{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Improving the speed of augmented data training using Keras 2\n", "\n", "As a complete data science newbie, I decided that it would be helpful to use the latest and greatest Anaconda/Python/Keras/cudnn rather than the official part 1 environment (Python 2.7/Keras 1/cudnn7). \n", "The switch is fairly straightforward with Keras 2 providing the most headaches (they changed the API without providing a complete backward compatible one). \n", "\n", "In this notebook, I am using Python 3 versions of Jeremy's utils.py and vgg16_bn.py. I'm currently about halfway the course and \"ported\" the first three lessons - my utils.py might still have incompatibilities I'm not aware of. \n", "\n", "## Rationale for this experiment\n", "\n", "I started this course using an old Z800 (24 GB memory, 2x6 2.8 Ghz cores) with a GTX 1080Ti graphics cards. Given that the GPU is one of the fastest ones around, I hoped to get fast training speeds - but alas, performance was disappointing. I switched to a Z640 (32 GB memory, 4 3.6 GHz cores) and I got improved performance - but I still did not understand why as my old Z800 should have done a good enough job. \n", "\n", "As Jeremy encourages experiments, I decided to figure out what the fundamental problem was. This notebook summarizes the problem and solution; I used my Z640, but I don't doubt that I would have been able to get similar performance on the 'old' Z800. \n", "\n", "## Cats vs dogs without data augmentation\n", "\n", "Let's start with training cats and dogs using batch normalization; fortunately, Jeremy already provided a nice wrapper for this. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n", "/home/karel/anaconda3/lib/python3.6/site-packages/theano/gpuarray/dnn.py:135: UserWarning: Your cuDNN version is more recent than Theano. If you encounter problems, try updating Theano or downgrading cuDNN to version 5.1.\n", " warnings.warn(\"Your cuDNN version is more recent than \"\n", "Using cuDNN version 6020 on context None\n", "Mapped name None to device cuda: Graphics Device (0000:02:00.0)\n" ] } ], "source": [ "import sys\n", "sys.path.append('..') # I am running this in an nbs subdirectory\n", "path = \"../data/dogscats/\"\n", "from utils3 import * # utils3 is my python3 version of Jeremy's utils.\n", "import subprocess" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 23000 images belonging to 2 classes.\n", "Found 2000 images belonging to 2 classes.\n" ] } ], "source": [ "# create batches for training\n", "batch_size = 100\n", "\n", "# do not use data augmentation.\n", "noAugmGen = image.ImageDataGenerator()\n", "defaultBatches = get_batches(path+'train', shuffle=True, batch_size=batch_size,gen=noAugmGen)\n", "val_batches = get_batches(path+'valid', shuffle=False,batch_size=batch_size,gen=noAugmGen)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create the vgg\n", "from vgg16bn_3 import * # my python 3 version of Jeremy's file\n", "vgg = Vgg16() # vgg with batch normalization and dropout, also loads precomputed coefficients\n", "vgg.ft(2) # reconfigure for two classes\n", "vgg.finetune(defaultBatches) # gets classes from training set" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "230/230 [==============================] - 111s - loss: 0.1224 - acc: 0.9667 - val_loss: 0.0494 - val_acc: 0.9845\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Start the training of the network. Note the changed API of Keras 2!\n", "batch_size = 64 # your mileage may vary\n", "epoch_steps = ceil(defaultBatches.samples/defaultBatches.batch_size)\n", "val_steps = ceil(val_batches.samples/val_batches.batch_size)\n", "\n", "vgg.model.fit_generator(defaultBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(OPTIONAL: jump to the Conclusions section here)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, what is the actual usage of the GPU here? I'll run another epoch, but now will capture the GPU load using the nvidia-smi utility. I need to do this as a background process, so I have to manually determine the time required for logging, in this case 120 seconds." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "230/230 [==============================] - 112s - loss: 0.0880 - acc: 0.9770 - val_loss: 0.0568 - val_acc: 0.9855\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.Popen(\"timeout 120 nvidia-smi --query-gpu=utilization.gpu,utilization.memory --format=csv -l 1 | sed s/%//g > ./GPU-stats.log\",shell=True)\n", "\n", "vgg.model.fit_generator(defaultBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sb\n", "sb.set_style(\"darkgrid\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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6BNvOwX6FXDFHyEMmCIIg7ILankAKmSAIgrAfBxV1Vb+8g6qsCYIgCJugHDKq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gpu = pd.read_csv(\"./GPU-stats.log\") # make sure that 120 seconds have expired before running this cell\n", "gpu.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is what I hoped to see: GPU running at about 100%, suggesting that the GPU is being used very well. The PCI bus is being used at about 30% of its capacity; it is quite \"bursty\" though. \n", "\n", "## Cats vs dogs with data augmentation\n", "\n", "Let's add data augmentation, which is done on the CPU. We have to generate new batches using an image generator that performs the data augmentation, as follows:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 23000 images belonging to 2 classes.\n" ] } ], "source": [ "# create a generator that performs data augmentation\n", "augmGen = image.ImageDataGenerator(rotation_range=10, width_shift_range=0.05,zoom_range=0.05,\n", " channel_shift_range=10, height_shift_range=0.05, shear_range=0.05, horizontal_flip=True)\n", "# and create new batches using that generator\n", "augBatches = get_batches(path+'train',gen=augmGen,shuffle=True, batch_size=batch_size)\n", "vgg.finetune(augBatches)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the batches don't contain a lot of information; it's actually an iterator that generates a new batch when it is called. I like to think that it is a \"recipe\" on how to get a batch. \n", "\n", "Let's train with data augmentation:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "230/230 [==============================] - 129s - loss: 0.1480 - acc: 0.9600 - val_loss: 0.0500 - val_acc: 0.9840\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vgg.model.fit_generator(augBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training now takes 20s longer (in previous experiments with low-memory condition, it was actually much slower). I suspect that the GPU is waiting for data, so let's do another epoch with logging (140s) so we can visualize it:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "230/230 [==============================] - 128s - loss: 0.1008 - acc: 0.9736 - val_loss: 0.0458 - val_acc: 0.9870\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.Popen(\"timeout 140 nvidia-smi --query-gpu=utilization.gpu,utilization.memory --format=csv -l 1 | sed s/%//g > ./GPU-stats.log\",shell=True)\n", "\n", "vgg.model.fit_generator(augBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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3DiISdOPr163Cxau6G5S6x+VAR7MfW/cN4/BAAhet7EIyU8B/PLwbL7w5gEjAjZuuW4VL\nzjGWvXhlV40ntvvwOP7n2T7MiQbxlx85Szes3RbxIZsvYdehceQKZbjdTmx4fB96ogH81UfO1pVt\njXiRK5Sw89A4cvkyHDyHe545iJ62AL780bNqjAg12XyhjJ2HxpHNlxq8tkc2H8Gr+0ewdlUXPnD+\n/EbZoiSbyZVqlDUAPPbyEWzvHcHFK7tw9XtrZQGgs8WP957ViePDSew5MoGt+4axsCuM1ohXlr1o\nZSeuee+CBtmOiuyJkRTePDiGrfuGsaAzhLaID79/5Si27RvGRWd34poLGmWVLOoOY/+xSew+MoHO\nFj/e7BvD1r3DuOCsTnzskkXoiQbwyp4h9PXHsXpJG257YBfKAvB3N6zWiDpIuJwOzGkP4uXdQzh4\nMo5zl0bx0wd2oSwI+NtPGsnymBOduuzcaBAv7xnCwZMxrF3VVRORPDqcwi8f3o32Zh++9rGVNQrW\n5eQxtz2El3cP4uDJGNYsk45bKknHrV9LnA4ea5ZF0d7kw1vHJ3Hu0jZ84xOr0NHibzgvjuOwuCeC\nVYtbceBEDEeHkhiJZTESyyKZKeI9Z7aryr7ZN4bjwylc/q45hnUeb5eAavRKwlAh79mzB9/85jex\nfft27N69G0899RS++93v4mc/+xnuu+8+uFwufO1rX4PH40F7eztuvvlmPProo/jSl76EVatWGZ6c\ncuF863gMf9h6DOcti9ZYVGp0twXw3I5+nBhJ4fLz5qDvZBwbtxzDmmVRw8XewfNY1B3G5l2D2Hlo\nHAdOxNDTFsBffVR/QWqQ7avKGi1ITHZxd0Q+7lsnYuhuC+ArJmRDIS86I15s3jWIXYfG8dbxGLpa\n/fjqtWebOK70kL60axA7+8bw1glJ9isfPVs1rN8oG8ZLuwax79gEjgwm8Yetx+BxOfCXH1mBD5w/\nX/MzeJ7Dkp4IXto5IJ9zR4sfX/3Y2YYFOrLsrgHsPTqJY0NJPL7lGNwuB66/dDHG4jnsOjSO3YfG\nsbNvDMOTWdx41RlYsaAFgYBHUyED0rMzPJnBnsMT2LJXUuyfuXIZVpgoIutuC2BkMoPdhycwMpnF\n/Zv6cHI0jXMWt+LvbliNnmhQU7arNYDRWFaSjVVlVy1uxd/esBpzDGTHKrI8z2F5JaqRzhXx7/e+\niWJJwN9+8hy0aOTNlCgN0td7R1AuC/ib603Kzq3IHh7H7sPjKAuiadkag3ROBO1NUgjz8EACv368\nFy1hL266blVNXrFB9rBkkLY3SwvqkcEE7tjYi5awB1//hLosAPg8TlxwViccPIedfePYvHsQ8XQB\nT796Es1hD75+3TmGsuGwF6/tG8HLeyTZZ147geaQviyD4zgsm9uEl3YNYvfhCfQenURT0INvfGIV\nXE4H2pv9SKQL2HVoHK/sGUIyW8QnLl2MNTqRPkZ7kw/JjEI2U8R1ly7GecuMC5SiTT6kMsVa2fct\nNlXcFG3yIZWVZAslAWdXalRyhRJ+dPfrSOeK+MZ156C92acqm87WHvfjlyzCu5ZrH3duexBXnz8P\n717eAZfBfstNQQ8uW9ODD14wHx++cEHlfwtx/gp12Z194zg2nMRla+aYKp58O+gpZE40m+w9RShz\nyLfevxO7Do3jOzeeJ+eo9Ljn6QN45vWT+IsPrcD23mHsPDSO73zmPM0cVT2PvHQYj718FA6ew3c/\n+y7M79SO7dfz6OYjeHTzkSnJPrb5CB6pyP7DZ89T5EC0iUZDGB1N4rGXj+CRl46A5yRZtVCRFr9/\n5SgefvHwlGQ3vnIUD714GIAUrvrLj6xAW6TxRVPj8S1H8eALh8FxwHduPA+Lu83dHwD4w9ZjeOD5\nQwCAxT1h/NWHz0Jbkw/5Qhl3P3MAm3cNApDCi9/4xCpwHCdfKz3SuSL+8Y7tmEzmsXJRK/7m+lWq\n+Ww1MrkivleRdTo4XH/pElzxrjmm5JWyDp7D9euW4MopyDod0t8LghT2/tgli/DhCxeYOn9AUmQ/\n/O/XJdm1C2tyl1Zkr127EB+xIHtsKIl//u/XIAgiHJXvUBZEiCLw//3ZubppjLcjq+TgyRj+67G9\nGK8UBP2vT602ZYxFoyFseeMkfvnYHln2m59ajbMsdANseqMfdz35liR7w+qaTpF8oYx/+vV2jMSy\nWDYngm+tX6NZAFhPvlDGP23YjpHJLJbOieDbU5RdMieCv7ciWyzj5g2vYngiIz+Toijdl6vPn1dT\n46Mnu7g7jL//zBpD5+JUcdeTb2HTG/34wZfOV00JTSd6OWTTIetTBfNk+sfS+J9nD2LJnEhNgYEe\nXa1+PPP6SRwdSuDIUBJLeiL46Frzi8OSORGkckVcft4c2bozLdsTQTpXxOVrrMsu7okgkyth3Zoe\nrFxkroqYeX1L5kSQyUqyWsUa2ucclo57rnXZxT1hpLJFnLs0is9fsxxBn/lKxMU9YWRzJbxvdTdW\nLzG2+GtkuyNIZYtYvaQNn7v6TLkC0ungce7SKDpb/HDwHD5z1RlyLYGRhwwAbqcDi3siKJUFfPqq\nZaYKxxgupwOLu6V7+KUPrcCaM6KmlbmrctxMroQvfuhMnHdGuyXZJXMiGIvlEPK50BT0oDnkwblL\n2/DxSxYZtnooaQ550Brxoqc9hA9fON+ybFuTF01BDz7+PmvHbQp6EI34EEvn0RTwyN/hQxfMx7vP\n7DCWbWqU/eAF8/EeA1klrWEvLlrZhVy+jPeu6MB76yqftQgEPPA6uRrZC0zKMhZ0hlAqi3j38vaG\n4zodPJbOaUK+UManr1yGgAVPzengsWxuE3KFMj7zNmQ/feUySx6i08Fj2ZwIRmNZBCvPZFPQg3ev\n6MTH1y7SjTg6HTzOmNuEXL6ET191BkIW1pTpZs+RCRwZTGDd6h5TrVxvh9PCQ/71H3qxedcgbvr4\nSt2CrHp+8egebO8dAQB87eMrTYV4TkfMeH2EBF0r89C1Mg9dK/Ocbtfqd88cxNOvncA/fe7dlqKd\nU0HPQ54Rs6yLJQFb9w6hvdmHc3QqFdX4wPlSG0hHs08ulScIgiAIszBPXrDXP337fcjTQbFURqks\noqctYCn8BQALOsP48kfPQmeL33TegyAIgiAYTHfUDzB5p5kRCpldhKkqVCv5I4IgCIJQwnSP3X3I\nMyJkzS6CUcsRQRAEQUw3TPWQQsbb95AJgiAIYqowZ7Bscw55Rihklkh3WMwfEwRBEMTbhTmDInnI\n1TABRx4yQRAE8Q7DBpLYXdQ1IxRymXLIBEEQhE1QDlmBQDlkgiAIwiZmStvTjFDIsodMOWSCIAji\nHWamDAaZEQqZXQTykAmCIIh3Go76kKtQDpkgCIKwCxadpZA1KIdMEARB2AdPIesqNKmLIAiCsAsH\nhayrkIdMEARB2AXNslbAxpVZ3emJIAiCIN4uPOWQq1DImiAIgrCLatuTvecxIxQybS5BEARB2AW1\nPSmgHDJBEARhF/JuT4Jg63nMCIVMfcgEQRCEXfAUsq5CHjJBEARhF2wwCIWsQbOsCYIgCPugzSUU\nkIdMEARB2AX1IStg48ooh0wQBEG809BuTwrIQyYIgiDsgqccchW5D5lyyARBEMQ7DOWQFdCkLoIg\nCMIuKIesQJ5lTQqZIAiCeIehHLIC8pAJgiAIu2Cqh0LWoFnWBEEQhH1QyFoBecgEQRCEXTh4SRVS\nyBrkIRMEQRD2QR6yAoGKugiCIAiboByyAoH6kAmCIAibcJCHXIW2XyQIgiDsgkLWCmh0JkEQBGEX\nLDpbpqIuqrImCIIg7IM8ZAVUZU0QBEHYBSlkBbKHTEVdBEEQxDsMz3HgOFLIAGiWNUEQBGEvPMdR\nDhmgHDJBEARhLw6eIw8ZoCprgiAIwl54noMg2HwO9h5egoq6CIIgCDvhOY4mdQE0qYsgCIKwF57n\nbN9cwjkVoXQ6jW9/+9uIx+MoFov467/+ayxZsgTf+ta3UC6XEY1Gccstt8Dtdpv6PJZIpxwyQRAE\nYQenbQ754YcfxsKFC3HXXXfhpz/9KX74wx/itttuw/r163HPPfdg/vz5eOCBB0x/HuWQCYIgCDvh\nT1eF3NzcjFgsBgBIJBJobm7Gtm3bcPnllwMA1q1bhy1btpj+PFLIBEEQhJ3MhBzylELWH/zgB/HQ\nQw/hyiuvRCKRwC9/+Ut85StfkUPUra2tGB0dNfVZ0WgIvIMHzwEd7eGpnM6sIRoN2X0Kpw10rcxD\n18o8dK3Mc7pdK7fLgUKpbOt5T0khP/roo+ju7sYdd9yB/fv34zvf+U7N70ULifHR0STy+RJ4nsPo\naHIqpzMriEZDdH1MQtfKPHStzEPXyjyn47USRRGlknDKz1tP4U8pZL1jxw5cfPHFAIDly5djZGQE\nPp8PuVwOADA8PIz29nbTn1cWRApXEwRBELbh4O0PWU9JIc+fPx87d+4EAPT39yMQCOCiiy7Ck08+\nCQB46qmnsHbtWtOfJ4giVVgTBEEQtsFx9hd1TSlkfcMNN+A73/kOPvOZz6BUKuHmm2/G4sWL8e1v\nfxv33nsvuru7ce2115r+PEEQqQeZIAiCsA0Hb/8s6ykp5EAggJ/+9KcNP9+wYcOUToJC1gRBEISd\n8DwH8XQMWU83AilkgiAIwkZ4HqdnDnm6KQuUQyYIgiDswzEDcsgzQiELIuWQCYIgCPvgeQ4iYOs8\n6xmhkMlDJgiCIOyE6SA7veQZoZAph0wQBEHYCUcKWUIgD5kgCIKwEUclbWpnYdfMUMgiecgEQRCE\nfTAdZGX087Sfg21HVlCmwSAEQRCEjTCFTB4yhawJgiAIG6Girgo0qYsgCIKwE6aD7GxFtl0hC6II\nUQR5yARBEIRtVIu6BNvOwX6FXDFHyEMmCIIg7ILankAKmSAIgrAfBxV1Vb+8g6qsCYIgCJugHDKq\nc0PJQyYIgiDsgrXeUsgapJAJgiAI+6C2J1S/PFVZEwRBEHZBg0FQ/fLkIRMEQRB2IXvIs3l0pkBF\nXQRBEITNcJRDBspU1EUQBEHYDLU9gXLIBEEQhP3wFLKmHDJBEARhP1RlDWp7IgiCIOyH5yhkXZ3U\nRQqZIAiCsAmePGTFpC6qsiYIgiBsgkLWoJA1QRAEYT9MBc3qoi6qsiYIgiDshiZ1gaqsCYIgCPuh\nHDLIQyYIgiDsh3LIIA+ZIAiCsB+57YlyyDTLmiAIgrAPClmDPGSCIAjCfihkjWqJOeWQCYIgCLvg\naben6pfnSCETBEEQNiG3Pc3mHHKZcsgEQRCEzVDIGjSpiyAIgrAfKupCNTxAOWSCIAjCLmi3J5CH\nTBAEQdiP7CFTDpk8ZIIgCMI+qjlk+87BdoVMHjJBEARhN5RDBs2yJgiCIOyHcsigSV0EQRCE/VDb\nE6oJdJ76kAmCIAiboKIuUMiaIAiCsB95Utds9pApZE0QBEHYDRV1gaqsCYIgCPth45vtDFk7pyr4\n2GOP4fbbb4fT6cTXv/51nHHGGfjWt76FcrmMaDSKW265BW632/BzqA+ZIAiCsJvT1kOenJzEz3/+\nc9xzzz34xS9+gWeffRa33XYb1q9fj3vuuQfz58/HAw88YOqzZA+ZiroIgiAImzhtc8hbtmzBBRdc\ngGAwiPb2dvzgBz/Atm3bcPnllwMA1q1bhy1btpj6LJplTRAEQdjNTGh7mlLI+uTJk8jlcvjyl7+M\nRCKBm266CdlsVg5Rt7a2YnR01NRnud3SKbS1BRGNhqZyOrMGuj7moWtlHrpW5qFrZZ7T7Vr5s0UA\ngNPlsO3cp5xDjsVi+NnPfoaBgQF89rOfhahIhIsWkuLpTAEAEI9l4CEnWZNoNITR0aTdp3FaQNfK\nPHStzEPXyjyn47XKFUoAgGyueErPXU/ZTylk3drainPPPRdOpxPz5s1DIBBAIBBALpcDAAwPD6O9\nvd3UZ4mUQyYIgiBsZiaErKekkC+++GJs3boVgiBgcnISmUwGF154IZ588kkAwFNPPYW1a9ea+iyW\nQ6a2J4IgCMIuZsKkrimFrDs6OvD+978fn/zkJwEA3/3ud7Fy5Up8+9vfxr333ovu7m5ce+21pj6L\nJnURBEEQdsOitKddURcAfOpTn8KnPvWpmp9t2LDB8ufQpC6CIAjCbjiOA8edhm1P0wl5yARBEMRM\nwMFzp18ZpU0JAAAgAElEQVQOeTohD5kgCIKYCfA8R7s9AeQhEwRBEPbCc9zsDlmTh0wQBEHMBGZ9\nyJpmWRMEQRAzASlkbePx7Tu0hCCK4Dipwo0gCIIg7ILnZ3nIWhBEyh8TBEEQtsNzHARBsO/4th25\nQlkQKX9MEARB2A7lkMlDJgiCIGYAsz6HXBZFKugiCIIgbMdBOWTykAmCIAj7kXLIs1ghUw6ZIAiC\nmAlQlTV5yARBEMQMYNaPziQPmSAIgpgJzPqQtUBFXQRBEMQMgNqeyEMmCIIgZgA8KWTKIRMEQRD2\n4+A5iIBteWTbFTLlkAmCIIiZAFNFdnnJtitk8pAJgiCImQDPSyrRrtYn2xUyecgEQRDETIA5h7Pb\nQ6Yqa4IgCMJmmCqalTlkQRAhAuQhEwRBELbDPORZGbJmX5pyyARBEITdMOdQnI0KmYUFOFLIBEEQ\nhM3Mbg+5LAAA5ZAJgiAI22FTI2dlURf70pRDJgiCIOyG6aLybCzqohwyQRAEMVOY1W1PZfKQCYIg\niBkCN6sVcpk8ZIIgCGJmwOqZZmdRlyAVdZGHTBAEQdgN00WzdjAIQB4yQRAEYT/VHLI9x58hOWTb\nJ3gSBEEQsxx+NueQ5bYncpAJgiAIm+HlHLI9LvIM8ZBJIxMEQRD2Ioes7XGQ7VbIlUldpJAJgiAI\nm5nVIWvykAmCIIiZAj+7Z1lTlTVBEAQxM6BZ1qheBIIgCIKwC8ds7kOmHDJBEAQxU6AcMiiHTBAE\nQdjP7M4hy5O6aDAIQRAEYS+zOmRNg0EIgiCImcKsLuqikDVBEAQxU2DB2lkZshao7YkgCIKYIczy\noi7afpEgCIKYGbB6plmZQ6aQ9emLKIp4degNZIpZu0+FmCLD6RHsGuq1+zSIGchYdhx7x/fbfRq6\nFMpFbB18DWWhPG2fyVTRaRmyzuVyuOKKK/DQQw9hcHAQN954I9avX49vfOMbKBQKhvJl2g/5tGX/\nxEHcue93eLF/i92nQkyRh/o24l9e/BmypZzdp0LMMB46uBH/uXMDMsWM3aeiycsD23BX733YPT59\nRuVpHbL+z//8T0QiEQDAbbfdhvXr1+Oee+7B/Pnz8cADDxjKk4d8+jKQHgIATOYmbT4TYqokCkmU\nRQEjmVG7T4WYYQxlRiBCRKKQtPtUNDmZGgAAxPLxafvM07bt6dChQ+jr68Oll14KANi2bRsuv/xy\nAMC6deuwZYux5ySU2aQu6kM+3RiuLOLJQsrmMzn92Tm6F3848vSUZTcefgriFBYQ5hkPn8YK+elj\nz2P70I4pyT5z/IUpyz57/MUpy75T7B7bh0f6/mD52RBEAePZCQBAspA+Fac2LQymhgEA6Wn04u32\nkJ1TFfzRj36E733ve3jkkUcAANlsFm63GwDQ2tqK0VHjl5x5yM1NPkSjoameyqxhJl2jyd3SC5sV\nszPqvBgz8Zy0eGHnS3hr/DA+fs5VCHmC1mR3bcZbY4fw0ZWXo8XfZEk2L+QBACkkTqvrxSgLZTy6\n6QlE/S344Mr3WZIVRAGPbnoCzb6IJdloNARRFPHo80+gyRO2fNx3ClEU8fD2xzGcGsV1q9+PFp/5\nZ2MsPYGSKOVlHX5hys/GqXymBFHAUEZSyIKzOG3HapmUjFSvz23LOzElhfzII49g9erVmDt3rurv\nzVpkLCyQTOYwOjpzQyMzgWg0NKOu0cm4FLKeSMdm1HkBM+9aGTGZTQAA9p88hgXheZZkYxlJ9s1j\nb2Fl2wpLspmCVJB3ZOzkaXW9GJO5GERRxEh6HCcGx+B1ekzLpoppyRPMTOJw/yBCbmNDiD1X2VIW\nZaGM8ax52XeaY4kTGE5JTtHRwSGUQw7Tsgcmj8n/7h8bw6jH+rNxqt/Bsew48mWpTmksMX1rUDKZ\nrfz/qdNJeop+SrHi559/Hs8++yw++clP4v7778d//Md/wO/3I5erhMCGh9He3m74OX9K2y+OZSfw\nj6/8K/ZPHLTluL3jByzLTuQm8U+v/Cv2jb9lSS5bysm5pZmcY3on+f2hP+LWHb+YUug4VQkLjmXG\npyx7PNlvSa5YLspekJ0ha1EU8X/f+BUeOrjRsmwsn5D/PZwZsSSbUqRaTiYHLMkqQ6QnLF73d4rX\nR3bK/7b6jo5mq89humg9ZP3M8Rfw/z/9rygKJcuyZhlMD8v/PiUh69Mph3zrrbfiwQcfxH333Yfr\nr78eX/3qV3HhhRfiySefBAA89dRTWLt2reHn/CkVde0Z78V4bgJvTfa9o8fdNbYX47kJ9E5aV8j7\nJw5iLDeB3glrssoioIJQRK6Ut3zsPzW2De3Awdhhy4tDWSgjU5Ks8tFK3m4qslYVQ0ZRWT2aGYMg\nCpbkp4vB9DD2Tx7Enim02MQVxTwDqSFLssraB6vXbqYrZEEUsGN4l/zfCYt1HmOK5zA1hRzy68M7\ncWji2CktFlTe79QUjAYt/mQ2l7jpppvwyCOPYP369YjFYrj22msNZeS2pxm0H/JYdmJKixN7MVPv\ncJHT4bgUXkrkrXuqrFLaqgXNPCoO0n2zs7BLCh1aU2TTTaqQxmQ+BsB6xWe6VF3cx7LWPORMKQsR\n0jtkVTHkStX+8YJQRFzhbb6T7K8Yg6mi9WdI6SGzZ9ksScUifjxl7dopFcB0K2RlQdVUOZo4jsl8\nDBG3FBpNvg0POWnxvgiiIHuvpzLywu63g3NMr4ds8yzrKRd1MW666Sb53xs2bLAkO9MmdR2OH8NP\nXv85PnPmJ3FB17ssybIXMzmN1poRoijicOwoACA+hdAxq1K0qsyZ5Ts31IPjyZNIFJKI+lstH386\neHlgO+478Ai+/e6vY26ox5ZzOKFY0GP5OOaEuk3LKj2QUYsKWWkIxfJxJAsp0/nMbFnykDlwECFi\nODOKZq+1orDpoLeS4skUsxBEATxn3kdQGj/KEKYZUjPQQ57ITeLOvb/DofhR3LT6L7C8ZemUPuf1\nYSlcvbbnQmw88qRlg3ssOw4H50BZLFtWduPZSRSFIgBgOH3qFPJgehhu3oWov83ye6OH40/FQ54K\nwgzbfvHg5CEAwEBq0JJcsVyUF4R30kOeyMUQL0heQmIKHs5gxcq0qsyZ5bukaSEA6xb4dML6Jfti\nR2w7B+WibNXTVHpbVj1klt9zco6G8zCCtTx1hzsA2JNHLpaLOBg7DAAQIVpe/JmH7OQclhUyM2bc\nvAtj2XFLE+fYPePAYSw3MS3DM3aO7sG/bL8Vh+JHK/+9d0qfI4gCdozsQsDlx7s7zwVgLYIliiLG\nsuOI+tvgdrgtr2eDikjFqXqmykIZw+kRdAU6EXIFUSgXUCwXp+WzmXNoUwbH5tGZ5ZmVQ2YLmlWL\nciA9JIe538nw7ZHKywtYV6qpYlqWSRSsKZHhzCjcvEv2SM3kqHonDlheNM3AFkM7c3nKY1sNWSsV\ncryQQMHCwsKiMYsqhtFUFPKSlgUA7FHIh+JHZW8KsF5AxHLIiyILEMvHLSlGdu2WNi8GUB0yYQZm\nOCwIz7UsW48gCrjvwKP4r93/jaJQxA3LPgavwyOH8q3SFzuCRCGJ1dGzEfGEAVjLIadLGWRLOUR9\nLQi6AkhZNDYGFO/4SPbUPFOj2XGUxDK6Ah0IuPwAalM/bwcWsi6fTkVd08VMG53JFjSrXo6yStNq\nzuXtcDgh5Y/9Th+ypaylxZyFqwFpcTYrK4gCRjJjaPdHEXGzF17fGCgJJfxi5wY8cOAx0+dnlvQM\nUMjK+x+z6iFXQtZehxeANS+ZKbAzK6FNawpZ8ggXt8wHAFumdbGOBGbYWR1CES8kEHQFMDcsyQ9Y\nMPiY4byi5QwA1q4dM6KWtywDYL3CXckbI7vwwsmX0RXowLfe9XVcMucCLG1ejJHsWE1xlVleH34T\nAHBe+2q4eCf8Tp8lB4M9f22+1opCTlnqHGAest/lw0hmdEpdB0aw/HFXsAMBVwDA9FVay5O6BHtc\nZNrtqUKmmMFYTnoBrHrIrCgk5AoiXy6gUDae4z0dHI4dhZN34szKwmDlvJm36uKdFVlziiSeT6Ao\nFNHub5PzlUYh63Qxg5JYPiUtUswyHkwPv2PXXUm2lMNIdgzzQ5K3FCtY9ZAlxcC8LSsKmSnzucEe\nBF2BKXnIrf5mRNwhWzzk/RMH4OQcWB1dCcC6hxzLxxHxhNEd6ARgLY+cLKTAgZPztFauHVv8pyKr\nRBRFPHP8BXDg8JcrP4vuoPQ92Pts1UsuC2W8OboHIXcQS5sXAQBC7pCllBJrvWMKuSiU5H5fMwyk\nhuB2uLGifRmypdwpcVAGKxXW3YFOBJmHPE21O9VJXdPycdaPb89hJVgOeQbo45qwU9xikdOJZD8c\nnEN+Cd6JcXO5Uh4nU4OYF5qDFm8zAGuhZ2bJLmmSztnsd2YLd4c/irCHVXHqv3RsAcuUpn9nKPbZ\nIkT0W2x9mQ6Yd7y0eRE8DvcUcsiV8GdEGgjCjEJzstJzFnQHMDfUYymfmaso5IDLh3Z/FJO5mKUI\ny5sju/Fw3+NT9oCShRROpAawuGkhWirFZFYKIrOlHPLlApo8EXQFpDz4oIVK61QxhYDLj3Z/G7wO\njzWFXHm/54fnwuvw4oTFPmbGwdhhHE/245zoWWj3R+Wfs4hHr8WZBgcmDyFVTGNN+yq5OC7sDiJd\nzJjeEYm13kV9rQi6mfdp7r6UhTJGMqPoCnSgJ1SpTTgFhV3M8JJC1tI5Wg2tazGri7pmUh8yCzvx\nHI9cOWfa2yoLZfSnBtEd7ESTR9poYyotHFY5ljgBESIWRebLitGKITGQHgIHDssqObS4SWXOFHK7\nPwq/0wee4w1zVEwRn4qdYzI294SyCuu5oR40eSLWFXLl2i2sTOiy5CEzhewKyGFfs/lM5iH7KwpZ\nhIjR7JjpY286uRnPHH8Bx5InTMsoYeHqM1uWIeSSIi1Wel5Z/jjiDqMz0AEOnKVeZFaRznM85oS6\nMZwZNe0JpksZeB1euHgn5oa6MZIZnVIv/rPHXwAAXD6vdvxm1NeGVm8z3prss7S1IHv+mYcNAGHW\n+mRyTaoPWQPm+3xHs2MoiWV0BzrRVVHIpyIVMpAehs/pRZMnUs0hT5OHzHGn4WCQ6UKYQTlk9jAv\nDEs5NbPKbSgzgpJQwtxgjyKEe+oVMus/XhRZIPcbmlWqoihiMDWMqK8Vrd4WAOZbn5QeMs/xCLmC\nhqFo9rIUhOK0Tu8RRAHZUg4Bp/RS2qKQk1WFHPFEkCqmLX1HttjNr4SsrbRwMAWmVMhm85lKhdxR\n8c6shK2ZR8JabKzChtEsb1mGgNv6ospy9U2eMDwON1p9LaZD1mygCjME5oZ6KhEWc8ZMupiRQ6VV\nWWudGYPpYewZ349FkQVYFJlf8zuO47C8ZRmypSyOJ0+a/kz2LClb39i/zaaLRrPj4MCh1dtsWSEP\nKDzX7tCpqd4vCiWMZsfQFegEx3HVcyyQh/y2mUke8onkAHxOr/xymFVuygWZveBWe5H7U4P4zuZ/\nxp4x8/t6Hq5UWC+KzEeYFVeZVKqJQgrpUgZdwU65EtPs9x1ReMgAEPYY56jSipYSK+0lRrDBGAsj\n8+HknTX9wGbYOboH39n8zziWmJqXB0j33+NwI+prRRO7lha85FQxDa/Dg5A7iIDLb9lD9jjccDlc\nmBvskc/HDGoK2Yo3w5TnjpFdlgfpiKKI/RMHEHIF0RPsVLw35g1ZVs3OolJdgQ6kimlTxnC94mLX\nzowxI4oiUsW0HCplhpBVY/CZind8RZ13zGBerpUpeix9E3AG5J/JHrJJJ2EsO45mbxOcvFMOWZuN\nXAwocrunqp1uJDMKQRTQXUlTVKusra25veMH8Peb/3eDIVVte5qNCrk8M/qQc6U8RjKjmBPsVrQK\nmFNuSoVcfYCtechPHn0O8ULC9MsniAKOJI4h6mtFyB1ExGPNQ2a5tq5Ah/zCWvGQI+4QfE6pKjjk\nDhqOz1R6Ptlpak8AquHqsDuInkAXBlJDKFnwTvtiRxAvJPCbffdayp8yCuUChtIjmBPsAc/xsnKw\n0vqUKqRlK7/N14rx7KRpBZcqphGsKLM2Xwt8TvP5TFZl7XN5ZePK7OIpitWe4Vg+LkdrzDKYHka8\nkMTylqXgOX5KlbLMQ2bvKyvsMhO2Zsop6K56yIA5pVoQiigJJdmrn4pCjucTeHXoDbT727Cy7UzV\nvzmjeTE4cJbyyMzQYEoKkIq6AHOtT4VyEfFCAm0+acgPey7NGkpybjfYgbAniIDTP+2tT+z+dlXu\nd2AKHrIoivj94SeRLKRwqG5+gdz2NCsVsk6V9VB6xFL+5O3I9qcGIUKshB2teTknkv3gOR49wa4p\nhazHshPYMbKr8m9z3tFQegTZUg6LIgsAwLKHzNoGugMdljzkQrmIyVyspgAl7GIvvPaxlcVc6Wn0\nkNkC7nf5MTfUjbJYtlRpyxaw4cwIfn/4j5aPz56beZVFufrsmFPIkmJLI1Ax5KK+VpTFMiZzxvLM\nU2OLJsdxmBM0n8/MlXLwONxw8A60epvh4BymFXKunIMgCrJRZjVszQxP5gW6HS7LQyjidR5yt1zY\nZXz/mYIJVa5dhz8KF+8ypVSZt8i8UFnWQnTm+ZMvoyyWcfncSzQnk/ldfiwIz8XRxHHZeDIiXcyA\n53j5vgCSsQoASRNrA1t/oj4pjcWMPbOG0mB6CD6nT26HbPdHMZadmPI6roa8dgWn7iH3xQ7LtQ/1\n67zjdNxcYrrQ6kN+qX8rfrDtx9g69Jrlz9w8BVmllyt7jCY8ZEEUcCI1gE5/O9wO15RCb5tOvCTP\nIza7uYAyXA0AXqdHqvA16yGnWK6nEx6HG16Hx9T3Hc2OQYRYq5BNVForX+jMNHrI1RCdf0qeClPI\nrd4WbDqxWZ7UZhblcwNA4SGbuw+5ch4lsSwrBuaZmDHM8uU8SkJJjsqw8zCbz8yWcvA5fQAAB+9A\n1Ndqum+UeSNnt56JoCuAN0atha3ZdK4zWpbIPwtZHEJRzSFXQtaVliEzM63rPWQH78CcYBcG08OG\n+X+28LMcMs/xmBPslmRNRlm2DL6KoCuA8zvP0/275S3LIIgCDph8LtPFNAJOv1yYBFRD1omieYXc\n5mUesvQdzRhKxXIRI5kxdAc65ON3+KMQRMHyBDo9qtE96X57HG44eael6Mozx1+U/13/rlbbnmah\nQq62PVUfoJHMGB46+HsA1rdFG82M48E+aRs3Ky0wbGGdF+qRrTszHvJoZgyFckFekIMWPeR0MYNX\nBrajyRNBT7AL4zlzG1soC7oYEXfYtIc8mB6Cg3Og3d8GQFKqZr6vsqCLYaYXuUYhT3MOGZCs5Ckp\n5EIaLt6Jz5+1HgBwV+99cjuQGRoVsvTsmA1Zp+UqaRZ2Nq+QmfJiHrLyPMxcg2wpB6/Ck+rwR033\njTKlFHIHsTp6NpKFFA5OHjaUY8Tycbh5l6xMASn0aGUIRSwfh5N3yh5Se6XI0EzrE1MwyuKnuaEe\naWMEg3UjXTFGAnXXXRAFU8ZAsVxEspDCnGA3XA6X7t9W88jmwtbpYkaOtjCsRO1khVyZS8/WMzOG\n0lBmFCJE2TACMKViQSMGU8MIugLy9+I4DgGnX25FM5RPD2PPeK/8rtS/q1TUBWWYQMBdvfehUBmn\nZ6XiVBAF/HfvvXK70oSFfs4TqX64eZc0fcpj3kOuX5A9DrcUejNZ1PVS/1YUhCLWzb0Ynf52lISS\nKcV4JH4MPqcXnYHqntNhTwipYtowPCSKIgbSQ2j3t8FZGQoScYdNyY6oKOSwy7iKM1PjIZ+akHV3\noBM8x1v2kAOuABZG5uH989dhPDeJh/rM78t7ItkPF++UrwdTMGbTHaxfneUjoxWFbOa5Z611SoU8\nz6RCFkUR2XIOPkdVIbfLhV3GrU9phTFwXsdqAMDrI28ayjES+aTsuTGCbmtDKOL5OCLusOyNuXgn\n2n1tGEwPGyp1VnTJIlqAeWMmrZKntVLhnpCNAe1N6hkLKn3OZmpLBFFAppSVOw4YcpW1CWOdReja\nKiFrn9MLnuNNtXEOpqsFXYz2wPQq5Hy5gLHcRM0xAOnZMTs687mKd3z1gsvhc/oQq4sqMt9wdnvI\nlbN49viLOBw/inPbVyHoClgKdciy0ZXwOrwYz06akmMbQ8wJdYPneLgdbngdXlOL6vFUrUIGpJfc\njDVaLBfx/MnN8Dq8uKj7fNk7MlqM8+UCRrJjmBeaU5N/irjDECEaejgTuRjy5ULNQ202TD9cV2EN\nVEPWekUjypdlOrdKk0PWLj9cDhe6Ah04mRo0nbNKFdNyuPjqhVegw9+OVwZeNVXgVRRKGEgPoyfY\nDQcvbe4QcgXBgTPtIafkXGa1MAsw6SGzlieFR9Tuj8LFO9Gf1g9ZF4RiTQ6YyQJSPt3ssQMuP5Y0\nLUTYHcKbI3tMXXdBFJAsphoUUlAu7DI2ZstCGYlCSo5IMLqCnciWcobXPykrxeq16wl2AQD6Dbxc\nOTJRkyqQdvc6aUIhJyuh47CJXbkcvANLmxdiLDtu2IaZKUodB8E6D9nJOxFw+pEwoVTHciyHLK1