_labels = utils.sample_batch(target_data_dir, batch_size=batch_size, seed=itr)\n",
    "            # target_samples_X.append(target_train_data)\n",
    "            # target_samples_Y.append(target_train_labels)\n",
    "\n",
    "            \n",
    "            # make sure the #samples from gaussians match the distribution of the labels\n",
    "            n_samples = np.zeros(num_classes, dtype=int)\n",
    "            cls, ns = np.unique(target_train_labels, return_counts=True)\n",
    "            for i in range(len(cls)):\n",
    "                if cls[i] == 0:\n",
    "                    continue\n",
    "                n_samples[cls[i]] = ns[i]\n",
    "\n",
    "            if np.sum(n_samples) % np.prod(img_shape) != 0:\n",
    "                remaining = np.prod(img_shape[:-1]) - np.sum(n_samples) % np.prod(img_shape[:-1])\n",
    "\n",
    "                aux = np.copy(n_samples) / np.sum(n_samples)\n",
    "                aux *= remaining\n",
    "                aux = np.floor(aux).astype('int')\n",
    "\n",
    "                n_samples += aux\n",
    "\n",
    "                # in case there are extra samples left, dump them on the highest represented class\n",
    "                n_samples[np.argmax(n_samples)] += remaining - np.sum(aux)\n",
    "\n",
    "            # print(prev_target, target_i, len(target_means), len(target_covs))\n",
    "\n",
    "            Yembed,Yembedlabels = utils.sample_from_gaussians(target_means[prev_target], target_covs[prev_target], n_samples = n_samples)\n",
    "            Yembed = Yembed.reshape(-1, img_shape[0], img_shape[1], num_classes)\n",
    "            Yembedlabels = Yembedlabels.reshape(-1, img_shape[0], img_shape[1])\n",
    "            Yembedlabels = tf.keras.utils.to_categorical(Yembedlabels, num_classes=num_classes)\n",
    "            \n",
    "            # rb_samples_X.append(Yembed)\n",
    "            # rb_samples_Y.append(Yembedlabels)\n",
    "            \n",
    "            theta_instance = tf.keras.backend.variable(wasserstein_utils.generateTheta(num_projections,num_classes))\n",
    "            loss.append(train(inputs=[target_train_data, Yembed, Yembedlabels, theta_instance]))\n",
    "            target_miou.append(combined_.evaluate(target_train_data, target_train_labels, verbose=False)[-1] * 14/13)\n",
    "\n",
    "        \n",
    "#         target_samples_X = tf.squeeze(tf.stack(target_samples_X))\n",
    "#         target_samples_Y = tf.squeeze(tf.stack(target_samples_Y))\n",
    "#         rb_samples_X = tf.squeeze(tf.stack(rb_samples_X))\n",
    "#         rb_samples_Y = tf.squeeze(tf.stack(rb_samples_Y))\n",
    "        \n",
    "#         theta_instance = tf.keras.backend.variable(wasserstein_utils.generateTheta(num_projections,num_classes))\n",
    "#         loss.append(train(inputs=[target_samples_X, rb_samples_X, rb_samples_Y, theta_instance]))\n",
    "#         target_miou.append(combined_.evaluate(target_samples_X, target_samples_Y, verbose=False)[-1] * 14/13)\n",
    "\n",
    "        if itr%epochstep==0:\n",
    "            # Debug info. First, the mIoU. Second, the categorical CE loss (ignoring class weights and containing) \n",
    "            # the ignore class\n",
    "            if itr != 0:\n",
    "                ax[0].clear()\n",
    "\n",
    "                ll = np.asarray(loss)\n",
    "                ax[0].plot(ll[:,0], label='total loss')\n",
    "                ax[0].plot(ll[:,1], label='ce loss')\n",
    "                ax[0].plot(ll[:,2] * lambda2, label='wasserstein loss')\n",
    "                ax[0].legend()\n",
    "\n",
    "            ax[0].set_title(\"Loss\")\n",
    "            ax[0].set_xlabel(\"Epochs\")\n",
    "            ax[0].set_ylabel(\"Loss\")\n",
    "\n",
    "            if itr != 0:\n",
    "                ax[1].clear()\n",
    "                ax[1].plot(np.asarray(target_miou))\n",
    "\n",
    "            ax[1].set_title(\"MIOU on target domain\")\n",
    "            ax[1].set_xlabel(\"Epochs\")\n",
    "            ax[1].set_ylabel(\"Mean IOU\")\n",
    "\n",
    "            display.display(plt.gcf())\n",
    "            display.clear_output(wait=True)\n",
    "            \n",
    "            \n",
    "            fn_w_adapted_dlv3 = \"weights/\" + dataset + str(target_i) + \"/\" + backbone +\"_deeplabv3_adapted.h5\"\n",
    "            fn_w_adapted_cls = \"weights/\" + dataset + str(target_i) + \"/\" + backbone + \"_deeplabv3_classifier_adapted.h5\"\n",
    "            \n",
    "            # print(fn_w_adapted_dlv3, fn_w_adapted_cls)\n",
    "\n",
    "            deeplabv3.save_weights(fn_w_adapted_dlv3)\n",
    "            classifier.save_weights(fn_w_adapted_cls)\n",
    "\n",
    "            time.sleep(1e-3)    \n",
    "            \n",
    "    print('complted adapting to target domain {}'.format(target_i), '\\t', prev_target_data_dir, '\\t', target_data_dir)\n",
    "\n",
    "    # calculate mIoU\n",
    "    start_time = time.time()\n",
    "    cit_cat_iou,cit_miou = utils.compute_miou(target_data_dir, combined_, data_utils.label_ids_13, 0)\n",
    "    for k in cit_cat_iou:\n",
    "        print(k, cit_cat_iou[k])\n",
    "    \n",
    "    print(cit_miou)\n",
    "    log_f.write(str(target_i) + ' : ' + str(cit_miou))    \n",
    "\n",
    "    print('Computed VIPER {} mIoU in'.format(target_i), time.time() - start_time)\n",
    "    \n",
    "log_f.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for itr in range(epochs2):\n",
    "    \n",
    "#     if itr%epochstep == 0:\n",
    "#         print('Iteration: {}'.format(itr))\n",
    "    \n",
    "#     target_train_data, target_train_labels = utils.sample_batch(cityscapes_data_dir, batch_size=batch_size, seed=itr)\n",
    "#     # print(target_train_data.shape)\n",
    "\n",
    "#     # make sure the #samples from gaussians match the distribution of the labels\n",
    "#     n_samples = np.zeros(num_classes, dtype=int)\n",
    "#     cls, ns = np.unique(target_train_labels, return_counts=True)\n",
    "#     for i in range(len(cls)):\n",
    "#         if cls[i] == 0:\n",
    "#             continue\n",
    "#         n_samples[cls[i]] = ns[i]\n",
    "\n",
    "#     if np.sum(n_samples) % np.prod(img_shape) != 0:\n",
    "#         remaining = np.prod(img_shape[:-1]) - np.sum(n_samples) % np.prod(img_shape[:-1])\n",
    "\n",
    "#         aux = np.copy(n_samples) / np.sum(n_samples)\n",
    "#         aux *= remaining\n",
    "#         aux = np.floor(aux).astype('int')\n",
    "        \n",
    "#         n_samples += aux\n",
    "\n",
    "#         # in case there are extra samples left, dump them on the highest represented class\n",
    "#         n_samples[np.argmax(n_samples)] += remaining - np.sum(aux)\n",
    "\n",
    "\n",
    "#     Yembed,Yembedlabels = utils.sample_from_gaussians(means, covs, n_samples = n_samples)\n",
    "#     Yembed = Yembed.reshape(-1, img_shape[0], img_shape[1], num_classes)\n",
    "#     Yembedlabels = Yembedlabels.reshape(-1, img_shape[0], img_shape[1])\n",
    "    \n",
    "#     # print('source shape: ',Yembed.shape)\n",
    "\n",
    "#     Yembedlabels = tf.keras.utils.to_categorical(Yembedlabels, num_classes=num_classes)\n",
    "    \n",
    "#     # print(Yembed.shape)\n",
    "\n",
    "#     theta_instance = tf.keras.backend.variable(wasserstein_utils.generateTheta(num_projections,num_classes))\n",
    "#     loss.append(train(inputs=[target_train_data, Yembed, Yembedlabels, theta_instance]))\n",
    "#     target_miou.append(combined_.evaluate(target_train_data, target_train_labels, verbose=False)[-1] * 20/19)\n",
    "    \n",
    "#     if itr%epochstep==0:\n",
    "#         # Debug info. First, the mIoU. Second, the categorical CE loss (ignoring class weights and containing) \n",