F\nPMcj4PSbqrJWDutgtPukCNx09SKPVozFDoUjAkgpq2wpZ/j8xfMJbB/agXZfG1a2rUCTJ9xQ78Fx\nHHiOm62zrKtFXf2pQWw8/CRC7iA+texjiPpaMZ6bNPWSD6SGqrJnfBytvmZM5CZNhb/YxhBKpRrx\nhEx6yFJIfU7lZQYkizRVMA69vTr8BpKFFNb2vBc+p9d0uJI9QM2e2q3yqsNB9A2J+hwMANOV5cOZ\nUTg4qQiIETKx52r6FHvIAUVPaFEoYiBpXNhTKBdRKBfksJyTd2JxZD5EiJjMGRtzg+khlMVyzXPj\n4B0Iu4Omc8ipYm34M+IOw1XZfchYttqDzOA5Hq2+Voxlx3WfP7nCui5kDZjzZpiBFXQFwHM8zm1f\nhXQpg/2TxqHVTEnaZrFeIVnpeU0WUxAh1oS8gaoyMJppnVQJWbN+/Fgupisre8gKT7TT3w4OnLmC\nssqx2ftqBHtPRwwMJbXzYoTcQVPjM8ey4wi4/HJtASAZHmbuSbX6uaqQo/42cOAwbCLqYga2PrG0\nIsPsBhMvnHwFJbGMy+ZdIndFsIlvSniem6VtT5UvzQH4be99KIllfHr5JxB0B9Dma4UgCpg0sHZF\nUcRdvffWyLZ4m5Ar500t/izMxHoRAcljTBXTui00oijiRLJfGr2nWNhC7gBKYhm5snYuUhRFPHv8\nRTg4By6dexGAamWjkYfMLOX6F9rsRg9s0WBVikDVQ9ZT5qIoYiQziqivVfYIAeXgAW0LPFPMyAvu\ndE7rYgVi/soCwpTjkUnjvmK10CMbQTpuQiFX0xW1ex9HPBHECwmTxVG1nhrHcWjztWA0O2Eor6aQ\nAek5ypZyuosTy5OrKWQz3ky6UHvt3tVxDgBz1dYsdFq/b7MVhcw84Eidh1ydaW3k5abh5Bzyhh5A\nJcrCOzGZN1DIpUYP2eVwIepvNRUul7+/y9hDBpRepr5SS9UZp0pCJsZnCqKA8eyk7Bgwgq4AMqWs\noWM0mB5CyB2sua8u3olWb/O0echsbQt7aq9dwES/dL5cwEv9W2qK6SIaNR88P1tzyGURPMchV87h\neLIfy5uXYmXbCgDmC1wShRSOJ/txZssyWbalYu1OWFpYlR6ydKP0Qs+pYhrZUhZd/o6an7MCHd2q\n41IGQ5kRLG9ZKlv55r9vpf+yzko0o1QBxU4pCku22vqkrcxz5RyypRxaK4YDw+/0wcE5NC3wYrmI\nglBEtLKwnBoPWXohWQ718ORxQ9lkXbgYsKaQ2QKpvI6AlEcuCSVTofn68wek5yBXzhnKq4WsmTyg\n/xxlZIVc6wkFXQEMpU2ErEu1570gPA8Bl7+hp1MN2UNUySED5oZQ1PcgM7oqoUyj75AspBB0B2uq\nkTmOQ7OnybDlLFVoNOQAyRjIlLKGnQ4Jje+vRYfJPKxcIFj3PCiPpZfOmszFURbLcriaIacSdA28\nPMZzkzVRN0Z7IIpkMTUtxZyaz46TTXrTPsej8ePIlLI4v+s8uCvFdNWaj8bCrlmbQ+Z5Tg4ZKPNK\nZgtc2MVUFhq1WvR0nJyjZmENmxhFKYdP6haFalWj9sLCrORmbzXsHPGE4eSdxiHrgoaHbEKpApKH\n7OKdNZZwdTiI3vdVfxl4jkfIHdT0kNmL3OJtAs/x0+ohp4sZuHin/IL1BLvBgTPlIVcX1uoCxowN\nM4Yci1TUG0ZWKq2TKoVZZp97LQ9ZVsgZbXk2pUsZ2QEk42IsO2E4xz1dd+14jkd3oBPjuUnDoixm\nuDXmkFlFr3kPuT5k3eZrBc/xhsorWUzJtQNKmrxNSBZTuu1LylY7JUwZKbc1VT+2+vurhdmhLSmN\n8wLM1YgoZ1grkSutddazoUwl6lZnnALTW2mdKKhHV5iHrGc0sD7xBZWZ8YDyXa1rfZrNOWSHQiF7\nFG0AZj1GtZeTKeQJg77eslDGQGVjCGUYtjrgQfsBZp5ouG5BDsmhN21rNC57udWXkud4tHlbDHuR\nExqKwIxSFUQBQ+lhdPrbawvCTChzLesUqOao1MJ1zDIOuPwIOP2WPWRRFPGT13+Oh/seV/nsTI1C\n9Tjc6PBHcXTyhPmQr8KjkA05E/3gLFJRfz0izOo20RPOvBplcZHZ5z5V1zLFqCp07e+QU8khA5JS\nESEaephVz77qYXcHmay+QpLDjm8jZB2v60FmOE2ESNn2qEGVoqrmyufppcnSxTTclXGlSszuOKUV\nstci6Aog4PIbhn31PGQzrU+aCpn1IuusZwOpxoIuxlTGsmqhtQYxI0Sv9UnZ2srQmqw3az3ksuwh\nS5OF3A63/DvzCrkxfNXiM+chx/IJlMRyTdUwoLQotRfVuOwh14fejB9+LaXa5mtFtpTVDb3IynwK\nHnKikERRKDV834iJ76tlnQLS9SoIRfk+KmELhd/lh9/ls1xlnSqmcTh+DHvH9zd+dikj548ZZitt\n1bMV6hcAACAASURBVDzMiCcMnuMxYVDYA0jX2e/0NSzMVjzkVCENR10u07RCLqQbpjKZlc+q5JCB\nal2BUXGSNH/bK7fNAVUP0aigKqFR1GRl7GzVCA83/K7DH0WqmNZ8ztR6kBksYhXTySOnihlVL7Q7\naP77c+AaIht6dPijGMtN6Na01Bc4KjEzHpcZIS11xaLVyIX2ezteedbq1xV27sA0e8h1Rmh1m0gd\nDznZD5/TKxfvAcoccl3r02xWyA6ek0NkHodH/l3YHYTb4TYdslZay2ZzgayAo75iuTocREe55dU9\nJDMha6ZU6xelqInFlL1U9Z653+mDk3fqvnSsFaxFUSUNSLlEJ+/U/74ang2gX9iVVgzv8Fc85Hrv\ntSyUNb2yyYpyrM/tlYWytNNT3QJkdgFIFxoVMs/xaPY0mephT+QTCKsoBCvTupLFNIKu2slKZov7\nUsUUgq5AjSwgefkcOF15LYVcVaoGwzGKmYbrbtZDlCucXeptT2aGUMhGuLvx+rcbeGRqtQMM2UPW\nySOni2lVL7Td1wYH5zC8dsliEkF3QHNkphrt8sQr7eeyWqQ4NQ85oWHom5nWxdbZ1rp1hZ07MD0K\nOVlIwef0NhjBAdmLV19zc6UcRjJjmBvsqXlftOYGOHhuFhd11YSsqx4yx3Fo87YYtnAot2FjBJx+\neBxuw1wga3Fo8dYpZI8VD7k+ZG08PlPPQwb0F+N4IQGf0yvnTRkcxyHsDumGSscriqbVV/vicByH\niFu/1UsvZK23o4yyHcPv8kEQhQZP+sX+LfjBth+rjnxkRhMrKmOwf9crBrO9j0mVcDEgLSrxQlI3\nj1gsF5EpZWtSDowmC/Os04pdgxgtFYVqHLLOqHpZTt6JZm+TSQ+5Lrogtw1pKxVRFJEuNR5bniVt\nkEPVirRYGUIRz8fl3vN6jBSAWssTo7miULQqrQulAgpCUdVDdvAOdPijGEwP607bS+RTpgu6GGaM\nTFMess74zLiGoV+d1qXtYIznJms2V1EScUvbY05HyDpRaBwoAyh72NWNuZOKvQrq5XiOb3hXeY4z\nPTFuurG3qEsUwXOQFbIyZA1IHmO+XNB9GNRyyBzHodXbYqiQWZimuU4hhy15yOpFXXoWpZaHbGYw\nhDTlqNEzACArVa0FgV2PFm9Lw+/C7rCurFYxjiSrXTTCcsh+lw/+ykJWn0ceqChitVGpSm9lUhFK\nlkPhdYuj1arUBoVYMVYmdMKW1WhB432ImPSQS0IJ2VKuIZfp5J1oMVCoUnQgqxn2bPO1Il5IaA44\n0fKQAy4/Iu6wrlLNlwvSbkd1C7/f5UeTJ2LsIRaS0vAdp6fm59KuT37TRV1qiz9grLyq1el6HrL6\nvU9qVLYzugIdKJQLmvKFchG5cs50yxPDTB42VUyDA9eQwgHMTetKFBJw8U6VZ8I4tz+Rm0STJ1JT\nh8PgOA7t/ihGs2OWt+lUUhbKSBczqoaUkYes1kkDSM9cxB1ueFdnr4dcF7KuV8hmPMZYIQGf09cg\n2+JtRraU0y23Z7nCprqQtc/phYt3GnrIHLgGD0veskxPIecrspoFOerftyiUkC5lVD0zAAh7whBE\nQdNSZCHr1joDBJCiAnqyZkLWaq1PytYef6UIqH7HJ/ZCqBlQE/nqz5TFNspQuBKzIetkQVrA6uXl\ngkAdY06rfgAAvA4P3A63YQ6ZXRe1al9JoSY1q53liloNxRA1MOzkKmuHt+F3XYEOTOZjNdEItfNW\nC412BToQy8d1dydKFFLyuNV6gq6AYdsTG+RQH5liGCmv+p2elDDDXMsYS+bVW54Y3QYbXCQ1OiSM\nMArDA9J98bt8qqFwMyHreMXQr0+BhAza0di43/ooo5IOfxRFoaRpqJghVUxDhKjqEHgdUnRFa+3S\nUsiAFNGKFxI1xgI/W3PIQlmQQtZCY8gaMFegEs/HVYs7zOSR5Ryyt9balsK/YUMPOewONrwADt4B\nv9OnH7IuJBB0BxosyhZfi264MqExFIQRMWhvqHrIjbmesMGmGolCCk7O0RDmlGR1cshyO4YPgYps\n/Z7ITHmpKcFYjYdc/b3aYA9ACsNGvGHDEFmqmFZdwFrkCn3t5yaRb6ySZ3AcVxnJp+8hp3RyftXn\nXj1nmNJRKrXy6s9RrsyiFo0KmSkVrcKu6oYYjUqpy2ALRK2xmQwzQyjkmhG3uoccdofgdXimFLL2\nOb3wOrw1z5wSFvVSu2eAcetTQufYerT5WisTr/Q9ZC1DwWh8JrsvagamkYc8mYtDhFhTLFXPdOSR\n9fq3OU4yrLW2YDyR7Ifb4ZY301ES8USk768wOPjZ7CHzPK8o6moMWQPaHmO+XEC2lFMNX7E8qV6B\nTiwXg4t3qeaEIp4QksWUaphFFEXEC0nVoh6Ajc/UK+pKqhakuHgnmjwRzYVYaygIw0ipTuQmEXIF\nG6IJQNXb06rSThakhbTeggaUm6CrhKxLSg+ZNfDXelDsfNWMJ2U+T+khK0Ph9XSHOjCRixn0k6Yb\nWoYAcz3s1V5w9fvQ5I4gVUzrbuVX3QKwcXE3eu7TKhXiSowUcqaYAweupoiSUVUq6l6eXq6STcoa\n0JDNFCtjMzUMSjNDKNRqRpRUQ6Tjqu+uHHbW8NKbvRHNHHLCyEM2KIqTPWSLOWQX70Srr0VTobGN\nJfQqt0PuoOaeyKliujLOVH1N8jo8mgqZ1aWoGfmM6ai01ovQAdL6wnbiUlIoFzGUGcGcYLdq9ECt\n5sPBcbBJH9uvkJV9yG7emocc1xihB5j1kONo9kZUlUzYLYV/1R7EXDmHolDUDB0HXUH5IW+QLeVQ\nKBc0F6WorxWxfFw1/6c1FIShp1QFUcBEblLOkdbDXka1PmZRFJEoJDUte/2iLukl8Tm9cn5LuSdy\nsVyUF2BVhZyLyy9STQ65pB067Q51QISoqdBYaF5tAWP59XEdQ07PQwYUvcg6XrKeUjV67qu7RGmF\nrPV7kXPlHDwOj+oCZdT6pOfZG7X+6LXOAeaGUMQ1pnQp6fBHURJKqu1rcnRB49o1e5qQLeVUt+Fk\nHnJQxYAHJCfAxbs0r51W244ZWDuX2mCdXCkHQRQ0DQVAekfTJfXxmfKQGx1DSeueTOhUWCvPHXh7\nvchJo2fH5ZfnpCsZSA9W9iroVpVT60We1W1PPMdpeshsupPWwlS/SbkSo1xgoVxEqphGi0f9QZKV\nm4pVqVWRyAi5gxAhquY05PyjhixbjNUUQkKn3UM6H+3hIIlCEiWxrPni6CnzXDmHklDStOz1xmem\nixn4nD4plF/xZpV5fWVV+GQuVvNCCaKAeCEhb95Rk0NWhMLr6Q5JSkXLItfaGQeQLGapF/lteMgm\nepGTb0MhM6WoHbI2ziHXF+8wOv36ldZ6HnKnQcha9nI0c8j6xTmA9pQuJXoeWbKQkrdJVYOlr9SG\ng8gesoYy5zkeXYF2DGVGVBWf1Y0llOh9p/pNStSQ88gqYevqkBv15zlQ2WBCrfJYbnnSMPSB6QlZ\n63V5ANJ3FyE21AydUNmrQIlaL/KsLeoSGiZ11b4kDt6BFk+TpqejNyCA5TS0coGs+b/Jq5WL0t6s\nQatnj6FXRGHkXen1Iid0iomkn2sPB9HLHwPK76umzNnLoL6QchxXGZ+pFrLOykqTLeLKKmvli1AW\nyzVeZTwvFVtEfW0IugI1O/FkdBah7rC+QlbbS5jh4B1o8kR0h4OoTVpTojUjV4laHzTDqBdZz0sF\npDx6wOWfkkL2Oj1o9TbrKGTt8/Y43Gj1tmiGu6v5W+3IEmCkkNm7p62QWa5QzSNLFlK6HmpzxUBX\nK0BK5VkOWdsT7Qp0oiSUNN5fa3OslegpNT0jiaE3HKQ6BlZjPXMFUBbLyKkM/tGabaDE43CjyROZ\nppC11rPD0mG1z45eQReg/q7y/Cxte6r2ITdO6mK0+VqRLKSQKzU+DHEdjzHg8sPNuzRD1mzBrR8K\nwojoeJuGHrLO+Ewj76rNr50/NDquXti5WmFtPSJQHfenvZCEK/Os6x/kdDEt545ZyFoZOWBGFav4\nVSrCatFdE5q9TZjMx+TPZ5+hlkPuCkmbDGiFyOQ9bTUUWqu3GfF8QjMHnMiz1h11paY1I7f2HLRb\naLxOr+5+4Cx8qFcc1FbZvrQ+hCeIAnI6ChmQlEqykFINU+rtKgRIIe9kMaVqjBpVGZvZYCKuY4Qz\n2v3S/a9XAKIoSntg61w32UNWUcgJHSOKoRe2NwrZ61EN+zbu+iQbSU7t89JrTZQ9ZI1rKhtKKvdl\nIjcJDpxuxAKQzj+WjxvOOtfC6NpVi89qo5JqexUoUXtXHZzkIduhlG0PWUttT1K+tN5DBqrhN7UQ\nrl74iuM4tPhaNBVytQdZw0PW8Ta1xlcy9MZnGnnIeuFGY888AA6c6jmPG3jIrElezUNmYS49yz7k\nDqFYNz6zUC6iqOhZ9at6yNJ9WBiZVznP6n2eVBhNzZ4mqe2r8sJpDfkHgPZAm+4mAymdgipAukbS\nvsjqXnK8kNC8f4CyF1nbQ9aaRc3Q2w/cqKiLyZfFcsN3yJcLECEaKGTtqVt6M5MlWe0qbaNIS1DH\nkGWMZsfhcbh1vzvzkOvvf7aUQ1ksa547UDXQ1Qq7qh6ytrzetUtWWiWtjM1k6IWs5XfBre0h60Xt\njDxk9rlq94X1ICvHqKrRrmNQmMEouhJQ8ZBLQgkDqSF0B7tUe6QB9Rwyz0s1RXY4yTNiMIhWHzKg\n34tsFL5q9TYjW8qq9kXGzHrIaiFcEzlkQH18ppGHrFdhGy8k4eJdqv2jgJTDCruDqh6yXHxRt32i\nUjbkCqp7yCYse7XxmfWtSXJRl8KKZVGORZH5NecJ1BpN1dyedN8ypQzcvEt1WpOTdyDqa8VwZlTV\nytXL3wL69QeCKCBVSGvee0BZuanjIcvelvoiqrcfeLLuumrJA43PUU5jpyclel6eniEE6FcaV4ua\n9KustcZnCqKAkewYOvxR1UJMhsfhRrOnqSFCojc2k1H1kFWuez4t7S7GNz5zjG6dmd7Sto/WxmYy\nWDuXWtTHKIUBGKTRZENfK+KnnkooCSXE8nHdcDWj6uHrb1ySLeVUx+gmCkn4nD64NBQ/++7K6Ntg\negQlsawZrgYkneNz+hBTrPNMIduRR7ZVIYsi5NGZDs6hamXp5VTj+Xhl+z+jhbXR2lWGQ9XQ2/Ep\nrrHTD0M3ZG1gjerl/xJ5yTPTW4yk8ZmNOy8Z5ZAByfNOqMgmDTwbQDn/W9GaVDe8w8k74Xa4VT3k\nRZEFAKqhdUDhIXubZMOJ3UtpEIK2Qmr3R5EtZVXzkUYeZovONozJQgoiRN3CnLA7ZLhJRaqYlovd\n1NAr7EoX0/A5vbpeiZa81thMJXozrbV2O6rKshGaah6ifh+u0QYTE7kYSiqbo6jBQqTKVFe11Uz7\nOW7S8ZCThRQCKvPDa+Uj8Dq8qt9fa/SjGaR2rjaMqEy8ShukYIBqmkA9ZJ2UJ6WpIYeD6xyMWL7S\ng6xT0MUwW9i1Ye89+NdXb214b5OFlO76o1YQaJQ/ZtTPDXBUFLIw20LWACpFXXnNqke9IQmx/9fe\nmQdGVd2L/3Nny8wkk32SEEhC2FdZBHGhKqK27lrcylPbp6211mrr6wOKWMuzoFLburbSgnWpViq1\nVVvbWq360xawgERA2RFCAiSEJJNkZpLZfn/cuXfuTGa5EwIzmPP5K5mZM/fMucv3fPduFwWW/IQ7\nzuIk7fSUHXBRAu0612xPaMJNVaBDz2402Y1ZaiuhxRPt/wsGg3KVoyS+M3lO+fiCvl4BGC3eo+SZ\nc+O6BdSxFmVsdMpHxCKQeM7KDviQZges+nk12pTdZIvSkNu6XRgkA0Pzq4AEGnJOobpxUh6UXT5P\nUg0xaVRqihKISiWzeO6OVAFdEKlr3BBOuYhHZ7ixRCKSWUo6w4IhGc4E902isplaKuxOJKS4BS4S\ndTtSKLc7MUiGBCbr+GUzFVIVoVDOpR6BrHym2RMxkSbr9KRgMZrJM+cmEMiJi28oSJLEoNxymjxH\nomIQ5LKZ3X0WyCD/pnjpXJ06LCbJfMhylS5HwueoWq0r5rykikvRoicX+UBHI1tbtuEL+tVyupC8\nbKZCPA05XsvFeBRY8vH4Paql1hDecGUi9SnjAlkpDJJIUCTyqSopMcmCO5LlIrd2t2Ez2RKa7mTz\nryOBhtxBrsme0Hyi+pDjPFgSte3TEvH/RbRNV3cHIUJJBQHED0aTc5DbUpqWEgV2KU3VkwV1DQrn\nrzZqHuLxoj9zzdE9kdu722VznMmKw5wXLZC9bZgNJvLMuaqG3OZtJxAM4A14UwoGiB/YFa/1ohY1\nFzlOhH6ixiCxVDkG0xPooTmOz0wJLkrkP4bEGq481p0w5SkyPn6ktidBL2QtZqMZp72Eg12H4gbp\nJdtImI1mnLZSGrsOx7G0dCRMeYLURSiUc1muU0OGaAGgx2QN8ia91dseNX9/0I/H5025EQI5sC0Y\nCkZde6nyaPWQSKjp0ZDVpjcxSoJcY8CVdKOQaKOkx+qmUGwtxGQwJc1Ffmv//1P/1pr8O3xhq1TS\nOfb2Idd3NGCQDKobIRGxfuQBa7KGsIYcTCyQlQd17IOlo0cuvJEs/SFSrSt+wYlE2rFCvsWBq8fV\n68Hi6nElNVnaTXJJxkRBXam0XKVjkTYwpNWbPBJSQREmWk21o6cTf9CfcierXJix6xUpm5lMqyqT\ntSrNnN1xBLLdZMMTLmQQCoVo73ap7oFiWxFHNZHBrd42CnPkwi1aH7Ii0FOZrCFR3mZygVyUU4CE\nFLfKW6LGILEoZjJll65F+f3JgosSCWRvQA5MSiUYCiz5mA1mtVet9tiQXCCD7At1+z1ReeI9AZ/c\n7SjFsStzy/HEjJXLM3alXLdkRSgOH6tA1lm6stBaiC/oi9K2lOpyqTRkgMpw3rz23B9LhLVCok1m\npNFKYjeE0WCUN7wxmr/H78UX9CcMFAWNbz/mvKQKFNVikAyU2UoTxnW0etvY0LRJdaVoTf6pArq0\nc1TO2a62vXzm2k+VY3BS5Qd6R1obBrLJWvEhJzJZg/xwOhoTcdrekzr9oUStuhQtYDx+D96AN6H/\nWKEgx4Ev3JVHoSfgw+P3JtWQDJIh/GCJFsjJ2vZpGRoOcNrbvk99rdUTrkqWYuyoouEAbDu6U31N\n3cmm8PUkKuzg6u5IWDZTwWK0UGorplGjVUVM1pEHhTbSusvnxh8KUBhey2JrEf5QAFdPB76gnw5f\np6oZF1jykZBo9bbpyrtMbrLuxGIwJ7zmlFzkeJYV3RpyuBDB/s7eAjlZHrRCon7gqToOKUiSRKmt\nmGbP0agHoCqQEwQGKsSry5yofnivsXm9S2h2+dwEQ8GkD1WQrUtdCYpQpGey7p2L3KEjXQwi7Vi1\nZms9ke0KtflyxoD2/j2WHGQFJZ2rt0B2YzNZE8YjKFTkltHiORrVtCRVURBIbLKOVOlKXMc6ev5O\nugM9cc3m79R/QDAU5PJhX8IgGaI05FTR+SBvMCUkOn1deP3dPP/JKgCuHnlZynnFZkWoPuSBqCFL\nBllQxZbN1KJEnGp9J+1JqnQp5JlzMRvMHI3xo6XyHyuotaE1O32XTg0pz5zbqypOsrZ9Wmrzq5GQ\n2KO5oZWC96k05KH5VViNVj49ukN9rUXnjRMvQjYUCtHh09fDtTK3gi6fW/3d8cpbaiOt29TSpwXh\n+UUsGsrvVTZNRoORgpx8WrvbdQnkPHMuNpMtgcnanTSwB2TrSnu3C39MLnKq8qUKQ8Kl+urjtJRM\nlQcNifuBK4JBT/nFUlsJ3oA3StPTE2UN8Xsjq6bRFJuBeGP1aqd5Zjv+BEUomtzNFOYUJI2DUCiy\nFmI2mFQhHggGVOtNKqGqpj7FafepR0MekleJxWCOun/7Wsdai7LJOBRHQ9ZnSq8gRCgqijlVkCnI\nNQKMkrHX86zFexQJKWHqaCyJNskev4d/Na6jwOLgjMrpOG2lHNS4PFIVBYFI+84un5s/7v4LR7xH\nOb/6HDVYNBmxWRFheTwwBbLBECREiBxT4ptscHjH/Zlrv/pa5GGeWEDJfZGLegVBpIqwVlACY7Qa\nY7tODclhycPj90Y90JO17dNiN9sYlFvOZ679qlVAr4ZsNBgZXTScZk+Lau48qjP4wmkrwSQZo7Qi\npWymHlNbrGYUMVlrNWSlnrWnV6W1Ek2npXjnqCinkLbudlXDTPZwlCSJ8nCTAa1lRfbBdqZ8KCu5\nyLG5xKnKlyrYTFbKbKXUdzT00vZS5UErxOsHrieARzseov3InkDqKGuIbCj2dxzofewkvnuAascQ\ngCiBpOehCpG87NiKS92BHtq623WZq0F+QDttpTS5mzniaeGnG3/BzrY9DMmrTHktq32RNedezyZK\nwWgwUpNfxcGuw2opx/4wWecYLZTaSmjoaFTdOkpMgZ7rId5GqT1FURCI3EsNnQejrIUtnlYKcvJT\