    "#         # the ignore class\n",
    "#         if itr != 0:\n",
    "#             ax[0].clear()\n",
    "            \n",
    "#             ll = np.asarray(loss)\n",
    "#             ax[0].plot(ll[:,0], label='total loss')\n",
    "#             ax[0].plot(ll[:,1], label='ce loss')\n",
    "#             ax[0].plot(ll[:,2] * lambda2, label='wasserstein loss')\n",
    "#             ax[0].legend()\n",
    "            \n",
    "#         ax[0].set_title(\"Loss\")\n",
    "#         ax[0].set_xlabel(\"Epochs\")\n",
    "#         ax[0].set_ylabel(\"Loss\")\n",
    "        \n",
    "#         if itr != 0:\n",
    "#             ax[1].clear()\n",
    "#             ax[1].plot(np.asarray(target_miou))\n",
    "        \n",
    "#         ax[1].set_title(\"MIOU on target domain\")\n",
    "#         ax[1].set_xlabel(\"Epochs\")\n",
    "#         ax[1].set_ylabel(\"Mean IOU\")\n",
    "        \n",
    "#         display.display(plt.gcf())\n",
    "#         display.clear_output(wait=True)\n",
    "        \n",
    "#         deeplabv3.save_weights(fn_w_adapted_dlv3)\n",
    "#         classifier.save_weights(fn_w_adapted_cls)\n",
    "        \n",
    "                \n",
    "#         time.sleep(1e-3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NugxJkqSUO5Hf8AyrmtzXgxwuZzi/4W3WEu/G158e3M4JBlepg5YuXcrQoW34d/R0LD7G9MPyeIvpVSUdngwpWzT1WQdBsCgMw8KmjrflVJIkKcVqW0fLqCAH2NWKhlupYQZLGmxby1Ym8RT38Trz+FLKapXUyMlFhtFO4phTSZKkFIqxlMnMpYwKYNfBdFeeYRVTmdfxwiQpzdhyKkmSlCIxlnIRT5HsQVQzWMJT/JMSxgM4ZlVSl2A4lSRJSoHDuY+3WJey65dRwSSe2mnbRTzFJJ5isEFVUoYxnEqSJO3CVOZxF0vqWkADIISdAmCMpVzDM6xla1Sl1tVYRgWTmQtgQJWUEQynkiRJLWhqVt36AfASnuIanq2bVTedVFLF5fzRcCopIzghkiRJUjNiLG12uZda1ZCWwbTWNkJO5DdRlyGpBVdccQVvvfXWTtvvv/9+vvGNb6TknitXrmTYsGEpv09b2HIqSZLUjCsT3WIz3a4CtqRo/fKXv4y6hLRgy6kkSVITpjKPTVRFXUbSuPyM1E7LY/BQPszMiT8uj3Xocps2beILX/gCBQUFDBs2jIcffpjjjz+e0tJSAO677z4OOeQQjjzySF588cW681avXs25557LmDFjGDNmTN2+4cOHs379esIwpH///vzqV78C4OKLL+bPf/4zK1euZPz48YwaNYpRo0bx0ksvtVjfH/7wB4455hjWrFnToffZHoZTSZKkRmIs5S6WRF1GUs1gCQE/JZ+ZxFgadTlSZlgeg/mTYWMZEMYf50/uUED905/+xP7778+SJUt44403OOWUU+r2vf/++9x44428+OKLLFiwoEFX32uuuYZvf/vbLFy4kDlz5nDFFVcAMHbsWF588UXefPNNPvOZzzB//nwAXn75ZY499lj22Wcf/vznP/Paa6/x8MMPc/XVVzdb2+OPP87NN9/MU089xYABA9r9HtvLbr2SJEmNFDM/6WuTposyKriMPwLO4ivt0sJiqKpsuK2qMr794KJ2XXL48OF897vf5fvf/z6nn34648ePr9v317/+leOPP569994bgAsuuIB//OMfAMybN69BWP3kk0/YuHEj48eP54UXXmDw4MFMmTKFmTNn8u6777LnnnvSp08fNmzYwDe+8Q0WL15Mbm5u3fUae/bZZyktLWXu3Lnstttu7XpvHWXLqSRJUiPlVERdQkptJ2QST9mCKu3KxvK2bW+FQw45hNdee43hw4dz/fXXc9NNN7XqvJqaGl555RUWL17M4sWLeffdd+nbty8TJkxg/vz5zJ8/vy7YPvroo3Wh97bbbmPfffdlyZIllJaWsm3btiavf9BBB1FRUdFseO0MhlNJkqR6YiwliLqITmJAlXah76C2bW+F9957j969ezNp0iSuvfZaXnvttbp9Rx11FM8//zxr165l+/btPPLII3X7Jk6cyC9+8Yu614sXLwZg4MCBrFmzhuXLl/OZz3yGcePGceuttzJhwgQANmzYwH777UdOTg4PPvgg1dXVTdY1ePBg5syZw8UXX8ybb77Z7vfXEYZTSZKkhBhLuYSnqIm6kE50EU+R41hUqWljSiCvd8Nteb3j29vp9ddf58gjj2TkyJH88Ic/5Prrr6/bt99++zFt2jSOOeYYxo4dy9ChO7re33HHHZSWljJixAgOO+ww7rrrrrp9Rx11FIcccggA48eP591332XcuHEATJ06lQceeICCggKWLVtGnz59mq3t0EMPJRaLcf7557NixYp2v8f2CsIwfUZUFBYWhrWzVEmSJHWmqcxjRhebBKmtepPHTCY6FlVd2tKlSxuEvl1aHouPMd1YHm8xHVPS7vGm2aapzzoIgkVhGBY2dbwTIkmSpKxnMI2rpIpi5htOpfoOLjKMdhK79UqSpKzX1ZaN6YiuPhmUpPRlOJUkSVkvfQY5RW8Q/aIuQVKWMpxKkqSs5iRADX1IpZ+JpEgYTiVJUlYrZn7UJaSVLVQziacYwP8aUiV1KsOpJEnKao6xbNpathpSJXUqw6kkScpqjrFs2Vq2Mpm5BlQpiVauXMmwYcNaffzvfvc7br75ZgCmTZvGrbfe2uI1S0tLufrqq5NTbCdyKRlJkpTVShjPJJ6Kuoy0VkkV1/CsS8xIETnzzDM588wzW318YWEhhYVNLiWa1mw5lSRJWa2IofQkN+oy0t5atjCVeVGXIXW+6hhU5UNVTvyxOpaUy1ZVVVFUVMTQoUM577zzqKysJD8/nzVr1gDx1s/jjz8egPvvv59vfOMbO11j0aJFFBQUUFBQwJ133lm3/bnnnuP0008H4i2tl19+Occffzyf+cxnuOOOO+qO+9GPfsSQIUMYN24cF154YZMtsp3JcCpJkrJajKVUUxN1GRlhBkvs3qvsUh2DcDJQRnzRqbL46yQE1LfffpupU6eydOlSdtttN6ZPn97ma1x22WX84he/YMmSltdqXrZsGU8//TSvvvoqP/zhD9m+fTsLFy5kzpw5LFmyhD/+8Y+Ulpa2960kjeFUkiRltWLms92VTlvtYp4yoCp7hMVAZaONlYntHTNw4EDGjh0LwKRJk1iwYEGbzl+/fj3r169nwoQJAFx00UXNHvuFL3yBHj16MGDAAPbZZx8+/PBDXnzxRb74xS/Ss2dP+vXrxxlnnNH+N5MkhlNJkpTVnK23bWqAy/ijAVVZoryN21svCIKdXufl5VFTE+/JsWXLlg7fo1aPHj3qnufm5lJVVZW0ayeT4VSSJGU1Z+ttu+2EXMMzUZchdYJBbdzeeuXl5bz88ssAPPTQQ4wbN478/HwWLVoEwJw5c1o8f4899mCPPfaoa3GNxdrW1Xjs2LE8+eSTbNmyhY0bN/L73/++He8iuQynkiQpq5UwnmDXh6mRtWyNugQp9YISoHejjb0T2ztmyJAh3HnnnQwdOpSPP/6YKVOmcOONN3LNNddQWFhIbu6uJ2q77777+PrXv87IkSMJw7YNTxgzZgxnnnkmI0aM4NRTT2X48OHsvvvu7X07SRG09U2kUmFhYZgOA3ElSVJ2mco8ZtDyhCLaWch3oy5BarOlS5cydGgblkWqjiXGmJYDg+LBNLcoVeV1qo0bN9K3b18qKyuZMGECM2fOZNSoUUm7flOfdRAEi8IwbHKdG9c5lSRJWW8sB3Avr7PNWXtbze53yhq5RUDXCKONTZ48mbfeeostW7ZwySWXJDWYtofhVJIkZb1i5htM28hPS8p8Dz30UNQlNOAvvSRJUtZzxt62y3WkrqQkM5xKkqSs54y9bVft2rCSksxwKkmSsl4J4+ntaKc2GWygl5RkhlNJkpT1ihjKTCbSnx67PlhAPNBLUjIZTiVJkogH1H3pE3UZGaOINizFIanO+vXrmT59ervOfeSRRxg6dCif+9znALjwwgsZMWIEt912GzfccAPz5s1r1XVWrlzJsGHDACgtLeXqq69u9fGN3X///bz33ntteBfNs/+KJEkScCK/4S3WRV1GxshnJiWMN6RKbVQbTqdOnbrTvqqqKvLymo9o9957L/fccw/jxo3jgw8+YOHChbzzzjsdqqewsJDCwiaXHW2V+++/n2HDhrH//vt3qA6w5VSSJAmAZ1gVdQkZpYwKJjOXGEujLkVKqRXEeJh8ZpHDw+SzgliHrnfdddexYsUKRo4cybXXXstzzz3H+PHjOfPMMznssMMAOOussxg9ejSHH344M2fOBOCmm25iwYIFfPWrX+Xaa69l4sSJvPvuu4wcOZL58+dz6aWX8uijjwKwcOFCjj32WAoKCjjyyCOpqGh+RvLnnnuO008/HYDVq1dz0kkncfjhh3PFFVcwePBg1qxZA0B1dTVf+9rXOPzww5k4cSKbN2/m0UcfpbS0lKKiIkaOHMnmzZs79NkYTiVJktQulVRRzPyoy5BSZgUxFjCZTZQBIZsoYwGTOxRQb775Zg466CAWL17MLbfcAsBrr73G7bffzj/+8Q8AZs2axaJFiygtLeWOO+5g7dq13HDDDRQWFhKLxbjlllv43e9+V3ed8eN3jAHftm0bF1xwAbfffjtLlixh3rx59OrVq1W1/fCHP+Tzn/88b775Jueddx7l5eV1+5YvX87Xv/513nzzTfbYYw/mzJnDeeedV1fT4sWLW32f5hhOJUmS1G6uEauurJRiqqlssK2aSkopTup9jjzySA488MC613fccQcFBQUcffTRrFq1iuXLl7f6Wm+//Tb77bcfY8aMAWC33XZrsatwfQsWLODLX/4yAKeccgp77rln3b4DDzyQkSNHAjB69GhWrlzZ6ppay3AqSZIEnMDAqEvISK4Rq65sE+Vt2t5effrsmIztueeeY968ebz88sssWbKEI444gi1btiT1fu3Ro8eO2cxzc3OpqqpK+j0Mp5IkScA8vkQeQdRlZJTe5LmkjLq0Pgxq0/bW6NevX4tjQDds2MCee+5J7969WbZsGa+88kqbrj9kyBDef/99Fi5cCEBFRUWrg+TYsWP5zW9+A8DcuXP5+OOPd3nOrt5PWxhOJUmSEu7nVLr741Gr9KcnM5nobL3q0gopIZfeDbbl0ptCStp9zf79+zN27FiGDRvGtddeu9P+U045haqqKoYOHcp1113H0Ucf3abrd+/enYcffphvfvObFBQUcNJJJ7W65fXGG29k7ty5DBs2jEceeYRPfepT9OvXcu+ISy+9lKuuuiopEyIFYRh26ALJVFhYGJaWlkZdhiRJymIxllLMfMocS9mibgTcx6mGU2WcpUuXMnRo6//eriBGKcVsopw+DKKQEg6iKIUVRmfr1q3k5uaSl5fHyy+/zJQpU1i8eHG7r9fUZx0EwaIwDJtcu8Z1TiVJkuopYihFDCXgp1GXkta2E3IRTwEYUNWlHURRlw2jjZWXl/OlL32Jmpoaunfvzj333NOp9zecSpIkNaEPeWwi+RN+dCUhcDl/AgyoUldw8MEH87e//S2y+zuoQpIkqQl3M5HcqIvIANuoca1TSUlhy6kkSVITalsCJyW6rqp5rnUqKRlsOZUkSWpGEUPpY/vpLrnWqaRkMJxKkiS1oJLqqEtIa3kErnUqKSkMp5IkSS2wVbBlu9PdyZCkNli/fj3Tp09v17mPPPIIQ4cO5XOf+xwAF154ISNGjOC2227jhhtuYN68eR2q7bTTTmP9+vUdukZHOOZUkiSpBSWM5zL+yHbSZ234dLKOrVGXIGWU2nA6derUnfZVVVWRl9d8RLv33nu55557GDduHB988AELFy7knXfeSVptTz0V7Rh7W04lSZJaUMRQ7uNU+tMj6lLSki3L6urmEuNc8hlPDueSz1xiHbreddddx4oVKxg5ciTXXnstzz33HOPHj+fMM8/ksMMOA+Css85i9OjRHH744cycOROAm266iQULFvDVr36Va6+9lokTJ/Luu+8ycuRI5s+fz6WXXsqjjz4KwMKFCzn22GMpKCjgyCOPpKKi4aRl77//PhMmTGDkyJEMGzaM+fPjM27n5+ezZs0aAH70ox8xZMgQxo0bx4UXXsitt94KwPHHH8/3v/99jjzySA455JC6c5PBllNJkqRdKGJog66rA/hf1tpiCOB4U3Vpc4nxEyazlUoAPqSMnzAZgIkUteuaN998M2+88QaLFy8G4LnnnuO1117jjTfe4MADDwRg1qxZ7LXXXmzevJkxY8Zw7rnncsMNN/Dss89y6623UlhYyNe//nVOP/30uuvce++9AGzbto0LLriAhx9+mDFjxvDJJ5/Qq1evBjU89NBDnHzyyRQXF1NdXU1lZWWD/QsXLmTOnDksWbKE7du3M2rUKEaPHl23v6qqildffZWnnnqKH/7whx3uTlzLllNJkqQ2up0T6O3v+OlPT8ebqku7m+K6YFprK5XcTXFS73PkkUfWBVOAO+64g4KCAo4++mhWrVrF8uXLW32tt99+m/32248xY8YAsNtuu+3UVXjMmDHcd999TJs2jddff51+/Rr2gHjxxRf54he/SM+ePenXrx9nnHFGg/3nnHMOAKNHj2blypVteastMpxKkiS1URFDmclE+tMz6lIidTufj7oEKaU+orxN29urT58+dc+fe+455s2bx8svv8ySJUs44ogj2LJlS1LvN2HCBF544QUOOOAALr30Un71q1+16fwePeLDHHJzc6mqqkpaXYZTSZKkdihiKGv4OrM5jcFZOu7SVlN1dfswqE3bW6Nfv347jQGtb8OGDey555707t2bZcuW8corr7Tp+kOGDOH9999n4cKFAFRUVOwUIMvKyth333352te+xhVXXMFrr73WYP/YsWN58skn2bJlCxs3buT3v/99m2poL8OpJElSBxQxlJVMzrqA2sduzcoCV1JCD3o32NaD3lxJSbuv2b9/f8aOHcuwYcO49tprd9p/yimnUFVVxdChQ7nuuus4+uij23T97t278/DDD/PNb36TgoICTjrppJ1aXp977jkKCgo44ogjePjhh7nmmmsa7B8zZgxnnnkmI0aM4NRTT2X48OHsvvvubX+zbRSEYfpMi15YWBiWlpZGXYYkSVKbxVjKZOZSSfK6uKWrXOABTrPlVBlp6dKlDB3a+r+7c4lxN8V8RDn7MIgrKWn3ZEiZZOPGjfTt25fKykomTJjAzJkzGTVqVJuu0dRnHQTBojAMC5s63l95SZIkJUFtUCtmPuVUkENAdRdcG3Uw/ShhvMFUWWMiRVkRRhubPHkyb731Flu2bOGSSy5pczBtD8OpJElSktRfcqapltTe5NGLPNaS3MlNOksArEwsoyGpa3vooYc6/Z6OOZUkSUqB2hl9B9OPgHiL40wmcjufJ4i6uHYalGXjaiV1LltOJUmSUqR+S2p9k3gqgmo6pjd5lDA+6jIkdWG2nEqSJHWyTJvZtz89mclEx5lKSinDqSRJUifLpBbI2ZzGGr5uMJWUcoZTSZKkTpYpQW8w/TKmVklxv/3tb3nrrbfafNwNN9zAvHnzUlnaLhlOJUmSIpAJXXszqYVXUlx7w+lNN93EiSeemMrSdslwKkmSFIESxtM7jeemnEKBraYSEONJ8vkcORxKPp8jxpMdut51113HnXfeWfd62rRp3Hrrrdxyyy2MGTOGESNGcOONN9bt/9GPfsSQIUMYN24cF154IbfeeisAK1as4JRTTmH06NGMHz+eZcuW8dJLL/G73/2Oa6+9lpEjR7JixQruuecexowZQ0FBAeeeey6VlZVNHnfppZfy6KOPAvDMM89wxBFHMHz4cC6//HK2bt0KQH5+PjfeeCOjRo1i+PDhLFu2rEOfRWOGU0mSpAjUX2omnXQnYDanMZ1oW1CkdBDjSSZzPWW8R0hIGe8xmes7FFAvuOACfvOb39S9/s1vfsPee+/N8uXLefXVV1m8eDGLFi3ihRdeYOHChcyZM4clS5bwxz/+kdLS0rrzJk+ezC9+8QsWLVrErbfeytSpUzn22GM588wzueWWW1i8eDEHHXQQ55xzDgsXLmTJkiUMHTqUe++9t8njam3ZsoVLL72Uhx9+mNdff52qqipmzJhRt3/AgAG89tprTJkypS4oJ0v6/rpOkiSpi6tdamYq85jBkqjLYQoFhlKpnmJ+RiVbGmyrZAvF/IwizmjXNY844gg++ugj3nvvPVavXs2ee+7J66+/zty5czniiCMA2LhxI8uXL6eiooIvfvGL9OzZk549e3LGGWfU7X/ppZc4//zz665b27rZ2BtvvMH111/P+vXr2bhxIyeffHKL9b399tsceOCBHHLIIQBccskl3HnnnXzrW98C4JxzzgFg9OjRPPbYY+36DJpjOJUkSYpYbSCMOqAaTKWGynm/Tdtb6/zzz+fRRx/lgw8+4IILLqCsrIwf/OAHXHnllQ2O+/nPf97k+TU1Neyxxx4sXrx4l/e69NJL+e1vf0tBQQH3338/zz33XIdq79GjBwC5ublUVVV16FqN2a1XkiQpDUznREK+u9PXFArIJUj5/dOte7GUDgaxX5u2t9YFF1zAr3/9ax599FHOP/98Tj75ZGbNmsXGjRsBePfdd/noo48YO3YsTz75JFu2bGHjxo38/ve/B2C33XbjwAMP5JFHHgEgDEOWLIn/cqtfv35UVFTU3auiooL99tuP7du3E4vF6rY3Pq7WkCFDWLlyJe+88w4ADz74IMcdd1yH3m9rGU4lSZLS2HROpIrvEPJdZnPaLkNkbjvu0Y3AmXmlJpTwHXrTs8G23vSkhO906LqHH344FRUVHHDAAey3335MnDiRr3zlKxxzzDEMHz6c8847j4qKCsaMGcOZZ57JiBEjOPXUUxk+fDi77747ALFYjHvvvZeCggIOP/xwnnjiCQC+/OUvc8stt3DEEUewYsUKfvSjH3HUUUcxduxYDj300LoaGh9Xq2fPntx3332cf/75DB8+nJycHK666qoOvd/WCsIw7JQbtUZhYWFYf5CvJEmSdhZjKZOZSyU7utQFwFUUMJYDKGY+ZVSQS0A1Tf2st2NbPwJmcJoz8yprLF26lKFDW//3PcaTFPMzynmfQexHCd9p93jT9ti4cSN9+/alsrKSCRMmMHPmTEaNGtVp9++Ipj7rIAgWhWFY2NTxjjmVJEnKMLVBspj5lFPBIPpRwvi67Y2DZoyldcf25BMG8nuGsZYrKWEiRZ1ev5RJijijU8NoY5MnT+att95iy5YtXHLJJRkTTNvDllNJkiRJWaOtLadqv7a2nDrmVJIkSZIUOcOpJEmSJClyhlNJkiRJUuQMp5IkSZKkyBlOJUmSJClC+fn5rFmzJuoyImc4lSRJkiRFznAqSZIkSc2IsZR8ZpLDT8lnJjGWduh6mzZt4gtf+AIFBQUMGzaMhx9+uG7f5s2bOfXUU7n77rs5+OCDWb16NQA1NTV89rOfrXvdVRlOJUmSJKkJMZYymbmUUUEIlFHBZOZ2KKD+6U9/Yv/992fJkiW88cYbnHLKKQBs3LiRM844gwsvvJArr7ySSZMmEYvFAJg3bx4FBQXsvffeyXhbactwKkmSJElNKGY+lVQ12FZJFcXMb/c1hw8fzp///Ge+//3vM3/+fHbffXcAvvjFL3LZZZdx8cUXA3D55Zfzq1/9CoBZs2Zx2WWXtfuemcJwKkmSJElNKKeiTdtb45BDDuG1115j+PDhXH/99dx0000AjB07lj/96U+EYQjAwIED2XfffXn22Wd59dVXOfXUU9t9z0xhOJUkSZKkJgyiX5u2t8Z7771H7969mTRpEtdeey2vvfYaADfddBN77rknX//61+uOveKKK5g0aRLnn38+ubm57b5npjCcSpIkSVITShhPb/IabOtNHiWMb/c1X3/9dY488khGjhzJD3/4Q66//vq6fbfffjubN2/me9/7HgBnnnkmGzduzIouvUCjT1qSJEmSBEARQ4H42NNyKhhEP0oYX7e9PU4++WROPvnkBttWrlxZ9/y+++6re75kyRIKCgo49NBD232/TGI4lSRJkqRmFDG0Q2G0vW6++WZmzJhRN2NvNrBbryRJkiSlmeuuu46ysjLGjRsXdSmdxnAqSZIkSYqc4VSSJEmSFDnDqSRJkiQpckkJp0EQzAqC4KMgCN6ot21aEATvBkGwOPF1WjLuJUmSJEnqepLVcno/cEoT228Lw3Bk4uupJN1LkiRJktTFJCWchmH4ArAuGdeSJEmSJDWtqqoq6hJSJtVjTr8RBMHfE91+90zxvSRJkiQpqWJsJZ/15LCOfNYTY2uHr7ly5UoOPfRQioqKGDp0KOeddx6VlZUsWrSI4447jtGjR3PyySfz/vvvA3D88cfzrW99i8LCQm6//XYeeeQRhg0bRkFBARMmTABgy5YtXHbZZQwfPpwjjjiCv/zlLwDcf//9nHPOOZxyyikcfPDBfO973+tw/amSl8JrzwB+BISJx58Clzc+KAiCycBkgEGDBqWwHEmSJElqvRhbmcwmKhOvy6hhMpsAKKJHh6799ttvc++99zJ27Fguv/xy7rzzTh5//HGeeOIJ9t57bx5++GGKi4uZNWsWANu2baO0tBSA4cOH8/TTT3PAAQewfv16AO68806CIOD1119n2bJlTJw4kX/84x8ALF68mL/97W/06NGDIUOG8M1vfpOBAwd2qP5USFnLaRiGH4ZhWB2GYQ1wD3BkM8fNDMOwMAzDwr333jtV5UiSJElSmxSzuS6Y1qpMbO+ogQMHMnbsWAAmTZrE008/zRtvvMFJJ53EyJEj+fGPf8y///3vuuMvuOCCuudjx47l0ksv5Z577qG6uhqABQsWMGnSJAAOPfRQBg8eXBdOTzjhBHbffXd69uzJYYcdRllZWYfrT4WUtZwGQbBfGIbvJ16eDbzR0vGSJEmSlE7KqWnT9rYIgqDB6379+nH44Yfz8ssvN3l8nz596p7fdddd/PWvf+UPf/gDo0ePZtGiRS3eq0ePHa28ubm5aTtuNVlLyfwf8DIwJAiCfwdB8FXgf4IgeD0Igr8DnwO+nYx7SZIkSVJnGNRMXGpue1uUl5fXBdGHHnqIo48+mtWrV9dt2759O2+++WaT565YsYKjjjqKm266ib333ptVq1Yxfvx4YrEYAP/4xz8oLy9nyJAhHa6zMyWl5TQMwwub2HxvMq4tSZIkSVEooVeDMacAvRPbO2rIkCHceeedXH755Rx22GF885vf5OSTT+bqq69mw4YNVFVV8a1vfYvDDz98p3OvvfZali9fThiGnHDCCRQUFHDooYcyZcoUhg8fTl5eHvfff3+DFtNMEIRhGHUNdQoLC8PaQb6SJEmSlGxLly5l6NChrT4+xlaK2Uw5NQwihxJ6dXgypJUrV3L66afzxhtde+RjU591EASLwjAsbOr4VM7WK0mSJEkZrYgeHQ6jap1Ur3MqSZIkSaonPz+/y7eatofhVJIkSZIUOcOpJEmSJClyhlNJkiRJUuQMp5IkSZKkyBlOJUmSJClCp512GuvXr99p+7Rp07j11ls7v6CIuJSMJEmSJEUkDEN+//vfk5PT8XbDqqoq8vIyN+LZcipJkiRJzYitgvy5kPNE/DG2quPXXLlyJUOGDOHiiy9m2LBh5ObmsmbNGgBKSko45JBDGDduHG+//XbdOStWrOCUU05h9OjRjB8/nmXLlgFw6aWXctVVV3HUUUfxve99r+PFRShzY7UkSZIkpVBsFUxeApXV8ddlm+OvAYoGduzay5cv54EHHuDoo48mPz8fgEWLFvHrX/+axYsXU1VVxahRoxg9ejQAkydP5q677uLggw/mr3/9K1OnTuXZZ58F4N///jcvvfQSubm5HSsqYoZTSZIkSWpC8dIdwbRWZXV8e0fD6eDBgzn66KMbbJs/fz5nn302vXv3BuDMM88EYOPGjbz00kucf/75dcdu3bq17vn555+f8cEUDKeSJEmS1KTyzW3b3hZ9+vRp9bE1NTXsscceLF68uMPXSmeOOZUkSZKkJgzq1bbtHTVhwgR++9vfsnnzZioqKnjyyScB2G233TjwwAN55JFHgPgkSkuWLElNEREynEqSJElSE0qGQu9GvWV758a3p8KoUaO44IILKCgo4NRTT2XMmDF1+2KxGPfeey8FBQUcfvjhPPHEE6kpIkJBGIZR11CnsLAwLC0tjboMSZIkSV3U0qVLGTq09ekytio+xrR8c7zFtGRox8ebZoumPusgCBaFYVjY1PGOOZUkSZKkZhQNNIx2Frv1SpIkSZIiZziVJEmSJEXOcCpJkiRJipzhVJIkSZIUOcOpJEmSJClyhlNJkiRJUuQMp5IkSZKU5qqqqqIuIeUMp5IkSZLUjNjzkH8F5JwVf4w93/Fr/upXv2LEiBEUFBRw0UUX8eSTT3LUUUdxxBFHcOKJJ/Lhhx8CMG3aNC666CLGjh3LRRdd1PEbp7m8qAuQJEmSpHQUex4m3wmVW+Ovy1bHXwMUHde+a7755pv8+Mc/5qWXXmLAgAGsW7eOIAh45ZVXCIKAX/7yl/zP//wPP/3pTwF46623WLBgAb169UrCO0pvhlNJkiRJakLxgzuCaa3KrfHt7Q2nzz77LOeffz4DBgwAYK+99uL111/nggsu4P3332fbtm0ceOCBdcefeeaZWRFMwW69kiRJktSk8jVt295e3/zmN/nGN77B66+/zt13382WLVvq9vXp0ye5N0tjhlNJkiRJasKgAW3b3hqf//zneeSRR1i7di0A69atY8OGDRxwwAEAPPDAA+2/eIYznEqSJElSE0ougt49Gm7r3SO+vb0OP/xwiouLOe644ygoKOA73/kO06ZN4/zzz2f06NF13X2zURCGYdQ11CksLAxLS0ujLkOSJElSF7V06VKGDh3a6uNjz8fHmJavibeYllzU/vGm2aapzzoIgkVhGBY2dbwTIkmSJElSM4qOM4x2Frv1SpIkSZIiZziVJEmSJEXOcCpJkiRJipzhVJIkSZIUOcOpJEmSJClyhlNJSgMriPEw+cwih4fJZwWxqEuSJEkpsnLlSoYNGxZ1GWnHcCpJnaxxEP0jJ/I8k9hEGRCyiTKeZxKzyOUlprb6OgZaSZKUyQynkpRiO0JkwCxydwqi7/NMM2fWsIwZ/JETd7refXRrMtDeRzdmM8DAKklSkjwWgzH5cEBO/PGxJH1rraqqoqioiKFDh3LeeedRWVlJfn4+a9asAaC0tJTjjz8egOeff56RI0cycuRIjjjiCCoqKpJTRJrJi7oASerKVhBjAZOppjKxpabN13ifZ5hF0KpjQ6rYxloANlHGAiYDcBBFbb6vJEnZ7rEYXDsZNie+jb9bFn8NcE4Hv7W+/fbb3HvvvYwdO5bLL7+c6dOnN3vsrbfeyp133snYsWPZuHEjPXv27NjN05Qtp5KUQqUU1wumna+aSl7hmsjuL0lSJvvv4h3BtNbmyvj2jho4cCBjx44FYNKkSSxYsKDZY8eOHct3vvMd7rjjDtavX09eXtdsYzScSlIKxbvdRmsra4kxwC6+kiS10XvlbdveFkEQ7PQ6Ly+Pmpp4L6stW7bU7bvuuuv45S9/yebNmxk7dizLli3reAFpqGtGbkmK2ApiadViuZW1PM9FPM8k+jCYQkrs6tvFzCXG3RTzIWXkkEsN1QTkENbrSt6LvkDIZjYB1O3fnf6EQAXr2IdBXEkJE/37IUnsPyjelbep7R1VXl7Oyy+/zDHHHMNDDz3EuHHjqKioYNGiRZx66qnMmTOn7tgVK1YwfPhwhg8fzsKFC1m2bBmHHnpox4tIM7acSlKSvcRUnmcSWxNjP9NHCMA89uIo3iLgVg7gNmIsjbgudcRcYpxEP25iEh8mWuprqAZoEEwBNrOxLpjW37+BtXzCWkJCPqSMnzCZnzKVc8lnPDmcSz5zm2h5n0tsl8dIUib7QQn06t1wW6/e8e0dNWTIEO68806GDh3Kxx9/zJQpU7jxxhu55pprKCwsJDc3t+7Yn//85wwbNowRI0bQrVs3Tj311I4XkIaCMAyjrqFOYWFhWFpaGnUZktRuLzGVZcyIuoxmvcwR/Irz2Ub3um09gV9yGkUMja4wtctcYvw3l7OdbZ1wt4CzuYrvEp+w46dM5XHuovaXHk0dI0npaOnSpQwd2vrveY/F4mNM3yuPt5j+oKTjkyFli6Y+6yAIFoVhWNjU8XbrlaQkWUGMZdwVdRktepxTGwRTgC1AMfMNpxnoboo7KZgChDzODB5v8ZcvIY9zF8MZa7dgSV3GOUWG0c5it15JSpL4GNP06Y3SlLXs2eT2crrmemldzY41c3O4iQF13XjTS8jdJGEaS0lS1rHlVJLaYQUxSilOi9l426I/H7OWvXbaPoh+EVSjtqi/Zu4K4IW0G9O8w0ckYRpLSVLWseVUktqoNiRkWjAFOJs/0r1RN9DubOMk7uJh8l1uJo3VXzP3ZdK7jT4k5AsMcIIkSVKbGE4lqY1e5pq6kJBpjuFvXMwj9GcdENKfdVzMIxzD39hEGQuYbEBNU5sSrZEroNNGmXbEBtZyE5P4KVOjLkWSlCHs1itJbbCCGNvSuDtlaxzD3ziGvzW5r5pKSil2DdQ01IdBbKKMTJvTvnYSpX0Z7PqpkqQW2XIqSW1QmgUTvWxyvGBaKqSEFVBvldLMUrt+ql19JaljnnvuOV566aVI7n388ceTyqU/DaeS1AbZENz6MCjqElTPXGKcRD8uYRLPR11MB22l0pl8JamD2hNOq6qqUlRNchlOJakNsiG4FVISdQlKmEuMEi5lMxujLiVpPqSM8eRwLvkNWlHnEuNc8pvcJ0lRWhyDW/Lh+pz44+Ik/Pe0cuVKDj30UC699FIOOeQQioqKmDdvHmPHjuXggw/m1VdfZd26dZx11lmMGDGCo48+mr///e+sXLmSu+66i9tuu42RI0cyf/58Vq5