n5iArJBLIHzSswxvo5twhMzEbTAyKcXno9b/nmu00eY7wQcNaKnMruGTYhbrmJXzIWiT5YZmTREMe\nUzwKIErrS1bHWkuxtYguvzvqQmpNkYOsUBs2He9p/0x9Ta8PUbl4tA/TVEVBYo/dE/TR0HUwPOfU\nGxCFyHrJZms9HVkgHB2cW8bBrkPqDR+JsNYhkGNM3pFqWlofcqTjU6yVQxuEFzlHkfNbZJVbpR0M\n7/DtKQRDud1JMBSMKjbSE5SLlaR6sKpdn2ICu9p7OjBKRl0PwCrHYDx+Ty/TdyoftkK8XOJIhLg+\nDRmi/dBuHUFdIMcx5JrtUUI1UoAidUBZqa2E7Ud3qZshPalz8nfbw5+PFsjpBHQplIdNpEs+/Dn7\nXPVML5/K96beljIPuNB6bBoyyGl8IUKqEpGqFrNehhXU0OV3q+vRHeiWi53o2CjEK9qiluNNMa+p\nZafgD/r5uHkrIFsc2ntcuvzHCvHcCP6gn3cP/Isco4WZg08H5BgEiGzs9a5drtlOMBTEIBm4adz1\nCYNuY4mYrOXn0YBOewoZ5SpdyXzIg/MqcJjz2HZ0h6ptJKtjrUXxm2rTHyIFJ5IL5GrHEIySkT1t\nmp2+DhMPREyKUbWZUxQF0aIUylCO3eZJnS6lMDYskLeFNzCR4IvkvxdkodoT9KlWhXR8X5XqDS/f\nSF1+NxJSlA9Z0ZY9USbrsIZsi/j8450jZQN1oLMx6rsSES+wS692Wpwg/qC9W45ITeZPV0gU2KVY\nWfQKZK1AVSwHySKdY8drH4BenUFdkiQxrKBGdh+Ez0WkRGPqY48tHoU34GVfRz2g1RD13TexOfzp\n+I8VtML7prHX8bXx16c01UP8al16GjhoUe/f8IamP3zI8b43VSlTLZXxNGQd3bMAppZPAmBjUx0g\nP3+DoaCulCeFeBryvxv/Q1t3O2dVzlCtZ4NienKna125eOgFCbs7xcMRLtbSrmrIslgckCZrDGEN\nOYlANkgGRhePoL2nQz1J7TqKzAMMD/sQthz5VH1Nr4ZsMZqpcgymvrNBDYRo1xEEATA4fEHs1mjX\n6WjIw2K0c6UNoS5Tpb2EUmsx21tlDeWot5Vcs13Xw2hQjFBVU0V0zLnMLpesVFKflGATrUai1rP2\ne3pZOYo1PuTWsCDUniNFODeoAjn5wzHeA0CvdhrxZ0e061AoJKfu6OzWk0ggb23ZhoREVdi0m4hY\nk3MgGGDtwfWYDSZq82tSHr/GMQQJiV1te9XX9EZZQ6RYi/Lw16shA4wtHgnApy3yplC3hpygWldf\nBPKMQdM4q3IGC6bdyYxBp+oeV5gTCSBUiAR16dOQY61rrnDZTL0adiJ6nxP9wWZ2s53CnIIol5Re\nYVdud1KVV8mnR3fS5XNr4lL0C2S72U6eOVc9l0c8R/nT7r9gM1mZXX22+rnYjYNLR0EXgPOqZnJh\nzSwurDlX95xAli8FlvxIlHV4rz0wTdaqQI5fLEBhbIzZuq27HZvJmjLAY2LpOEwGExsO16natXKj\nJer0pGVYQQ3BUJB9LtmX5krROzQy35FR84X0NGSlw5Fy47V52pMm78cypngkHr+soRz1tuq+cWLN\nRZHGEqlNpCaDiXK7U81fdcfxbalR1j4PbTGR8kqN4hbv0bi1xhXzdbNbFlDJ0jwgfpqIXoFcHKfK\nW5dfjgpP5T9WUAO7NJHWLZ5W9rr2MbJoeMprKFZD3tS8mRZvK6cPmp5SwwfZClDlqGR3+2dqUX+P\n34uElPJ+A+3D/zNAX1chhVFFwzFIBtVtovfB70hQrasvJusyeylzx8yhPLdM9xiQXTf5Fkf8dp86\nBWqu2U6FvUyNA+kI9xPvS9lMLYNyy7EarX06J8r41u42NR9dUWz03N9TyycRCAWoa94SsbrpqNKl\npdzuDEd6+3j+01V0B3q4dtSVUa5Hp600KpalI0XZTIWRRcO5YvhFKaPN41GYk097j9xdzjCQo6xD\nkhz0ZEmRKzYmRsC1dbtSascgawITSsZwyN2k7rhau9twmPN0+RhiH0p6NeTCnAIG5Zazs3WPWrFH\nT5UnBdlkOJTW7jZavW20el26BQFENjD/OfQRvqBfNcGmIlLHWPHfpBcdOii3HG+gm6PeNrr8nl65\nwmqUtd9Ne7cLqzEnSnMvthaFf28bVmNOVPCRoiGHkG8UPdWqDJIhSkNNVaUrcqyCcPpFxLyXqkJb\nLHnmXIqtRdS7IoFdislvWtmklOO1/cBDoRBv7X8PCYnzqr6g6/ggxxMEQgF2tu4G5IhWm8mqy+Su\numxU86gS1JV6M2Az2RiaX81nrv24fR7dJmvlOjsSkwN+2N2MyWDS5XbpD4qthapZtq55Kx8f+QSj\nwZi0mEsswwqG0h3oobHrsNz28RgCuhQMkoHagmoOu5vp7OnSVcdaS2ych6ungzxzrq7ArFPD1+yG\nw3Vq5orelCeFcruTECFe3vEqu9r2Mtk5genlU6I+o8ayuA8TDAWPqeSoXgpy8gmGgnT53APclilP\neAAAIABJREFUhxwO6krmQwZZwFXmVrCrbQ9dPjcev0fNX03FVM2FFArJkbN6Q/V7+4I6yElS/k/L\nmOKR+II+9rR9Jo9N0bYv0bG3tHyKP+hPq7H5qKIRSEh8eOgjQJ//WP5cERaDWXPD6jM1Kih+5H0d\n9fiD/l4RublaDbm7vdemqsRahC/o55C7qZePP/b/eK0XtZiNZkYVDmd/R4NaQlKvhmwymBhdNIL6\njgZVQ01nQ6VQ5RhMh69THbuhqQ6DZGBS2YSUY7X9wHe27WF/RwOTnOPV4Bg9KJYaJS89WS/kWFSX\nTUcDPQGf7rQn7bFDhNjRuouOnk4sRktKi9aQvEpyzXY+bt4aFUnc5G6mzFZ6zBqmXgqthQRDQZ77\nZBW/2vws/qCPb077L133vYJy/25v3XnMZTO1KObwva59aWvIlTH55UrZTD2U2IoZml/Njrbd7AtH\n36e7QVJcDv8++CEOcx7Xj/5y3M3hoNxyudKdp4Uun1v386evRCp9udXSmSeVyXrZsmVcd911zJkz\nhzfffJODBw9y4403MnfuXO666y56enT2vTTI2qOe3EJZwPnZcFjWMlJFWCtMKB2LxWhhQ1Mdnb4u\nfEE/RTpNuAU5+ZRYi9nbvi9SWUrnRiDWzJ6qbV8syo23qWmLPJc0xtrNsoai1KXWu5M1SAYqcss5\n3NUkm9p0lM3UogRkKH7LXibrsMbb3uOiy+fuFZSnmMCCoWAvH3+eOReTJJujLEaLLgvHqTHBKKna\nHmpRNnIbD38M6C8KokUpEFLf0UCTu5n6jgbGFI/U3YJP6Qf+xt5/AHB+9Tm6jw1QWzAUi8GsXoNe\nv1f3hhC0Lpt6On1dcoOQJN2OtGiv/w6dWo7RYGSycwKung71GmrvcdEd6EnLf3ysKO6R/xz+iEG5\n5cybdifn1p6R1ncoArmuWb5/+0sga5WEdH3bWgtYT6AHb8CrK3ND4dTySQRDQba2bAPQ/RxV0Loc\nvjJmTkKrgRLLsiNs2ekP60IyFEucx+85+UzWa9euZefOnaxatYoVK1awdOlSHnvsMebOncuLL75I\nTU0Nq1ev1vVdQUkRyKl3nsoNvvbgeiB1hLVCjtHCxJKxHPG0qGH7qYqCaFFSDQ52HaZTR/k/hRGF\nwzBJRrYd3aGrbV8sNWGT4Y42+aLU43vWomhHgK6OLAqVuRX4QwGaPS24ujsxGUy6tSrFJLarbQ/Q\nu7ylNVxRR9HAYx8G2o1DrBXDIBnUTViqXFiFSc4JGCQDG8ObuFRtD7VMdo7HKBlZ37QJ0J/ypqVK\nLRDSwIawYD9Vh7laodQu+5F3tu1heMFQdZOmF7PBxMii4RxyN9HiacUb6NZ9LkGjjbXL2lheim5H\nWqodQ7CZbHxydIdcNlPnQ/XUsskAbDgsr3tf/MfHypA8+bydVTmDedO+owqydCizO8k129nbLqc+\n9ZdQGaoWDvpMoyHr2+CVh6t9NXYdVqP909koTC07BQn5/MvlWfWlFilU5w/BJBk5c9B0JjnHJ/yc\nEsuyvXVX2nPsCxFXmufkq9Q1ffp0Hn30UQDy8/PxeDysW7eO2bNnAzBr1izWrFmj67uCqg85tYY8\norAWk8GkplLo8SErnFou3+Rv18sFzFOlPGlR/MjKTlevhpRjtDCssJb6zkYaOw/JzSHSeJibjWaq\nHYNV0106GjLA2JJR6t/p5AuqjSK6DuHq6cBhztP9EHbaSjAZTGpQWKyGbJAM2ExWTdpa9DksjpPm\npEU5b+kE14wrHkV9Z6Psd/Ppz+O1m+2MLR5FQ+dBDnU19U1DDkdS13c0srGpDpNkTPogikWJtAaY\nnaZ2rKBsZOuaNwP6IqwVVG3M9Vm4IpT+KGG5SM0ItT65XivLyKJhOCx5bGreQiAY0ERY6zfVHyun\nVUzlgZn3MnfMHF3PpngoqWNKzEN/CWSbyUplXgX7XPVqoKje82I15VBiLeZg56GICyaNjX5hToH6\nPExnk68dv+SsRcwdc3XSzyka8k5VQz7OAlkpWOQ7CTVko9GI3S5fAKtXr+bss8/G4/FgscgXbklJ\nCc3NyfteKgSR85CTFQZRsBgtjCioVf/XqyEDjCsZjdVoVW/udDVkgI/CD7R0NCRFS/3w0EYgvYc5\nEKURpXPjANQ45DKakKZAVv1Mh3SXzVQwSAYG2csigVdxNFltdHQyDbkwzqZJ6VebjmCImJ5ll4Wy\nKdCDYvLe0FTXJw25IMdBgcXBtqM7aOw6xLiSMSmrZGlRIq3L7U4mlo7VPU6Lcg1ubJI19HSOX5hT\nQIm1iN1tn+ENdOvWxGKPDfq1HINkYGrZKXT6utjeuiutphL9hSRJ/aKVDcsfqv7dn1resIKh+IJ+\nVWClc14q88rp8HWqm+Z05zUtfE+k80zRkmdJbWUpsRVhNpjVDfTx9iFHmawVH3IGgrrSszfE8NZb\nb7F69WqefvppLrwwUqYsXmH4hBOwAF4oLy3EWZr6wphWPYFtrXKASm3FIJzF+i+mGVWTee+zteGx\nlbqOB1BSMhLbR1Yawj06K4ucOJ36xp5pmsKru//K+mbZ/DaouFT3WIAp3WP5Z/37ANSUV6T1ewEu\nGzObho7DVA/S/zCbmDsc6qDecwB/0E+pozCtOdeWVFEfzhWuKC7uNbbA5lCjaGucFVHv5xWa4cPw\n95QP6jV2cLGT/xyGorz8pHPSvnde4Qx+t/0PbGrZTCAUwGHJpbxM34bsvMIZvLj9D9Qd2Uy+NQ8J\niWGVg9JKrRheUsPGg7J1ZdbIGWmt5Yz8ibxVX8U1Ey7VPedYSkvzKPm4iL3hqlHFMWuXaj5jy0bw\nwf7/xB2birNsU/jd9lcAqCjsfS0kYjZn8N6Bf7PV9QntfnkjNK66VndA2fEind8OMCU0llf3/BWA\nKmdZ2uMTMalrNO83rMHt92A15VBZrl84DndWs/nIp+zt2tuneX0xfyabWj7mnBGn6b4H+0J1QSW7\nW/f1aY7pMsgvb3ylnCD5Dnmz7nDYjusx49Fngfz+++/z1FNPsWLFChwOB3a7Ha/Xi9Vq5fDhw5SV\n6cv98/rkoCO3y09zqHcXkFiqLBGNMeg20xxIPUZhfME43kMWyJLHQnOz/rE1jip1I2D06R9rD+WT\nZ86lPdw+0eTLSeu4JUTWMeg2pvV7Ac4tPwfKSeuYoZDcanFbs+y/yQnZ0hpfZIpouQGvoddYMxFr\niNRt7vW+UiTe4O29ztagrBmbAr3HKTidjl7vjSsZQ13zFiQkKnLL0vo944tHs6l5C01dZvIsuRxt\ncacepKEspxzYgtlgptpcm9axAb4/9TtAeucwllGFI1jjkYUqvsg5ibdWsQy2DgbksaZgeveNRA5l\ntlKaPEcw+vWPLQo5KcwpYF39JqzGHPLMuXjag3jo+xocK3rWKpaCoJx6FwwFCXqMx3QOo+ZiiPi0\nc032tL63QJKF9+bDcmAW3sT3UiLunHQbkPia7Mta9fqOHCe7kQVyf65dPHxuWYlsbmsj1yMHJLe2\nuo/LMZMJ+T6ZrDs6Oli2bBnLly+nsFA2IZ555pn8/e9/B+DNN9/kC1/QlyuZjg8Z5ChBJcHekeZu\neUzRSHJNdrkyS5rm32F9NB0bJIOaQw3pmTuVY5VYi5EkSVeHn/5AkiQG5Zar+dPpmou0DcHjlbfU\nmpvjRcorRUwK4/mQw6/pjVJWUAKpQoTSHxuOP/AFfWm7HCBSsWti6di00mb6E63pOJ0oa5AjtRXS\nXTuI1FZP1/UxtewUPH4Prd1tJ9Rc3Z8oqWPQvybrEmuR+n3pVv9S7k+laltfrukTgRLLAsc/qCuu\nyToDPuQ+achvvPEGra2tfPe731Vfe/DBB1m0aBGrVq2isrKSK6+8Utd3BdGf9gTyjfqV0V+ms6cr\n7ZxEo8HIf429GldPZ9pjtW280r04xhaPYn04YrQvF/+1o67Ab+7uUwWavjIot0LNvXakuYkYpBXI\ncYpI2MJCWiL+JuPyYRdxxNsSt1jMmOKRXFgzizMqT0trThNKx2IxmOkJ+tIWKhNKxmAxWugJ9KS9\noQIYXzKG86q+oBbPzwSji0YiIREilFZQF8gRrzlGC92Bnj6Vfryg5hxMBiNji0enNe7U8kmqu+Zk\nFcgAVw2/mH0dB/o1dUcJGNvUvCVtv3653alq7ZC66mCm0D5H9ARhHgvaKGtDBguD9EkgX3fddVx3\n3XW9Xv/Nb36T9ncFlKCuNCIZJzlTF1Xo77FDC6rVB1q62vWxaMggC5P+MAGlg1bLTXcDUmwt1DzA\n42jI4UCvfIsj7iZDGx0ei8lg4orhF6U1HwinvpWOY0NTna5azFosRgunlI5j/eFNfdpQmQ0m5uho\nlH48kctoDmZ/x4G0grpA3sjW5tewrXVnnzTkYmtRn35/jaOKEmsxLd6jJzQHub8ZWTSckUXD+/17\nhxUMDQvk9DZJZqMZp62Uw+4mrEZrn6PIjzdK6pNdR9nMYyXSp/0kTHvqTwKkZ7LOFDaTlSpHJTaT\nLWUN5VgKcwoYkleJzWTVnT+baZR8YiBtU7kkSVQ7hvQqi6mgpBjoLezSX0yvkEv0leSkHx06LWy2\nLrWlVyowmxhfMgZIP1of5Jx6SN2QpT+RJInp4XUfnDfohB33ZGFk+Jz0JdpZub+zVTsG+fngMOep\nXeCOJ0aDEYvRkvHCIMd326GDQMiH2WA6YSXxjoWbx9+AN+DVnZOr5esTbsQT8PRpbCbQFkLoi1Z/\n49hrw2Xoep9XpY9xOmlr/cGEkrF8e9ItUfEA6Yy9vY9js4Uv1syiJn+I2gEtHc6vPpshjkGMKKxN\n/eF+5EtDZ1OTX6XmUgsiVOcP4c7Jt6bValChMrecTc2bj7tv9liQJInvTPmGrjrb/YHdZJNN1taT\nzIfcn/jxZb12rOC0l6T+0HEYmwkcljzyzLlyZbI++G9KbMUJd7aKhpxOYZf+QJIkxpWk58fUjh3f\nx7HZgtloZmLpuBM+9lgwG82ckkYhlYHG6OIRfRqnlLjti7XkRHIiLSN2k43W7raB3VzCH/LpKpsp\nOPGMKR6J01aiFhfpLypzyzFIBobmV/Xr9woEAn3U5ldjMZipSdGTeyBhM9nw+rtRjJgD1mRtMZ4c\nftWBxo1jryUUCvW7mb3M7uThs/9Pd5MCgUDQvxRZC3lg5g9Ttr0dSNjNNkKE8EtyHvLANFmH/LrK\nZgpOPMfTd5NOVL1AIOh/MpUTn60owbq+UDcwIKOsQwTwi4ezQCAQCDKKIpADhAXygPMhGwJA9qc8\nCQQCgeDzjVKwqGfAasjG9Kp0CQQCgUBwPFBN1sglRTPhQ86oQJaEhiwQCASCLEAVyEE5qGsAasiy\nQBYaskAgEAgyiV01Wcsa8oDzIQsNWSAQCATZgFLjXfEhDziTtRLUJTRkgUAgEGQSxWTdEwxryANO\nIKsma5EPJxAIBILMoZisu4MDNqhL6fQkqsUIBAKBIHMoJuvu4ADPQxaVugQCgUCQSSwGM0bJSHdg\ngJqsJaMI6hIIBAJB5pEkCbvJhnfA+pANwocsEAgEguzAZrbiDXiAgWiyFhqyQCAQCLIEu8lOd6Ab\nCA3EoC6R9iQQCASC7MBushEIBcAQGIgmayXKWghkgUAgEGQWm8kq/2H0D0ANWZTOFAgEAkGWYDfb\nAZBMPjIgjzOtIQuBLBAIBILsQKnWJRl9A9FkHUBCwmwQhUEEAoFAkFlUk7XJP/AEsmQMYDGakSQp\nk9MQCAQCgUAtnykZfQMw7cngxyKqdAkEAoEgC7CbZB8yJt/ADOoS/mOBQCAQZAOKyVoyDkCTNYbA\nSZPytGnTRlpbjwKwYMHdANxxx63s2bOLN954nffeeyet79u1ayf79+8D4L77fkB3t7d/J9xHVq5c\nzvXXX8XTT/+KW2/9GrfccmOmpyQQCAQnBNVkPRA1ZAyBk6Zs5l/+8poqkB988GdR71188WWcc86s\ntL7vvff+SX39fgAWL36AnBxr/0y0H7jmmuu5+eZbWbx4aaanIhAIBCcMxWQtmfwZ8SGbTvgRNUiG\nUNaZrN9443X27NnNHXd8F7fbzU03Xcf8+ffw/vvvsnfvHn7842Xccst/8Ze/vK2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gpu = pd.read_csv(\"./GPU-stats.log\") # make sure that 140 seconds have expired before running this cell\n", "gpu.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph above clearly shows that the GPU doesn't get the data fast enough; with data augmentation, the CPU is now the bottleneck. I'm pretty sure that this was the problem I was running into with my Z800, which has slower CPUs and memory; while the 1080 Ti was very fast, it was not fed data fast enough. \n", "\n", "As default, Keras only uses one thread to do the preprocessing. If we have multiple cores, an obvious solution would be to use more than one thread for data augmentation. How would we do that?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Faster data augmentation: use multiple threads\n", "\n", "There is limited information available on how to implement custom data generators - I suggest you Google it if you're interested. The main issue that folks are running into is that the iterator used by fit_generator (in our case: batches) need to be thread-safe. I believe that some bugs were reported against Keras regarding this.\n", "\n", "But: as I'm using the latest Keras (2.0), I suspected that the Keras developers might have made the iterator(s) threadsafe, which turned out to be the case indeed. \n", "\n", "Making the data augmentation faster is absolutely trivial: just specify the number of worker threads and you're done!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "230/230 [==============================] - 132s - loss: 0.1244 - acc: 0.9683 - val_loss: 0.0502 - val_acc: 0.9820\n", "Epoch 1/1\n", "230/230 [==============================] - 75s - loss: 0.1112 - acc: 0.9730 - val_loss: 0.0460 - val_acc: 0.9855\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subprocess.Popen(\"timeout 220 nvidia-smi --query-gpu=utilization.gpu,utilization.memory --format=csv -l 1 | sed s/%//g > ./GPU-stats.log\",shell=True)\n", "\n", "nrThreads = 3 \n", "vgg.model.fit_generator(augBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps) # default\n", "vgg.model.fit_generator(augBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps, workers=nrThreads) # multiple threads" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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6pBOEqt/NqhtDtoq6xl1lbf5eiTiyqqrIFWgYsmiLlcVBviijKClUjHssrzFb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\ntW+w3zYKQlhluQMm6hmyFHU1aXsCvCEEs5U5bB3vh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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gpu = pd.read_csv(\"./GPU-stats.log\") \n", "gpu.