cyec//3lGjBjBCSecQHl5/Bfpl156KVdddRVHHXUU3/ve93jnnXc48cQTKSgoYNSoUaxYsYKLL76Y3/72t3V1FRUV8cQTT1BdXc1//Md/MGzYMEaMGMEvfvGLnd7D3LlzOeaYYxg1ahTnn38+Gzd2/HuVY04lqQ0KKeF5JkVdRsr0oL/jTdPAXGLcTXGarmPacSEhH1LGTUziJiaxG/3ZTAXbE1M91XYBBhyjKilSi2PwxGTYnpgHcUNZ/DXAyA7+9/TOO+/wyCOPMGvWLMaMGcNDDz3EggUL+N3vfsf/+3//j4EDB3LEEUfw29/+lmeffZaLL76YxYsXc9VVV9G3b1/+4z/+A4AzzjiDSy65hEsuuYRZs2Zx9dVX1wXOf//737z00kvk5uZy1FFHcd1113H22WezZcsWampq+OpXv8ptt93GWWedxYYNG3jppZd44IEHmDlzJitXrmTx4sXk5eWxbt26BrWvWbOGH//4x8ybN48+ffrwk5/8hJ/97GfccMMNHfpMDKeS1Ao71jXt2t16D+RLUZeQ1eYS4xauZHPGjixtn0+amGSstguw4VRSlP5cvCOY1tpeGd/e0XB64IEHMnz4cAAOP/xwTjjhBIIgYPjw4axcuZKysjLmzJkDwOc//3nWrl3LJ598stN1Xn75ZR577DEALrroIr73ve/V7Tv//PPJzc2loqKCd999l7PPPhuAnj17AnDccccxdepUVq9ezZw5czj33HPJy8tj3rx5XHXVVeTlxePiXns1XCP9lVde4a233mLs2LEAbNu2jWOOOaZjHwiGU0napdp1TTN1+Zi2WMYMVvEUhZTYgtpJfspUHucu0nvl0mh81MV/GSQp/W1o5r+h5ra3RY8ePeqe5+Tk1L3OycmhqqqKbt26dfgeffr02eUxF198MbNnz+bXv/419913X6uuG4YhJ510Ev/3f//X0RIbcMypJLVgBTGeZ1JWBNNarnfaOWonOnqcGXT1YHoDs8kht83n7ZMFY7wlpbfdm/lvqLntyTR+/Hhisfj34ueee44BAwaw22670a9fPyoqKuqOO/bYY/n1r38NQCwWY/z48Ttdq1+/fnz605+u6+67detWKivjP9tceuml/PznPwfgsMMOA+Ckk07i7rvvrptIqXG33qOPPpoXX3yRd955B4BNmzbxj3/8o8Pv2XAqSfWsIMbD5DOLgFnkdrnxpSuAh4FZiccVzRxXu96pUmMuMX7C5C410VFLbmISNVS3+bwPKeOnTK177aRJkjrbSSXQrXfDbd16x7en2rRp01i0aBEjRozguuuu44EHHgDiY0wff/zxugmRfvGLX3DfffcxYsQIHnzwQW6//fYmr/fggw9yxx13MGLECI499lg++OADAPbdd1+GDh3KZZddVnfsFVdcwaBBgxgxYgQFBQU89NBDDa619957c//993PhhRcyYsQIjjnmGJYtW9bh9xyEYfr8trawsDBM5bo5ktSSrt59dwWwABpEhFxgHHBQk2cEXE5N6gvLQueS32UnO0qFAzmMlSwjbOLv427051vc7thUSa22dOlShg4d2urjF8fiY0w3lMdbTE8q6fh403RSWVnJ8OHDee2119h9992Teu2mPusgCBaFYVjY1PG2nEpSQinFXTaYApTCTm1X1YntTcmGZXOi4ljKtvkXbzUZTCE+mdJNTGIcARPIa9DSKknJMLIIrl0JP66JP3alYDpv3jyGDh3KN7/5zaQH0/ZwQiRJIt5quqmLt2Q1N/9rc9td7zR19mGQLacpUEM1jzODVfyDnzMv6nIkKe2deOKJlJWlz/cjW04lZb3a7rxdXXPz9TW33dl6U+dKSuhB710fqHYp5RlbUCUpAxlOJWW9rt6dt1YhNDlfahXNT4yk1JhIEd9nJvsyOOpSuqzHucsJkyQpwxhOJWWtP3Iiswi6fHfeWgcRn/yoe6PtW4lPlGRA7VwTKeJYTovs/gFBZPfuHCG3c03URUiS2sBwKikrzeFw3ueZqMvodAcBTS3p3XhipD626HWKJ5gZ2b37sVdk9+4sG1jLOAKXnZGkDGE4lZR1VhBjA29FXUZkWjMx0ibKeJh8VvgDfUq1Z+3PZPmEtZHdu7N9SBk3cZHjUCWljZUrVzJs2LCdtl9xxRW89Vbyfkbp27dv0q7VGZytV1JW+SMnZmWLaX19aDqgNp4YaRNldRNFOTlSauSQG2lAzS4hj3MXwxnrmqiS0tYvf/nLqEuIlC2nkrKGwTSuqYmRchPbG6umklKKU19UlvpiFswSnV5Cfu44VEltVBGD8nz4Z078sSJJnYqqqqooKipi6NChnHfeeVRWVnL88cdTWhofaPOnP/2JUaNGUVBQwAknnEBNTQ0HH3wwq1evBqCmpobPfvazrF69mg8//JCzzz6bgoICCgoKeOmll3a63y233MKYMWMYMWIEN954Y3LeRJIZTiVlDYNpXO3ESLUtpX0Srw9q5vhNlHdGWVnpu0yPuoSs8wlrHX8qqdUqYrBmMlSVAWH8cc3k5ATUt99+m6lTp7J06VJ22203pk/f8T1h9erVfO1rX2POnDksWbKERx55hJycHCZNmkQsFr/5vHnzKCgoYO+99+bqq6/muOOOY8mSJbz22mscfvjhDe41d+5cli9fzquvvsrixYtZtGgRL7zwQsffRJIZTiUpCx0EXABcnnhsLpgC9Am7/sQ5USrkhKhLyDp32xtAUit9XAxho9Xmwsr49o4aOHAgY8eOBWDSpEksWLCgbt8rr7zChAkTOPDAAwHYa6/49+LLL7+cX/3qVwDMmjWLyy67DIBnn32WKVOmAJCbm8vuu+/e4F5z585l7ty5HHHEEYwaNYply5axfPnyjr+JJHPMqaSs8EdOjLqEzBRCYQ1NL5CqpPg585hAnmNPO9FH9gaQ1EpVzfx30dz2tgiCoMXXTRk4cCD77rsvzz77LK+++mpdK+quhGHID37wA6688sp21dpZbDmV1OWtIGaX3nbqARwUrou6jC4vpCbqErLKPgyKugRJGSKvmf8umtveFuXl5bz88ssAPPTQQ4wbN65u39FHH80LL7zAv/71LwDWrdvxvfiKK65g0qRJnH/++eTmxn97fMIJJzBjxgwAqqur2bBhQ4N7nXzyycyaNYuNGzcC8O677/LRRx91/E0kmeFUUpf3ihOgtEtuCEfXAP4gn3KGpY7rRvdWH3ssp6WwEkldyZ4lEPRuuC3oHd/eUUOGDOHOO+9k6NChfPzxx3XdcgH23ntvZs6cyTnnnENBQQEXXHBB3b4zzzyTjRs31nXpBbj99tv5y1/+wvDhwxk9evROy9FMnDiRr3zlKxxzzDEMHz6c8847j4qKio6/iSQLwjCMuoY6hYWFYe3sVJKULLPYdTcZ1RPGJ0kqrIGDwt4QzIRcl95IpbnE+G8uZzvboi4lK+SSRzH3u6SMlKWWLl3K0KFDW318RSw+xrSqPN5iumcJ9Ivwv4/S0lK+/e1vM3/+/OiKaKWmPusgCBaFYdjUIgGOOZUk1RPCcTX9E115B0FQYjDtBLUh6XauYQNrI66m66umih9zCYABVdIu9SuKNozWd/PNNzNjxoxWjzXNNHbrldSlrXDJiDbJDfpwUO4ayKuBvJUG0040kSL+wBp2p3/UpWSFGqr5CZNdVkZSRrnuuusoKytrMD61KzGcSurSSl0yok2qqdz1QUqpa7g96hKyxlYqXVZGktKI4VRSl7bJJSPapI8T80RuIkWczZRdH6ik+JCyqEuQFIF0mnenq2rPZ2w4ldSlGbZaL5feFJKE6QfVYd9lOsEuvkXb/Tc5AidMk7JOz549Wbt2rQE1hcIwZO3atfTs2bNN5zkhkqQurZASFjDZ7qo7yQFqCMglpJo+DKaQEg5ycpi0cRZX8jgzmt3/CR93YjVdV0jItziRnzMv6lIkdZJPf/rT/Pvf/2b16tVRl9Kl9ezZk09/+tNtOsdwKqnLq6Eq6hLSTkAuE/iVYTSNfZfprOIflPJMoz0BeXSjymVnkqaUZ/gpU/ku06MuRVIn6NatGwceeGDUZagJduuV1GWtIMYLXEboD/E7CdnuZFEZ4OfM4wZmsy+DCQjYjf7szl4G0xR4nBl8gQHO3itJETKcSuqySikmZHvUZaQtJ4vKDBMpYg4r+S8eZCubXQc1hTaw1uVlJClChlNJXZbhq2VOFpVZ7qaYrY6dTjmXl5Gk6BhOJXVZhq/mOTNv5nHJk87zkb/YkqRIGE4ldVmGr6b1YTDjmOlkSBlkBTH6uuRJp9nHX2xJUiQMp5K6LMNX0y5gpZ9NhimlmNGE5EZdSBbIIZcr/cWWJEXCpWQkKYsExpuMtIlyDko8LwU2RVlMFxcSMtFf3khSJAynkpRFhjA56hLUDn0YxCbKOAjqQurDGFJTIaQm6hIkKWvZrVeSssixTI+6BLVDISXk0rvBtiPpRq4t4UmX42cqSZExnErq0vLoG3UJaaMPg6MuQe10EEWMY2bizzCgD4O5lPso5gF2o3/U5XUpX7R3gdR+1TGoGgBVQb2vAfHt7b5ePlTlxB/bex1ljCAMw6hrqFNYWBiWlpZGXYakLmQFMV7gUkKqoi4lUrn0dobeLmwuMf6by9nOtqhLyWgHchgP8mbUZUiZp2oqcBfQXK7oDsEsyN3F96DqGITFQBnxNrTmutnnApMhz95AmSgIgkVhGBY2tc+WU0ld2kEUMYSvRV1G5A7mEoNpFzaRIn7ArKjLyGhnM8VgKrVVdQyq+gIzaD6YAmyLh86qqVCV16hltd5XOAnq1nRuafx3dfyedef2sFW1izCcSury/sVvoi4hcqt4KuoSlGITKWJfu263y74M5ruOx5bapmpqIky2dmq2MuIhtjoFxWyL12JAzXiGU0ld2gpibGVt1GVEbhPlUZegTnAlJeTSLeoyMo7rmkptVHUi8aCZZsJLoq5AHWQ4ldSllVIcdQlpoQ+Doi5BnWAiRRRzH3l0j7qUjLEb/V3XVGpOUxMSVU0Fnom4sOZUJ+pTpjKcSurSbDGMT4ZUaMtQ1phIEc+xlbOZQuC3+V36FrdHXYIUreZmxK2OQTiZeHfcMP4YTgbujqrSVpoRHwfrDL8Zye9akrq0bGsxzKMvxzG7wZIjztKbnb7LdOZTzQJCbmB21OWkLVtNldWaC6B1s+ZWNjqhkpYnKkoXm9jxfi5LLG9jWM0ELiUjqUtbQYwFTKZ6p2+wXU9AHhO43yCqJp1LPh/WzYIpgF705c9URF2GFJ2qfGjy/4XBzWzvCnpDMHPXy9ooZVxKRlLWOogixjEz0ZLYleUYTNWiKykhx2/7dQICruWuqMuQItbc0JeuGkwBKhOtwkpHfpeS1OUdRBEXsJJDmRJ1KSlzKFcaTNWiiRRxPb+iOz0bbM8lL6KKotOdnvwXD9qlV9mhwZjS2rGYifVBW1ybtCtzPop0lX3fkSRlra681mdXfm9KnokUNRnIsqXLby/6cC13G0qVPerGlNYObWntmqRdXRD/bOzam3ZsOZWUNbryzL1d+b0p9a6khB70jrqMpOtFXwIC9mUwNzCbP7PRYKrs0uSkRoIaCCe57EwaMpxKyhpdeebervzelHoTKeL7zGQ3+kddStKczRT+TAXzqWEOKw2lyj7VMbr22NFkmJHo4pzvLL5pwnAqKWt01bU+XcdUyTCRIp5iDTcwm33rJhALGh3V+HX6+i7Toy5Bik7V1HjLoFqpDMLLDahpwHAqKWt0hQmDutOfQ5niOqZKmYkUMYeVifVRH2RfBtd1jT2bq+hG96hL3KXdu1ALsNQm1bH4pEfMiLqSDLQNwmuiLiLrOSGSpKzSnf5sY23UZbRaHwZTSInhU5FoagKl4Yzldq5hQ5r+O+pGd67h9qjLkDpXdQzCq4CNUVeS4dLz/7VsYsuppKxyDLcT0C3qMnYpoBvHMZsLWGkwVVqZSBF/YE2iZXV21OU0sC+D+QGzHF+q7FC3REyQ6MJrMFXmM5xKyioHUcQE7kt0i01P3enPBO4zlCrtTaSIszth/eDd6c/ZTNlpRuEe9OYGZrOAkAWETnyk7FEdg/AynPAo2TJnXH1XZbdeSVnnIIoaBL//4wA2816EFcUF9OAytkRdhtQm32U6wxnLz7mGT5LQJW43+vMtbm8yZA5nLHdTzEeUsw+DuJISw6iyS3UssTyMoTQ1wqgLyHpBGKbPH0JhYWFYWloadRmSstAfOZH3eSay+wfkMYH7bS1VRptLbJchtZATeJ2X2Vpv7cUe9Ob7zDRoSi2pmgrchQEqlQZD3sqoi+jygiBYFIZhYVP77NYrScCpzOPQTuie2JRc+hhM1SXULkdT2832bKaQQy4AOeRyNlP4OfP4PjMbzAJsMJV2oWoq8Rl4DaYpFbgsW9RsOZWkeu6jByHbOvGOOVxOdSfeT5KUUapjrlnaKfpD3pqoi8gKtpxKUitNYBYkWnoaSs1/l4dyZUquK0nqIsKroq4gCwQQuARVOjCcSlI9B1HEcTyQmM03oA+DOY7ZHMevyG00U2jHBBzKFI5lehKvKUnqUqqm4hIxqRYAV0GuQwvSQVJm6w2CYBZwOvBRGIbDEtv2Ah4G8oGVwJfCMPw4GfeTpFRqPJtvfaUUs4ly+jCIgZzGKp6qe51HHzbwVovXzqEn4/ml40slSS2rjhGfAEnJMZj4LMc5QE1iW/94i6nBNG0kaymZ+4H/BX5Vb9t1wDNhGN4cBMF1idffT9L9JKnTtRRaJUlKqrAYJ0BKpjLiraQ19bZtjqgWNScp3XrDMHwBWNdo8xeBBxLPHwDOSsa9JEmSpC6tOoZrmaZC47BfmfglgNJFKsec7huG4fuJ5x8A+zZ1UBAEk4MgKA2CoHT16tUpLEeSJElKc9UxCCdHXUUWKY+6ANXTKRMihfH1aprslxCG4cwwDAvDMCzce++9O6McSZIkKT2FxUBl1FVkkUFRF6B6UhlOPwyCYD+AxONHKbyXJEmS1AXYnbdTBSVRV6B6UhlOfwdcknh+CfBECu8lSZIkdQFNrbUtZYekhNMgCP4PeBkYEgTBv4Mg+CpwM3BSEATLgRMTryVJkiQ1qzrqArKLEyKllaQsJROG4YXN7DohGdeXJEmSurzqWNQVZCEnREonnTIhkiRJkqRdsBUvAk6IlE4Mp5IkSVJasBWvc/V2QqQ0YziVJEmS0oKteJ1nMAQzIbco6kJUj+FUkiRJSgunRV1AF3cC5IWJr5UG0zRkOJUkSZLSwlNRF9CFnQB586IuQrtgOJUkSZLSgmNOU8NgmikMp5IkSVJacMxp8hlMM4nhVJIkSUoHQQnQPeoquog+EMw2mGaYvKgLkCRJkkR8gp5qIJwUdSUZrLez8GYwW04lSZKkdJFbhD+it1d/g2mG82++JEmSlFZqoi4gQwwGgvhjMBvy1hhMM5zdeiVJkiRlnryVUVegJLPlVJIkSUor/aMuQIqE4VSSJElKJ8HtxLurqnkG+K7IcCpJkiSlk9wiCB4E+kRdSZrqngjw6moMp5IkSVK6yS2CvI3xiX6cJqae/hDMcuKjLspwKkmSJKWr3CII7sdurABTnJG3izOcSpIkSekstygeyrI2oOYmloqZHnUhSjHDqSRJkpQJgtuB3lFX0clOgLwqW0uzhOFUkiRJygS5RRDMBAYnNmT6j/JTIC/c8RXMJv7egvhjMBvy5kVcozpTpv+NliRJkrJHbhHkrUwEuup6gS5q3dtwbP+mu+nWvbea+KOtpVnHcCpJkiRlqtpAF8wmmi6/idbPYBbQrfn9Db6c1EhNM5xKkiRJma5Bl99Et1im0HASpdb86N8ncU6QeKw9P2h0XO+GrZ+5RRDcx87dcp3ESK1nOJUkSZK6gsbdYvOmx1sp61osq1toYa3tarsxcU5N4jFxfvAgDYPnzJ1bP+2Wqw5yRV9JkiQpW+QWQTUQFgPlwCAISnYdJHOLAMOmUstwKkmSJGUTg6bSlN16JUmSJEmRM5xKkiRJkiJnOJUkSZIkRc5wKkmSJEmKnOFUkiRJkhQ5w6kkSZIkKXKGU0mSJElS5AynkiRJkqTIGU4lSZIkSZEznEqSJEmSImc4lSRJkiRFznAqSZIkSYqc4VSSJEmSFDnDqSRJkiQpcoZTSZIkSVLkDKeSJEmSpMgZTiVJkiRJkTOcSpIkSZIiZziVJEmSJEXOcCpJkiRJipzhVJIkSZIUOcOpJEmSJClyhlNJkiRJUuQMp5IkSZKkyBlOJUmSJEmRM5xKkiRJkiJnOJUkSZIkRc5wKkmSJEmKnOFUkiRJkhQ5w6kkSZIkKXKGU0mSJElS5AynkiRJkqTIGU4lSZIkSZEznEqSJEmSImc4lSRJkiRFznAqSZIkSYqc4VSSJEmSFDnDqSRJkiQpcoZTSZIkSVLkDKeSJEmSpMgZTiVJkiRJkTOcSpIkSZIiZziVJEmSJEXOcCpJkiRJipzhVJIkSZIUOcOpJEmSJClyhlNJkiRJUuQMp5IkSZKkyBlOJUmSJEmRM5xKkiRJkiJnOJUkSZIkRc5wKkmSJEmKnOFUkiRJkhQ5w6kkSZIkKXKGU0mSJElS5AynkiRJkqTIGU4lSZIkSZEznEqSJEmSImc4lSRJkiRFznAqSZIkSYqc4VSSJHWKGE8ygKMIGELAEAZwFDGerNs/lWnkcGjd/trn+XyuwXH1r5fP58jh0GaPkSRljiAMw6hrqFNYWBiWlpZGXYYkSUqyqUxjBv/X7vO7kcd93EwRZwDxYHoZ17GdqrpjcgjYk91ZxwYGsR8lfKfueElSegiCYFEYhoVN7jOcSpKkVIrxJJP4j6RcazD7cxrHcRe/JqTln2F605OZ/LjJgBrjSa7hx6xlfd22/uzBlziVp3iect6nNz3ZzBZqEvcJCAgJySWXyXyJ6UxLynuSpGxiOJUkSZEZwFENQmBnGsz+rOQvda+bCqXt1YdebGJzs/v7swcA69jQIOgabiVls5bCaV5nFyNJkrq+GE9SzM8o5/1dtnCmUhnvETAkJdduKZgCDQJw/WOrqa7r4mxAlaQdnBBJkiQlVYwnmcz1lPFepME03d3Nr53QSZLqseVUkiS1W+Nusrvq6qodaggp4z0g3sJbOy7XSZwkZStbTiVJ0i41tWxLjCe5nB80231VbXdRkiaOkqRMZMupJElqUW0I3cZ2YEcrXx961W1TcoTEl91xLKqkbGTLqSRJataJXMok/qPJEGoraWrM5DdRlyBJkbDlVJIkNZhddxD78VkG8wwvR11WVqqmOuoSJCkShlNJkrJc7ey6lWwB4t12ayfqUefLJTfqEiQpEoZTSZKyVOOZdpUejufIqEuQpEgYTiVJyhL1u+7uxe58zAZqXIc07bxDWdQlSFIkDKeSJGWBxl13bS1NX+W8H3UJkhQJZ+uVJCkLFPOzumCq9DaI/aIuQZIiYTiVJCkLOMFRZuhNT0r4TtRlSFIkDKeSJHUxMZ4kn88RMIQ8DiNgSNQlqRUGsz8z+TFFnBF1KZIUCcecSpLUhZzIpQ3WJ3XNzMzQh16s5C9RlyFJkbLlVJKkDFbbSprDofTjiAbBVJmjJz2iLkGSImfLqSRJGWgq05jB/zXYtpHKiKpRR61jQ9QlSFLkbDmVJCnDNBVMldmcoVeSDKeSJGWUGE8aTLsYZ+iVpDi79UqSlAFiPMk1/Ji1rI+6FCXRYPanhO84Q68kYTiVJCntxXiSyVxPJVuiLkVJ0pueLhsjSY3YrVeSpDRXzM8Mpl2I65lKUtNsOZUkKY3FeJIy3ou6DHVQH3pxNz8ykEpSCwynkiSlqRO51HVLu4ApXMh0pkVdhiSlPcOpJElpaCrTDKYZznGlktQ2jjmVJCkNzeQ3UZegdgoIHFcqSe2Q8pbTIAhWAhVANVAVhmFhqu8pSVKmq6Y66hLUDnbhlaT266xuvZ8Lw3BNJ91LkiSp0xlMJaljHHMqSZLUTjkEXMmXDaWSlASdMeY0BOYGQbAoCILJnXA/dZLHYjAmHw7IiT8+Fou6IknqOgazf9QlqBl96MVsbiXkbapZZjCVpCTpjHA6LgzDUcCpwNeDIJhQf2cQBJODICgNgqB09erVnVCOkuGeqfDRJPhlGbwQxh8/mgTnHhJ1ZZLUNZTwnahLUEJ/9qgLoyFvs5HFTnQkSSmQ8nAahuG7icePgMeBIxvtnxmGYWEYhoV77713qsvJGhUxKM+Hfwbwz7z4Y3l+fHt9370D9t8X9suJP373jl1fe3EMghnwOaAfECQePwecshwuG5fkNyNJWaiIM+hL76jLyHoncAxr+KthVJI6QUrDaRAEfYIg6Ff7HJgIvJHKewr+dAes+ipUlSU2JCZ8rCqD9y/aEVC/dzFUXQM/+gj+GML8j+Dca+DiAH4wdefrxlZB/ly499vx3zB0a7S/GzAW2P3FlLwtSco6d3FT1CVkrRwCpnAh87g/6lIkKWukekKkfYHHgyCovddDYRj+KcX3zGqxVTD4x9Bza9P780JYcRm8tBaCB6GAeKCsDZqfAm4ENs+AX/4Drpi347qTl0DNs7DbaujTzP17AN8AVgTx9YPKT4Az5yXt7UlSViniDF5kETP4v6hLyQp96U0Ff4u6DEnKWiltOQ3D8J9hGBYkvg4Pw7AklfcTFC+F/XYxdLffdlj643ggLWTnFtAA6A187hlYPXXHdWuehfPugK+0cO2A+F+qANgNOPQZ+OXh7XknkiSA6Uwjt1OmiMhu3elmS7UkRczvdl1M+WaobsWf6m6JVWebawGFeMCsuCsxfnUznDcd/qsaeiX2tUYecPxb8GwT3YQhPn51Vi68GcRbW1cE8ec/HhxvrZUkwQP8T9QldDkncAyD2Z+AgMHszyz+23GlkhQxw2kXM6gX5NS0fMx2YHPfHc9bFMLHxXDAy/DNre3rB54DfOqunSdjemMqdJsEx9XsCLwB8ecXlMPysQZUSQIMTUlUO/PuPO5nJX+hhmWs5C9+xpKUBgynXUzJUPhgF5MedwO+WAFHsXOX3qZUlUO/2dC/A3X1TITcWhUx6H5X862wucD5q+LdiSVJELS6z0pm6Yz31ZuedUvBOPOuJKUvw2kXUzQQNl4Pm1to4gyATwNDaV333PU5cNa7sLGDtVWV73j+cXF8cqaW9CTenViSBFfx5ahLSLrZ3MqD3EJveib1ut3pRn/2qOuyO5MfG0glKQOkerZeReCUq+FbN8E31zYfPlv7e+oQ6FsNk4jPxBu24dzGtgBv9oOeG1tfw6Be7byZJHUx05kGwN38mhp28du9DDCY/RsExmJ+Rhnvteta/dkDgHVsYBD7UcJ3DKOSlIFsOc1wL14My3PiEwm9E8DjAdwzFR5Zl5zrB8R/g9GTHWNCW6Pxj01VQPcQem1s/XW2Ag+dHH9fi/ruPGZVkrLNdKZRzTJC3q77ms2tDGZ/AHLJbfJxMPszhQuT3kLZWo277vamJyV8p+51EWewkr8Q8nbde2mNKVxY11V3DX91/KgkZTjDaRo6cQGc+R2Yvw+8kxN//NMdO/Y/FoMx+XB3AJ96EHLDHUu4jAD2nQEHhlC9i/ukYpRPSLz771Li65yGQA3xMaRt+csWEm+p/VTivD03wbuXGFAlqbH6wa6Kt5p8XMlfmM40ZvLjBjPUnsAxKRvxWX+c54Pc0uC+LXWzLeE7zYbo2smMaoN5bWuyJKlrCMIwfboGFRYWhqWlpVGXEYmKWHwc5rZyqMyBPtUNw2MV8OZF8GpfmD8DjgauounAFwJ/Jh4Oz6FtIbQj3XZD4qH0lcTro2n9uNb616CFc/IGw6CVO16f/AM44zYYvxX6AmsCmPEFOGl6fPytJKllMZ6kmJ9RzvsMYj9O4zie4vlddrHtS2960J21rCeXXKqprnsczP4d6lrbuCa76UpS1xEEwaIwDAub3Gc4TY3HYvDfxfBeOew/CH5QAucUNX1sRQzWTIawsuVrVrNzl9jmQtx24HfAWZDo1NU5agPqR8BxtD3o7ioch8B9QE0ODMyDz22LzzjcOMjfcBqccZcBVZKSwbAoSUoWw2kneywG106GzZVwIvEWzn2ANTlQeSV8fnrD48vzoaps19dta6tmNZ0bTGvtqvWzOVXE623pvI1AKXAsO4fS+j4BznoaVk5sYxGSJEmSUqalcOqY0xT47+IdwfT77Bg3uU8NDJ4BbwcNx07WX2KlJW0Ne1H94bZ14qQQ2NaK80KgHBgPdN/Fsf1wGRpJkiQpkxhOU+C9RNi8Cmi8EkpAvMXvg0k7AmreoNTUkQnLtdcG0m7supV3OzCoFcfVchkaSZIkKXMYTlNg/0TY3KeFY3KBFd+IP9+zBILeqa4qve0qSFcBLwF9Wnm9zUDJ0A6VJEmSJKkTGU5T4Acl0Kt3fFKglvRbn3gsgqfPjY+TrO3mqh1CYO0e8E9gUyuOrwZeGutkSJIkSVImMZymwDlFcMtMeLBPy0FzY+Lxu3fAgw/ChsTrVK0/mqmhNwD2WQ/H7w+v0vT6rbXvrxL4y1iYvKATC5QkSZLUYYbTFDmnCGZshFU0HQprgPn94s8f+i/4D+DTpNc40XQKswEw+D1YtEd8/daw3tdm4uu6TjoNlpQbTCVJkqRMZDhNseVT4C3iYbT+zLTvAqdUwIoA5n8CI0hdMK1dgiadgm975AD/sR72YMf72QL8BCjpBtd+ZeeuvM9OhdLc+Oe8JIAH+sLiGJIkSZLSjOucpsDiGPy5GDaUw+6DIO+z8O4z0COx/xhgKA3DYlvXMNUOHwDnAQcMhoUr49tiq+DpkfCf6+LLztTaDiwIYPSDMLKosyuVJEmSspvrnHaixTF4YjJsKAPC+OMnL8OEKZCTCwexczClide1WvrVQfr8WiFa+wJfAnqUxV/HVsG3zoCrGwVTiC9Zc0wIT1zTuTVKkiRJalle1AV0Jcd+CYoegfPZ0Uq6FXilEtbMgCLi4ailINra0LqrfdlkE9AXGB/EfzlQvDfkLYE9mzm+B/DptZ1XnyRJkqRds+U0SY79Enz2EZgI9GTHmMiewHhgAvFWPANlcm0HajuC54bx7tTlm+Ovm2tZDoDRqS9NkiRJUhvYcpok/5oDtwK5TexraltTDK5tU0N8WZnjgELiIXVFGVx9FvSh5c+zX+rLkyRJktQGhtNkqYF9oq4hi9Suddoz8diXeEg9LvF6a+KrJ03b3j91tUmSJElqO7v1JksOfBR1DVkkh51bpAMadqfuzo4QW191Lhxwe2rrkyRJktQ2htMkOfBcuIv4GqaNVePMusnWmi7QOcTHpG5hxxqz2/eETz0A/VxGRpIkSUorhtMkeek38M758P+A9ewIQ5uIh1PHk7ZdMgJ9D+AhYBawZDYMWWcwlSRJktKR4TSJXvoN/CqEkotgXA5cTDyYNl5rU62TjEC/KfEYAv9dnIQLSpIkSUoJJ0RKsjP+Hyz8DXy3Jr6sTN+oC8piIVCeeB4A75W3cLAkSZKkSNlymgSLY3BLPlyfA5/5L7htK5xDfLkSu/NGJwAGJZ6HwKVh/M9pcSy6miRJkiQ1zZbTDlocgycmw8BKOBnokxgoaShND32Ao4ChJP5MyoBJsGISdBsMe5Y4BlWSJElKB4bTDvpzMYyurBd+diFs5XFKju3AYTT9mVeVwUeXx58bUCVJkqRo2a23g/qXtT6YkjgubPSl1KgButHyn02wDT68ppMKkiRJktQsw2kHxFbBaNrXEvp84tFW1OSqDfzbaP0SPsHalJYkSZIkqRUMpx1QvLT9s/EWYjBNlQ+Jt5h2a+Xxm3Z9iCRJkqQUM5x2QMGf2t8tt09SK1F9n6L1wX87sMiFaCVJkqTIGU7bqSIGt/y87R9gbbdTW01TY1efa/2xvjXEZwQbtQ3uOSTVlUmSJElqieG0HSpi8NEkyGum2bS51tSQeEudH3p0gnpfOYnHfsCE5fD4xVFWJkmSJGU3c1IrVcSgPB/+GcDqi3bdQlfd6PV24DlaPw5SnasbcGAs6iokSZKk7OU6p61QEYM1kyGsTGzYxUDTgHgY3Ux8bGlI/IMeA2wFeqasUnVEv5qoK5AkSZKyly2nrfBxcb1g2ko9gFKgih3dR/smtru2aXqq8F+DJEmSFBl/HG+FqvL2nXccO3fjrR3vqM7R2l8EbAf+VZTKSiRJkiS1xHDaCnmD2n6OITQzhMA2YD5Q+iBM+zQsduypJEmS1OkMp62wZ0nUFai9AlpuPQ2It24fDxwDVL0Lc75mQJUkSZI6m+G0FfrZ3TOj1QbU2q+m9ucAQ4GjgXAz/Lm48+qTJEnalRhbyWc9AevIYx0B68hnPTG2Rl2alDSG01bKGxx1BeqIoNFjc8ccmni+oZ3jjCVJkpItxlYuZxNlxJcWqF2ysIwaJrPJgKouw3DaSnuWQNA76irUEa0ZA1x7zO7tGGcsSZKUClexiW3N7KsELjGgqoswnLZSvyIYMBNW5zY/hrGlrqPKDCEQ9IKTHGcsSZIiEGMrAxLddgPW0Y91bNzFOdUYUNU1GE7boF8RvPsAVDSzv/a/g86YpdcA3D4tfW4hsKwbnHsPjHScsSRJ6mQxtnIZm1hbb9uugmmtamASmxjAx4ZUZSzDaRudUwS/3ze+LmZ9ta87a/kYl6lpu5D4n1N14nkNDVu71wIvfNlgKkmSdq12gqKcJE1MFGMrl7Bpp58x22otIZPYxNRWx1opfRhO2+H6D+DpfeMtqCHx32i9CPSItiztQgB0B3LZMUNvUO+rP/CFB+F7F0dWoiRJygAxtjI5MUFRSMOJiVozq2686+7HdV13A9YxiU11Ex0lwwy2GVCVcQyn7XT1BxDOhscGw28CWDu49d0ulJ4C4NPAZx+MuhJJkhSl+q2iA/iYAaxr0EJazGYqG51TCVzMJiY1M6vuRWwiYB0DWMdFbGJtJwzSmsE2erLObr7KGEEYps/oxcLCwrC0tDTqMtplcQwWTYLjaV2X29pP3e656acCKEiffxaSJKkT1baKNg6fma47NJjxNwf4HLm8Q0g5NQwihxJ6UWRfQKVYEASLwjAsbGqfLadJsDgGf7sYxrbxvKU4sVE66ht1AZIkKRK14z67WjAFdlqKpgZ4huoGXZMn1WvdHcDHSRtPK7WW4TQJXvsajK+J/0aqtS2hW4FD23C8Oo/dsyVJyj61LabJHPeZqdYSn1ipfmg9kQ1Rl6UsYDjtoNgqOGpz2z7IGuJB1g8//WwHlra1CVySJGW0GFu5uIu2mCbLM1Q7wZJSznzUQcVLoWcL+2uXKamvdqZYpYfaP6MK4G9j4csLIi5IkiSlVOMJjy5hU2IKI7Xkrp06B0vJlRd1AZmufHPL+5vqtmtX3vRSAfxzdnwN24Koi5EkSSk1lY3MqBeyOmPW3K7CT0qpZgNeB124wH+omawayJsSD6aSJKlraxxMJaUXW07boSIGq74PPd6Fm7AlNJ3VEJ98qmfiMQfolti3FVh9Ahw3PaLiJElSysXYyjVsYm3UhXQB/syrVDOctlFFDD74GvTcRXfeWjWJLz/ozhUSn3X3Vz3gH/vA8FUN/0Pt3h++eDscZ4upJEldVoytXMQme7kliZ+jUs3M1EYfF0PuLoJp7T/cLcBfE88LgT74G6fOUDvBUQ9g6laoWAUPjYSC30HRwGhrkyRJnecqg2lSDXZEoFLMv2FtVFXeuuMOCmH7bFg7GFYADxNfpiQddPX/pGtnQ65dd3Y34KuLoeC/o6xKkiQlW+NZdwewrsFzFz5JrhJ6RV2CujjDaRvlDdr1Mdv3iD+OLIJrV8Lzie3dmjm+swV0/YDaWDcgnBl1FZIkKVlibGUymyijhpD4rLtrocFzJdeLadPUoq7KcNpGe5a0HOyqgQP+N/58cQxuyYfLgNYMbezMwJiN3Yt7V0ddgSRJ6qgYW+nHOiaxicqoi8kyM53pWClmOG2jfkWw6uCdg2QIbANWTYkfszgGT0yG/mVwHPHZYncVCDMxMNaO78wElblRVyBJktorxlYG8DGT2GR33Yj4e36lmuG0HY7/B7w+FqqIB7Mq4Hfd4K3Z8PnEsiR/LobtlXAsXftDDtjRTTjqkNrS/auAYHJnVSJJkpKlfkvp2sh/2pCUSs7W205nLWj4+pBG+zeUxR/TZZxpqkXd6hsC64A92RGWq4h//pW58WA6zPVMJUnKCDG2UsxmyqiJuhRJnchwmgK/PADOB/pGXUgWCYG/A6/vA5f+LD4ZlSRJyjwxtnI5mxzdKGWhrtzjNBL3HALHvQf92NHltSU1RN8dtivIAY4B+n4Ev74Upt8RcUGSJKlVaseSBqwjSHTfNZimpz5RF6Auz3CaRNPvgGOXQ1vm3QmAx8iskJqsOpM9TrUH8BXgkCpYdj3EViXx4pIkKelqW0kdS5r+AuBu46lSzHCaRP8oic/K2xYbgduA+1JQT6p0dHxpCGwhPuNbMseqBsQ//+OAkyqgeGkSLy5JkpKumM22kqah/gT0b/T6QfpQRI/IalJ2cMxpEvVZ3bbjtwML8oBesKkCNpE941SraHuQrxXScqgNgMOAgj8BE9t5E0mSlHJOeJR+egO309sgqkjYcppEm/aGrbs4prb77kbg+QDuvQZueRMunQ2l3eKBtbNE1YFmIx0bs1DJrj+nAPh+rAM3kSRJKRXb5U9N6mz9CZhpC6kiZDhNokOK4cUg3irYlO3Ac8As4J694aHvwXevgaKB8dllR90HL+8BFcSDY22Qrf+VTFEs/xLS8dbhXsBbF7HL37Xu+xGsntrBm0mSpKSJsZV81tdNfKT0MJgcZtOHNexpMFWkgjBMnwHohYWFYWlpadRldMj0OyD4Hhy7NR7CarugbtkTBv0C+rViiZMx+fC5MugOfAYoZEdL464CZe2fZtTrjqbStv7wZF/oXwbjaXkCqhBYcwIcNa+TipMkSU06gHW8F3UR2kl/YA17RV2GskgQBIvCMCxsap9jTpNs6tXA1R27xnvl8BJwKbsOX40FZM6sv+1RFcABt8OGi2BDYtuxQDeaDuQBsOcz8FgMznHtU0mSInE4HxtM09S6qAuQ6rFbbxrafxD8ExhD24JppqshPoNvU0Kgqjvs92C89Xn3QfHt/wRm0/JY31zgv4uTWKgkSWqTt7r0r84z2yDjgNKIfxvT0A9KoFfv9o/NbG4sZrp/WwiAF6g35jaRzPMGwz6z4ZCtO7pFn1QC3XrvOLel0RHVxFujJUmStENvoIReUZch1TGcpqFziuC6S3Z9XFOTJFX2gFcG7rw9BFYB25o4JxXac48PgZ/1hxWz4aAQPlsFnwlh0Mqdx+qOLIIvzoTdB8dfb2yhjieIt0ZLkiRlu8HkECQenZlX6cYxp2lq/VO7PmYr8Ao7Jkz6pAfkbodjVzUcf1kDPA78jHhQG5D0andWTbw7bWsnZtoG/M934D+/BecMbN05I4viXwC/ORwK3oqPPa0VAq8C/9sLbitpZSGSJEld1GByWMkeUZchNctwmqY2lO+Y6bc5PYiPufwncCAwbmvDcFYrBziZeMtk/2QX2oTacaOtCaYh8YmNbsuDJybAn5fEtxe1MqDW+tKbUBGDVd+H7u/Chzlwdw2UHgC3/cTJkCRJUnazC68ygeE0Te0+CJaVwVCaD3n1Vwc7hqaDaa0+wPGJczq6zmhzQuJruS4HDmvlOdXA6UCYWBy2shqKl7Y9nEK86+9hiRD6WWBs2y8hSZKSbGqzg2/UGQLikx6V0MsuvEp7jjlNUyeVwKLe8C5Nj9+sBuqvCLur/2oC4kG3vJnrJcts4muztrbV9C/ADcCfgHfOgr+dDafdlrr6JElS55nKRmawLeoystZgcqhhL1ayh8FUGcFwmqZqJ/z562B4HtjCjgmQNhOf1XYFQA4cOaV114z/5gyW0nxA7Whw/QzQs4X9te+hBpgH7AecBPRL1LdbCN/+Ezx/YgcLkSRJkZtpMI2U3XiVaezWm8bqT/jT2OJVEFsK5ZthUC/4/Yz4WIJd6UN8EqWPgKNp2OK6kdZ1+a0NsI1bRwPgqFac+wvgEeB64PAmrpML7PsMLI41//4lSVL6a279cnUOW0uVaQynGaYiBssvhaOr4jPvLgSeGAj3A1PYdXfa2nGqtRMpQXyc6IvAOz3gxa27rqGliZp6Jq7XvZnzlgLDpsBLT8HYsuav0w2Yc43hVJKkTJZD8+uvK7UG20FSGci/tRmkIgbvT4I9quKhrh/wOeA/VsW7+L5Ny91ytxMPs7XdakNgU248mG4dDFu/0bo6WvpLEySuXdVoewi81w0GzoavTYeFK3fdSrt5bevqkSRJ6SfGVoNphOzSq0xky2kGefeinVskA+DTwOXAV3NgWg2ckNheO3tud+C9veGWSfDkcfHz+neD24c3nBU3tgoqfxrv+tsRPYDngEOBfYFtB8DAn8BBjVpBw/4QNBNAUzlpkyRJSr1iNkddQtaaQne79CojGU4zREUMujWT2Gpn4q3+DvxwJkz7JL6t1x5wy//G1/g8CBi/i3sUDYTLe0Hx5tbNttucTcDfc2DPX8HYFrrl7nt7vCW48V/CEFgG9O6MRVklSVJKlNtu2um6A7PoYzBVxrJbb4b4uLjlwBgAt10N722A90N4L4QVH8eDaVucfg88xq5bLiuhyfn3qolPuLTk2l3fu18R7Dc7fq36s/guBRZ2h9Nub1vtkiQpfQzyx8ykyCE+WWR93YDZ9CFkrwZfW9nLYKqM5v8aGaKqvOX9IQ276LbXOUXwQgn8meYDahVwC/D/gPU0XOJmLnDPafC1r7fufv2KYFgIFbPhscFwfwBLB8PZs5wMSZKkTFZCL3/QbIcTyGUwOQTEJzX6FX14gD4Ntt1n66i6KLv1Zoi8QVBV1vS+ENh+QvLu9eR/QuEWWP5juDxsuG7pJ8CiE+DFl2FzZXyt0lrV/aD7VLj5620Pyi0tmyNJkjJPbXi6kk3x1QLCEIKODBzqmnKJ9zzLBSbTnenNTBlpGFU28BdaGWLPEggaLWRa22K57QQYOq+ps9qv9Cbg53DSPjAugPH7wPTb4YgQrpgHt8yEAwbHv8ccMBj+dzZ8+Amsujk5LbiSJCnzFdGDjYkup1OCHg2GKPVs9qzs0Jt419yqxOdTxV7NBlMpWwRhmD7zohYWFoalpaVRl5G2KmLxsadV5fGW1D1L4t1iJUmSMtFUNjKjyVkskuz57vBgL1iTAwNq4KLNcFwn3LcZg8mhhF62hiorBUGwKAzDwqb22a03g/QrMoxKkqSuYzp9GctWitlMGTXkQPLn+H2+O9zZB7Ym2m1X58ZfQ8oDau3SfrV6AzMdLyo1y269kiRJikwRPVjJHoTsRTV7Mbve5D+N13dvlwd77QimtbYG8e0p1Bu4iu4NJjIymEots+VUkiRJaaOIHhTRgxhbmcymjnf6Xd1MW0xz21urdmhcE5M89SfgdnobRKU2suVUkiRJaaeYzVQm40LN/bTbmp+Cw3DHVz3dq7cy+9krmf38N+lftSM+9ydgNn1Yw54GU6kdDKeSJElKO+XJGn3a3GVaunwY0n/Lx8x+9krC/xvN7A+WxLvnhiGDN73HrOe+SdGHpRQd8AXW5H2KMDHjrqFU6hi79UqSJCntDCKHsmQE1L1r4pMgNbW9kcHksJI94jMZ9ewPJzwKQFHiiwDos2O7pOSy5VSSJElpp4Re9N71YbvU7aLNdO/RaOnEHmF8OZl6eifuKSk6hlNJkiSlnSJ6MLPezL39CegPdTPfTqF7k7P5nkBugxly7zuuG7O+HjB47/jcRYP3htlfD5h9XDdn0pXSTBA2GuAdpcLCwrC0tDTqMiRJkpQBYok1UsupYRA5lNDLgCmluSAIFoVhWNjUPsecSpIkKSPVLjsjqWuwW68kSZIkKXKGU0mSJElS5AynkiRJkqTIGU4lSZIkSZEznEqSJEmSImc4lSRJkiRFznAqSZIkSYqc4VSSJEmSFDnDqSRJkiQpcoZTSZIkSVLkDKeSJEmSpMgZTiVJkiRJkTOcSpIkSZIil/JwGgTBKUEQvB0EwTtBEFyX6vtJkiRJkjJPSsNpEAS5wJ3AqcBhwIVBEByWyntKkiRJkjJPqltOjwTeCcPwn2EYbgN+DXwxxfeUJEmSJGWYVIfTA4BV9V7/O7GtThAEk4MgKA2CoHT16tUpLkeSJEmSlI4inxApDMOZYRgWhmFYuPfee0ddjiRJkiQpAqkOp+8CA+u9/nRimyRJkiRJdVIdThcCBwdBcGAQBN2BLwO/S/E9JUmSJEkZJi+VFw/DsCoIgm8ATwO5wKwwDN9M5T0lSZIkSZknpeEUIAzDp4CnUn0fSZIkSVLminxCJEmSJEmSDKeSJEmSpMgZTiVJkiRJkTOcSpIkSZIiZziVJEmSJEXOcCpJkiRJipzhVJIk7drTMTg7H47NiT8+HYu6IklSF2M4lSRJxNhKPuvJYR35rCfG1h07n47BzZPhgzIIw/jjtElwygBDqiQpaQynkiRluRhbmcwmyqghBMqoYTKb4gH16RjcdAlsqdz5xA1rYdpFcMvUTq9ZktT1GE4lScpyxWymcfSsBIrfXxZvIa2pbuHsEB6bAccEtqRKkjrEcCpJUjZ6OgYnD4BjAsprqpo8pHzfA9p2zQ1r42H2mMBxqZKkNjOcSpKUDb55Yjw01n5NmwSfrAVg0IfvNnlKc9tbpXZc6uf7GVIlSa1iOJUkqSuo1xLa5FfpM82eWnLXj+i9uWHH3t6bKym560cdr2vzRii53IAqSdolw6kkSZmudtKiREtoWxXNncPMm7/F4PdXEdTUMPj9Vcy8+VsUzZ2TnPq2b4u3otrVV5LUgiAMw6hrqFNYWBiWlpZGXYYkSZnh6Rj85ErYvCnqStpmt/7wndvh5KKoK5EkdbIgCBaFYVjY1L68zi5GkiR10NMxuO2a+AREBwKFQB9gE1AK/CvS6nbtk8TESX9/Ea6dHnU1kqQ0YbdeSZIyyS1T42uL1gbTcUBfIEg8jiO+PRPULkHj7L6SJAynkiRljqdj8NhdQGJITiE794HKi2+PTTyX/McWk/PiavIfW0xs4rmdW2tbfVAGN082oEpSFjOcSpKUKe4qpi6YQrwrbxNiI85l8nU/p2y/gYQ5OZTtN5DJ1/08/QPqlsrEe5QkZSPDqSRJmeLD8oavm5kHqfjI/6KyV+8G2yp79ab4qv9KUWFJ9EFZ1BVIkiJiOJUkKVPsO6jh61KgqtExVVDe94AmTy/ft+ntaeeYnPjYWklSVjGcSpKUKa4qafj6X8ACYCPx3r4b468Hffhuk6c3tz39hPHJkgyokpRVDKeSJGWC2uVjGvsX8Ahwf+LxX1By14/ovbmywWG9N1dSctePUl9nMj02I+oKJEmdyHAqSVK6ezoWn8l2w9pWHV40dw4zb/4Wg99fRVBTw+D3VzHz5m9RNHdOigtNAVtPJSlrBGEY7vqoTlJYWBiWlpZGXYYkSenllAGtDqZdTk4uvNh4YK0kKVMFQbAoDMPCpvbZcipJUjp7OrYjmB4InA9cmng8MLKqOk9NNZw8wPVPJSkLGE4lSUpXT8dg2qT48wOBcUBfIEg8jiM7Auona+HHlxlQJaW/5TF4KB9m5sQfl/v/VlvkRV2AJElqwi1TG04IVMjO37XzEtv/1XllRaZqO9xVDCcXRV2JpGw2fyosmwlh9Y5tPfrHH7c2Gn6xsQzmT44/P7iV/3ctj8HCYthYDn0HwZiS1p/bBRhOJUlKN0/HGgbTA4E+zRzb3Pau6MPyqCuQlK3mT4Wlzcwg3jiU1ldVGQ+bTQXM5TF46Zrmz29PuM1whlNJktLNXcU7ntd25w2aOXZTJ9STLvYdFHUFkrJJXStmWceus7GJX6wtj8FfLiK+SHULqirhL5PiX4316A/H3t6lgqtjTiVJSjf1Wwib6s5bqwrIlknuc3LhqpKoq5CULZbH4q2WHQ2mEO+e29gLV7LLYLorW9fGQ+v8rrPkluFUkqR0U7+FsLluuyGwgOwYbwrQvafjTSWlVv3JjP5ycbzVMhm2b9x5YqTqJHZ7WToDHhjQJSZfMpxKkpRuriqBbt3jz5v7+WUT2RNMAbZscrZeSanToKU0BGqSd+2ta+PXrg2PqQiRW9fC85dnfEA1nEqSlG5OLoLiWfHnpcS779aXTd1563M5GUmpsDwGz12SvJbSplRVxu8xf+qOSY6SrWZbfIKlDGY4lSQpHZ1cBNNmx1tHFwAbif8yfyPZ1Z23vtrlZCQpWWpbTOsvDZMqYTUsvSu1IbilmYMzgLP1SpKUrk4ugr+/GF9WJhvDaFNcTkZSstS2mHZGMK3TwUmQWuOePDh0Moyfnvp7JZktp5IkpbNrp0PhCVFXkT5cTkZSMsyfGp/ptlODaScJq+OTJGXgLL6GU0mS0t0v5sE5UyDw2zbHnhZ1BZIy2fJYfGbbpTOiriT1ls2MuoI287ucJEmZ4Nrp8FI1BEHUlUTrqQecFElS69WG0ZlB/OsvkzJ+XGarZWCrsOFUkqRM0qN31BVEa0ulkyJJiqu/LulD+Tsvo/L7E7MrjHYBTogkSVIm2ZrCWR4zhZMiSaqdZbd25tuNZfEg+pdJ0dalDrHlVJKkTOKEQH4GUrbrjHVJFQnDqSRJmeSqkqgriFbP3n4GUrZaHoN7+3bdWXbbIXbQueRfuJicr60m/8LFxA46N+qSOsRwKklSJjm5KOoKovOpwXDdzOz+DKRstTwGz10G1ZuiriRtxA46l8kTfk5Zv4GEQQ5l/QYyecLPMzqgGk4lSVL6+9RgeHylwVTKVguLIdwedRVppfjI/6KyW8NJ8iq79ab4yP+Kv9g/89bINpxKkpRpdusfdQWdy668kjaWRV1B2inve0Dz27vtAafP69yCksBwKklSpvnO7ZDXLeoqOsfu/e3KK2W7xkvECIBBG99tenvlR3DZx51cTXIYTiVJyjQnF8H198WDW1cSBNCzT/zxU4Nh2mz40xqDqZTtFrq2cVNKXv0R/7+9+4+RtCzsAP59dhfOHne15SAqyh0NoanYNKRZTWqrp8EWNSZgTU3NSmnacsBpBE1MbC6mJmYTYyt4TQRdW9TC2oaEUI3V0kKag9Q2uJcYC1qDWA68Wj0wbUAiertP/5g97o7bvdu9+fHM7Hw+yWRm3pl95wt5szvfe57nfTf/7PgzFm9e/Glmz7qwUaLuuc4pAIyiy2Y6t1dPJnWpdZrufeh2JRTojJJ+bU/y9GPJlu3JK2c7jznBzCN3JumsPX1sy0uz/emDmd36K5nJpsbJTp+RUwAYZW+9pnWC7immQNIppvfvWl5fWjv39+9KNp3dOtlpGcRlXmYeuTOP/u0lWfr0uXn0jt8c6WKaKKcAMNref3PrBN154TbFFOj46vXJ4eOnqebwM8nh0bt8TC8v87LmkvvLf9Bl6vaUUwAYdSO79rQk793bOgQwDB6eT559cuXXFn8y2Cw9cMrLvKzRukru41/uJvJQUE4BYNS9d29yxpmtU6zf715r1BTo+Or1rRP01Ekv87IO6yq5G2BtrnIKAKPusplkz62dM9yOghdu66wzHfUpyUDvrDZqOqJWvczLKttXs66Su2X7uvY9jJRTANgILptJ7no0+bfaKX4v2HzKHxm4F27r5HN5GGCDW/EyLz97JrMPfHhd+1lzyZ3a3Dmz8YhTTgFgo7lsJvnAXLuR1OlLTyzHL9hsfSmwsvt3t07QczOP3Jm5+27IjqceT6lL2fHU45m774bnLv+yVmsquVNbktfMJReN/j/6lVpr6wzPmZ6ergsLC61jAMDGcvd8ctP1yf/1edrci3ck1852yvHd88kn9yQ/eCx50faj2wGO9aU3JP99b+sUQ23+wrcdfy3TBz58tOSed2nylnvaBlynUsr+Wuv0iq8ppwAwZv58d/L3c8nSYm/294LNnZFa5RNYj/t3J9+6pXWK0bVpW3LVE61TrNvJyqlpvQAwbt5/c/KvhzvrP/+trn/67/SlnZ8ppXOvmAKnQzHtzqs33lKJqdYBAIDGrp1NPrIr+ckzJ3/fxGRyxS5n2QW69/B86wSjbwOsMX0+5RQAxt2RUc8ja0R//uykJnnqR9aLAv3xL1e1TsAQUk4BgE75VECBQbh/d5IerXkfV5u2tU7QF9acAgAAg/Ofc60TjL4NuN40UU4BAIBBqkZNu3LepRtyvWminAIAAAOlgpyWMpm8/LqRu67pelhzCgAADM7kzyWLP26dYoSU5PW3bdjR0mP5ZwsAAGBwFk9x2SqOMT7FNFFOAQCAQdqyvXWC0TImxTRRTgEAgEE6/82tE4yOMSvyyikAADA4372jdYLRMLU5eeVs6xQDpZwCAACD8fB88uyTrVMMvzKZvGZurKb0JsopAAAwKPdf2zrBSc1f+LZc8I6vZ+LqQ7ngHV/P/IVvG3yIqc3J6z43dsU0cSkZAABgEB6eTw4/3TrFquYvfFt2vfbjeeaMzUmSA1vPz67XfjxJMvPInYMJsWlb8uq9Y1lMEyOnAADAIHz1+tYJTmrPqz74XDE94pkzNmfPqz44mACbtiVXPTG2xTRRTgEAgEEY8rWmj2156bq299yzPxrM5wwx5RQAABgZ/VoXuv3pg+va3nNjdtmYlSinAADASDiyLvTA1vNTy8Rz60J7UVBnH/hwNv/smeO2bf7ZM5l94MNd7/uUxvCyMStRTgEAgP7btK3rXfRzXejMI3dm7r4bsuOpx1PqUnY89Xjm7ruhPydDmjxr+f9HSbbsGMvLxqzE2XoBAID+e/Xe5F/e2dUu+r0udOaRO/tTRstUUhc7U3dfOauIrkI5BQAA+u+ima7L6fanD+bA1vNX3D6cSvL625TRNTKtFwAAGAlN14Wu18SZiuk6KacAAMBg/MLFXf34QNeFrtfUluPXke68VTFdp1JrbZ3hOdPT03VhYaF1DAAAoF/ueEXyv99snaI3Nm3rrKVVQteslLK/1jq90mvWnAIAAIPz9oeOPn54vut1qAP38uuS19zcOsWGZFovAADQxkUzXU/1HZyimPaZcgoAALTz9oeS8y5dwxsnktff3rlt2bG8rfQzWcfkWZ3P3LWkmPaZab0AAEBbb7nn+Of3706+9akkS53nk2clr/3U0bWdR+4fnk++tid5+rFk09nJ4Z8kiz/uLot1pM04IRIAALBx3L87+dYtq78+tTm56Krk4duSw08vbyzJy681MjoATogEAACMhyMF8z/nkrqYZCKZ/Llk8Zlky/bklbOdUVFFdOgopwAAwMbympuVzxHkhEgAAAA0p5wCAADQnHIKAABAc8opAAAAzSmnAAAANKecAgAA0JxyCgAAQHPKKQAAAM0ppwAAADSnnAIAANCccgoAAEBzyikAAADNKacAAAA0p5wCAADQnHIKAABAc8opAAAAzSmnAAAANKecAgAA0JxyCgAAQHPKKQAAAM0ppwAAADSnnAIAANCccgoAAEBzyikAAADNKacAAAA0p5wCAADQXN/KaSnlQ6WUg6WUry/f3tyvzwIAAGC0TfV5/zfVWv+iz58BAADAiDOtFwAAgOb6XU7fXUr5Rinl1lLKL/b5swAAABhRXZXTUso9pZQHV7hdnuSWJBcmuSTJ95N8bJV97CqlLJRSFg4dOtRNHAAAAEZUqbX2/0NKuSDJl2qtv3qy901PT9eFhYW+5wEAAGDwSin7a63TK73Wz7P1vuSYp29N8mC/PgsAAIDR1s+z9X60lHJJkprk0STX9PGzAAAAGGF9K6e11iv7tW8AAAA2FpeSAQAAoDnlFAAAgOaUUwAAAJpTTgEAAGhOOQUAAKA55RQAAIDmlFMAAACaU04BAABoTjkFAACgOeUUAACA5pRTAAAAmlNOAQAAaE45BQAAoDnlFAAAgOaUUwAAAJpTTgEAAGhOOQUAAKA55RQAAIDmlFMAAACaU04BAABoTjkFAACgOeUUAACA5pRTAAAAmlNOAQAAaE45BQAAoDnlFAAAgOaUUwAAAJpTTgEAAGhOOQUAAKA55RQAAIDmlFMAAACaU04BAABoTjkFAACgOeUUAACA5pRTAAAAmlNOAQAAaE45BQAAoDnlFAAAgOaUUwAAAJpTTgEAAGhOOQWAITK/L7ngT5KJKzr38/tO7z0AMGpKrbV1hudMT0/XhYWF1jEAYCB2fzKZuztZXFr9PWdOJX/828mXF5IDh5KSZC1/ubdtTfZenczs7FVaAOheKWV/rXV6pdeMnAJAn6w2wjm/L5m6IrnlKycvpkny08Od9x041Hm+1n9SfvKp5I/+0qgqAKPDyCkA9Nj8vuT6T3cK4rEmczhLdSK1lHTGQNsxsgpAC0ZOAWBA5vd1RiyfX0yTZDFTqWUirYtp0sn3zhuTySuScrm1qwC0p5wCQA9d84nOVNxRsbQ8gerAoeTKGxVVANpRTgGgR+b3JT9+tnWK03dkoc+BQ8muT5xYUJ0lGIB+suYUAHrkgj85euKijWJyInndryb//u0Ti/eZU8mt77FuFYC1s+YUAAbgsSdaJ+i9xaXk3m+sPCL808OdEz8BQC8opwDQI9vPaZ1g8J58yjpVAHpDOQWAHpm9snWCdg4cSv7gJgUVgNOnnAIAPbFUO2crBoDToZwCQI/sua11gvZG+WzFALSlnAJAj2y0M/Wert2fbJ0AgFGknAJADyhkR93yleQNH2ydAoBRo5wCQA/M3d06wXC59xvJxOVOkATA2imnANADi0utEwyfmuSdNyqoAKyNcgoA9JUz+AKwFsopAHTJyODJOYMvAGuhnAJAl1xCBgC6p5wCQJcee6J1AgAYfcopAHRp+zmtEwDA6FNOAaBLs1e2TjDcSqzLBeDUlFMA6NLMztYJhltNctVNrVMAMOyUUwDogeve1DrBcFusySve3ToFAMNMOQWAHrj52tYJht83H2+dAIBhppwCQI9s29o6AQCMLuUUAHpk79WtEwy/icuT3Z9snQKAYaScAkCPzOw0enoqNcktX1FQATiRcgoAPbT36mSytE4x/G75SusEAAwb5RQAemhmZ/K59yZnbWqdZPi59ikAx1JOAaDHZnYmT9+R3P6+1kmG2zWfaJ0AgGGinAJAn8zsbJ1guP342dYJABgmyikA9JETJJ2cEyMBcIRyCgB9tPfq5IzJ1imG19zdrRMAMCyUUwDoo5mdyWeuT3acmxRn8T3B4lLrBAAMC+UUAPpsZmfy6F8lS3+fXPem1mmGy6RvIgAs8ycBAAbo5muTTVOtUwyPXZe1TgDAsFBOAWDA/vo9yZkKapJOWQeAJPGnEQAG7MglZt55Y9scrU1YgwvAMYycAkADMzs7J0kaZ9e8sXUCAIaJcgoAjcxemWze1DpFG+edbUovAMdTTgGgkZmdydy7jl5mZtvW1okG49JfSw5+pnUKAIaNNacA0NDMzqNrUJNk4oqk1mZx+mbb1mTv1cf/twLAsZRTABgi289JDhxqnaJ3bn+fQgrA2pjWCwBDZCOtQ91xrmIKwNoppwAwRJ6/DnXHuZ3Rx/qFzv3UkPzlPmPyaK7r3pQ8/6owmzd1ijYArFWpQ7SwZXp6ui4sLLSOAQBDa35fcv2nkyefapdhoiR/897jR0Xn9yV7bksee6IzNXn2SqOmAJyolLK/1jq94mvKKQCMvvl9yR9+PDm81Lt9Tk2cuL8zp5Jb36N4AnB6TlZOh2RyEADQjZmdyWdvSLa8YOXXLz7/xKm3J7Nta2d/t7/v+CnGiikA/WLkFAA2mNWm2B7ZfuBQMjmRLC51Cuebp5MvL5iSC0D/mdYLAABAc6b1AgAAMNSUUwAAAJpTTgEAAGhOOQUAAKA55RQAAIDmlFMAAACaU04BAABoTjkFAACgOeUUAACA5pRTAAAAmlNOAQAAaE45BQAAoDnlFAAAgOaUUwAAAJpTTgEAAGhOOQUAAKA55RQAAIDmlFMAAACaU04BAABoTjkFAACgOeUUAACA5pRTAAAAmlNOAQAAaE45BQAAoDnlFAAAgOa6KqellN8rpTxUSlkqpUw/77U/LaV8p5Ty7VLKZd3FBAAAYCOb6vLnH0zyu0k+dezGUsrFSX4/ySuSnJfknlLKL9daF7v8PAAAADagrkZOa63fqrV+e4WXLk/yd7XWZ2ut/5XkO0le1c1nAQAAsHH1a83pS5M8fszz7y1vO0EpZVcpZaGUsnDo0KE+xQEAAGCYnXJabynlniQvXuGlPbXWL3QboNY6l2QuSaanp2u3+wMAAGD0nLKc1lrfcBr7PZjk/GOev2x520nt37//iVLKgdP4PIbfOUmeaB2Csec4ZFg4FhkGjkOGhWNxvOxY7YVuT4i0mi8m+Xwp5cZ0Toh0UZIHTvVDtdZz+5SHxkopC7XW6VO/E/rHcciwcCwyDByHDAvHIkd0eymZt5ZSvpfkN5L8Qynl7iSptT6U5I4k30zyj0ne5Uy9AAAArKarkdNa611J7lrltdkks93sHwAAgPHQr7P1wvPNtQ4AcRwyPByLDAPHIcPCsUiSpNTqBLkAAAC0ZeQUAACA5pRTBqaU8qFSysFSyteXb29unYnxUUp5Yynl26WU75RSPtA6D+OrlPJoKeU/ln8PLrTOw3gopdxaSvlhKeXBY7adXUr551LKw8v3v9gyI+NhlWPRd0SSKKcM3k211kuWb19uHYbxUEqZTPKJJG9KcnGSd5RSLm6bijH3+uXfgy6dwKB8Nskbn7ftA0nurbVelOTe5efQb5/Nicdi4jsiUU6B8fCqJN+ptX631vrTJH+X5PLGmQAGptZ6X5IfPW/z5Uk+t/z4c0muGGQmxtMqxyIkUU4ZvHeXUr6xPKXD9CEG5aVJHj/m+feWt0ELNck/lVL2l1J2tQ7DWHtRrfX7y4//J8mLWoZh7PmOiHJKb5VS7imlPLjC7fIktyS5MMklSb6f5GMtswI08lu11l9PZ5r5u0opr20dCGrn8g0u4UArviOSJJlqHYCNpdb6hrW8r5Ty6SRf6nMcOOJgkvOPef6y5W0wcLXWg8v3Pyyl3JXOtPP72qZiTP2glPKSWuv3SykvSfLD1oEYT7XWHxx57DvieDNyysAs/+E74q1JHlztvdBjX0tyUSnll0opZyb5/SRfbJyJMVRKOauUsvXI4yS/E78LaeeLSa5afnxVki80zMIY8x2RI4ycMkgfLaVcks60oUeTXNM0DWOj1nq4lPLuJHcnmUxya631ocaxGE8vSnJXKSXp/A3+fK31H9tGYhyUUv42yeuSnFNK+V6SP0vykSR3lFL+OMmBJG9vl5Bxscqx+DrfEUmS0lliAAAAAO2Y1gsAAEBzyikAAADNKacAAAA0p5wCAADQnHIKAABAc8opAAAAzSmnAAAANKecAgAA0Nz/A34HP1UT/EU9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17.204325437545776\n"
     ]
    }
   ],
   "source": [
    "import data_utils_new\n",
    "import matplotlib.pyplot as plt \n",
    "import umap.umap_ as umap\n",
    "# Generate data from the gmm model and plot it\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "n_samples = np.ones(num_classes, dtype=int)\n",
    "n_samples[0] = 0\n",
    "n_samples *= 2000\n",
    "\n",
    "xx, yy = utils.sample_from_gaussians(means, covs, n_samples=n_samples)\n",
    "\n",
    "NUM_COLORS = num_classes\n",
    "\n",
    "reducer = umap.UMAP()\n",
    "\n",
    "umap_embedding = reducer.fit_transform(xx)\n",
    "\n",
    "plt.figure(figsize=(16,14))\n",
    "cm = plt.get_cmap('gist_rainbow')\n",
    "\n",
    "shift = 1 / len(data_utils_new.label_ids_13.keys())\n",
    "idx = 0\n",
    "for label in data_utils_new.label_ids_13:\n",
    "    ind = yy == data_utils_new.label_ids_13[label]\n",
    "    \n",
    "    plt.scatter(umap_embedding[:,0][ind], umap_embedding[:,1][ind], label=label, \\\n",
    "                color=cm(1.*idx/NUM_COLORS))\n",
    "    idx += 1\n",
    "\n",
    "plt.title(\"Embedding scatter-plot\")\n",
    "plt.legend()\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "print(time.time() - start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model weights\n"
     ]
    }
   ],
   "source": [
    "deeplabv3.load_weights(fn_w_adapted_dlv3)\n",
    "classifier.load_weights(fn_w_adapted_cls)\n",
    "print(\"Loaded model weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "road 0.8308608968675483\n",
      "sidewalk 0.27348768244553456\n",
      "building 0.6513443882341332\n",
      "traffic light 0.37291672961621636\n",
      "traffic sign 0.0038099773782593166\n",
      "vegetation 0.16323353477649255\n",
      "sky 0.888175399577542\n",
      "person 0.19304997344212002\n",
      "rider 0.0\n",
      "car 0.8840926390088035\n",
      "bus 0.0\n",
      "motorcycle 0.0\n",
      "bicycle 0.0\n",
      "0.32776701702666533\n",
      "Computed SYNTHIA_VIPER_DENSE_13_1 mIoU in 159.04997277259827\n"
     ]
    }
   ],
   "source": [
    "import utils\n",
    "importlib.reload(utils)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "source_cat_iou,source_miou = utils.compute_miou('./processed-data/1024x512/13_classes/VIPER_DENSE_SEQ/Sequence001/', combined_, data_utils.label_ids_13, 0)\n",
    "\n",
    "for k in source_cat_iou:\n",
    "    print(k, source_cat_iou[k])\n",
    "print(source_miou)\n",
    "\n",
    "print('Computed ' + dataset + ' mIoU in', time.time() - start_time)\n",
    "\n",
    "road 0.8308608968675483\n",
    "sidewalk 0.27348768244553456\n",
    "building 0.6513443882341332\n",
    "traffic light 0.37291672961621636\n",
    "traffic sign 0.0038099773782593166\n",
    "vegetation 0.16323353477649255\n",
    "sky 0.888175399577542\n",
    "person 0.19304997344212002\n",
    "rider 0.0\n",
    "car 0.8840926390088035\n",
    "bus 0.0\n",
    "motorcycle 0.0\n",
    "bicycle 0.0\n",
    "0.32776701702666533\n",
    "Computed SYNTHIA_VIPER_DENSE_13_1 mIoU in 159.04997277259827"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# road 0.8615069121641106\n",
    "# sidewalk 0.9000754476234025\n",
    "# building 0.9430840666603088\n",
    "# traffic light 0.5906668306793044\n",
    "# traffic sign 0.4751323753940848\n",
    "# vegetation 0.8432769737226227\n",
    "# sky 0.9643724556369807\n",
    "# person 0.7238095621515034\n",
    "# rider 0.6191529188583779\n",
    "# car 0.8826804768268254\n",
    "# bus 0.9113284943230776\n",
    "# motorcycle 0.6885963930665517\n",
    "# bicycle 0.3477612301913242\n",
    "# 0.7501110874844981\n",
    "# Computed SYNTHIA_VIPER_DENSE_13 mIoU in 209.03599977493286\n",
    "\n",
    "# VIPER1\n",
    "# road 0.7598092688219289\n",
    "# sidewalk 0.3371948453270736\n",
    "# building 0.5888429412251485\n",
    "# traffic light 0.2412746294793218\n",
    "# traffic sign 0.007871538466283783\n",
    "# vegetation 0.07906209988581193\n",
    "# sky 0.8681167733611675\n",
    "# person 0.15913067911219309\n",
    "# rider 0.0\n",
    "# car 0.8381470531819963\n",
    "# bus 0.0\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.2984192176046866\n",
    "# Computed SYNTHIA_VIPER_DENSE_13_2 mIoU in 12.690653085708618\n",
    "\n",
    "# VIPER2\n",
    "# road 0.12746921970993438\n",
    "# sidewalk 0.01654514397536747\n",
    "# building 0.08359767761356621\n",
    "# traffic light 0.0014628747427203985\n",
    "# traffic sign 0.0004797761683849531\n",
    "# vegetation 0.0010155479171658137\n",
    "# sky 0.06442882520719335\n",
    "# person 1.3905304873809358e-05\n",
    "# rider 0.0\n",
    "# car 0.013538577154181806\n",
    "# bus 0.0040766048179061275\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.024048319431638026\n",
    "# Computed SYNTHIA_VIPER_DENSE_13_3 mIoU in 16.93789768218994\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "road 0.16361121759652467\n",
      "sidewalk 5.5880594346001465e-06\n",
      "building 0.0537824356711514\n",
      "traffic light 0.004082077289533864\n",
      "traffic sign 0.0\n",
      "vegetation 0.004795258655464463\n",
      "sky 0.03790733322264466\n",
      "person 0.0\n",
      "rider 0.0\n",
      "car 0.02297664564513909\n",
      "bus 0.0\n",
      "motorcycle 0.0\n",
      "bicycle 0.0\n",
      "0.022089273549222518\n",
      "Computed VIPER mIoU in 8.972235918045044\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "cit_cat_iou,cit_miou = utils.compute_miou(cityscapes_data_dir, combined_, data_utils.label_ids_13, 0)\n",
    "\n",
    "for k in cit_cat_iou:\n",
    "    print(k, cit_cat_iou[k])\n",
    "print(cit_miou)\n",
    "\n",
    "print('Computed VIPER mIoU in', time.time() - start_time)\n",
    "\n",
    "# road 0.7883116582422506\n",
    "# sidewalk 0.6215464735885322\n",
    "# building 0.751691007583865\n",
    "# wall 0.3993120030432491\n",
    "# fence 0.39173486089325144\n",
    "# pole 0.302509334397975\n",
    "# traffic light 0.3019573318279201\n",
    "# traffic sign 0.38603017233885695\n",
    "# vegetation 0.7792104073113252\n",
    "# terrain 0.484321694588526\n",
    "# sky 0.8323752669785053\n",
    "# person 0.5206867614087272\n",
    "# rider 0.2377222662760571\n",
    "# car 0.8020324193022697\n",
    "# truck 0.4754062370048565\n",
    "# bus 0.593789975462107\n",
    "# train 0.5966522990567177\n",
    "# motorcycle 0.3119814031424603\n",
    "# bicycle 0.47793033593638945\n",
    "# 0.5292211530728338\n",
    "# Computed CITYSCAPES mIoU in 232.43373942375183"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NO ADAPTATION\n",
    "\n",
    "# road 0.4343089698314718\n",
    "# sidewalk 0.11156745414108413\n",
    "# building 0.3455360124643591\n",
    "# traffic light 0.13244824374732939\n",
    "# traffic sign 0.020891181218774212\n",
    "# vegetation 0.16245698445827797\n",
    "# sky 0.7351399630612382\n",
    "# person 0.18702045190416963\n",
    "# rider 0.0\n",
    "# car 0.8301180914913947\n",
    "# bus 0.06320265566955835\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.23251461599905057\n",
    "# Computed VIPER mIoU in 94.76635837554932\n",
    "\n",
    "# ALL IN ONE\n",
    "\n",
    "# road 0.6900469076232928\n",
    "# sidewalk 0.3999559532819935\n",
    "# building 0.4477707606588546\n",
    "# traffic light 0.14471677916664455\n",
    "# traffic sign 0.005208014396949388\n",
    "# vegetation 0.22113673614692125\n",
    "# sky 0.8054719866917823\n",
    "# person 0.20232922242992005\n",
    "# rider 0.0\n",
    "# car 0.7608467046022307\n",
    "# bus 0.5757047777388474\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.32716829559518745\n",
    "# Computed VIPER mIoU in 133.14108419418335\n",
    "\n",
    "# SYNTHIA-VIPER1\n",
    "# road 0.8153079075418371\n",
    "# sidewalk 0.3638901684131552\n",
    "# building 0.6422570606708572\n",
    "# traffic light 0.3448658440152766\n",
    "# traffic sign 0.030519858531548108\n",
    "# vegetation 0.11682109473012166\n",
    "# sky 0.86401077761864\n",
    "# person 0.18156178781292293\n",
    "# rider 0.0\n",
    "# car 0.8640675468674487\n",
    "# bus 0.0\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.32486938816936983\n",
    "# Computed VIPER mIoU in 12.139243841171265\n",
    "\n",
    "\n",
    "# VIPER1-VIPER2\n",
    "# road 0.588499111128223\n",
    "# sidewalk 0.369365725539779\n",
    "# building 0.6204748941380721\n",
    "# traffic light 0.1665936557512192\n",
    "# traffic sign 0.01265457278785601\n",
    "# vegetation 0.043019551365977134\n",
    "# sky 0.8271666403331733\n",
    "# person 0.3732262212752325\n",
    "# rider 0.0\n",
    "# car 0.816343619490096\n",
    "# bus 0.02369959332673774\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.29546489116433583\n",
    "# Computed VIPER mIoU in 11.642083406448364\n",
    "\n",
    "# VIPER2-VIPER3\n",
    "# road 0.14499194020624828\n",
    "# sidewalk 0.010885014754372853\n",
    "# building 0.052795147047877924\n",
    "# traffic light 0.00418806705772967\n",
    "# traffic sign 0.0018961732294710642\n",
    "# vegetation 0.006274219959573628\n",
    "# sky 0.06364095194179961\n",
    "# person 0.0\n",
    "# rider 0.0\n",
    "# car 0.013660761584344825\n",
    "# bus 0.0\n",
    "# motorcycle 0.0\n",
    "# bicycle 0.0\n",
    "# 0.022948636598570604\n",
    "# Computed VIPER mIoU in 14.073742628097534\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deeplabv3.save_weights(fn_w_adapted_dlv3)\n",
    "classifier.save_weights(fn_w_adapted_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deeplabv3.load_weights(fn_w_adapted_dlv3)\n",
    "classifier.load_weights(fn_w_adapted_cls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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