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I was surprised at the performance improvement (73s for the training step, down from 130s). The 73s is much faster compared to the 110s we obtained for the non-augmented data. I spent quite some time figuring out what is going on and reached the tentative conclusion that specifying multiple worker threads changes the behavior of the iterator. \n", "\n", "If we use the original batches, we obtain the original performance. But if we generate a new batch, we suddenly have much better performance!" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 23000 images belonging to 2 classes.\n", "Found 2000 images belonging to 2 classes.\n", "Epoch 1/1\n", "230/230 [==============================] - 111s - loss: 0.0911 - acc: 0.9782 - val_loss: 0.0429 - val_acc: 0.9835\n", "Epoch 1/1\n", "230/230 [==============================] - 71s - loss: 0.0982 - acc: 0.9777 - val_loss: 0.0475 - val_acc: 0.9836\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Changing the number of threads changes the behavior of the iterator. Create new batches and do a benchmark.\n", "defaultBatches2 = get_batches(path+'train', shuffle=True, batch_size=batch_size,gen=noAugmGen)\n", "val_batches2 = get_batches(path+'valid', shuffle=False,batch_size=batch_size,gen=noAugmGen)\n", "\n", "subprocess.Popen(\"timeout 200 nvidia-smi --query-gpu=utilization.gpu,utilization.memory --format=csv -l 1 | sed s/%//g > ./GPU-stats.log\",shell=True)\n", "\n", "# first epoch without data augmentation, should recreate the original performance\n", "vgg.model.fit_generator(defaultBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps)\n", "# second epoch without data augmentation, still single thread, but is faster!\n", "vgg.model.fit_generator(defaultBatches2, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches2, validation_steps=val_steps)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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nhwKi8vfNTy2Ws47d3gAuttsxszQLBTkGrJpfjINnOrFqfrG8XjIME5bgKH0m\n9WV5oQnXTcsGL4hri83hC3tvz7/XhMpiC2aUWDC9yAyLUYMhlx+bqssxPZhNvTeYm7OwKg8r5xXh\nxT3NcHoC2FwtKp6bqsvw2v5LWDyrAGUFJuRl6fDSBxfg8XG4aXlIGYg3FqXP5HDlFJHIqh//+Mc/\nTmUDBgbdeG53I6YVmnHbqgoU5Rpw5mIf6i/3o6vfhXarE2sXlWBhVR7K8k1oszphMWqQn6XHJ9dX\nobzQDIZhUJClR5/dgxyzFsW5Bnxm0+yo7S4S5QUmDAx58akbq8KENssw0GpUONlkRWOrDe1WBxgw\n2HbzHPnFsiyDXIseFoMGN6+YPqwANpl0cLl8YZ/lWnTw+DisWVQSc6sWwzBo7RlCS7foIbhjdQWm\nRaT7A0BhjgFDLj82LiuLEkRlBSa0WR2wGDTIy9LhrnUz5K0HoT4wo8/uwdYNVbKVE0lpgQkdvaH+\n3rq+CmUFoWQxk0mHbIMa/XYvvvyx+ShQeAvC7pNnRGefE1lGLfKz9bj7xplyop1ESZ4RXX0uZJm0\nyDHrkGPWIdeiw60rp8fd6pFr0cHlCWDVgmLZTR5JQbYeNocPapZBrkWH5dcVYsPS6G1hAFBZYoFG\nzaKy2IK1i0pjvj+9VgW9RoWZZVlYOS9aoQIQdNWKW+SunxN7O5dJrwHLMlhYlYclswpiXiO9lyWz\n8uVQRiRZJi04nsfyOYWYX5kX85ocsw4eXwDXzynCTQo3vPL58iw6vFPTApZhsDG42O050QaPn8Nd\na2fEvO9IHK7rRO+gB59YVwWGYXClawinL/TijtUVyDJqcdP10zC9SJy/xblGAALuXDsDahWL4jwj\nBhxe3LJiOsoKTGJYKUcPlmVw55pKqFgWJXlGDNi9uPWGCpTmi9cUZOuhVatw+6oKWMx6ZOnVsNrc\n+OT6KiyYkYeiXAOKcw1yboQ01pRj7uYV02TXf3mBCXanD5/aMBN6rRq5Fh28fg43zCuSLbSyfBMc\nbj+2bpgJnUaFPMUcl/JDSvNNcHkD+OT6Kmg1KhRk6+F0B7B+Sam8jbIk3wifn8Mn1lVBo2bD5rg0\nf7NNWtSc60Hd5T6smFuEh146jSuddkwrNCMvSx/2LFkmHaw2D+xOn5yTU3epDzXnu7FhSSnmVeai\nvNCMvkEPPrVxVtTWv0hK8o3ot3tw+6oKFOcawTIM8rP0MOhUuPUGMQelrMCIvkEPPramEoU5huB6\nqYPFqMVpvZznAAAgAElEQVTN10+DUa/G8YYeeRvpPVuuQ45Zh2yTFnkWvaxIlReaYLV58IU75sGo\nUUGtYmE2aJBr0ePvbr4uylsUj/4hLw6d7cSc6TlhxkQqMZl0cX/HCCmOdn9QcwW/frkWd6yqwGc3\ni+7OfrsH//P0cdiDL+3f71km7+ObbARBwJNvN+DQWTHOtGJuIf7lU4vHdK+xZEAC4kImbcf6zbfW\nx7Rg04GxPt9U4Vp7vn995CCMeg3+92viVqv/+uMReHwcfn3/+jHd/5cvnsK5KwP403c2ifH+0+14\n5t1G/NNdC8ISJyeLTH1/rx+4hLcOX4Feq4LHx2HdohJ85c75MZXM/33uBC60DeLn/7waRblGPLe7\nER+cbMd/fmF5VF5Gsnjh/Wa8d7wVeVk6PPiNtcMaNeN9h81tNvzsuZP4+NpK3H3jrDHfZyIZLss6\n5bWsm4KZiPNnhLSXvCw97vvUIqhYBlo1izkxik9MFgzD4N7b5mJWuai1RiZUJYMFM/LAAKgstqSt\nMCYyD71ODbcvtMfc4+eist5HgxTPkxJqJNV/PBnCBLB1QxWqryuAx8dhbmUu/v72uXGFmhRW2X+6\nAwNDXhxv6IFeq4pKJk0mi2aKa+qiqrwRQ3zjRXZZ8yNcmCakvHSJxyvGqbIjzPjrpuXg//n8MgQ4\nPiqeMdlICUJHz3VjbZz45WSSYxazheO5gAliMjBo1fK+YEDc9pRtGrtCGCriIP4sCWaSx+ODZRj8\n010LcLS+G7eurYLP7Yt77Yq5hXjhfQ0OnulEQ4sNdpcfn79p9piql00Ui6ry8KU75kXtGpgMpLE2\nVbY9pVwgS8H2WCGBVPr8LUYtblkxfeQLJ4nlcWKPBDFZGHQqeP0ceF4Aw4gCWa+ZAAs5OMdDc50k\n8njRa9XYVF2ObLMO1mEEskatwvolpXi3pgUOtx9rF5WE7TVPBQzDxM35mGhCtRWS8u/GTcpd1vIk\nTTBITxDE5KDXivq5xxeAz89DAKDTjl1nl+SuNMcFnuZ6Kti0rAwqVqzz/Q/DuLczkUilMN1JuYXM\n0SQliLTAoBOtYbeXg1otzkv9BMaQeYohp4SiXCMe+MdVyLXokh7+SzWRSmG6k3KBLGvNNEkJIqXo\ng3vy3b4AtLzoPBtPUhfDStaJ+LMge8PG0UhiTEgnlF1rMFPMZZ1ygUxxJYJIDwySy9rLyVbt+GLI\n4lchIoZ8LblMidRCSV2jhFzWBJEeyC5rXwC8IH4/kdueePKGEUlmqiV1pVwgy3ElEsgEkVKkpC7l\nedfjiSFHli0MxZDHfEuCGBVkIY+SUAw5xQ0hiGscyUL2+DhZmOrHkWUtxYolQcyTN4xIMpRlPUrI\nZU0Q6YFBYSFLloVuAvYhS9aJAIohE8kllGWd2nYkSsoFMiV1EUR6IGdZewNQsZKFPA6BzEbGkIOf\n01wnkgQToRSmO+kjkMlCJoiUonRZS6UVJyaGjODXoIVM256IJEFJXaOE9iETRHpgUFTq0qjHvw9Z\n3vZEWdZEiqDCIKNEiiGryEImiJQScllz0KjFQ18mIoYsl86kSl1EkqHCIKNEcmfRHCWI1GLQhvYh\nazVBl7VuPFnWEac9yYVBxtFIghgFkcVp0p2UR3N4QTxZhjIvCSK1aNQsVCwDj5eD1ydayJNy2hN5\nw4gkEVm+Nd1JvUDmBXJXE0QawDAM9FoV3L4APP6gy3pcSV3iVyl2TPkiRLKRk7qmiEROC4FME5Qg\n0gO9Vg2PNxAsDgJo1WNfIuRtT2QhEyliqiV1pV4gC4LsViAIIrUYdCq4gy5rvVY1rlCSvO0p6vjF\ncTeTIBKCwdRK6kq9QOYFqMhCJoi0QK9Tiy5rX2BcGdaAMqEm+JWnSl1EcplqtaxTL5AFcmERRLpg\n0KohCIDd6R9XHWtgGJc1CWQiSciZ/iluR6KkXiDzArmwCCJNkKp1ef3cuBK6gGFOe6IJTyQJNiKx\nMN1JD4FME5Qg0gKlVTyeLU+AcjGUvtLJbkRymWq1rFMvkAUSyASRLkgWMjC+LU9AtMtaECiGTCSX\nqXbaU3oIZJqgBJEWGJQW8ngFcoR1Qi5rItlEjsF0J+UCmSOXNUGkDcpSmeMVyKFtTwh+JZc1kVwi\nTxxLd1IukAUqDEIQaYNBIYR1mnFmWUdsOeHJZU2kAIYhCzlhaNsTQaQPhgm0kKNjyOGfE0QyYBmG\nCoMkCkcWMkGkDXpFUtdExZBDlbrIZU0kH4ZhyEJOFDHLOtWtIAgCCE/qmqgsa/n4RarURaQAlqEY\ncsJQDJkg0ocJTeoKfo3c9kQuayKZMAxDh0skCi/Q8YsEkS5MaFIXG+GypuMXiRRASV2jgOPptCeC\nSBcmNKkrsnQmSBgTyYehpK7EEAQBgkCTlCDSBWXceNy1rIOrixS/E3gBNNWJZMOShZwYdD4qQaQX\nLMPIlvHEV+qiIkBE8hFjyKluRWKkViAHS/hQDJkg0gfJbT3+wyUiY8jkDSOSD1nICcJJ2yBIIBNE\n2iBZxhN3/CKCX2mLI5F8yEJOEMq6JIj0Q7aQtePNsha/SvNcoINkiBTAslOnMMj4Ztw4kSYquawJ\nIn1YVJUHnUYFtWp88zLWaU9UFIRINgwTkjXpTkoFMkcWMkGkHVs3zJyQ+0TWsuZ5gRI4iaTDgLY9\nJYR8+gvNUoLIOCIPh+cFqjlAJB+GAQRMDYmcFjFkclkTROYhu6x5pYVMc51ILnTaU4JwdGA5QWQs\nkZW6xCJAqWwRcS3CsFOnlvWYYshOpxPf+973MDg4CL/fj/vuuw+zZ8/Gd7/7XXAch8LCQjz44IPQ\narXD3oeyrAkicwnFkBH8SnXrieTDMiEvTbozJgt5x44dqKqqwrPPPouHH34YP/3pT/HII49g27Zt\n2L59OyorK/Hqq6+OeB9ZINMkJYiMgyp1EelAxu9Dzs3Nhc1mAwDY7Xbk5uaipqYGW7ZsAQBs3rwZ\nR44cGfE+HAlkgshY5KQuaR8yxZCJFDCVTnsak8v6zjvvxOuvv45bbrkFdrsdf/zjH/GNb3xDdlHn\n5+fDarWOeB/Jr280alFYaBlLU9KeTH0uCXq+qc1kPt+AOwAA0Ok14v9hGGg0qqT2aaa/PyDzn3G8\nz6fVqACGmRL9NCaB/MYbb6CsrAxPPPEEGhoa8P3vfz/s94lqI5Lm7PMGYLUOjaUpaU1hoSUjn0uC\nnm9qM9nPZx90AwCcTh+s1iFwHA+e55PWp5n+/oDMf8aJeD6O48FxyRt3IzGcYjAml/XJkyexfv16\nAMC8efPQ09MDg8EAj8cDAOju7kZRUdGI96HCIASRuUjTOiyGTHOdSDIZfx5yZWUlamtrAQDt7e0w\nmUxYt24ddu3aBQDYvXs3NmzYMOJ9QkldY2kFQRDpTOS2JzrtiUgFU+m0pzG5rD//+c/j+9//Pr74\nxS8iEAjgxz/+MWbNmoXvfe97eOmll1BWVoatW7eOeB9polJSF0FkHkyMbU+kfBPJZiplWY9JIJtM\nJjz88MNRnz/55JOjug/HkcuaIDIVNjLLmlzWRAoQK3VNDYmcFrWsaZISROYRfbgEnfZEJB+GwZSp\n1EW1rAmCmBRiFgahqU4kmYxP6poopCxrOgGGIDKPUGEQyhchUgcbke2fzqSFhUxzlCAyD6WFLM11\nclkTyYaRx2GKG5IAaRFDVlHqJUFkHMoYsrQYkvJNJBs5uXAKSOTUuqw5spAJIlOR9yHzgrwYUniK\nSDZMRC5DOpMWFjJNUoLIPCQLWRDoqFUidTBM+H74dCYtYsiUZU0QmQejcBUKtMWRSBGU1JUgVMua\nIDKXcJd18DNSvokkI1vIfIobkgBpYSHTJCWIzINVuArl8BRNdSLJyIecgCzkYQlthUhlKwiCmAyk\nzRO8IEAgbxiRIlja9pQYHMWQCSJjUWa3ksuaSBUMbXtKDKplTRCZS1gMmYoAESlCme2f7qSFy5q0\nZoLIPEKWCSjLmkgZjEIxTHdS7LIW095okhJE5sEwjHzSTiipi+Y6kVwY2vaUGFIaOlnIBJGZsAwD\nIWzbU2rbQ1x7MCCXdUJQ9R6CyGxYlhG3PdFcJ1KEpASShTwCdCQbQWQ2US5rmutEkgmVziSBPCxy\nDJncWASRkUgu69BpTySQieQSKp2Z2nYkQnrEkGmSEkRGwjKMaCFTESAiRZCFnCB0uARBZDZyDJm2\nPREpgqFKXYkhuaxpKwRBZCYMI1XqonwRIjWwVKkrMagwCEFkNizDgKcYMpFCyEJOEGlvIrmsCSIz\nEV3WFEMmUgcVBkkQqtRFEJkNy4jJm1Q6k0gVdNpTgpDWTBCZDRORZU3hKSLZsJRlnRiUZU0QmQ3L\nMOHHL9JUJ5IMHb+YIBxpzQSR0TDBbU8CZVkTKYKSuhKE6tsSRGYjxpDptCcidVBSV4LQ3kSCyGxY\nNuiypqp8RIogCzlBOLKQCSKjEUtnKit1pbhBxDWHXBiET3+JnB4ua5qlBJGRyKc9STsqaK4TSSa0\n7YkE8rCQQCaIzEY+7UnxM0EkEznLOrXNSIg0cVmnshUEQUwWkZW6aK4TyYYhCzkxyEImiMxGrGUd\niiGTy5pINpJ8mQLyOE2yrMmNRRAZCSud9kQJnESKYCipKzHIQiaIzIZhGAgg5ZtIHQzIQk4I2vZE\nEJmNpGxzVLeeSBEsFQZJjJCFnMpWEAQxWUiLIceRN4xIDVLewhTwWFMMmSCIyUNaDAMcHbVKpAba\nh5wgHCeAYai+LUFkKtJiKAtkspCJJEOnPSUILwikMRNEBiPNb6o5QKQKqmWdIDwv0FnIBJHBMBEx\nZPKGEcmGTntKEI4XqFAAQWQwkos6wFMMmUgN0pgjl/UI8Dy5rAkik5Fd1hztqCBSQ8hCTm07EiHl\nMWRyWRNE5iIthpRlTaSKqWQhq8f6h2+++Sb+/Oc/Q61W41vf+hbmzp2L7373u+A4DoWFhXjwwQeh\n1WqHvQfHCZTkQRAZTFRhEJrwRJLJeAt5YGAAjz76KLZv344//OEP2LNnDx555BFs27YN27dvR2Vl\nJV599dUR78MLFEMmiEwmymWdysYQ1yQZvw/5yJEjWLNmDcxmM4qKivDAAw+gpqYGW7ZsAQBs3rwZ\nR44cGfE+lGVNEJmNvA+Zp33IRGpgZJd1ihuSAGNyWbe1tcHj8eDrX/867HY77r//frjdbtlFnZ+f\nD6vVOuJ9OF6AWq1CYaFlLM2YEmTyswH0fFOdyX4+o1FcE1RqFQAgN9eU1D7N9PcHZP4zjvf5srPt\nAACTSZf2fTXmGLLNZsPvfvc7dHR04O///u/D3AGJugZ4XgALwGodGmsz0prCQkvGPhtAzzfVScbz\neb1+AIDL5QMA2O3upPVppr8/IPOfcSKezzHkAQAMDXnSoq+GUwrG5LLOz89HdXU11Go1KioqYDKZ\nYDKZ4PGID97d3Y2ioqIR78MLArmwCCKDia7URfOdSC4ZXzpz/fr1OHr0KHiex8DAAFwuF9auXYtd\nu3YBAHbv3o0NGzaMeB+OI4FMEJlMKKlLjCGTPCaSjZzUNQWCyGNyWRcXF+O2227D5z73OQDAD37w\nAyxevBjf+9738NJLL6GsrAxbt24d8T5iLeuxtIAgiKkAE1T5AxxZyERqyPikLgC45557cM8994R9\n9uSTT47qHjxPFjJBZDKUZU2kGnkfMtJfIqe8UhdpzASRuUTtQ6bpTiQZOu0pQSiGTBCZjeSylit1\nkQJOJBmpfnrGFgaZKCjLmiAyG9llzZHLmkgNcgx5CgSR6bQngiAmDXJZE6lGEnJTwEBOfWlZmqAE\nkbnQechEqmGm0GlPKRfIVMuaIDIXSf6GzkOm+U4kl4w/7WkiodOeCCJziazURUldRLKRlECykBNp\nAE1QgshYZJe1lNRF051IMrTtaTQNIIFMEBlLKMs6aCGTRCaSTMhlnf4SOeUCmWLIBJG5hGLIlNRF\npAaWLOTEIY2ZIDIXeTGM+JkgkgVLWdajaADNT4LIWCKzqkkeE8km449fnEjIZU0QmUvk9KZtT0Sy\noaSu0TSAVGaCyFgiQ1I034lkQ0ldo4BiyASRuUQKYDblKw5xrUFJXaOAXNYEkblEGsRUGIRINhRD\nHk0DaIISRMYSZSHTfCeSTMhCJoE8cgNoghJExqJM4qK5TqSCkIWc2nYkQuoFMrmsCSJjUQphih8T\nqYAhC3kUDUh5CwiCmCyURjFZyEQqkAuD8CluSAKkXBzSJCWIzEU5vymhi0gF8rYnkIU8cgPIZU0Q\nGUtYDDnlqw1xLUKFQUbTANKaCSJjCYsh01wnUgBLhUFG0QCykAkiY1FaxeSyJlKBVHyKsqwTaQAJ\nZILIWJiwLGua60TykfchTwGJnHqBTHOUIDKW8KSuFDaEuGahSl2jaQBJZILIWFja9kSkGAaU1JUw\nKpqkBJGxUKUuItXQaU+jgE57IojMhaFKXUSKYSmpaxQNIK2ZIDIW2vZEpBra9jQK6PhFgshcGNr2\nRKQYqmU9mgaQQCaIjIWlbU9EiqHTnkYBKc0EkbmEu6xT2BDimoXOQx5NA0giE0TGQqc9EalGclmT\nhZwAFEMmiMxF6aamGDKRKhiGLOTEGkACmSAyFpa2PRFpAMswVBgkoQaQ1kwQGQsVBiHSAbKQE21A\nhlrItdZ6bD+zM9XNIIiUopzeVASISBUsw1At64QakKFa8/62D7Hz/C54Ap5UN4UgUgZDhUGINIBh\nGErqSqgBKW/B5ODj/AAANwlk4hqGtj0R6QC5rBNtQIbOUh/vA0ACmbi2oSxrIh1gKKkrwQZk6CT1\nBy1kD0cCmbh2CTt+MUOVbyL9YRk6DzmxBkyCQB7w2Cb8nqPFx2eWy9ruG4KfD6S6GePCE/DC6Xel\nuhnXFAxlWRNpAFnIiTZggrXmhv5m/ODw/+JcX+OE3ne0+LjMcVk7/S7895Ff4J3L76e6KePi2fMv\n4xfHHk51M64pKIZMpAMsxZATbMAEz9IeVy8AoMvZPaH3HS2ZZCHbvIPwcT50ODtT3ZRx0eXqQZ9n\nQFaWiMknbNsTWchEiqAs60QbMMGT1Mt5AQCugHtc9/FxftR0ngAv8KP+W17gEQi6dzNh25PUpw6f\nM8UtGR/Su7iW3dbdLisa+puT9v/Ctj2RiUxAtFSPdh7HoHcoaf+TZRkIU0Aij0sgezwe3HzzzXj9\n9dfR2dmJe++9F9u2bcO3v/1t+HyJWSETXctasn6c/vEJ5Jqu43jm/Es423tuDG3wy99ngoXsCYgC\necifGQLZcQ0L5Fea3sBjtX+Rkw4nm/BKXUn5l0Sa0+pox7PnX8a+tkNJ+5/MtZDU9dhjjyE7OxsA\n8Mgjj2Dbtm3Yvn07Kisr8eqrryZ0j4n2YnmDAtkVGN+iK2lvA97BUf+tn88wgZwBFjIv8PJzOKe4\nYjEerO4+BARO7ovJhs5DJiKR1pHxejFHA4MMT+q6ePEiLly4gE2bNgEAampqsGXLFgDA5s2bceTI\nkcQaMMGTdKJc1pJAH4sQUsYohxPIp6112HXlg9E3bgIRBAE7L7yNpoELca/xBi1kD+dJ20zr09Y6\nvHtlT9zfS1Y+MH6XdU3nCexr/XBc95gM6vsa8dalXXF/zws8bMEdCMr+mEwYiiETEUhrojeQvFwO\nhgEEpL9EVo/1D3/xi1/ghz/8IXbuFOs1u91uaLVaAEB+fj6sVmtC9ynIN6Ow0DLWZkTBXBQ73S94\nx3Vf7qIoeDiVb9T38Q465O8FVSDu3+89fQAX+q/gnuV3Qq0a86sYF1ZnH95r2YcBbgDr5lTHvEY9\nEPpeb2GQZww9z0S+u/Hwwen9uNh/FZ+r/hh0am3U763O0ORn9VzC7Y513fvH9sHq6sdnqm9LKyFz\noO4Q6noa8Zllt8OiM0f93uaxIyBwAABjlgrA5L8/XhG3Mxg0SR8v6TI+J5Op9oxqu/iVUfMJtX0i\nnk+jVsEXSHzep4oxSYGdO3di2bJlmD59eszfjya9fNDmgn4CU8vsTtGiHfQ4YLWOPWlgwCGOGuuQ\nbdT36bKHJNigK347+l2iO7y1uxdmjWmMLR0fbUNiVnrf0EDcdvYN2uXvr3R1g7OEFvPx9PFE0usQ\n+/xSRycKDHlRv2939Mnfd/X3J9TueM/n9nnh5/y43NEFizZa8KUKq6MfANDe3Yd8Q/QcvGpvk7/v\n7B3AjNzpSX1/fm8gqf8vncbnZDEVn9FqE700dpdzxLZP1PPxgoBAgE+LvhpOKRiTQN63bx9aW1ux\nb98+dHV1QavVwmg0wuPxQK/Xo7u7G0VFRQnda6JPgJFjyONM6nL5JZe1Y4Qro0kkqUsQBNh94uDw\nBryTIpA5nsOvT/4By4sW46aKG2NeI7VPaksslO5Nhz92f3gCHvzqxO9x0/QNWFO2csxtdvideOjE\n73HXzNtRXbQ4ob/hBV5uv903FFMgK9/DcC7r15r/il53P/55yT/EvUYqizrgsaWVQB70iopTvPhw\nv6JgznDZ/29d2oUr9lbct/SrcT0Ar194C53Obty39Ktx7/Na81/R77GBZaaBFwQ67YkAoHBZJ3H7\n4VTZhzwmgfyb3/xG/v63v/0tysvLcerUKezatQuf/OQnsXv3bmzYsCGhe6kmfNuTlNTlhiAIY3Yp\nSjFoxxgSgHwJJHV5OE9oa9QkJdgM+R24bL8KnUobVyBLpT3tPkfc/vIq2hcvpt7p7EGHswu1vfXj\nEshtQx3odllR13c+YYE85HPI8aGhOIqFW5FTMFyW9bm+RnS5euR3EwspQ7nfa0MFpiXUxsnGE/DI\n4yhefHjAE/LceIcZc7XWenQ4u8AJHNRM7CXiRHctbN5B+PkANGzsa2qt9bD77GDZaeA5qtRFiEhr\noqTYJoNrbh/y/fffj507d2Lbtm2w2WzYunVrYg2YpKQuZVbtWHD6x57U5VdofvEsEbvC8o61gA56\nh8adeOOVtyvFt/KlyeHn/XEXaWU/xlNQJEHY5+4fU1sj7z+a8qeSZQiE96uSMAs5EP+dSgpdPI+B\nIAiywpUOJVolBhXtjWshe20jXiPeS+xPX5ytUe6AG7bg7gOlB6nX3R+2b38oWG5VksNkII+eAY8t\nqZZkMvDISV3JSSwEps5pT+POJLr//vvl75988slR//1ET1JlhrPL74ZBrR/1PQRBkC1kZ8AFXuDB\nMonrLmEWMueJaXkOKQVyxOLICzz+96OHMN1Sjm8u+8dRt19CmsjDKRVKQWX3OaCP0V/ewMgCWRJg\nve6+cXkmpLaOSiD7QgI5noXsSdBlLSklSiGvRGk59ysszlSjbG88xWogzGUd+xo/H5D7x8f7YIQh\n6pouZ4/8vcPvRK4+B1ftrfi/47/FvfM/h9WlK+AJeOV5wLLiQkgu69Hh5wN4oOaXWFa4GH+/4POp\nbs6EIbusk2ghs9eahTzmBkxSDBkY+9YnL+eTNX1e4Ed9H6VSwAt8mICWUFpgkQuol/PC4XfifH/T\nuEqAyvuH/c642mG4QI4jzMJc1rEtUEnB8PH+YePRIyHFqPu9toQ1WttoLeQ4AlkQBPlZ4wlk5btM\nKwtZ0d54wjYshhxHaNsV94lnIXcqBXJQgeoIjtN2h1heVTkGWJU4l8hlPTpcfje8nC+tFL+JQAof\nJdPynyoWcgYK5NBC40pgv+nJnjNRC2tkUZHRuq2lRVuKrbljCHTlghW5gCoH6sH2o6P63+H3Ee/L\nCVzcYyCVluNQHGGWiMtaKQit7r6Y1ySCVA0swAfk/9Xl7Eb9MIeFDCqKt8SPIY8skAMCJytitjgC\nWVn0RekCHg997n6cttaN6x5KL0EiFnI8d6HyPv4YiiQQXideCodI/S4pBsqxxKjEhTBdBLLV1Tem\nCnzJRnqPmeqy9ikMn8mGTntKtAETOEkjrdGRLNu2oQ48Ufccdl/dF/Z5ZIb2aBO7JMsixyBWMYsV\nR1YuWJELqNLCruk6MeYJqfy7oThKhVJZiCfMvAEvTGojGDBx76P8295xCGSl8iNZBi817sQfzjwZ\n12JLKIYcVEjMGhPcATc4nou6RvkelIJJifLdTJSFvPPi23j87DNxrfJEGMlC9nN+DPkdyNZmidfE\nEdq2MAs59rjrcoW7rIGQgmmTBXJoPDBs0EJO+Woj8tblXfjjmafHlLCZTKT5m2kCWakcJ6vQ0FTJ\nsk75FJlICzlywR7JQr5sbwEQvZVHspD1KjGeOtqtT1JSV65eFMixMq2HhrWQxedQMSq4Ax6c6K4d\n1f+X75NA7Fe5MMcTZh7OC4NaD6PGMGIMGRinQFa8C6lsabuzMxg6iP0+bUHhqVNpR4wh5we3RMVS\n1pT9FU84KheQiTojut3RBWBsGf0SI8WQpb4sNRXHvSbyPvEUIKWFLClQkoIpKTLKscQGBXK6FFFx\n+JwQIMCZ5qVgQxZy8pKfkoFb4a1L1rNdc1nWY27ABE5SKY1ey2oAjGwhtwQLJUQKRMlCLjIWABj9\noQqSlZ5jEK2RWAJ5uBiyZJksL1oKBgwOjdFt7U0g9usOc1nHE2Ze6NQ6mDXmuPuQh3wOMBDfZe84\nMq2VFvKAx4Yhn0N2McdzNQ967dCrdCgw5Md1u0vPWaDPi3svpSUSN4YcYa0Mxql1vrf1EB468Rge\nOvEYnj73YkyLHBBd81Z3b1gbY1HXex6vNr8ZV8u3KdoRy/qVrHlJIMeLM4cJ5BhJN17Ohz7PALK1\nYnGDkIXskP9eucceAJDCGDLHc3jm3EtoGrgofyb1jyvY3xzPYXvDa7gSVNBTxRV7C56s3y4rQrEE\n8lV7K15oeE0eT4IgYMeFv4WFdC4NXsWLjTtkd7AgCHi1+c2knvIVD0EQwsZ5so5CZRmGLOSEGjCB\nLZBqo+bqcwGMXLP46lArAETFVyVBLgnkUceQE7CQ7cNkWUt/X2IqxIysClwdah1TrEUpYOJZX5FZ\n1pEIggAv54VepYNZY4LL747ZFrvPgRJTEViGHaeFHO6yVlpj8Yq9DHrtyNZlIUtrgYfzxpzk7oAH\nDP6c49EAACAASURBVBjkDTM2lAufLY7LWoqrSln3/XHc1u+37MfFwcu4OHgZH3WdRLcrdilZq7tP\n7s+RinXsbT0UJniVDHrtskIUS9hK8e4Sk1iwJ55lYhvBQu4OJnTNzpkJIPS+JGXOz/vhDnjClDs2\nhS7rLlcParpOoKbrhPyZNN+kcE2rox0fdtTgYNvY8zUmghPdtTjefRotQ6KhoHRZS8LkaOdxHOqo\nQaujHQAw4BnE+y37caAtVFv9w44aHGw/IifY9Xn6sbf1EPa2Hkzm48TEz/vD1o9kueOvidOeJqQB\nE6g1S4tMrk4UhMNZyD7Oj87gYh+5gEmLdbGxEED86lRx7y1ZyPpgvC5ODFla1KNc1pKlr9LCpDGK\nn43BtRMmkOMoFZ6ABwa1ASzDxrSQfbwfAgTo1DpYtCbR1RchzHycHx7Og2xtFvL1uWNO6uIFHk6/\nC0UGUREa8NjCMnpjuayl5K9sbZZcNSuWYuEJeKBX6+X+jHXik1Ixim8hi+82PyjY48WRXX4XplvK\ncWvl5mDbY4/FToXCEc9CHvI50OroABC7qIkgCBj02eU2xXRZB+Px+YY8aFXauDFkZew81u4Aqb2z\ncqrAgJE9L0qLeNBnD0/qYlOX1CXNLWU4QpqPkkCWjmodmKAkvbEitSv0NVRTQQqNuAOSMiFeIymp\nLsXYkX4X6Vkay8l1E03kGE+my3oKyOPUCmSWmdi4kuRiy9XnABheILc7OmRNLXKQhCxkSSCPLakr\nN5jU5Y6wwAVBwJBvSLbW4rmsdaxW3kc9lmMcE8mOdgc8MKr1sGjMcQSZeA/JQgais7ElQW7RWlBg\nyIfD7xzW2ouH0++CAAGl5hKoGRX6vTZ0uYa3kCVBkK0LCeRYioU74IFBrYcp+AwjuazdAXfM02gk\nIVVsFC3NWBaynw/Ax/thVBtgVBvk+8WiKwGBrHQ1xlIk3AE3/HwARSZxvA7nss7T5UCv0sXPslYo\nIv4Y1ouU0FVqKoZJY4TD7wTHc2H9afMOho8lVnSvJkMg7287jJ999Bu5mprk/VL2iTfCZe2WBFYS\nt7HxAo+HTjyGv11+T/7MLVVai+Gq9sq/k5SJoED2uYM/h8aX2x9eZVD6Gs+bE8mT9dvxzLmXRvlE\niRG5LsSzkAe9dvzo8M9wrH1s+TORSKlK6e62Tq1AnugtT5LLWrKQh3FZKwvtRy5g0t+N2WUdVAxy\n4rispWMMCw35Mf+/NEi1qvEJZG+CAlmv1iNLZ8GQbyhqwHqDC4BepYM5KPAihYK0+GbpzPIzWccQ\nR5baaNGYkKPPibKQnTEsZMnFKrmsle1R4uEkgWyMey9JSEmu3wFPtEUhLfSS92TAG71HVFIcjBqj\nwiKPPRaVRTbiKTHhAjl+H+TpcqBh1TGFrbQY5+pFgRzXQvYObyFL7S01FcOsMcHhc0ZVghv02sMs\nZkYlJXXF/JcTSmN/M9ocHbK1G2l1CoIgK5myhRwIWcjJWrA9AS8uDl4Oe7eRbVUKK+n7yGtc/nAB\nLX4vWf7hFrI74B5RURYEAbXWOtT1nR/jkw2PZJyoGPGAmngCuWWoDX2eAdR3x9/uOBokwy/d3dYp\ntpBHP0O7Xda4k0YSQAaNAVqVVrZ0h3yOKKEqxWlMGiM8AU/YPaW/y9Zmi5m7cVzWgiDELNwhLdqS\nyzpSmEoCI1eXDXWMBdSnEMj6BASy0++KGVsM2/YU4xl4gYc3mEFt0Zrhi1E+U1q4xaSuoIUcIZAl\nizRLa5GzmMcSR5bcn2aNCXm6HNh9Q2gPumqBkOavRBIgObpshUAOt5B5gYcn4IVepZefYTgLWXqG\nAXe0RRGykEWBHMvqcAeFvdJCjuetUW4hivSkAOIYO9/fJP8cq92SmzlHlw1dHGE74LXBrDFBq9JC\np459jSfghYfzQB3cP++PEUPucnbDpDHCrDHBrDXBGXDJ70BSxga99ohtT0GX9SgV8H7PQNgY9nF+\n9LkHoq6JdZiLO8LtK1mf/mAIRnmNpED5FXvfx8ugdyiuVyS8faF3LrV1eAs5nss69L9cssvaGfwa\nGjOS29rH+WImXzr8Tvj5QNxckfEitTtbJ66N8Tw1dq84fuJtsxwtIQt5Qm43aaRUIOu0o6vc2TRw\nEf9z9EHU9tbH/L00eXUqLUxqo2zpPnLqT3jsTHhZz6tDbdCptKiwTIMAIXz/smzhGMTM4jiDYk/r\nATxQ8ytcHrwa9rmP90PDqmHWitZRpFYquXwtWktMa8WneA7JQo5fE3sIP/voN/jl8Uej+yM42FWM\nKuYzeDkvBAgwqPXI0sS2Lr0Kl7UlKMwi7yUJQIsmZCGPRSBLgt6sNcthB6ffhVxd/BDEoMJCjuey\n9nI++TmHiyFLi57kGemPIZAlN65Za4JJbYzp5pRikiaNEcbg/4vlreF4Dt0uK3Qq8fzmWEpXp7Mb\ngz47LBrJOxFDICv6QK/SxRwrA95B5AQ9R3qVLmZRBkmwS5nokRayw+eE1d2HEmMxGIaBOdgmKa48\nzVIOAOhx9Yb9LTMGl7XD78QDR3+JNy6+LX/2t8u78UDNg/L8cfic+J+jv8TbYW7fcIEs/SxblsrC\nQQFJmCkE1gS4rXmBxy+O/QbPN7wW95pI17Pys5CFHEMgRygc0lrl43xy5nW0haxMlBSf76+XduGB\now9G1Z6Xnj9WrshEILU7JyiQ4x0wYVe844lAspDJZT0M3/ni9aO6XspU7XHGzlgNCWQdjBoDXAE3\nhnwOdDi75K0lgDiou509qLBMiynwXAEXNKwmKFRNcMYoPckLPPa1ipmNkZqmn/NDy2ph1Ejxw0gL\nWYq5mqFT6WKUzkzMZe3nA3j87LMY8Now4LVFba3xcl6oGBWytJaYmr90T73KoBBm4QJZtpAVLuvI\nJDfpb7KCMWSxT8ZiIQcFssaEvKBABoBZOTMADG8dZmtDLuuoZwg+Z7hAjp9lXWwIuqPd0V4HXzC5\nRstqkaPPjlni05Wghdzn6UeAD6AyqwJA7Dhzw4Do0lxevERsd4yDMZQCWaeOHk8cz8HH+eRn16l0\nAKKTCWVL1yi+w8hs9SOdxyBAkE/hMmtFBa3DKe6jrjCLAlnKAJYq1TFj2Ifc5eyBj/fLWxMBccuP\nnw/IBWP6PQPw8370KOZ2pOUpW50RX4GQx0WZmzAR1dc8AQ8GfUOwunrjXhNpycdqozKHQfo+dE24\nQJbuxQt83KQuIJTcd3nwKgICF1UBT/n8k1E4RWq3VKAmnst6yB+0kL0TK5DTfS9ySgVy9dzEzkyW\nCMV8YmtukralU2lhVBvgDnjkvYVuhVu6dagDAgRUWKbJxT/CNGe/W168LBoTAgIXZcXW9zWE4lQR\nrkYf54NGpYFBIwnT8IXWLrt4zdCrdVELo/I5DKr4AvmVpjdwafCKHPOM7Bcv54NOpYVZa4q5D9kt\nCyodsnSSMIuwLiULWeGyjpyodtniNysE8vAx5Pq+BjQPXAr7TBL0Zq1JtpABYGb2DAAjW8jxYsiy\n4qHWywJyOJe1lMzXH0MgSxayRqVBnj4HPs4X9X6VHhaTbCFHt12Kj88MCuRYlq3krr6+aFncdiuV\nEr1KF7ZNBgiNbSn8oVeLAtntD/9/Idez6CFQuoJ5gcehjhpoWA1WlSwHANlj0ukQLeRSczFUjEq2\nmKWkRUjbnkbhsZYUOmXGvjSmomKjir6NtCCluenlvHKIRsIVYz1JxEI+19cYpii4/C582F4jexwc\ncsw21L8OnxOHO47J7yVUz9kb2vbGhVv1CSV1+ZWuarfsDVL2T7hAFhVIaexJCl+s5x/LWfCx6HJ2\no9ZaH9ZuyVsTTyDHclm3DXWgvq9B/pkXeBxqP5qQJS/pgnyaS+SUb3saDbEGoRJJeOhUWtlVKCVN\niFsHxAVGytwtN5fKi1PYaUABt7xwS265SNeJslhHtED1Q6vSQMWqxC0mw7isJQtZuYDKFjIbiiFH\n3sMT8OLDjhoUG4uwsqRabHfEwPRwXtGy1Zjg4/1RFo9SUGVpYm8ZCreQY7us5RiyzgKdSguLxjxi\nQfyn6l/A704/HubulwS9WWNGni5X/rzCUg41q4753mWBrLXApJHKe4YrFW6FhaxiVTCoDTEnsfQe\nJZf1cDFkLatBTtCVHrmdRFrojWojDJICEEOJlPIPKrKmgwETU+lqsbehwJCPcnOJeJ9hkrokC1mA\nELbQeWRPiDjWJQs58v9Jgl0WyAp3YmP/BfS6+3B98VJ5bklzQ7KQs7QWZGktsoCRBTITtJBHIZEl\ngSxl7Pv5gJwnEemKlYSbsuhEyFUtvlMBAnycL9xCjrGejDRuA3wAfzz7NJ5veFX+7IPWQ9je+Jpc\nfERqn3LOHmg/jOcbXsGl4HiX/rf4rrxhyWaRsWTp+wAfCG1/4mJZyO4w5TCWQO732jDos8uCvbH/\nQphnTfn8oy2IFI+dF9/B42efgcPnjI4hxzvkRHJZe0Pr0avNb+IPZ56SFcXz/c14ofF1vN+yf8Q2\nsLLLeuzPkQymlECOFfNRonT1moIL4XlFFmPkBDRrzQqBF9rz5wl4ZHezLIQUbto+dz/q+xqhDcb+\nomPAossaAAwqfdTCp0yC0qt0UTHsmEldEVa4NPEqLOVyjDVysfZyoQpb4v8Nn2BKV64lTkKUbF2p\n4id12X1DYMDIvzdpTcNqrRzPwRVwIyBw+NPZZxRn6wazrLUmuagK8P+3950BdhRX1qfTy+9NDppR\nRhGUEBISYJGDSSZHg+xFBBsLDIYVAoNB6wWMsL322t4FTDJpLSNjL174DCYuJgiMQAgZowjKmqCJ\nL4f+fnRXdXV199Mk6c3M1vkjzUy/ftXd1XXrnnvuvUYxi7AadH3u7ekOhNUQNEWDLMmI+MKOayD3\nirANYS20zxiyBMmDsjYbhygaIh70N4kXh9Qg/IoPsiS7CtLYFKKA6nfME9IGNKoZoQ1FUjxjyIqk\nIKKFqdFNc4s5wHjIhLLOFqesWVHXWzveBQAsaJxPf0feDfL8Yr4ojQ0CsMIOfYghswxLc3IvWpN7\nHZ5ftzkfiIgpW8ghr5M4qotoKp+23RcyL5K5JKOsL+4hNydbkSvksDvRRA0Z2ZCQ+0c3CnmLlevi\n8rXZcSVzKZtn66WyTuWLbyaSuZTtZ0vUFYcqq8acTrVTpbwsyUjlU7RAEn/9AxW/bU+1Q4eO5mSL\nRVn7e0ZZx7NW3fm2dAcKegGtKWNuNJkhzC86t7megwWZeuQeD1YMOoOs6zqe+cfvsWrXh46/ucV8\nWKQLVgw5aBrUPayKlaOzQmoAQbI4MYIKHTpCKvECnDTtOzvfhw4dRzUcTj/DIlPIwKcY5TsDasBh\nTHnKmj+HJerSPGPI7CLrJVQilHXUZVPBnpOorIHilLUqqwiqAQeV1ZXpRkQL00InYTWERM5bpUk2\nVkE1gM5MFx5a+wQKeoHe47AWpt5nmS+GoBpEUAu5e8iZTvpyA4ZR8IohE4NkGOSES4qXcd9DahAR\nX9jVIBMj5ZM1z5xmkkYT0kKQJAlhNeTpIauyiupgJQIuG7eMWdUoqAaM83hsJEilMkmSLGPrsniT\nue5XPTxknrI2Nx/t6Q6sbf0Mo6KNGBMdRY8n7wb92RexPQviIUsSqdTVew+Z/J/9mVcPW4aVjcc6\nRVypXMpV1BXPJlAZKIdi5r4XA6Hjc4UcNQyE6SCZDGRcbLMb3plIcgaZDXt5qaxZ7z5FzseEHRK8\nh2x+V3c2gagWQcwXNVMJjfHOrJkGAPis1VLx22PIvaess4Ucnvzsd9jc8QX9HVnvmpOtDsqarHWr\nmz7BHze+6NjAkOtiz8OHM7Z1bXesNVs6tuLxdf9Fzy885D6iI9OJt3euwpOf/c42UQCmAo2Xh5xj\nY8ghx9/prpIahKDDQyYKWUpZ+5ze5ZdmytQRI+baPgsY3l9BL1geshqwxa8Bg45RJYM6dVtA3UVd\ndmPE0pBuQqV8IY9cIWfzbPnYr7VQB5gYsjdlDRjGkjdAnZluatABIKKFoEP33DiRz8+unYEZ1Yfg\ny85t2N69E93ZOAJKAJqsIqD6MaP6EErHGx6y3cjnC3kkcymbYbDKZzKpMHlr42FcQwg5Pe/YnZMF\n0Kf4UO6LYW+qw2G0WQ/ZayOU4OZQSAu63ou9qXZUBsohSzKCasChRUgyGxcybjcPuTvbTVXYZD67\nVabiPWQ+htxult+s8JdBgkQXs+1dRhGdWTXTbMIs9pkH1SA0WXU1yDr1kB1D90SzwyBbHjN5/+NM\nrNZgtuzeInvtxu/StvvCrgdhLYwKf9k+PWQ2zXFXvAnZQo6OlXiU7PrEbxbIPHAYZJfn5fCQuc8A\n9vh5Mpd0VOzKF/KI5xIIayFUBMrRlu7ALtOjP6bxSEiQbHFkm4fcB8p6U/sWvLfrb3hn5wcAjE0J\nYdRakq30XeQ95Ne3/RV/2foGurLdyOaztvsTzyaQzmfofCT3mxjmZC7lEJG+svUNfLDnIxpGoDHk\nQW6RB51BZmMrj6x7mtISxt+Ke8gZZkENmx4ywFB01EN2GmQakyEKWUJZm4suu1tM5lJQJIVpZcd4\ntwUi+lHN7wjY4teA6VH6IpAkiXor7EKRyRtpU2ShZsdOQCtoeSiH02zs1yNdiYybiJ1kSXamPTGU\nNWAYGVZcRcpmEkEVgKKpRYA9xnpYraEe/kfrBnRnuikNCgDXzPgGzplwunGsFqTxNmv8Zt65eY8A\nuHr6LBMAAGHV3bNN59PwyQb1XeaPIZ1LO8IRdg/ZnbImechkDoZMxoA17iTeSTaOQTWAVC5t23C4\nefbE+LDjyRZydL76XTZ4Fpti/C3gpbLOdCLmi5jaB41uPsj9I9dLYN8IGfedvBOASwy5h5R1KpdC\ndzZOvahmh4dsjyGTmDnLRFmxZPY+2D3knJlvmzWrqlUEyvfZwYst5LI7vgfNiRZGzOXM++XXHDL3\n2bGmvDxk5vnwc9GLsnYTkBKFfUWgHHk9j/VtmyBBwtjYKIyNjcYXndvM+5BDZ6arzwWRjHti3B+i\nR2DzmVuSe60Yss+eh9zFeL/8GhTPJrj2rntt/wKwiezyhTw+b9tojMdkSAk7owtRV3F8sPsj/OCd\ne+lkJhOqPlSLZC6Jx//+W3psgpvcPGxpT6plkEkhfD5HMaQGHIuT5d3YhSvs5CRlGC262W5MAVAP\nmS/sQcpmkgXM1UMuZGh82q/4XQU/SWpM/a7UKZuTTYwcXxzEMlSGMTbKZ3IxZMbwA8ainC1kqWFi\ny2YS0PGYhmlj+xYsfetfqHdBY6xaEJMrJwIw1MQGtWanQQnI84i7KGoDLga508Ugk+No7DfnQvGb\nz5Ts4PmCK9RDLmKQ49kkZEmmxjGkBR0K32whi7yep5uEoBpwiLEcGwkt7GAeeO+fzMl03ulx+XnK\nmvGQdV2n1DdgzF8yl1kmhQVroMmGjHxelmQrntzLGDKp8jalwpgbLclWm8fsJlYyBE0ulDWXVkR+\nJpsJQjuHtRDdQHh18AIMyprEm3fFm2y1yLs5ytoYF9noEwPqFHx5e8hp+lwNytp5fSxlncwmHZun\nJjP1ihTbAYwNTnWwEpqiYWrlRBT0Ata3b6LXPcpMXyNr8uaOL7DkrbuwrWuH532h98cUzJLwB9+W\nNZVLQpM1qq0gDgxh5lqSe2n8mITA4tm4zUi3mA1ZWlN7oZoVvwhrSf5P7gN5PiLtqYfY2L4Zrak2\nurMii828+sMwOjoS27t2Us+CeK+pfNqjwXwGsiRDlRSqBAWAyRUHAbDvUlVZhaZoDhVzgvNuaNpK\njjUESQTUgHEOWbXtdskippkxZD5tKWsqJYnR8ruIcDL5DDXoZGHnPTW2xrSbR2p5yD66qXCosLkF\nP+pztldMc5Q1n1dLy2Yy9CVvqDa0bUJXthubO7baPhtWQ4j6IhgVbcTGji3I63l6X3gQD5AVdvGe\nHwBK3bJ0G3+ddMOQ4e9Hml4nSd9q4nJJs/ksFEmBIiuennbCVOmTRYBsJuzVlOy0tpuanjxjy7N3\nPmdCWRI1t1uOMc8kUMqa+S5jXmbpXPEpGqUI3TY+AKimALA2QsQgR7UINHMO61Lvuj0Rb7ghUo+Y\nL0pjyAYtrrlWoDK8Q6eHzBs6ci9IWh3xsoJakP7Oq+ZzvpBHU6IZI6MNUGUVexJ7bJXWKGWdtW/e\nAes5xXNelLXdQ84X8sgUsogx+brsMTk9j2w+i0TWEqQlcik6r+j8TRoMI/GQCerNNpxTKicBMLJR\nyHXXhKoRUAL0HfqsdT3i2QQ+aXYvyMSCbLq9DDJxZiRJgl/xUSqaPJfmZCs1zqTJTDybcJynI92J\nXCGHKZWTIEGylUJmK9sRu0JFXYKyLg7yINh4EGC8IDFfFHk975pE75aTmikYIiZJkuhCVxWoRJU5\nOa3zJB2Lk9UjlYv/uTQHSDI714ASsHvIBStlCbAWQSt/kF+I3bzsDK3eRM7hVg/b+Lw7ZW0reUnT\nlbxFXYCxi07nMzZ1bTKXhgSJjifEfVcXU+SEIExpfuMYmsJgGnuy6SLCu6mVkyitxVLWLOhGgKPo\nANBccuPzzpi/4zo9GIM0c99J32C+NGqmkIFm9tsOe3jaiWyCbiAAazMRdxk7uQdBOs+YhTpvN4Ru\ndbiJ50XnkxtlzegN2PPZvTR7vFpTfDTMYnnh1jUREE+TMCSEZo75IrQwCKWsAezs3r3PRZEY5Opg\nFaqDVdibakdLai9qgpW2ODo73xNZe53mZD7pYCVYlTXJTCAeckgNUg/SK47cktqLnJ5HQ7gedaEa\n7I43YVe3EY9VJIXGSt09ZNMQu8SQU7mUjVoH7MJPMnb+mGTeUFWTTRCb9lTDbSgNBsAyyGR+j42N\nQkAJ4LO9620NSNjaBYSx2Mp4oV4gBpCU4GQ1KR2ZLnRluukc85s587z3S3KQyaYhnrNT1q3JvfS6\nGiL1GBGuw7buHXQN+cfeDaYWohy7403QdZ1uWga5PS69QU5zhtAyWAFbylE2n0WOie24lSJMMx4O\nodDGlY1m4rBmHdisPXZn/M0UjFFjYf8728w8k89Qz9ev2ksVUsqaeMiEss7aDTI7KQGnqMu3L4PM\nLLIhNWi0wmMp65xF30c90pUcVK7PKf5K59N0kwM4PWS2ShcB77GTl4kXvRCPj1CTgOXh8gi5MhUW\nbW993qko5w1y1EXkRvo+k+dRHzINMuMBAQbVzD5bWZJtCzBJVWJFhdamzin2IX/jN27sMWy6FsAZ\nIm4+uVPW9rADzUNm0p7od5kbBJ/s9JCDnIcMWCEdYjjK/WWQJRmVgQrGIBtsVkt+J+5+/6d4e+cq\nx3lYEINcE6xCTbAKOnTkCjlUB6s8DTJPWSdzKaRzRioRGYdBWRMP2dg4kNKRrAfp5SETY1MfrsWI\ncB0yhSw+b9sIv+JDXaiG8ZDZjXGKsg+ANfdT3MaL/EzGSmKw5L1iKWu2ql6+kKeGNplL0nXGYniI\nh2wvtlNvditTZAWTKw5CS7IV69s3mfemHFEtjG4zE4E8jy87txfdTHVlum3vVGe6i777ZOOWyqfp\nPPQpGtL5NBcftmLIZNNgeMhWnfucnsfGdqOoUE2wCqOjI5HJZ7An0YxkLokvOrdibGw0xsZGIZVP\noSPTSdkZIeraB6zYrT2nMKgGbfRjgjNIbh4y6+FUBMrx7Rn/hHMmnG4zuoaYJokQt4ClOFEXoaxJ\ncQ+yYPH0X1Dx2xbRLPWQzUWbo1rZ6wPgyBslAjDWQw6oAUcDDJbOlCUZITVo85xYyprEiHmRRjKX\nsnm/5H6znmOKMVIAQx0TD5nWn7Y8Wz6mTXb7VCfAVLICgPHlY6nXuS8P2Y0FsHvITgEbWajJddAS\noMwxpOkAeR5VQcOgODzkfJaOlTAx7JgyZmyY9ZAtQ8pQ1lm7IXWryOYWQwbsPZFZgSJ7jfyCz94n\nN8qaZ4aIqIsttuFqkM37TQxHUA1g8cwrce7EM2iTCt00yPG8YWS8atETEBq5KliJarPRBwDTIIdN\nFX3G9t4luE5GqVyaGqcy02tP5VMOD5l8V0gNUsPm1sELsNiSEeE6umFL5JKoD9ch4osglU8hV8g5\nPGRbWV4PD5m8z2SshPINqH74TGo3xY2dFPEoMzdBbNoTH3IJayFbsR1i7ACLtl69x2h1WBkwPGTC\nThKD3JXtdm1iY90fkx42vdGOTCc1pKTaHmDNdctDtgu2SAx5RNjYNMSzcbrxJ+ch9SWqg1UYHRsJ\nwBB2rW/bhIJewJTKidTD3hXfI2pZ9xReHnLQzAUFDPqRF3K5lj4s2D3LadVTUe4vs+XyZgs55PS8\nZ8yNT1kh/+epJqsMYQBpplC/5SEb4whz8UO2aITxefv385837kXARWFspyH5HFW2oIfRCCDsiA+n\n8ikEVD8VT7gZs3QubfNA+XgoLXepsQaZp7WNY2ieJq31bBynySomVox3nIcFOadbmUR2fBG3GHI+\nhYDCXKeLF82K4AAjdt8Qq8fueJNd1cx4yMa4whx1an++7P8TOSeVGeRiyG7CJDbtCeBjlBajBHhR\n1pzKmsw5xkPmNwgk5JIt5BxznkWUUtYWszG5cgKqg1WQJAmqrNIYck437vGGts1FlczNyVbEfEbV\nN2JYAMMbIveAiLxIG79kLkU3HiE1iGwhS+cdEZexMWRCrbekjPOEtBDNff+8bRP+sPEFvLPzfdu4\nSLnJ+lAdNRYAMCJUZ2Nd4px+wU07kMqnGObEUlmTsRKD7Ff8ZjlUy0Mmni6hmENqAEE1YI8hmw1C\nWhjRWlgLUQ+8jhn/FFNYSQSL5f5y+h61pvbaNuhfcrT1tq4dtCQoqYA4MtpAr4EY2/FlY+hn2DmW\nzWdtBrkz00U3SfU2D9l+HlISuSZYhTGmQf7fHe/iL18aVbumVk6iz2h3vEl0e+opeENoo6yppxUH\nT/XyHnJBLzhirwRsahNZFMl5DNGUVd6SLNKsuIiljHlvgacI2TxVgPUo7Qad1LnmRV00Bs0Zr1Ua\nFwAAIABJREFUZPaz7H3zKnbBKs4BY8Hkc4yTuZTdu3ShcvflIVsNIbxFXXznlgTnIQPA7Boj/Ynd\nubNwa9LglvZkVRNjvPxcymZMXDcenHgNAEbG6pEpZG0xRdZDJtcazyao0aaCNUZUyM8BwGlI3Z4x\nbwjdKGviBRImxi2Njqau8ZS1qzdueciAMR95JoVFY6QBsiR7PjdNVqmHnIPxbmQLWWxhCkewyBfy\naEu3U0NcwxjkasYgk0YzVaYyOsFQ1sRgtSYMD7KceshGHrKPKa2712znGFKDCKh+VAcq0ZJsxStb\n38TT/1hpUxbvTuyBJquoClZQYwEYFDaZU22pDmQKWco28alIyZxReSqVS9Nxsl40GWs7Ncg+Q/zE\n0u3mMYRaD6gBBE2nIZFLGp3ZzA0SmZdhs0jN6OhIjI422p5lTbAKVaYBJ/eBvEfE8BGB1ZdMVazm\nRCt+8dGv8fQ/nsUXnVvphoWEoIw2nMZ7OM7FIPtVH3TotKUmedZfdGyFBInWlCdpT7IkU+OrQ4dq\n5r03hkcgqAbxRedWbOn8ElFfBGNjo1w95MFOWfeu/+F+gCWm4ihdLWjLn034zdhIoBLbunc6DDLZ\ncbMLKgFLB/JiGsCg8ohgoi3VYbywjIcTVINUHJDiNga0OUUujaAatMpemh4GpVpphR47xcinXdEq\nXbKdsibjJ6STm+CnoBeQMgVnLGUNGHl/O7p3IcV4vMlcir7cAEvlGi9RvpBHtpClYwRAS5JaHrJV\n7tI6xvLkSJ4ye2wiZ6QFseedP2IOplZNogsSD15MZtwzO0tArleTVQdlzRasIDnXXbZYuXnfVdYg\njwBgvNBVwUrouu7iIRtFUIxyq1Z80+4hu6ms7QbQLd/cKbrzjiHvS9SlSgr1jkiOO5v2xG8QiEI6\naz4/lklhsaBxPg6rm2nzkFmosoo0TIOsW2LBz/ZuwKSKCY7j96baUdALdHHmPeSIak/nqQ5WoSnZ\ngiRDWVcGyrGjexdaTINMKF7iIRu6i4A5przt/v3z3OvQkmzF1s4dWLH+D3hrx3u4dMp5KOgF7I43\noTZUA1mSUROsgizJKOgF1Idr6UacxGyrg5XoynYba46jAEsHdOgo95dhZ/duY2NszjsyVtZD9it+\ndGW6GS/aeEdIc5ugEkBIDWB3upPWaudzxklGwLUzr3AUj5QkCVMrJ+KvO1fRTQLZYHzRYRjg2bUz\n8OcvX6P5vqlcCg+ufZyua2/teI/S2ZMqDsJftr5hUtZd8Ck+NEZG0O/jN4aE7RhfNhbNyVbEcwlE\ntDA0WUVYM0JCxiYnTKvIAYZgV5ZkyIqMO+bdTEMNVYFKKLKC2lANJEjYHW9CtWRUJRvk9ngQeMh5\nzkMm1JnClHPMdtMFgyimeVEXW2WJhyIr8MkaUkyMhV0wSYwWMGigSn+5rYhBSLWKUrhR1oC1ePKi\nrhBHtfILsZ/zsNkqXQRB2pHK7iFLkGismo/bkvMEuLzaTlMsYmwuUh7eZdx2DtZIhbh4aHemGz5Z\ns41XkRUElIAjoZ8Y+kQ2YUsLAoxFwcsYAx4eMscSkPNEtIhVNELX6SaFQJZkhNWQjbIm52I9h5Fl\nxiJChF1k08d7yIAV16UGklkQwxyrALhszIpQ1tamy5lm5RlDZotIMEIaco/8ir12Nj8vqYeczziY\nFBaKrHgaY8C4V9RDthnk9a7HE9qTxI5JfW7iDVn5tcTwmRkUWWuzTRXUpkGO+aNmLn+aGmReMU7O\nG9HCGBsbja80zkNloAIf7PkIyVwKTYkWZAtZygSQBR8wWB1CWZO5UmWOn6esAaDVjP2SWgapvNND\nJqIuYpDT5nMA4BCfEQ85U8iiO9ONkBa0rW/s9QVMepvHVDOOTOLoZC3YYnrIo2IjUROswlazTOVv\n/r4Cu+J7cMzII1EbrMbqpjXY3rUTFf5y1Jn3pT3dYdRc0CIIMown9ZDNTR+JUbNeNNEkRPwRM4bc\nhagvijJ/jGoTahh9QZk/agq5RtP5qMkqakJV2B3fA0kyLPFg95BLapAJzQywHmQKmpkjzFKolkG2\nKCoWbNlMNxDamY+VATBfijTS+QziuYRNjWgcaxkDvkgCL8rKcKIuy0PmKXl3jyaTd16HO2Vt0LDE\nqPHxRavzld0gk513Op82REwuBpmeg6vSxY6bMBpd2Tj1rFkQKpdNaSAdp0iebm9gpT25pHZxrEhE\nC1kpKDmDTubzmyO+sC0P2Y2ybozVA7DEKkQpy24+eBrZTYMQJIxBrm+UNZlrlrDNLaWLGGQfJEiO\n0pkB7h4FFD9Sbh6yxlPWWZo72hdosgqdesjG3C73l2Fb1w5HCKU7E8ez658HAEwsN2oHSJKEw+tn\n4/C6Q42NlPkcCWVNFmWistZk1Sr6kSCGL2jm8qeMTAzVaZD5n2VJxlEN85DJZ/DOzvfx5Ge/A2AJ\noABgXt1sTK2chMpABX0HyLhIDJelrImRsQxykNYwJ+mFpIQt8Tb9io9Su52ZLqjM9ZFQStA0yIDx\nvNiuZuRa9vX8plROxJjYKMyoNjzJCL3PTeZ9NtTMiVwST372O3zSsg6TKibgvAln4qjGecgWcojn\nEhgRrkOMFNVJdaArG6cpcWTzZDkjlkEmlcMIiFGN+sLozHQjnc8g6otAlmR6b1n2xAv1oTrEcwnk\nJON9GOT2uLQG2bVZeC5BHxirhqVy/gDxkO0Gme0h7IaA2R+ZX8AAY8HLFXK0oTjZYROENIbyzrt7\nyORaspwoizck/EJMO0bleA/Z8sJcDbK50ydweshOyhqwYlNuylk+tsrmMlv3wqJfdV1HdzbuKsQK\nayFH/iBgCPSMPF1nrfFiMLxuv2vaE7/YRHwRZPIZZPJW/LeS22RFtDDiuQQtMEMZBeae1kdqIUsy\nVdeSzRJN5wEQUe2bGFp6laWsTSOXdFFZk/sQcFNZ51PwyUYbT3IPgmrAg7I2Pk8KLvCiLl6Q5Vf9\nSNrqOts9bRJyIQUp3ARdPYEqqyiYHnLW9JBJU4PPmU5s+UIeD3/6JFpTe3Hq2BOpyA8ALpp8Dr4+\n9QIAcHjIlDEzKWvWAySxyaDiR0D1I5FNImOGYELM9SiS4rpuHDFiLmRJxh82voAvOrdibt1szK8/\njP795LHHYfGsK40uY8SAmZu3iC9iZGfk2WIdlea4zGIk5liJqIutMEgpa9VPN4kd6U7TuzfGTrzo\noBqwXQ95huRe8WyUGwJqAEvmXIcjG4z6/DzrURWoxBjTYL6/ezWqA5VYNO3rUGQF80fMoV5rfbjW\npJpD2BnfjYJeoClx5Pr5NqCpfBpRX8SmFyCbl6g/TDs0xTjD3iODbAq7UpIxF4TKughYlSfrIYeY\n3b4ma+jOdtNJTaggvhWfm4fDImDmC/MGkfwNANNY3cNDZooP8KIuYqhpv1zToPKGhI2RA6Cx1DTn\nIbNemBedyRoi3lPjRV3UQ87YDTK70PKxVbYaGIFP1qBKinkvjB6tbqlKYS2EXCFnU3kCRqpGTs/3\n2kMGjOfgnvbEe8iWiprQevwmi7IBTPU3wL6hU01acpepH6AestwDD5nZcGiyCp+suaqsA5yHnMrb\ni4fwm42wGgKfVkOqzhH4FT/1kElxDFaJDhj3zI2yZtOeACPMoEPvh4esMR6ycf9mmQZ51Z7VdIFc\nueFP2NC+GTNrpuG0cSd6no+vnBf1Rei1WCVtjWsgMeSAGkBADVClbkD1Q1M0urHyMlhl/ihm1UyD\nDh2joyNx6ZTzPA0bmU8kHhrWQrT1KrnPRDhF0pWCSsCsYZ4yNxN++pzI9QUUP52TCbNCIHkWRLDF\nesjketh75VX9rhjYTXZUM7rSjY4agiq/4sM1M75Jj4loYcw2a9ITA1jmi1n6EtPrJwbXynW33qOo\n2QqXiOHIhoBdW4hBtvQFFmXtBRJiSEoG4zDI7XFpRV08PVfQC0jkkrbAfUQLG16VOUFJd5x4tpeU\ntRJATs/Tl5KdwMQ7IX1NnZQ1MYhJh2cZVOwesuVFWeMIMa0D2Rg5AVsasxhlTTYDJC7KLrK8YeDp\nXD6dgiqUmXHwsVU3ylqSJAQ1ozdxl9k83K2YBxkP8S4bwvXY0L6ZlvJjFdY9RVgL2ZoMpHJGkw9V\ntk9j1tP39JAZ9iXmi1obOs5wjQjVYnd8DzoynY6yqOx1xhmKHIBjw2EIvuzpL5psGQVWaMdeH7+Y\nhrUwdsR3GdWHJAls1TmCgBqg48mYfXb5GHBA8ZuivZxR/tUcGy3aYIZcOuj70g/KWioA0GmO/kFl\nYzGhfBz+3vo5XvryNUS1CP53xztoCNdj4dSLXMVj1vWHHD8H1aDxbuZTqAxU0LHupQbZj6Dip72S\nyTsRVIPIZrqKzsUzxp0Mv+LHGeNPtrFWPIgBId8R1sIIqgGzhoLdQyZpPcS46tDRkelCdbDK8Zz8\nis/2/gVd4t8BziDzaXL8PesJ2DAU8UTHl43BMSOPxMzqaWiI1NuOP2PcydBkFYea2RJl/hhdT2Pm\n+nBkw+GIZxM42KT9CWUN2L3frmy35SEzBpnc4wWN81GAbgsfeIGsTXnJmHsihlwErPqwoBfQlek2\n+r9qdhqVTXsKqUEz544zyC7pQizoS0rjLqyoi3jIxgTiF282X9Ar7YkIrnhRF/l8wiVGzn5/mqes\nZdYg28sqZgs5FPQCl7LExZA5w87HkL2KPRgl87wpa+N6jA1GZ9qZg0xADAlJhSAvcHOilZ6jtwip\nQVsd86S5KeG9lggtcBLHXlN5WRGo4I6x5yJnPDZ0bOqE5SHb85ABpvRr1pn2RMae4GLILEtDmBK2\nCYmrh+wzmAe2zy5v/FnGxa3eN+BMjzLmpbVB0Mz7YHmVffeQAQBSAVk9Qyn4K6ddjgp/Of60+SX8\ndv0fENZCuGbGNx3j5OHcoIQQ0oLozHQjV8hRGhiwRHgBJWAbf4AxyEDxuVgXrsVlUy8oKjgk4yAF\nMQDjfQyYdDSZEw4PWbXGlSvkKLXOgoi66NjVgONZBJWgbVNBKWsznNIXg0zYScAyyIqs4MJJZ2Ny\npVMdXxWsxKVTzqfjYLMaSAy5MlCBiyafwzTOsXvIxndV2n6O+COOY+rCtbhw0lm20JEXaHEak6UR\nBrkI+JaCbNUcgogWRqaQpYYkqBoKQofK2kUly8IyyCTvkKWsTQ+52/DmKvwV3GctUZezt6zde+VF\nXYDhHaXzGbN/b9KxwyWCE8DaWBQTdVl1rF1iyDkrhqzJKo0/RrUIJEg0hkwMNz+WiBZGwsyVJF49\nv2sPa4Zx6TBrznpR1oAVBhgRNgzyHlrKr/ceMl8+M+Wh/qVFGjLdRTxkq+gM4C0QIxTcnnhzUQ+5\nO8eprB0esuHFEZrRMLZOTyfFbLrYblD0+7iYtdt5/Krf+Hwh79kYghcT8hsEMn87yXvnobLeF+ii\nKReQ1bO2vPhrZnyTLvpXTrusRxRkUA1QwydBoht0slkKqAGHUQuofntqnEoapZhiuT7MRR6yJDuq\nswXVAPIMK0eujwi2gpxxDagBFw/Zb1sLAqofmqzaWCF2E8JeV6QfHrLxeWOu1fTgufBgNzAxDxW+\nX3YaZLIBJvnlURfKujcgbXALpkEe5Pa41JS1sRhIkIwE8RQRO7AG2XhQTckWmksZ0kJoc7TFs8dM\neQR4g8xMUvKyku8vD9h3w+6UtXulLV7UBdhTdkgzdNvYFGsBdY0hc4IfK/+2eAyZvReKrCDmi9AW\naySPsyZkF0ZQKjeboMKZGk48EVKNdoJN3VZrNx60slbO6EZDaueSc/Ylhszex6gvglQuTVX39muw\n1PltqXbIkux4mfl61l4aBGLI29Md9LuKecjxbMLwNDl6k+RmE40EH5oBjHlmhRTcDSmbixzRItQr\nZMEq/8k7xhtUviBNIpe0PUcy/v5S1tRwyHlkCxkENOv+joo24ObDvoNMIWur5FQMhtI6hO5snGoe\neLqWv9b+eMi9gZFuR0rvhmxrjiIptCwmESkFuLGSeDFZDwGD1mUZKkq3KwF0FbrpNfN1E8gY2H97\ni6gvbCvS0huwfbGjHoaUvS7yfh478ijUhWpoy9yo30lZ9wZkw0dS74SoqwisWrPGwyPxQXvDeeLJ\ndCNoii9CatCg7ZiuRDxFy4Ockxhy+87UPjF4KoSnrGVJposyVVnzoi5Zc3w+nk2YFKNT8WpcQ9o9\nD5n7Dr4UIsAW4zANci7tuBdl/hg6Mp1GmTumLi8LtjkDyQnljyGbmZ1dxt/dDDL7u4gWpukcRPQS\n7MMiQQU9ZmWsdD7t6iGz5TP3ptpRYdb6dT0mQ+Ll7vOHLCwdmU4rD9nmIdtrbHuldAWZal2ZQtYR\nmgGM55zKp+31oz16EMezCdeceuMaSDw67cqmsD+ncmnaEIM1bA4PuZ+UtSQVaDc2FiOjDT02xgTU\n0PgsFTEBK+oCQCuMsddvGeSB85ABa84b4QfL2LalOwyjyX0P79mSNMYAZ4B5ypodu0/RbClOgDXX\naG612ntRl/F543N9MsgMZe3l2bJhOXJMQPXj0NrpNAzlJurqDciGkHjIhUHeEHlQUNbEC2llytgR\nsAt7iJtorGp135S18dmCXrDFygC7p8kLutjPJkzhSFCx8n8DLqIuCZKNUiLjbU93GAuxS8wPAC2a\nz1+HtXhylDXzomqKBp+s2Tw+3tsr88fM2sRJ7Eo0IaAEbDtZgImtZuLYFW9CRAs7KGnyfHZ1WSke\nPNhdedQXoeelopc+eMhWEY44FSsFXWKOZLyklq7bM+U7W3nFWmNM7J2vwgYYL3xA8VtpT1zrRQI2\nf9vLkAbUgJGbX8gWTekCjNguX07WOo+1wbOU8vZj2PG4bRDIhrCjnzFk1aQMJaWAbCHjyWD1BtQg\nq/ZCE4Cz8IXfrOXOXj/ZAFPD1Ye56AbiPIRVo0wlq4YOqc5iHQ6DTNpjmmNVTEbQFr4i9Q+4zYQb\nZd0YMWpKN0atKlm9wchIA4JqwFa3u6cot8WQPShr1UlZ84iaMWTCjPQWGhdDHuQOcqk9ZK7zSsrp\nIbPGwKKYnLWB0z2krI3Puy9gAGhPVBZBJg+Zz8kkBoFNe9IUzVHpC7DnH7LwM96KG2XNC36SLhWq\nAHujA56yBixvrzXVjqZEM0aEa52CKPPF2JtuR2tyr2uN4p54yOzLE/NFQbpS8efoDcgOuTPdxRhQ\n7xjy9u6d0KE7NAHGmC3hF+BNWZOcyo50JyPqsjMobD1rg5J2Xpvl3SeZ4jTcAk1DE041PwEpitCc\nbLVSlbh7yW7w3BpwAFY95LZUu+sGgYgSSfGO/qisAeD6iw6GDt1zw9wb8FQsT1lrskqbTvANNQDr\n/tCe532kdJ3jsouogrZ1Iugo0OGMIdvH6qfiJ7/jGN67dxN1HVQ+Fvd95U4cXDm5T9dz5vhT8C9H\n3Nqn+0M8ZJ+seQr12Ovy8n7JJieihYuq771AGJqCoKz3DbIQVJoBfJrI7xJDNn5PJqGzpi/5/75U\n1vz5gR54yGShzCYdaSaarEGWZFu3JpauNsZr5kWSGLnm7iGzlDW/cBHFJuDe5QiwOj4RQZAbZQ0A\nG9s3mzV4ncaWGNfN7V9Ah+56DFnIWhLG9UTdRF0qG/uJOna4fYkhk/G3pztc61gTkMWPCMoqAk6F\nLInFUso6l7GVIrV9ry+G9jSb9mS/r8Qgp3Ip6NCLesjxXMLT2LLpbV4dlgh92Jrc6+kh0/iwWSoS\ncN6ncqb8otsGgbAAJJbJvzM9BVVZK+7j6At49bCtKIbJXllZEPb0RIBNezLXk4HykDmDHOCMr/Fd\nxt8MKt0P3rsHrPXITz1mdjNhp6wtD9lpkAHDodlXURAvKLLSZzqfiEi94seAMw/ZDRG/s5tYb6BR\nyjpn/ju4MSg8ZFY4A9hfEHaxD/EesrkgbWjbhPd3r0ZEC1N1Hg/2hXRTtxK4GWRS3COeSyCdz9he\nIlIXmKqs8xnHpoBsIFpdVOSAtTtsS3cwKm37OUgBAcC9DzBgLATpfIYq0HmDTQza521GhaR6FyqK\nGGTSANztGHb8iqS4xnHtHrKZ7M9srvryorN1fr1YAsB4JqTZBuBUWAOgcbdumq9rxNzdFq9yfxlS\n+RQ9ljfaYS2MbCGLj5s/BeDesYrcey+PFLCnt3kZbZKH35xstXLaHfPJuM9t7MbF5TzGMe30u9w8\nZGts/RN1kXs3oJS1h4cMsIbYxUM2/zalciIawvWYUD6u32MCLHaJeMo2J4DzZP1mO1B7PQTOQ+Y8\nZfZvPGXtMx0DwMkAlgKKrODw+tmYUzfL8xhCWUuQPNuu+hUfZtVMx2G1M/s0DuohY2h4yIOiMAjx\nkMlu3J4+YG+DCNjLUbYm9+LhT58CAFw1fWHPPGSHmKY4ZW0cE6QpNG4pJMRIZgtZx+SiHmXK3SBb\nqTVNSJteGO/dBtUA9iSaaVMIt3Gw1bCMc7hT1hvaDGPrZjhIiKApaSioR4Scx7DGNqK578D9ig+q\npCCn562cQlYP0AdlK5tLbcXR3RefqGa1m+SrdFnHhG0xZC86lXwvEaQ5FNTm/Xj5y9cBGCUXebD5\nzOR8bjFkgIRG3L1fRVZQ6S9HC0tZc8eQ7yKiPOPczrkgSRL2MhsEO/PDMTT9THuiBlkdSMraNHxs\n5zZO9MSXuAWs92J0dCS+P+97/R4PQaQIZU2eERFfBrlxuo3VnbJ295CJ2DWeTfQ53j/QWHjwRUX/\nTq4r4vOmoyVJwlXTL+/zGGRJhgSJEXX1+VQHBIOidGZlgM/7ZVTWmksMmRFJPbj2N+jOxnHhpLOL\n7nT5EpG2v+2DsiZjSngsksR71XUd6XzGsWCTl8YrhlwfshbrTD4DRVJo/jA7fir4oX2A3UtGbu74\nEoDTqLN9YdnvtZ/DTg25UtaanRJzA/FSAaZzi3msJqtFqx55IaD44VN8hkF2UZqzYI0/P7/Yscep\nYttbcMRnATg9ZOM6m5OtmFIxEbUhezoTYMR+VUnB7niTawtQwJoXiSIxZMCgrTszXTSFjfeQ65nG\n7F6iLkVWUBksR1uq3TV3eqA8ZOKhDKSHbJVsdKGsOYqaF0oZx/R/DG7gN55uYbIgJ8IKcGlPxtjt\ntZ5dFeKk4QjjXIQ0o5dxX2KtpYAqKVAkpU/q6Z5CkiRosio85J6AbydGwMdDSN9RnrJ+6cvXkC3k\nsKDxCCxonF/0u4rFkNl8OK/F240WIwiofqQSabSm2pAtZB1pAmRX3O1RjKMiUAaf4sPuhFEz2c3L\nZwU/bnnIAHBY3Sz8decq/Pem/2dcl4vKmsAna0Vjq2Scbkn9rHfrVjaTIKyF0ZHpsgyyeWxfY3aS\nJKHcF0N7ptOTJSBgNwpem6yIFqFCrHQ+bbs/LAizQKqM8cYqzNyPBSOPcD0Hade3K7HHteMYYOV7\n74rvYcqWuhnkSqAN2Nq9wzyP/X6GtRCivgh2x/c4aFDbeUKV2NC6heov2A0Cv+nYVwUtL1DKOjNw\nBvnQ2unYEd9N6yfbq+55ecjOXN6BxsTy8Thh9NG0QUPAZc2hXd44cZZ9rPbNhFuRILeUrTPHf5Vq\nUIYCJEnCBZPOsimy9wc0WUMhb8SQB7k9LrWHnKIKYre8W4D0tyXUlEn7mEYjW8hhYvl4XDDxa/v8\nLjeBBQGpfMO2NuPB7kSddYEN73VT+xYAwBizCLv1Wa5qE7eAypKM+lAt9iSakcylXKlTq6tUiomf\n2heWCeXjcO6EMxw1ewnCWoiqT+vDta47aUVW6PjcVNj89Xh5yOT7AGeh+P6oWsv8MXRn4rQiWdBj\ncSXGn08tsR9j5bgbHnJxyprQ5JpLDBkwDPf0qqmeY68P1yKTz2BnfBcA5zwYHTPmzdbO7Z6iLsAS\ndm3r3GGex3nMiFAdWlNtVulLN8MeqkBBL9CcdPY+KbJC54pf8fXZ6yKUdZx6yP2nrENaCBdOOovO\no2IxZBpzdfFEBxqKrODcCWfQPslBxRoXpaw5wRf7Dgc9DLEma7Q6mbXBMg28z/qO2bUzcMSIOQN8\nVfsXCxrnY3r1wfv1O9Qh5CGXWGWdpnmCxMvwyZqzWQClgIzJF/UZCr7KQAUWTbvMQe+6gS035+ah\nlfmiqA1WeyoS9+UhA8DnbRsBAGNidoMcUOwpP3wMGzDiublCDm3pdlc6l7zIXZl40fjpsSOPwnzz\npYz47IaPrVjlRkUTEMPpRmkD3nniPCpMARKpfEbCD/1RtZb5Y9Ch00pj+/KQveLH7DEf7PkIgN3T\nZcHv4HkGg1zfUY3zis5Fcs9JSMGRY6yFURWoxNau7Z55yIDlSXeZdbh56pv9rq1d22lxDB7VISOF\nakf3bgDO50LmYV8V1oBlkEl62f4whrbCIJwK2VIlG98rS7JjfdlfcGPlyFjJOIlDYozVLtgiz4y0\n1GT/RjaJfFVBASdYynqw17IuOWVNcwK1INrS7a4vf8QXAeLWZI76Ivj2zCvQEK7rlRyelJtzW+Su\nnv4NqEUWUzdajP5svlzr2zYBAEZFG21/J2kYlBp0uUZWzeyXnYsnEWBt797pmfZEvuuSyedietVU\n124o5f4Y2tLtrmItgogWQRNaPAsCqLIKn+JDJp8papDPOuhUHNUwz4r5mc+qXx6ySR/vNptWeFGp\nxPi7Kaz5Y/78xatQJQUnjTnO/Ts5g8x7yNOrpuLKaZdjerW3dwxYz7DdI/YLGF7yR02fYEf3LvMY\nbw+ZwK3ONHl2JCvAbaNJDDLx2Pnv8skakuh7L2QAUM17FSeU9X6I3wZUPyRIUCSZ6jd4D5RNIepr\nGlBfxkXA5wvzVLXRvY0bM0ezs8ccXDkJV0//Bo4Zexja99p7AgjYoSoa8jDW3kFuj/tukJcvX44P\nP/wQuVwO11xzDaZPn44lS5Ygn8+jpqYG999/P3y+4vRUKptCmIsruu32o5yHDACHVPU+2T2oBtCV\n7XY1CCOjDUU/6yYcISAvUFu6HbWhateFliggyf95kFrPgHsuNaHBt3ZtRyqfhq8IjajG3BlpAAAg\nAElEQVTKKmbVTnf9GzEubulMBOR+F/OiQ2rQMMhFKOtyf5mtyHxkgDxkANiTMA2yF2VtGv/yIgaZ\nrTB2yZTzMK5stOtxJKeSZAHwDIYiKzjU436zYJ8x4G5sx0QNg9ycbHU0ESBgmzDwncPodzHPzksh\nXRUy9BIk7sjPW5Jv3dfGEmR8ACvq6j9lzUOWZARUP6XYAae4S5EV+GRtv8WP3aDKKjRZRbaQYzxk\nJ80eUANAusMhQGPH6ld9kDJWnrwkSZhZc4j57IVBLgZNVlHQh4aH3CfK+r333sOGDRuwYsUKPPzw\nw7jnnnvw7//+77j00kvxzDPPYMyYMVi5cuU+z5PMpRkP2amYJBgVbYRP1nrUDaYYeMFHb1CcsrZ+\nHhMd5fp5WzMLl+9nF1A3g1wTqkZA8WNrp0FnesVO94XR0ZHwyRqNV7phZLQBAcXv8PRZ0Dq5RURd\nPOpCNdBkDSMjfSvlB1j0MVngvby3hnA9ZEnGuJi7kQUsj/WE0UdTmt8NpDEHYChD+xpPrQ1V08+6\nhWYAe7jD69rYRu5edDKb0ubFIhAPmcDNQ3b7fW9ADLJXN62BQkN4BBqZeUX+PyJi3YeaUDVqXBTw\n+xM0X9j8tyFcB1mSbf2EG8P1qAvV0HAHafjCNnWpCVajJlR1wLz74QRNVpEfIqUz++Qhz507FzNm\nGArHWCyGZDKJVatWYdmyZQCA4447Do8++iguvfTSoufJFXKOMnZuC8wJo4/Ggsb5/c6v609lnmIG\nmTWOXoaOfCdfR5ugOlgJVVaRK+RcvQhZkjEq2oiN7VvgU7R99mf1wkljjsXRI48oGhc8ZczxOG7U\ngqILMbmeYpQ1jzJ/DD/6yh2eueI9O4f9ur085PpwLZYvuLNo/uyoaCPu+8qdRb1863tj6Mh0uXqj\nPYUqq6gJVmNPosnz/rOboGL3vzpYabA9HueJaGFaQczrHlSHrYwCtw0CeU79McgqR+/vL4N83awr\nbT9PrZyEx875CRIdefq7782+9oCnBAXVAG2MAxi9fPl5ufDgi2gRG8DYNN/3lTtt+f5XTbucijUF\negdV1qCjAKAwPEVdiqIgFDImy8qVK3H00UcjmUxSirqqqgrNzc09OhftTarZRQ+2QUrygCS7uyXj\n9xS2/sm8ypr52+iou0F2K/PHQpZk1JnqTC+DNSY2CjqMXOe+FmrgqwO5wahiVfz8Iaqg7l0nmYAa\n6NeiyDbDkCAVNe6kO1gx9MQYA/bavP0Bie16VSoLqkGax8yqdHmQOLLXs5QkiYryvDzksBaimz+3\n8/AdzfoCfvO5PyhrwCjW4ijYwokaA6q/T/nv/QF5hnwqJzsviSaDBV/yUlO0QVPwY6iB7ck92Cnr\nfom6XnnlFaxcuRKPPvooTj75ZPr73uxCysJh1NREUdNsxPoqozHU1OyfRPGvjJ8DXclj8sjRtAtN\nTzGiYNFHI+uqURWyxlibMsYuSRIOHTfZtgCSa6mMxoA9QDQQ8by+sZWN2NG9i94THtOSE/HK1jcB\nALFQaL/dp55gwfg5gJLH1FFje30v+4NYjimSoPlRV7t/VabkHteVVWNti/Gd/bnv42tG4ePmTxEL\nes+DSdXj0LS1BbGQ+zwAgDHVI/DBno9QHvY+z7jqkdjUsQVlRY6pCVdhe+cuxALO74oEg0A7UBXr\n+zuZ5RiNxroqlAcO3Lwt5TsCAEePPxyb2urQUFex3+jmUl/j/kZ/ry8cNDcycgGRSGBQ368+r6Rv\nvfUWHnjgATz88MOIRqMIhUJIpVIIBALYs2cPamt72LIrq6C5uQvIGPETify8HzA1fDCmHnww2vYm\n930wh0zc2mQkOnIoxLscfxsRqkNXWwZdMEQyNTVRei1yztiZ+ySf5/VVKEZMr5CRXI+pgLUpkAva\nfrtPPcHU8ME4+ph5JRlDUA0imUvCL/v36/ezz89fMMVBUPv1nTEYmzdN935+dX4jvqgUecahQnSf\nx5TLBiUt59zfqZqaKGJqDMAuaJLzXup5g8nQM3Kfr7kzaS9U0d2eRVY5MHOGfX6lwvyqeZhfNQ8t\nLd375fyD4Rr3Jwbi+swmbYBUQEdnsuT3q9iGoE/cYVdXF5YvX44HH3wQ5eXGAnPkkUfipZdeAgC8\n/PLLWLBgQY/ORbxJt7q0gwmEciJdWux/MxbrYiKoYjFyAiLs8koNqQpU0lzZ/VXcYCiA0McHksIj\nMXs+5am3IM84WKSWNwl7FGsSQIQ/xfQQRElf7D6RKmZu3zUglDXDnnh10xIQ2J8g76wkF4anqOvF\nF19EW1sbbrjhBvq7H/3oR7j99tuxYsUKNDQ04Oyzz+7RuYhxm1Y1BSeMOhqH18/uy5D2O8iCFXDJ\n6RwTG4WTxxyHefWHeX/epeA8j2nVU3HS6GMx3+M8kiRhdGwkPtu7vs+lDIcDyn0x7I7v6bPSvC+g\nMeR+xiBHRkbgzPGnFO1RO75sDE4bdxJmVh/ieczY2GicOvZEWj7SDZPKD8JXxxxf9J0ihVNcY8gD\nmPYEwLObloDA/oQVQ84PzxjyRRddhIsucnbyeOyxx3p9LuLp+RQfzp14Rl+Gc0BQLGVKlmScddCp\nRT/P1+F2gyarOHvCaUXPMzpqGuR+LJJDHaXwkImYrL8esiRJ+OrYE4oeI0syTh930j6POWP8yUWP\nUWQFZx701aLHkMIpxURdA6Wy3l+CLgGBYqDZA9Lg95BL3hZkf1Tu2R+QJRllvlifC6GTRg582k5v\nMb5sDAAg5h+8woT9DWqQD6CHXBkohyIpfW6UPlhRFzaU/W7z2qpF3ve5pkoKrcN8IItyCAgQ2Cnr\nwW2RS1o6E/BuDjAY8Z1Zi/rsIY2OjsTiWVcWLVTRExxSNQXXzrwCE8vH9+s8Qxml8JBDWgg3zv6W\nZzewoYqxsdGe8/LokUegPlyHsTH3Yjc9gSRJUGUV2UJ2yGy+BYYX2LSnQW6PS2+Qh9JL2tiPClOA\nUaygv5AkCYdUTen3eYYyyn3EIB/YuTPOZCeGG7zmZVANYmaNdxy7p9CIQRaUtUAJoLIx5MLgtsgl\np6z/L6uFBfqGkdEGqJKChnD/NkgCBwbEQxGUtUApQFlNSVDW+4R4SQV6i+pgFZYfvUyk0AwREGGX\n2HwLlAIk9U6SCxjkDnLpDfL/5fQdgb5D0J9DB5aHLJ6ZwIEH9ZDlPO3YNlghKGsBAYH9CkFZC5QS\nIu2pFxAvqYDA8AahrIWHLFAKkA2hJBeEqKsY/IqP9gAVEBAYnqAesghPCZQAFmU9+EVdJTXIC2ed\nX8qvFxAQOAAg3cAEGyZQCliUdV5Q1sVw0oSeNaAQEBAYutAEZS1QQgylfsgljyELCAgMb5AFUQg4\nBUoBtnTmIA8hC4MsICCwf6EKlbVACWFXWQ9uiywMsoCAwH4FpaxVQVkLHHiw7RcHuT0WBllAQGD/\nojZYBVVShl1jDoGhAdHtSUBAQMDEcaMW4MiGww9ody4BAQJ1CHV7Eh6ygIDAfoUkScIYC5QMGhND\nFiprAQEBAQGBEkGRFUiQjPaLwiALCAgICAiUDqqsmjHkUo+kOIRBFhAQEBAY1lAlVaQ9CQgICAgI\nlBqqrJqVuko9kuIQBllAQEBAYFhDlVRIcl54yAICAgICAqUE8ZCFQRYQEBAQECghSAxZUNYCAgIC\nAgIlhCprRtpToVDqoRSFMMgCAgICAsMamqxCkiDykAUEBAQEBEoJUq0rj1yJR1IcwiD3EB9/vBpt\nbXsBAEuXfg8AsHjx1di8eSNefPFPePPN13t1vo0bN2Dr1i8BAHfeeSvS6dTADriPeOSRB3Hxxefg\n0UcfwtVXfxOLFl1e6iEJCAgI9Auq2WCioOdLPJLiEAa5h3jhheepQf7Rj35q+9tpp52JY445rlfn\ne/PN17Bt21YAwLJl98LvHzy1fi+44GJcccXVWLbsnlIPRUBAQKDfGCoesuj2xOHFF/+EzZs3YfHi\nG5BIJLBw4UW45Zbv46233sCWLZvxr/+6HIsWfR0vvPAq/cwjjzyI8vJyVFfX4tln/wsA0NS0BwsW\nfAXf/vaNuPvuu9Dc3IRkMokrrrga9fUj8N///RzefPM1VFRU4Ac/uBVPPLEC3d1duPfef0E2m4Us\ny1i69A5IkoS7774LDQ2N2LhxAyZNmoylS++wjfnPf34BzzzzBGpr61BWVo7DDpsLAFi16h3E43E0\nNzfhwgsvxemnfw3nn38mnnhiBUKhEH75y59h/PiDcNppZx64GywgICBwgKEppkHW7Qa5tSOFPCP0\nkiQJ1WUBSJK0z3Pm8gXs7bQzm36firKwz/WYaMiHoL+4yR3UBvl3r23EB/9oGtBzzp1SiwuPn9C7\nz8ydjwkTJuF731uC+vp6z+OOOeY4HHPMcUgk4li8+GpcddVV6OrqxOGHz8epp56BHTu24447luLR\nR5/CvHlH4NhjT8DBB0+jn3/44Qdwxhln4YQTTsbrr7+CRx99CIsWXYPPP/8My5bdg4qKSpxzzmno\n6upCNBoFABQKBTz44K/wyCNPIhgMYeHCi6hB3rJlMx599Gl0d3fjm9+8BKeeekYf7piAgIDA0IbG\nUda6ruM3f/4c/7tmp+PYGQdV4brzpkORvQnkeCqLe59ajZ0tcdvvJQCXnDgRJ84Zha5EBvc8+SH2\ntCUBAAGfgn++5FDU1EQ9zzuoDfJQxU9+ch8uvvhyjBo1Crt2teGzz9bh+eefgyTJ6Ozs8Pzc559/\nhm99azEAYPbsOXj88YcBAI2No1BVVQ0AqK6uQTzeTQ1yR0c7wuEwKiurAIAaYwCYNWs2VFVFeXk5\notEoOjra98v1CggICAxmqBxl/ZcPtuF/1+zEiKoQDmoso8dtb+rGJ5ta8ezrm3DxCRNdz5UvFPDA\nH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gpu = pd.read_csv(\"./GPU-stats.log\") \n", "gpu.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "CPU performance can seriously limit the overall throughput during neural training. A solution for this problem is to use multiple threads to feed the GPU; this requires a thread-safe iterator. In Keras 2, the required iterators seem to be threadsafe and adding a simple \"workers=N\" statement to fit_generator provides a significant speedup. \n", "\n", "Once multi-threading is enabled, the iterators seem to change their behavior to take advantage of multi-threading and even simple \"single-thread\" performance without data augmentation is significantly faster. \n", "\n", "For those that jumped to the conclusions, here is a demonstration of the performance improvement of single-threaded " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "230/230 [==============================] - 112s - loss: 0.0831 - acc: 0.9797 - val_loss: 0.0695 - val_acc: 0.9835\n", "Epoch 1/1\n", "230/230 [==============================] - 112s - loss: 0.0897 - acc: 0.9763 - val_loss: 0.0550 - val_acc: 0.9835\n", "Found 23000 images belonging to 2 classes.\n", "Found 2000 images belonging to 2 classes.\n", "Epoch 1/1\n", "230/230 [==============================] - 72s - loss: 0.0914 - acc: 0.9791 - val_loss: 0.0849 - val_acc: 0.9773\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# step 1: non-accelerated training \n", "vgg.model.fit_generator(defaultBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps)\n", "# step 2: throw more threads at it - might not improve performance, but changes the iterator behavior\n", "vgg.model.fit_generator(defaultBatches, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches, validation_steps=val_steps,workers=3)\n", "\n", "# step 3: regenerate the batches after activating multi-threading. \n", "defaultBatches2 = get_batches(path+'train', shuffle=True, batch_size=batch_size,gen=noAugmGen)\n", "val_batches2 = get_batches(path+'valid', shuffle=False,batch_size=batch_size,gen=noAugmGen)\n", "vgg.model.fit_generator(defaultBatches2, steps_per_epoch=epoch_steps, epochs=1, \n", " validation_data=val_batches2, validation_steps=val_steps)\n", "\n", "# VOILA, instant performance improvement!!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }