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apache / madlib-site / ac2c01e2a555798c3b4d7ed919e5438fca652c16 / . / community-artifacts / Deep-learning / MADlib-Keras-imagenet-inference-v1.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inference for ImageNet dataset using imported models\n",
"The predict BYOM function allows you to do inference using models that have not been trained with MADlib, but rather imported from elsewhere. It was added in MADlib 1.17.\n",
"\n",
"In this workbook we load models and trained weights from \n",
"https://keras.io/applications/\n",
"and run inference on the ImageNet validation set.\n",
"\n",
"## Table of contents\n",
"\n",
"<a href=\"#setup\">1. Setup</a>\n",
"\n",
"<a href=\"#load_model\">2. Load model architecture and weights</a>\n",
"\n",
"* <a href=\"#vgg16\">2a. VGG16</a>\n",
"\n",
"* <a href=\"#resnet50\">2b. ResNet50</a>\n",
"\n",
"<a href=\"#load_images\">3. Load validation set images</a>\n",
"\n",
"<a href=\"#predict\">4. Inference</a>\n",
"\n",
"* <a href=\"#vgg16_predict\">4a. VGG16</a>\n",
"\n",
"* <a href=\"#resnet50_predict\">4b. ResNet50</a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"setup\"></a>\n",
"# 1. Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
" \"You should import from traitlets.config instead.\", ShimWarning)\n",
"/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
" warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
]
}
],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"u'Connected: gpadmin@madlib'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
"#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
"\n",
"# Greenplum Database 5.x on GCP - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
"#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
" <td>MADlib version: 1.17-dev, git revision: rel/v1.16-10-g205bdba, cmake configuration time: Mon Aug 26 16:15:40 UTC 2019, build type: release, build system: Linux-3.10.0-957.21.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-10-g205bdba, cmake configuration time: Mon Aug 26 16:15:40 UTC 2019, build type: release, build system: Linux-3.10.0-957.21.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select madlib.version();\n",
"#%sql select version();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"load_model\"></a>\n",
"# 2. Load model architecture and weights\n",
"\n",
"First drop model architecture table"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n"
]
},
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS model_arch_library_imagenet;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"vgg16\"></a>\n",
"## 2a. VGG16\n",
"Create a PL/Python function to load the model architecture and weights for VGG16\n",
"\n",
"Ref:\n",
"Very Deep Convolutional Networks for Large-Scale Image Recognition (Karen Simonyan, Andrew Zisserman)\n",
"https://arxiv.org/abs/1409.1556"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>model_id</th>\n",
" <th>name</th>\n",
" <th>description</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>VGG16</td>\n",
" <td>VGG16 model with weights pre-trained on ImageNet.</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, u'VGG16', u'VGG16 model with weights pre-trained on ImageNet.')]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"CREATE OR REPLACE FUNCTION load_model_vgg16() RETURNS VOID AS\n",
"$$\n",
"import keras\n",
"from keras.applications.vgg16 import VGG16\n",
"import numpy as np\n",
"import plpy\n",
"\n",
"# create model\n",
"model = VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)\n",
"\n",
"# get weights, flatten and serialize\n",
"weights = model.get_weights()\n",
"weights_flat = [w.flatten() for w in weights]\n",
"weights1d = np.concatenate(weights_flat).ravel()\n",
"weights_bytea = weights1d.tostring()\n",
"\n",
"# load query\n",
"load_query = plpy.prepare(\"\"\"SELECT madlib.load_keras_model(\n",
" 'model_arch_library_imagenet',\n",
" $1, $2, $3, $4)\n",
" \"\"\", ['json','bytea', 'text', 'text'])\n",
"plpy.execute(load_query, [model.to_json(), weights_bytea, \"VGG16\", \"VGG16 model with weights pre-trained on ImageNet.\"])\n",
"$$ language plpythonu;\n",
"\n",
"-- Call load function\n",
"SELECT load_model_vgg16();\n",
"\n",
"-- Check weights loaded OK\n",
"SELECT model_id, name, description FROM model_arch_library_imagenet;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"resnet50\"></a>\n",
"## 2b. ResNet50\n",
"Create a PL/Python function to load the model architecture and weights for ResNet\n",
"\n",
"Ref:\n",
"Deep Residual Learning for Image Recognition (Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun)\n",
"https://arxiv.org/abs/1512.03385"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"2 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>model_id</th>\n",
" <th>name</th>\n",
" <th>description</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>VGG16</td>\n",
" <td>VGG16 model with weights pre-trained on ImageNet.</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>ResNet50</td>\n",
" <td>ResNet50 model with weights pre-trained on ImageNet.</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, u'VGG16', u'VGG16 model with weights pre-trained on ImageNet.'),\n",
" (2, u'ResNet50', u'ResNet50 model with weights pre-trained on ImageNet.')]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"CREATE OR REPLACE FUNCTION load_model_resnet() RETURNS VOID AS\n",
"$$\n",
"import keras\n",
"from keras.applications.resnet50 import ResNet50\n",
"import numpy as np\n",
"import plpy\n",
"\n",
"# create model\n",
"model = ResNet50(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)\n",
"\n",
"# get weights, flatten and serialize\n",
"weights = model.get_weights()\n",
"weights_flat = [w.flatten() for w in weights]\n",
"weights1d = np.concatenate(weights_flat).ravel()\n",
"weights_bytea = weights1d.tostring()\n",
"\n",
"# load query\n",
"load_query = plpy.prepare(\"\"\"SELECT madlib.load_keras_model(\n",
" 'model_arch_library_imagenet',\n",
" $1, $2, $3, $4)\n",
" \"\"\", ['json','bytea', 'text', 'text'])\n",
"plpy.execute(load_query, [model.to_json(), weights_bytea, \"ResNet50\", \"ResNet50 model with weights pre-trained on ImageNet.\"])\n",
"$$ language plpythonu;\n",
"\n",
"-- Call load function\n",
"SELECT load_model_resnet();\n",
"\n",
"-- Check weights loaded OK\n",
"SELECT model_id, name, description FROM model_arch_library_imagenet;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"load_images\"></a>\n",
"# 3. Load validation set images\n",
"Load validation set images (test set image labels not readily available) from http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads\n",
"\n",
"We use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning which uses the Python Imaging Library so supports multiple formats http://www.pythonware.com/products/pil/\n",
"\n",
"First initialize loader:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n",
"sys.path.append(madlib_site_dir)\n",
"\n",
"# Import image loader module\n",
"from madlib_image_loader import ImageLoader, DbCredentials\n",
"\n",
"# Specify database credentials, for connecting to db\n",
"db_creds = DbCredentials(user='gpadmin',\n",
" host='localhost',\n",
" port='8000',\n",
" password='')\n",
"\n",
"# Specify database credentials, for connecting to db\n",
"#db_creds = DbCredentials(user='fmcquillan',\n",
"# host='localhost',\n",
"# port='5432',\n",
"# password='')\n",
"\n",
"# Initialize ImageLoader (increase num_workers to run faster)\n",
"iloader = ImageLoader(num_workers=8, db_creds=db_creds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the validation dataset images in chunks for memory reasons. Labels are in a separate file that must be mapped from Caffe format:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Couldn't import dot_parser, loading of dot files will not be possible.\n",
"Done.\n",
"Chunk: 1/100\n",
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE imagenet_validation_data (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-1 [pid 80631]\n",
"PoolWorker-1: Created temporary directory /tmp/madlib_YEtJdtGq7X\n",
"Initializing PoolWorker-2 [pid 80632]\n",
"PoolWorker-2: Created temporary directory /tmp/madlib_PgVGQNJEMV\n",
"Initializing PoolWorker-3 [pid 80633]\n",
"PoolWorker-3: Created temporary directory /tmp/madlib_dNsqB0RZBX\n",
"Initializing PoolWorker-4 [pid 80634]\n",
"PoolWorker-4: Created temporary directory /tmp/madlib_NODr6Za6YY\n",
"Initializing PoolWorker-5 [pid 80635]\n",
"PoolWorker-5: Created temporary directory /tmp/madlib_PWuHnhXgoA\n",
"Initializing PoolWorker-6 [pid 80636]\n",
"PoolWorker-6: Created temporary directory /tmp/madlib_kVvTfJwi1G\n",
"Initializing PoolWorker-7 [pid 80637]\n",
"PoolWorker-7: Created temporary directory /tmp/madlib_r4iKOVq1zJ\n",
"Initializing PoolWorker-8 [pid 80638]\n",
"PoolWorker-8: Created temporary directory /tmp/madlib_IwlQzDfllA\n",
"PoolWorker-1: Connected to madlib db.\n",
"PoolWorker-2: Connected to madlib db.\n",
"PoolWorker-3: Connected to madlib db.\n",
"PoolWorker-4: Connected to madlib db.\n",
"PoolWorker-5: Connected to madlib db.\n",
"PoolWorker-6: Connected to madlib db.\n",
"PoolWorker-7: Connected to madlib db.\n",
"PoolWorker-8: Connected to madlib db.\n",
"PoolWorker-1: Wrote 500 images to /tmp/madlib_YEtJdtGq7X/imagenet_validation_data0000.tmp\n",
"PoolWorker-1: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-2: Removed temporary directory /tmp/madlib_PgVGQNJEMV\n",
"PoolWorker-7: Removed temporary directory /tmp/madlib_r4iKOVq1zJ\n",
"PoolWorker-3: Removed temporary directory /tmp/madlib_dNsqB0RZBX\n",
"PoolWorker-6: Removed temporary directory /tmp/madlib_kVvTfJwi1G\n",
"PoolWorker-8: Removed temporary directory /tmp/madlib_IwlQzDfllA\n",
"PoolWorker-5: Removed temporary directory /tmp/madlib_PWuHnhXgoA\n",
"PoolWorker-4: Removed temporary directory /tmp/madlib_NODr6Za6YY\n",
"PoolWorker-1: Removed temporary directory /tmp/madlib_YEtJdtGq7X\n",
"Done! Loaded 500 images in 293.43257308s\n",
"8 workers terminated.\n",
"Chunk: 2/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-9 [pid 82087]\n",
"PoolWorker-9: Created temporary directory /tmp/madlib_vliWWopeXt\n",
"Initializing PoolWorker-10 [pid 82088]\n",
"PoolWorker-10: Created temporary directory /tmp/madlib_5xhdh1ruzX\n",
"Initializing PoolWorker-11 [pid 82089]\n",
"PoolWorker-11: Created temporary directory /tmp/madlib_YuA4hATaPX\n",
"Initializing PoolWorker-12 [pid 82090]\n",
"PoolWorker-12: Created temporary directory /tmp/madlib_yDugflQzxa\n",
"Initializing PoolWorker-13 [pid 82091]\n",
"PoolWorker-13: Created temporary directory /tmp/madlib_s9WlMEYxJf\n",
"Initializing PoolWorker-14 [pid 82092]\n",
"PoolWorker-14: Created temporary directory /tmp/madlib_4PeW6Ck2PZ\n",
"Initializing PoolWorker-15 [pid 82093]\n",
"PoolWorker-15: Created temporary directory /tmp/madlib_R4NGNdbi4U\n",
"Initializing PoolWorker-16 [pid 82094]\n",
"PoolWorker-16: Created temporary directory /tmp/madlib_ARFjOa5539\n",
"PoolWorker-9: Connected to madlib db.\n",
"PoolWorker-10: Connected to madlib db.\n",
"PoolWorker-11: Connected to madlib db.\n",
"PoolWorker-12: Connected to madlib db.\n",
"PoolWorker-13: Connected to madlib db.\n",
"PoolWorker-14: Connected to madlib db.\n",
"PoolWorker-15: Connected to madlib db.\n",
"PoolWorker-16: Connected to madlib db.\n",
"PoolWorker-9: Wrote 500 images to /tmp/madlib_vliWWopeXt/imagenet_validation_data0000.tmp\n",
"PoolWorker-9: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-11: Removed temporary directory /tmp/madlib_YuA4hATaPX\n",
"PoolWorker-10: Removed temporary directory /tmp/madlib_5xhdh1ruzX\n",
"PoolWorker-12: Removed temporary directory /tmp/madlib_yDugflQzxa\n",
"PoolWorker-13: Removed temporary directory /tmp/madlib_s9WlMEYxJf\n",
"PoolWorker-14: Removed temporary directory /tmp/madlib_4PeW6Ck2PZ\n",
"PoolWorker-15: Removed temporary directory /tmp/madlib_R4NGNdbi4U\n",
"PoolWorker-16: Removed temporary directory /tmp/madlib_ARFjOa5539\n",
"PoolWorker-9: Removed temporary directory /tmp/madlib_vliWWopeXt\n",
"Done! Loaded 500 images in 329.386959076s\n",
"8 workers terminated.\n",
"Chunk: 3/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-17 [pid 82123]\n",
"PoolWorker-17: Created temporary directory /tmp/madlib_bUUOm8GhCp\n",
"Initializing PoolWorker-18 [pid 82124]\n",
"PoolWorker-18: Created temporary directory /tmp/madlib_mjhXhFEE8z\n",
"Initializing PoolWorker-19 [pid 82125]\n",
"PoolWorker-19: Created temporary directory /tmp/madlib_BeEVwKm70A\n",
"Initializing PoolWorker-20 [pid 82126]\n",
"PoolWorker-20: Created temporary directory /tmp/madlib_b5n0WlaxFf\n",
"Initializing PoolWorker-21 [pid 82127]\n",
"PoolWorker-21: Created temporary directory /tmp/madlib_gMvkBAMaaG\n",
"Initializing PoolWorker-22 [pid 82128]\n",
"PoolWorker-22: Created temporary directory /tmp/madlib_F9X2PeBqi2\n",
"Initializing PoolWorker-23 [pid 82129]\n",
"PoolWorker-24: Connected to madlib db.\n",
"PoolWorker-23: Created temporary directory /tmp/madlib_3TYdbtBjeo\n",
"Initializing PoolWorker-24 [pid 82130]\n",
"PoolWorker-24: Created temporary directory /tmp/madlib_zKIEGCgpXT\n",
"PoolWorker-17: Connected to madlib db.\n",
"PoolWorker-18: Connected to madlib db.\n",
"PoolWorker-20: Connected to madlib db.\n",
"PoolWorker-19: Connected to madlib db.\n",
"PoolWorker-21: Connected to madlib db.\n",
"PoolWorker-22: Connected to madlib db.\n",
"PoolWorker-23: Connected to madlib db.\n",
"PoolWorker-17: Wrote 500 images to /tmp/madlib_bUUOm8GhCp/imagenet_validation_data0000.tmp\n",
"PoolWorker-17: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-18: Removed temporary directory /tmp/madlib_mjhXhFEE8z\n",
"PoolWorker-19: Removed temporary directory /tmp/madlib_BeEVwKm70A\n",
"PoolWorker-24: Removed temporary directory /tmp/madlib_zKIEGCgpXT\n",
"PoolWorker-20: Removed temporary directory /tmp/madlib_b5n0WlaxFf\n",
"PoolWorker-21: Removed temporary directory /tmp/madlib_gMvkBAMaaG\n",
"PoolWorker-22: Removed temporary directory /tmp/madlib_F9X2PeBqi2\n",
"PoolWorker-23: Removed temporary directory /tmp/madlib_3TYdbtBjeo\n",
"PoolWorker-17: Removed temporary directory /tmp/madlib_bUUOm8GhCp\n",
"Done! Loaded 500 images in 321.439842939s\n",
"8 workers terminated.\n",
"Chunk: 4/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-25 [pid 82159]\n",
"PoolWorker-25: Created temporary directory /tmp/madlib_aL8m9O86Wy\n",
"Initializing PoolWorker-26 [pid 82160]\n",
"PoolWorker-26: Created temporary directory /tmp/madlib_ErWj86oGJ4\n",
"Initializing PoolWorker-27 [pid 82161]\n",
"PoolWorker-27: Created temporary directory /tmp/madlib_BCmQipPLKc\n",
"Initializing PoolWorker-28 [pid 82162]\n",
"PoolWorker-28: Created temporary directory /tmp/madlib_DTcFD7cyFL\n",
"Initializing PoolWorker-29 [pid 82163]\n",
"PoolWorker-29: Created temporary directory /tmp/madlib_pEFV3GMLIO\n",
"Initializing PoolWorker-30 [pid 82164]\n",
"PoolWorker-30: Created temporary directory /tmp/madlib_9DseGWD1Q2\n",
"PoolWorker-32: Connected to madlib db.\n",
"Initializing PoolWorker-31 [pid 82165]\n",
"PoolWorker-31: Created temporary directory /tmp/madlib_4grXGY2O51\n",
"Initializing PoolWorker-32 [pid 82166]\n",
"PoolWorker-32: Created temporary directory /tmp/madlib_NLliJhB0RR\n",
"PoolWorker-25: Connected to madlib db.\n",
"PoolWorker-26: Connected to madlib db.\n",
"PoolWorker-27: Connected to madlib db.\n",
"PoolWorker-28: Connected to madlib db.\n",
"PoolWorker-29: Connected to madlib db.\n",
"PoolWorker-30: Connected to madlib db.\n",
"PoolWorker-31: Connected to madlib db.\n",
"PoolWorker-25: Wrote 500 images to /tmp/madlib_aL8m9O86Wy/imagenet_validation_data0000.tmp\n",
"PoolWorker-25: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-27: Removed temporary directory /tmp/madlib_BCmQipPLKc\n",
"PoolWorker-26: Removed temporary directory /tmp/madlib_ErWj86oGJ4\n",
"PoolWorker-29: Removed temporary directory /tmp/madlib_pEFV3GMLIO\n",
"PoolWorker-28: Removed temporary directory /tmp/madlib_DTcFD7cyFL\n",
"PoolWorker-31: Removed temporary directory /tmp/madlib_4grXGY2O51\n",
"PoolWorker-32: Removed temporary directory /tmp/madlib_NLliJhB0RR\n",
"PoolWorker-30: Removed temporary directory /tmp/madlib_9DseGWD1Q2\n",
"PoolWorker-25: Removed temporary directory /tmp/madlib_aL8m9O86Wy\n",
"Done! Loaded 500 images in 331.78370595s\n",
"8 workers terminated.\n",
"Chunk: 5/100\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-33 [pid 82246]\n",
"PoolWorker-33: Created temporary directory /tmp/madlib_U6ZWOEzzNo\n",
"Initializing PoolWorker-34 [pid 82247]\n",
"PoolWorker-34: Created temporary directory /tmp/madlib_504qdPbrdc\n",
"Initializing PoolWorker-35 [pid 82248]\n",
"PoolWorker-35: Created temporary directory /tmp/madlib_vEAoSLL3Ug\n",
"Initializing PoolWorker-36 [pid 82249]\n",
"PoolWorker-36: Created temporary directory /tmp/madlib_EH5piNTNZX\n",
"Initializing PoolWorker-37 [pid 82250]\n",
"PoolWorker-37: Created temporary directory /tmp/madlib_VBk1dpvNjI\n",
"Initializing PoolWorker-38 [pid 82251]\n",
"PoolWorker-38: Created temporary directory /tmp/madlib_ugURfgUPuW\n",
"PoolWorker-40: Connected to madlib db.\n",
"Initializing PoolWorker-39 [pid 82252]\n",
"PoolWorker-39: Created temporary directory /tmp/madlib_qKDbQ7UOxd\n",
"Initializing PoolWorker-40 [pid 82253]\n",
"PoolWorker-40: Created temporary directory /tmp/madlib_DAs94XRBNT\n",
"PoolWorker-33: Connected to madlib db.\n",
"PoolWorker-34: Connected to madlib db.\n",
"PoolWorker-35: Connected to madlib db.\n",
"PoolWorker-36: Connected to madlib db.\n",
"PoolWorker-37: Connected to madlib db.\n",
"PoolWorker-38: Connected to madlib db.\n",
"PoolWorker-39: Connected to madlib db.\n",
"PoolWorker-33: Wrote 500 images to /tmp/madlib_U6ZWOEzzNo/imagenet_validation_data0000.tmp\n",
"PoolWorker-33: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-34: Removed temporary directory /tmp/madlib_504qdPbrdc\n",
"PoolWorker-35: Removed temporary directory /tmp/madlib_vEAoSLL3Ug\n",
"PoolWorker-36: Removed temporary directory /tmp/madlib_EH5piNTNZX\n",
"PoolWorker-37: Removed temporary directory /tmp/madlib_VBk1dpvNjI\n",
"PoolWorker-38: Removed temporary directory /tmp/madlib_ugURfgUPuW\n",
"PoolWorker-40: Removed temporary directory /tmp/madlib_DAs94XRBNT\n",
"PoolWorker-39: Removed temporary directory /tmp/madlib_qKDbQ7UOxd\n",
"PoolWorker-33: Removed temporary directory /tmp/madlib_U6ZWOEzzNo\n",
"Done! Loaded 500 images in 330.478680134s\n",
"8 workers terminated.\n",
"Chunk: 6/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-41 [pid 82271]\n",
"Initializing PoolWorker-42 [pid 82272]\n",
"PoolWorker-41: Created temporary directory /tmp/madlib_XOwcBTvJxn\n",
"PoolWorker-42: Created temporary directory /tmp/madlib_zu6Gv6L6fM\n",
"Initializing PoolWorker-43 [pid 82273]\n",
"PoolWorker-43: Created temporary directory /tmp/madlib_YEREV83bBt\n",
"Initializing PoolWorker-44 [pid 82274]\n",
"PoolWorker-44: Created temporary directory /tmp/madlib_41AYQw0EBA\n",
"Initializing PoolWorker-45 [pid 82275]\n",
"PoolWorker-45: Created temporary directory /tmp/madlib_8JlToQSNV9\n",
"Initializing PoolWorker-46 [pid 82276]\n",
"PoolWorker-46: Created temporary directory /tmp/madlib_ZhEkMhcEUD\n",
"PoolWorker-48: Connected to madlib db.\n",
"Initializing PoolWorker-47 [pid 82277]\n",
"PoolWorker-47: Created temporary directory /tmp/madlib_GxYo2n5GWG\n",
"Initializing PoolWorker-48 [pid 82278]\n",
"PoolWorker-48: Created temporary directory /tmp/madlib_sy6Hg5Swz5\n",
"PoolWorker-42: Connected to madlib db.\n",
"PoolWorker-41: Connected to madlib db.\n",
"PoolWorker-43: Connected to madlib db.\n",
"PoolWorker-44: Connected to madlib db.\n",
"PoolWorker-45: Connected to madlib db.\n",
"PoolWorker-46: Connected to madlib db.\n",
"PoolWorker-47: Connected to madlib db.\n",
"PoolWorker-41: Wrote 500 images to /tmp/madlib_XOwcBTvJxn/imagenet_validation_data0000.tmp\n",
"PoolWorker-41: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-42: Removed temporary directory /tmp/madlib_zu6Gv6L6fM\n",
"PoolWorker-44: Removed temporary directory /tmp/madlib_41AYQw0EBA\n",
"PoolWorker-46: Removed temporary directory /tmp/madlib_ZhEkMhcEUD\n",
"PoolWorker-45: Removed temporary directory /tmp/madlib_8JlToQSNV9\n",
"PoolWorker-47: Removed temporary directory /tmp/madlib_GxYo2n5GWG\n",
"PoolWorker-48: Removed temporary directory /tmp/madlib_sy6Hg5Swz5\n",
"PoolWorker-43: Removed temporary directory /tmp/madlib_YEREV83bBt\n",
"PoolWorker-41: Removed temporary directory /tmp/madlib_XOwcBTvJxn\n",
"Done! Loaded 500 images in 436.764898062s\n",
"8 workers terminated.\n",
"Chunk: 7/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-49 [pid 82461]\n",
"PoolWorker-49: Created temporary directory /tmp/madlib_mILBwLxpvW\n",
"Initializing PoolWorker-50 [pid 82462]\n",
"PoolWorker-50: Created temporary directory /tmp/madlib_HGSwHuZAjE\n",
"Initializing PoolWorker-51 [pid 82463]\n",
"PoolWorker-51: Created temporary directory /tmp/madlib_TJj4a1iHxz\n",
"Initializing PoolWorker-52 [pid 82464]\n",
"PoolWorker-52: Created temporary directory /tmp/madlib_XgIfLZ1Tfr\n",
"Initializing PoolWorker-53 [pid 82465]\n",
"PoolWorker-53: Created temporary directory /tmp/madlib_c2llGEYPIn\n",
"Initializing PoolWorker-54 [pid 82466]\n",
"PoolWorker-54: Created temporary directory /tmp/madlib_XIRnV6dwve\n",
"PoolWorker-56: Connected to madlib db.\n",
"Initializing PoolWorker-55 [pid 82467]\n",
"PoolWorker-55: Created temporary directory /tmp/madlib_HK2NbnQtOf\n",
"Initializing PoolWorker-56 [pid 82468]\n",
"PoolWorker-56: Created temporary directory /tmp/madlib_hPOBdAsLwD\n",
"PoolWorker-49: Connected to madlib db.\n",
"PoolWorker-50: Connected to madlib db.\n",
"PoolWorker-51: Connected to madlib db.\n",
"PoolWorker-52: Connected to madlib db.\n",
"PoolWorker-53: Connected to madlib db.\n",
"PoolWorker-54: Connected to madlib db.\n",
"PoolWorker-55: Connected to madlib db.\n",
"PoolWorker-50: Wrote 500 images to /tmp/madlib_HGSwHuZAjE/imagenet_validation_data0000.tmp\n",
"PoolWorker-50: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-49: Removed temporary directory /tmp/madlib_mILBwLxpvW\n",
"PoolWorker-52: Removed temporary directory /tmp/madlib_XgIfLZ1Tfr\n",
"PoolWorker-51: Removed temporary directory /tmp/madlib_TJj4a1iHxz\n",
"PoolWorker-53: Removed temporary directory /tmp/madlib_c2llGEYPIn\n",
"PoolWorker-56: Removed temporary directory /tmp/madlib_hPOBdAsLwD\n",
"PoolWorker-55: Removed temporary directory /tmp/madlib_HK2NbnQtOf\n",
"PoolWorker-54: Removed temporary directory /tmp/madlib_XIRnV6dwve\n",
"PoolWorker-50: Removed temporary directory /tmp/madlib_HGSwHuZAjE\n",
"Done! Loaded 500 images in 385.487308025s\n",
"8 workers terminated.\n",
"Chunk: 8/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-57 [pid 83814]\n",
"Initializing PoolWorker-58 [pid 83815]\n",
"PoolWorker-57: Created temporary directory /tmp/madlib_sKjccDwylO\n",
"PoolWorker-58: Created temporary directory /tmp/madlib_YWaMxRe4UR\n",
"Initializing PoolWorker-59 [pid 83816]\n",
"PoolWorker-59: Created temporary directory /tmp/madlib_3hEzE87DsB\n",
"Initializing PoolWorker-60 [pid 83817]\n",
"PoolWorker-60: Created temporary directory /tmp/madlib_YozcyXglsx\n",
"Initializing PoolWorker-61 [pid 83818]\n",
"PoolWorker-61: Created temporary directory /tmp/madlib_YhWwEWG4zK\n",
"Initializing PoolWorker-62 [pid 83819]\n",
"PoolWorker-62: Created temporary directory /tmp/madlib_i6n2cTmYuN\n",
"Initializing PoolWorker-63 [pid 83820]\n",
"PoolWorker-63: Created temporary directory /tmp/madlib_hTdxJRMSEc\n",
"Initializing PoolWorker-64 [pid 83821]\n",
"PoolWorker-64: Created temporary directory /tmp/madlib_0kwDKutZ0U\n",
"PoolWorker-57: Connected to madlib db.\n",
"PoolWorker-58: Connected to madlib db.\n",
"PoolWorker-59: Connected to madlib db.\n",
"PoolWorker-60: Connected to madlib db.\n",
"PoolWorker-61: Connected to madlib db.\n",
"PoolWorker-62: Connected to madlib db.\n",
"PoolWorker-63: Connected to madlib db.\n",
"PoolWorker-64: Connected to madlib db.\n",
"PoolWorker-57: Wrote 500 images to /tmp/madlib_sKjccDwylO/imagenet_validation_data0000.tmp\n",
"PoolWorker-57: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-58: Removed temporary directory /tmp/madlib_YWaMxRe4UR\n",
"PoolWorker-59: Removed temporary directory /tmp/madlib_3hEzE87DsB\n",
"PoolWorker-63: Removed temporary directory /tmp/madlib_hTdxJRMSEc\n",
"PoolWorker-62: Removed temporary directory /tmp/madlib_i6n2cTmYuN\n",
"PoolWorker-61: Removed temporary directory /tmp/madlib_YhWwEWG4zK\n",
"PoolWorker-60: Removed temporary directory /tmp/madlib_YozcyXglsx\n",
"PoolWorker-64: Removed temporary directory /tmp/madlib_0kwDKutZ0U\n",
"PoolWorker-57: Removed temporary directory /tmp/madlib_sKjccDwylO\n",
"Done! Loaded 500 images in 326.529043198s\n",
"8 workers terminated.\n",
"Chunk: 9/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-65 [pid 83910]\n",
"PoolWorker-65: Created temporary directory /tmp/madlib_hJrdejv3ix\n",
"Initializing PoolWorker-66 [pid 83911]\n",
"PoolWorker-66: Created temporary directory /tmp/madlib_0bhUvB7VrQ\n",
"Initializing PoolWorker-67 [pid 83912]\n",
"PoolWorker-67: Created temporary directory /tmp/madlib_2kUMNDL4G5\n",
"Initializing PoolWorker-68 [pid 83913]\n",
"PoolWorker-68: Created temporary directory /tmp/madlib_efJziQmlhn\n",
"Initializing PoolWorker-69 [pid 83914]\n",
"PoolWorker-69: Created temporary directory /tmp/madlib_I2vO29IpEg\n",
"Initializing PoolWorker-70 [pid 83915]\n",
"PoolWorker-70: Created temporary directory /tmp/madlib_VgCg4h7OHS\n",
"PoolWorker-72: Connected to madlib db.\n",
"Initializing PoolWorker-71 [pid 83916]\n",
"PoolWorker-71: Created temporary directory /tmp/madlib_OG3jUx2eiJ\n",
"Initializing PoolWorker-72 [pid 83917]\n",
"PoolWorker-72: Created temporary directory /tmp/madlib_8T058VFWCS\n",
"PoolWorker-65: Connected to madlib db.\n",
"PoolWorker-66: Connected to madlib db.\n",
"PoolWorker-67: Connected to madlib db.\n",
"PoolWorker-68: Connected to madlib db.\n",
"PoolWorker-69: Connected to madlib db.\n",
"PoolWorker-70: Connected to madlib db.\n",
"PoolWorker-71: Connected to madlib db.\n",
"PoolWorker-65: Wrote 500 images to /tmp/madlib_hJrdejv3ix/imagenet_validation_data0000.tmp\n",
"PoolWorker-65: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-67: Removed temporary directory /tmp/madlib_2kUMNDL4G5\n",
"PoolWorker-66: Removed temporary directory /tmp/madlib_0bhUvB7VrQ\n",
"PoolWorker-68: Removed temporary directory /tmp/madlib_efJziQmlhn\n",
"PoolWorker-69: Removed temporary directory /tmp/madlib_I2vO29IpEg\n",
"PoolWorker-71: Removed temporary directory /tmp/madlib_OG3jUx2eiJ\n",
"PoolWorker-70: Removed temporary directory /tmp/madlib_VgCg4h7OHS\n",
"PoolWorker-72: Removed temporary directory /tmp/madlib_8T058VFWCS\n",
"PoolWorker-65: Removed temporary directory /tmp/madlib_hJrdejv3ix\n",
"Done! Loaded 500 images in 325.044052839s\n",
"8 workers terminated.\n",
"Chunk: 10/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-73 [pid 83981]\n",
"PoolWorker-73: Created temporary directory /tmp/madlib_zshw0TYnVv\n",
"Initializing PoolWorker-74 [pid 83982]\n",
"PoolWorker-74: Created temporary directory /tmp/madlib_h67bVA9KmB\n",
"Initializing PoolWorker-75 [pid 83983]\n",
"PoolWorker-75: Created temporary directory /tmp/madlib_9NOHaoTvLk\n",
"Initializing PoolWorker-76 [pid 83984]\n",
"PoolWorker-76: Created temporary directory /tmp/madlib_0wbF7WtMDy\n",
"Initializing PoolWorker-77 [pid 83985]\n",
"PoolWorker-77: Created temporary directory /tmp/madlib_AGety7Nv1y\n",
"Initializing PoolWorker-78 [pid 83986]\n",
"PoolWorker-78: Created temporary directory /tmp/madlib_9zoZ2Q5NbA\n",
"Initializing PoolWorker-79 [pid 83987]\n",
"PoolWorker-79: Created temporary directory /tmp/madlib_i7CDhjuVqU\n",
"Initializing PoolWorker-80 [pid 83988]\n",
"PoolWorker-80: Created temporary directory /tmp/madlib_nLBeAdLmj3\n",
"PoolWorker-73: Connected to madlib db.\n",
"PoolWorker-74: Connected to madlib db.\n",
"PoolWorker-75: Connected to madlib db.\n",
"PoolWorker-76: Connected to madlib db.\n",
"PoolWorker-77: Connected to madlib db.\n",
"PoolWorker-78: Connected to madlib db.\n",
"PoolWorker-79: Connected to madlib db.\n",
"PoolWorker-80: Connected to madlib db.\n",
"PoolWorker-73: Wrote 500 images to /tmp/madlib_zshw0TYnVv/imagenet_validation_data0000.tmp\n",
"PoolWorker-73: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-75: Removed temporary directory /tmp/madlib_9NOHaoTvLk\n",
"PoolWorker-74: Removed temporary directory /tmp/madlib_h67bVA9KmB\n",
"PoolWorker-78: Removed temporary directory /tmp/madlib_9zoZ2Q5NbA\n",
"PoolWorker-76: Removed temporary directory /tmp/madlib_0wbF7WtMDy\n",
"PoolWorker-77: Removed temporary directory /tmp/madlib_AGety7Nv1y\n",
"PoolWorker-79: Removed temporary directory /tmp/madlib_i7CDhjuVqU\n",
"PoolWorker-80: Removed temporary directory /tmp/madlib_nLBeAdLmj3\n",
"PoolWorker-73: Removed temporary directory /tmp/madlib_zshw0TYnVv\n",
"Done! Loaded 500 images in 433.54046607s\n",
"8 workers terminated.\n",
"Chunk: 11/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-81 [pid 84453]\n",
"PoolWorker-81: Created temporary directory /tmp/madlib_uaiUHFf0J1\n",
"Initializing PoolWorker-82 [pid 84454]\n",
"PoolWorker-82: Created temporary directory /tmp/madlib_Jq6Wd7T1Lh\n",
"Initializing PoolWorker-83 [pid 84455]\n",
"PoolWorker-83: Created temporary directory /tmp/madlib_tQB0XtguLS\n",
"Initializing PoolWorker-84 [pid 84456]\n",
"PoolWorker-84: Created temporary directory /tmp/madlib_xlooSZ849r\n",
"Initializing PoolWorker-85 [pid 84457]\n",
"PoolWorker-85: Created temporary directory /tmp/madlib_2iqInGoRpR\n",
"Initializing PoolWorker-86 [pid 84458]\n",
"PoolWorker-86: Created temporary directory /tmp/madlib_YDN83ufIwU\n",
"Initializing PoolWorker-87 [pid 84459]\n",
"PoolWorker-88: Connected to madlib db.\n",
"PoolWorker-87: Created temporary directory /tmp/madlib_HN0bs5vstF\n",
"Initializing PoolWorker-88 [pid 84460]\n",
"PoolWorker-88: Created temporary directory /tmp/madlib_OGDQwV5Ipg\n",
"PoolWorker-81: Connected to madlib db.\n",
"PoolWorker-82: Connected to madlib db.\n",
"PoolWorker-83: Connected to madlib db.\n",
"PoolWorker-84: Connected to madlib db.\n",
"PoolWorker-85: Connected to madlib db.\n",
"PoolWorker-86: Connected to madlib db.\n",
"PoolWorker-87: Connected to madlib db.\n",
"PoolWorker-81: Wrote 500 images to /tmp/madlib_uaiUHFf0J1/imagenet_validation_data0000.tmp\n",
"PoolWorker-81: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-82: Removed temporary directory /tmp/madlib_Jq6Wd7T1Lh\n",
"PoolWorker-83: Removed temporary directory /tmp/madlib_tQB0XtguLS\n",
"PoolWorker-85: Removed temporary directory /tmp/madlib_2iqInGoRpR\n",
"PoolWorker-84: Removed temporary directory /tmp/madlib_xlooSZ849r\n",
"PoolWorker-88: Removed temporary directory /tmp/madlib_OGDQwV5Ipg\n",
"PoolWorker-86: Removed temporary directory /tmp/madlib_YDN83ufIwU\n",
"PoolWorker-87: Removed temporary directory /tmp/madlib_HN0bs5vstF\n",
"PoolWorker-81: Removed temporary directory /tmp/madlib_uaiUHFf0J1\n",
"Done! Loaded 500 images in 325.673794031s\n",
"8 workers terminated.\n",
"Chunk: 12/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-89 [pid 84592]\n",
"PoolWorker-89: Created temporary directory /tmp/madlib_CDTYVRrmyD\n",
"Initializing PoolWorker-90 [pid 84593]\n",
"PoolWorker-90: Created temporary directory /tmp/madlib_hFoDfSsZcQ\n",
"Initializing PoolWorker-91 [pid 84594]\n",
"PoolWorker-91: Created temporary directory /tmp/madlib_ef2c5LBNKS\n",
"Initializing PoolWorker-92 [pid 84595]\n",
"PoolWorker-92: Created temporary directory /tmp/madlib_w4M4Rla4Ry\n",
"Initializing PoolWorker-93 [pid 84596]\n",
"PoolWorker-93: Created temporary directory /tmp/madlib_YJTwfIOZct\n",
"Initializing PoolWorker-94 [pid 84597]\n",
"PoolWorker-94: Created temporary directory /tmp/madlib_ZJ73A7YTiD\n",
"Initializing PoolWorker-95 [pid 84598]\n",
"PoolWorker-95: Created temporary directory /tmp/madlib_6NkWMa3Woy\n",
"Initializing PoolWorker-96 [pid 84599]\n",
"PoolWorker-96: Created temporary directory /tmp/madlib_CLVeabn7i6\n",
"PoolWorker-89: Connected to madlib db.\n",
"PoolWorker-90: Connected to madlib db.\n",
"PoolWorker-91: Connected to madlib db.\n",
"PoolWorker-92: Connected to madlib db.\n",
"PoolWorker-93: Connected to madlib db.\n",
"PoolWorker-94: Connected to madlib db.\n",
"PoolWorker-95: Connected to madlib db.\n",
"PoolWorker-96: Connected to madlib db.\n",
"PoolWorker-89: Wrote 500 images to /tmp/madlib_CDTYVRrmyD/imagenet_validation_data0000.tmp\n",
"PoolWorker-89: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-90: Removed temporary directory /tmp/madlib_hFoDfSsZcQ\n",
"PoolWorker-92: Removed temporary directory /tmp/madlib_w4M4Rla4Ry\n",
"PoolWorker-91: Removed temporary directory /tmp/madlib_ef2c5LBNKS\n",
"PoolWorker-93: Removed temporary directory /tmp/madlib_YJTwfIOZct\n",
"PoolWorker-95: Removed temporary directory /tmp/madlib_6NkWMa3Woy\n",
"PoolWorker-96: Removed temporary directory /tmp/madlib_CLVeabn7i6\n",
"PoolWorker-94: Removed temporary directory /tmp/madlib_ZJ73A7YTiD\n",
"PoolWorker-89: Removed temporary directory /tmp/madlib_CDTYVRrmyD\n",
"Done! Loaded 500 images in 306.039473057s\n",
"8 workers terminated.\n",
"Chunk: 13/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-97 [pid 84612]\n",
"PoolWorker-97: Created temporary directory /tmp/madlib_d29mZeGYc8\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-98 [pid 84613]\n",
"PoolWorker-98: Created temporary directory /tmp/madlib_rmqg3k6Til\n",
"Initializing PoolWorker-99 [pid 84614]\n",
"PoolWorker-99: Created temporary directory /tmp/madlib_Yl65HB83Ze\n",
"Initializing PoolWorker-100 [pid 84615]\n",
"PoolWorker-100: Created temporary directory /tmp/madlib_H5ksg5o4yE\n",
"Initializing PoolWorker-101 [pid 84616]\n",
"PoolWorker-101: Created temporary directory /tmp/madlib_Cq4veSAXLM\n",
"Initializing PoolWorker-102 [pid 84617]\n",
"PoolWorker-102: Created temporary directory /tmp/madlib_EYtNeF64in\n",
"Initializing PoolWorker-103 [pid 84618]\n",
"PoolWorker-103: Created temporary directory /tmp/madlib_R0gt0dyFOh\n",
"Initializing PoolWorker-104 [pid 84619]\n",
"PoolWorker-104: Created temporary directory /tmp/madlib_yJfhPJozNk\n",
"PoolWorker-97: Connected to madlib db.\n",
"PoolWorker-98: Connected to madlib db.\n",
"PoolWorker-99: Connected to madlib db.\n",
"PoolWorker-100: Connected to madlib db.\n",
"PoolWorker-101: Connected to madlib db.\n",
"PoolWorker-102: Connected to madlib db.\n",
"PoolWorker-103: Connected to madlib db.\n",
"PoolWorker-104: Connected to madlib db.\n",
"PoolWorker-97: Wrote 500 images to /tmp/madlib_d29mZeGYc8/imagenet_validation_data0000.tmp\n",
"PoolWorker-97: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-98: Removed temporary directory /tmp/madlib_rmqg3k6Til\n",
"PoolWorker-100: Removed temporary directory /tmp/madlib_H5ksg5o4yE\n",
"PoolWorker-99: Removed temporary directory /tmp/madlib_Yl65HB83Ze\n",
"PoolWorker-101: Removed temporary directory /tmp/madlib_Cq4veSAXLM\n",
"PoolWorker-102: Removed temporary directory /tmp/madlib_EYtNeF64in\n",
"PoolWorker-103: Removed temporary directory /tmp/madlib_R0gt0dyFOh\n",
"PoolWorker-104: Removed temporary directory /tmp/madlib_yJfhPJozNk\n",
"PoolWorker-97: Removed temporary directory /tmp/madlib_d29mZeGYc8\n",
"Done! Loaded 500 images in 434.285313845s\n",
"8 workers terminated.\n",
"Chunk: 14/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-105 [pid 84639]\n",
"PoolWorker-105: Created temporary directory /tmp/madlib_olJz48GfWV\n",
"Initializing PoolWorker-106 [pid 84640]\n",
"PoolWorker-106: Created temporary directory /tmp/madlib_CILmx4vqq7\n",
"Initializing PoolWorker-107 [pid 84641]\n",
"PoolWorker-107: Created temporary directory /tmp/madlib_IfptplH7gX\n",
"Initializing PoolWorker-108 [pid 84642]\n",
"PoolWorker-108: Created temporary directory /tmp/madlib_iDMBdj10Cy\n",
"Initializing PoolWorker-109 [pid 84643]\n",
"PoolWorker-109: Created temporary directory /tmp/madlib_w0wZZHHSPo\n",
"Initializing PoolWorker-110 [pid 84644]\n",
"PoolWorker-110: Created temporary directory /tmp/madlib_Ilb4RbJ1e7\n",
"PoolWorker-112: Connected to madlib db.\n",
"Initializing PoolWorker-111 [pid 84645]\n",
"PoolWorker-111: Created temporary directory /tmp/madlib_YvmtmvmrV1\n",
"Initializing PoolWorker-112 [pid 84646]\n",
"PoolWorker-112: Created temporary directory /tmp/madlib_lWLtj3mgOO\n",
"PoolWorker-105: Connected to madlib db.\n",
"PoolWorker-106: Connected to madlib db.\n",
"PoolWorker-107: Connected to madlib db.\n",
"PoolWorker-108: Connected to madlib db.\n",
"PoolWorker-109: Connected to madlib db.\n",
"PoolWorker-110: Connected to madlib db.\n",
"PoolWorker-111: Connected to madlib db.\n",
"PoolWorker-105: Wrote 500 images to /tmp/madlib_olJz48GfWV/imagenet_validation_data0000.tmp\n",
"PoolWorker-105: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-107: Removed temporary directory /tmp/madlib_IfptplH7gX\n",
"PoolWorker-106: Removed temporary directory /tmp/madlib_CILmx4vqq7\n",
"PoolWorker-108: Removed temporary directory /tmp/madlib_iDMBdj10Cy\n",
"PoolWorker-109: Removed temporary directory /tmp/madlib_w0wZZHHSPo\n",
"PoolWorker-112: Removed temporary directory /tmp/madlib_lWLtj3mgOO\n",
"PoolWorker-110: Removed temporary directory /tmp/madlib_Ilb4RbJ1e7\n",
"PoolWorker-111: Removed temporary directory /tmp/madlib_YvmtmvmrV1\n",
"PoolWorker-105: Removed temporary directory /tmp/madlib_olJz48GfWV\n",
"Done! Loaded 500 images in 321.748677015s\n",
"8 workers terminated.\n",
"Chunk: 15/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-113 [pid 84659]\n",
"PoolWorker-113: Created temporary directory /tmp/madlib_cAD6JBcG9A\n",
"Initializing PoolWorker-114 [pid 84660]\n",
"PoolWorker-114: Created temporary directory /tmp/madlib_AYupN0qfj3\n",
"Initializing PoolWorker-115 [pid 84661]\n",
"PoolWorker-115: Created temporary directory /tmp/madlib_WCGJ2yWIsl\n",
"Initializing PoolWorker-116 [pid 84662]\n",
"PoolWorker-116: Created temporary directory /tmp/madlib_7Fm01utR46\n",
"Initializing PoolWorker-117 [pid 84663]\n",
"PoolWorker-117: Created temporary directory /tmp/madlib_QlVrLiutJT\n",
"Initializing PoolWorker-118 [pid 84664]\n",
"PoolWorker-118: Created temporary directory /tmp/madlib_KcsCzOrNXo\n",
"Initializing PoolWorker-119 [pid 84665]\n",
"PoolWorker-120: Connected to madlib db.\n",
"PoolWorker-119: Created temporary directory /tmp/madlib_Skvpyhwvss\n",
"Initializing PoolWorker-120 [pid 84666]\n",
"PoolWorker-120: Created temporary directory /tmp/madlib_0sBPvvi2Ap\n",
"PoolWorker-113: Connected to madlib db.\n",
"PoolWorker-114: Connected to madlib db.\n",
"PoolWorker-115: Connected to madlib db.\n",
"PoolWorker-116: Connected to madlib db.\n",
"PoolWorker-117: Connected to madlib db.\n",
"PoolWorker-118: Connected to madlib db.\n",
"PoolWorker-119: Connected to madlib db.\n",
"PoolWorker-113: Wrote 500 images to /tmp/madlib_cAD6JBcG9A/imagenet_validation_data0000.tmp\n",
"PoolWorker-113: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-117: Removed temporary directory /tmp/madlib_QlVrLiutJT\n",
"PoolWorker-116: Removed temporary directory /tmp/madlib_7Fm01utR46\n",
"PoolWorker-114: Removed temporary directory /tmp/madlib_AYupN0qfj3\n",
"PoolWorker-120: Removed temporary directory /tmp/madlib_0sBPvvi2Ap\n",
"PoolWorker-118: Removed temporary directory /tmp/madlib_KcsCzOrNXo\n",
"PoolWorker-115: Removed temporary directory /tmp/madlib_WCGJ2yWIsl\n",
"PoolWorker-119: Removed temporary directory /tmp/madlib_Skvpyhwvss\n",
"PoolWorker-113: Removed temporary directory /tmp/madlib_cAD6JBcG9A\n",
"Done! Loaded 500 images in 498.033784866s\n",
"8 workers terminated.\n",
"Chunk: 16/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-121 [pid 85275]\n",
"PoolWorker-121: Created temporary directory /tmp/madlib_IjEacukabr\n",
"Initializing PoolWorker-122 [pid 85276]\n",
"PoolWorker-122: Created temporary directory /tmp/madlib_gwoVVywxJL\n",
"Initializing PoolWorker-123 [pid 85277]\n",
"PoolWorker-123: Created temporary directory /tmp/madlib_kxbLWxos6b\n",
"Initializing PoolWorker-124 [pid 85278]\n",
"PoolWorker-124: Created temporary directory /tmp/madlib_6WOAqEojz7\n",
"Initializing PoolWorker-125 [pid 85279]\n",
"PoolWorker-125: Created temporary directory /tmp/madlib_gHRp8aa1pj\n",
"Initializing PoolWorker-126 [pid 85280]\n",
"PoolWorker-126: Created temporary directory /tmp/madlib_b4bw0yh1lR\n",
"Initializing PoolWorker-127 [pid 85281]\n",
"PoolWorker-128: Connected to madlib db.\n",
"PoolWorker-127: Created temporary directory /tmp/madlib_qeqec1xg0R\n",
"Initializing PoolWorker-128 [pid 85282]\n",
"PoolWorker-128: Created temporary directory /tmp/madlib_D5Ak0nXdtU\n",
"PoolWorker-121: Connected to madlib db.\n",
"PoolWorker-122: Connected to madlib db.\n",
"PoolWorker-123: Connected to madlib db.\n",
"PoolWorker-124: Connected to madlib db.\n",
"PoolWorker-125: Connected to madlib db.\n",
"PoolWorker-126: Connected to madlib db.\n",
"PoolWorker-127: Connected to madlib db.\n",
"PoolWorker-121: Wrote 500 images to /tmp/madlib_IjEacukabr/imagenet_validation_data0000.tmp\n",
"PoolWorker-121: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-123: Removed temporary directory /tmp/madlib_kxbLWxos6b\n",
"PoolWorker-122: Removed temporary directory /tmp/madlib_gwoVVywxJL\n",
"PoolWorker-125: Removed temporary directory /tmp/madlib_gHRp8aa1pj\n",
"PoolWorker-126: Removed temporary directory /tmp/madlib_b4bw0yh1lR\n",
"PoolWorker-124: Removed temporary directory /tmp/madlib_6WOAqEojz7\n",
"PoolWorker-128: Removed temporary directory /tmp/madlib_D5Ak0nXdtU\n",
"PoolWorker-127: Removed temporary directory /tmp/madlib_qeqec1xg0R\n",
"PoolWorker-121: Removed temporary directory /tmp/madlib_IjEacukabr\n",
"Done! Loaded 500 images in 481.063435793s\n",
"8 workers terminated.\n",
"Chunk: 17/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-129 [pid 85306]\n",
"PoolWorker-129: Created temporary directory /tmp/madlib_fo7OGP9t8b\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-130 [pid 85307]\n",
"PoolWorker-130: Created temporary directory /tmp/madlib_6mESApdFR8\n",
"Initializing PoolWorker-131 [pid 85308]\n",
"PoolWorker-131: Created temporary directory /tmp/madlib_TjII4pygvp\n",
"Initializing PoolWorker-132 [pid 85309]\n",
"PoolWorker-132: Created temporary directory /tmp/madlib_fX7A5ZDm9X\n",
"Initializing PoolWorker-133 [pid 85310]\n",
"PoolWorker-133: Created temporary directory /tmp/madlib_Lah7W3jS8E\n",
"Initializing PoolWorker-134 [pid 85311]\n",
"PoolWorker-134: Created temporary directory /tmp/madlib_OB2c5uuT4h\n",
"Initializing PoolWorker-135 [pid 85312]\n",
"PoolWorker-136: Connected to madlib db.\n",
"PoolWorker-135: Created temporary directory /tmp/madlib_uHkGkvPU3N\n",
"Initializing PoolWorker-136 [pid 85313]\n",
"PoolWorker-136: Created temporary directory /tmp/madlib_gaaMXSoWdc\n",
"PoolWorker-129: Connected to madlib db.\n",
"PoolWorker-130: Connected to madlib db.\n",
"PoolWorker-131: Connected to madlib db.\n",
"PoolWorker-132: Connected to madlib db.\n",
"PoolWorker-133: Connected to madlib db.\n",
"PoolWorker-134: Connected to madlib db.\n",
"PoolWorker-135: Connected to madlib db.\n",
"PoolWorker-129: Wrote 500 images to /tmp/madlib_fo7OGP9t8b/imagenet_validation_data0000.tmp\n",
"PoolWorker-129: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-130: Removed temporary directory /tmp/madlib_6mESApdFR8\n",
"PoolWorker-131: Removed temporary directory /tmp/madlib_TjII4pygvp\n",
"PoolWorker-132: Removed temporary directory /tmp/madlib_fX7A5ZDm9X\n",
"PoolWorker-136: Removed temporary directory /tmp/madlib_gaaMXSoWdc\n",
"PoolWorker-133: Removed temporary directory /tmp/madlib_Lah7W3jS8E\n",
"PoolWorker-135: Removed temporary directory /tmp/madlib_uHkGkvPU3N\n",
"PoolWorker-134: Removed temporary directory /tmp/madlib_OB2c5uuT4h\n",
"PoolWorker-129: Removed temporary directory /tmp/madlib_fo7OGP9t8b\n",
"Done! Loaded 500 images in 425.296325922s\n",
"8 workers terminated.\n",
"Chunk: 18/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-137 [pid 86633]\n",
"PoolWorker-137: Created temporary directory /tmp/madlib_vXUMEIX7nn\n",
"Initializing PoolWorker-138 [pid 86634]\n",
"PoolWorker-138: Created temporary directory /tmp/madlib_KOw6ZgRdAN\n",
"Initializing PoolWorker-139 [pid 86635]\n",
"PoolWorker-139: Created temporary directory /tmp/madlib_2YV8mqlp40\n",
"Initializing PoolWorker-140 [pid 86636]\n",
"PoolWorker-140: Created temporary directory /tmp/madlib_CNzQ3drK79\n",
"Initializing PoolWorker-141 [pid 86637]\n",
"PoolWorker-141: Created temporary directory /tmp/madlib_MtmWT8LhXk\n",
"Initializing PoolWorker-142 [pid 86638]\n",
"PoolWorker-142: Created temporary directory /tmp/madlib_2dIRdYqmxH\n",
"Initializing PoolWorker-143 [pid 86639]\n",
"PoolWorker-143: Created temporary directory /tmp/madlib_JHIN2l9cm4\n",
"Initializing PoolWorker-144 [pid 86640]\n",
"PoolWorker-144: Created temporary directory /tmp/madlib_UfE6XkvICW\n",
"PoolWorker-137: Connected to madlib db.\n",
"PoolWorker-138: Connected to madlib db.\n",
"PoolWorker-139: Connected to madlib db.\n",
"PoolWorker-140: Connected to madlib db.\n",
"PoolWorker-141: Connected to madlib db.\n",
"PoolWorker-142: Connected to madlib db.\n",
"PoolWorker-143: Connected to madlib db.\n",
"PoolWorker-144: Connected to madlib db.\n",
"PoolWorker-137: Wrote 500 images to /tmp/madlib_vXUMEIX7nn/imagenet_validation_data0000.tmp\n",
"PoolWorker-137: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-140: Removed temporary directory /tmp/madlib_CNzQ3drK79\n",
"PoolWorker-138: Removed temporary directory /tmp/madlib_KOw6ZgRdAN\n",
"PoolWorker-144: Removed temporary directory /tmp/madlib_UfE6XkvICW\n",
"PoolWorker-141: Removed temporary directory /tmp/madlib_MtmWT8LhXk\n",
"PoolWorker-139: Removed temporary directory /tmp/madlib_2YV8mqlp40\n",
"PoolWorker-142: Removed temporary directory /tmp/madlib_2dIRdYqmxH\n",
"PoolWorker-143: Removed temporary directory /tmp/madlib_JHIN2l9cm4\n",
"PoolWorker-137: Removed temporary directory /tmp/madlib_vXUMEIX7nn\n",
"Done! Loaded 500 images in 345.722942114s\n",
"8 workers terminated.\n",
"Chunk: 19/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-145 [pid 86669]\n",
"PoolWorker-145: Created temporary directory /tmp/madlib_gZJJjShndj\n",
"Initializing PoolWorker-146 [pid 86670]\n",
"PoolWorker-146: Created temporary directory /tmp/madlib_bakxnq4A1A\n",
"Initializing PoolWorker-147 [pid 86671]\n",
"PoolWorker-147: Created temporary directory /tmp/madlib_YjQ2XQoQt5\n",
"Initializing PoolWorker-148 [pid 86672]\n",
"PoolWorker-148: Created temporary directory /tmp/madlib_TYoAbln4cP\n",
"Initializing PoolWorker-149 [pid 86673]\n",
"PoolWorker-149: Created temporary directory /tmp/madlib_IRAyH4hCHU\n",
"Initializing PoolWorker-150 [pid 86674]\n",
"PoolWorker-150: Created temporary directory /tmp/madlib_2BVyU0vjLB\n",
"Initializing PoolWorker-151 [pid 86675]\n",
"PoolWorker-151: Created temporary directory /tmp/madlib_ixrIltsrR7\n",
"Initializing PoolWorker-152 [pid 86676]\n",
"PoolWorker-152: Created temporary directory /tmp/madlib_tpviSJjSHe\n",
"PoolWorker-145: Connected to madlib db.\n",
"PoolWorker-146: Connected to madlib db.\n",
"PoolWorker-147: Connected to madlib db.\n",
"PoolWorker-148: Connected to madlib db.\n",
"PoolWorker-149: Connected to madlib db.\n",
"PoolWorker-150: Connected to madlib db.\n",
"PoolWorker-151: Connected to madlib db.\n",
"PoolWorker-152: Connected to madlib db.\n",
"PoolWorker-145: Wrote 500 images to /tmp/madlib_gZJJjShndj/imagenet_validation_data0000.tmp\n",
"PoolWorker-145: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-146: Removed temporary directory /tmp/madlib_bakxnq4A1A\n",
"PoolWorker-147: Removed temporary directory /tmp/madlib_YjQ2XQoQt5\n",
"PoolWorker-150: Removed temporary directory /tmp/madlib_2BVyU0vjLB\n",
"PoolWorker-152: Removed temporary directory /tmp/madlib_tpviSJjSHe\n",
"PoolWorker-148: Removed temporary directory /tmp/madlib_TYoAbln4cP\n",
"PoolWorker-149: Removed temporary directory /tmp/madlib_IRAyH4hCHU\n",
"PoolWorker-151: Removed temporary directory /tmp/madlib_ixrIltsrR7\n",
"PoolWorker-145: Removed temporary directory /tmp/madlib_gZJJjShndj\n",
"Done! Loaded 500 images in 338.026529074s\n",
"8 workers terminated.\n",
"Chunk: 20/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-153 [pid 86696]\n",
"PoolWorker-153: Created temporary directory /tmp/madlib_ATQi1uQL4j\n",
"Initializing PoolWorker-154 [pid 86697]\n",
"PoolWorker-154: Created temporary directory /tmp/madlib_75YUDOxV4a\n",
"Initializing PoolWorker-155 [pid 86698]\n",
"PoolWorker-155: Created temporary directory /tmp/madlib_iqsBoJxjkR\n",
"Initializing PoolWorker-156 [pid 86699]\n",
"PoolWorker-156: Created temporary directory /tmp/madlib_gTyVVqbGxY\n",
"Initializing PoolWorker-157 [pid 86700]\n",
"PoolWorker-157: Created temporary directory /tmp/madlib_Olhm12z5G1\n",
"Initializing PoolWorker-158 [pid 86701]\n",
"PoolWorker-158: Created temporary directory /tmp/madlib_afhuEBY2yV\n",
"PoolWorker-159: Connected to madlib db.\n",
"Initializing PoolWorker-159 [pid 86702]\n",
"PoolWorker-159: Created temporary directory /tmp/madlib_pVknhXjxt6\n",
"Initializing PoolWorker-160 [pid 86703]\n",
"PoolWorker-160: Created temporary directory /tmp/madlib_ngxfLJdxtt\n",
"PoolWorker-153: Connected to madlib db.\n",
"PoolWorker-154: Connected to madlib db.\n",
"PoolWorker-155: Connected to madlib db.\n",
"PoolWorker-156: Connected to madlib db.\n",
"PoolWorker-157: Connected to madlib db.\n",
"PoolWorker-158: Connected to madlib db.\n",
"PoolWorker-160: Connected to madlib db.\n",
"PoolWorker-153: Wrote 500 images to /tmp/madlib_ATQi1uQL4j/imagenet_validation_data0000.tmp\n",
"PoolWorker-153: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-154: Removed temporary directory /tmp/madlib_75YUDOxV4a\n",
"PoolWorker-155: Removed temporary directory /tmp/madlib_iqsBoJxjkR\n",
"PoolWorker-156: Removed temporary directory /tmp/madlib_gTyVVqbGxY\n",
"PoolWorker-157: Removed temporary directory /tmp/madlib_Olhm12z5G1\n",
"PoolWorker-158: Removed temporary directory /tmp/madlib_afhuEBY2yV\n",
"PoolWorker-160: Removed temporary directory /tmp/madlib_ngxfLJdxtt\n",
"PoolWorker-159: Removed temporary directory /tmp/madlib_pVknhXjxt6\n",
"PoolWorker-153: Removed temporary directory /tmp/madlib_ATQi1uQL4j\n",
"Done! Loaded 500 images in 475.534565926s\n",
"8 workers terminated.\n",
"Chunk: 21/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-161 [pid 86795]\n",
"PoolWorker-161: Created temporary directory /tmp/madlib_jgWBPA8vRQ\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-162 [pid 86796]\n",
"PoolWorker-162: Created temporary directory /tmp/madlib_c7KvZLGABg\n",
"Initializing PoolWorker-163 [pid 86797]\n",
"PoolWorker-163: Created temporary directory /tmp/madlib_Xla8oB358k\n",
"Initializing PoolWorker-164 [pid 86798]\n",
"PoolWorker-164: Created temporary directory /tmp/madlib_NqcigSQb6L\n",
"Initializing PoolWorker-165 [pid 86799]\n",
"PoolWorker-165: Created temporary directory /tmp/madlib_D1mTob4Fgp\n",
"Initializing PoolWorker-166 [pid 86800]\n",
"PoolWorker-166: Created temporary directory /tmp/madlib_SumJMN8HoN\n",
"PoolWorker-168: Connected to madlib db.\n",
"Initializing PoolWorker-167 [pid 86801]\n",
"PoolWorker-167: Created temporary directory /tmp/madlib_ykP0oITTmf\n",
"Initializing PoolWorker-168 [pid 86802]\n",
"PoolWorker-168: Created temporary directory /tmp/madlib_CKXJSXXh2u\n",
"PoolWorker-162: Connected to madlib db.\n",
"PoolWorker-161: Connected to madlib db.\n",
"PoolWorker-163: Connected to madlib db.\n",
"PoolWorker-164: Connected to madlib db.\n",
"PoolWorker-165: Connected to madlib db.\n",
"PoolWorker-166: Connected to madlib db.\n",
"PoolWorker-167: Connected to madlib db.\n",
"PoolWorker-162: Wrote 500 images to /tmp/madlib_c7KvZLGABg/imagenet_validation_data0000.tmp\n",
"PoolWorker-162: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-161: Removed temporary directory /tmp/madlib_jgWBPA8vRQ\n",
"PoolWorker-165: Removed temporary directory /tmp/madlib_D1mTob4Fgp\n",
"PoolWorker-163: Removed temporary directory /tmp/madlib_Xla8oB358k\n",
"PoolWorker-168: Removed temporary directory /tmp/madlib_CKXJSXXh2u\n",
"PoolWorker-164: Removed temporary directory /tmp/madlib_NqcigSQb6L\n",
"PoolWorker-166: Removed temporary directory /tmp/madlib_SumJMN8HoN\n",
"PoolWorker-167: Removed temporary directory /tmp/madlib_ykP0oITTmf\n",
"PoolWorker-162: Removed temporary directory /tmp/madlib_c7KvZLGABg\n",
"Done! Loaded 500 images in 348.429680109s\n",
"8 workers terminated.\n",
"Chunk: 22/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-169 [pid 86824]\n",
"PoolWorker-169: Created temporary directory /tmp/madlib_w45egOYdnj\n",
"Initializing PoolWorker-170 [pid 86825]\n",
"PoolWorker-170: Created temporary directory /tmp/madlib_gjEiV62ZJV\n",
"Initializing PoolWorker-171 [pid 86826]\n",
"PoolWorker-171: Created temporary directory /tmp/madlib_MpZQ1LKqh5\n",
"Initializing PoolWorker-172 [pid 86827]\n",
"PoolWorker-172: Created temporary directory /tmp/madlib_drM88KR9IX\n",
"Initializing PoolWorker-173 [pid 86828]\n",
"PoolWorker-173: Created temporary directory /tmp/madlib_QSnaE144MN\n",
"Initializing PoolWorker-174 [pid 86829]\n",
"PoolWorker-174: Created temporary directory /tmp/madlib_msRkwJB7A0\n",
"Initializing PoolWorker-175 [pid 86830]\n",
"PoolWorker-176: Connected to madlib db.\n",
"PoolWorker-175: Created temporary directory /tmp/madlib_ct463nNDZt\n",
"Initializing PoolWorker-176 [pid 86831]\n",
"PoolWorker-176: Created temporary directory /tmp/madlib_cXqWnRiNIn\n",
"PoolWorker-170: Connected to madlib db.\n",
"PoolWorker-169: Connected to madlib db.\n",
"PoolWorker-171: Connected to madlib db.\n",
"PoolWorker-172: Connected to madlib db.\n",
"PoolWorker-173: Connected to madlib db.\n",
"PoolWorker-174: Connected to madlib db.\n",
"PoolWorker-175: Connected to madlib db.\n",
"PoolWorker-169: Wrote 500 images to /tmp/madlib_w45egOYdnj/imagenet_validation_data0000.tmp\n",
"PoolWorker-169: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-170: Removed temporary directory /tmp/madlib_gjEiV62ZJV\n",
"PoolWorker-171: Removed temporary directory /tmp/madlib_MpZQ1LKqh5\n",
"PoolWorker-172: Removed temporary directory /tmp/madlib_drM88KR9IX\n",
"PoolWorker-174: Removed temporary directory /tmp/madlib_msRkwJB7A0\n",
"PoolWorker-176: Removed temporary directory /tmp/madlib_cXqWnRiNIn\n",
"PoolWorker-173: Removed temporary directory /tmp/madlib_QSnaE144MN\n",
"PoolWorker-175: Removed temporary directory /tmp/madlib_ct463nNDZt\n",
"PoolWorker-169: Removed temporary directory /tmp/madlib_w45egOYdnj\n",
"Done! Loaded 500 images in 334.61874795s\n",
"8 workers terminated.\n",
"Chunk: 23/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-177 [pid 86873]\n",
"PoolWorker-177: Created temporary directory /tmp/madlib_jsJoCBx7aq\n",
"Initializing PoolWorker-178 [pid 86874]\n",
"PoolWorker-178: Created temporary directory /tmp/madlib_9mCVGDi10o\n",
"Initializing PoolWorker-179 [pid 86875]\n",
"PoolWorker-179: Created temporary directory /tmp/madlib_rD0YDyaiLv\n",
"Initializing PoolWorker-180 [pid 86876]\n",
"PoolWorker-180: Created temporary directory /tmp/madlib_3oU4NrwKnX\n",
"Initializing PoolWorker-181 [pid 86877]\n",
"PoolWorker-181: Created temporary directory /tmp/madlib_ZL4mnWHr2p\n",
"Initializing PoolWorker-182 [pid 86878]\n",
"PoolWorker-182: Created temporary directory /tmp/madlib_BqSbhz5Elh\n",
"Initializing PoolWorker-183 [pid 86879]\n",
"PoolWorker-183: Created temporary directory /tmp/madlib_Wv8ma6SWg4\n",
"Initializing PoolWorker-184 [pid 86880]\n",
"PoolWorker-184: Created temporary directory /tmp/madlib_TUQkHlWoEa\n",
"PoolWorker-177: Connected to madlib db.\n",
"PoolWorker-178: Connected to madlib db.\n",
"PoolWorker-179: Connected to madlib db.\n",
"PoolWorker-180: Connected to madlib db.\n",
"PoolWorker-181: Connected to madlib db.\n",
"PoolWorker-182: Connected to madlib db.\n",
"PoolWorker-183: Connected to madlib db.\n",
"PoolWorker-184: Connected to madlib db.\n",
"PoolWorker-177: Wrote 500 images to /tmp/madlib_jsJoCBx7aq/imagenet_validation_data0000.tmp\n",
"PoolWorker-177: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-178: Removed temporary directory /tmp/madlib_9mCVGDi10o\n",
"PoolWorker-180: Removed temporary directory /tmp/madlib_3oU4NrwKnX\n",
"PoolWorker-181: Removed temporary directory /tmp/madlib_ZL4mnWHr2p\n",
"PoolWorker-179: Removed temporary directory /tmp/madlib_rD0YDyaiLv\n",
"PoolWorker-182: Removed temporary directory /tmp/madlib_BqSbhz5Elh\n",
"PoolWorker-183: Removed temporary directory /tmp/madlib_Wv8ma6SWg4\n",
"PoolWorker-184: Removed temporary directory /tmp/madlib_TUQkHlWoEa\n",
"PoolWorker-177: Removed temporary directory /tmp/madlib_jsJoCBx7aq\n",
"Done! Loaded 500 images in 326.023793936s\n",
"8 workers terminated.\n",
"Chunk: 24/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-185 [pid 86977]\n",
"PoolWorker-185: Created temporary directory /tmp/madlib_KX6hXRxY7f\n",
"Initializing PoolWorker-186 [pid 86978]\n",
"PoolWorker-186: Created temporary directory /tmp/madlib_WtcrLNc2dP\n",
"Initializing PoolWorker-187 [pid 86979]\n",
"PoolWorker-187: Created temporary directory /tmp/madlib_T0rvSOnF4w\n",
"Initializing PoolWorker-188 [pid 86980]\n",
"PoolWorker-188: Created temporary directory /tmp/madlib_2hfSXrpcn2\n",
"Initializing PoolWorker-189 [pid 86981]\n",
"PoolWorker-189: Created temporary directory /tmp/madlib_jrMMm4NqCm\n",
"Initializing PoolWorker-190 [pid 86982]\n",
"PoolWorker-190: Created temporary directory /tmp/madlib_phyCAdpen0\n",
"PoolWorker-192: Connected to madlib db.\n",
"Initializing PoolWorker-191 [pid 86983]\n",
"PoolWorker-191: Created temporary directory /tmp/madlib_I6G6ZvvDeD\n",
"Initializing PoolWorker-192 [pid 86984]\n",
"PoolWorker-192: Created temporary directory /tmp/madlib_WPtnA8t5AA\n",
"PoolWorker-185: Connected to madlib db.\n",
"PoolWorker-186: Connected to madlib db.\n",
"PoolWorker-187: Connected to madlib db.\n",
"PoolWorker-188: Connected to madlib db.\n",
"PoolWorker-189: Connected to madlib db.\n",
"PoolWorker-190: Connected to madlib db.\n",
"PoolWorker-191: Connected to madlib db.\n",
"PoolWorker-185: Wrote 500 images to /tmp/madlib_KX6hXRxY7f/imagenet_validation_data0000.tmp\n",
"PoolWorker-185: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-187: Removed temporary directory /tmp/madlib_T0rvSOnF4w\n",
"PoolWorker-186: Removed temporary directory /tmp/madlib_WtcrLNc2dP\n",
"PoolWorker-188: Removed temporary directory /tmp/madlib_2hfSXrpcn2\n",
"PoolWorker-191: Removed temporary directory /tmp/madlib_I6G6ZvvDeD\n",
"PoolWorker-190: Removed temporary directory /tmp/madlib_phyCAdpen0\n",
"PoolWorker-189: Removed temporary directory /tmp/madlib_jrMMm4NqCm\n",
"PoolWorker-192: Removed temporary directory /tmp/madlib_WPtnA8t5AA\n",
"Done! Loaded 500 images in 429.464191914s\n",
"PoolWorker-185: Removed temporary directory /tmp/madlib_KX6hXRxY7f\n",
"8 workers terminated.\n",
"Chunk: 25/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-193 [pid 87549]\n",
"PoolWorker-193: Created temporary directory /tmp/madlib_GRAqHbpZxr\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-194 [pid 87550]\n",
"PoolWorker-194: Created temporary directory /tmp/madlib_Z5dZ29bbfQ\n",
"Initializing PoolWorker-195 [pid 87551]\n",
"PoolWorker-195: Created temporary directory /tmp/madlib_AMNwM8N7va\n",
"Initializing PoolWorker-196 [pid 87552]\n",
"PoolWorker-196: Created temporary directory /tmp/madlib_cDG7F9sTmA\n",
"Initializing PoolWorker-197 [pid 87553]\n",
"PoolWorker-197: Created temporary directory /tmp/madlib_F3pzfUAOFc\n",
"Initializing PoolWorker-198 [pid 87554]\n",
"PoolWorker-198: Created temporary directory /tmp/madlib_AYO7rkbWuV\n",
"PoolWorker-200: Connected to madlib db.\n",
"Initializing PoolWorker-199 [pid 87555]\n",
"PoolWorker-199: Created temporary directory /tmp/madlib_oOryhdy9cy\n",
"Initializing PoolWorker-200 [pid 87556]\n",
"PoolWorker-200: Created temporary directory /tmp/madlib_J4sg3FMVIM\n",
"PoolWorker-193: Connected to madlib db.\n",
"PoolWorker-194: Connected to madlib db.\n",
"PoolWorker-195: Connected to madlib db.\n",
"PoolWorker-196: Connected to madlib db.\n",
"PoolWorker-197: Connected to madlib db.\n",
"PoolWorker-198: Connected to madlib db.\n",
"PoolWorker-199: Connected to madlib db.\n",
"PoolWorker-193: Wrote 500 images to /tmp/madlib_GRAqHbpZxr/imagenet_validation_data0000.tmp\n",
"PoolWorker-193: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-196: Removed temporary directory /tmp/madlib_cDG7F9sTmA\n",
"PoolWorker-195: Removed temporary directory /tmp/madlib_AMNwM8N7va\n",
"PoolWorker-197: Removed temporary directory /tmp/madlib_F3pzfUAOFc\n",
"PoolWorker-194: Removed temporary directory /tmp/madlib_Z5dZ29bbfQ\n",
"PoolWorker-198: Removed temporary directory /tmp/madlib_AYO7rkbWuV\n",
"PoolWorker-200: Removed temporary directory /tmp/madlib_J4sg3FMVIM\n",
"PoolWorker-199: Removed temporary directory /tmp/madlib_oOryhdy9cy\n",
"PoolWorker-193: Removed temporary directory /tmp/madlib_GRAqHbpZxr\n",
"Done! Loaded 500 images in 344.489638805s\n",
"8 workers terminated.\n",
"Chunk: 26/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-201 [pid 87571]\n",
"PoolWorker-201: Created temporary directory /tmp/madlib_zWftvr2YrH\n",
"Initializing PoolWorker-202 [pid 87572]\n",
"PoolWorker-202: Created temporary directory /tmp/madlib_2tsxO08Ekk\n",
"Initializing PoolWorker-203 [pid 87573]\n",
"PoolWorker-203: Created temporary directory /tmp/madlib_OWpMfhVap4\n",
"Initializing PoolWorker-204 [pid 87574]\n",
"PoolWorker-204: Created temporary directory /tmp/madlib_ZH6Csf3lUp\n",
"Initializing PoolWorker-205 [pid 87575]\n",
"PoolWorker-205: Created temporary directory /tmp/madlib_DxFXf7UVyt\n",
"Initializing PoolWorker-206 [pid 87576]\n",
"PoolWorker-206: Created temporary directory /tmp/madlib_eRc7lO7lXN\n",
"Initializing PoolWorker-207 [pid 87577]\n",
"PoolWorker-207: Created temporary directory /tmp/madlib_V9lk461TYP\n",
"Initializing PoolWorker-208 [pid 87578]\n",
"PoolWorker-208: Created temporary directory /tmp/madlib_Vf2FVOgqXN\n",
"PoolWorker-201: Connected to madlib db.\n",
"PoolWorker-202: Connected to madlib db.\n",
"PoolWorker-203: Connected to madlib db.\n",
"PoolWorker-204: Connected to madlib db.\n",
"PoolWorker-205: Connected to madlib db.\n",
"PoolWorker-206: Connected to madlib db.\n",
"PoolWorker-207: Connected to madlib db.\n",
"PoolWorker-208: Connected to madlib db.\n",
"PoolWorker-201: Wrote 500 images to /tmp/madlib_zWftvr2YrH/imagenet_validation_data0000.tmp\n",
"PoolWorker-201: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-204: Removed temporary directory /tmp/madlib_ZH6Csf3lUp\n",
"PoolWorker-207: Removed temporary directory /tmp/madlib_V9lk461TYP\n",
"PoolWorker-203: Removed temporary directory /tmp/madlib_OWpMfhVap4\n",
"PoolWorker-208: Removed temporary directory /tmp/madlib_Vf2FVOgqXN\n",
"PoolWorker-205: Removed temporary directory /tmp/madlib_DxFXf7UVyt\n",
"PoolWorker-202: Removed temporary directory /tmp/madlib_2tsxO08Ekk\n",
"PoolWorker-206: Removed temporary directory /tmp/madlib_eRc7lO7lXN\n",
"PoolWorker-201: Removed temporary directory /tmp/madlib_zWftvr2YrH\n",
"Done! Loaded 500 images in 349.646668196s\n",
"8 workers terminated.\n",
"Chunk: 27/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-209 [pid 87597]\n",
"PoolWorker-209: Created temporary directory /tmp/madlib_UXsxrcxAiA\n",
"Initializing PoolWorker-210 [pid 87598]\n",
"PoolWorker-210: Created temporary directory /tmp/madlib_WlrINAv1p0\n",
"Initializing PoolWorker-211 [pid 87599]\n",
"PoolWorker-211: Created temporary directory /tmp/madlib_RZVAoUhzyr\n",
"Initializing PoolWorker-212 [pid 87600]\n",
"PoolWorker-212: Created temporary directory /tmp/madlib_FJgv9iYB6o\n",
"Initializing PoolWorker-213 [pid 87601]\n",
"PoolWorker-213: Created temporary directory /tmp/madlib_JKSjjQCeDV\n",
"Initializing PoolWorker-214 [pid 87602]\n",
"PoolWorker-214: Created temporary directory /tmp/madlib_rzOUEuUeNO\n",
"Initializing PoolWorker-215 [pid 87603]\n",
"PoolWorker-216: Connected to madlib db.\n",
"PoolWorker-215: Created temporary directory /tmp/madlib_BA24bnhKpp\n",
"Initializing PoolWorker-216 [pid 87604]\n",
"PoolWorker-216: Created temporary directory /tmp/madlib_MEKh2TKLN1\n",
"PoolWorker-209: Connected to madlib db.\n",
"PoolWorker-210: Connected to madlib db.\n",
"PoolWorker-211: Connected to madlib db.\n",
"PoolWorker-212: Connected to madlib db.\n",
"PoolWorker-213: Connected to madlib db.\n",
"PoolWorker-214: Connected to madlib db.\n",
"PoolWorker-215: Connected to madlib db.\n",
"PoolWorker-209: Wrote 500 images to /tmp/madlib_UXsxrcxAiA/imagenet_validation_data0000.tmp\n",
"PoolWorker-209: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-210: Removed temporary directory /tmp/madlib_WlrINAv1p0\n",
"PoolWorker-212: Removed temporary directory /tmp/madlib_FJgv9iYB6o\n",
"PoolWorker-211: Removed temporary directory /tmp/madlib_RZVAoUhzyr\n",
"PoolWorker-216: Removed temporary directory /tmp/madlib_MEKh2TKLN1\n",
"PoolWorker-215: Removed temporary directory /tmp/madlib_BA24bnhKpp\n",
"PoolWorker-214: Removed temporary directory /tmp/madlib_rzOUEuUeNO\n",
"PoolWorker-213: Removed temporary directory /tmp/madlib_JKSjjQCeDV\n",
"PoolWorker-209: Removed temporary directory /tmp/madlib_UXsxrcxAiA\n",
"Done! Loaded 500 images in 401.236725092s\n",
"8 workers terminated.\n",
"Chunk: 28/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-217 [pid 88917]\n",
"PoolWorker-217: Created temporary directory /tmp/madlib_5WMfcqBGUe\n",
"Initializing PoolWorker-218 [pid 88918]\n",
"PoolWorker-218: Created temporary directory /tmp/madlib_SVwqXbrZEA\n",
"Initializing PoolWorker-219 [pid 88919]\n",
"PoolWorker-219: Created temporary directory /tmp/madlib_akIJna5TB4\n",
"Initializing PoolWorker-220 [pid 88920]\n",
"PoolWorker-220: Created temporary directory /tmp/madlib_PkQwib4l5i\n",
"Initializing PoolWorker-221 [pid 88921]\n",
"PoolWorker-221: Created temporary directory /tmp/madlib_wuI1nOZ9aw\n",
"Initializing PoolWorker-222 [pid 88922]\n",
"PoolWorker-222: Created temporary directory /tmp/madlib_FM3Q9KdSMP\n",
"Initializing PoolWorker-223 [pid 88923]\n",
"PoolWorker-224: Connected to madlib db.\n",
"PoolWorker-223: Created temporary directory /tmp/madlib_2rhXPSL5DN\n",
"Initializing PoolWorker-224 [pid 88924]\n",
"PoolWorker-224: Created temporary directory /tmp/madlib_AWc1BK3RDZ\n",
"PoolWorker-217: Connected to madlib db.\n",
"PoolWorker-218: Connected to madlib db.\n",
"PoolWorker-219: Connected to madlib db.\n",
"PoolWorker-220: Connected to madlib db.\n",
"PoolWorker-221: Connected to madlib db.\n",
"PoolWorker-222: Connected to madlib db.\n",
"PoolWorker-223: Connected to madlib db.\n",
"PoolWorker-218: Wrote 500 images to /tmp/madlib_SVwqXbrZEA/imagenet_validation_data0000.tmp\n",
"PoolWorker-218: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-220: Removed temporary directory /tmp/madlib_PkQwib4l5i\n",
"PoolWorker-221: Removed temporary directory /tmp/madlib_wuI1nOZ9aw\n",
"PoolWorker-222: Removed temporary directory /tmp/madlib_FM3Q9KdSMP\n",
"PoolWorker-217: Removed temporary directory /tmp/madlib_5WMfcqBGUe\n",
"PoolWorker-219: Removed temporary directory /tmp/madlib_akIJna5TB4\n",
"PoolWorker-224: Removed temporary directory /tmp/madlib_AWc1BK3RDZ\n",
"PoolWorker-223: Removed temporary directory /tmp/madlib_2rhXPSL5DN\n",
"PoolWorker-218: Removed temporary directory /tmp/madlib_SVwqXbrZEA\n",
"Done! Loaded 500 images in 323.454668999s\n",
"8 workers terminated.\n",
"Chunk: 29/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-225 [pid 88963]\n",
"PoolWorker-225: Created temporary directory /tmp/madlib_jmpsuFxy3W\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-226 [pid 88964]\n",
"PoolWorker-226: Created temporary directory /tmp/madlib_zA2OTBhEwr\n",
"Initializing PoolWorker-227 [pid 88965]\n",
"PoolWorker-227: Created temporary directory /tmp/madlib_5ml6wTukQw\n",
"Initializing PoolWorker-228 [pid 88966]\n",
"PoolWorker-228: Created temporary directory /tmp/madlib_PO8KJ3Eiqj\n",
"Initializing PoolWorker-229 [pid 88967]\n",
"PoolWorker-229: Created temporary directory /tmp/madlib_GHfniNPp3X\n",
"Initializing PoolWorker-230 [pid 88968]\n",
"PoolWorker-230: Created temporary directory /tmp/madlib_lUcRSMgVmK\n",
"Initializing PoolWorker-231 [pid 88969]\n",
"PoolWorker-231: Created temporary directory /tmp/madlib_7ZGN6FoQfe\n",
"Initializing PoolWorker-232 [pid 88970]\n",
"PoolWorker-232: Created temporary directory /tmp/madlib_SXERPpbAlP\n",
"PoolWorker-225: Connected to madlib db.\n",
"PoolWorker-226: Connected to madlib db.\n",
"PoolWorker-227: Connected to madlib db.\n",
"PoolWorker-228: Connected to madlib db.\n",
"PoolWorker-229: Connected to madlib db.\n",
"PoolWorker-230: Connected to madlib db.\n",
"PoolWorker-231: Connected to madlib db.\n",
"PoolWorker-232: Connected to madlib db.\n",
"PoolWorker-225: Wrote 500 images to /tmp/madlib_jmpsuFxy3W/imagenet_validation_data0000.tmp\n",
"PoolWorker-225: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-226: Removed temporary directory /tmp/madlib_zA2OTBhEwr\n",
"PoolWorker-229: Removed temporary directory /tmp/madlib_GHfniNPp3X\n",
"PoolWorker-227: Removed temporary directory /tmp/madlib_5ml6wTukQw\n",
"PoolWorker-228: Removed temporary directory /tmp/madlib_PO8KJ3Eiqj\n",
"PoolWorker-230: Removed temporary directory /tmp/madlib_lUcRSMgVmK\n",
"PoolWorker-231: Removed temporary directory /tmp/madlib_7ZGN6FoQfe\n",
"PoolWorker-232: Removed temporary directory /tmp/madlib_SXERPpbAlP\n",
"PoolWorker-225: Removed temporary directory /tmp/madlib_jmpsuFxy3W\n",
"Done! Loaded 500 images in 438.763128996s\n",
"8 workers terminated.\n",
"Chunk: 30/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-233 [pid 89003]\n",
"Initializing PoolWorker-234 [pid 89004]\n",
"PoolWorker-233: Created temporary directory /tmp/madlib_c65DY8N5K3\n",
"PoolWorker-234: Created temporary directory /tmp/madlib_jsg5nAQ8fA\n",
"Initializing PoolWorker-235 [pid 89005]\n",
"PoolWorker-235: Created temporary directory /tmp/madlib_s929WjUZxv\n",
"Initializing PoolWorker-236 [pid 89006]\n",
"PoolWorker-236: Created temporary directory /tmp/madlib_Qq3E9ALvWr\n",
"Initializing PoolWorker-237 [pid 89007]\n",
"PoolWorker-237: Created temporary directory /tmp/madlib_YTl7dBJa0d\n",
"Initializing PoolWorker-238 [pid 89008]\n",
"PoolWorker-238: Created temporary directory /tmp/madlib_R5cOCwW6lZ\n",
"Initializing PoolWorker-239 [pid 89009]\n",
"PoolWorker-239: Created temporary directory /tmp/madlib_I2Femk7vD0\n",
"Initializing PoolWorker-240 [pid 89010]\n",
"PoolWorker-240: Created temporary directory /tmp/madlib_a5Q3dWTDbU\n",
"PoolWorker-234: Connected to madlib db.\n",
"PoolWorker-235: Connected to madlib db.\n",
"PoolWorker-237: Connected to madlib db.\n",
"PoolWorker-233: Connected to madlib db.\n",
"PoolWorker-236: Connected to madlib db.\n",
"PoolWorker-238: Connected to madlib db.\n",
"PoolWorker-239: Connected to madlib db.\n",
"PoolWorker-240: Connected to madlib db.\n",
"PoolWorker-234: Wrote 500 images to /tmp/madlib_jsg5nAQ8fA/imagenet_validation_data0000.tmp\n",
"PoolWorker-234: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-235: Removed temporary directory /tmp/madlib_s929WjUZxv\n",
"PoolWorker-233: Removed temporary directory /tmp/madlib_c65DY8N5K3\n",
"PoolWorker-237: Removed temporary directory /tmp/madlib_YTl7dBJa0d\n",
"PoolWorker-236: Removed temporary directory /tmp/madlib_Qq3E9ALvWr\n",
"PoolWorker-240: Removed temporary directory /tmp/madlib_a5Q3dWTDbU\n",
"PoolWorker-239: Removed temporary directory /tmp/madlib_I2Femk7vD0\n",
"PoolWorker-238: Removed temporary directory /tmp/madlib_R5cOCwW6lZ\n",
"PoolWorker-234: Removed temporary directory /tmp/madlib_jsg5nAQ8fA\n",
"Done! Loaded 500 images in 332.930665016s\n",
"8 workers terminated.\n",
"Chunk: 31/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-241 [pid 89027]\n",
"PoolWorker-241: Created temporary directory /tmp/madlib_nQm4yY9ICJ\n",
"Initializing PoolWorker-242 [pid 89028]\n",
"PoolWorker-242: Created temporary directory /tmp/madlib_4aEwBLXAia\n",
"Initializing PoolWorker-243 [pid 89029]\n",
"PoolWorker-243: Created temporary directory /tmp/madlib_W8ROmg1bcU\n",
"Initializing PoolWorker-244 [pid 89030]\n",
"PoolWorker-244: Created temporary directory /tmp/madlib_NJJJAYOiTm\n",
"Initializing PoolWorker-245 [pid 89031]\n",
"PoolWorker-245: Created temporary directory /tmp/madlib_5gBih3p1lx\n",
"Initializing PoolWorker-246 [pid 89032]\n",
"PoolWorker-246: Created temporary directory /tmp/madlib_gT0mRsVtAx\n",
"PoolWorker-248: Connected to madlib db.\n",
"Initializing PoolWorker-247 [pid 89033]\n",
"PoolWorker-247: Created temporary directory /tmp/madlib_IyB5euIOXP\n",
"Initializing PoolWorker-248 [pid 89034]\n",
"PoolWorker-248: Created temporary directory /tmp/madlib_RwHBnTz8Zo\n",
"PoolWorker-241: Connected to madlib db.\n",
"PoolWorker-242: Connected to madlib db.\n",
"PoolWorker-243: Connected to madlib db.\n",
"PoolWorker-244: Connected to madlib db.\n",
"PoolWorker-245: Connected to madlib db.\n",
"PoolWorker-246: Connected to madlib db.\n",
"PoolWorker-247: Connected to madlib db.\n",
"PoolWorker-241: Wrote 500 images to /tmp/madlib_nQm4yY9ICJ/imagenet_validation_data0000.tmp\n",
"PoolWorker-241: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-243: Removed temporary directory /tmp/madlib_W8ROmg1bcU\n",
"PoolWorker-244: Removed temporary directory /tmp/madlib_NJJJAYOiTm\n",
"PoolWorker-242: Removed temporary directory /tmp/madlib_4aEwBLXAia\n",
"PoolWorker-245: Removed temporary directory /tmp/madlib_5gBih3p1lx\n",
"PoolWorker-246: Removed temporary directory /tmp/madlib_gT0mRsVtAx\n",
"PoolWorker-248: Removed temporary directory /tmp/madlib_RwHBnTz8Zo\n",
"PoolWorker-247: Removed temporary directory /tmp/madlib_IyB5euIOXP\n",
"PoolWorker-241: Removed temporary directory /tmp/madlib_nQm4yY9ICJ\n",
"Done! Loaded 500 images in 336.3992939s\n",
"8 workers terminated.\n",
"Chunk: 32/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-249 [pid 89048]\n",
"PoolWorker-249: Created temporary directory /tmp/madlib_QctqDvC7BT\n",
"Initializing PoolWorker-250 [pid 89049]\n",
"PoolWorker-250: Created temporary directory /tmp/madlib_1VxHZicZ0I\n",
"Initializing PoolWorker-251 [pid 89050]\n",
"PoolWorker-251: Created temporary directory /tmp/madlib_QI3s6kR9OS\n",
"Initializing PoolWorker-252 [pid 89051]\n",
"PoolWorker-252: Created temporary directory /tmp/madlib_7lWLvCqMO9\n",
"Initializing PoolWorker-253 [pid 89052]\n",
"PoolWorker-253: Created temporary directory /tmp/madlib_ZDuZHp1OMK\n",
"Initializing PoolWorker-254 [pid 89053]\n",
"PoolWorker-254: Created temporary directory /tmp/madlib_BZ1N6zl9iq\n",
"PoolWorker-256: Connected to madlib db.\n",
"Initializing PoolWorker-255 [pid 89054]\n",
"PoolWorker-255: Created temporary directory /tmp/madlib_7qydQatEto\n",
"Initializing PoolWorker-256 [pid 89055]\n",
"PoolWorker-256: Created temporary directory /tmp/madlib_8SXvCc30wD\n",
"PoolWorker-249: Connected to madlib db.\n",
"PoolWorker-250: Connected to madlib db.\n",
"PoolWorker-251: Connected to madlib db.\n",
"PoolWorker-252: Connected to madlib db.\n",
"PoolWorker-253: Connected to madlib db.\n",
"PoolWorker-254: Connected to madlib db.\n",
"PoolWorker-255: Connected to madlib db.\n",
"PoolWorker-249: Wrote 500 images to /tmp/madlib_QctqDvC7BT/imagenet_validation_data0000.tmp\n",
"PoolWorker-249: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-253: Removed temporary directory /tmp/madlib_ZDuZHp1OMK\n",
"PoolWorker-250: Removed temporary directory /tmp/madlib_1VxHZicZ0I\n",
"PoolWorker-252: Removed temporary directory /tmp/madlib_7lWLvCqMO9\n",
"PoolWorker-255: Removed temporary directory /tmp/madlib_7qydQatEto\n",
"PoolWorker-251: Removed temporary directory /tmp/madlib_QI3s6kR9OS\n",
"PoolWorker-254: Removed temporary directory /tmp/madlib_BZ1N6zl9iq\n",
"PoolWorker-256: Removed temporary directory /tmp/madlib_8SXvCc30wD\n",
"PoolWorker-249: Removed temporary directory /tmp/madlib_QctqDvC7BT\n",
"Done! Loaded 500 images in 325.048389912s\n",
"8 workers terminated.\n",
"Chunk: 33/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-257 [pid 89069]\n",
"PoolWorker-257: Created temporary directory /tmp/madlib_NvHodVx80l\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-258 [pid 89070]\n",
"PoolWorker-258: Created temporary directory /tmp/madlib_756ETQwp9T\n",
"Initializing PoolWorker-259 [pid 89071]\n",
"PoolWorker-259: Created temporary directory /tmp/madlib_tNsjmIuNLE\n",
"Initializing PoolWorker-260 [pid 89072]\n",
"PoolWorker-260: Created temporary directory /tmp/madlib_3lf86o7i8f\n",
"Initializing PoolWorker-261 [pid 89073]\n",
"PoolWorker-261: Created temporary directory /tmp/madlib_U4i1fcR1Qi\n",
"Initializing PoolWorker-262 [pid 89074]\n",
"PoolWorker-262: Created temporary directory /tmp/madlib_RTT5AW2LIi\n",
"Initializing PoolWorker-263 [pid 89075]\n",
"PoolWorker-263: Created temporary directory /tmp/madlib_w4a2ENCDy5\n",
"Initializing PoolWorker-264 [pid 89076]\n",
"PoolWorker-264: Created temporary directory /tmp/madlib_FHPl90Bb5p\n",
"PoolWorker-257: Connected to madlib db.\n",
"PoolWorker-258: Connected to madlib db.\n",
"PoolWorker-259: Connected to madlib db.\n",
"PoolWorker-260: Connected to madlib db.\n",
"PoolWorker-261: Connected to madlib db.\n",
"PoolWorker-262: Connected to madlib db.\n",
"PoolWorker-263: Connected to madlib db.\n",
"PoolWorker-264: Connected to madlib db.\n",
"PoolWorker-257: Wrote 500 images to /tmp/madlib_NvHodVx80l/imagenet_validation_data0000.tmp\n",
"PoolWorker-257: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-258: Removed temporary directory /tmp/madlib_756ETQwp9T\n",
"PoolWorker-259: Removed temporary directory /tmp/madlib_tNsjmIuNLE\n",
"PoolWorker-263: Removed temporary directory /tmp/madlib_w4a2ENCDy5\n",
"PoolWorker-264: Removed temporary directory /tmp/madlib_FHPl90Bb5p\n",
"PoolWorker-262: Removed temporary directory /tmp/madlib_RTT5AW2LIi\n",
"PoolWorker-261: Removed temporary directory /tmp/madlib_U4i1fcR1Qi\n",
"PoolWorker-260: Removed temporary directory /tmp/madlib_3lf86o7i8f\n",
"PoolWorker-257: Removed temporary directory /tmp/madlib_NvHodVx80l\n",
"Done! Loaded 500 images in 330.621911049s\n",
"8 workers terminated.\n",
"Chunk: 34/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-265 [pid 89091]\n",
"PoolWorker-265: Created temporary directory /tmp/madlib_D4ASKzuY22\n",
"Initializing PoolWorker-266 [pid 89092]\n",
"PoolWorker-266: Created temporary directory /tmp/madlib_tjWBbkdxI8\n",
"Initializing PoolWorker-267 [pid 89093]\n",
"PoolWorker-267: Created temporary directory /tmp/madlib_yb3e99bCp2\n",
"Initializing PoolWorker-268 [pid 89094]\n",
"PoolWorker-268: Created temporary directory /tmp/madlib_JegMbe0EAf\n",
"Initializing PoolWorker-269 [pid 89095]\n",
"PoolWorker-269: Created temporary directory /tmp/madlib_hVUbuKhtmf\n",
"Initializing PoolWorker-270 [pid 89096]\n",
"PoolWorker-270: Created temporary directory /tmp/madlib_ufrLKrRPjW\n",
"Initializing PoolWorker-271 [pid 89097]\n",
"PoolWorker-271: Created temporary directory /tmp/madlib_2MEgRFlte7\n",
"Initializing PoolWorker-272 [pid 89098]\n",
"PoolWorker-272: Created temporary directory /tmp/madlib_Ep1MaAROw7\n",
"PoolWorker-265: Connected to madlib db.\n",
"PoolWorker-266: Connected to madlib db.\n",
"PoolWorker-267: Connected to madlib db.\n",
"PoolWorker-268: Connected to madlib db.\n",
"PoolWorker-269: Connected to madlib db.\n",
"PoolWorker-270: Connected to madlib db.\n",
"PoolWorker-271: Connected to madlib db.\n",
"PoolWorker-272: Connected to madlib db.\n",
"PoolWorker-265: Wrote 500 images to /tmp/madlib_D4ASKzuY22/imagenet_validation_data0000.tmp\n",
"PoolWorker-265: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-266: Removed temporary directory /tmp/madlib_tjWBbkdxI8\n",
"PoolWorker-267: Removed temporary directory /tmp/madlib_yb3e99bCp2\n",
"PoolWorker-268: Removed temporary directory /tmp/madlib_JegMbe0EAf\n",
"PoolWorker-269: Removed temporary directory /tmp/madlib_hVUbuKhtmf\n",
"PoolWorker-270: Removed temporary directory /tmp/madlib_ufrLKrRPjW\n",
"PoolWorker-271: Removed temporary directory /tmp/madlib_2MEgRFlte7\n",
"PoolWorker-272: Removed temporary directory /tmp/madlib_Ep1MaAROw7\n",
"PoolWorker-265: Removed temporary directory /tmp/madlib_D4ASKzuY22\n",
"Done! Loaded 500 images in 335.043764114s\n",
"8 workers terminated.\n",
"Chunk: 35/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-273 [pid 89661]\n",
"PoolWorker-273: Created temporary directory /tmp/madlib_2rCDXzJsSc\n",
"Initializing PoolWorker-274 [pid 89662]\n",
"PoolWorker-274: Created temporary directory /tmp/madlib_dQpCPeAZ9k\n",
"Initializing PoolWorker-275 [pid 89663]\n",
"PoolWorker-275: Created temporary directory /tmp/madlib_AUMxAbeXlw\n",
"Initializing PoolWorker-276 [pid 89664]\n",
"PoolWorker-276: Created temporary directory /tmp/madlib_KLgBtrdzie\n",
"Initializing PoolWorker-277 [pid 89665]\n",
"PoolWorker-277: Created temporary directory /tmp/madlib_FARxU4sL3t\n",
"Initializing PoolWorker-278 [pid 89666]\n",
"PoolWorker-278: Created temporary directory /tmp/madlib_AVJ60C8fN8\n",
"Initializing PoolWorker-279 [pid 89667]\n",
"PoolWorker-279: Created temporary directory /tmp/madlib_i6bQ8SWPQ3\n",
"Initializing PoolWorker-280 [pid 89668]\n",
"PoolWorker-280: Created temporary directory /tmp/madlib_dhSwzg5EWX\n",
"PoolWorker-273: Connected to madlib db.\n",
"PoolWorker-274: Connected to madlib db.\n",
"PoolWorker-275: Connected to madlib db.\n",
"PoolWorker-276: Connected to madlib db.\n",
"PoolWorker-277: Connected to madlib db.\n",
"PoolWorker-278: Connected to madlib db.\n",
"PoolWorker-279: Connected to madlib db.\n",
"PoolWorker-280: Connected to madlib db.\n",
"PoolWorker-273: Wrote 500 images to /tmp/madlib_2rCDXzJsSc/imagenet_validation_data0000.tmp\n",
"PoolWorker-273: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-276: Removed temporary directory /tmp/madlib_KLgBtrdzie\n",
"PoolWorker-275: Removed temporary directory /tmp/madlib_AUMxAbeXlw\n",
"PoolWorker-274: Removed temporary directory /tmp/madlib_dQpCPeAZ9k\n",
"PoolWorker-280: Removed temporary directory /tmp/madlib_dhSwzg5EWX\n",
"PoolWorker-279: Removed temporary directory /tmp/madlib_i6bQ8SWPQ3\n",
"PoolWorker-277: Removed temporary directory /tmp/madlib_FARxU4sL3t\n",
"PoolWorker-278: Removed temporary directory /tmp/madlib_AVJ60C8fN8\n",
"PoolWorker-273: Removed temporary directory /tmp/madlib_2rCDXzJsSc\n",
"Done! Loaded 500 images in 322.24575901s\n",
"8 workers terminated.\n",
"Chunk: 36/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-281 [pid 89681]\n",
"PoolWorker-281: Created temporary directory /tmp/madlib_0CpJM1kqxH\n",
"Initializing PoolWorker-282 [pid 89682]\n",
"PoolWorker-282: Created temporary directory /tmp/madlib_yRF5hBxeWA\n",
"Initializing PoolWorker-283 [pid 89683]\n",
"PoolWorker-283: Created temporary directory /tmp/madlib_LdlKlBhOOz\n",
"Initializing PoolWorker-284 [pid 89684]\n",
"PoolWorker-284: Created temporary directory /tmp/madlib_TSXM5NFNPd\n",
"Initializing PoolWorker-285 [pid 89685]\n",
"PoolWorker-285: Created temporary directory /tmp/madlib_eTnvznKD3m\n",
"Initializing PoolWorker-286 [pid 89686]\n",
"PoolWorker-286: Created temporary directory /tmp/madlib_lSmqBIS2FZ\n",
"Initializing PoolWorker-287 [pid 89687]\n",
"PoolWorker-287: Created temporary directory /tmp/madlib_s5qC6cj3Gi\n",
"Initializing PoolWorker-288 [pid 89688]\n",
"PoolWorker-288: Created temporary directory /tmp/madlib_hMWsk7Yhae\n",
"PoolWorker-281: Connected to madlib db.\n",
"PoolWorker-282: Connected to madlib db.\n",
"PoolWorker-283: Connected to madlib db.\n",
"PoolWorker-284: Connected to madlib db.\n",
"PoolWorker-285: Connected to madlib db.\n",
"PoolWorker-286: Connected to madlib db.\n",
"PoolWorker-287: Connected to madlib db.\n",
"PoolWorker-288: Connected to madlib db.\n",
"PoolWorker-281: Wrote 500 images to /tmp/madlib_0CpJM1kqxH/imagenet_validation_data0000.tmp\n",
"PoolWorker-281: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-283: Removed temporary directory /tmp/madlib_LdlKlBhOOz\n",
"PoolWorker-282: Removed temporary directory /tmp/madlib_yRF5hBxeWA\n",
"PoolWorker-285: Removed temporary directory /tmp/madlib_eTnvznKD3m\n",
"PoolWorker-284: Removed temporary directory /tmp/madlib_TSXM5NFNPd\n",
"PoolWorker-287: Removed temporary directory /tmp/madlib_s5qC6cj3Gi\n",
"PoolWorker-288: Removed temporary directory /tmp/madlib_hMWsk7Yhae\n",
"PoolWorker-286: Removed temporary directory /tmp/madlib_lSmqBIS2FZ\n",
"PoolWorker-281: Removed temporary directory /tmp/madlib_0CpJM1kqxH\n",
"Done! Loaded 500 images in 325.482316017s\n",
"8 workers terminated.\n",
"Chunk: 37/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-289 [pid 89701]\n",
"PoolWorker-289: Created temporary directory /tmp/madlib_ETDryE9dSv\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-290 [pid 89702]\n",
"PoolWorker-290: Created temporary directory /tmp/madlib_7ysWzurxu2\n",
"Initializing PoolWorker-291 [pid 89703]\n",
"PoolWorker-291: Created temporary directory /tmp/madlib_Ui8U0xG3Em\n",
"Initializing PoolWorker-292 [pid 89704]\n",
"PoolWorker-292: Created temporary directory /tmp/madlib_LOia3DJWUg\n",
"Initializing PoolWorker-293 [pid 89705]\n",
"PoolWorker-293: Created temporary directory /tmp/madlib_kX0kyW5u0B\n",
"Initializing PoolWorker-294 [pid 89706]\n",
"PoolWorker-294: Created temporary directory /tmp/madlib_vjc33RTCOq\n",
"Initializing PoolWorker-295 [pid 89707]\n",
"PoolWorker-296: Connected to madlib db.\n",
"PoolWorker-295: Created temporary directory /tmp/madlib_UeRv8HvtIA\n",
"Initializing PoolWorker-296 [pid 89708]\n",
"PoolWorker-296: Created temporary directory /tmp/madlib_9qC8rP6Nxu\n",
"PoolWorker-289: Connected to madlib db.\n",
"PoolWorker-290: Connected to madlib db.\n",
"PoolWorker-291: Connected to madlib db.\n",
"PoolWorker-292: Connected to madlib db.\n",
"PoolWorker-293: Connected to madlib db.\n",
"PoolWorker-294: Connected to madlib db.\n",
"PoolWorker-295: Connected to madlib db.\n",
"PoolWorker-289: Wrote 500 images to /tmp/madlib_ETDryE9dSv/imagenet_validation_data0000.tmp\n",
"PoolWorker-289: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-290: Removed temporary directory /tmp/madlib_7ysWzurxu2\n",
"PoolWorker-291: Removed temporary directory /tmp/madlib_Ui8U0xG3Em\n",
"PoolWorker-294: Removed temporary directory /tmp/madlib_vjc33RTCOq\n",
"PoolWorker-293: Removed temporary directory /tmp/madlib_kX0kyW5u0B\n",
"PoolWorker-292: Removed temporary directory /tmp/madlib_LOia3DJWUg\n",
"PoolWorker-295: Removed temporary directory /tmp/madlib_UeRv8HvtIA\n",
"PoolWorker-296: Removed temporary directory /tmp/madlib_9qC8rP6Nxu\n",
"PoolWorker-289: Removed temporary directory /tmp/madlib_ETDryE9dSv\n",
"Done! Loaded 500 images in 332.323100805s\n",
"8 workers terminated.\n",
"Chunk: 38/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-297 [pid 89720]\n",
"Initializing PoolWorker-298 [pid 89721]\n",
"PoolWorker-297: Created temporary directory /tmp/madlib_KMTAxV9AZC\n",
"PoolWorker-298: Created temporary directory /tmp/madlib_miis3N8A8n\n",
"Initializing PoolWorker-299 [pid 89722]\n",
"PoolWorker-299: Created temporary directory /tmp/madlib_LkoJlScWrz\n",
"Initializing PoolWorker-300 [pid 89723]\n",
"PoolWorker-300: Created temporary directory /tmp/madlib_uuQwy9njhI\n",
"Initializing PoolWorker-301 [pid 89724]\n",
"PoolWorker-301: Created temporary directory /tmp/madlib_GvZt9OVSVQ\n",
"Initializing PoolWorker-302 [pid 89725]\n",
"PoolWorker-302: Created temporary directory /tmp/madlib_l75EmvjT1g\n",
"PoolWorker-304: Connected to madlib db.\n",
"Initializing PoolWorker-303 [pid 89726]\n",
"PoolWorker-303: Created temporary directory /tmp/madlib_nwQu6LiXPy\n",
"Initializing PoolWorker-304 [pid 89727]\n",
"PoolWorker-304: Created temporary directory /tmp/madlib_f9EQ5KDO4e\n",
"PoolWorker-298: Connected to madlib db.\n",
"PoolWorker-297: Connected to madlib db.\n",
"PoolWorker-299: Connected to madlib db.\n",
"PoolWorker-300: Connected to madlib db.\n",
"PoolWorker-301: Connected to madlib db.\n",
"PoolWorker-302: Connected to madlib db.\n",
"PoolWorker-303: Connected to madlib db.\n",
"PoolWorker-298: Wrote 500 images to /tmp/madlib_miis3N8A8n/imagenet_validation_data0000.tmp\n",
"PoolWorker-298: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-297: Removed temporary directory /tmp/madlib_KMTAxV9AZC\n",
"PoolWorker-299: Removed temporary directory /tmp/madlib_LkoJlScWrz\n",
"PoolWorker-300: Removed temporary directory /tmp/madlib_uuQwy9njhI\n",
"PoolWorker-301: Removed temporary directory /tmp/madlib_GvZt9OVSVQ\n",
"PoolWorker-302: Removed temporary directory /tmp/madlib_l75EmvjT1g\n",
"PoolWorker-303: Removed temporary directory /tmp/madlib_nwQu6LiXPy\n",
"PoolWorker-304: Removed temporary directory /tmp/madlib_f9EQ5KDO4e\n",
"PoolWorker-298: Removed temporary directory /tmp/madlib_miis3N8A8n\n",
"Done! Loaded 500 images in 320.624463081s\n",
"8 workers terminated.\n",
"Chunk: 39/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-305 [pid 89890]\n",
"PoolWorker-305: Created temporary directory /tmp/madlib_2dw8cMqo3w\n",
"Initializing PoolWorker-306 [pid 89891]\n",
"PoolWorker-306: Created temporary directory /tmp/madlib_s4mIV6Z7Fn\n",
"Initializing PoolWorker-307 [pid 89893]\n",
"PoolWorker-307: Created temporary directory /tmp/madlib_sfBdhj2CZN\n",
"Initializing PoolWorker-308 [pid 89894]\n",
"PoolWorker-308: Created temporary directory /tmp/madlib_nWdFbsF2Np\n",
"Initializing PoolWorker-309 [pid 89895]\n",
"PoolWorker-309: Created temporary directory /tmp/madlib_O92D2f0Das\n",
"Initializing PoolWorker-310 [pid 89897]\n",
"PoolWorker-310: Created temporary directory /tmp/madlib_TT8yBgSTbN\n",
"PoolWorker-312: Connected to madlib db.\n",
"Initializing PoolWorker-311 [pid 89898]\n",
"PoolWorker-311: Created temporary directory /tmp/madlib_TD1PYESfBK\n",
"Initializing PoolWorker-312 [pid 89899]\n",
"PoolWorker-312: Created temporary directory /tmp/madlib_K2VuTpiKOR\n",
"PoolWorker-305: Connected to madlib db.\n",
"PoolWorker-306: Connected to madlib db.\n",
"PoolWorker-307: Connected to madlib db.\n",
"PoolWorker-308: Connected to madlib db.\n",
"PoolWorker-309: Connected to madlib db.\n",
"PoolWorker-310: Connected to madlib db.\n",
"PoolWorker-311: Connected to madlib db.\n",
"PoolWorker-305: Wrote 500 images to /tmp/madlib_2dw8cMqo3w/imagenet_validation_data0000.tmp\n",
"PoolWorker-305: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-308: Removed temporary directory /tmp/madlib_nWdFbsF2Np\n",
"PoolWorker-310: Removed temporary directory /tmp/madlib_TT8yBgSTbN\n",
"PoolWorker-306: Removed temporary directory /tmp/madlib_s4mIV6Z7Fn\n",
"PoolWorker-312: Removed temporary directory /tmp/madlib_K2VuTpiKOR\n",
"PoolWorker-309: Removed temporary directory /tmp/madlib_O92D2f0Das\n",
"PoolWorker-307: Removed temporary directory /tmp/madlib_sfBdhj2CZN\n",
"PoolWorker-311: Removed temporary directory /tmp/madlib_TD1PYESfBK\n",
"PoolWorker-305: Removed temporary directory /tmp/madlib_2dw8cMqo3w\n",
"Done! Loaded 500 images in 328.809534073s\n",
"8 workers terminated.\n",
"Chunk: 40/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-313 [pid 91057]\n",
"PoolWorker-313: Created temporary directory /tmp/madlib_5vbP4fK0x8\n",
"Initializing PoolWorker-314 [pid 91058]\n",
"PoolWorker-314: Created temporary directory /tmp/madlib_P69WwTNe1d\n",
"Initializing PoolWorker-315 [pid 91059]\n",
"PoolWorker-315: Created temporary directory /tmp/madlib_tFUaHsMmLC\n",
"Initializing PoolWorker-316 [pid 91060]\n",
"PoolWorker-316: Created temporary directory /tmp/madlib_0gfnTi8uXl\n",
"Initializing PoolWorker-317 [pid 91061]\n",
"PoolWorker-317: Created temporary directory /tmp/madlib_4loMB7KbOq\n",
"Initializing PoolWorker-318 [pid 91062]\n",
"PoolWorker-318: Created temporary directory /tmp/madlib_jbgsgokUno\n",
"PoolWorker-320: Connected to madlib db.\n",
"Initializing PoolWorker-319 [pid 91063]\n",
"PoolWorker-319: Created temporary directory /tmp/madlib_Q6ysXPtj44\n",
"Initializing PoolWorker-320 [pid 91064]\n",
"PoolWorker-320: Created temporary directory /tmp/madlib_oTW0cUyiUd\n",
"PoolWorker-313: Connected to madlib db.\n",
"PoolWorker-314: Connected to madlib db.\n",
"PoolWorker-315: Connected to madlib db.\n",
"PoolWorker-316: Connected to madlib db.\n",
"PoolWorker-317: Connected to madlib db.\n",
"PoolWorker-318: Connected to madlib db.\n",
"PoolWorker-319: Connected to madlib db.\n",
"PoolWorker-313: Wrote 500 images to /tmp/madlib_5vbP4fK0x8/imagenet_validation_data0000.tmp\n",
"PoolWorker-313: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-314: Removed temporary directory /tmp/madlib_P69WwTNe1d\n",
"PoolWorker-316: Removed temporary directory /tmp/madlib_0gfnTi8uXl\n",
"PoolWorker-315: Removed temporary directory /tmp/madlib_tFUaHsMmLC\n",
"PoolWorker-317: Removed temporary directory /tmp/madlib_4loMB7KbOq\n",
"PoolWorker-318: Removed temporary directory /tmp/madlib_jbgsgokUno\n",
"PoolWorker-319: Removed temporary directory /tmp/madlib_Q6ysXPtj44\n",
"PoolWorker-320: Removed temporary directory /tmp/madlib_oTW0cUyiUd\n",
"PoolWorker-313: Removed temporary directory /tmp/madlib_5vbP4fK0x8\n",
"Done! Loaded 500 images in 436.756713867s\n",
"8 workers terminated.\n",
"Chunk: 41/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-321 [pid 91090]\n",
"PoolWorker-321: Created temporary directory /tmp/madlib_y2YWNPDzu9\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-322 [pid 91091]\n",
"PoolWorker-322: Created temporary directory /tmp/madlib_cQ3vaKgbze\n",
"Initializing PoolWorker-323 [pid 91092]\n",
"PoolWorker-323: Created temporary directory /tmp/madlib_o22HqzR7p5\n",
"Initializing PoolWorker-324 [pid 91093]\n",
"PoolWorker-324: Created temporary directory /tmp/madlib_zp3JP1B4SV\n",
"Initializing PoolWorker-325 [pid 91094]\n",
"PoolWorker-325: Created temporary directory /tmp/madlib_RZFNbDfiHO\n",
"Initializing PoolWorker-326 [pid 91095]\n",
"PoolWorker-326: Created temporary directory /tmp/madlib_Axm3UK1QzV\n",
"PoolWorker-328: Connected to madlib db.\n",
"Initializing PoolWorker-327 [pid 91096]\n",
"PoolWorker-327: Created temporary directory /tmp/madlib_wrmQyybE2M\n",
"Initializing PoolWorker-328 [pid 91097]\n",
"PoolWorker-328: Created temporary directory /tmp/madlib_VZHhItVmwZ\n",
"PoolWorker-321: Connected to madlib db.\n",
"PoolWorker-322: Connected to madlib db.\n",
"PoolWorker-323: Connected to madlib db.\n",
"PoolWorker-324: Connected to madlib db.\n",
"PoolWorker-325: Connected to madlib db.\n",
"PoolWorker-326: Connected to madlib db.\n",
"PoolWorker-327: Connected to madlib db.\n",
"PoolWorker-321: Wrote 500 images to /tmp/madlib_y2YWNPDzu9/imagenet_validation_data0000.tmp\n",
"PoolWorker-321: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-326: Removed temporary directory /tmp/madlib_Axm3UK1QzV\n",
"PoolWorker-322: Removed temporary directory /tmp/madlib_cQ3vaKgbze\n",
"PoolWorker-323: Removed temporary directory /tmp/madlib_o22HqzR7p5\n",
"PoolWorker-324: Removed temporary directory /tmp/madlib_zp3JP1B4SV\n",
"PoolWorker-327: Removed temporary directory /tmp/madlib_wrmQyybE2M\n",
"PoolWorker-328: Removed temporary directory /tmp/madlib_VZHhItVmwZ\n",
"PoolWorker-325: Removed temporary directory /tmp/madlib_RZFNbDfiHO\n",
"PoolWorker-321: Removed temporary directory /tmp/madlib_y2YWNPDzu9\n",
"Done! Loaded 500 images in 326.080613852s\n",
"8 workers terminated.\n",
"Chunk: 42/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-329 [pid 91117]\n",
"PoolWorker-329: Created temporary directory /tmp/madlib_L5qieznDHc\n",
"Initializing PoolWorker-330 [pid 91118]\n",
"PoolWorker-330: Created temporary directory /tmp/madlib_kGRS7Ab3WY\n",
"Initializing PoolWorker-331 [pid 91119]\n",
"PoolWorker-331: Created temporary directory /tmp/madlib_N5QA3eDms1\n",
"Initializing PoolWorker-332 [pid 91120]\n",
"PoolWorker-332: Created temporary directory /tmp/madlib_TJCRXh4qkN\n",
"Initializing PoolWorker-333 [pid 91121]\n",
"PoolWorker-333: Created temporary directory /tmp/madlib_p8LgS8icrx\n",
"Initializing PoolWorker-334 [pid 91122]\n",
"PoolWorker-334: Created temporary directory /tmp/madlib_GI9bf4aDPM\n",
"PoolWorker-336: Connected to madlib db.\n",
"Initializing PoolWorker-335 [pid 91123]\n",
"PoolWorker-335: Created temporary directory /tmp/madlib_S4WbUHWj3D\n",
"Initializing PoolWorker-336 [pid 91124]\n",
"PoolWorker-336: Created temporary directory /tmp/madlib_lOa2VDgQ9K\n",
"PoolWorker-329: Connected to madlib db.\n",
"PoolWorker-330: Connected to madlib db.\n",
"PoolWorker-331: Connected to madlib db.\n",
"PoolWorker-332: Connected to madlib db.\n",
"PoolWorker-333: Connected to madlib db.\n",
"PoolWorker-334: Connected to madlib db.\n",
"PoolWorker-335: Connected to madlib db.\n",
"PoolWorker-329: Wrote 500 images to /tmp/madlib_L5qieznDHc/imagenet_validation_data0000.tmp\n",
"PoolWorker-329: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-332: Removed temporary directory /tmp/madlib_TJCRXh4qkN\n",
"PoolWorker-330: Removed temporary directory /tmp/madlib_kGRS7Ab3WY\n",
"PoolWorker-331: Removed temporary directory /tmp/madlib_N5QA3eDms1\n",
"PoolWorker-333: Removed temporary directory /tmp/madlib_p8LgS8icrx\n",
"PoolWorker-334: Removed temporary directory /tmp/madlib_GI9bf4aDPM\n",
"PoolWorker-336: Removed temporary directory /tmp/madlib_lOa2VDgQ9K\n",
"PoolWorker-335: Removed temporary directory /tmp/madlib_S4WbUHWj3D\n",
"PoolWorker-329: Removed temporary directory /tmp/madlib_L5qieznDHc\n",
"Done! Loaded 500 images in 320.313257933s\n",
"8 workers terminated.\n",
"Chunk: 43/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-337 [pid 91155]\n",
"PoolWorker-337: Created temporary directory /tmp/madlib_R8cHHknzq2\n",
"Initializing PoolWorker-338 [pid 91156]\n",
"PoolWorker-338: Created temporary directory /tmp/madlib_tplpjkQmaK\n",
"Initializing PoolWorker-339 [pid 91157]\n",
"PoolWorker-339: Created temporary directory /tmp/madlib_12IZ7Tl7sT\n",
"Initializing PoolWorker-340 [pid 91158]\n",
"PoolWorker-340: Created temporary directory /tmp/madlib_DkWvRRw6BW\n",
"Initializing PoolWorker-341 [pid 91159]\n",
"PoolWorker-341: Created temporary directory /tmp/madlib_AaZU17hnvk\n",
"Initializing PoolWorker-342 [pid 91160]\n",
"PoolWorker-342: Created temporary directory /tmp/madlib_TSgzH222wW\n",
"Initializing PoolWorker-343 [pid 91161]\n",
"PoolWorker-343: Created temporary directory /tmp/madlib_nWMUguqlyK\n",
"Initializing PoolWorker-344 [pid 91162]\n",
"PoolWorker-344: Created temporary directory /tmp/madlib_JiLYV7U1Kz\n",
"PoolWorker-337: Connected to madlib db.\n",
"PoolWorker-338: Connected to madlib db.\n",
"PoolWorker-339: Connected to madlib db.\n",
"PoolWorker-340: Connected to madlib db.\n",
"PoolWorker-341: Connected to madlib db.\n",
"PoolWorker-342: Connected to madlib db.\n",
"PoolWorker-343: Connected to madlib db.\n",
"PoolWorker-344: Connected to madlib db.\n",
"PoolWorker-337: Wrote 500 images to /tmp/madlib_R8cHHknzq2/imagenet_validation_data0000.tmp\n",
"PoolWorker-337: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-340: Removed temporary directory /tmp/madlib_DkWvRRw6BW\n",
"PoolWorker-339: Removed temporary directory /tmp/madlib_12IZ7Tl7sT\n",
"PoolWorker-341: Removed temporary directory /tmp/madlib_AaZU17hnvk\n",
"PoolWorker-343: Removed temporary directory /tmp/madlib_nWMUguqlyK\n",
"PoolWorker-338: Removed temporary directory /tmp/madlib_tplpjkQmaK\n",
"PoolWorker-344: Removed temporary directory /tmp/madlib_JiLYV7U1Kz\n",
"PoolWorker-342: Removed temporary directory /tmp/madlib_TSgzH222wW\n",
"PoolWorker-337: Removed temporary directory /tmp/madlib_R8cHHknzq2\n",
"Done! Loaded 500 images in 301.689789057s\n",
"8 workers terminated.\n",
"Chunk: 44/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-345 [pid 91202]\n",
"PoolWorker-345: Created temporary directory /tmp/madlib_y1851oFbd2\n",
"Initializing PoolWorker-346 [pid 91203]\n",
"PoolWorker-346: Created temporary directory /tmp/madlib_jrtNpQuAlP\n",
"Initializing PoolWorker-347 [pid 91204]\n",
"PoolWorker-347: Created temporary directory /tmp/madlib_RIQNnNt4tp\n",
"Initializing PoolWorker-348 [pid 91205]\n",
"PoolWorker-348: Created temporary directory /tmp/madlib_eQQ9fSXe8o\n",
"Initializing PoolWorker-349 [pid 91206]\n",
"PoolWorker-349: Created temporary directory /tmp/madlib_qHVyon5hOg\n",
"Initializing PoolWorker-350 [pid 91207]\n",
"PoolWorker-350: Created temporary directory /tmp/madlib_EN6WrGriJz\n",
"Initializing PoolWorker-351 [pid 91208]\n",
"PoolWorker-351: Created temporary directory /tmp/madlib_hlcyso9SRN\n",
"Initializing PoolWorker-352 [pid 91209]\n",
"PoolWorker-352: Created temporary directory /tmp/madlib_BrJ8bh5xGx\n",
"PoolWorker-345: Connected to madlib db.\n",
"PoolWorker-346: Connected to madlib db.\n",
"PoolWorker-347: Connected to madlib db.\n",
"PoolWorker-348: Connected to madlib db.\n",
"PoolWorker-349: Connected to madlib db.\n",
"PoolWorker-350: Connected to madlib db.\n",
"PoolWorker-351: Connected to madlib db.\n",
"PoolWorker-352: Connected to madlib db.\n",
"PoolWorker-345: Wrote 500 images to /tmp/madlib_y1851oFbd2/imagenet_validation_data0000.tmp\n",
"PoolWorker-345: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-347: Removed temporary directory /tmp/madlib_RIQNnNt4tp\n",
"PoolWorker-348: Removed temporary directory /tmp/madlib_eQQ9fSXe8o\n",
"PoolWorker-346: Removed temporary directory /tmp/madlib_jrtNpQuAlP\n",
"PoolWorker-349: Removed temporary directory /tmp/madlib_qHVyon5hOg\n",
"PoolWorker-352: Removed temporary directory /tmp/madlib_BrJ8bh5xGx\n",
"PoolWorker-351: Removed temporary directory /tmp/madlib_hlcyso9SRN\n",
"PoolWorker-350: Removed temporary directory /tmp/madlib_EN6WrGriJz\n",
"PoolWorker-345: Removed temporary directory /tmp/madlib_y1851oFbd2\n",
"Done! Loaded 500 images in 325.93063283s\n",
"8 workers terminated.\n",
"Chunk: 45/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-353 [pid 91667]\n",
"PoolWorker-353: Created temporary directory /tmp/madlib_QbzDqzR6PN\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-354 [pid 91668]\n",
"PoolWorker-354: Created temporary directory /tmp/madlib_Yqix9SZ2ub\n",
"Initializing PoolWorker-355 [pid 91669]\n",
"PoolWorker-355: Created temporary directory /tmp/madlib_vtlvbUTCCy\n",
"Initializing PoolWorker-356 [pid 91670]\n",
"PoolWorker-356: Created temporary directory /tmp/madlib_Za77wMUjuw\n",
"Initializing PoolWorker-357 [pid 91671]\n",
"PoolWorker-357: Created temporary directory /tmp/madlib_Vx7pwTI3zy\n",
"Initializing PoolWorker-358 [pid 91672]\n",
"PoolWorker-358: Created temporary directory /tmp/madlib_XhSqe6DPZn\n",
"Initializing PoolWorker-359 [pid 91673]\n",
"PoolWorker-360: Connected to madlib db.\n",
"PoolWorker-359: Created temporary directory /tmp/madlib_aqdXOMctrk\n",
"Initializing PoolWorker-360 [pid 91674]\n",
"PoolWorker-360: Created temporary directory /tmp/madlib_FHVYKbpTxh\n",
"PoolWorker-353: Connected to madlib db.\n",
"PoolWorker-354: Connected to madlib db.\n",
"PoolWorker-355: Connected to madlib db.\n",
"PoolWorker-356: Connected to madlib db.\n",
"PoolWorker-357: Connected to madlib db.\n",
"PoolWorker-358: Connected to madlib db.\n",
"PoolWorker-359: Connected to madlib db.\n",
"PoolWorker-353: Wrote 500 images to /tmp/madlib_QbzDqzR6PN/imagenet_validation_data0000.tmp\n",
"PoolWorker-353: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-360: Removed temporary directory /tmp/madlib_FHVYKbpTxh\n",
"PoolWorker-355: Removed temporary directory /tmp/madlib_vtlvbUTCCy\n",
"PoolWorker-356: Removed temporary directory /tmp/madlib_Za77wMUjuw\n",
"PoolWorker-357: Removed temporary directory /tmp/madlib_Vx7pwTI3zy\n",
"PoolWorker-359: Removed temporary directory /tmp/madlib_aqdXOMctrk\n",
"PoolWorker-354: Removed temporary directory /tmp/madlib_Yqix9SZ2ub\n",
"PoolWorker-358: Removed temporary directory /tmp/madlib_XhSqe6DPZn\n",
"PoolWorker-353: Removed temporary directory /tmp/madlib_QbzDqzR6PN\n",
"Done! Loaded 500 images in 328.965209007s\n",
"8 workers terminated.\n",
"Chunk: 46/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-361 [pid 91790]\n",
"PoolWorker-361: Created temporary directory /tmp/madlib_zWw5DNdwZj\n",
"Initializing PoolWorker-362 [pid 91791]\n",
"PoolWorker-362: Created temporary directory /tmp/madlib_Dd7wKD1L9b\n",
"Initializing PoolWorker-363 [pid 91792]\n",
"PoolWorker-363: Created temporary directory /tmp/madlib_vyBnvI4dJ4\n",
"Initializing PoolWorker-364 [pid 91793]\n",
"PoolWorker-364: Created temporary directory /tmp/madlib_vd6Y6e97xI\n",
"Initializing PoolWorker-365 [pid 91794]\n",
"PoolWorker-365: Created temporary directory /tmp/madlib_BkReUQ3tAi\n",
"Initializing PoolWorker-366 [pid 91795]\n",
"PoolWorker-366: Created temporary directory /tmp/madlib_zB7WN3v10Y\n",
"Initializing PoolWorker-367 [pid 91796]\n",
"PoolWorker-367: Created temporary directory /tmp/madlib_qut72f1oPY\n",
"Initializing PoolWorker-368 [pid 91797]\n",
"PoolWorker-368: Created temporary directory /tmp/madlib_iH272jelVC\n",
"PoolWorker-361: Connected to madlib db.\n",
"PoolWorker-362: Connected to madlib db.\n",
"PoolWorker-363: Connected to madlib db.\n",
"PoolWorker-365: Connected to madlib db.\n",
"PoolWorker-364: Connected to madlib db.\n",
"PoolWorker-366: Connected to madlib db.\n",
"PoolWorker-367: Connected to madlib db.\n",
"PoolWorker-368: Connected to madlib db.\n",
"PoolWorker-361: Wrote 500 images to /tmp/madlib_zWw5DNdwZj/imagenet_validation_data0000.tmp\n",
"PoolWorker-361: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-368: Removed temporary directory /tmp/madlib_iH272jelVC\n",
"PoolWorker-362: Removed temporary directory /tmp/madlib_Dd7wKD1L9b\n",
"PoolWorker-363: Removed temporary directory /tmp/madlib_vyBnvI4dJ4\n",
"PoolWorker-367: Removed temporary directory /tmp/madlib_qut72f1oPY\n",
"PoolWorker-364: Removed temporary directory /tmp/madlib_vd6Y6e97xI\n",
"PoolWorker-366: Removed temporary directory /tmp/madlib_zB7WN3v10Y\n",
"PoolWorker-365: Removed temporary directory /tmp/madlib_BkReUQ3tAi\n",
"PoolWorker-361: Removed temporary directory /tmp/madlib_zWw5DNdwZj\n",
"Done! Loaded 500 images in 320.71963501s\n",
"8 workers terminated.\n",
"Chunk: 47/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-369 [pid 91811]\n",
"PoolWorker-369: Created temporary directory /tmp/madlib_WgU5a2NMRh\n",
"Initializing PoolWorker-370 [pid 91812]\n",
"PoolWorker-370: Created temporary directory /tmp/madlib_tppBOYYh08\n",
"Initializing PoolWorker-371 [pid 91813]\n",
"PoolWorker-371: Created temporary directory /tmp/madlib_gCqbU09tai\n",
"Initializing PoolWorker-372 [pid 91814]\n",
"PoolWorker-372: Created temporary directory /tmp/madlib_pPEgvOBPXp\n",
"Initializing PoolWorker-373 [pid 91815]\n",
"PoolWorker-373: Created temporary directory /tmp/madlib_VUpUUxi8Jk\n",
"Initializing PoolWorker-374 [pid 91816]\n",
"PoolWorker-374: Created temporary directory /tmp/madlib_nHJQb9X1kR\n",
"Initializing PoolWorker-375 [pid 91817]\n",
"PoolWorker-375: Created temporary directory /tmp/madlib_tXo7WWFd6f\n",
"Initializing PoolWorker-376 [pid 91818]\n",
"PoolWorker-376: Created temporary directory /tmp/madlib_rs6OOKIIJG\n",
"PoolWorker-369: Connected to madlib db.\n",
"PoolWorker-370: Connected to madlib db.\n",
"PoolWorker-371: Connected to madlib db.\n",
"PoolWorker-372: Connected to madlib db.\n",
"PoolWorker-374: Connected to madlib db.\n",
"PoolWorker-373: Connected to madlib db.\n",
"PoolWorker-375: Connected to madlib db.\n",
"PoolWorker-376: Connected to madlib db.\n",
"PoolWorker-369: Wrote 500 images to /tmp/madlib_WgU5a2NMRh/imagenet_validation_data0000.tmp\n",
"PoolWorker-369: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-376: Removed temporary directory /tmp/madlib_rs6OOKIIJG\n",
"PoolWorker-370: Removed temporary directory /tmp/madlib_tppBOYYh08\n",
"PoolWorker-373: Removed temporary directory /tmp/madlib_VUpUUxi8Jk\n",
"PoolWorker-372: Removed temporary directory /tmp/madlib_pPEgvOBPXp\n",
"PoolWorker-371: Removed temporary directory /tmp/madlib_gCqbU09tai\n",
"PoolWorker-374: Removed temporary directory /tmp/madlib_nHJQb9X1kR\n",
"PoolWorker-375: Removed temporary directory /tmp/madlib_tXo7WWFd6f\n",
"PoolWorker-369: Removed temporary directory /tmp/madlib_WgU5a2NMRh\n",
"Done! Loaded 500 images in 320.442999125s\n",
"8 workers terminated.\n",
"Chunk: 48/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-377 [pid 91833]\n",
"PoolWorker-377: Created temporary directory /tmp/madlib_vx5CmjvGx0\n",
"Initializing PoolWorker-378 [pid 91834]\n",
"PoolWorker-378: Created temporary directory /tmp/madlib_csNWsMAW4j\n",
"Initializing PoolWorker-379 [pid 91835]\n",
"PoolWorker-379: Created temporary directory /tmp/madlib_dEi19WgHpr\n",
"Initializing PoolWorker-380 [pid 91836]\n",
"PoolWorker-380: Created temporary directory /tmp/madlib_tTOhp5yDkh\n",
"Initializing PoolWorker-381 [pid 91837]\n",
"PoolWorker-381: Created temporary directory /tmp/madlib_q3dloUWwcJ\n",
"Initializing PoolWorker-382 [pid 91838]\n",
"PoolWorker-382: Created temporary directory /tmp/madlib_SParIcmX9X\n",
"Initializing PoolWorker-383 [pid 91839]\n",
"PoolWorker-383: Created temporary directory /tmp/madlib_zzz0NBUKUg\n",
"Initializing PoolWorker-384 [pid 91840]\n",
"PoolWorker-384: Created temporary directory /tmp/madlib_crLklyLWpL\n",
"PoolWorker-378: Connected to madlib db.\n",
"PoolWorker-377: Connected to madlib db.\n",
"PoolWorker-379: Connected to madlib db.\n",
"PoolWorker-380: Connected to madlib db.\n",
"PoolWorker-381: Connected to madlib db.\n",
"PoolWorker-382: Connected to madlib db.\n",
"PoolWorker-383: Connected to madlib db.\n",
"PoolWorker-384: Connected to madlib db.\n",
"PoolWorker-377: Wrote 500 images to /tmp/madlib_vx5CmjvGx0/imagenet_validation_data0000.tmp\n",
"PoolWorker-377: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-380: Removed temporary directory /tmp/madlib_tTOhp5yDkh\n",
"PoolWorker-379: Removed temporary directory /tmp/madlib_dEi19WgHpr\n",
"PoolWorker-383: Removed temporary directory /tmp/madlib_zzz0NBUKUg\n",
"PoolWorker-381: Removed temporary directory /tmp/madlib_q3dloUWwcJ\n",
"PoolWorker-384: Removed temporary directory /tmp/madlib_crLklyLWpL\n",
"PoolWorker-378: Removed temporary directory /tmp/madlib_csNWsMAW4j\n",
"PoolWorker-382: Removed temporary directory /tmp/madlib_SParIcmX9X\n",
"PoolWorker-377: Removed temporary directory /tmp/madlib_vx5CmjvGx0\n",
"Done! Loaded 500 images in 319.580028057s\n",
"8 workers terminated.\n",
"Chunk: 49/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-385 [pid 91854]\n",
"PoolWorker-385: Created temporary directory /tmp/madlib_3QZ5lPrMGz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-386 [pid 91855]\n",
"PoolWorker-386: Created temporary directory /tmp/madlib_rhUWR2aBQu\n",
"Initializing PoolWorker-387 [pid 91856]\n",
"PoolWorker-387: Created temporary directory /tmp/madlib_OVtm6gwlNn\n",
"Initializing PoolWorker-388 [pid 91857]\n",
"PoolWorker-388: Created temporary directory /tmp/madlib_j7PwBzshTm\n",
"Initializing PoolWorker-389 [pid 91858]\n",
"PoolWorker-389: Created temporary directory /tmp/madlib_v1O15ETmE1\n",
"Initializing PoolWorker-390 [pid 91859]\n",
"PoolWorker-390: Created temporary directory /tmp/madlib_A1MxBB1Ct8\n",
"PoolWorker-392: Connected to madlib db.\n",
"Initializing PoolWorker-391 [pid 91860]\n",
"PoolWorker-391: Created temporary directory /tmp/madlib_DjJ3IPQvBI\n",
"Initializing PoolWorker-392 [pid 91861]\n",
"PoolWorker-392: Created temporary directory /tmp/madlib_V22Zk76gbK\n",
"PoolWorker-386: Connected to madlib db.\n",
"PoolWorker-385: Connected to madlib db.\n",
"PoolWorker-387: Connected to madlib db.\n",
"PoolWorker-388: Connected to madlib db.\n",
"PoolWorker-389: Connected to madlib db.\n",
"PoolWorker-390: Connected to madlib db.\n",
"PoolWorker-391: Connected to madlib db.\n",
"PoolWorker-385: Wrote 500 images to /tmp/madlib_3QZ5lPrMGz/imagenet_validation_data0000.tmp\n",
"PoolWorker-385: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-386: Removed temporary directory /tmp/madlib_rhUWR2aBQu\n",
"PoolWorker-387: Removed temporary directory /tmp/madlib_OVtm6gwlNn\n",
"PoolWorker-390: Removed temporary directory /tmp/madlib_A1MxBB1Ct8\n",
"PoolWorker-388: Removed temporary directory /tmp/madlib_j7PwBzshTm\n",
"PoolWorker-391: Removed temporary directory /tmp/madlib_DjJ3IPQvBI\n",
"PoolWorker-389: Removed temporary directory /tmp/madlib_v1O15ETmE1\n",
"PoolWorker-392: Removed temporary directory /tmp/madlib_V22Zk76gbK\n",
"PoolWorker-385: Removed temporary directory /tmp/madlib_3QZ5lPrMGz\n",
"Done! Loaded 500 images in 315.122698069s\n",
"8 workers terminated.\n",
"Chunk: 50/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-393 [pid 91874]\n",
"PoolWorker-393: Created temporary directory /tmp/madlib_28MDlPwvAs\n",
"Initializing PoolWorker-394 [pid 91875]\n",
"PoolWorker-394: Created temporary directory /tmp/madlib_F5m6OHBEE7\n",
"Initializing PoolWorker-395 [pid 91876]\n",
"PoolWorker-395: Created temporary directory /tmp/madlib_ADB2HjyVXE\n",
"Initializing PoolWorker-396 [pid 91877]\n",
"PoolWorker-396: Created temporary directory /tmp/madlib_5IKHFHngKd\n",
"Initializing PoolWorker-397 [pid 91878]\n",
"PoolWorker-397: Created temporary directory /tmp/madlib_zr8NDG2pVs\n",
"Initializing PoolWorker-398 [pid 91879]\n",
"PoolWorker-398: Created temporary directory /tmp/madlib_ZYjEDSBEFv\n",
"PoolWorker-400: Connected to madlib db.\n",
"Initializing PoolWorker-399 [pid 91880]\n",
"PoolWorker-399: Created temporary directory /tmp/madlib_Fv4OINqdSW\n",
"Initializing PoolWorker-400 [pid 91881]\n",
"PoolWorker-400: Created temporary directory /tmp/madlib_nNOPJXcCo1\n",
"PoolWorker-393: Connected to madlib db.\n",
"PoolWorker-394: Connected to madlib db.\n",
"PoolWorker-395: Connected to madlib db.\n",
"PoolWorker-396: Connected to madlib db.\n",
"PoolWorker-397: Connected to madlib db.\n",
"PoolWorker-398: Connected to madlib db.\n",
"PoolWorker-399: Connected to madlib db.\n",
"PoolWorker-393: Wrote 500 images to /tmp/madlib_28MDlPwvAs/imagenet_validation_data0000.tmp\n",
"PoolWorker-393: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-395: Removed temporary directory /tmp/madlib_ADB2HjyVXE\n",
"PoolWorker-394: Removed temporary directory /tmp/madlib_F5m6OHBEE7\n",
"PoolWorker-396: Removed temporary directory /tmp/madlib_5IKHFHngKd\n",
"PoolWorker-397: Removed temporary directory /tmp/madlib_zr8NDG2pVs\n",
"PoolWorker-398: Removed temporary directory /tmp/madlib_ZYjEDSBEFv\n",
"PoolWorker-400: Removed temporary directory /tmp/madlib_nNOPJXcCo1\n",
"PoolWorker-399: Removed temporary directory /tmp/madlib_Fv4OINqdSW\n",
"PoolWorker-393: Removed temporary directory /tmp/madlib_28MDlPwvAs\n",
"Done! Loaded 500 images in 332.146434069s\n",
"8 workers terminated.\n",
"Chunk: 51/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-401 [pid 93187]\n",
"PoolWorker-401: Created temporary directory /tmp/madlib_3r8PP58oNN\n",
"Initializing PoolWorker-402 [pid 93188]\n",
"PoolWorker-402: Created temporary directory /tmp/madlib_P2tzgLxdOU\n",
"Initializing PoolWorker-403 [pid 93189]\n",
"PoolWorker-403: Created temporary directory /tmp/madlib_jGUiC4AN0Y\n",
"Initializing PoolWorker-404 [pid 93190]\n",
"PoolWorker-404: Created temporary directory /tmp/madlib_JOCEfXVno6\n",
"Initializing PoolWorker-405 [pid 93191]\n",
"PoolWorker-405: Created temporary directory /tmp/madlib_r19mubRxTz\n",
"Initializing PoolWorker-406 [pid 93192]\n",
"PoolWorker-406: Created temporary directory /tmp/madlib_rz2ie4T6at\n",
"Initializing PoolWorker-407 [pid 93193]\n",
"PoolWorker-408: Connected to madlib db.\n",
"PoolWorker-407: Created temporary directory /tmp/madlib_S74gwerhlV\n",
"Initializing PoolWorker-408 [pid 93194]\n",
"PoolWorker-408: Created temporary directory /tmp/madlib_cGB1k63Qxt\n",
"PoolWorker-401: Connected to madlib db.\n",
"PoolWorker-402: Connected to madlib db.\n",
"PoolWorker-403: Connected to madlib db.\n",
"PoolWorker-404: Connected to madlib db.\n",
"PoolWorker-405: Connected to madlib db.\n",
"PoolWorker-406: Connected to madlib db.\n",
"PoolWorker-407: Connected to madlib db.\n",
"PoolWorker-401: Wrote 500 images to /tmp/madlib_3r8PP58oNN/imagenet_validation_data0000.tmp\n",
"PoolWorker-401: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-403: Removed temporary directory /tmp/madlib_jGUiC4AN0Y\n",
"PoolWorker-405: Removed temporary directory /tmp/madlib_r19mubRxTz\n",
"PoolWorker-402: Removed temporary directory /tmp/madlib_P2tzgLxdOU\n",
"PoolWorker-404: Removed temporary directory /tmp/madlib_JOCEfXVno6\n",
"PoolWorker-406: Removed temporary directory /tmp/madlib_rz2ie4T6at\n",
"PoolWorker-408: Removed temporary directory /tmp/madlib_cGB1k63Qxt\n",
"PoolWorker-407: Removed temporary directory /tmp/madlib_S74gwerhlV\n",
"PoolWorker-401: Removed temporary directory /tmp/madlib_3r8PP58oNN\n",
"Done! Loaded 500 images in 413.640304089s\n",
"8 workers terminated.\n",
"Chunk: 52/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-409 [pid 93217]\n",
"PoolWorker-409: Created temporary directory /tmp/madlib_Zg4stI8FUt\n",
"Initializing PoolWorker-410 [pid 93218]\n",
"PoolWorker-410: Created temporary directory /tmp/madlib_LrOyYLYyTY\n",
"Initializing PoolWorker-411 [pid 93219]\n",
"PoolWorker-411: Created temporary directory /tmp/madlib_fyRMeYU0pd\n",
"Initializing PoolWorker-412 [pid 93220]\n",
"PoolWorker-412: Created temporary directory /tmp/madlib_3C6GoxKGQO\n",
"Initializing PoolWorker-413 [pid 93221]\n",
"PoolWorker-413: Created temporary directory /tmp/madlib_H1o6XvsaCT\n",
"Initializing PoolWorker-414 [pid 93222]\n",
"PoolWorker-414: Created temporary directory /tmp/madlib_8S7uOf4k98\n",
"PoolWorker-416: Connected to madlib db.\n",
"Initializing PoolWorker-415 [pid 93223]\n",
"PoolWorker-415: Created temporary directory /tmp/madlib_vaJkQy93rL\n",
"Initializing PoolWorker-416 [pid 93224]\n",
"PoolWorker-416: Created temporary directory /tmp/madlib_eZk6AcN1g8\n",
"PoolWorker-409: Connected to madlib db.\n",
"PoolWorker-410: Connected to madlib db.\n",
"PoolWorker-411: Connected to madlib db.\n",
"PoolWorker-412: Connected to madlib db.\n",
"PoolWorker-413: Connected to madlib db.\n",
"PoolWorker-414: Connected to madlib db.\n",
"PoolWorker-415: Connected to madlib db.\n",
"PoolWorker-409: Wrote 500 images to /tmp/madlib_Zg4stI8FUt/imagenet_validation_data0000.tmp\n",
"PoolWorker-409: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-411: Removed temporary directory /tmp/madlib_fyRMeYU0pd\n",
"PoolWorker-412: Removed temporary directory /tmp/madlib_3C6GoxKGQO\n",
"PoolWorker-413: Removed temporary directory /tmp/madlib_H1o6XvsaCT\n",
"PoolWorker-410: Removed temporary directory /tmp/madlib_LrOyYLYyTY\n",
"PoolWorker-415: Removed temporary directory /tmp/madlib_vaJkQy93rL\n",
"PoolWorker-414: Removed temporary directory /tmp/madlib_8S7uOf4k98\n",
"PoolWorker-416: Removed temporary directory /tmp/madlib_eZk6AcN1g8\n",
"PoolWorker-409: Removed temporary directory /tmp/madlib_Zg4stI8FUt\n",
"Done! Loaded 500 images in 421.918645144s\n",
"8 workers terminated.\n",
"Chunk: 53/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-417 [pid 93242]\n",
"PoolWorker-417: Created temporary directory /tmp/madlib_cSnzYUOdsq\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-418 [pid 93243]\n",
"PoolWorker-418: Created temporary directory /tmp/madlib_TY06TeuEUl\n",
"Initializing PoolWorker-419 [pid 93244]\n",
"PoolWorker-419: Created temporary directory /tmp/madlib_CIOT47IVzI\n",
"Initializing PoolWorker-420 [pid 93245]\n",
"PoolWorker-420: Created temporary directory /tmp/madlib_3bHuc6HdcY\n",
"Initializing PoolWorker-421 [pid 93246]\n",
"PoolWorker-421: Created temporary directory /tmp/madlib_4USWWA3lZs\n",
"Initializing PoolWorker-422 [pid 93247]\n",
"PoolWorker-422: Created temporary directory /tmp/madlib_EvxYfThfox\n",
"PoolWorker-424: Connected to madlib db.\n",
"Initializing PoolWorker-423 [pid 93248]\n",
"PoolWorker-423: Created temporary directory /tmp/madlib_yQZkCPzOM5\n",
"Initializing PoolWorker-424 [pid 93249]\n",
"PoolWorker-424: Created temporary directory /tmp/madlib_ZPNelvznmA\n",
"PoolWorker-417: Connected to madlib db.\n",
"PoolWorker-418: Connected to madlib db.\n",
"PoolWorker-419: Connected to madlib db.\n",
"PoolWorker-420: Connected to madlib db.\n",
"PoolWorker-421: Connected to madlib db.\n",
"PoolWorker-422: Connected to madlib db.\n",
"PoolWorker-423: Connected to madlib db.\n",
"PoolWorker-417: Wrote 500 images to /tmp/madlib_cSnzYUOdsq/imagenet_validation_data0000.tmp\n",
"PoolWorker-417: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-418: Removed temporary directory /tmp/madlib_TY06TeuEUl\n",
"PoolWorker-419: Removed temporary directory /tmp/madlib_CIOT47IVzI\n",
"PoolWorker-420: Removed temporary directory /tmp/madlib_3bHuc6HdcY\n",
"PoolWorker-421: Removed temporary directory /tmp/madlib_4USWWA3lZs\n",
"PoolWorker-422: Removed temporary directory /tmp/madlib_EvxYfThfox\n",
"PoolWorker-424: Removed temporary directory /tmp/madlib_ZPNelvznmA\n",
"PoolWorker-423: Removed temporary directory /tmp/madlib_yQZkCPzOM5\n",
"PoolWorker-417: Removed temporary directory /tmp/madlib_cSnzYUOdsq\n",
"Done! Loaded 500 images in 397.270573139s\n",
"8 workers terminated.\n",
"Chunk: 54/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-425 [pid 93264]\n",
"PoolWorker-425: Created temporary directory /tmp/madlib_VWrVgS5SYd\n",
"Initializing PoolWorker-426 [pid 93265]\n",
"PoolWorker-426: Created temporary directory /tmp/madlib_ohwRfGcwHT\n",
"Initializing PoolWorker-427 [pid 93266]\n",
"PoolWorker-427: Created temporary directory /tmp/madlib_WomHDo9CDf\n",
"Initializing PoolWorker-428 [pid 93267]\n",
"PoolWorker-428: Created temporary directory /tmp/madlib_oF6fUzzUDX\n",
"Initializing PoolWorker-429 [pid 93268]\n",
"PoolWorker-429: Created temporary directory /tmp/madlib_cUjSftj62P\n",
"Initializing PoolWorker-430 [pid 93269]\n",
"PoolWorker-430: Created temporary directory /tmp/madlib_UnIVVV6xLP\n",
"Initializing PoolWorker-431 [pid 93270]\n",
"PoolWorker-431: Created temporary directory /tmp/madlib_w8K2Tym9Uw\n",
"Initializing PoolWorker-432 [pid 93271]\n",
"PoolWorker-432: Created temporary directory /tmp/madlib_zIKV82RGGz\n",
"PoolWorker-425: Connected to madlib db.\n",
"PoolWorker-426: Connected to madlib db.\n",
"PoolWorker-427: Connected to madlib db.\n",
"PoolWorker-428: Connected to madlib db.\n",
"PoolWorker-429: Connected to madlib db.\n",
"PoolWorker-430: Connected to madlib db.\n",
"PoolWorker-431: Connected to madlib db.\n",
"PoolWorker-432: Connected to madlib db.\n",
"PoolWorker-425: Wrote 500 images to /tmp/madlib_VWrVgS5SYd/imagenet_validation_data0000.tmp\n",
"PoolWorker-425: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-426: Removed temporary directory /tmp/madlib_ohwRfGcwHT\n",
"PoolWorker-431: Removed temporary directory /tmp/madlib_w8K2Tym9Uw\n",
"PoolWorker-428: Removed temporary directory /tmp/madlib_oF6fUzzUDX\n",
"PoolWorker-430: Removed temporary directory /tmp/madlib_UnIVVV6xLP\n",
"PoolWorker-427: Removed temporary directory /tmp/madlib_WomHDo9CDf\n",
"PoolWorker-429: Removed temporary directory /tmp/madlib_cUjSftj62P\n",
"PoolWorker-432: Removed temporary directory /tmp/madlib_zIKV82RGGz\n",
"PoolWorker-425: Removed temporary directory /tmp/madlib_VWrVgS5SYd\n",
"Done! Loaded 500 images in 330.216993093s\n",
"8 workers terminated.\n",
"Chunk: 55/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-433 [pid 93752]\n",
"PoolWorker-433: Created temporary directory /tmp/madlib_nfhcDFBMLU\n",
"Initializing PoolWorker-434 [pid 93753]\n",
"PoolWorker-434: Created temporary directory /tmp/madlib_0J695r8J58\n",
"Initializing PoolWorker-435 [pid 93754]\n",
"PoolWorker-435: Created temporary directory /tmp/madlib_YfuSV7BZ5E\n",
"Initializing PoolWorker-436 [pid 93755]\n",
"Initializing PoolWorker-437 [pid 93756]\n",
"PoolWorker-436: Created temporary directory /tmp/madlib_0dCN0DRALn\n",
"PoolWorker-437: Created temporary directory /tmp/madlib_FwvOuObUHD\n",
"Initializing PoolWorker-438 [pid 93757]\n",
"PoolWorker-438: Created temporary directory /tmp/madlib_N0vsG5BiEH\n",
"Initializing PoolWorker-439 [pid 93758]\n",
"PoolWorker-439: Created temporary directory /tmp/madlib_auMgAx1mLl\n",
"Initializing PoolWorker-440 [pid 93759]\n",
"PoolWorker-440: Created temporary directory /tmp/madlib_9Mmnql1IKK\n",
"PoolWorker-433: Connected to madlib db.\n",
"PoolWorker-434: Connected to madlib db.\n",
"PoolWorker-435: Connected to madlib db.\n",
"PoolWorker-436: Connected to madlib db.\n",
"PoolWorker-437: Connected to madlib db.\n",
"PoolWorker-438: Connected to madlib db.\n",
"PoolWorker-439: Connected to madlib db.\n",
"PoolWorker-440: Connected to madlib db.\n",
"PoolWorker-433: Wrote 500 images to /tmp/madlib_nfhcDFBMLU/imagenet_validation_data0000.tmp\n",
"PoolWorker-433: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-434: Removed temporary directory /tmp/madlib_0J695r8J58\n",
"PoolWorker-435: Removed temporary directory /tmp/madlib_YfuSV7BZ5E\n",
"PoolWorker-436: Removed temporary directory /tmp/madlib_0dCN0DRALn\n",
"PoolWorker-437: Removed temporary directory /tmp/madlib_FwvOuObUHD\n",
"PoolWorker-438: Removed temporary directory /tmp/madlib_N0vsG5BiEH\n",
"PoolWorker-439: Removed temporary directory /tmp/madlib_auMgAx1mLl\n",
"PoolWorker-440: Removed temporary directory /tmp/madlib_9Mmnql1IKK\n",
"PoolWorker-433: Removed temporary directory /tmp/madlib_nfhcDFBMLU\n",
"Done! Loaded 500 images in 333.938501835s\n",
"8 workers terminated.\n",
"Chunk: 56/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-441 [pid 93910]\n",
"PoolWorker-441: Created temporary directory /tmp/madlib_SweQ1ft7NF\n",
"Initializing PoolWorker-442 [pid 93911]\n",
"PoolWorker-442: Created temporary directory /tmp/madlib_sva2JZbIfP\n",
"Initializing PoolWorker-443 [pid 93912]\n",
"PoolWorker-443: Created temporary directory /tmp/madlib_xu7x5S8vtC\n",
"Initializing PoolWorker-444 [pid 93913]\n",
"PoolWorker-444: Created temporary directory /tmp/madlib_o0tnwoPWwW\n",
"Initializing PoolWorker-445 [pid 93914]\n",
"PoolWorker-445: Created temporary directory /tmp/madlib_UJE4HP9Z6T\n",
"Initializing PoolWorker-446 [pid 93915]\n",
"PoolWorker-446: Created temporary directory /tmp/madlib_U5NYDXkXym\n",
"PoolWorker-448: Connected to madlib db.\n",
"Initializing PoolWorker-447 [pid 93916]\n",
"PoolWorker-447: Created temporary directory /tmp/madlib_c0p8q2tYSj\n",
"Initializing PoolWorker-448 [pid 93917]\n",
"PoolWorker-448: Created temporary directory /tmp/madlib_oM8rI0NShk\n",
"PoolWorker-441: Connected to madlib db.\n",
"PoolWorker-442: Connected to madlib db.\n",
"PoolWorker-443: Connected to madlib db.\n",
"PoolWorker-444: Connected to madlib db.\n",
"PoolWorker-445: Connected to madlib db.\n",
"PoolWorker-446: Connected to madlib db.\n",
"PoolWorker-447: Connected to madlib db.\n",
"PoolWorker-441: Wrote 500 images to /tmp/madlib_SweQ1ft7NF/imagenet_validation_data0000.tmp\n",
"PoolWorker-441: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-442: Removed temporary directory /tmp/madlib_sva2JZbIfP\n",
"PoolWorker-445: Removed temporary directory /tmp/madlib_UJE4HP9Z6T\n",
"PoolWorker-444: Removed temporary directory /tmp/madlib_o0tnwoPWwW\n",
"PoolWorker-443: Removed temporary directory /tmp/madlib_xu7x5S8vtC\n",
"PoolWorker-446: Removed temporary directory /tmp/madlib_U5NYDXkXym\n",
"PoolWorker-448: Removed temporary directory /tmp/madlib_oM8rI0NShk\n",
"PoolWorker-447: Removed temporary directory /tmp/madlib_c0p8q2tYSj\n",
"PoolWorker-441: Removed temporary directory /tmp/madlib_SweQ1ft7NF\n",
"Done! Loaded 500 images in 307.906814098s\n",
"8 workers terminated.\n",
"Chunk: 57/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-449 [pid 93933]\n",
"PoolWorker-449: Created temporary directory /tmp/madlib_xXzA5MgDHF\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-450 [pid 93934]\n",
"PoolWorker-450: Created temporary directory /tmp/madlib_HsuhYtNxYL\n",
"Initializing PoolWorker-451 [pid 93935]\n",
"PoolWorker-451: Created temporary directory /tmp/madlib_G1nVTzph2K\n",
"Initializing PoolWorker-452 [pid 93936]\n",
"PoolWorker-452: Created temporary directory /tmp/madlib_Jeq4NLQS0e\n",
"Initializing PoolWorker-453 [pid 93937]\n",
"PoolWorker-453: Created temporary directory /tmp/madlib_UqLrXJss3a\n",
"Initializing PoolWorker-454 [pid 93938]\n",
"PoolWorker-454: Created temporary directory /tmp/madlib_uD7l5davzP\n",
"PoolWorker-456: Connected to madlib db.\n",
"Initializing PoolWorker-455 [pid 93939]\n",
"PoolWorker-455: Created temporary directory /tmp/madlib_YXAO3lb4io\n",
"Initializing PoolWorker-456 [pid 93940]\n",
"PoolWorker-456: Created temporary directory /tmp/madlib_kZw5tPoSzi\n",
"PoolWorker-449: Connected to madlib db.\n",
"PoolWorker-450: Connected to madlib db.\n",
"PoolWorker-451: Connected to madlib db.\n",
"PoolWorker-452: Connected to madlib db.\n",
"PoolWorker-453: Connected to madlib db.\n",
"PoolWorker-454: Connected to madlib db.\n",
"PoolWorker-455: Connected to madlib db.\n",
"PoolWorker-449: Wrote 500 images to /tmp/madlib_xXzA5MgDHF/imagenet_validation_data0000.tmp\n",
"PoolWorker-449: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-450: Removed temporary directory /tmp/madlib_HsuhYtNxYL\n",
"PoolWorker-452: Removed temporary directory /tmp/madlib_Jeq4NLQS0e\n",
"PoolWorker-451: Removed temporary directory /tmp/madlib_G1nVTzph2K\n",
"PoolWorker-454: Removed temporary directory /tmp/madlib_uD7l5davzP\n",
"PoolWorker-453: Removed temporary directory /tmp/madlib_UqLrXJss3a\n",
"PoolWorker-455: Removed temporary directory /tmp/madlib_YXAO3lb4io\n",
"PoolWorker-456: Removed temporary directory /tmp/madlib_kZw5tPoSzi\n",
"PoolWorker-449: Removed temporary directory /tmp/madlib_xXzA5MgDHF\n",
"Done! Loaded 500 images in 322.033977985s\n",
"8 workers terminated.\n",
"Chunk: 58/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-457 [pid 93954]\n",
"PoolWorker-457: Created temporary directory /tmp/madlib_71Wbhfox8O\n",
"Initializing PoolWorker-458 [pid 93955]\n",
"PoolWorker-458: Created temporary directory /tmp/madlib_uTGMabOAdf\n",
"Initializing PoolWorker-459 [pid 93956]\n",
"PoolWorker-459: Created temporary directory /tmp/madlib_x2THv90b5y\n",
"Initializing PoolWorker-460 [pid 93957]\n",
"PoolWorker-460: Created temporary directory /tmp/madlib_8m7jLCpTBo\n",
"Initializing PoolWorker-461 [pid 93958]\n",
"PoolWorker-461: Created temporary directory /tmp/madlib_NFRPNyjLbn\n",
"Initializing PoolWorker-462 [pid 93959]\n",
"PoolWorker-462: Created temporary directory /tmp/madlib_znBbY0m4is\n",
"Initializing PoolWorker-463 [pid 93960]\n",
"PoolWorker-464: Connected to madlib db.\n",
"PoolWorker-463: Created temporary directory /tmp/madlib_eQFktMNj9N\n",
"Initializing PoolWorker-464 [pid 93961]\n",
"PoolWorker-464: Created temporary directory /tmp/madlib_UzymoNSuo0\n",
"PoolWorker-457: Connected to madlib db.\n",
"PoolWorker-458: Connected to madlib db.\n",
"PoolWorker-459: Connected to madlib db.\n",
"PoolWorker-460: Connected to madlib db.\n",
"PoolWorker-461: Connected to madlib db.\n",
"PoolWorker-462: Connected to madlib db.\n",
"PoolWorker-463: Connected to madlib db.\n",
"PoolWorker-457: Wrote 500 images to /tmp/madlib_71Wbhfox8O/imagenet_validation_data0000.tmp\n",
"PoolWorker-457: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-458: Removed temporary directory /tmp/madlib_uTGMabOAdf\n",
"PoolWorker-462: Removed temporary directory /tmp/madlib_znBbY0m4is\n",
"PoolWorker-460: Removed temporary directory /tmp/madlib_8m7jLCpTBo\n",
"PoolWorker-461: Removed temporary directory /tmp/madlib_NFRPNyjLbn\n",
"PoolWorker-464: Removed temporary directory /tmp/madlib_UzymoNSuo0\n",
"PoolWorker-463: Removed temporary directory /tmp/madlib_eQFktMNj9N\n",
"PoolWorker-459: Removed temporary directory /tmp/madlib_x2THv90b5y\n",
"PoolWorker-457: Removed temporary directory /tmp/madlib_71Wbhfox8O\n",
"Done! Loaded 500 images in 326.859508038s\n",
"8 workers terminated.\n",
"Chunk: 59/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-465 [pid 93989]\n",
"PoolWorker-465: Created temporary directory /tmp/madlib_SCKHFAeZmW\n",
"Initializing PoolWorker-466 [pid 93990]\n",
"PoolWorker-466: Created temporary directory /tmp/madlib_JqB9BZx9sz\n",
"Initializing PoolWorker-467 [pid 93991]\n",
"PoolWorker-467: Created temporary directory /tmp/madlib_sFPe7fxIZZ\n",
"Initializing PoolWorker-468 [pid 93992]\n",
"PoolWorker-468: Created temporary directory /tmp/madlib_BsuTKQsSOz\n",
"Initializing PoolWorker-469 [pid 93993]\n",
"PoolWorker-469: Created temporary directory /tmp/madlib_Gnzc6VqdYN\n",
"Initializing PoolWorker-470 [pid 93994]\n",
"PoolWorker-470: Created temporary directory /tmp/madlib_bOcpwAfQvx\n",
"Initializing PoolWorker-471 [pid 93995]\n",
"PoolWorker-471: Created temporary directory /tmp/madlib_IEAqRGiwkM\n",
"Initializing PoolWorker-472 [pid 93996]\n",
"PoolWorker-472: Created temporary directory /tmp/madlib_b8sM9jWEHh\n",
"PoolWorker-465: Connected to madlib db.\n",
"PoolWorker-466: Connected to madlib db.\n",
"PoolWorker-467: Connected to madlib db.\n",
"PoolWorker-468: Connected to madlib db.\n",
"PoolWorker-469: Connected to madlib db.\n",
"PoolWorker-470: Connected to madlib db.\n",
"PoolWorker-471: Connected to madlib db.\n",
"PoolWorker-472: Connected to madlib db.\n",
"PoolWorker-465: Wrote 500 images to /tmp/madlib_SCKHFAeZmW/imagenet_validation_data0000.tmp\n",
"PoolWorker-465: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-466: Removed temporary directory /tmp/madlib_JqB9BZx9sz\n",
"PoolWorker-468: Removed temporary directory /tmp/madlib_BsuTKQsSOz\n",
"PoolWorker-467: Removed temporary directory /tmp/madlib_sFPe7fxIZZ\n",
"PoolWorker-469: Removed temporary directory /tmp/madlib_Gnzc6VqdYN\n",
"PoolWorker-470: Removed temporary directory /tmp/madlib_bOcpwAfQvx\n",
"PoolWorker-471: Removed temporary directory /tmp/madlib_IEAqRGiwkM\n",
"PoolWorker-472: Removed temporary directory /tmp/madlib_b8sM9jWEHh\n",
"PoolWorker-465: Removed temporary directory /tmp/madlib_SCKHFAeZmW\n",
"Done! Loaded 500 images in 314.851197004s\n",
"8 workers terminated.\n",
"Chunk: 60/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-473 [pid 94025]\n",
"PoolWorker-473: Created temporary directory /tmp/madlib_TeKc3XXbzP\n",
"Initializing PoolWorker-474 [pid 94026]\n",
"PoolWorker-474: Created temporary directory /tmp/madlib_8VSoNUmwcK\n",
"Initializing PoolWorker-475 [pid 94027]\n",
"PoolWorker-475: Created temporary directory /tmp/madlib_1cYVgcYkBw\n",
"Initializing PoolWorker-476 [pid 94028]\n",
"PoolWorker-476: Created temporary directory /tmp/madlib_TlqBDQv4Kq\n",
"Initializing PoolWorker-477 [pid 94029]\n",
"PoolWorker-477: Created temporary directory /tmp/madlib_MRTB1m1lio\n",
"Initializing PoolWorker-478 [pid 94030]\n",
"PoolWorker-478: Created temporary directory /tmp/madlib_EcaLvuSAFu\n",
"Initializing PoolWorker-479 [pid 94031]\n",
"PoolWorker-479: Created temporary directory /tmp/madlib_LuwoCzjELt\n",
"Initializing PoolWorker-480 [pid 94032]\n",
"PoolWorker-480: Created temporary directory /tmp/madlib_ktbtyIMWpL\n",
"PoolWorker-473: Connected to madlib db.\n",
"PoolWorker-474: Connected to madlib db.\n",
"PoolWorker-475: Connected to madlib db.\n",
"PoolWorker-476: Connected to madlib db.\n",
"PoolWorker-477: Connected to madlib db.\n",
"PoolWorker-478: Connected to madlib db.\n",
"PoolWorker-479: Connected to madlib db.\n",
"PoolWorker-480: Connected to madlib db.\n",
"PoolWorker-473: Wrote 500 images to /tmp/madlib_TeKc3XXbzP/imagenet_validation_data0000.tmp\n",
"PoolWorker-473: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-474: Removed temporary directory /tmp/madlib_8VSoNUmwcK\n",
"PoolWorker-475: Removed temporary directory /tmp/madlib_1cYVgcYkBw\n",
"PoolWorker-476: Removed temporary directory /tmp/madlib_TlqBDQv4Kq\n",
"PoolWorker-477: Removed temporary directory /tmp/madlib_MRTB1m1lio\n",
"PoolWorker-478: Removed temporary directory /tmp/madlib_EcaLvuSAFu\n",
"PoolWorker-480: Removed temporary directory /tmp/madlib_ktbtyIMWpL\n",
"PoolWorker-479: Removed temporary directory /tmp/madlib_LuwoCzjELt\n",
"PoolWorker-473: Removed temporary directory /tmp/madlib_TeKc3XXbzP\n",
"Done! Loaded 500 images in 336.931374073s\n",
"8 workers terminated.\n",
"Chunk: 61/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-481 [pid 94055]\n",
"PoolWorker-481: Created temporary directory /tmp/madlib_AZkMba4qQI\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-482 [pid 94056]\n",
"PoolWorker-482: Created temporary directory /tmp/madlib_EEsQi0Iz8a\n",
"Initializing PoolWorker-483 [pid 94057]\n",
"PoolWorker-483: Created temporary directory /tmp/madlib_6IlgdX5l80\n",
"Initializing PoolWorker-484 [pid 94058]\n",
"PoolWorker-484: Created temporary directory /tmp/madlib_8VYEREhUsN\n",
"Initializing PoolWorker-485 [pid 94059]\n",
"PoolWorker-485: Created temporary directory /tmp/madlib_WYVG9wM2By\n",
"Initializing PoolWorker-486 [pid 94060]\n",
"PoolWorker-486: Created temporary directory /tmp/madlib_e2AW4T2tno\n",
"Initializing PoolWorker-487 [pid 94061]\n",
"PoolWorker-487: Created temporary directory /tmp/madlib_aC3SlgsOhs\n",
"Initializing PoolWorker-488 [pid 94062]\n",
"PoolWorker-488: Created temporary directory /tmp/madlib_0Ia4vOEnAO\n",
"PoolWorker-481: Connected to madlib db.\n",
"PoolWorker-482: Connected to madlib db.\n",
"PoolWorker-483: Connected to madlib db.\n",
"PoolWorker-484: Connected to madlib db.\n",
"PoolWorker-485: Connected to madlib db.\n",
"PoolWorker-486: Connected to madlib db.\n",
"PoolWorker-487: Connected to madlib db.\n",
"PoolWorker-488: Connected to madlib db.\n",
"PoolWorker-481: Wrote 500 images to /tmp/madlib_AZkMba4qQI/imagenet_validation_data0000.tmp\n",
"PoolWorker-481: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-482: Removed temporary directory /tmp/madlib_EEsQi0Iz8a\n",
"PoolWorker-484: Removed temporary directory /tmp/madlib_8VYEREhUsN\n",
"PoolWorker-485: Removed temporary directory /tmp/madlib_WYVG9wM2By\n",
"PoolWorker-486: Removed temporary directory /tmp/madlib_e2AW4T2tno\n",
"PoolWorker-483: Removed temporary directory /tmp/madlib_6IlgdX5l80\n",
"PoolWorker-488: Removed temporary directory /tmp/madlib_0Ia4vOEnAO\n",
"PoolWorker-487: Removed temporary directory /tmp/madlib_aC3SlgsOhs\n",
"PoolWorker-481: Removed temporary directory /tmp/madlib_AZkMba4qQI\n",
"Done! Loaded 500 images in 357.574882984s\n",
"8 workers terminated.\n",
"Chunk: 62/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-489 [pid 95378]\n",
"PoolWorker-489: Created temporary directory /tmp/madlib_Gd6RAGBtw0\n",
"Initializing PoolWorker-490 [pid 95379]\n",
"PoolWorker-490: Created temporary directory /tmp/madlib_5COuM3Gguf\n",
"Initializing PoolWorker-491 [pid 95380]\n",
"PoolWorker-491: Created temporary directory /tmp/madlib_HB5koUs5y0\n",
"Initializing PoolWorker-492 [pid 95381]\n",
"PoolWorker-492: Created temporary directory /tmp/madlib_khPnh8Og20\n",
"Initializing PoolWorker-493 [pid 95382]\n",
"PoolWorker-493: Created temporary directory /tmp/madlib_gYpqAQvWcZ\n",
"Initializing PoolWorker-494 [pid 95383]\n",
"PoolWorker-494: Created temporary directory /tmp/madlib_qmztmdNQRy\n",
"PoolWorker-496: Connected to madlib db.\n",
"PoolWorker-495: Connected to madlib db.\n",
"Initializing PoolWorker-495 [pid 95384]\n",
"PoolWorker-495: Created temporary directory /tmp/madlib_bbQ36TEQN4\n",
"Initializing PoolWorker-496 [pid 95385]\n",
"PoolWorker-496: Created temporary directory /tmp/madlib_IFHcqO2QQf\n",
"PoolWorker-489: Connected to madlib db.\n",
"PoolWorker-490: Connected to madlib db.\n",
"PoolWorker-491: Connected to madlib db.\n",
"PoolWorker-492: Connected to madlib db.\n",
"PoolWorker-493: Connected to madlib db.\n",
"PoolWorker-494: Connected to madlib db.\n",
"PoolWorker-489: Wrote 500 images to /tmp/madlib_Gd6RAGBtw0/imagenet_validation_data0000.tmp\n",
"PoolWorker-489: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-490: Removed temporary directory /tmp/madlib_5COuM3Gguf\n",
"PoolWorker-491: Removed temporary directory /tmp/madlib_HB5koUs5y0\n",
"PoolWorker-492: Removed temporary directory /tmp/madlib_khPnh8Og20\n",
"PoolWorker-493: Removed temporary directory /tmp/madlib_gYpqAQvWcZ\n",
"PoolWorker-495: Removed temporary directory /tmp/madlib_bbQ36TEQN4\n",
"PoolWorker-496: Removed temporary directory /tmp/madlib_IFHcqO2QQf\n",
"PoolWorker-494: Removed temporary directory /tmp/madlib_qmztmdNQRy\n",
"PoolWorker-489: Removed temporary directory /tmp/madlib_Gd6RAGBtw0\n",
"Done! Loaded 500 images in 356.144065857s\n",
"8 workers terminated.\n",
"Chunk: 63/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-497 [pid 95399]\n",
"PoolWorker-497: Created temporary directory /tmp/madlib_61LU6TGGOE\n",
"Initializing PoolWorker-498 [pid 95400]\n",
"PoolWorker-498: Created temporary directory /tmp/madlib_B3FMYIgQQb\n",
"Initializing PoolWorker-499 [pid 95401]\n",
"PoolWorker-499: Created temporary directory /tmp/madlib_ps5LnJxLw1\n",
"Initializing PoolWorker-500 [pid 95402]\n",
"PoolWorker-500: Created temporary directory /tmp/madlib_y3Ysc0deTK\n",
"Initializing PoolWorker-501 [pid 95403]\n",
"PoolWorker-501: Created temporary directory /tmp/madlib_JHmX6lRcN3\n",
"Initializing PoolWorker-502 [pid 95404]\n",
"PoolWorker-502: Created temporary directory /tmp/madlib_D7iXssO62K\n",
"Initializing PoolWorker-503 [pid 95405]\n",
"PoolWorker-503: Created temporary directory /tmp/madlib_nOwTOyw4dt\n",
"Initializing PoolWorker-504 [pid 95406]\n",
"PoolWorker-504: Created temporary directory /tmp/madlib_sDR7agbXJf\n",
"PoolWorker-497: Connected to madlib db.\n",
"PoolWorker-498: Connected to madlib db.\n",
"PoolWorker-499: Connected to madlib db.\n",
"PoolWorker-500: Connected to madlib db.\n",
"PoolWorker-501: Connected to madlib db.\n",
"PoolWorker-502: Connected to madlib db.\n",
"PoolWorker-503: Connected to madlib db.\n",
"PoolWorker-504: Connected to madlib db.\n",
"PoolWorker-497: Wrote 500 images to /tmp/madlib_61LU6TGGOE/imagenet_validation_data0000.tmp\n",
"PoolWorker-497: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-498: Removed temporary directory /tmp/madlib_B3FMYIgQQb\n",
"PoolWorker-500: Removed temporary directory /tmp/madlib_y3Ysc0deTK\n",
"PoolWorker-499: Removed temporary directory /tmp/madlib_ps5LnJxLw1\n",
"PoolWorker-501: Removed temporary directory /tmp/madlib_JHmX6lRcN3\n",
"PoolWorker-502: Removed temporary directory /tmp/madlib_D7iXssO62K\n",
"PoolWorker-504: Removed temporary directory /tmp/madlib_sDR7agbXJf\n",
"PoolWorker-503: Removed temporary directory /tmp/madlib_nOwTOyw4dt\n",
"PoolWorker-497: Removed temporary directory /tmp/madlib_61LU6TGGOE\n",
"Done! Loaded 500 images in 351.062001944s\n",
"8 workers terminated.\n",
"Chunk: 64/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-505 [pid 95420]\n",
"PoolWorker-505: Created temporary directory /tmp/madlib_6jVU7pG1pp\n",
"Initializing PoolWorker-506 [pid 95421]\n",
"PoolWorker-506: Created temporary directory /tmp/madlib_l4wZUduqkk\n",
"Initializing PoolWorker-507 [pid 95422]\n",
"PoolWorker-507: Created temporary directory /tmp/madlib_ceOqBVexpr\n",
"Initializing PoolWorker-508 [pid 95423]\n",
"PoolWorker-508: Created temporary directory /tmp/madlib_MmebtWAaSD\n",
"Initializing PoolWorker-509 [pid 95424]\n",
"PoolWorker-509: Created temporary directory /tmp/madlib_ipzFRL7j9v\n",
"Initializing PoolWorker-510 [pid 95425]\n",
"PoolWorker-510: Created temporary directory /tmp/madlib_O2rMaelTsV\n",
"PoolWorker-512: Connected to madlib db.\n",
"Initializing PoolWorker-511 [pid 95426]\n",
"PoolWorker-511: Created temporary directory /tmp/madlib_3eWNxBhFPa\n",
"Initializing PoolWorker-512 [pid 95427]\n",
"PoolWorker-512: Created temporary directory /tmp/madlib_nUX6FMPH8M\n",
"PoolWorker-505: Connected to madlib db.\n",
"PoolWorker-506: Connected to madlib db.\n",
"PoolWorker-507: Connected to madlib db.\n",
"PoolWorker-508: Connected to madlib db.\n",
"PoolWorker-509: Connected to madlib db.\n",
"PoolWorker-510: Connected to madlib db.\n",
"PoolWorker-511: Connected to madlib db.\n",
"PoolWorker-505: Wrote 500 images to /tmp/madlib_6jVU7pG1pp/imagenet_validation_data0000.tmp\n",
"PoolWorker-505: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-506: Removed temporary directory /tmp/madlib_l4wZUduqkk\n",
"PoolWorker-507: Removed temporary directory /tmp/madlib_ceOqBVexpr\n",
"PoolWorker-508: Removed temporary directory /tmp/madlib_MmebtWAaSD\n",
"PoolWorker-509: Removed temporary directory /tmp/madlib_ipzFRL7j9v\n",
"PoolWorker-510: Removed temporary directory /tmp/madlib_O2rMaelTsV\n",
"PoolWorker-511: Removed temporary directory /tmp/madlib_3eWNxBhFPa\n",
"PoolWorker-512: Removed temporary directory /tmp/madlib_nUX6FMPH8M\n",
"PoolWorker-505: Removed temporary directory /tmp/madlib_6jVU7pG1pp\n",
"Done! Loaded 500 images in 415.761136055s\n",
"8 workers terminated.\n",
"Chunk: 65/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-513 [pid 95452]\n",
"PoolWorker-513: Created temporary directory /tmp/madlib_7Ooj4e0Av9\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-514 [pid 95453]\n",
"PoolWorker-514: Created temporary directory /tmp/madlib_mTN1dnwA5o\n",
"Initializing PoolWorker-515 [pid 95454]\n",
"PoolWorker-515: Created temporary directory /tmp/madlib_GUqLJ1PVQB\n",
"Initializing PoolWorker-516 [pid 95455]\n",
"PoolWorker-516: Created temporary directory /tmp/madlib_NH1E0I8W1P\n",
"Initializing PoolWorker-517 [pid 95456]\n",
"PoolWorker-517: Created temporary directory /tmp/madlib_oSWEqhWFFg\n",
"Initializing PoolWorker-518 [pid 95457]\n",
"PoolWorker-518: Created temporary directory /tmp/madlib_iktcd3veRt\n",
"PoolWorker-520: Connected to madlib db.\n",
"Initializing PoolWorker-519 [pid 95458]\n",
"PoolWorker-519: Created temporary directory /tmp/madlib_NvVgDTmcmv\n",
"Initializing PoolWorker-520 [pid 95459]\n",
"PoolWorker-520: Created temporary directory /tmp/madlib_Xf6ZhfeFxH\n",
"PoolWorker-513: Connected to madlib db.\n",
"PoolWorker-514: Connected to madlib db.\n",
"PoolWorker-515: Connected to madlib db.\n",
"PoolWorker-516: Connected to madlib db.\n",
"PoolWorker-517: Connected to madlib db.\n",
"PoolWorker-518: Connected to madlib db.\n",
"PoolWorker-519: Connected to madlib db.\n",
"PoolWorker-513: Wrote 500 images to /tmp/madlib_7Ooj4e0Av9/imagenet_validation_data0000.tmp\n",
"PoolWorker-513: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-514: Removed temporary directory /tmp/madlib_mTN1dnwA5o\n",
"PoolWorker-515: Removed temporary directory /tmp/madlib_GUqLJ1PVQB\n",
"PoolWorker-517: Removed temporary directory /tmp/madlib_oSWEqhWFFg\n",
"PoolWorker-516: Removed temporary directory /tmp/madlib_NH1E0I8W1P\n",
"PoolWorker-519: Removed temporary directory /tmp/madlib_NvVgDTmcmv\n",
"PoolWorker-518: Removed temporary directory /tmp/madlib_iktcd3veRt\n",
"PoolWorker-520: Removed temporary directory /tmp/madlib_Xf6ZhfeFxH\n",
"PoolWorker-513: Removed temporary directory /tmp/madlib_7Ooj4e0Av9\n",
"Done! Loaded 500 images in 320.424824953s\n",
"8 workers terminated.\n",
"Chunk: 66/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-521 [pid 96025]\n",
"PoolWorker-521: Created temporary directory /tmp/madlib_Itc2KFnu5u\n",
"Initializing PoolWorker-522 [pid 96026]\n",
"PoolWorker-522: Created temporary directory /tmp/madlib_8CdjslPgnx\n",
"Initializing PoolWorker-523 [pid 96027]\n",
"PoolWorker-523: Created temporary directory /tmp/madlib_XteUEz1OlE\n",
"Initializing PoolWorker-524 [pid 96028]\n",
"PoolWorker-524: Created temporary directory /tmp/madlib_exyBus5cLD\n",
"Initializing PoolWorker-525 [pid 96029]\n",
"PoolWorker-525: Created temporary directory /tmp/madlib_gowl0RMGZe\n",
"Initializing PoolWorker-526 [pid 96030]\n",
"PoolWorker-526: Created temporary directory /tmp/madlib_fkngT4q3vf\n",
"PoolWorker-528: Connected to madlib db.\n",
"Initializing PoolWorker-527 [pid 96031]\n",
"PoolWorker-527: Created temporary directory /tmp/madlib_SZBBVCANxA\n",
"Initializing PoolWorker-528 [pid 96032]\n",
"PoolWorker-528: Created temporary directory /tmp/madlib_nUn80APirW\n",
"PoolWorker-521: Connected to madlib db.\n",
"PoolWorker-522: Connected to madlib db.\n",
"PoolWorker-523: Connected to madlib db.\n",
"PoolWorker-524: Connected to madlib db.\n",
"PoolWorker-525: Connected to madlib db.\n",
"PoolWorker-526: Connected to madlib db.\n",
"PoolWorker-527: Connected to madlib db.\n",
"PoolWorker-521: Wrote 500 images to /tmp/madlib_Itc2KFnu5u/imagenet_validation_data0000.tmp\n",
"PoolWorker-521: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-522: Removed temporary directory /tmp/madlib_8CdjslPgnx\n",
"PoolWorker-524: Removed temporary directory /tmp/madlib_exyBus5cLD\n",
"PoolWorker-525: Removed temporary directory /tmp/madlib_gowl0RMGZe\n",
"PoolWorker-526: Removed temporary directory /tmp/madlib_fkngT4q3vf\n",
"PoolWorker-523: Removed temporary directory /tmp/madlib_XteUEz1OlE\n",
"PoolWorker-527: Removed temporary directory /tmp/madlib_SZBBVCANxA\n",
"PoolWorker-528: Removed temporary directory /tmp/madlib_nUn80APirW\n",
"PoolWorker-521: Removed temporary directory /tmp/madlib_Itc2KFnu5u\n",
"Done! Loaded 500 images in 314.109488964s\n",
"8 workers terminated.\n",
"Chunk: 67/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-529 [pid 96050]\n",
"PoolWorker-529: Created temporary directory /tmp/madlib_EerFcrsQTJ\n",
"Initializing PoolWorker-530 [pid 96051]\n",
"PoolWorker-530: Created temporary directory /tmp/madlib_x5klhgO6hh\n",
"Initializing PoolWorker-531 [pid 96052]\n",
"PoolWorker-531: Created temporary directory /tmp/madlib_gvCuNp8OFg\n",
"Initializing PoolWorker-532 [pid 96053]\n",
"PoolWorker-532: Created temporary directory /tmp/madlib_RIqNpBedtZ\n",
"Initializing PoolWorker-533 [pid 96054]\n",
"PoolWorker-533: Created temporary directory /tmp/madlib_o4M3xHM5SJ\n",
"Initializing PoolWorker-534 [pid 96055]\n",
"PoolWorker-534: Created temporary directory /tmp/madlib_S5EoqRpqUe\n",
"PoolWorker-536: Connected to madlib db.\n",
"Initializing PoolWorker-535 [pid 96056]\n",
"PoolWorker-535: Created temporary directory /tmp/madlib_49FOCzNbhw\n",
"Initializing PoolWorker-536 [pid 96057]\n",
"PoolWorker-536: Created temporary directory /tmp/madlib_mXxrnDYTgn\n",
"PoolWorker-529: Connected to madlib db.\n",
"PoolWorker-530: Connected to madlib db.\n",
"PoolWorker-531: Connected to madlib db.\n",
"PoolWorker-532: Connected to madlib db.\n",
"PoolWorker-533: Connected to madlib db.\n",
"PoolWorker-534: Connected to madlib db.\n",
"PoolWorker-535: Connected to madlib db.\n",
"PoolWorker-529: Wrote 500 images to /tmp/madlib_EerFcrsQTJ/imagenet_validation_data0000.tmp\n",
"PoolWorker-529: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-536: Removed temporary directory /tmp/madlib_mXxrnDYTgn\n",
"PoolWorker-530: Removed temporary directory /tmp/madlib_x5klhgO6hh\n",
"PoolWorker-532: Removed temporary directory /tmp/madlib_RIqNpBedtZ\n",
"PoolWorker-531: Removed temporary directory /tmp/madlib_gvCuNp8OFg\n",
"PoolWorker-534: Removed temporary directory /tmp/madlib_S5EoqRpqUe\n",
"PoolWorker-533: Removed temporary directory /tmp/madlib_o4M3xHM5SJ\n",
"PoolWorker-535: Removed temporary directory /tmp/madlib_49FOCzNbhw\n",
"PoolWorker-529: Removed temporary directory /tmp/madlib_EerFcrsQTJ\n",
"Done! Loaded 500 images in 329.371101856s\n",
"8 workers terminated.\n",
"Chunk: 68/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-537 [pid 96071]\n",
"PoolWorker-537: Created temporary directory /tmp/madlib_0BvB3vsgBU\n",
"Initializing PoolWorker-538 [pid 96072]\n",
"PoolWorker-538: Created temporary directory /tmp/madlib_Gejc9U5ydg\n",
"Initializing PoolWorker-539 [pid 96073]\n",
"PoolWorker-539: Created temporary directory /tmp/madlib_4NjNZZQ7VW\n",
"Initializing PoolWorker-540 [pid 96074]\n",
"PoolWorker-540: Created temporary directory /tmp/madlib_6jqpbHi4Cc\n",
"Initializing PoolWorker-541 [pid 96075]\n",
"PoolWorker-541: Created temporary directory /tmp/madlib_mZ5NSTbC1D\n",
"Initializing PoolWorker-542 [pid 96076]\n",
"PoolWorker-542: Created temporary directory /tmp/madlib_xRFxTBtsDs\n",
"PoolWorker-544: Connected to madlib db.\n",
"Initializing PoolWorker-543 [pid 96077]\n",
"PoolWorker-543: Created temporary directory /tmp/madlib_88WSlXwjdG\n",
"Initializing PoolWorker-544 [pid 96078]\n",
"PoolWorker-544: Created temporary directory /tmp/madlib_Ti1fo4JTK8\n",
"PoolWorker-537: Connected to madlib db.\n",
"PoolWorker-538: Connected to madlib db.\n",
"PoolWorker-539: Connected to madlib db.\n",
"PoolWorker-540: Connected to madlib db.\n",
"PoolWorker-541: Connected to madlib db.\n",
"PoolWorker-542: Connected to madlib db.\n",
"PoolWorker-543: Connected to madlib db.\n",
"PoolWorker-537: Wrote 500 images to /tmp/madlib_0BvB3vsgBU/imagenet_validation_data0000.tmp\n",
"PoolWorker-537: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-539: Removed temporary directory /tmp/madlib_4NjNZZQ7VW\n",
"PoolWorker-538: Removed temporary directory /tmp/madlib_Gejc9U5ydg\n",
"PoolWorker-541: Removed temporary directory /tmp/madlib_mZ5NSTbC1D\n",
"PoolWorker-540: Removed temporary directory /tmp/madlib_6jqpbHi4Cc\n",
"PoolWorker-542: Removed temporary directory /tmp/madlib_xRFxTBtsDs\n",
"PoolWorker-543: Removed temporary directory /tmp/madlib_88WSlXwjdG\n",
"PoolWorker-544: Removed temporary directory /tmp/madlib_Ti1fo4JTK8\n",
"PoolWorker-537: Removed temporary directory /tmp/madlib_0BvB3vsgBU\n",
"Done! Loaded 500 images in 325.923799992s\n",
"8 workers terminated.\n",
"Chunk: 69/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-545 [pid 96125]\n",
"PoolWorker-545: Created temporary directory /tmp/madlib_ccCZsREghj\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-546 [pid 96126]\n",
"PoolWorker-546: Created temporary directory /tmp/madlib_dQFlPmH6fO\n",
"Initializing PoolWorker-547 [pid 96127]\n",
"PoolWorker-547: Created temporary directory /tmp/madlib_uA7UTZVQlT\n",
"Initializing PoolWorker-548 [pid 96128]\n",
"PoolWorker-548: Created temporary directory /tmp/madlib_yIEppHU4ds\n",
"Initializing PoolWorker-549 [pid 96129]\n",
"PoolWorker-549: Created temporary directory /tmp/madlib_JRIeJU8E4E\n",
"Initializing PoolWorker-550 [pid 96130]\n",
"PoolWorker-550: Created temporary directory /tmp/madlib_OYO35FfqVn\n",
"Initializing PoolWorker-551 [pid 96131]\n",
"PoolWorker-551: Created temporary directory /tmp/madlib_AiZSIl8RVH\n",
"Initializing PoolWorker-552 [pid 96132]\n",
"PoolWorker-552: Created temporary directory /tmp/madlib_LAPYogF0KB\n",
"PoolWorker-545: Connected to madlib db.\n",
"PoolWorker-546: Connected to madlib db.\n",
"PoolWorker-547: Connected to madlib db.\n",
"PoolWorker-548: Connected to madlib db.\n",
"PoolWorker-549: Connected to madlib db.\n",
"PoolWorker-550: Connected to madlib db.\n",
"PoolWorker-551: Connected to madlib db.\n",
"PoolWorker-552: Connected to madlib db.\n",
"PoolWorker-545: Wrote 500 images to /tmp/madlib_ccCZsREghj/imagenet_validation_data0000.tmp\n",
"PoolWorker-545: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-546: Removed temporary directory /tmp/madlib_dQFlPmH6fO\n",
"PoolWorker-547: Removed temporary directory /tmp/madlib_uA7UTZVQlT\n",
"PoolWorker-548: Removed temporary directory /tmp/madlib_yIEppHU4ds\n",
"PoolWorker-549: Removed temporary directory /tmp/madlib_JRIeJU8E4E\n",
"PoolWorker-550: Removed temporary directory /tmp/madlib_OYO35FfqVn\n",
"PoolWorker-551: Removed temporary directory /tmp/madlib_AiZSIl8RVH\n",
"PoolWorker-552: Removed temporary directory /tmp/madlib_LAPYogF0KB\n",
"PoolWorker-545: Removed temporary directory /tmp/madlib_ccCZsREghj\n",
"Done! Loaded 500 images in 319.375603914s\n",
"8 workers terminated.\n",
"Chunk: 70/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-558 [pid 96247]\n",
"Initializing PoolWorker-554 [pid 96243]\n",
"Initializing PoolWorker-553 [pid 96242]\n",
"Initializing PoolWorker-560 [pid 96249]\n",
"Initializing PoolWorker-556 [pid 96245]\n",
"Initializing PoolWorker-555 [pid 96244]\n",
"Initializing PoolWorker-557 [pid 96246]\n",
"Initializing PoolWorker-559 [pid 96248]\n",
"PoolWorker-558: Created temporary directory /tmp/madlib_0N0e7LESN9\n",
"PoolWorker-560: Created temporary directory /tmp/madlib_fyIBKC1Vsq\n",
"PoolWorker-554: Created temporary directory /tmp/madlib_aCeOHBID6a\n",
"PoolWorker-555: Created temporary directory /tmp/madlib_9nQxtawL5z\n",
"PoolWorker-553: Created temporary directory /tmp/madlib_iRMYyD1kEd\n",
"PoolWorker-556: Created temporary directory /tmp/madlib_TYypwWfO9t\n",
"PoolWorker-557: Created temporary directory /tmp/madlib_40uYwmiPnV\n",
"PoolWorker-559: Created temporary directory /tmp/madlib_ue7cVkxUJR\n",
"PoolWorker-560: Connected to madlib db.\n",
"PoolWorker-554: Connected to madlib db.\n",
"PoolWorker-555: Connected to madlib db.\n",
"PoolWorker-557: Connected to madlib db.\n",
"PoolWorker-556: Connected to madlib db.\n",
"PoolWorker-553: Connected to madlib db.\n",
"PoolWorker-558: Connected to madlib db.\n",
"PoolWorker-559: Connected to madlib db.\n",
"PoolWorker-560: Wrote 500 images to /tmp/madlib_fyIBKC1Vsq/imagenet_validation_data0000.tmp\n",
"PoolWorker-560: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-554: Removed temporary directory /tmp/madlib_aCeOHBID6a\n",
"PoolWorker-553: Removed temporary directory /tmp/madlib_iRMYyD1kEd\n",
"PoolWorker-557: Removed temporary directory /tmp/madlib_40uYwmiPnV\n",
"PoolWorker-555: Removed temporary directory /tmp/madlib_9nQxtawL5z\n",
"PoolWorker-556: Removed temporary directory /tmp/madlib_TYypwWfO9t\n",
"PoolWorker-558: Removed temporary directory /tmp/madlib_0N0e7LESN9\n",
"PoolWorker-559: Removed temporary directory /tmp/madlib_ue7cVkxUJR\n",
"PoolWorker-560: Removed temporary directory /tmp/madlib_fyIBKC1Vsq\n",
"Done! Loaded 500 images in 313.465673208s\n",
"8 workers terminated.\n",
"Chunk: 71/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-561 [pid 96262]\n",
"PoolWorker-561: Created temporary directory /tmp/madlib_OQ20aWnwH0\n",
"Initializing PoolWorker-562 [pid 96263]\n",
"PoolWorker-562: Created temporary directory /tmp/madlib_j09PPdFRKA\n",
"Initializing PoolWorker-563 [pid 96264]\n",
"PoolWorker-563: Created temporary directory /tmp/madlib_vMeCSYmwtb\n",
"Initializing PoolWorker-564 [pid 96265]\n",
"PoolWorker-564: Created temporary directory /tmp/madlib_LZLkWIj509\n",
"Initializing PoolWorker-565 [pid 96266]\n",
"PoolWorker-565: Created temporary directory /tmp/madlib_DTbACZBcni\n",
"Initializing PoolWorker-566 [pid 96267]\n",
"PoolWorker-566: Created temporary directory /tmp/madlib_0ZurXzOdFC\n",
"Initializing PoolWorker-567 [pid 96268]\n",
"PoolWorker-568: Connected to madlib db.\n",
"PoolWorker-567: Created temporary directory /tmp/madlib_29OeGqfRBz\n",
"Initializing PoolWorker-568 [pid 96269]\n",
"PoolWorker-568: Created temporary directory /tmp/madlib_i2nxY9BX22\n",
"PoolWorker-561: Connected to madlib db.\n",
"PoolWorker-562: Connected to madlib db.\n",
"PoolWorker-563: Connected to madlib db.\n",
"PoolWorker-564: Connected to madlib db.\n",
"PoolWorker-565: Connected to madlib db.\n",
"PoolWorker-566: Connected to madlib db.\n",
"PoolWorker-567: Connected to madlib db.\n",
"PoolWorker-561: Wrote 500 images to /tmp/madlib_OQ20aWnwH0/imagenet_validation_data0000.tmp\n",
"PoolWorker-561: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-563: Removed temporary directory /tmp/madlib_vMeCSYmwtb\n",
"PoolWorker-565: Removed temporary directory /tmp/madlib_DTbACZBcni\n",
"PoolWorker-566: Removed temporary directory /tmp/madlib_0ZurXzOdFC\n",
"PoolWorker-562: Removed temporary directory /tmp/madlib_j09PPdFRKA\n",
"PoolWorker-568: Removed temporary directory /tmp/madlib_i2nxY9BX22\n",
"PoolWorker-564: Removed temporary directory /tmp/madlib_LZLkWIj509\n",
"PoolWorker-567: Removed temporary directory /tmp/madlib_29OeGqfRBz\n",
"PoolWorker-561: Removed temporary directory /tmp/madlib_OQ20aWnwH0\n",
"Done! Loaded 500 images in 421.046489s\n",
"8 workers terminated.\n",
"Chunk: 72/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-569 [pid 97337]\n",
"PoolWorker-569: Created temporary directory /tmp/madlib_23XN6A0I8H\n",
"Initializing PoolWorker-570 [pid 97338]\n",
"PoolWorker-570: Created temporary directory /tmp/madlib_HVatG8PgZN\n",
"Initializing PoolWorker-571 [pid 97339]\n",
"PoolWorker-571: Created temporary directory /tmp/madlib_GKYUqK5UoN\n",
"Initializing PoolWorker-572 [pid 97340]\n",
"PoolWorker-572: Created temporary directory /tmp/madlib_DMF48YwVxl\n",
"Initializing PoolWorker-573 [pid 97341]\n",
"PoolWorker-573: Created temporary directory /tmp/madlib_nNvJlJEb3d\n",
"Initializing PoolWorker-574 [pid 97342]\n",
"PoolWorker-574: Created temporary directory /tmp/madlib_Yo59I2M41l\n",
"Initializing PoolWorker-575 [pid 97343]\n",
"PoolWorker-576: Connected to madlib db.\n",
"PoolWorker-575: Created temporary directory /tmp/madlib_BTQyhsFsdm\n",
"Initializing PoolWorker-576 [pid 97344]\n",
"PoolWorker-576: Created temporary directory /tmp/madlib_wE8vkJ22D3\n",
"PoolWorker-569: Connected to madlib db.\n",
"PoolWorker-570: Connected to madlib db.\n",
"PoolWorker-571: Connected to madlib db.\n",
"PoolWorker-572: Connected to madlib db.\n",
"PoolWorker-574: Connected to madlib db.\n",
"PoolWorker-573: Connected to madlib db.\n",
"PoolWorker-575: Connected to madlib db.\n",
"PoolWorker-569: Wrote 500 images to /tmp/madlib_23XN6A0I8H/imagenet_validation_data0000.tmp\n",
"PoolWorker-569: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-570: Removed temporary directory /tmp/madlib_HVatG8PgZN\n",
"PoolWorker-571: Removed temporary directory /tmp/madlib_GKYUqK5UoN\n",
"PoolWorker-574: Removed temporary directory /tmp/madlib_Yo59I2M41l\n",
"PoolWorker-573: Removed temporary directory /tmp/madlib_nNvJlJEb3d\n",
"PoolWorker-572: Removed temporary directory /tmp/madlib_DMF48YwVxl\n",
"PoolWorker-576: Removed temporary directory /tmp/madlib_wE8vkJ22D3\n",
"PoolWorker-575: Removed temporary directory /tmp/madlib_BTQyhsFsdm\n",
"PoolWorker-569: Removed temporary directory /tmp/madlib_23XN6A0I8H\n",
"Done! Loaded 500 images in 331.624581099s\n",
"8 workers terminated.\n",
"Chunk: 73/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-577 [pid 97606]\n",
"PoolWorker-577: Created temporary directory /tmp/madlib_bzaeHFjwZA\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-578 [pid 97607]\n",
"PoolWorker-578: Created temporary directory /tmp/madlib_cgEaUhLwwk\n",
"Initializing PoolWorker-579 [pid 97608]\n",
"PoolWorker-579: Created temporary directory /tmp/madlib_zLKsLIKK8h\n",
"Initializing PoolWorker-580 [pid 97609]\n",
"PoolWorker-580: Created temporary directory /tmp/madlib_eLU8vetDpY\n",
"Initializing PoolWorker-581 [pid 97610]\n",
"PoolWorker-581: Created temporary directory /tmp/madlib_r0DNBNcnxE\n",
"Initializing PoolWorker-582 [pid 97611]\n",
"PoolWorker-582: Created temporary directory /tmp/madlib_sU5cJOt0bI\n",
"Initializing PoolWorker-583 [pid 97612]\n",
"PoolWorker-583: Created temporary directory /tmp/madlib_IskrjUHsH9\n",
"PoolWorker-584: Connected to madlib db.\n",
"Initializing PoolWorker-584 [pid 97613]\n",
"PoolWorker-584: Created temporary directory /tmp/madlib_xwbzNFbOYC\n",
"PoolWorker-577: Connected to madlib db.\n",
"PoolWorker-578: Connected to madlib db.\n",
"PoolWorker-579: Connected to madlib db.\n",
"PoolWorker-580: Connected to madlib db.\n",
"PoolWorker-581: Connected to madlib db.\n",
"PoolWorker-582: Connected to madlib db.\n",
"PoolWorker-583: Connected to madlib db.\n",
"PoolWorker-577: Wrote 500 images to /tmp/madlib_bzaeHFjwZA/imagenet_validation_data0000.tmp\n",
"PoolWorker-577: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-579: Removed temporary directory /tmp/madlib_zLKsLIKK8h\n",
"PoolWorker-580: Removed temporary directory /tmp/madlib_eLU8vetDpY\n",
"PoolWorker-581: Removed temporary directory /tmp/madlib_r0DNBNcnxE\n",
"PoolWorker-578: Removed temporary directory /tmp/madlib_cgEaUhLwwk\n",
"PoolWorker-582: Removed temporary directory /tmp/madlib_sU5cJOt0bI\n",
"PoolWorker-583: Removed temporary directory /tmp/madlib_IskrjUHsH9\n",
"PoolWorker-584: Removed temporary directory /tmp/madlib_xwbzNFbOYC\n",
"PoolWorker-577: Removed temporary directory /tmp/madlib_bzaeHFjwZA\n",
"Done! Loaded 500 images in 328.247517109s\n",
"8 workers terminated.\n",
"Chunk: 74/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-585 [pid 97628]\n",
"PoolWorker-585: Created temporary directory /tmp/madlib_derP8vcquX\n",
"Initializing PoolWorker-586 [pid 97629]\n",
"PoolWorker-586: Created temporary directory /tmp/madlib_AsgwhKLf9s\n",
"Initializing PoolWorker-587 [pid 97630]\n",
"PoolWorker-587: Created temporary directory /tmp/madlib_7lPwZkFaVK\n",
"Initializing PoolWorker-588 [pid 97631]\n",
"PoolWorker-588: Created temporary directory /tmp/madlib_QU4E1lcxaa\n",
"Initializing PoolWorker-589 [pid 97632]\n",
"PoolWorker-589: Created temporary directory /tmp/madlib_V9bBHl1bra\n",
"Initializing PoolWorker-590 [pid 97633]\n",
"PoolWorker-590: Created temporary directory /tmp/madlib_gEZTdP1rNS\n",
"Initializing PoolWorker-591 [pid 97634]\n",
"PoolWorker-591: Created temporary directory /tmp/madlib_EsVFN96CIC\n",
"Initializing PoolWorker-592 [pid 97635]\n",
"PoolWorker-592: Created temporary directory /tmp/madlib_aFplRMfQYG\n",
"PoolWorker-585: Connected to madlib db.\n",
"PoolWorker-586: Connected to madlib db.\n",
"PoolWorker-587: Connected to madlib db.\n",
"PoolWorker-588: Connected to madlib db.\n",
"PoolWorker-589: Connected to madlib db.\n",
"PoolWorker-590: Connected to madlib db.\n",
"PoolWorker-591: Connected to madlib db.\n",
"PoolWorker-592: Connected to madlib db.\n",
"PoolWorker-585: Wrote 500 images to /tmp/madlib_derP8vcquX/imagenet_validation_data0000.tmp\n",
"PoolWorker-585: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-587: Removed temporary directory /tmp/madlib_7lPwZkFaVK\n",
"PoolWorker-588: Removed temporary directory /tmp/madlib_QU4E1lcxaa\n",
"PoolWorker-589: Removed temporary directory /tmp/madlib_V9bBHl1bra\n",
"PoolWorker-586: Removed temporary directory /tmp/madlib_AsgwhKLf9s\n",
"PoolWorker-592: Removed temporary directory /tmp/madlib_aFplRMfQYG\n",
"PoolWorker-591: Removed temporary directory /tmp/madlib_EsVFN96CIC\n",
"PoolWorker-590: Removed temporary directory /tmp/madlib_gEZTdP1rNS\n",
"PoolWorker-585: Removed temporary directory /tmp/madlib_derP8vcquX\n",
"Done! Loaded 500 images in 327.616983891s\n",
"8 workers terminated.\n",
"Chunk: 75/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-593 [pid 97649]\n",
"PoolWorker-593: Created temporary directory /tmp/madlib_1Irl2nWlnJ\n",
"Initializing PoolWorker-594 [pid 97650]\n",
"PoolWorker-594: Created temporary directory /tmp/madlib_WMKPkN4naq\n",
"Initializing PoolWorker-595 [pid 97651]\n",
"PoolWorker-595: Created temporary directory /tmp/madlib_FX8NaaAei9\n",
"Initializing PoolWorker-596 [pid 97652]\n",
"PoolWorker-596: Created temporary directory /tmp/madlib_ECbOyPM6jd\n",
"Initializing PoolWorker-597 [pid 97653]\n",
"PoolWorker-597: Created temporary directory /tmp/madlib_wafkhz032W\n",
"Initializing PoolWorker-598 [pid 97654]\n",
"PoolWorker-598: Created temporary directory /tmp/madlib_jSnYB8u70G\n",
"PoolWorker-600: Connected to madlib db.\n",
"Initializing PoolWorker-599 [pid 97655]\n",
"PoolWorker-599: Created temporary directory /tmp/madlib_juslKlVSX9\n",
"Initializing PoolWorker-600 [pid 97656]\n",
"PoolWorker-600: Created temporary directory /tmp/madlib_TDw31vkRQU\n",
"PoolWorker-593: Connected to madlib db.\n",
"PoolWorker-594: Connected to madlib db.\n",
"PoolWorker-595: Connected to madlib db.\n",
"PoolWorker-596: Connected to madlib db.\n",
"PoolWorker-597: Connected to madlib db.\n",
"PoolWorker-598: Connected to madlib db.\n",
"PoolWorker-599: Connected to madlib db.\n",
"PoolWorker-593: Wrote 500 images to /tmp/madlib_1Irl2nWlnJ/imagenet_validation_data0000.tmp\n",
"PoolWorker-593: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-594: Removed temporary directory /tmp/madlib_WMKPkN4naq\n",
"PoolWorker-596: Removed temporary directory /tmp/madlib_ECbOyPM6jd\n",
"PoolWorker-595: Removed temporary directory /tmp/madlib_FX8NaaAei9\n",
"PoolWorker-597: Removed temporary directory /tmp/madlib_wafkhz032W\n",
"PoolWorker-598: Removed temporary directory /tmp/madlib_jSnYB8u70G\n",
"PoolWorker-599: Removed temporary directory /tmp/madlib_juslKlVSX9\n",
"PoolWorker-600: Removed temporary directory /tmp/madlib_TDw31vkRQU\n",
"PoolWorker-593: Removed temporary directory /tmp/madlib_1Irl2nWlnJ\n",
"Done! Loaded 500 images in 319.321337223s\n",
"8 workers terminated.\n",
"Chunk: 76/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-601 [pid 98113]\n",
"PoolWorker-601: Created temporary directory /tmp/madlib_DcDpZASLgC\n",
"Initializing PoolWorker-602 [pid 98114]\n",
"PoolWorker-602: Created temporary directory /tmp/madlib_G6w0dX5X5k\n",
"Initializing PoolWorker-603 [pid 98115]\n",
"PoolWorker-603: Created temporary directory /tmp/madlib_E7L1NRFan4\n",
"Initializing PoolWorker-604 [pid 98116]\n",
"PoolWorker-604: Created temporary directory /tmp/madlib_enTA9X4IcM\n",
"Initializing PoolWorker-605 [pid 98117]\n",
"PoolWorker-605: Created temporary directory /tmp/madlib_GFbm2bYVWg\n",
"Initializing PoolWorker-606 [pid 98118]\n",
"PoolWorker-606: Created temporary directory /tmp/madlib_Dp9ewVs7uU\n",
"Initializing PoolWorker-607 [pid 98119]\n",
"PoolWorker-607: Created temporary directory /tmp/madlib_8uSkQieFAh\n",
"Initializing PoolWorker-608 [pid 98120]\n",
"PoolWorker-608: Created temporary directory /tmp/madlib_TTQp7P63bG\n",
"PoolWorker-601: Connected to madlib db.\n",
"PoolWorker-602: Connected to madlib db.\n",
"PoolWorker-603: Connected to madlib db.\n",
"PoolWorker-604: Connected to madlib db.\n",
"PoolWorker-605: Connected to madlib db.\n",
"PoolWorker-606: Connected to madlib db.\n",
"PoolWorker-607: Connected to madlib db.\n",
"PoolWorker-608: Connected to madlib db.\n",
"PoolWorker-601: Wrote 500 images to /tmp/madlib_DcDpZASLgC/imagenet_validation_data0000.tmp\n",
"PoolWorker-601: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-603: Removed temporary directory /tmp/madlib_E7L1NRFan4\n",
"PoolWorker-602: Removed temporary directory /tmp/madlib_G6w0dX5X5k\n",
"PoolWorker-604: Removed temporary directory /tmp/madlib_enTA9X4IcM\n",
"PoolWorker-607: Removed temporary directory /tmp/madlib_8uSkQieFAh\n",
"PoolWorker-608: Removed temporary directory /tmp/madlib_TTQp7P63bG\n",
"PoolWorker-605: Removed temporary directory /tmp/madlib_GFbm2bYVWg\n",
"PoolWorker-606: Removed temporary directory /tmp/madlib_Dp9ewVs7uU\n",
"PoolWorker-601: Removed temporary directory /tmp/madlib_DcDpZASLgC\n",
"Done! Loaded 500 images in 417.491924047s\n",
"8 workers terminated.\n",
"Chunk: 77/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-609 [pid 98238]\n",
"PoolWorker-609: Created temporary directory /tmp/madlib_fWbf6TqjJJ\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-610 [pid 98239]\n",
"PoolWorker-610: Created temporary directory /tmp/madlib_bXEuFtwQMV\n",
"Initializing PoolWorker-611 [pid 98240]\n",
"PoolWorker-611: Created temporary directory /tmp/madlib_8PT2uhHNhv\n",
"Initializing PoolWorker-612 [pid 98241]\n",
"PoolWorker-612: Created temporary directory /tmp/madlib_3dM59RVfqt\n",
"Initializing PoolWorker-613 [pid 98242]\n",
"PoolWorker-613: Created temporary directory /tmp/madlib_cdZaBvr8xT\n",
"Initializing PoolWorker-614 [pid 98243]\n",
"PoolWorker-614: Created temporary directory /tmp/madlib_QWEZZtghwo\n",
"Initializing PoolWorker-615 [pid 98244]\n",
"PoolWorker-615: Created temporary directory /tmp/madlib_wECXZb4wsx\n",
"Initializing PoolWorker-616 [pid 98245]\n",
"PoolWorker-616: Created temporary directory /tmp/madlib_EmtRQ39Do3\n",
"PoolWorker-609: Connected to madlib db.\n",
"PoolWorker-610: Connected to madlib db.\n",
"PoolWorker-611: Connected to madlib db.\n",
"PoolWorker-612: Connected to madlib db.\n",
"PoolWorker-613: Connected to madlib db.\n",
"PoolWorker-614: Connected to madlib db.\n",
"PoolWorker-615: Connected to madlib db.\n",
"PoolWorker-616: Connected to madlib db.\n",
"PoolWorker-609: Wrote 500 images to /tmp/madlib_fWbf6TqjJJ/imagenet_validation_data0000.tmp\n",
"PoolWorker-609: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-610: Removed temporary directory /tmp/madlib_bXEuFtwQMV\n",
"PoolWorker-611: Removed temporary directory /tmp/madlib_8PT2uhHNhv\n",
"PoolWorker-612: Removed temporary directory /tmp/madlib_3dM59RVfqt\n",
"PoolWorker-613: Removed temporary directory /tmp/madlib_cdZaBvr8xT\n",
"PoolWorker-615: Removed temporary directory /tmp/madlib_wECXZb4wsx\n",
"PoolWorker-614: Removed temporary directory /tmp/madlib_QWEZZtghwo\n",
"PoolWorker-616: Removed temporary directory /tmp/madlib_EmtRQ39Do3\n",
"PoolWorker-609: Removed temporary directory /tmp/madlib_fWbf6TqjJJ\n",
"Done! Loaded 500 images in 422.398868084s\n",
"8 workers terminated.\n",
"Chunk: 78/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-617 [pid 98263]\n",
"PoolWorker-617: Created temporary directory /tmp/madlib_NevH0mUEGq\n",
"Initializing PoolWorker-618 [pid 98264]\n",
"PoolWorker-618: Created temporary directory /tmp/madlib_jDHK8X24gb\n",
"Initializing PoolWorker-619 [pid 98265]\n",
"PoolWorker-619: Created temporary directory /tmp/madlib_CMjTCNL6gU\n",
"Initializing PoolWorker-620 [pid 98266]\n",
"PoolWorker-620: Created temporary directory /tmp/madlib_OGuG2csNS8\n",
"Initializing PoolWorker-621 [pid 98267]\n",
"PoolWorker-621: Created temporary directory /tmp/madlib_tJNBGABmZ0\n",
"Initializing PoolWorker-622 [pid 98268]\n",
"PoolWorker-622: Created temporary directory /tmp/madlib_NldpIwZQD4\n",
"Initializing PoolWorker-623 [pid 98269]\n",
"PoolWorker-623: Created temporary directory /tmp/madlib_lQXfXtEm3p\n",
"Initializing PoolWorker-624 [pid 98270]\n",
"PoolWorker-624: Created temporary directory /tmp/madlib_SAUwHCFTMI\n",
"PoolWorker-617: Connected to madlib db.\n",
"PoolWorker-618: Connected to madlib db.\n",
"PoolWorker-619: Connected to madlib db.\n",
"PoolWorker-620: Connected to madlib db.\n",
"PoolWorker-621: Connected to madlib db.\n",
"PoolWorker-622: Connected to madlib db.\n",
"PoolWorker-623: Connected to madlib db.\n",
"PoolWorker-624: Connected to madlib db.\n",
"PoolWorker-617: Wrote 500 images to /tmp/madlib_NevH0mUEGq/imagenet_validation_data0000.tmp\n",
"PoolWorker-617: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-623: Removed temporary directory /tmp/madlib_lQXfXtEm3p\n",
"PoolWorker-619: Removed temporary directory /tmp/madlib_CMjTCNL6gU\n",
"PoolWorker-620: Removed temporary directory /tmp/madlib_OGuG2csNS8\n",
"PoolWorker-624: Removed temporary directory /tmp/madlib_SAUwHCFTMI\n",
"PoolWorker-622: Removed temporary directory /tmp/madlib_NldpIwZQD4\n",
"PoolWorker-618: Removed temporary directory /tmp/madlib_jDHK8X24gb\n",
"PoolWorker-621: Removed temporary directory /tmp/madlib_tJNBGABmZ0\n",
"PoolWorker-617: Removed temporary directory /tmp/madlib_NevH0mUEGq\n",
"Done! Loaded 500 images in 329.124665022s\n",
"8 workers terminated.\n",
"Chunk: 79/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-625 [pid 98283]\n",
"PoolWorker-625: Created temporary directory /tmp/madlib_w4NEQVNO68\n",
"Initializing PoolWorker-626 [pid 98284]\n",
"PoolWorker-626: Created temporary directory /tmp/madlib_9wuqO2zsRt\n",
"Initializing PoolWorker-627 [pid 98285]\n",
"PoolWorker-627: Created temporary directory /tmp/madlib_nQalvafXNS\n",
"Initializing PoolWorker-628 [pid 98286]\n",
"PoolWorker-628: Created temporary directory /tmp/madlib_eDmTokhsAf\n",
"Initializing PoolWorker-629 [pid 98287]\n",
"PoolWorker-629: Created temporary directory /tmp/madlib_jK6XQKx2vw\n",
"Initializing PoolWorker-630 [pid 98288]\n",
"PoolWorker-630: Created temporary directory /tmp/madlib_u8Ww8xzNLD\n",
"PoolWorker-632: Connected to madlib db.\n",
"Initializing PoolWorker-631 [pid 98289]\n",
"PoolWorker-631: Created temporary directory /tmp/madlib_jR1p2hB8TA\n",
"Initializing PoolWorker-632 [pid 98290]\n",
"PoolWorker-632: Created temporary directory /tmp/madlib_TysNGkl1GF\n",
"PoolWorker-625: Connected to madlib db.\n",
"PoolWorker-626: Connected to madlib db.\n",
"PoolWorker-627: Connected to madlib db.\n",
"PoolWorker-628: Connected to madlib db.\n",
"PoolWorker-629: Connected to madlib db.\n",
"PoolWorker-630: Connected to madlib db.\n",
"PoolWorker-631: Connected to madlib db.\n",
"PoolWorker-625: Wrote 500 images to /tmp/madlib_w4NEQVNO68/imagenet_validation_data0000.tmp\n",
"PoolWorker-625: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-626: Removed temporary directory /tmp/madlib_9wuqO2zsRt\n",
"PoolWorker-631: Removed temporary directory /tmp/madlib_jR1p2hB8TA\n",
"PoolWorker-630: Removed temporary directory /tmp/madlib_u8Ww8xzNLD\n",
"PoolWorker-632: Removed temporary directory /tmp/madlib_TysNGkl1GF\n",
"PoolWorker-629: Removed temporary directory /tmp/madlib_jK6XQKx2vw\n",
"PoolWorker-627: Removed temporary directory /tmp/madlib_nQalvafXNS\n",
"PoolWorker-628: Removed temporary directory /tmp/madlib_eDmTokhsAf\n",
"PoolWorker-625: Removed temporary directory /tmp/madlib_w4NEQVNO68\n",
"Done! Loaded 500 images in 319.679324865s\n",
"8 workers terminated.\n",
"Chunk: 80/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-633 [pid 98305]\n",
"PoolWorker-633: Created temporary directory /tmp/madlib_obWvR0MRgH\n",
"Initializing PoolWorker-634 [pid 98306]\n",
"PoolWorker-634: Created temporary directory /tmp/madlib_lElDhZ94Ba\n",
"Initializing PoolWorker-635 [pid 98307]\n",
"PoolWorker-635: Created temporary directory /tmp/madlib_qvn619Ck8d\n",
"Initializing PoolWorker-636 [pid 98308]\n",
"PoolWorker-636: Created temporary directory /tmp/madlib_u4FiO32FWI\n",
"Initializing PoolWorker-637 [pid 98309]\n",
"PoolWorker-637: Created temporary directory /tmp/madlib_1cixcNsPX7\n",
"Initializing PoolWorker-638 [pid 98310]\n",
"Initializing PoolWorker-639 [pid 98311]\n",
"PoolWorker-638: Created temporary directory /tmp/madlib_UWyFT6RBsM\n",
"PoolWorker-639: Created temporary directory /tmp/madlib_0rkN2d6Sk8\n",
"Initializing PoolWorker-640 [pid 98312]\n",
"PoolWorker-640: Created temporary directory /tmp/madlib_7LCarwX17s\n",
"PoolWorker-633: Connected to madlib db.\n",
"PoolWorker-634: Connected to madlib db.\n",
"PoolWorker-635: Connected to madlib db.\n",
"PoolWorker-636: Connected to madlib db.\n",
"PoolWorker-637: Connected to madlib db.\n",
"PoolWorker-638: Connected to madlib db.\n",
"PoolWorker-639: Connected to madlib db.\n",
"PoolWorker-640: Connected to madlib db.\n",
"PoolWorker-633: Wrote 500 images to /tmp/madlib_obWvR0MRgH/imagenet_validation_data0000.tmp\n",
"PoolWorker-633: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-637: Removed temporary directory /tmp/madlib_1cixcNsPX7\n",
"PoolWorker-638: Removed temporary directory /tmp/madlib_UWyFT6RBsM\n",
"PoolWorker-635: Removed temporary directory /tmp/madlib_qvn619Ck8d\n",
"PoolWorker-636: Removed temporary directory /tmp/madlib_u4FiO32FWI\n",
"PoolWorker-634: Removed temporary directory /tmp/madlib_lElDhZ94Ba\n",
"PoolWorker-640: Removed temporary directory /tmp/madlib_7LCarwX17s\n",
"PoolWorker-639: Removed temporary directory /tmp/madlib_0rkN2d6Sk8\n",
"PoolWorker-633: Removed temporary directory /tmp/madlib_obWvR0MRgH\n",
"Done! Loaded 500 images in 319.444750071s\n",
"8 workers terminated.\n",
"Chunk: 81/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-641 [pid 98326]\n",
"PoolWorker-641: Created temporary directory /tmp/madlib_a1kKQ2ioKu\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-642 [pid 98327]\n",
"PoolWorker-642: Created temporary directory /tmp/madlib_srTiduLpLs\n",
"Initializing PoolWorker-643 [pid 98328]\n",
"PoolWorker-643: Created temporary directory /tmp/madlib_CIcbFDDuzr\n",
"Initializing PoolWorker-644 [pid 98329]\n",
"PoolWorker-644: Created temporary directory /tmp/madlib_gYwIGRX0bV\n",
"Initializing PoolWorker-645 [pid 98330]\n",
"PoolWorker-645: Created temporary directory /tmp/madlib_Ry8NDHKNZt\n",
"Initializing PoolWorker-646 [pid 98331]\n",
"PoolWorker-646: Created temporary directory /tmp/madlib_UOkzGAeW1X\n",
"PoolWorker-648: Connected to madlib db.\n",
"Initializing PoolWorker-647 [pid 98332]\n",
"PoolWorker-647: Created temporary directory /tmp/madlib_2q473NbUoK\n",
"Initializing PoolWorker-648 [pid 98333]\n",
"PoolWorker-648: Created temporary directory /tmp/madlib_tn5hTVFluH\n",
"PoolWorker-641: Connected to madlib db.\n",
"PoolWorker-642: Connected to madlib db.\n",
"PoolWorker-643: Connected to madlib db.\n",
"PoolWorker-644: Connected to madlib db.\n",
"PoolWorker-645: Connected to madlib db.\n",
"PoolWorker-646: Connected to madlib db.\n",
"PoolWorker-647: Connected to madlib db.\n",
"PoolWorker-641: Wrote 500 images to /tmp/madlib_a1kKQ2ioKu/imagenet_validation_data0000.tmp\n",
"PoolWorker-641: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-643: Removed temporary directory /tmp/madlib_CIcbFDDuzr\n",
"PoolWorker-642: Removed temporary directory /tmp/madlib_srTiduLpLs\n",
"PoolWorker-645: Removed temporary directory /tmp/madlib_Ry8NDHKNZt\n",
"PoolWorker-647: Removed temporary directory /tmp/madlib_2q473NbUoK\n",
"PoolWorker-646: Removed temporary directory /tmp/madlib_UOkzGAeW1X\n",
"PoolWorker-644: Removed temporary directory /tmp/madlib_gYwIGRX0bV\n",
"PoolWorker-648: Removed temporary directory /tmp/madlib_tn5hTVFluH\n",
"PoolWorker-641: Removed temporary directory /tmp/madlib_a1kKQ2ioKu\n",
"Done! Loaded 500 images in 420.132057905s\n",
"8 workers terminated.\n",
"Chunk: 82/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-649 [pid 98352]\n",
"PoolWorker-649: Created temporary directory /tmp/madlib_A7C0MBlmMu\n",
"Initializing PoolWorker-650 [pid 98353]\n",
"PoolWorker-650: Created temporary directory /tmp/madlib_5BTayvEWUI\n",
"Initializing PoolWorker-651 [pid 98354]\n",
"PoolWorker-651: Created temporary directory /tmp/madlib_9yNIEsvnC4\n",
"Initializing PoolWorker-652 [pid 98355]\n",
"PoolWorker-652: Created temporary directory /tmp/madlib_XfsceclcLq\n",
"Initializing PoolWorker-653 [pid 98356]\n",
"PoolWorker-653: Created temporary directory /tmp/madlib_Z0tCpRNCmX\n",
"Initializing PoolWorker-654 [pid 98357]\n",
"PoolWorker-654: Created temporary directory /tmp/madlib_Z5RF2TW1R8\n",
"Initializing PoolWorker-655 [pid 98358]\n",
"PoolWorker-656: Connected to madlib db.\n",
"PoolWorker-655: Created temporary directory /tmp/madlib_Q9lTeGww4j\n",
"Initializing PoolWorker-656 [pid 98359]\n",
"PoolWorker-656: Created temporary directory /tmp/madlib_jrjlvuezUq\n",
"PoolWorker-649: Connected to madlib db.\n",
"PoolWorker-650: Connected to madlib db.\n",
"PoolWorker-651: Connected to madlib db.\n",
"PoolWorker-652: Connected to madlib db.\n",
"PoolWorker-653: Connected to madlib db.\n",
"PoolWorker-654: Connected to madlib db.\n",
"PoolWorker-655: Connected to madlib db.\n",
"PoolWorker-649: Wrote 500 images to /tmp/madlib_A7C0MBlmMu/imagenet_validation_data0000.tmp\n",
"PoolWorker-649: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-650: Removed temporary directory /tmp/madlib_5BTayvEWUI\n",
"PoolWorker-652: Removed temporary directory /tmp/madlib_XfsceclcLq\n",
"PoolWorker-653: Removed temporary directory /tmp/madlib_Z0tCpRNCmX\n",
"PoolWorker-651: Removed temporary directory /tmp/madlib_9yNIEsvnC4\n",
"PoolWorker-654: Removed temporary directory /tmp/madlib_Z5RF2TW1R8\n",
"PoolWorker-656: Removed temporary directory /tmp/madlib_jrjlvuezUq\n",
"PoolWorker-655: Removed temporary directory /tmp/madlib_Q9lTeGww4j\n",
"PoolWorker-649: Removed temporary directory /tmp/madlib_A7C0MBlmMu\n",
"Done! Loaded 500 images in 325.07364893s\n",
"8 workers terminated.\n",
"Chunk: 83/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-657 [pid 99668]\n",
"PoolWorker-657: Created temporary directory /tmp/madlib_w2h1wfmVk8\n",
"Initializing PoolWorker-658 [pid 99669]\n",
"PoolWorker-658: Created temporary directory /tmp/madlib_CiTLp9Umm1\n",
"Initializing PoolWorker-659 [pid 99670]\n",
"PoolWorker-659: Created temporary directory /tmp/madlib_QWlyGFWCVr\n",
"Initializing PoolWorker-660 [pid 99671]\n",
"PoolWorker-660: Created temporary directory /tmp/madlib_UT3fCkbFk2\n",
"Initializing PoolWorker-661 [pid 99672]\n",
"PoolWorker-661: Created temporary directory /tmp/madlib_jP7z9ES8vC\n",
"Initializing PoolWorker-662 [pid 99673]\n",
"PoolWorker-662: Created temporary directory /tmp/madlib_LKGZJON202\n",
"Initializing PoolWorker-663 [pid 99674]\n",
"PoolWorker-664: Connected to madlib db.\n",
"PoolWorker-663: Connected to madlib db.\n",
"PoolWorker-663: Created temporary directory /tmp/madlib_QoAM2CS1QK\n",
"Initializing PoolWorker-664 [pid 99675]\n",
"PoolWorker-664: Created temporary directory /tmp/madlib_lIYcdlwIBD\n",
"PoolWorker-657: Connected to madlib db.\n",
"PoolWorker-658: Connected to madlib db.\n",
"PoolWorker-659: Connected to madlib db.\n",
"PoolWorker-660: Connected to madlib db.\n",
"PoolWorker-661: Connected to madlib db.\n",
"PoolWorker-662: Connected to madlib db.\n",
"PoolWorker-657: Wrote 500 images to /tmp/madlib_w2h1wfmVk8/imagenet_validation_data0000.tmp\n",
"PoolWorker-657: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-659: Removed temporary directory /tmp/madlib_QWlyGFWCVr\n",
"PoolWorker-658: Removed temporary directory /tmp/madlib_CiTLp9Umm1\n",
"PoolWorker-660: Removed temporary directory /tmp/madlib_UT3fCkbFk2\n",
"PoolWorker-661: Removed temporary directory /tmp/madlib_jP7z9ES8vC\n",
"PoolWorker-664: Removed temporary directory /tmp/madlib_lIYcdlwIBD\n",
"PoolWorker-662: Removed temporary directory /tmp/madlib_LKGZJON202\n",
"PoolWorker-663: Removed temporary directory /tmp/madlib_QoAM2CS1QK\n",
"PoolWorker-657: Removed temporary directory /tmp/madlib_w2h1wfmVk8\n",
"Done! Loaded 500 images in 324.853332043s\n",
"8 workers terminated.\n",
"Chunk: 84/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-665 [pid 99690]\n",
"PoolWorker-665: Created temporary directory /tmp/madlib_A4xAsVQ5dW\n",
"Initializing PoolWorker-666 [pid 99691]\n",
"PoolWorker-666: Created temporary directory /tmp/madlib_aYgYZPwHgg\n",
"Initializing PoolWorker-667 [pid 99692]\n",
"PoolWorker-667: Created temporary directory /tmp/madlib_RIXMAreurS\n",
"Initializing PoolWorker-668 [pid 99693]\n",
"PoolWorker-668: Created temporary directory /tmp/madlib_qjSHG8oyu2\n",
"Initializing PoolWorker-669 [pid 99694]\n",
"PoolWorker-669: Created temporary directory /tmp/madlib_n1y5yeNn3t\n",
"Initializing PoolWorker-670 [pid 99695]\n",
"PoolWorker-670: Created temporary directory /tmp/madlib_u2B3R1UN8u\n",
"Initializing PoolWorker-671 [pid 99696]\n",
"PoolWorker-672: Connected to madlib db.\n",
"PoolWorker-671: Created temporary directory /tmp/madlib_WKuP4E9d0n\n",
"Initializing PoolWorker-672 [pid 99697]\n",
"PoolWorker-672: Created temporary directory /tmp/madlib_oEizAADw0D\n",
"PoolWorker-665: Connected to madlib db.\n",
"PoolWorker-666: Connected to madlib db.\n",
"PoolWorker-667: Connected to madlib db.\n",
"PoolWorker-668: Connected to madlib db.\n",
"PoolWorker-669: Connected to madlib db.\n",
"PoolWorker-670: Connected to madlib db.\n",
"PoolWorker-671: Connected to madlib db.\n",
"PoolWorker-665: Wrote 500 images to /tmp/madlib_A4xAsVQ5dW/imagenet_validation_data0000.tmp\n",
"PoolWorker-665: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-666: Removed temporary directory /tmp/madlib_aYgYZPwHgg\n",
"PoolWorker-667: Removed temporary directory /tmp/madlib_RIXMAreurS\n",
"PoolWorker-669: Removed temporary directory /tmp/madlib_n1y5yeNn3t\n",
"PoolWorker-671: Removed temporary directory /tmp/madlib_WKuP4E9d0n\n",
"PoolWorker-670: Removed temporary directory /tmp/madlib_u2B3R1UN8u\n",
"PoolWorker-668: Removed temporary directory /tmp/madlib_qjSHG8oyu2\n",
"PoolWorker-672: Removed temporary directory /tmp/madlib_oEizAADw0D\n",
"PoolWorker-665: Removed temporary directory /tmp/madlib_A4xAsVQ5dW\n",
"Done! Loaded 500 images in 323.293869019s\n",
"8 workers terminated.\n",
"Chunk: 85/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-673 [pid 99710]\n",
"PoolWorker-673: Created temporary directory /tmp/madlib_QN0F9LCrVn\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-674 [pid 99711]\n",
"PoolWorker-674: Created temporary directory /tmp/madlib_DuLzZ4X0Cn\n",
"Initializing PoolWorker-675 [pid 99712]\n",
"PoolWorker-675: Created temporary directory /tmp/madlib_PpIswy1ITx\n",
"Initializing PoolWorker-676 [pid 99713]\n",
"PoolWorker-676: Created temporary directory /tmp/madlib_3ZkdQBIaLC\n",
"Initializing PoolWorker-677 [pid 99714]\n",
"PoolWorker-677: Created temporary directory /tmp/madlib_tWuCziD18M\n",
"Initializing PoolWorker-678 [pid 99715]\n",
"PoolWorker-678: Created temporary directory /tmp/madlib_ZBt9jpAABU\n",
"Initializing PoolWorker-679 [pid 99716]\n",
"PoolWorker-679: Created temporary directory /tmp/madlib_fBbtF0SnJq\n",
"PoolWorker-680: Connected to madlib db.\n",
"Initializing PoolWorker-680 [pid 99717]\n",
"PoolWorker-680: Created temporary directory /tmp/madlib_sBcUjJ6tXH\n",
"PoolWorker-673: Connected to madlib db.\n",
"PoolWorker-674: Connected to madlib db.\n",
"PoolWorker-675: Connected to madlib db.\n",
"PoolWorker-676: Connected to madlib db.\n",
"PoolWorker-677: Connected to madlib db.\n",
"PoolWorker-678: Connected to madlib db.\n",
"PoolWorker-679: Connected to madlib db.\n",
"PoolWorker-673: Wrote 500 images to /tmp/madlib_QN0F9LCrVn/imagenet_validation_data0000.tmp\n",
"PoolWorker-673: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-674: Removed temporary directory /tmp/madlib_DuLzZ4X0Cn\n",
"PoolWorker-677: Removed temporary directory /tmp/madlib_tWuCziD18M\n",
"PoolWorker-676: Removed temporary directory /tmp/madlib_3ZkdQBIaLC\n",
"PoolWorker-678: Removed temporary directory /tmp/madlib_ZBt9jpAABU\n",
"PoolWorker-675: Removed temporary directory /tmp/madlib_PpIswy1ITx\n",
"PoolWorker-679: Removed temporary directory /tmp/madlib_fBbtF0SnJq\n",
"PoolWorker-680: Removed temporary directory /tmp/madlib_sBcUjJ6tXH\n",
"PoolWorker-673: Removed temporary directory /tmp/madlib_QN0F9LCrVn\n",
"Done! Loaded 500 images in 428.354425907s\n",
"8 workers terminated.\n",
"Chunk: 86/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-681 [pid 432]\n",
"PoolWorker-681: Created temporary directory /tmp/madlib_3aqOIOqbBK\n",
"Initializing PoolWorker-682 [pid 433]\n",
"PoolWorker-682: Created temporary directory /tmp/madlib_2aBqu7GBzr\n",
"Initializing PoolWorker-683 [pid 434]\n",
"PoolWorker-683: Created temporary directory /tmp/madlib_JImiTEbhJ8\n",
"Initializing PoolWorker-684 [pid 435]\n",
"PoolWorker-684: Created temporary directory /tmp/madlib_kvItZKd78s\n",
"Initializing PoolWorker-685 [pid 436]\n",
"PoolWorker-685: Created temporary directory /tmp/madlib_llgLRwQ39g\n",
"Initializing PoolWorker-686 [pid 437]\n",
"PoolWorker-686: Created temporary directory /tmp/madlib_FrRHoTbfPP\n",
"Initializing PoolWorker-687 [pid 438]\n",
"PoolWorker-688: Connected to madlib db.\n",
"PoolWorker-687: Created temporary directory /tmp/madlib_adq7brXrUd\n",
"Initializing PoolWorker-688 [pid 439]\n",
"PoolWorker-688: Created temporary directory /tmp/madlib_ZtXjl8ADCL\n",
"PoolWorker-681: Connected to madlib db.\n",
"PoolWorker-682: Connected to madlib db.\n",
"PoolWorker-683: Connected to madlib db.\n",
"PoolWorker-684: Connected to madlib db.\n",
"PoolWorker-685: Connected to madlib db.\n",
"PoolWorker-686: Connected to madlib db.\n",
"PoolWorker-687: Connected to madlib db.\n",
"PoolWorker-681: Wrote 500 images to /tmp/madlib_3aqOIOqbBK/imagenet_validation_data0000.tmp\n",
"PoolWorker-681: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-683: Removed temporary directory /tmp/madlib_JImiTEbhJ8\n",
"PoolWorker-682: Removed temporary directory /tmp/madlib_2aBqu7GBzr\n",
"PoolWorker-685: Removed temporary directory /tmp/madlib_llgLRwQ39g\n",
"PoolWorker-684: Removed temporary directory /tmp/madlib_kvItZKd78s\n",
"PoolWorker-686: Removed temporary directory /tmp/madlib_FrRHoTbfPP\n",
"PoolWorker-688: Removed temporary directory /tmp/madlib_ZtXjl8ADCL\n",
"PoolWorker-687: Removed temporary directory /tmp/madlib_adq7brXrUd\n",
"PoolWorker-681: Removed temporary directory /tmp/madlib_3aqOIOqbBK\n",
"Done! Loaded 500 images in 321.920161963s\n",
"8 workers terminated.\n",
"Chunk: 87/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-689 [pid 452]\n",
"PoolWorker-689: Created temporary directory /tmp/madlib_0MHmzJfF1Y\n",
"Initializing PoolWorker-690 [pid 453]\n",
"PoolWorker-690: Created temporary directory /tmp/madlib_L72E11P5bF\n",
"Initializing PoolWorker-691 [pid 454]\n",
"PoolWorker-691: Created temporary directory /tmp/madlib_JsPCZFYEkX\n",
"Initializing PoolWorker-692 [pid 455]\n",
"PoolWorker-692: Created temporary directory /tmp/madlib_kuXfoVwSyw\n",
"Initializing PoolWorker-693 [pid 456]\n",
"PoolWorker-693: Created temporary directory /tmp/madlib_AntB26VZg5\n",
"Initializing PoolWorker-694 [pid 457]\n",
"PoolWorker-694: Created temporary directory /tmp/madlib_NSd3ME7IDB\n",
"PoolWorker-696: Connected to madlib db.\n",
"Initializing PoolWorker-695 [pid 458]\n",
"PoolWorker-695: Created temporary directory /tmp/madlib_S65g3aPsAa\n",
"Initializing PoolWorker-696 [pid 459]\n",
"PoolWorker-696: Created temporary directory /tmp/madlib_F7S3nMtIII\n",
"PoolWorker-689: Connected to madlib db.\n",
"PoolWorker-690: Connected to madlib db.\n",
"PoolWorker-691: Connected to madlib db.\n",
"PoolWorker-692: Connected to madlib db.\n",
"PoolWorker-693: Connected to madlib db.\n",
"PoolWorker-694: Connected to madlib db.\n",
"PoolWorker-695: Connected to madlib db.\n",
"PoolWorker-689: Wrote 500 images to /tmp/madlib_0MHmzJfF1Y/imagenet_validation_data0000.tmp\n",
"PoolWorker-689: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-694: Removed temporary directory /tmp/madlib_NSd3ME7IDB\n",
"PoolWorker-690: Removed temporary directory /tmp/madlib_L72E11P5bF\n",
"PoolWorker-691: Removed temporary directory /tmp/madlib_JsPCZFYEkX\n",
"PoolWorker-692: Removed temporary directory /tmp/madlib_kuXfoVwSyw\n",
"PoolWorker-696: Removed temporary directory /tmp/madlib_F7S3nMtIII\n",
"PoolWorker-693: Removed temporary directory /tmp/madlib_AntB26VZg5\n",
"PoolWorker-695: Removed temporary directory /tmp/madlib_S65g3aPsAa\n",
"PoolWorker-689: Removed temporary directory /tmp/madlib_0MHmzJfF1Y\n",
"Done! Loaded 500 images in 319.714130878s\n",
"8 workers terminated.\n",
"Chunk: 88/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-697 [pid 474]\n",
"PoolWorker-697: Created temporary directory /tmp/madlib_deTAxwcSOu\n",
"Initializing PoolWorker-698 [pid 475]\n",
"PoolWorker-698: Created temporary directory /tmp/madlib_DpWioBdIvl\n",
"Initializing PoolWorker-699 [pid 476]\n",
"PoolWorker-699: Created temporary directory /tmp/madlib_tAfmVeWq25\n",
"Initializing PoolWorker-700 [pid 477]\n",
"PoolWorker-700: Created temporary directory /tmp/madlib_aferUUQsaE\n",
"Initializing PoolWorker-701 [pid 478]\n",
"PoolWorker-701: Created temporary directory /tmp/madlib_qEIXWoNw6C\n",
"Initializing PoolWorker-702 [pid 479]\n",
"PoolWorker-702: Created temporary directory /tmp/madlib_vYhQgmo77l\n",
"PoolWorker-704: Connected to madlib db.\n",
"Initializing PoolWorker-703 [pid 480]\n",
"PoolWorker-703: Created temporary directory /tmp/madlib_3s9zT78uZ6\n",
"Initializing PoolWorker-704 [pid 481]\n",
"PoolWorker-704: Created temporary directory /tmp/madlib_02pMdL19dU\n",
"PoolWorker-697: Connected to madlib db.\n",
"PoolWorker-698: Connected to madlib db.\n",
"PoolWorker-700: Connected to madlib db.\n",
"PoolWorker-699: Connected to madlib db.\n",
"PoolWorker-701: Connected to madlib db.\n",
"PoolWorker-702: Connected to madlib db.\n",
"PoolWorker-703: Connected to madlib db.\n",
"PoolWorker-697: Wrote 500 images to /tmp/madlib_deTAxwcSOu/imagenet_validation_data0000.tmp\n",
"PoolWorker-697: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-699: Removed temporary directory /tmp/madlib_tAfmVeWq25\n",
"PoolWorker-698: Removed temporary directory /tmp/madlib_DpWioBdIvl\n",
"PoolWorker-701: Removed temporary directory /tmp/madlib_qEIXWoNw6C\n",
"PoolWorker-703: Removed temporary directory /tmp/madlib_3s9zT78uZ6\n",
"PoolWorker-700: Removed temporary directory /tmp/madlib_aferUUQsaE\n",
"PoolWorker-702: Removed temporary directory /tmp/madlib_vYhQgmo77l\n",
"PoolWorker-704: Removed temporary directory /tmp/madlib_02pMdL19dU\n",
"PoolWorker-697: Removed temporary directory /tmp/madlib_deTAxwcSOu\n",
"Done! Loaded 500 images in 422.198534012s\n",
"8 workers terminated.\n",
"Chunk: 89/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-705 [pid 498]\n",
"PoolWorker-705: Created temporary directory /tmp/madlib_GlTWlheU7U\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing PoolWorker-706 [pid 499]\n",
"PoolWorker-706: Created temporary directory /tmp/madlib_pzmOnXR5zS\n",
"Initializing PoolWorker-707 [pid 500]\n",
"PoolWorker-707: Created temporary directory /tmp/madlib_hs7bvIsu1o\n",
"Initializing PoolWorker-708 [pid 501]\n",
"PoolWorker-708: Created temporary directory /tmp/madlib_C20epcePqE\n",
"Initializing PoolWorker-709 [pid 502]\n",
"PoolWorker-709: Created temporary directory /tmp/madlib_vU2T2Axs2i\n",
"Initializing PoolWorker-710 [pid 503]\n",
"PoolWorker-710: Created temporary directory /tmp/madlib_NOPnBATlii\n",
"Initializing PoolWorker-711 [pid 504]\n",
"PoolWorker-711: Created temporary directory /tmp/madlib_qEKBOD0ovD\n",
"Initializing PoolWorker-712 [pid 506]\n",
"PoolWorker-712: Created temporary directory /tmp/madlib_yc09U8bg1U\n",
"PoolWorker-705: Connected to madlib db.\n",
"PoolWorker-706: Connected to madlib db.\n",
"PoolWorker-707: Connected to madlib db.\n",
"PoolWorker-708: Connected to madlib db.\n",
"PoolWorker-709: Connected to madlib db.\n",
"PoolWorker-710: Connected to madlib db.\n",
"PoolWorker-711: Connected to madlib db.\n",
"PoolWorker-712: Connected to madlib db.\n",
"PoolWorker-705: Wrote 500 images to /tmp/madlib_GlTWlheU7U/imagenet_validation_data0000.tmp\n",
"PoolWorker-705: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-707: Removed temporary directory /tmp/madlib_hs7bvIsu1o\n",
"PoolWorker-708: Removed temporary directory /tmp/madlib_C20epcePqE\n",
"PoolWorker-711: Removed temporary directory /tmp/madlib_qEKBOD0ovD\n",
"PoolWorker-706: Removed temporary directory /tmp/madlib_pzmOnXR5zS\n",
"PoolWorker-709: Removed temporary directory /tmp/madlib_vU2T2Axs2i\n",
"PoolWorker-710: Removed temporary directory /tmp/madlib_NOPnBATlii\n",
"PoolWorker-712: Removed temporary directory /tmp/madlib_yc09U8bg1U\n",
"PoolWorker-705: Removed temporary directory /tmp/madlib_GlTWlheU7U\n",
"Done! Loaded 500 images in 409.745854139s\n",
"8 workers terminated.\n",
"Chunk: 90/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-713 [pid 524]\n",
"PoolWorker-713: Created temporary directory /tmp/madlib_Ym8cCBlwk0\n",
"Initializing PoolWorker-714 [pid 525]\n",
"PoolWorker-714: Created temporary directory /tmp/madlib_tehd1whxTD\n",
"Initializing PoolWorker-715 [pid 526]\n",
"PoolWorker-715: Created temporary directory /tmp/madlib_qwtKl6cYPE\n",
"Initializing PoolWorker-716 [pid 527]\n",
"PoolWorker-716: Created temporary directory /tmp/madlib_quQOmOfy89\n",
"Initializing PoolWorker-717 [pid 528]\n",
"PoolWorker-717: Created temporary directory /tmp/madlib_EjWCwNQxti\n",
"Initializing PoolWorker-718 [pid 529]\n",
"PoolWorker-718: Created temporary directory /tmp/madlib_RWS19TveFx\n",
"PoolWorker-720: Connected to madlib db.\n",
"Initializing PoolWorker-719 [pid 530]\n",
"PoolWorker-719: Created temporary directory /tmp/madlib_vry8jbScxM\n",
"Initializing PoolWorker-720 [pid 531]\n",
"PoolWorker-720: Created temporary directory /tmp/madlib_PPFyLLzyPS\n",
"PoolWorker-713: Connected to madlib db.\n",
"PoolWorker-714: Connected to madlib db.\n",
"PoolWorker-715: Connected to madlib db.\n",
"PoolWorker-716: Connected to madlib db.\n",
"PoolWorker-717: Connected to madlib db.\n",
"PoolWorker-718: Connected to madlib db.\n",
"PoolWorker-719: Connected to madlib db.\n",
"PoolWorker-713: Wrote 500 images to /tmp/madlib_Ym8cCBlwk0/imagenet_validation_data0000.tmp\n",
"PoolWorker-713: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-715: Removed temporary directory /tmp/madlib_qwtKl6cYPE\n",
"PoolWorker-714: Removed temporary directory /tmp/madlib_tehd1whxTD\n",
"PoolWorker-717: Removed temporary directory /tmp/madlib_EjWCwNQxti\n",
"PoolWorker-716: Removed temporary directory /tmp/madlib_quQOmOfy89\n",
"PoolWorker-718: Removed temporary directory /tmp/madlib_RWS19TveFx\n",
"PoolWorker-720: Removed temporary directory /tmp/madlib_PPFyLLzyPS\n",
"PoolWorker-719: Removed temporary directory /tmp/madlib_vry8jbScxM\n",
"PoolWorker-713: Removed temporary directory /tmp/madlib_Ym8cCBlwk0\n",
"Done! Loaded 500 images in 319.002321005s\n",
"8 workers terminated.\n",
"Chunk: 91/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-721 [pid 545]\n",
"PoolWorker-721: Created temporary directory /tmp/madlib_uqo6l1bPz0\n",
"Initializing PoolWorker-722 [pid 546]\n",
"PoolWorker-722: Created temporary directory /tmp/madlib_0sWs6eH8Hd\n",
"Initializing PoolWorker-723 [pid 547]\n",
"PoolWorker-723: Created temporary directory /tmp/madlib_LV3vd64UoU\n",
"Initializing PoolWorker-724 [pid 548]\n",
"PoolWorker-724: Created temporary directory /tmp/madlib_y0oPgIa7ZG\n",
"Initializing PoolWorker-725 [pid 549]\n",
"PoolWorker-725: Created temporary directory /tmp/madlib_bgQqQ4g30l\n",
"Initializing PoolWorker-726 [pid 550]\n",
"PoolWorker-726: Created temporary directory /tmp/madlib_nnrdvGmaZg\n",
"PoolWorker-728: Connected to madlib db.\n",
"Initializing PoolWorker-727 [pid 551]\n",
"PoolWorker-727: Created temporary directory /tmp/madlib_lXTGji6W4h\n",
"Initializing PoolWorker-728 [pid 552]\n",
"PoolWorker-728: Created temporary directory /tmp/madlib_ZMGAKcwk2F\n",
"PoolWorker-721: Connected to madlib db.\n",
"PoolWorker-722: Connected to madlib db.\n",
"PoolWorker-723: Connected to madlib db.\n",
"PoolWorker-724: Connected to madlib db.\n",
"PoolWorker-725: Connected to madlib db.\n",
"PoolWorker-726: Connected to madlib db.\n",
"PoolWorker-727: Connected to madlib db.\n",
"PoolWorker-721: Wrote 500 images to /tmp/madlib_uqo6l1bPz0/imagenet_validation_data0000.tmp\n",
"PoolWorker-721: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-725: Removed temporary directory /tmp/madlib_bgQqQ4g30l\n",
"PoolWorker-722: Removed temporary directory /tmp/madlib_0sWs6eH8Hd\n",
"PoolWorker-724: Removed temporary directory /tmp/madlib_y0oPgIa7ZG\n",
"PoolWorker-728: Removed temporary directory /tmp/madlib_ZMGAKcwk2F\n",
"PoolWorker-726: Removed temporary directory /tmp/madlib_nnrdvGmaZg\n",
"PoolWorker-723: Removed temporary directory /tmp/madlib_LV3vd64UoU\n",
"PoolWorker-727: Removed temporary directory /tmp/madlib_lXTGji6W4h\n",
"PoolWorker-721: Removed temporary directory /tmp/madlib_uqo6l1bPz0\n",
"Done! Loaded 500 images in 326.13105607s\n",
"8 workers terminated.\n",
"Chunk: 92/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-729 [pid 566]\n",
"PoolWorker-729: Created temporary directory /tmp/madlib_yr8PnIcMpZ\n",
"Initializing PoolWorker-730 [pid 567]\n",
"PoolWorker-730: Created temporary directory /tmp/madlib_dJ5puXbHpL\n",
"Initializing PoolWorker-731 [pid 568]\n",
"PoolWorker-731: Created temporary directory /tmp/madlib_ay3LtvThvh\n",
"Initializing PoolWorker-732 [pid 569]\n",
"PoolWorker-732: Created temporary directory /tmp/madlib_wFWAusu9af\n",
"Initializing PoolWorker-733 [pid 570]\n",
"PoolWorker-733: Created temporary directory /tmp/madlib_dSCVTElrCi\n",
"Initializing PoolWorker-734 [pid 571]\n",
"PoolWorker-734: Created temporary directory /tmp/madlib_o2fyi9SHu6\n",
"Initializing PoolWorker-735 [pid 572]\n",
"PoolWorker-736: Connected to madlib db.\n",
"PoolWorker-735: Created temporary directory /tmp/madlib_UNU8NFyQZ5\n",
"Initializing PoolWorker-736 [pid 573]\n",
"PoolWorker-736: Created temporary directory /tmp/madlib_PBCG3MX91K\n",
"PoolWorker-729: Connected to madlib db.\n",
"PoolWorker-730: Connected to madlib db.\n",
"PoolWorker-731: Connected to madlib db.\n",
"PoolWorker-732: Connected to madlib db.\n",
"PoolWorker-733: Connected to madlib db.\n",
"PoolWorker-734: Connected to madlib db.\n",
"PoolWorker-735: Connected to madlib db.\n",
"PoolWorker-729: Wrote 500 images to /tmp/madlib_yr8PnIcMpZ/imagenet_validation_data0000.tmp\n",
"PoolWorker-729: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-731: Removed temporary directory /tmp/madlib_ay3LtvThvh\n",
"PoolWorker-732: Removed temporary directory /tmp/madlib_wFWAusu9af\n",
"PoolWorker-730: Removed temporary directory /tmp/madlib_dJ5puXbHpL\n",
"PoolWorker-733: Removed temporary directory /tmp/madlib_dSCVTElrCi\n",
"PoolWorker-736: Removed temporary directory /tmp/madlib_PBCG3MX91K\n",
"PoolWorker-735: Removed temporary directory /tmp/madlib_UNU8NFyQZ5\n",
"PoolWorker-734: Removed temporary directory /tmp/madlib_o2fyi9SHu6\n",
"PoolWorker-729: Removed temporary directory /tmp/madlib_yr8PnIcMpZ\n",
"Done! Loaded 500 images in 319.203655958s\n",
"8 workers terminated.\n",
"Chunk: 93/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-737 [pid 591]\n",
"PoolWorker-737: Created temporary directory /tmp/madlib_HqtoT1O03E\n",
"Initializing PoolWorker-738 [pid 592]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PoolWorker-738: Created temporary directory /tmp/madlib_EZaWxuQF2u\n",
"Initializing PoolWorker-739 [pid 593]\n",
"PoolWorker-739: Created temporary directory /tmp/madlib_tBpiJEHXlj\n",
"Initializing PoolWorker-740 [pid 594]\n",
"PoolWorker-740: Created temporary directory /tmp/madlib_1tLYRnXMte\n",
"Initializing PoolWorker-741 [pid 595]\n",
"PoolWorker-741: Created temporary directory /tmp/madlib_jhgOLCfiiw\n",
"Initializing PoolWorker-742 [pid 596]\n",
"PoolWorker-742: Created temporary directory /tmp/madlib_3kLbxRO6Gx\n",
"Initializing PoolWorker-743 [pid 597]\n",
"PoolWorker-744: Connected to madlib db.\n",
"PoolWorker-743: Created temporary directory /tmp/madlib_h4raxdZhFg\n",
"Initializing PoolWorker-744 [pid 598]\n",
"PoolWorker-744: Created temporary directory /tmp/madlib_uR9Hf8CiTq\n",
"PoolWorker-737: Connected to madlib db.\n",
"PoolWorker-738: Connected to madlib db.\n",
"PoolWorker-739: Connected to madlib db.\n",
"PoolWorker-740: Connected to madlib db.\n",
"PoolWorker-741: Connected to madlib db.\n",
"PoolWorker-742: Connected to madlib db.\n",
"PoolWorker-743: Connected to madlib db.\n",
"PoolWorker-737: Wrote 500 images to /tmp/madlib_HqtoT1O03E/imagenet_validation_data0000.tmp\n",
"PoolWorker-737: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-739: Removed temporary directory /tmp/madlib_tBpiJEHXlj\n",
"PoolWorker-738: Removed temporary directory /tmp/madlib_EZaWxuQF2u\n",
"PoolWorker-740: Removed temporary directory /tmp/madlib_1tLYRnXMte\n",
"PoolWorker-741: Removed temporary directory /tmp/madlib_jhgOLCfiiw\n",
"PoolWorker-743: Removed temporary directory /tmp/madlib_h4raxdZhFg\n",
"PoolWorker-742: Removed temporary directory /tmp/madlib_3kLbxRO6Gx\n",
"PoolWorker-744: Removed temporary directory /tmp/madlib_uR9Hf8CiTq\n",
"PoolWorker-737: Removed temporary directory /tmp/madlib_HqtoT1O03E\n",
"Done! Loaded 500 images in 317.473318815s\n",
"8 workers terminated.\n",
"Chunk: 94/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-745 [pid 1911]\n",
"PoolWorker-745: Created temporary directory /tmp/madlib_ctiBPyLV4u\n",
"Initializing PoolWorker-746 [pid 1912]\n",
"PoolWorker-746: Created temporary directory /tmp/madlib_5RAvfQ0g9E\n",
"Initializing PoolWorker-747 [pid 1913]\n",
"PoolWorker-747: Created temporary directory /tmp/madlib_W28YcmJU2S\n",
"Initializing PoolWorker-748 [pid 1914]\n",
"PoolWorker-748: Created temporary directory /tmp/madlib_zRitpSb8FW\n",
"Initializing PoolWorker-749 [pid 1915]\n",
"PoolWorker-749: Created temporary directory /tmp/madlib_IcAQAhVAE1\n",
"Initializing PoolWorker-750 [pid 1916]\n",
"PoolWorker-750: Created temporary directory /tmp/madlib_4YQU4ZeVEz\n",
"Initializing PoolWorker-751 [pid 1917]\n",
"PoolWorker-751: Created temporary directory /tmp/madlib_8tX1FyvZYb\n",
"Initializing PoolWorker-752 [pid 1918]\n",
"PoolWorker-752: Created temporary directory /tmp/madlib_kTTSebB8mP\n",
"PoolWorker-745: Connected to madlib db.\n",
"PoolWorker-746: Connected to madlib db.\n",
"PoolWorker-747: Connected to madlib db.\n",
"PoolWorker-748: Connected to madlib db.\n",
"PoolWorker-749: Connected to madlib db.\n",
"PoolWorker-750: Connected to madlib db.\n",
"PoolWorker-751: Connected to madlib db.\n",
"PoolWorker-752: Connected to madlib db.\n",
"PoolWorker-745: Wrote 500 images to /tmp/madlib_ctiBPyLV4u/imagenet_validation_data0000.tmp\n",
"PoolWorker-745: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-746: Removed temporary directory /tmp/madlib_5RAvfQ0g9E\n",
"PoolWorker-747: Removed temporary directory /tmp/madlib_W28YcmJU2S\n",
"PoolWorker-748: Removed temporary directory /tmp/madlib_zRitpSb8FW\n",
"PoolWorker-751: Removed temporary directory /tmp/madlib_8tX1FyvZYb\n",
"PoolWorker-749: Removed temporary directory /tmp/madlib_IcAQAhVAE1\n",
"PoolWorker-750: Removed temporary directory /tmp/madlib_4YQU4ZeVEz\n",
"PoolWorker-752: Removed temporary directory /tmp/madlib_kTTSebB8mP\n",
"PoolWorker-745: Removed temporary directory /tmp/madlib_ctiBPyLV4u\n",
"Done! Loaded 500 images in 322.200166941s\n",
"8 workers terminated.\n",
"Chunk: 95/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-753 [pid 1932]\n",
"PoolWorker-753: Created temporary directory /tmp/madlib_HImKT4pg1B\n",
"Initializing PoolWorker-754 [pid 1933]\n",
"PoolWorker-754: Created temporary directory /tmp/madlib_E1lkeTo3uE\n",
"Initializing PoolWorker-755 [pid 1934]\n",
"PoolWorker-755: Created temporary directory /tmp/madlib_h3E0LErtQL\n",
"Initializing PoolWorker-756 [pid 1935]\n",
"PoolWorker-756: Created temporary directory /tmp/madlib_xPHVhBTPfU\n",
"Initializing PoolWorker-757 [pid 1936]\n",
"PoolWorker-757: Created temporary directory /tmp/madlib_wtmIXtxPzr\n",
"Initializing PoolWorker-758 [pid 1937]\n",
"PoolWorker-758: Created temporary directory /tmp/madlib_s62VfDOPNC\n",
"Initializing PoolWorker-759 [pid 1938]\n",
"PoolWorker-760: Connected to madlib db.\n",
"PoolWorker-759: Created temporary directory /tmp/madlib_IeTN8KzDHF\n",
"Initializing PoolWorker-760 [pid 1939]\n",
"PoolWorker-760: Created temporary directory /tmp/madlib_m7ZyJIuZdV\n",
"PoolWorker-753: Connected to madlib db.\n",
"PoolWorker-754: Connected to madlib db.\n",
"PoolWorker-755: Connected to madlib db.\n",
"PoolWorker-756: Connected to madlib db.\n",
"PoolWorker-757: Connected to madlib db.\n",
"PoolWorker-758: Connected to madlib db.\n",
"PoolWorker-759: Connected to madlib db.\n",
"PoolWorker-753: Wrote 500 images to /tmp/madlib_HImKT4pg1B/imagenet_validation_data0000.tmp\n",
"PoolWorker-753: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-756: Removed temporary directory /tmp/madlib_xPHVhBTPfU\n",
"PoolWorker-755: Removed temporary directory /tmp/madlib_h3E0LErtQL\n",
"PoolWorker-759: Removed temporary directory /tmp/madlib_IeTN8KzDHF\n",
"PoolWorker-758: Removed temporary directory /tmp/madlib_s62VfDOPNC\n",
"PoolWorker-757: Removed temporary directory /tmp/madlib_wtmIXtxPzr\n",
"PoolWorker-754: Removed temporary directory /tmp/madlib_E1lkeTo3uE\n",
"PoolWorker-760: Removed temporary directory /tmp/madlib_m7ZyJIuZdV\n",
"PoolWorker-753: Removed temporary directory /tmp/madlib_HImKT4pg1B\n",
"Done! Loaded 500 images in 318.991536856s\n",
"8 workers terminated.\n",
"Chunk: 96/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-761 [pid 1955]\n",
"PoolWorker-761: Created temporary directory /tmp/madlib_nws7sMUEy6\n",
"Initializing PoolWorker-762 [pid 1956]\n",
"PoolWorker-762: Created temporary directory /tmp/madlib_iKjPZVTPaG\n",
"Initializing PoolWorker-763 [pid 1957]\n",
"PoolWorker-763: Created temporary directory /tmp/madlib_I5xo6I3X17\n",
"Initializing PoolWorker-764 [pid 1958]\n",
"PoolWorker-764: Created temporary directory /tmp/madlib_jayzF7HR86\n",
"Initializing PoolWorker-765 [pid 1959]\n",
"PoolWorker-765: Created temporary directory /tmp/madlib_virkdg6zTm\n",
"Initializing PoolWorker-766 [pid 1960]\n",
"PoolWorker-766: Created temporary directory /tmp/madlib_OhaWnv4yml\n",
"Initializing PoolWorker-767 [pid 1961]\n",
"PoolWorker-768: Connected to madlib db.\n",
"PoolWorker-767: Created temporary directory /tmp/madlib_eJhBMUwTQ4\n",
"Initializing PoolWorker-768 [pid 1962]\n",
"PoolWorker-768: Created temporary directory /tmp/madlib_eeWNIZniUq\n",
"PoolWorker-761: Connected to madlib db.\n",
"PoolWorker-762: Connected to madlib db.\n",
"PoolWorker-763: Connected to madlib db.\n",
"PoolWorker-764: Connected to madlib db.\n",
"PoolWorker-765: Connected to madlib db.\n",
"PoolWorker-766: Connected to madlib db.\n",
"PoolWorker-767: Connected to madlib db.\n",
"PoolWorker-761: Wrote 500 images to /tmp/madlib_nws7sMUEy6/imagenet_validation_data0000.tmp\n",
"PoolWorker-761: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-762: Removed temporary directory /tmp/madlib_iKjPZVTPaG\n",
"PoolWorker-765: Removed temporary directory /tmp/madlib_virkdg6zTm\n",
"PoolWorker-763: Removed temporary directory /tmp/madlib_I5xo6I3X17\n",
"PoolWorker-766: Removed temporary directory /tmp/madlib_OhaWnv4yml\n",
"PoolWorker-764: Removed temporary directory /tmp/madlib_jayzF7HR86\n",
"PoolWorker-767: Removed temporary directory /tmp/madlib_eJhBMUwTQ4\n",
"PoolWorker-768: Removed temporary directory /tmp/madlib_eeWNIZniUq\n",
"PoolWorker-761: Removed temporary directory /tmp/madlib_nws7sMUEy6\n",
"Done! Loaded 500 images in 303.105370998s\n",
"8 workers terminated.\n",
"Chunk: 97/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-769 [pid 2563]\n",
"PoolWorker-769: Created temporary directory /tmp/madlib_x7gYR7lXs0\n",
"Initializing PoolWorker-770 [pid 2564]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PoolWorker-770: Created temporary directory /tmp/madlib_YXphViiByR\n",
"Initializing PoolWorker-771 [pid 2565]\n",
"PoolWorker-771: Created temporary directory /tmp/madlib_z0IapYbC9f\n",
"Initializing PoolWorker-772 [pid 2566]\n",
"PoolWorker-772: Created temporary directory /tmp/madlib_hdlVHqeCx1\n",
"Initializing PoolWorker-773 [pid 2567]\n",
"PoolWorker-773: Created temporary directory /tmp/madlib_FVOG97jRbl\n",
"Initializing PoolWorker-774 [pid 2569]\n",
"PoolWorker-774: Created temporary directory /tmp/madlib_iBR2cQlvCm\n",
"Initializing PoolWorker-775 [pid 2570]\n",
"PoolWorker-776: Connected to madlib db.\n",
"PoolWorker-775: Created temporary directory /tmp/madlib_wkXqwmbBqF\n",
"Initializing PoolWorker-776 [pid 2571]\n",
"PoolWorker-776: Created temporary directory /tmp/madlib_NExsGcBjzD\n",
"PoolWorker-769: Connected to madlib db.\n",
"PoolWorker-770: Connected to madlib db.\n",
"PoolWorker-771: Connected to madlib db.\n",
"PoolWorker-772: Connected to madlib db.\n",
"PoolWorker-773: Connected to madlib db.\n",
"PoolWorker-774: Connected to madlib db.\n",
"PoolWorker-775: Connected to madlib db.\n",
"PoolWorker-769: Wrote 500 images to /tmp/madlib_x7gYR7lXs0/imagenet_validation_data0000.tmp\n",
"PoolWorker-769: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-771: Removed temporary directory /tmp/madlib_z0IapYbC9f\n",
"PoolWorker-773: Removed temporary directory /tmp/madlib_FVOG97jRbl\n",
"PoolWorker-770: Removed temporary directory /tmp/madlib_YXphViiByR\n",
"PoolWorker-772: Removed temporary directory /tmp/madlib_hdlVHqeCx1\n",
"PoolWorker-775: Removed temporary directory /tmp/madlib_wkXqwmbBqF\n",
"PoolWorker-774: Removed temporary directory /tmp/madlib_iBR2cQlvCm\n",
"PoolWorker-776: Removed temporary directory /tmp/madlib_NExsGcBjzD\n",
"PoolWorker-769: Removed temporary directory /tmp/madlib_x7gYR7lXs0\n",
"Done! Loaded 500 images in 328.722644091s\n",
"8 workers terminated.\n",
"Chunk: 98/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-777 [pid 2587]\n",
"PoolWorker-777: Created temporary directory /tmp/madlib_EC1jKX0Ql8\n",
"Initializing PoolWorker-778 [pid 2588]\n",
"PoolWorker-778: Created temporary directory /tmp/madlib_lOenAOdzA2\n",
"Initializing PoolWorker-779 [pid 2589]\n",
"PoolWorker-779: Created temporary directory /tmp/madlib_o03e7eTVG5\n",
"Initializing PoolWorker-780 [pid 2590]\n",
"PoolWorker-780: Created temporary directory /tmp/madlib_sDmeIWCgm6\n",
"Initializing PoolWorker-781 [pid 2592]\n",
"PoolWorker-781: Created temporary directory /tmp/madlib_CyCHsGlooi\n",
"Initializing PoolWorker-782 [pid 2593]\n",
"PoolWorker-782: Created temporary directory /tmp/madlib_o3Fq2G4t7F\n",
"Initializing PoolWorker-783 [pid 2594]\n",
"PoolWorker-784: Connected to madlib db.\n",
"PoolWorker-783: Created temporary directory /tmp/madlib_yrHLGo2lWO\n",
"Initializing PoolWorker-784 [pid 2595]\n",
"PoolWorker-784: Created temporary directory /tmp/madlib_VP1seVdtkq\n",
"PoolWorker-777: Connected to madlib db.\n",
"PoolWorker-778: Connected to madlib db.\n",
"PoolWorker-779: Connected to madlib db.\n",
"PoolWorker-780: Connected to madlib db.\n",
"PoolWorker-781: Connected to madlib db.\n",
"PoolWorker-782: Connected to madlib db.\n",
"PoolWorker-783: Connected to madlib db.\n",
"PoolWorker-777: Wrote 500 images to /tmp/madlib_EC1jKX0Ql8/imagenet_validation_data0000.tmp\n",
"PoolWorker-777: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-782: Removed temporary directory /tmp/madlib_o3Fq2G4t7F\n",
"PoolWorker-784: Removed temporary directory /tmp/madlib_VP1seVdtkq\n",
"PoolWorker-778: Removed temporary directory /tmp/madlib_lOenAOdzA2\n",
"PoolWorker-780: Removed temporary directory /tmp/madlib_sDmeIWCgm6\n",
"PoolWorker-779: Removed temporary directory /tmp/madlib_o03e7eTVG5\n",
"PoolWorker-783: Removed temporary directory /tmp/madlib_yrHLGo2lWO\n",
"PoolWorker-781: Removed temporary directory /tmp/madlib_CyCHsGlooi\n",
"PoolWorker-777: Removed temporary directory /tmp/madlib_EC1jKX0Ql8\n",
"Done! Loaded 500 images in 316.947548866s\n",
"8 workers terminated.\n",
"Chunk: 99/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-785 [pid 2611]\n",
"PoolWorker-785: Created temporary directory /tmp/madlib_BEaYjoVTNy\n",
"Initializing PoolWorker-786 [pid 2612]\n",
"PoolWorker-786: Created temporary directory /tmp/madlib_FUpjUCkvSH\n",
"Initializing PoolWorker-787 [pid 2613]\n",
"PoolWorker-787: Created temporary directory /tmp/madlib_XwqGnyR0n7\n",
"Initializing PoolWorker-788 [pid 2614]\n",
"PoolWorker-788: Created temporary directory /tmp/madlib_Kza1wjD3Jy\n",
"Initializing PoolWorker-789 [pid 2615]\n",
"PoolWorker-789: Created temporary directory /tmp/madlib_f8lcKQF3tc\n",
"Initializing PoolWorker-790 [pid 2616]\n",
"PoolWorker-790: Created temporary directory /tmp/madlib_P3uhT3X0fu\n",
"Initializing PoolWorker-791 [pid 2617]\n",
"PoolWorker-792: Connected to madlib db.\n",
"PoolWorker-791: Created temporary directory /tmp/madlib_aAVvKSZkWn\n",
"Initializing PoolWorker-792 [pid 2618]\n",
"PoolWorker-792: Created temporary directory /tmp/madlib_KpbHJNv8tp\n",
"PoolWorker-785: Connected to madlib db.\n",
"PoolWorker-786: Connected to madlib db.\n",
"PoolWorker-787: Connected to madlib db.\n",
"PoolWorker-788: Connected to madlib db.\n",
"PoolWorker-789: Connected to madlib db.\n",
"PoolWorker-790: Connected to madlib db.\n",
"PoolWorker-791: Connected to madlib db.\n",
"PoolWorker-785: Wrote 500 images to /tmp/madlib_BEaYjoVTNy/imagenet_validation_data0000.tmp\n",
"PoolWorker-785: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-789: Removed temporary directory /tmp/madlib_f8lcKQF3tc\n",
"PoolWorker-792: Removed temporary directory /tmp/madlib_KpbHJNv8tp\n",
"PoolWorker-790: Removed temporary directory /tmp/madlib_P3uhT3X0fu\n",
"PoolWorker-788: Removed temporary directory /tmp/madlib_Kza1wjD3Jy\n",
"PoolWorker-787: Removed temporary directory /tmp/madlib_XwqGnyR0n7\n",
"PoolWorker-786: Removed temporary directory /tmp/madlib_FUpjUCkvSH\n",
"PoolWorker-791: Removed temporary directory /tmp/madlib_aAVvKSZkWn\n",
"PoolWorker-785: Removed temporary directory /tmp/madlib_BEaYjoVTNy\n",
"Done! Loaded 500 images in 315.309365034s\n",
"8 workers terminated.\n",
"Chunk: 100/100\n",
"MainProcess: Connected to madlib db.\n",
"Appending to table imagenet_validation_data in madlib db\n",
"Spawning 8 workers...\n",
"Initializing PoolWorker-793 [pid 2631]\n",
"PoolWorker-793: Created temporary directory /tmp/madlib_nPYFSXKJd7\n",
"Initializing PoolWorker-794 [pid 2632]\n",
"PoolWorker-794: Created temporary directory /tmp/madlib_BHczPibO7R\n",
"Initializing PoolWorker-795 [pid 2633]\n",
"PoolWorker-795: Created temporary directory /tmp/madlib_yGfMO0BWZE\n",
"Initializing PoolWorker-796 [pid 2634]\n",
"PoolWorker-796: Created temporary directory /tmp/madlib_PLH4lYSR8O\n",
"Initializing PoolWorker-797 [pid 2635]\n",
"PoolWorker-797: Created temporary directory /tmp/madlib_TmsisOWIGL\n",
"Initializing PoolWorker-798 [pid 2636]\n",
"PoolWorker-798: Created temporary directory /tmp/madlib_zrjr8oRCy0\n",
"PoolWorker-800: Connected to madlib db.\n",
"Initializing PoolWorker-799 [pid 2637]\n",
"PoolWorker-799: Created temporary directory /tmp/madlib_z0tnGc2BQ7\n",
"Initializing PoolWorker-800 [pid 2638]\n",
"PoolWorker-800: Created temporary directory /tmp/madlib_kuOJTPMn9i\n",
"PoolWorker-793: Connected to madlib db.\n",
"PoolWorker-794: Connected to madlib db.\n",
"PoolWorker-795: Connected to madlib db.\n",
"PoolWorker-796: Connected to madlib db.\n",
"PoolWorker-797: Connected to madlib db.\n",
"PoolWorker-798: Connected to madlib db.\n",
"PoolWorker-799: Connected to madlib db.\n",
"PoolWorker-793: Wrote 500 images to /tmp/madlib_nPYFSXKJd7/imagenet_validation_data0000.tmp\n",
"PoolWorker-793: Loaded 500 images into imagenet_validation_data\n",
"PoolWorker-795: Removed temporary directory /tmp/madlib_yGfMO0BWZE\n",
"PoolWorker-796: Removed temporary directory /tmp/madlib_PLH4lYSR8O\n",
"PoolWorker-794: Removed temporary directory /tmp/madlib_BHczPibO7R\n",
"PoolWorker-797: Removed temporary directory /tmp/madlib_TmsisOWIGL\n",
"PoolWorker-799: Removed temporary directory /tmp/madlib_z0tnGc2BQ7\n",
"PoolWorker-798: Removed temporary directory /tmp/madlib_zrjr8oRCy0\n",
"PoolWorker-800: Removed temporary directory /tmp/madlib_kuOJTPMn9i\n",
"PoolWorker-793: Removed temporary directory /tmp/madlib_nPYFSXKJd7\n",
"Done! Loaded 500 images in 317.671030998s\n",
"8 workers terminated.\n"
]
}
],
"source": [
"from scipy.io import loadmat\n",
"from keras.preprocessing import image\n",
"from keras.applications.vgg16 import preprocess_input # Only import the model-specific preprocessing function\n",
"import glob\n",
"import os\n",
"import numpy as np\n",
"\n",
"CAFFE_LABELS = {\"0\": [\"n01440764\", \"tench\"], \"1\": [\"n01443537\", \"goldfish\"], \"2\": [\"n01484850\", \"great_white_shark\"], \"3\": [\"n01491361\", \"tiger_shark\"], \"4\": [\"n01494475\", \"hammerhead\"], \"5\": [\"n01496331\", \"electric_ray\"], \"6\": [\"n01498041\", \"stingray\"], \"7\": [\"n01514668\", \"cock\"], \"8\": [\"n01514859\", \"hen\"], \"9\": [\"n01518878\", \"ostrich\"], \"10\": [\"n01530575\", \"brambling\"], \"11\": [\"n01531178\", \"goldfinch\"], \"12\": [\"n01532829\", \"house_finch\"], \"13\": [\"n01534433\", \"junco\"], \"14\": [\"n01537544\", \"indigo_bunting\"], \"15\": [\"n01558993\", \"robin\"], \"16\": [\"n01560419\", \"bulbul\"], \"17\": [\"n01580077\", \"jay\"], \"18\": [\"n01582220\", \"magpie\"], \"19\": [\"n01592084\", \"chickadee\"], \"20\": [\"n01601694\", \"water_ouzel\"], \"21\": [\"n01608432\", \"kite\"], \"22\": [\"n01614925\", \"bald_eagle\"], \"23\": [\"n01616318\", \"vulture\"], \"24\": [\"n01622779\", \"great_grey_owl\"], \"25\": [\"n01629819\", \"European_fire_salamander\"], \"26\": [\"n01630670\", \"common_newt\"], \"27\": [\"n01631663\", \"eft\"], \"28\": [\"n01632458\", \"spotted_salamander\"], \"29\": [\"n01632777\", \"axolotl\"], \"30\": [\"n01641577\", \"bullfrog\"], \"31\": [\"n01644373\", \"tree_frog\"], \"32\": [\"n01644900\", \"tailed_frog\"], \"33\": [\"n01664065\", \"loggerhead\"], \"34\": [\"n01665541\", \"leatherback_turtle\"], \"35\": [\"n01667114\", \"mud_turtle\"], \"36\": [\"n01667778\", \"terrapin\"], \"37\": [\"n01669191\", \"box_turtle\"], \"38\": [\"n01675722\", \"banded_gecko\"], \"39\": [\"n01677366\", \"common_iguana\"], \"40\": [\"n01682714\", \"American_chameleon\"], \"41\": [\"n01685808\", \"whiptail\"], \"42\": [\"n01687978\", \"agama\"], \"43\": [\"n01688243\", \"frilled_lizard\"], \"44\": [\"n01689811\", \"alligator_lizard\"], \"45\": [\"n01692333\", \"Gila_monster\"], \"46\": [\"n01693334\", \"green_lizard\"], \"47\": [\"n01694178\", \"African_chameleon\"], \"48\": [\"n01695060\", \"Komodo_dragon\"], \"49\": [\"n01697457\", \"African_crocodile\"], \"50\": [\"n01698640\", \"American_alligator\"], \"51\": [\"n01704323\", \"triceratops\"], \"52\": [\"n01728572\", \"thunder_snake\"], \"53\": [\"n01728920\", \"ringneck_snake\"], \"54\": [\"n01729322\", \"hognose_snake\"], \"55\": [\"n01729977\", \"green_snake\"], \"56\": [\"n01734418\", \"king_snake\"], \"57\": [\"n01735189\", \"garter_snake\"], \"58\": [\"n01737021\", \"water_snake\"], \"59\": [\"n01739381\", \"vine_snake\"], \"60\": [\"n01740131\", \"night_snake\"], \"61\": [\"n01742172\", \"boa_constrictor\"], \"62\": [\"n01744401\", \"rock_python\"], \"63\": [\"n01748264\", \"Indian_cobra\"], \"64\": [\"n01749939\", \"green_mamba\"], \"65\": [\"n01751748\", \"sea_snake\"], \"66\": [\"n01753488\", \"horned_viper\"], \"67\": [\"n01755581\", \"diamondback\"], \"68\": [\"n01756291\", \"sidewinder\"], \"69\": [\"n01768244\", \"trilobite\"], \"70\": [\"n01770081\", \"harvestman\"], \"71\": [\"n01770393\", \"scorpion\"], \"72\": [\"n01773157\", \"black_and_gold_garden_spider\"], \"73\": [\"n01773549\", \"barn_spider\"], \"74\": [\"n01773797\", \"garden_spider\"], \"75\": [\"n01774384\", \"black_widow\"], \"76\": [\"n01774750\", \"tarantula\"], \"77\": [\"n01775062\", \"wolf_spider\"], \"78\": [\"n01776313\", \"tick\"], \"79\": [\"n01784675\", \"centipede\"], \"80\": [\"n01795545\", \"black_grouse\"], \"81\": [\"n01796340\", \"ptarmigan\"], \"82\": [\"n01797886\", \"ruffed_grouse\"], \"83\": [\"n01798484\", \"prairie_chicken\"], \"84\": [\"n01806143\", \"peacock\"], \"85\": [\"n01806567\", \"quail\"], \"86\": [\"n01807496\", \"partridge\"], \"87\": [\"n01817953\", \"African_grey\"], \"88\": [\"n01818515\", \"macaw\"], \"89\": [\"n01819313\", \"sulphur-crested_cockatoo\"], \"90\": [\"n01820546\", \"lorikeet\"], \"91\": [\"n01824575\", \"coucal\"], \"92\": [\"n01828970\", \"bee_eater\"], \"93\": [\"n01829413\", \"hornbill\"], \"94\": [\"n01833805\", \"hummingbird\"], \"95\": [\"n01843065\", \"jacamar\"], \"96\": [\"n01843383\", \"toucan\"], \"97\": [\"n01847000\", \"drake\"], \"98\": [\"n01855032\", \"red-breasted_merganser\"], \"99\": [\"n01855672\", \"goose\"], \"100\": [\"n01860187\", \"black_swan\"], \"101\": [\"n01871265\", \"tusker\"], \"102\": [\"n01872401\", \"echidna\"], \"103\": [\"n01873310\", \"platypus\"], \"104\": [\"n01877812\", \"wallaby\"], \"105\": [\"n01882714\", \"koala\"], \"106\": [\"n01883070\", \"wombat\"], \"107\": [\"n01910747\", \"jellyfish\"], \"108\": [\"n01914609\", \"sea_anemone\"], \"109\": [\"n01917289\", \"brain_coral\"], \"110\": [\"n01924916\", \"flatworm\"], \"111\": [\"n01930112\", \"nematode\"], \"112\": [\"n01943899\", \"conch\"], \"113\": [\"n01944390\", \"snail\"], \"114\": [\"n01945685\", \"slug\"], \"115\": [\"n01950731\", \"sea_slug\"], \"116\": [\"n01955084\", \"chiton\"], \"117\": [\"n01968897\", \"chambered_nautilus\"], \"118\": [\"n01978287\", \"Dungeness_crab\"], \"119\": [\"n01978455\", \"rock_crab\"], \"120\": [\"n01980166\", \"fiddler_crab\"], \"121\": [\"n01981276\", \"king_crab\"], \"122\": [\"n01983481\", \"American_lobster\"], \"123\": [\"n01984695\", \"spiny_lobster\"], \"124\": [\"n01985128\", \"crayfish\"], \"125\": [\"n01986214\", \"hermit_crab\"], \"126\": [\"n01990800\", \"isopod\"], \"127\": [\"n02002556\", \"white_stork\"], \"128\": [\"n02002724\", \"black_stork\"], \"129\": [\"n02006656\", \"spoonbill\"], \"130\": [\"n02007558\", \"flamingo\"], \"131\": [\"n02009229\", \"little_blue_heron\"], \"132\": [\"n02009912\", \"American_egret\"], \"133\": [\"n02011460\", \"bittern\"], \"134\": [\"n02012849\", \"crane\"], \"135\": [\"n02013706\", \"limpkin\"], \"136\": [\"n02017213\", \"European_gallinule\"], \"137\": [\"n02018207\", \"American_coot\"], \"138\": [\"n02018795\", \"bustard\"], \"139\": [\"n02025239\", \"ruddy_turnstone\"], \"140\": [\"n02027492\", \"red-backed_sandpiper\"], \"141\": [\"n02028035\", \"redshank\"], \"142\": [\"n02033041\", \"dowitcher\"], \"143\": [\"n02037110\", \"oystercatcher\"], \"144\": [\"n02051845\", \"pelican\"], \"145\": [\"n02056570\", \"king_penguin\"], \"146\": [\"n02058221\", \"albatross\"], \"147\": [\"n02066245\", \"grey_whale\"], \"148\": [\"n02071294\", \"killer_whale\"], \"149\": [\"n02074367\", \"dugong\"], \"150\": [\"n02077923\", \"sea_lion\"], \"151\": [\"n02085620\", \"Chihuahua\"], \"152\": [\"n02085782\", \"Japanese_spaniel\"], \"153\": [\"n02085936\", \"Maltese_dog\"], \"154\": [\"n02086079\", \"Pekinese\"], \"155\": [\"n02086240\", \"Shih-Tzu\"], \"156\": [\"n02086646\", \"Blenheim_spaniel\"], \"157\": [\"n02086910\", \"papillon\"], \"158\": [\"n02087046\", \"toy_terrier\"], \"159\": [\"n02087394\", \"Rhodesian_ridgeback\"], \"160\": [\"n02088094\", \"Afghan_hound\"], \"161\": [\"n02088238\", \"basset\"], \"162\": [\"n02088364\", \"beagle\"], \"163\": [\"n02088466\", \"bloodhound\"], \"164\": [\"n02088632\", \"bluetick\"], \"165\": [\"n02089078\", \"black-and-tan_coonhound\"], \"166\": [\"n02089867\", \"Walker_hound\"], \"167\": [\"n02089973\", \"English_foxhound\"], \"168\": [\"n02090379\", \"redbone\"], \"169\": [\"n02090622\", \"borzoi\"], \"170\": [\"n02090721\", \"Irish_wolfhound\"], \"171\": [\"n02091032\", \"Italian_greyhound\"], \"172\": [\"n02091134\", \"whippet\"], \"173\": [\"n02091244\", \"Ibizan_hound\"], \"174\": [\"n02091467\", \"Norwegian_elkhound\"], \"175\": [\"n02091635\", \"otterhound\"], \"176\": [\"n02091831\", \"Saluki\"], \"177\": [\"n02092002\", \"Scottish_deerhound\"], \"178\": [\"n02092339\", \"Weimaraner\"], \"179\": [\"n02093256\", \"Staffordshire_bullterrier\"], \"180\": [\"n02093428\", \"American_Staffordshire_terrier\"], \"181\": [\"n02093647\", \"Bedlington_terrier\"], \"182\": [\"n02093754\", \"Border_terrier\"], \"183\": [\"n02093859\", \"Kerry_blue_terrier\"], \"184\": [\"n02093991\", \"Irish_terrier\"], \"185\": [\"n02094114\", \"Norfolk_terrier\"], \"186\": [\"n02094258\", \"Norwich_terrier\"], \"187\": [\"n02094433\", \"Yorkshire_terrier\"], \"188\": [\"n02095314\", \"wire-haired_fox_terrier\"], \"189\": [\"n02095570\", \"Lakeland_terrier\"], \"190\": [\"n02095889\", \"Sealyham_terrier\"], \"191\": [\"n02096051\", \"Airedale\"], \"192\": [\"n02096177\", \"cairn\"], \"193\": [\"n02096294\", \"Australian_terrier\"], \"194\": [\"n02096437\", \"Dandie_Dinmont\"], \"195\": [\"n02096585\", \"Boston_bull\"], \"196\": [\"n02097047\", \"miniature_schnauzer\"], \"197\": [\"n02097130\", \"giant_schnauzer\"], \"198\": [\"n02097209\", \"standard_schnauzer\"], \"199\": [\"n02097298\", \"Scotch_terrier\"], \"200\": [\"n02097474\", \"Tibetan_terrier\"], \"201\": [\"n02097658\", \"silky_terrier\"], \"202\": [\"n02098105\", \"soft-coated_wheaten_terrier\"], \"203\": [\"n02098286\", \"West_Highland_white_terrier\"], \"204\": [\"n02098413\", \"Lhasa\"], \"205\": [\"n02099267\", \"flat-coated_retriever\"], \"206\": [\"n02099429\", \"curly-coated_retriever\"], \"207\": [\"n02099601\", \"golden_retriever\"], \"208\": [\"n02099712\", \"Labrador_retriever\"], \"209\": [\"n02099849\", \"Chesapeake_Bay_retriever\"], \"210\": [\"n02100236\", \"German_short-haired_pointer\"], \"211\": [\"n02100583\", \"vizsla\"], \"212\": [\"n02100735\", \"English_setter\"], \"213\": [\"n02100877\", \"Irish_setter\"], \"214\": [\"n02101006\", \"Gordon_setter\"], \"215\": [\"n02101388\", \"Brittany_spaniel\"], \"216\": [\"n02101556\", \"clumber\"], \"217\": [\"n02102040\", \"English_springer\"], \"218\": [\"n02102177\", \"Welsh_springer_spaniel\"], \"219\": [\"n02102318\", \"cocker_spaniel\"], \"220\": [\"n02102480\", \"Sussex_spaniel\"], \"221\": [\"n02102973\", \"Irish_water_spaniel\"], \"222\": [\"n02104029\", \"kuvasz\"], \"223\": [\"n02104365\", \"schipperke\"], \"224\": [\"n02105056\", \"groenendael\"], \"225\": [\"n02105162\", \"malinois\"], \"226\": [\"n02105251\", \"briard\"], \"227\": [\"n02105412\", \"kelpie\"], \"228\": [\"n02105505\", \"komondor\"], \"229\": [\"n02105641\", \"Old_English_sheepdog\"], \"230\": [\"n02105855\", \"Shetland_sheepdog\"], \"231\": [\"n02106030\", \"collie\"], \"232\": [\"n02106166\", \"Border_collie\"], \"233\": [\"n02106382\", \"Bouvier_des_Flandres\"], \"234\": [\"n02106550\", \"Rottweiler\"], \"235\": [\"n02106662\", \"German_shepherd\"], \"236\": [\"n02107142\", \"Doberman\"], \"237\": [\"n02107312\", \"miniature_pinscher\"], \"238\": [\"n02107574\", \"Greater_Swiss_Mountain_dog\"], \"239\": [\"n02107683\", \"Bernese_mountain_dog\"], \"240\": [\"n02107908\", \"Appenzeller\"], \"241\": [\"n02108000\", \"EntleBucher\"], \"242\": [\"n02108089\", \"boxer\"], \"243\": [\"n02108422\", \"bull_mastiff\"], \"244\": [\"n02108551\", \"Tibetan_mastiff\"], \"245\": [\"n02108915\", \"French_bulldog\"], \"246\": [\"n02109047\", \"Great_Dane\"], \"247\": [\"n02109525\", \"Saint_Bernard\"], \"248\": [\"n02109961\", \"Eskimo_dog\"], \"249\": [\"n02110063\", \"malamute\"], \"250\": [\"n02110185\", \"Siberian_husky\"], \"251\": [\"n02110341\", \"dalmatian\"], \"252\": [\"n02110627\", \"affenpinscher\"], \"253\": [\"n02110806\", \"basenji\"], \"254\": [\"n02110958\", \"pug\"], \"255\": [\"n02111129\", \"Leonberg\"], \"256\": [\"n02111277\", \"Newfoundland\"], \"257\": [\"n02111500\", \"Great_Pyrenees\"], \"258\": [\"n02111889\", \"Samoyed\"], \"259\": [\"n02112018\", \"Pomeranian\"], \"260\": [\"n02112137\", \"chow\"], \"261\": [\"n02112350\", \"keeshond\"], \"262\": [\"n02112706\", \"Brabancon_griffon\"], \"263\": [\"n02113023\", \"Pembroke\"], \"264\": [\"n02113186\", \"Cardigan\"], \"265\": [\"n02113624\", \"toy_poodle\"], \"266\": [\"n02113712\", \"miniature_poodle\"], \"267\": [\"n02113799\", \"standard_poodle\"], \"268\": [\"n02113978\", \"Mexican_hairless\"], \"269\": [\"n02114367\", \"timber_wolf\"], \"270\": [\"n02114548\", \"white_wolf\"], \"271\": [\"n02114712\", \"red_wolf\"], \"272\": [\"n02114855\", \"coyote\"], \"273\": [\"n02115641\", \"dingo\"], \"274\": [\"n02115913\", \"dhole\"], \"275\": [\"n02116738\", \"African_hunting_dog\"], \"276\": [\"n02117135\", \"hyena\"], \"277\": [\"n02119022\", \"red_fox\"], \"278\": [\"n02119789\", \"kit_fox\"], \"279\": [\"n02120079\", \"Arctic_fox\"], \"280\": [\"n02120505\", \"grey_fox\"], \"281\": [\"n02123045\", \"tabby\"], \"282\": [\"n02123159\", \"tiger_cat\"], \"283\": [\"n02123394\", \"Persian_cat\"], \"284\": [\"n02123597\", \"Siamese_cat\"], \"285\": [\"n02124075\", \"Egyptian_cat\"], \"286\": [\"n02125311\", \"cougar\"], \"287\": [\"n02127052\", \"lynx\"], \"288\": [\"n02128385\", \"leopard\"], \"289\": [\"n02128757\", \"snow_leopard\"], \"290\": [\"n02128925\", \"jaguar\"], \"291\": [\"n02129165\", \"lion\"], \"292\": [\"n02129604\", \"tiger\"], \"293\": [\"n02130308\", \"cheetah\"], \"294\": [\"n02132136\", \"brown_bear\"], \"295\": [\"n02133161\", \"American_black_bear\"], \"296\": [\"n02134084\", \"ice_bear\"], \"297\": [\"n02134418\", \"sloth_bear\"], \"298\": [\"n02137549\", \"mongoose\"], \"299\": [\"n02138441\", \"meerkat\"], \"300\": [\"n02165105\", \"tiger_beetle\"], 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[\"n02281406\", \"sulphur_butterfly\"], \"326\": [\"n02281787\", \"lycaenid\"], \"327\": [\"n02317335\", \"starfish\"], \"328\": [\"n02319095\", \"sea_urchin\"], \"329\": [\"n02321529\", \"sea_cucumber\"], \"330\": [\"n02325366\", \"wood_rabbit\"], \"331\": [\"n02326432\", \"hare\"], \"332\": [\"n02328150\", \"Angora\"], \"333\": [\"n02342885\", \"hamster\"], \"334\": [\"n02346627\", \"porcupine\"], \"335\": [\"n02356798\", \"fox_squirrel\"], \"336\": [\"n02361337\", \"marmot\"], \"337\": [\"n02363005\", \"beaver\"], \"338\": [\"n02364673\", \"guinea_pig\"], \"339\": [\"n02389026\", \"sorrel\"], \"340\": [\"n02391049\", \"zebra\"], \"341\": [\"n02395406\", \"hog\"], \"342\": [\"n02396427\", \"wild_boar\"], \"343\": [\"n02397096\", \"warthog\"], \"344\": [\"n02398521\", \"hippopotamus\"], \"345\": [\"n02403003\", \"ox\"], \"346\": [\"n02408429\", \"water_buffalo\"], \"347\": [\"n02410509\", \"bison\"], \"348\": [\"n02412080\", \"ram\"], \"349\": [\"n02415577\", \"bighorn\"], \"350\": [\"n02417914\", \"ibex\"], \"351\": [\"n02422106\", \"hartebeest\"], \"352\": [\"n02422699\", \"impala\"], \"353\": [\"n02423022\", \"gazelle\"], \"354\": [\"n02437312\", \"Arabian_camel\"], \"355\": [\"n02437616\", \"llama\"], \"356\": [\"n02441942\", \"weasel\"], \"357\": [\"n02442845\", \"mink\"], \"358\": [\"n02443114\", \"polecat\"], \"359\": [\"n02443484\", \"black-footed_ferret\"], \"360\": [\"n02444819\", \"otter\"], \"361\": [\"n02445715\", \"skunk\"], \"362\": [\"n02447366\", \"badger\"], \"363\": [\"n02454379\", \"armadillo\"], \"364\": [\"n02457408\", \"three-toed_sloth\"], \"365\": [\"n02480495\", \"orangutan\"], \"366\": [\"n02480855\", \"gorilla\"], \"367\": [\"n02481823\", \"chimpanzee\"], \"368\": [\"n02483362\", \"gibbon\"], \"369\": [\"n02483708\", \"siamang\"], \"370\": [\"n02484975\", \"guenon\"], \"371\": [\"n02486261\", \"patas\"], \"372\": [\"n02486410\", \"baboon\"], \"373\": [\"n02487347\", \"macaque\"], \"374\": [\"n02488291\", \"langur\"], \"375\": [\"n02488702\", \"colobus\"], \"376\": [\"n02489166\", \"proboscis_monkey\"], \"377\": [\"n02490219\", \"marmoset\"], \"378\": [\"n02492035\", \"capuchin\"], \"379\": [\"n02492660\", \"howler_monkey\"], \"380\": [\"n02493509\", \"titi\"], \"381\": [\"n02493793\", \"spider_monkey\"], \"382\": [\"n02494079\", \"squirrel_monkey\"], \"383\": [\"n02497673\", \"Madagascar_cat\"], \"384\": [\"n02500267\", \"indri\"], \"385\": [\"n02504013\", \"Indian_elephant\"], \"386\": [\"n02504458\", \"African_elephant\"], \"387\": [\"n02509815\", \"lesser_panda\"], \"388\": [\"n02510455\", \"giant_panda\"], \"389\": [\"n02514041\", \"barracouta\"], \"390\": [\"n02526121\", \"eel\"], \"391\": [\"n02536864\", \"coho\"], \"392\": [\"n02606052\", \"rock_beauty\"], \"393\": [\"n02607072\", \"anemone_fish\"], \"394\": [\"n02640242\", \"sturgeon\"], \"395\": [\"n02641379\", \"gar\"], \"396\": [\"n02643566\", \"lionfish\"], \"397\": [\"n02655020\", \"puffer\"], \"398\": [\"n02666196\", \"abacus\"], \"399\": [\"n02667093\", \"abaya\"], \"400\": [\"n02669723\", \"academic_gown\"], \"401\": [\"n02672831\", \"accordion\"], \"402\": [\"n02676566\", \"acoustic_guitar\"], \"403\": [\"n02687172\", \"aircraft_carrier\"], \"404\": [\"n02690373\", \"airliner\"], \"405\": [\"n02692877\", \"airship\"], \"406\": [\"n02699494\", \"altar\"], \"407\": [\"n02701002\", \"ambulance\"], \"408\": [\"n02704792\", \"amphibian\"], \"409\": [\"n02708093\", \"analog_clock\"], \"410\": [\"n02727426\", \"apiary\"], \"411\": [\"n02730930\", \"apron\"], \"412\": [\"n02747177\", \"ashcan\"], \"413\": [\"n02749479\", \"assault_rifle\"], \"414\": [\"n02769748\", \"backpack\"], \"415\": [\"n02776631\", \"bakery\"], \"416\": [\"n02777292\", \"balance_beam\"], \"417\": [\"n02782093\", \"balloon\"], \"418\": [\"n02783161\", \"ballpoint\"], \"419\": [\"n02786058\", \"Band_Aid\"], \"420\": [\"n02787622\", \"banjo\"], \"421\": [\"n02788148\", \"bannister\"], \"422\": [\"n02790996\", \"barbell\"], \"423\": [\"n02791124\", \"barber_chair\"], \"424\": [\"n02791270\", \"barbershop\"], \"425\": [\"n02793495\", \"barn\"], \"426\": [\"n02794156\", \"barometer\"], \"427\": [\"n02795169\", \"barrel\"], \"428\": [\"n02797295\", \"barrow\"], \"429\": [\"n02799071\", \"baseball\"], \"430\": [\"n02802426\", \"basketball\"], \"431\": [\"n02804414\", \"bassinet\"], \"432\": [\"n02804610\", \"bassoon\"], \"433\": [\"n02807133\", \"bathing_cap\"], \"434\": [\"n02808304\", \"bath_towel\"], \"435\": [\"n02808440\", \"bathtub\"], \"436\": [\"n02814533\", \"beach_wagon\"], \"437\": [\"n02814860\", \"beacon\"], \"438\": [\"n02815834\", \"beaker\"], \"439\": [\"n02817516\", \"bearskin\"], \"440\": [\"n02823428\", \"beer_bottle\"], \"441\": [\"n02823750\", \"beer_glass\"], \"442\": [\"n02825657\", \"bell_cote\"], \"443\": [\"n02834397\", \"bib\"], \"444\": [\"n02835271\", \"bicycle-built-for-two\"], \"445\": [\"n02837789\", \"bikini\"], \"446\": [\"n02840245\", \"binder\"], \"447\": [\"n02841315\", \"binoculars\"], \"448\": [\"n02843684\", \"birdhouse\"], \"449\": [\"n02859443\", \"boathouse\"], \"450\": [\"n02860847\", \"bobsled\"], \"451\": [\"n02865351\", \"bolo_tie\"], \"452\": [\"n02869837\", \"bonnet\"], \"453\": [\"n02870880\", \"bookcase\"], \"454\": [\"n02871525\", \"bookshop\"], \"455\": [\"n02877765\", \"bottlecap\"], \"456\": [\"n02879718\", \"bow\"], \"457\": [\"n02883205\", \"bow_tie\"], \"458\": [\"n02892201\", \"brass\"], \"459\": [\"n02892767\", \"brassiere\"], \"460\": [\"n02894605\", \"breakwater\"], \"461\": [\"n02895154\", \"breastplate\"], \"462\": [\"n02906734\", \"broom\"], \"463\": [\"n02909870\", \"bucket\"], \"464\": [\"n02910353\", \"buckle\"], \"465\": [\"n02916936\", \"bulletproof_vest\"], \"466\": [\"n02917067\", \"bullet_train\"], \"467\": [\"n02927161\", \"butcher_shop\"], \"468\": [\"n02930766\", \"cab\"], \"469\": [\"n02939185\", \"caldron\"], \"470\": [\"n02948072\", \"candle\"], \"471\": [\"n02950826\", \"cannon\"], \"472\": [\"n02951358\", \"canoe\"], \"473\": [\"n02951585\", \"can_opener\"], \"474\": [\"n02963159\", \"cardigan\"], \"475\": [\"n02965783\", \"car_mirror\"], \"476\": [\"n02966193\", \"carousel\"], \"477\": [\"n02966687\", \"carpenter's_kit\"], \"478\": [\"n02971356\", \"carton\"], \"479\": [\"n02974003\", \"car_wheel\"], \"480\": [\"n02977058\", \"cash_machine\"], \"481\": [\"n02978881\", \"cassette\"], \"482\": [\"n02979186\", \"cassette_player\"], \"483\": [\"n02980441\", \"castle\"], \"484\": [\"n02981792\", \"catamaran\"], \"485\": [\"n02988304\", \"CD_player\"], \"486\": [\"n02992211\", \"cello\"], \"487\": [\"n02992529\", \"cellular_telephone\"], \"488\": [\"n02999410\", \"chain\"], \"489\": [\"n03000134\", \"chainlink_fence\"], \"490\": [\"n03000247\", \"chain_mail\"], \"491\": [\"n03000684\", \"chain_saw\"], \"492\": [\"n03014705\", \"chest\"], \"493\": [\"n03016953\", \"chiffonier\"], \"494\": [\"n03017168\", \"chime\"], \"495\": [\"n03018349\", \"china_cabinet\"], \"496\": [\"n03026506\", \"Christmas_stocking\"], \"497\": [\"n03028079\", \"church\"], \"498\": [\"n03032252\", \"cinema\"], \"499\": [\"n03041632\", \"cleaver\"], \"500\": [\"n03042490\", \"cliff_dwelling\"], \"501\": [\"n03045698\", \"cloak\"], \"502\": [\"n03047690\", \"clog\"], \"503\": [\"n03062245\", \"cocktail_shaker\"], \"504\": [\"n03063599\", \"coffee_mug\"], \"505\": [\"n03063689\", \"coffeepot\"], \"506\": [\"n03065424\", \"coil\"], \"507\": [\"n03075370\", \"combination_lock\"], \"508\": [\"n03085013\", \"computer_keyboard\"], \"509\": [\"n03089624\", \"confectionery\"], \"510\": [\"n03095699\", \"container_ship\"], \"511\": [\"n03100240\", \"convertible\"], \"512\": [\"n03109150\", \"corkscrew\"], \"513\": [\"n03110669\", \"cornet\"], \"514\": [\"n03124043\", \"cowboy_boot\"], \"515\": [\"n03124170\", \"cowboy_hat\"], \"516\": [\"n03125729\", \"cradle\"], \"517\": [\"n03126707\", \"crane\"], \"518\": [\"n03127747\", \"crash_helmet\"], \"519\": [\"n03127925\", \"crate\"], \"520\": [\"n03131574\", \"crib\"], \"521\": [\"n03133878\", \"Crock_Pot\"], \"522\": [\"n03134739\", \"croquet_ball\"], \"523\": [\"n03141823\", \"crutch\"], \"524\": [\"n03146219\", \"cuirass\"], \"525\": [\"n03160309\", \"dam\"], \"526\": [\"n03179701\", \"desk\"], \"527\": [\"n03180011\", \"desktop_computer\"], \"528\": [\"n03187595\", \"dial_telephone\"], \"529\": [\"n03188531\", \"diaper\"], \"530\": [\"n03196217\", \"digital_clock\"], \"531\": [\"n03197337\", \"digital_watch\"], \"532\": [\"n03201208\", \"dining_table\"], \"533\": [\"n03207743\", \"dishrag\"], \"534\": [\"n03207941\", \"dishwasher\"], \"535\": [\"n03208938\", \"disk_brake\"], \"536\": [\"n03216828\", \"dock\"], \"537\": [\"n03218198\", \"dogsled\"], \"538\": [\"n03220513\", \"dome\"], \"539\": [\"n03223299\", \"doormat\"], \"540\": [\"n03240683\", \"drilling_platform\"], \"541\": [\"n03249569\", \"drum\"], \"542\": [\"n03250847\", \"drumstick\"], \"543\": [\"n03255030\", \"dumbbell\"], \"544\": [\"n03259280\", \"Dutch_oven\"], \"545\": [\"n03271574\", \"electric_fan\"], \"546\": [\"n03272010\", \"electric_guitar\"], \"547\": [\"n03272562\", \"electric_locomotive\"], \"548\": [\"n03290653\", \"entertainment_center\"], \"549\": [\"n03291819\", \"envelope\"], \"550\": [\"n03297495\", \"espresso_maker\"], \"551\": [\"n03314780\", \"face_powder\"], \"552\": [\"n03325584\", \"feather_boa\"], \"553\": [\"n03337140\", \"file\"], \"554\": [\"n03344393\", \"fireboat\"], \"555\": [\"n03345487\", \"fire_engine\"], \"556\": [\"n03347037\", \"fire_screen\"], \"557\": [\"n03355925\", \"flagpole\"], \"558\": [\"n03372029\", \"flute\"], \"559\": [\"n03376595\", \"folding_chair\"], \"560\": [\"n03379051\", \"football_helmet\"], \"561\": [\"n03384352\", \"forklift\"], \"562\": [\"n03388043\", \"fountain\"], \"563\": [\"n03388183\", \"fountain_pen\"], \"564\": [\"n03388549\", \"four-poster\"], \"565\": [\"n03393912\", \"freight_car\"], \"566\": [\"n03394916\", \"French_horn\"], \"567\": [\"n03400231\", \"frying_pan\"], \"568\": [\"n03404251\", \"fur_coat\"], \"569\": [\"n03417042\", \"garbage_truck\"], \"570\": [\"n03424325\", \"gasmask\"], \"571\": [\"n03425413\", \"gas_pump\"], \"572\": [\"n03443371\", \"goblet\"], \"573\": [\"n03444034\", \"go-kart\"], \"574\": [\"n03445777\", \"golf_ball\"], \"575\": [\"n03445924\", \"golfcart\"], \"576\": [\"n03447447\", \"gondola\"], \"577\": [\"n03447721\", \"gong\"], \"578\": [\"n03450230\", \"gown\"], \"579\": [\"n03452741\", \"grand_piano\"], \"580\": [\"n03457902\", \"greenhouse\"], \"581\": [\"n03459775\", \"grille\"], \"582\": [\"n03461385\", \"grocery_store\"], \"583\": [\"n03467068\", \"guillotine\"], \"584\": [\"n03476684\", \"hair_slide\"], \"585\": [\"n03476991\", \"hair_spray\"], \"586\": [\"n03478589\", \"half_track\"], \"587\": [\"n03481172\", \"hammer\"], \"588\": [\"n03482405\", \"hamper\"], \"589\": [\"n03483316\", \"hand_blower\"], \"590\": [\"n03485407\", \"hand-held_computer\"], \"591\": [\"n03485794\", \"handkerchief\"], \"592\": [\"n03492542\", \"hard_disc\"], \"593\": [\"n03494278\", \"harmonica\"], \"594\": [\"n03495258\", \"harp\"], \"595\": [\"n03496892\", \"harvester\"], \"596\": [\"n03498962\", \"hatchet\"], \"597\": [\"n03527444\", \"holster\"], \"598\": [\"n03529860\", \"home_theater\"], \"599\": [\"n03530642\", \"honeycomb\"], \"600\": [\"n03532672\", \"hook\"], \"601\": [\"n03534580\", \"hoopskirt\"], \"602\": [\"n03535780\", \"horizontal_bar\"], \"603\": [\"n03538406\", \"horse_cart\"], \"604\": [\"n03544143\", \"hourglass\"], \"605\": [\"n03584254\", \"iPod\"], \"606\": [\"n03584829\", \"iron\"], \"607\": [\"n03590841\", \"jack-o'-lantern\"], \"608\": [\"n03594734\", \"jean\"], \"609\": [\"n03594945\", \"jeep\"], \"610\": [\"n03595614\", \"jersey\"], \"611\": [\"n03598930\", \"jigsaw_puzzle\"], \"612\": [\"n03599486\", \"jinrikisha\"], \"613\": [\"n03602883\", \"joystick\"], \"614\": [\"n03617480\", \"kimono\"], \"615\": [\"n03623198\", \"knee_pad\"], \"616\": [\"n03627232\", \"knot\"], \"617\": [\"n03630383\", \"lab_coat\"], \"618\": [\"n03633091\", \"ladle\"], \"619\": [\"n03637318\", \"lampshade\"], \"620\": [\"n03642806\", \"laptop\"], \"621\": [\"n03649909\", \"lawn_mower\"], \"622\": [\"n03657121\", \"lens_cap\"], \"623\": [\"n03658185\", \"letter_opener\"], \"624\": [\"n03661043\", \"library\"], \"625\": [\"n03662601\", \"lifeboat\"], \"626\": [\"n03666591\", \"lighter\"], \"627\": [\"n03670208\", \"limousine\"], \"628\": [\"n03673027\", \"liner\"], \"629\": [\"n03676483\", \"lipstick\"], \"630\": [\"n03680355\", \"Loafer\"], \"631\": [\"n03690938\", \"lotion\"], \"632\": [\"n03691459\", \"loudspeaker\"], \"633\": [\"n03692522\", \"loupe\"], \"634\": [\"n03697007\", \"lumbermill\"], \"635\": [\"n03706229\", \"magnetic_compass\"], \"636\": [\"n03709823\", \"mailbag\"], \"637\": [\"n03710193\", \"mailbox\"], \"638\": [\"n03710637\", \"maillot\"], \"639\": [\"n03710721\", \"maillot\"], \"640\": [\"n03717622\", \"manhole_cover\"], \"641\": [\"n03720891\", \"maraca\"], \"642\": [\"n03721384\", \"marimba\"], \"643\": [\"n03724870\", \"mask\"], \"644\": [\"n03729826\", \"matchstick\"], \"645\": [\"n03733131\", \"maypole\"], \"646\": [\"n03733281\", \"maze\"], \"647\": [\"n03733805\", \"measuring_cup\"], \"648\": [\"n03742115\", \"medicine_chest\"], \"649\": [\"n03743016\", \"megalith\"], \"650\": [\"n03759954\", \"microphone\"], \"651\": [\"n03761084\", \"microwave\"], \"652\": [\"n03763968\", \"military_uniform\"], \"653\": [\"n03764736\", \"milk_can\"], \"654\": [\"n03769881\", \"minibus\"], \"655\": [\"n03770439\", \"miniskirt\"], \"656\": [\"n03770679\", \"minivan\"], \"657\": [\"n03773504\", \"missile\"], \"658\": [\"n03775071\", \"mitten\"], \"659\": [\"n03775546\", \"mixing_bowl\"], \"660\": [\"n03776460\", \"mobile_home\"], \"661\": [\"n03777568\", \"Model_T\"], \"662\": [\"n03777754\", \"modem\"], \"663\": [\"n03781244\", \"monastery\"], \"664\": [\"n03782006\", \"monitor\"], \"665\": [\"n03785016\", \"moped\"], \"666\": [\"n03786901\", \"mortar\"], \"667\": [\"n03787032\", \"mortarboard\"], \"668\": [\"n03788195\", \"mosque\"], \"669\": [\"n03788365\", \"mosquito_net\"], \"670\": [\"n03791053\", \"motor_scooter\"], \"671\": [\"n03792782\", \"mountain_bike\"], \"672\": [\"n03792972\", \"mountain_tent\"], \"673\": [\"n03793489\", \"mouse\"], \"674\": [\"n03794056\", \"mousetrap\"], \"675\": [\"n03796401\", \"moving_van\"], \"676\": [\"n03803284\", \"muzzle\"], \"677\": [\"n03804744\", \"nail\"], \"678\": [\"n03814639\", \"neck_brace\"], \"679\": [\"n03814906\", \"necklace\"], \"680\": [\"n03825788\", \"nipple\"], \"681\": [\"n03832673\", \"notebook\"], \"682\": [\"n03837869\", \"obelisk\"], \"683\": [\"n03838899\", \"oboe\"], \"684\": [\"n03840681\", \"ocarina\"], \"685\": [\"n03841143\", \"odometer\"], \"686\": [\"n03843555\", \"oil_filter\"], \"687\": [\"n03854065\", \"organ\"], \"688\": [\"n03857828\", \"oscilloscope\"], \"689\": [\"n03866082\", \"overskirt\"], \"690\": [\"n03868242\", \"oxcart\"], \"691\": [\"n03868863\", \"oxygen_mask\"], \"692\": [\"n03871628\", \"packet\"], \"693\": [\"n03873416\", \"paddle\"], \"694\": [\"n03874293\", \"paddlewheel\"], \"695\": [\"n03874599\", \"padlock\"], \"696\": [\"n03876231\", \"paintbrush\"], \"697\": [\"n03877472\", \"pajama\"], \"698\": [\"n03877845\", \"palace\"], \"699\": [\"n03884397\", \"panpipe\"], \"700\": [\"n03887697\", \"paper_towel\"], \"701\": [\"n03888257\", \"parachute\"], \"702\": [\"n03888605\", \"parallel_bars\"], \"703\": [\"n03891251\", \"park_bench\"], \"704\": [\"n03891332\", \"parking_meter\"], \"705\": [\"n03895866\", \"passenger_car\"], \"706\": [\"n03899768\", \"patio\"], \"707\": [\"n03902125\", \"pay-phone\"], \"708\": [\"n03903868\", \"pedestal\"], \"709\": [\"n03908618\", \"pencil_box\"], \"710\": [\"n03908714\", \"pencil_sharpener\"], \"711\": [\"n03916031\", \"perfume\"], \"712\": [\"n03920288\", \"Petri_dish\"], \"713\": [\"n03924679\", \"photocopier\"], \"714\": [\"n03929660\", \"pick\"], \"715\": [\"n03929855\", \"pickelhaube\"], \"716\": [\"n03930313\", \"picket_fence\"], \"717\": [\"n03930630\", \"pickup\"], \"718\": [\"n03933933\", \"pier\"], \"719\": [\"n03935335\", \"piggy_bank\"], \"720\": [\"n03937543\", \"pill_bottle\"], \"721\": [\"n03938244\", \"pillow\"], \"722\": [\"n03942813\", \"ping-pong_ball\"], \"723\": [\"n03944341\", \"pinwheel\"], \"724\": [\"n03947888\", \"pirate\"], \"725\": [\"n03950228\", \"pitcher\"], \"726\": [\"n03954731\", \"plane\"], \"727\": [\"n03956157\", \"planetarium\"], \"728\": [\"n03958227\", \"plastic_bag\"], \"729\": [\"n03961711\", \"plate_rack\"], \"730\": [\"n03967562\", \"plow\"], \"731\": [\"n03970156\", \"plunger\"], \"732\": [\"n03976467\", \"Polaroid_camera\"], \"733\": [\"n03976657\", \"pole\"], \"734\": [\"n03977966\", \"police_van\"], \"735\": [\"n03980874\", \"poncho\"], \"736\": [\"n03982430\", \"pool_table\"], \"737\": [\"n03983396\", \"pop_bottle\"], \"738\": [\"n03991062\", \"pot\"], \"739\": [\"n03992509\", \"potter's_wheel\"], \"740\": [\"n03995372\", \"power_drill\"], \"741\": [\"n03998194\", \"prayer_rug\"], \"742\": [\"n04004767\", \"printer\"], \"743\": [\"n04005630\", \"prison\"], \"744\": [\"n04008634\", \"projectile\"], \"745\": [\"n04009552\", \"projector\"], \"746\": [\"n04019541\", \"puck\"], \"747\": [\"n04023962\", \"punching_bag\"], \"748\": [\"n04026417\", \"purse\"], \"749\": [\"n04033901\", \"quill\"], \"750\": [\"n04033995\", \"quilt\"], \"751\": [\"n04037443\", \"racer\"], \"752\": [\"n04039381\", \"racket\"], \"753\": [\"n04040759\", \"radiator\"], \"754\": [\"n04041544\", \"radio\"], \"755\": [\"n04044716\", \"radio_telescope\"], \"756\": [\"n04049303\", \"rain_barrel\"], \"757\": [\"n04065272\", \"recreational_vehicle\"], \"758\": [\"n04067472\", \"reel\"], \"759\": [\"n04069434\", \"reflex_camera\"], \"760\": [\"n04070727\", \"refrigerator\"], \"761\": [\"n04074963\", \"remote_control\"], \"762\": [\"n04081281\", \"restaurant\"], \"763\": [\"n04086273\", \"revolver\"], \"764\": [\"n04090263\", \"rifle\"], \"765\": [\"n04099969\", \"rocking_chair\"], \"766\": [\"n04111531\", \"rotisserie\"], \"767\": [\"n04116512\", \"rubber_eraser\"], \"768\": [\"n04118538\", \"rugby_ball\"], \"769\": [\"n04118776\", \"rule\"], \"770\": [\"n04120489\", \"running_shoe\"], \"771\": [\"n04125021\", \"safe\"], \"772\": [\"n04127249\", \"safety_pin\"], \"773\": [\"n04131690\", \"saltshaker\"], \"774\": [\"n04133789\", \"sandal\"], \"775\": [\"n04136333\", \"sarong\"], \"776\": [\"n04141076\", \"sax\"], \"777\": [\"n04141327\", \"scabbard\"], \"778\": [\"n04141975\", \"scale\"], \"779\": [\"n04146614\", \"school_bus\"], \"780\": [\"n04147183\", \"schooner\"], \"781\": [\"n04149813\", \"scoreboard\"], \"782\": [\"n04152593\", \"screen\"], \"783\": [\"n04153751\", \"screw\"], \"784\": [\"n04154565\", \"screwdriver\"], \"785\": [\"n04162706\", \"seat_belt\"], \"786\": [\"n04179913\", \"sewing_machine\"], \"787\": [\"n04192698\", \"shield\"], \"788\": [\"n04200800\", \"shoe_shop\"], \"789\": [\"n04201297\", \"shoji\"], \"790\": [\"n04204238\", \"shopping_basket\"], \"791\": [\"n04204347\", \"shopping_cart\"], \"792\": [\"n04208210\", \"shovel\"], \"793\": [\"n04209133\", \"shower_cap\"], \"794\": [\"n04209239\", \"shower_curtain\"], \"795\": [\"n04228054\", \"ski\"], \"796\": [\"n04229816\", \"ski_mask\"], \"797\": [\"n04235860\", \"sleeping_bag\"], \"798\": [\"n04238763\", \"slide_rule\"], \"799\": [\"n04239074\", \"sliding_door\"], \"800\": [\"n04243546\", \"slot\"], \"801\": [\"n04251144\", \"snorkel\"], \"802\": [\"n04252077\", \"snowmobile\"], \"803\": [\"n04252225\", \"snowplow\"], \"804\": [\"n04254120\", \"soap_dispenser\"], \"805\": [\"n04254680\", \"soccer_ball\"], \"806\": [\"n04254777\", \"sock\"], \"807\": [\"n04258138\", \"solar_dish\"], \"808\": [\"n04259630\", \"sombrero\"], \"809\": [\"n04263257\", \"soup_bowl\"], \"810\": [\"n04264628\", \"space_bar\"], \"811\": [\"n04265275\", \"space_heater\"], \"812\": [\"n04266014\", \"space_shuttle\"], \"813\": [\"n04270147\", \"spatula\"], \"814\": [\"n04273569\", \"speedboat\"], \"815\": [\"n04275548\", \"spider_web\"], \"816\": [\"n04277352\", \"spindle\"], \"817\": [\"n04285008\", \"sports_car\"], \"818\": [\"n04286575\", \"spotlight\"], \"819\": [\"n04296562\", \"stage\"], \"820\": [\"n04310018\", \"steam_locomotive\"], \"821\": [\"n04311004\", \"steel_arch_bridge\"], \"822\": [\"n04311174\", \"steel_drum\"], \"823\": [\"n04317175\", \"stethoscope\"], \"824\": [\"n04325704\", \"stole\"], \"825\": [\"n04326547\", \"stone_wall\"], \"826\": [\"n04328186\", \"stopwatch\"], \"827\": [\"n04330267\", \"stove\"], \"828\": [\"n04332243\", \"strainer\"], \"829\": [\"n04335435\", \"streetcar\"], \"830\": [\"n04336792\", \"stretcher\"], \"831\": [\"n04344873\", \"studio_couch\"], \"832\": [\"n04346328\", \"stupa\"], \"833\": [\"n04347754\", \"submarine\"], \"834\": [\"n04350905\", \"suit\"], \"835\": [\"n04355338\", \"sundial\"], \"836\": [\"n04355933\", \"sunglass\"], \"837\": [\"n04356056\", \"sunglasses\"], \"838\": [\"n04357314\", \"sunscreen\"], \"839\": [\"n04366367\", \"suspension_bridge\"], \"840\": [\"n04367480\", \"swab\"], \"841\": [\"n04370456\", \"sweatshirt\"], \"842\": [\"n04371430\", \"swimming_trunks\"], \"843\": [\"n04371774\", \"swing\"], \"844\": [\"n04372370\", \"switch\"], \"845\": [\"n04376876\", \"syringe\"], \"846\": [\"n04380533\", \"table_lamp\"], \"847\": [\"n04389033\", \"tank\"], \"848\": [\"n04392985\", \"tape_player\"], \"849\": [\"n04398044\", \"teapot\"], \"850\": [\"n04399382\", \"teddy\"], \"851\": [\"n04404412\", \"television\"], \"852\": [\"n04409515\", \"tennis_ball\"], \"853\": [\"n04417672\", \"thatch\"], \"854\": [\"n04418357\", \"theater_curtain\"], \"855\": [\"n04423845\", \"thimble\"], \"856\": [\"n04428191\", \"thresher\"], \"857\": [\"n04429376\", \"throne\"], \"858\": [\"n04435653\", \"tile_roof\"], \"859\": [\"n04442312\", \"toaster\"], \"860\": [\"n04443257\", \"tobacco_shop\"], \"861\": [\"n04447861\", \"toilet_seat\"], \"862\": [\"n04456115\", \"torch\"], \"863\": [\"n04458633\", \"totem_pole\"], \"864\": [\"n04461696\", \"tow_truck\"], \"865\": [\"n04462240\", \"toyshop\"], \"866\": [\"n04465501\", \"tractor\"], \"867\": [\"n04467665\", \"trailer_truck\"], \"868\": [\"n04476259\", \"tray\"], \"869\": [\"n04479046\", \"trench_coat\"], \"870\": [\"n04482393\", \"tricycle\"], \"871\": [\"n04483307\", \"trimaran\"], \"872\": [\"n04485082\", \"tripod\"], \"873\": [\"n04486054\", \"triumphal_arch\"], \"874\": [\"n04487081\", \"trolleybus\"], \"875\": [\"n04487394\", \"trombone\"], \"876\": [\"n04493381\", \"tub\"], \"877\": [\"n04501370\", \"turnstile\"], \"878\": [\"n04505470\", \"typewriter_keyboard\"], \"879\": [\"n04507155\", \"umbrella\"], \"880\": [\"n04509417\", \"unicycle\"], \"881\": [\"n04515003\", \"upright\"], \"882\": [\"n04517823\", \"vacuum\"], \"883\": [\"n04522168\", \"vase\"], \"884\": [\"n04523525\", \"vault\"], \"885\": [\"n04525038\", \"velvet\"], \"886\": [\"n04525305\", \"vending_machine\"], \"887\": [\"n04532106\", \"vestment\"], \"888\": [\"n04532670\", \"viaduct\"], \"889\": [\"n04536866\", \"violin\"], \"890\": [\"n04540053\", \"volleyball\"], \"891\": [\"n04542943\", \"waffle_iron\"], \"892\": [\"n04548280\", \"wall_clock\"], \"893\": [\"n04548362\", \"wallet\"], \"894\": [\"n04550184\", \"wardrobe\"], \"895\": [\"n04552348\", \"warplane\"], \"896\": [\"n04553703\", \"washbasin\"], \"897\": [\"n04554684\", \"washer\"], \"898\": [\"n04557648\", \"water_bottle\"], \"899\": [\"n04560804\", \"water_jug\"], \"900\": [\"n04562935\", \"water_tower\"], \"901\": [\"n04579145\", \"whiskey_jug\"], \"902\": [\"n04579432\", \"whistle\"], \"903\": [\"n04584207\", \"wig\"], \"904\": [\"n04589890\", \"window_screen\"], \"905\": [\"n04590129\", \"window_shade\"], \"906\": [\"n04591157\", \"Windsor_tie\"], \"907\": [\"n04591713\", \"wine_bottle\"], \"908\": [\"n04592741\", \"wing\"], \"909\": [\"n04596742\", \"wok\"], \"910\": [\"n04597913\", \"wooden_spoon\"], \"911\": [\"n04599235\", \"wool\"], \"912\": [\"n04604644\", \"worm_fence\"], \"913\": [\"n04606251\", \"wreck\"], \"914\": [\"n04612504\", \"yawl\"], \"915\": [\"n04613696\", \"yurt\"], \"916\": [\"n06359193\", \"web_site\"], \"917\": [\"n06596364\", \"comic_book\"], \"918\": [\"n06785654\", \"crossword_puzzle\"], \"919\": [\"n06794110\", \"street_sign\"], \"920\": [\"n06874185\", \"traffic_light\"], \"921\": [\"n07248320\", \"book_jacket\"], \"922\": [\"n07565083\", \"menu\"], \"923\": [\"n07579787\", \"plate\"], \"924\": [\"n07583066\", \"guacamole\"], \"925\": [\"n07584110\", \"consomme\"], \"926\": [\"n07590611\", \"hot_pot\"], \"927\": [\"n07613480\", \"trifle\"], \"928\": [\"n07614500\", \"ice_cream\"], \"929\": [\"n07615774\", \"ice_lolly\"], \"930\": [\"n07684084\", \"French_loaf\"], \"931\": [\"n07693725\", \"bagel\"], \"932\": [\"n07695742\", \"pretzel\"], \"933\": [\"n07697313\", \"cheeseburger\"], \"934\": [\"n07697537\", \"hotdog\"], \"935\": [\"n07711569\", \"mashed_potato\"], \"936\": [\"n07714571\", \"head_cabbage\"], \"937\": [\"n07714990\", \"broccoli\"], \"938\": [\"n07715103\", \"cauliflower\"], \"939\": [\"n07716358\", \"zucchini\"], \"940\": [\"n07716906\", \"spaghetti_squash\"], \"941\": [\"n07717410\", \"acorn_squash\"], \"942\": [\"n07717556\", \"butternut_squash\"], \"943\": [\"n07718472\", \"cucumber\"], \"944\": [\"n07718747\", \"artichoke\"], \"945\": [\"n07720875\", \"bell_pepper\"], \"946\": [\"n07730033\", \"cardoon\"], \"947\": [\"n07734744\", \"mushroom\"], \"948\": [\"n07742313\", \"Granny_Smith\"], \"949\": [\"n07745940\", \"strawberry\"], \"950\": [\"n07747607\", \"orange\"], \"951\": [\"n07749582\", \"lemon\"], \"952\": [\"n07753113\", \"fig\"], \"953\": [\"n07753275\", \"pineapple\"], \"954\": [\"n07753592\", \"banana\"], \"955\": [\"n07754684\", \"jackfruit\"], \"956\": [\"n07760859\", \"custard_apple\"], \"957\": [\"n07768694\", \"pomegranate\"], \"958\": [\"n07802026\", \"hay\"], \"959\": [\"n07831146\", \"carbonara\"], \"960\": [\"n07836838\", \"chocolate_sauce\"], \"961\": [\"n07860988\", \"dough\"], \"962\": [\"n07871810\", \"meat_loaf\"], \"963\": [\"n07873807\", \"pizza\"], \"964\": [\"n07875152\", \"potpie\"], \"965\": [\"n07880968\", \"burrito\"], \"966\": [\"n07892512\", \"red_wine\"], \"967\": [\"n07920052\", \"espresso\"], \"968\": [\"n07930864\", \"cup\"], \"969\": [\"n07932039\", \"eggnog\"], \"970\": [\"n09193705\", \"alp\"], \"971\": [\"n09229709\", \"bubble\"], \"972\": [\"n09246464\", \"cliff\"], \"973\": [\"n09256479\", \"coral_reef\"], \"974\": [\"n09288635\", \"geyser\"], \"975\": [\"n09332890\", \"lakeside\"], \"976\": [\"n09399592\", \"promontory\"], \"977\": [\"n09421951\", \"sandbar\"], \"978\": [\"n09428293\", \"seashore\"], \"979\": [\"n09468604\", \"valley\"], \"980\": [\"n09472597\", \"volcano\"], \"981\": [\"n09835506\", \"ballplayer\"], \"982\": [\"n10148035\", \"groom\"], \"983\": [\"n10565667\", \"scuba_diver\"], \"984\": [\"n11879895\", \"rapeseed\"], \"985\": [\"n11939491\", \"daisy\"], \"986\": [\"n12057211\", \"yellow_lady's_slipper\"], \"987\": [\"n12144580\", \"corn\"], \"988\": [\"n12267677\", \"acorn\"], \"989\": [\"n12620546\", \"hip\"], \"990\": [\"n12768682\", \"buckeye\"], \"991\": [\"n12985857\", \"coral_fungus\"], \"992\": [\"n12998815\", \"agaric\"], \"993\": [\"n13037406\", \"gyromitra\"], \"994\": [\"n13040303\", \"stinkhorn\"], \"995\": [\"n13044778\", \"earthstar\"], \"996\": [\"n13052670\", \"hen-of-the-woods\"], \"997\": [\"n13054560\", \"bolete\"], \"998\": [\"n13133613\", \"ear\"], \"999\": [\"n15075141\", \"toilet_tissue\"]}\n",
"\n",
"#IMAGE_ROOT = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_img_val_test'\n",
"#CLASS_FILE = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet_test/ILSVRC2012_validation_ground_truth_test.txt'\n",
"\n",
"IMAGE_ROOT = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_img_val'\n",
"CLASS_FILE = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt'\n",
"IMAGENET_MAT_FILE = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_devkit_t12/data/meta.mat'\n",
"\n",
"synsets = loadmat(IMAGENET_MAT_FILE)['synsets']\n",
"\n",
"imagenet_2_caffe = {}\n",
"caffe_2_imagenet = {}\n",
"caffe_2_class = {}\n",
"wn_id_2_imagenet_id = {}\n",
"\n",
"for i in range(1000):\n",
" synset = synsets[i][0]\n",
" imagenet_id = synset[0][0][0]\n",
" wn_id = synset[1][0]\n",
" words = synset[2][0]\n",
" wn_id_2_imagenet_id[wn_id] = imagenet_id\n",
"\n",
"for caffe_id, [wn_id, words] in CAFFE_LABELS.items():\n",
" caffe_id = int(caffe_id)\n",
" imagenet_id = wn_id_2_imagenet_id[wn_id]\n",
" caffe_2_imagenet[caffe_id] = imagenet_id\n",
" imagenet_2_caffe[imagenet_id] = caffe_id\n",
" caffe_2_class[caffe_id] = words\n",
"\n",
"with open(CLASS_FILE) as f:\n",
" all_label_list = f.read().splitlines()\n",
"all_label_list = [imagenet_2_caffe[int(x)] for x in all_label_list]\n",
"all_label_list = np.array(all_label_list)\n",
"\n",
"all_file_list = sorted(glob.glob(os.path.join(IMAGE_ROOT, '*.JPEG')))\n",
"\n",
"assert len(all_file_list) == len(all_label_list), 'Image count has to be equal'\n",
"\n",
"%sql DROP TABLE IF EXISTS imagenet_validation_data;\n",
"chunk_size = 500\n",
"for i in range(0, len(all_file_list), chunk_size):\n",
" print (\"Chunk: {}/{}\".format(i/chunk_size+1, len(all_file_list)/chunk_size))\n",
" file_chunk = all_file_list[i:i+chunk_size]\n",
" labels_chunk = all_label_list[i:i+chunk_size]\n",
" img_chunk = [image.load_img(img_path, target_size=(224, 224)) for img_path in file_chunk]\n",
" img_chunk = [image.img_to_array(img) for img in img_chunk]\n",
" x = np.stack(img_chunk)\n",
" preprocessed_imgs = preprocess_input(x)\n",
" append = False if i == 0 else True\n",
" iloader.load_dataset_from_np(\n",
" preprocessed_imgs,\n",
" labels_chunk,\n",
" 'imagenet_validation_data',\n",
" append=append\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"predict\"></a>\n",
"# 4. Inference\n",
"\n",
"Use default NULL for the class values parameter which means predictions will be returned as an index into a vector {0, 1, ...n-1}, where n-1 is the number of classes. This corresponds to how ImageNet class values are 1-hot encoded."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"vgg16_predict\"></a>\n",
"## 4a. VGG16"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n"
]
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS imagenet_predict_vgg16;\n",
"\n",
"SELECT madlib.madlib_keras_predict_byom('model_arch_library_imagenet', -- model arch for ImageNet\n",
" 1, -- model arch id\n",
" 'imagenet_validation_data', -- validation data\n",
" 'id', -- id column\n",
" 'x', -- independent var\n",
" 'imagenet_predict_vgg16', -- output table\n",
" 'response', -- prediction type\n",
" FALSE, -- use gpus\n",
" NULL, -- class values\n",
" NULL -- normalizing const\n",
" );\n",
"SELECT COUNT(*) FROM imagenet_predict_vgg16;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count missclassifications:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <td>17863</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(17863L,)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT COUNT(*) FROM imagenet_predict_vgg16 JOIN imagenet_validation_data USING (id)\n",
"WHERE imagenet_predict_vgg16.estimated_dependent_var != imagenet_validation_data.y;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predict accuracy. From https://keras.io/applications/ top-1 accuracy claim is 0.713. Difference in accuracy is likely due to differences in image cropping and pre-processing."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>test_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
" <td>64.27</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('64.27'),)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT round(count(*)*100.0/50000,2) as test_accuracy_percent from\n",
" (select imagenet_validation_data.y as actual, imagenet_predict_vgg16.estimated_dependent_var as estimated\n",
" from imagenet_predict_vgg16 inner join imagenet_validation_data\n",
" on imagenet_validation_data.id=imagenet_predict_vgg16.id) q\n",
"WHERE q.actual=q.estimated;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call predict again but this time get the probability vector. Just run for a few images:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"10 rows affected.\n",
"1 rows affected.\n",
"3 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>prob</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
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" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
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" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
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" </tr>\n",
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]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS imagenet_predict_vgg16_prob, imagenet_validation_data_sample;\n",
"\n",
"CREATE TABLE imagenet_validation_data_sample AS\n",
"SELECT * FROM imagenet_validation_data ORDER BY id LIMIT 10;\n",
"\n",
"SELECT madlib.madlib_keras_predict_byom('model_arch_library_imagenet', -- model arch for ImageNet\n",
" 1, -- model arch id = 1 for VGG16\n",
" 'imagenet_validation_data_sample', -- sample of validation dataset\n",
" 'id', -- id column\n",
" 'x', -- independent var\n",
" 'imagenet_predict_vgg16_prob', -- output table\n",
" 'prob', -- prediction type\n",
" FALSE, -- use gpus\n",
" NULL, -- class values\n",
" NULL -- normalizing const\n",
" );\n",
"SELECT * FROM imagenet_predict_vgg16_prob order by id LIMIT 3;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show a few predictions with the photos:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 rows affected.\n",
"(10, 1, 1000)\n"
]
}
],
"source": [
"from keras.applications.vgg16 import decode_predictions\n",
"import numpy as np\n",
"\n",
"prob_vector = %sql select prob from imagenet_predict_vgg16_prob order by id;\n",
"print np.array(prob_vector).shape\n",
"label = decode_predictions(np.array(prob_vector).reshape(10,1000))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(\"/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_img_val/ILSVRC2012_val_00000001.JPEG\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(u'n01751748', u'sea_snake', 0.5346089),\n",
" (u'n01737021', u'water_snake', 0.1408455),\n",
" (u'n01697457', u'African_crocodile', 0.05819438),\n",
" (u'n01744401', u'rock_python', 0.055647947),\n",
" (u'n01755581', u'diamondback', 0.041236367)]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label [0]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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CkAHJGMef7zwlSSQ6bDXTY9oWmeR5zYG3vT5gaKlZWV4/p/hI7d/654XNoaf32gCsGhL7CDW+3ODUcLZ2AgEgjyPI9v07e/nhEyckf5oBw/vaH/sW7Grvaj8623QsQpII1G3t7Yb24HAk19xEHhUG4HQq7tHOMqwDHOT+g8fbP0zx7pgFVtCScIJNNmfK8aF4+2dzKcDAPnhicTEi/fXKLLBLqvrVoLzPIuQWOPP5u+f18f6cFgLeTXP75QopqEpIAPTtfjbfGpcsJDtKgggEDtj+/wDLheFjQ1ilrSAMxzrvtWCvVPdXVzn2yPP+3FzEhsZPzCSU4wVWzcU94BeYYU+FP6j+/HA4CCKQ1aUk4jRz/K0HKMNKyjG8j9Dj28cGVVtAy1FiaU5PoxoJ3DKpZd4Ht/f1xxf6sIesUkqYAGr1jZZQGYgjBBGVbOT9/wCnFubUgFSkhYWBw38Yz1xnI7ruyMY/n/l/HglKAoL7Y0hYCiwccPf3gVJTkgBv4gjtwxSGYPCEoZTlhxrr1gwKmJlBDEEHcMt/T9O/EXKL8YZJlpCio9bfVI++KQHDr8vv3z2/T+PESlw41vi/1BKiSBXOtdze+cCLURKTmL5h27n8vbv2+nAsQXBbhEQ4LquaUp294H+KjXO1SpB7+DxAaNt1s9IBSwkBN+Xv6wYirICV27wpIJHjvni3D0Fd308QLBGEGj9ucG1qomYEMwOe3fOcdscCUEF4qVMUb7+O7cO5hQp5oMZVgy9u2CM/y/p/54BWw9YYhnxk7oV450Y4YgjPb5f5f37cUEhnyghNSUhrCsKERX5RI2GzkZz8v6niKD218RQQcLh976btHzKu5QxAHYkE44Jyav3g5yQCmjcifgDhG+MOsmCMYPnPb/bgwkVAvx3bfSAM8sFPR+XEapBiORvJYggE+fGPqRwPlbCbwwAkkuSb0r9xuCFYDLnt2J74/wBv+/FMXhYBS1ajZ8PG587mwVznz5+/vwAL2hqi5OKx1kIMKCV+VQVIzjdjB/X34Ns1cIVNlqJbLWY7wKrNlck7vJ3Hz/vwDDMwxEwuxAzu1N38gdGUNnev6kf3/twCwFkDPjEUaBy2tsDp08oFK/wH8O39fPEV5U1OveIJTKCwPn+8XEbqrB1DN32nuTnt7cUQk2hIIcYi6vXp9VgyrRllYZ2KPOOy+Pb744WC17wSQCMQPr7W4d4GCqBhic47YPg/2f6cWoAjd8RHdRw01lovAphD91G4ZHYHB+2ftwtUxrZ6MUuazB+2tb42WDcQzDYB82O/jiyKYrxQThGJ8L7Nb43SHBUf4f8AqHgfr78CBhqctc4tBLUIB405vGnTKogUoQf/AHd/OMZ/T34ileZ3tBFJ/WLHi0BMgRWPhsfmz+vv9uCbEWFeWn5xQOA4bHIxoWRBucYPf69vof8ALinLMdfyKSksSTwb1a8avI6lg5ffj+P8fvg8CSlrUgJhZLEuYAM7FSM+RgYPf+/9+GJfOBqCCC3AWEatUOe28fQAEYA+mP5/zHAJluaiFlZY4FODdm70vvjV5nyO/v8AlJ8/Tv8AXiBDuSNcKRSsQYk3ytAamZmAy5H079jxJi0qFawpglToy3E14UeNek7Biu4k5Ayf5n+OOLmLxEADWUHgYHE/CB1p5nfsD1MeCfr/AJHieUAbNZ+kCGCyZbvCjHbJWClgCT5z4z/54W/6y2caBiNz765QdWylcksFbB/UAfz+vGWZM2imt/zACUg0OXxm9veFCGwszBX759yft3/88KC6OBrjFYWNTw/kKaWWNWJUMwGSSRkngsTHER8QCqOkUANvbOhG3rClT21g2VQ57eM+3f8Av9eM8w5KprXzDsBSXli+T9LQcFvTd88YUkn2z/3z4/hw4MaWAt8QkkkOH1fh1gzT0kChzgDPnB/Mfp/f04UtVMR5mHiaQP8AOtre8HfhYtjOsSscEdiPP9/pwn9hAcRclRsa8IzHR04OHRT/AByCPt44tAJo8VKBDBLEi1ToQoxU0IBQgSgH2bJB/X38f0PClpUKAsYqWqrk1GVb35wN8PEuPl74/wAJ7Z88CAVDhrN+3SNWElRTcHlXZBmOkVgmQpbx7DH3/v68CZgYkW3QSFqSXLPbd8wdSGP5m2RA59/fPnt24EMpTu8HOlnCEu5G4hn9xpoMrHBgkqMdiQfof6k+OLUurAvr2iTJYUca2ci1eHbbfhAyxI5A+QFe+M9jn+/14zGaC7BzrlwgRhcAdCX663wIlMhf94QV7g9u4/Tx/wB+IqZurDpigHDhjtd+uXvAiQwjps3TyfBYggHv2P24XPmqdxfdr4gEzF3YHaC3oYOBacgNsjwe4+Td2/XhYmKTR46UkYkgpJbgr5jmK2GbBAT6e39ffj35W42xzJiEvZm6cPiAGV2+QnIGc/U/rwZICcRDa0IzrUS1H3ivbbBZ1Lkl/m+59/04pSQVapF/sWxSQ3Es3e2jBdlkySzKwx3yf8x/D24uURr2gln/AK5jZBSTcuQQyEHxk5X/AL8GVMHEYsQcPQjr0NO8FWYhsgdz3Jz38ePv78Gt8xBhZsRQwEZ2OGGAcE9u/b/fgVS2pllBuC5U705ch6c4DEzDYrbt35j7dv7/AMuHJSKlOtViphL0Fbi9eUbpL3Tcfl9iMY/r9uBIqM4clJor/t24n+QPvyX+RO+Ow/kfvxUsUpBJmKSp1BzstTo9f5AwUyoxYgZ9wR/P/v8AfglKSFMICaDMIOcbmJzna2D7g9zk/wAv7PFqU/lI+YsA0Cv9cK9o1NPIpba8ZHvhscNK6gCg4QEwFJbbrKBDTVO1iRG6j3DDsPf288ApQBYWg5SUjypzg0lNUNg5Rmx2OR2P1+vAYg4AFPmELklKf/qOp7/cG46edR2jX6EZ7/cef9+ItZcvbWcNdBUxL8cuLtAopahmyYUcH/3eO/t/f14gmpDOYSp/9vQa6e0HIYphgGFAAcE7vt/X24LFUAmBxEjEKj15N0g/EtQCV/IMdvv/AN+FLlBt/bWmjZVRbMcvmFGKWcYXqtj2znB7fT+/fiKajVbv1i5hKksrlqkHklkY4ZwwY+5znz9+CwgWgEHAouaevrXnSB1lJGCRux2J9j7cLqKxTqUMKg1aaaBY5A43DDt7qrePft9+GKLEA1BisKlMQ+vTk0CRz92Bf5wPY+f0/v68LWMLUvFpQSfMand1sx5QOlQArFdhx27D2HfGf79+AwmggkhJW4DPuy3wMJpNwUK4bAHj+x/Z4tAD8dcoiyS7NrLhA5nHcqQgAyAD4z9/7/14oEcd8Gl0KplrpsgT4v5vlCdu/j9e/FAnOBJH/Vxuz47LxsKtSw+gwD79/wBPGO3FlgG2wuapL/N+muEDCo+XLbSP5AEf+eADYqUgb+UW4tTh98oMCqYEMGAUdgS2e/EBqxhsz9gYDXKBEqV29UMqqO4OfHf2/pwJyr9wIH+SSKcm6Rj9ojwp7E57YH8/14r9aTWLVNahZ9esCG5xr4kJHuB3IP8At34UJLqxWi5i28oDijcc6+vaNVuYwuGGfBP/AG+nnggzMc4WpILbd7et+jb42WuBc4dM5PY9/fiYGIbbBCaWv7+3vBpFLhvmQ/pnwfHAqcFy5gEgsVNQ3PDPQ5wZSlZnXEwAHY4Of7+nbgTNYMT/AHlFkLKXYltUgYUAY4Mq7wewz3Hb+/8AtwvGDfjr4ilpK3NjS+6t7/Mbi0DdjeCAAPzef9uImaauK66QK5uZJAejR8ltgKhchRnA7Y/v+/PD3er7db4pCAAUgseftTmRugybdFsOCO+M9/f7Y4zBbWo1ssuvWBlkN5DfWrRmOgp1fazK+ME/39OLTMUrc0MUEviHa+jByKkptwXaCQvv2x48e3v/AE4Bzhwgb4GWDUCp7dmrzg98LTbk+RT7fcn/AMcEFEjZAqKgcSPvv7vBqGGJRtVcHGD37H9SeAs6TbnAJF0EPw1ygwEh+UEFmxn2Of77cIQwL0ftwhmAUbLVqQYDxI4BCEnsO3nt5/r/AJcMSS1viAKQk4jU14FunWDQlGAvcg+ct4+v9/rwvALJ1zcQQXd6duzwIKlCAdwYN9G7NwpRawtrZFCWAoYrfW7tBJatZZikfyjyT7f5/b/PhlSMRr0+otDFW/Y9OOfJ84OieMIdhYDHf7/r9uM6k1ccoMJD1qeH8A6/MfNWB94fuuRtA/Xx9u/+XBI8pcX4ZQseemSTRqe8DpWYUHbhSMDt4/h4PAf9aAHV337I0LmkqwF2yz3wMK4OGADbWH5dxOPp49vbgFEAs8EoqYKU7M99c4E/aYbBUq4AycHIPkf344NMlJSTcmF4saqXpYm+TZ6rBta4bjllYj/3eR3x/f34Bi2hbfDUg1QRV7Wr0d9m21INivAVQpcdyPlPv9/t7cJYXp8waViijRzbYe3QwPHXEsm3IUnxu7/qD9e/9eKYNXZyHH5i1qSAFl3FCS3TdBhK5cCQHchH1wf7PAUX5W2atFE0Ymh2OLbNj9INpWxFR1JSrfY+f7+ueErCklgQTz16QwTATUkG2T0yvn12RlaxCSQ2cbh37n+v+fC0qUkDZuz5xCnGFEGtOXAObbIMGrjAHjuM9kDf19+CSJxDpAaNKChnCcT5xzDNXIQXKyE9+2cfXv8Abj3eGuG54RhWrGcZDXbZ7U94LtWSqcl2Kg4U7j9+334YQxYXglnCb2EFf2g4wQwYAeD2/hxagAWjIJjuk96t9QSluXTOFk2kgnPn+n9OCBpb7ixMBNwDx+LvBRrtGuQCq9uwB7f18/pwBl5msLUog1NqHRpBd7tEpCsI/bHn+f8ADh6kkVi/3YRhXbbu5QEbrCwdQexGCQ57H9B7cVhoFG/DLWcFMJJIAfZ8VfnGP2hF3KHL58mQ+ftweFmUPSBIABxVIzz0PuBBWBgAkSucgg9X7/XgR5a25RSwkKcH546eBVmmdRimYA/SYZHv/rwwBIIL04UhygwrfWqwIGqkVisNQB9VdT/nwJIJObRAkNhVnw9fiN1qJVJTdKT907gfr9ODLEkJOvqCIqygSQM/XXrA6VM6sW6h6eAD27ePP37cRYbjA/sqQCANbfaDS18yqoWRVyMEkHv/AC8/9uEAUxOaa4RaZoYhNAbtrtGDdKgZKyrkYy3f5h/v24abuc9avCpgKku2sm+DBR9SmE7ZXcnOMqrN/l/DgxKc+WJ+1k0Dt2/vaDUOrqdgd01Shydv7t8/x+334oyfLTX8hQKnD63QbTV0bEAVEre3/pn/AC+n99+IJCBVJjR+0gUye/pBqHU0bHDvOf1Q9/4/biCX/wBqQtWYO0ddsKEV/ibDRMxGO2R3HCcDiotDVLSBuzq779Vg8t3yO5RfbIPniBFzoRQUQkMXO3bXbeDyXVe5ZT289/H2yeKCDkdcIa4U4NG0PuDYuMZ//JnH17/+BxRRitnwgJcxnGezXXZkY3/aKN8xZE7Hx4H9P4cUmWzmDM0nz7BrXSNhc4sAlijHtkN4+3/biiggsqGJmh2djt3bjGRdNw/PlvBOe2Pr/PiYQL01aFBOHzKqr22uIFW6AflOQfy9/wCGeIGdidbIYuWpqiw1TVIE/ap77i2PYfT7/wBeAWgNa8VMejqfnt2UbpAq3EHbsbCnyD+X+f8AHiLSFB4UpVWwwZiuAKZ3dgM4PYfT+HArQ9v7yhSCrCTTK9eX9gwKxhg5O7ODg/6cLSqjaMa0YlEF6nLRrweNzVh2PdSSSAPfH9/5cNWlgAD8QK6moYE8+FP5AgdCw3ySnGQO3t/ee/68CElqQsFqA0e7W+tsbjawLhgw2+3+X9eBTUEC+yKxKDmheNhub/8AIN/kY8j/AH9+EhSTe0MAZ9uuQgeGR1YFGhyPBGO/f++324JqFjU2hYlqdhXDZ/7CpT1EqqAuEGc9gfl4XML1ikqUFlINt/WFKO4MAvfevfwMf2f9DwJlhiB8/wBiGZQYQ6drmvrBgXBFCsIcIAB3Y9v7+vFtViaxS1EMoC+72Z+cHaa5bgQsKHHYYJ/ljjP+kYSHoYZ+xKEhQLcNNB41McoUhEP+v/fgJZShRRAtj8pt1c8Y+eVGKmPCj9fI/v8Az4YlJTp+0Wugqz+vvAiORudyuAO/bxwMsklganXKBUgf6WK5f2/eBgxbuWLAH+HCyoJLD1vCmws5FdnZ9cYFSRR8pYrlQBxS1pGWviIhBJ8ttnyIGFVsKN+vf2zj+P0/vHBLU93IhqVEf5p7/WzPhGwnGWQ4BwfP6e314gBUAX7+sKIY4lB24dNGBBWBHILAHGTngAlILpL9IFOJIYUJjBqTtyjGJwcDJ/N98/pj6cNmEE1Ot+fDZER5/wDtQd9CC8tVI5YAlwfHCyrN+Xxti1S8QAL6FO1o3p3ZD2bYT3OT+X/LPAMRer69P5BhABKgGO+nSBeuTt3qApHge3bx/Hi0qyOXOFpSkAJNm1qu2PjONpAkOfBOcD+/twqaoIABPWCRMIqlT7x80PzGvxS7XVnIB7kgfm/n7f7+/BIu6WipaQr/AERxe+/5ePnqkYsQ0WcnBJOAcdvP99+KSGFRTrD5aEqHmDb9bYybgnTYkgjB8H2+v+nCSMKc68veKWVk4QCKce3WBFu4jAO9kUecnuv6f5cVgDMNutvUw0TFM9gOY3UFT1jf9r712kjBA98fwHngcLk4faBCVhJD/wB9TzjJvYAbdKqgnO7Hk/XiyklTtBzDXDccr8NogZb6qgyHaqdjgjP8z5x54VhSQ4f6ggFEKFdbWt6QPHfoi6q0qHyT39v8jwpaPKXBfWhBSUrIwtXjXjw77oFj1BGF3STAY9s+f9frxaklnbZrZWKckeUdPhvdzCmuoqZRgyROfqzgH+XGVKFJDR0JEyalIAPcDs0cy5b7MxwVXGPY+P5/w4+kBOKr64COMZhqkDPXPdmbQC98Lksqrntn5sf354EoAAfKmtc4pKnJSnfQn67QQe7Mez5UE7vz5x/f0/jwaUAFxaM5nnDhNzYacHnCc11JG4/MMAED+/H8Pf34oS02EV+zMkqtv7wnPci5KhUJII7j3/0HgcMw5G3P0iisEOoO3CvHMHZaEqW5zklhuAyMH2P9/pwxEtIYZwBnFxR2689Xgo1ynIQK0hAxj7nPngwQr7+IVLmkqchiMvnW6MG6VCg/M+POCOGAJoBSNAJIbDXuzdvQCBhdJcqXkmTv2+TJH19/68UAKw9U3IgDhX3g9HeFUlUnrEbOTkAfw7frxMBAB+YFU4A4Tlq0KUd9qEOesWGAe4/39u/EVLcNF/s8zJsexhWg1BMQA0qM2PcAfw4UZD2pBLW5ZYqNaEKMV/ydwVWJHk9v6/x4pcgP5oMTQoOctvs8H1vVK+C8e1ie5JwP5jgf1KajQqZNcs2hAc1VBMVZaggDuAT44sILNnFTCFs5PPfu2QSZGYgmct9w2N3FlNGhWJIPmNtkalYR8zk5wT8x+XH+38eAMwuwNIsJb/QYa1tOUFLjX2iyXOwWi73Sks93u0ksNqpKyZIJrrJEhkkjpUkIaV1QM5VMsAODAJTjFBth+JAoDXLVjC9CCE3rI7A5AyfPf7f6cLJvr2hMvyukgtk4yEHog4+ZpCy+Scnv+nEEwUrDQkq8xNd19boMxsMKBI5yvcZ9+KxEWgpiiGY3vTlnvgUzMhIZiB3x3OV/7YA4oCjQD4RfXp06mNjWEKQNytnPnx+nAhNThZoYlTBy/wAxsLgFdC5CjOP/AJdvP68WEXGqxDOOGlG/uyBFubqMsx8eB578UUYmGyGKwgHFGyXTAABHjv8AbiJQAXMCiaCHa+z77wILiSGBLY9+KLjzxP2UL6+INLc2wV+XJY+c9u3AKqaZxambza6aMDpdxjym/PcZOP0+/A/rb/IgQcJpXn8H5g1HeguAW3HGe54hSTU2iiqtYEjvZOWZgVz4Psf7/wA+JhFWH8gZZexfOvtB5Lz329vAOPoCfc8JKWFNcobiBUxp8cYMpec9vlC+AQMg/b+nAFGzXp1iIm4i6iKc+sCpdySuGLqT3O7xxa5QBqOUIWVNWvv/ADiIPRXGQsBu9gfJ7cUUs31DwkKfCNbeHCDcdaxIOcLgAY8jgaWAgJMgBNbem/8AsHIa9S4PYx5Pv4/78CQnI11rbCzSwJzqNDpClHXK2EUsM+wIPb+zjgTLcuId+4kMpqvns4U7QpipUMpyNp7+OFoU75kQtQIUHtxPx9QfhqvkyAE75ABOB9e//jhU1Lmp1whi1Ojt/NDpBxKpQNxwDjvlvvxYSkEOe0RRGQgx8YSFJz37Dt47f334XKQ5ZOtXgP34g5Tw1nzgWKuBx+XH0B8ffPv44pSQRhfWtkUvNTBsvloEWuQPl2YnyM9/7PBFIcDZBGdiqAxNL94yK9Ae7Ij+P0P6fw4EyA7EUEAlTpZQJO5s9u4R98fGCWCgHGDjsc4+3ATC1U++tPAlBpTntjPxygKSoPfOAe2PHBJlnbTPbAqXVxbt3D+kYSvVySAA2fAON32/y4YJIbEBrlC1kVf16RuKpdo2EJ2JPbGP778ApdABZ+sUZqUnAEu/Ec7B+EDw1EaeJC3se+fb278UtLnDrs5hiALEUO/ZsfiY+NWir3ddozkjt2Hn+n+fFBJPlTftrjFpmH72cTALVqAKQ+W8DBBI/l/nxf6cVDEVOALu+35+s4Ky3KJcqwx2+b5sZ/18e/FCWrFe/TVYBE0ENmdUz40MFmvK9ypywzk5/v8Al9OBMkbdCCVNzSK16Z5fMFJLujfkcsPZcj+ePccClOL/AE29/TZGkTAlIKX694L/ALXZgvz4BHv7fU5/34glJelzq/zGdc0AsRo7dvWCz3ztkSKzd+yn+h4P9QLoJg/+SQbH2tt9oKT3kxYAnG0dhkjx/Pt44JsQxG/xtgUTCmpsKcH458oBN6YZAkRhg9iew/7cLUmpx8M4audgThSebk/Q39jABvkhDEuFOe21vlz9c/14IyAp3tTKvGkK/a4GIYTx313tetTADX2cMSZQ3bGA2cfXPvn/AH/XgVSknCGvDhOVhdT9f56boDGo6jDMZcMx2+Mnx/57fYcKPh8TAC1dkK/eovh/7bPbYesbLqVm3Fp4UO49mcZ/z4fLlnCIk1OJROE9opUbhIzHcFUnxkkf3/349dgpSoEBgxLIbq3r7u8aNcDjO2MDwCrd8+/8uFsRnTV9PEnTVBNmPSnC/rBCW4sXbA2rtz2b6/3/AJcOQGDJhRWEpxOxgE1UhCbo+wyQVPEIA94ZjVhBJoN3p8WjX4hWGOmFLH2yf68LURYZQASHcjh/NsBrOm4qNv1H644sl2IMWAMRpTjup04RqJ4e27BGQF7+f04gWa4YWmUCQWofbY9BAiSwZy8ePoM+P9+IZarbItUsMXy2QMXiHzbl7rgfb+8f04K4fKHGd5QRfZnG4VMY7oRkkk+P77cEkh6aHpBoQAMQr87YMxpEwDbxnHknx/H27+3FFVOETAgqJT29f5aDqBGACMwY9iv1PBFRFjAoSpI8pyOh/YNoVUF8YU+D4P1/TgQRESgUIFePxGJKiOMMMHJ84Pj9OAIJFDFS1JJa9N5gM18QADkgeT9v4/68MAra0RTuQ/tpuTQzdZ8y7Xou33CQUtVqHUEaAwWSiljFXVSMcLkSMBHHnBeVuyLlsN2UuleGJZ7XhUyYCkhJck60I53c8vUTrqn0pU6f5oVtXps1pWWSj0qZIFoRlM088ruJK5BhSqssQYmQuXAQJ1fC+FSVBSc9rdYQqYSQAXI1T7oNzxzOq+aWpLjqm2Xqz6g1FYWtl1W90bTVxqaijr98O6vTfhetmnpiQB/+NM5G5j2VeHT+spvGWXRXkDH59X/kdS+R/wCI4twvNysHOmlkhFZd6eK0Xqz2qOClo4Jm6bLWQCUmKGOURsJVLkJMwZcQAnz/AIr8SEjEiwyjbLWVEA5x1Ce/V1LUSQ1FLVJNG5jdSndHBIZT9CMEccsyhQUe++NJmqTehOs7nuIMxajdsBo3XIA78K/QmtYGV4hWJhQ24wZW8M8Rk2u0Y7FgvbP0zwZku4N+doYi7A+nz6REmqOfWkLJd6nSlgA1xrGB+nV0Nvq4ljtZwGPxtWxMVOQCD0/nl8fIAc8XL8MSA/tAqnJH+La03eOX/Oj1z84arUfwnL3Vtp0tZbdWVO6otVEslPdPmQJG7VaymeKPYwDhYuqXZtqgJjsSfASwGIc0jOVOcQNt+r74RdHeu/1Qa1q7Lpy33LlhQ3Cp6NtStm06AHqZWVEmmbq7EfKtgKoiO85Tsu25vgZKXLU4ntFImEuolhrrE3cq/XfzEodQ09Jz+0daKLSNwrmoqK82O11NK9rmBw6S08jyfEwocbmjIlQfMBMuF4XN8AhST+urX1lFy5qgfNV92ue6OnVJqChr6O0V1ur6Out9wpBcLfUQyh4rhS72QT07j5Zot6MhdMhWBU4PbjipJJIJ3bNe8dGYnCWy0A2niP8AXXPHl/oKrp7Tf9SW6TVNQM0lngqENZU/LkfIWHSTuD1JCgxjG7sC+R4JSw5DDbl8xmmT0oLE1Gz3pDEtXq55PTrVR3zUFTo+vgub2meKvp2lSGVI97SvUU4khWBSGRpGZdrgBgoYMTneAUnyuDSnxAJnKKASCk7/AGtEqW3nJy8vFxFutOu9I3Sq6sdMPh7jFLE07xdVIRMGMZlZAWEYYu2CAN3y8KPhVhLqH9g5apaaJVrVINPzY0jFqKTSf7WqKu9RTmkq/hqWSaK3TiITCCplUbIZWjYOsbEM3jz24VMkK/2aPFhTEpD069/j5iGuUvq2tHM7X3MTRdNp66VNBaf3ttu9it9xutJckBxLTs0VOSKqMtFjYpjlLyLGW6O+Up0hCGOMObhx/K9t8EiUpdcPP+3GTxc21LeLlQxXOntF6SicFh16aSCQd/DxSBXQjHdWUMD5A4wqWHhhRMUCBXoRwG/nB2nauaZohQVpnUjcuw5HY+38D34XMNqwSEhzr6+YXqWjvQKqlorwD7lMFh/v7Y4DIgmIpO3208Az1VdRgJcKeeiJ9pUKZ/ie38uBAFxFUT5aOenB2MfJcFULLK7IjDKuT2Ye+PbhswFQqONIRgSaj665d4OQ3aAbWMju3k9vH+/vwqZidjEEtKikrHf1EKMd/o49itKiuc7V3d2P2B8ngFI2wwLSlOF6n0g2mpKdCSs0Xuchs5+nFBNHi8JYEAgXOXs0bLqoAhOp83nsfI9uInw5anCFKnBykWMBnV67hkHv3/T9DxZkB/KYktQBxAHdekA/8ZCU7k6u3PfH6fQfbxw39DGvp9wpU1KvMvXP3ZzAZ1ZIxCozKPbuQD9uAMggP6xf7Q4OvmGvq7mzY9DaXv8ArHV96o9P6XtlM1ZXVs7Nsp4gwUEhQzFmZ0jVVDMzuiqCWA4ZJ8ISoJQHGt/9hK1sQXI9s+kc3IPxT7VUcxr3BQ6Qpp+T0MqUlJcZ5Jae7VjYINS0bOYYYzIpUQsu4KyySSx/Oq9VX4YhACi6+zQtM2mIZXq/LY+546V6b5p2nVtgseqtP3enuum7hSx1tFVKcLNAwwG8naRhlK+zKy9yp45C5DAgCsAJwAAL/HeFYaxmUBlmDKCR/DxxafDZmClzlJJBNTx+NZGMprat+UoyoM9sHxkf9x9OGq8KGYwwL8uFJYjV37RkayrlYqj+cDI7Y/Ufy4zq8O4haUKQQ7tw+4DfVtymikZGIjUZJ8hPH8h3H9OD/wCNLSrfwhf+iSLa5iNP+LLn2HV7bSQQPGT5+/8A34OV4ZJtrdDAp6AtrlWC82oblIUJmRe48djnHnP8sD/vxRlpAZIo3KFqnJJuyTwJ1sr2ggb7X4GJiyhcklSB58cQSEKJALCGKUhNAD0Ipr6pBd73cUDL1S5LAY3eB54ESkkbCNCDUCCwUTsrffoQG98uR3KuRjGe+P6eBxWFJDi+s9+cVNDuxoOHLZBQ3a5MdqmVj9FHYfyxkcUUJqFU1tiwghiunBmty7QVkuV0IAaWbYDggHv/AC/v/XglywPKBwg5omAUVl09+0AftC6disz7M9iWzj+P9OKcYanKFeHk1ZV+dd1mgBq+7HBEsrA9sbsbT9v8uISirj72Qf8Axz/oAl+HL+ZQF+0L0CBvmY47gd8e2ffvxFIQ41rVIoyiElVchn30ISLleqy30ktzuVxpbZb4gBJUVNSsMUft87uQqnPbufOPPuUsh2SH4V7ewgVoUgBU6x369Yiq7c7+X1qqvhrhzQ0fTzkbgq3DqjGSPzQ7l7EEYJz2+mON8vw6lJBwH/8AtgR4eWv/APiJpSqvu0bmkJ77SD4b6Dt/44P9gd43EgJd9D1gDoqmCFLNg/mHk8SYoEULZa+YmHzBz8/zvBVol7Fc+MgE9/788EkEbotaEkJe+bXpbP65xqySDcVdQO2CRjglEswgVBJU5qfX2gGTrqjEvGSD2Hfx7j+fFK/2QBeACiUuok+3s0FnnnwB0mdR9/A/n588EN9z32/UAFpqrJ/7rZAD1E+0gRY9hnHzD7+//ngAkXLNu2RPEzGUVKt6e/xvgq1RV79whJB9sg+PrxpBDN9QJm+QHIh9U+9sDrVV4JVoSx85BHfiiAQHMGhRBwqNs3rBqOSpmIRYnLk7VxjuT7D6niygCptq+mi0zA2FRvzgWmraWaW2xw3yzB6ud6WkC18JaqmX80UQDHfIMgGNcsD2xniyoP5n1quyCUPK707cb2hzR26WN5DV1nQKgs+TgIAMnOfAGD3PjuT9lSp1PKKfMTCzVprjSI0t3O/kzdrrqSx2bmvo28XWz08lVcaanq97wQJHvkkUAfvkVe7ND1ADgfmIB0L8FNQ2MM+uUKR4kVYu2x+7Q8ptQVs2m7TqTTGjNV6ypq9YZaeFFjtbrHJG0gkqGuLU4plGFVlkHVDOFEZOQM6JiQ7m2so0hyQw5P8AD+kVT51+oC58vaOlsmoNRac0zzMq3mWi0vpirqLl8LE+FjqbpdRBGYwgV5OhSojSOVRXZR1Rv8P4J/MoU357veM01YIGZ3ZfMRHbeZNRU22y6H5fXHRtm1XW29Z6kUstY8twkKgyVEtVMGnLAiRpIpXWQf4AcjLwMIxL2tCgo2TYDVq8D1jn9z+0vzXgusNbq3Sl0pLbM+6mqKdXlhbckcrRswziRC6xtv7HYdjOBnjueEnymdJ6xjnA5X10998QhRwTR0ldRt1KSGQ9dllpstNsG/c0p/JgDICn5s4Ocji1qKlUTrZECsLYjlB+n01d62eojSCont2w/ESpGTA8YjL7Cfykkf4c9h7djxFzg1f7DDUEWIjs9yK9YvK+Hkzp6g5iXm8WXXFh00sc6VsvUGoHpU6CikmGepUS7If3MgDjeWy6q7jzM3wSzMKUkM/bWyN86ckIcXOQ20zfvGvL/wBX1fzYsmrQ50zyeuUDvLR1dVELjBbYF2FPi5Z9kEqsTMrOOjt2qFA3BuGL8FhWCA/PvFK8QQCFX4Z7/asR3yV5k6wtfNu48zL9zm05zsrqankoatqK5qKC2Um8BHMEaRxo5kGUgRRks7MZB83B+KkAp/W2Hpr3hMrxASCUcCavwGuUSRfa2r5pvd6Z9HWS1WuemnFpvNNGqU9tcttkSQAjaWk3b4v8Sl9px34BJw/5qdhvBYThxKLDLWwaa0cpZtLXzVWsjoqwRz3fUpqJLfSwpADFUxK5USxsBvMe0O4YgfKAffA6hISkKMXLQVULte/rFyeR3pIqNSXeesg1xyssdBTVixUrahrulJeKsSyU8JSli/5inRpmgdZVbYQn5WDLu5fivHYaJB+PmNXh/DoBTMUpibf146qa19BNPq2kWRK676bvclKYZhSzuYahijAkJNlgd0jHemwspKnAcnjkS/y9AkiNc7wabpNddW9YYvLr0vc0PTnLV19gNRrqhrZ4YayytBT1FvhLz7paqSGokGxsMpYwFZJApVnbsvFzfHS5qgVXD1zijLmSwQlq61eIa55+min5g3G76xodEV+iNag9aQSxSJFWzYAR1mdyDKioqASZVgqhm7bjrkeLIo4OuUKVKJHmps03SGbN6dNe6itCi9Q2v9uq6RiShllijrYUTYHZFXpxyeA23KsM5XP5nJ8QkK8oprjCh4erNVntr25wLZvS9yLt9+s2iq46h15zpkxcKqx2q608b2alSWF3rLg46a0FOspgCxiRqiWRk2o0mSiJni5gGJyBbidzesMSpJLIHF8vY8o6c6N5Y6N0tPcZNJaNtVvudckUddcK15ppaxVyVDu7tLIoJLBC2NzFvzEnjjzJlXVGmWMI/WKcu8SpcI7pR2GGzW2ro6OnACCCnb4SmiT/ABP00H0z2AySe/ueFkgEnnFLIUaGvCphi3fWWndN9fT9NzT0bo+7rNBSGnlqInnSaXIhiMIbeZX2tsjYbm2tgHB4cZUykwpgFKQSElRfr2rCby81xpG71+p6Cx3y6cwLxaayjprzJFAolp5KiJpYd+4RR46cZbYjMyKRlfmXITpak1apFtf2CEwm23TcM4e+t+c1i0ANO2+qr9P02o71VLQ2OhuNa1Kt0qiCwjEqRylQQj4YrsJUrvUkZTLkuCrLPdFpdsCTfP3y+IRKb1E0iapsXLvUFJbdPa0nH7y31k8CyV5WBpn+CgMzSuifIj9iULgjK5kFiSSkqFvTjFmYzBJ93NuHWEimhslNqe7DTXJlNMftFzWXS60kFPb6WpnVVAZoEYmaZwAplEasQuWkYKAbxtZT5Z+8VNnqIJm5U2tzvDynrNNaetHx+ofhKKmjIjM007F3Y57AjG5zg4VVJ7dhxUtK1FhCKAEG/rrjAukNS2K7Wel1JbrbRrQ1JL07xyR1wlpiQqyoYg2GYDuiscYxuOOwTHqM31rtFJCk1J76ESKtTGoWOWmtsnhSCNo7D+PFMWcWg0zAonEzapugrU0VJVosdRR20QFskFVcdsYPYKSewJ7/AM+KCyzJMRZIAFjptsEGsVplZU+FoUj8KYZZIz/DuRnx+nFiasVr9QBULrqTnbt8iCdXo2hnjToXK7Ue3cu6B0IYdiCyyRvlu3kEDue3EC1Etr6gFS0qTnXfs1u5wzq3RupYpopLXrGCojHd4rpZkIILqflMDwYcAMoJYg7gSpK92/ucthpAKkgixxcbDWx4plzD5BQeoe7i16656a71lo2jqmu0WnLDa6a2UMMio0SbZY45Z5ZFBlw8jzOd7GPZv41o/IGWh5YAJz0W7Qj9Vc6bfj0ipXOT0Z8lbLo+71GiKjmBpO/Wqsp42a7TSCmvCSwO5cRVSo0SqyqnxBdlYjayx7g41Sfyc0qqQQdavFTEppiFuv30is3LvndzW5U6FqtAaV5i/D6IErtS1NmiC1Vmq/iHkliNRIoQMw3mSKKTB3x5beSr9VknzqS7tQ/FveETvDvY1d/5F4PTR60qe60VBpTnTfDVVMssgo9WyzAxyM7M6U1xGF6BXIjSoOF7BZdm0SFXj/x4IC5NjcfHxGbEH85cPv7vE8XP1Z2K33vTlANIwVFoq5jFLWR6qtk69M5aN6SSKVqaqZowH6YnV0G4SBMbjjleAOHE9eGrQxZdTDPfT44jtG1D6t9O12mK64z6K1Vp3UhrfgoaCqaCZcEHMnWRxueMAM0SqRtdMOxIUyZ+MWlQ8wKbvqkClQYIUNu9t134Q6dPcmOY3Nq+XDnbeKy52i/W+3LDpa02eqK1GmqKdTM09VGVjaSSVOhMxjaeKRWZCRsEcWSZ+RQj/wBNhmSL8KCHp8KQAq5baOTV70iJuXfq7fT+u6PlNzmtdJDOlTJQ/wDEfXkiemkD9OIXCBhgIzxzKalWX5Xp2aMjqyjZ4n8eVyjMknl8H2PXKM6PFJRMwk1fZy3seHekWf1Fz+5caNvOobJq6m1VpaS2yCCeartUhjdypbZGsbPKWKB5ArxqzoDIgdCrHBK/HzZtZZfZXQ4Q+ZPTLYLPd+3fjlEsW66W+7UlHdrWUutuqIknp6qlbqxTRsMrIjr2ZT3Of9iOMEwFCsKhaNAmDC5Nt2Wsq1gN7lDGw6dLTqvZfPbHnHn9e/29+BQkEMddoWFkf6DAM+0Btlwd7QlXK+0Fqp0rLnU2+10e9YxPVTpBG8jAkIHYgFiAcDOTg9uxw+VKUpkip3B/uK/5KWJUSBll6bdsV11L6qOXdnvdx0zZ4L3quvpIOrM9GI4qVWBxtM8xAIAILSKrIF7gt446CPxS1gTDS8UfHIH+g3b1ivGqPURzvltOoNSW2t0/p6x76e3uVoEZbFVGYxtseaNjKzbkId8+CRGgXvukfifDpWEEEn1puhU3xa1kqSTyIHf0rE7cm+Z+pKvTopNaXG6aueGrlopL5BSmVY5xIqCCdookVgC3acAjGd5AXeeX+R8LLxvLSBanxXPZ0h0qeQhlrdt5P95OYevMXmzPo62RV1j0xX6yjkpzN8ZDJi3Uo25UTVAz8xGCAOxBJLLtbGbw3hRMcLLeuuLQYnUxEEiutg43ipHMPm9rnWFJDLUajXltZaEU5ENulmjnulfv7qXRt35ShWnBbYHBYliGHU8N4WWglLYn27OGzf2MZlz1FkrOFgczXgWYRU7XEusNUVtJQam1bqnXNcqsKaqramaoaYRPhU6TNsgZMuSWUsC3c5UDjtyzLlp8gAAZwAP6RwjIqbUAkk5Pccfum+DNBr+ksQqqJrD/AMeP1ixuFbRSVbMcBdqS/Fx5Qbe3yjJJPvwj/jKVUEp3AgdmvGxASQ6iX1atov7Z75S6jsVp1Np7U1u1Bp2ujMtFcbfWpUUtYnuY50JRgMgHByp7MAcjhCJ6VJKgAQBy/vOHKBDpKn2hzyhQFbUh9prVdu3Yyeftj+vEOEpqKaaATMTcHQjE01YUy9RG2ATgsR/5/wDHCVYXYBhyhoUtRJUQ+6+hn3hJmlqmKstQrtj5vm8caEEMzQODENpbjBA1FUnydUbv/nn/AMfx78GSkKcDtCFAgh6DVM+kArNcA5br7TkH8/5j/nxRUhgGpBGaQTi1wgwtbeMbTUlcdx82c/f+/rxCJLuQ+t0NT+0Ie3rwjc1laH+WVpB7d89sf9uIgJIYBiYQskOoQI9fVRoZZpcRICWbIGAB3JJIAAHfJ7DBz2zwQlpsL84tU2pJZtVjn1q/1K665m8xrjy+5XXm70HLGOJ6Gqulgjh+Pu5eMEyU9XOFShp9x6Rqd0Yji6kpZmaOMdM+HRKAVTF1bpeDkj9r4gydlur5bc2hn8lZOb2ruZdsqeW9boi6aQs1GdM1U8dqkrNHWm3S4eW3yUzGP4tdyCYyIomlmZX3qgSYZ/Gfp/X+sjzXZ6v87Y2eF/ziJpbdTYNmzvF3LF6f9MXV7zLzA5q+oXmHo6RZIhYZ9R1lusdQGyHp9rVUtVNEQNoiepZNpVTJJ34wK8XNJAAA5V6wKJKSMQDneH7N7RPFt1bojR1Cml9Lad0joi3xx5hpKOhhgipplQOrVBQ9MsrMSWYuynBz34xHw6lPn7w5figzFRxC3Hbl6RSz1ec7bpyvtNnptC6iq6HXt/Zt95sN7nExpiRk0yHD7mYIpkGEjLhVLMxWPsfj/C40kzBQbdNGSYwJQK7ctNziuPJP003rnBpe4cyl11LpCwpc6qGGruVtkqLhXVKKGmqHYSAbBPvj/Nv3dTcSQVdnivHoQv8AWA5prhDEeGGAE09W35GLQtR6Q5UWC3UmnbJEltp5lSOrr6Q1NymSWcDe7yKhYCRlYkEqvy7UJwOEIxEk511ugVCjJIHpD3XVdi1HqWax6Wlq9RVFP+7r56IJJS2yY91FRIfmVyA21cYYK3jiLSUgE22bYGT5kummT/AqTFZdd+nS4XO5UtrNssWobbX3WplarS0LGbFC6l0EsyhV6avnO0hzkhcdw+mX4xQejHZtifqUQyrc9e0LGgfR2stq1VcLw1JPJLRPS0UFnlWV4C8h/eyvsjWqlbagAEcYCArk+VVO/JFSgE5Q1MgJodC/9htau1Lyw5J6Ei5cUnLyy8xuZFvMktXNLRq8dJVsylvjak7tropi20tLuUbQXkUsXNSUrUv9ijfXLnFlTgITY5/G077boqFqfmHqaOkk0hqDT8L2GleOs/ZqCqgg+Kan+StmphJieowd4ll3gbSqhF3BtwkywnEDXOsJAWC/dssuv9h6cr7bX6E1XQUMuojopAIFrap6SaWOCpnhLoKpFcFdiyOy4IO7HyMBuAeIWCl2BN+WXeDMvAGUN1AKP677xcPlFYaq82i2R1ldZf8A7d2etmoaCOClalmu9SkzxszU5kbajsEmLNiVnLlgAMnnzVAOkXN84MSSCMR1vpBnm9Q3q12292TR9HQ0dyuzijoltsbWuCliCNLWz3K5RspMaIkkg7xIH2bixxkvDrAOI26k7IMoKjhem/Llt2X4QxdJ6NgqNEaHo9M6d5ic+aOkqTBW1Oh6GKeyWO4n940QpvhqiabeJ0Dt0lhjK9zu3KuKd4lIJC1AHZm28+0bZXhZpYpHl7b6UZ46b+h6z+oblzzJ582Hm3y7OntF3iS1Xa2V9ReJq+K4V6QGF/g5JJJN6NTCAysyxMky4KgNsj5f5FcuaEKlmrMcqZPvh8iXMQ6VNy+u0dOqueAR7qipoqMn5dlQww328/r245QGysMmYRQltnOGDqPSWrbtX2iaw3+1W6zoHarokt8ZNU3+H98yvhckggbTkdj540omJwEEa6+3GKwKJcmmtXMMa7Wm7xmVK69U9tCOpwm1ztHkAAeT385x34ZKmJuA8JmSyGBhlW6waB0rervqGyabtY1NcUhir7m0INTWRxFjFG8mN2xDLKQgwAXYkFiTwbrX/q3pEQwGFIHbXWBNd849O8sNIz6z11f6DS2mIz00kMQeSvnOdlNSQ5U1FS5G1IVOSfJRQzKUjw6lqZAr6c4XMngJDmuQGeumUUNrfWVqW46HvGptb3Sl5F2as6sVPBMsy3e1o4Qwbqz5kgqij7mCwHpFwQy4VuNx8I62kB276yjOVqFVm+Qf4c+kUt5D1nK2/wCqq646k0d/x1yxqamW3U93S9S0tZU3FnZm60aup6crNkLLs6mWcb386/EyzhqWN9zRUsjF/kEbNg1tjoRcdc2rkrojTFH6eNF6J0ppW71tW/7VvNStjtVHVxsyNHVtWD/9/kliaIwzASMscm1g0a45iZWJbzCVKGQqdcI0LJIc05UrkNnHbSEfRWkucXMbVXNvU9JqDQHKvUlXS09nt2rLQtVfJ4aIdUTzU1y+Nh6VfIJQMdKNYREhKSgjIrnpDIAcDI7d7BoktGEVIrso26vuBF3LPW8v9D00GmdJ2OShpKKORqagttG85hgdyzBZpD8+9sliZC0r5Y7mOeMZSot+yp36MOKmXiDUyZ/eGhzu9QeneVFgpZzTw6l1bdp0t2nrFTzxJUXurckAAFhsgQqTLN3WMA57kDh8jwxJBNAM9Z7oxF8OFIcj0eKI/wDFJ5nc5NGWzmVrHTnMPVFvqprhT2HRUjNS6XxHsDXeomqY3aBmGwQwQzdeVlSd2i2JxqKVJThAZO+vpbe8ChIByftw2mLL6f59Wus1MNJ2jV+mNO0NDKlNLH+z6usqGbYBDCscVP8ADxxPuBGZ1mRIc9ERv1Uwz5OFIKh1b5eDkTFBVNa2RZeK+VDsskZqZYySvyp9GILDOBtyD82SPcZyOBKCzmCY3FdbIX7XVXSraqWppkggRh0nWYt1BgZYgDCd8gAkntntnHAKCb5QS/KDm2Q1TieEKTXqhpKgx1dSqTB0jYAYBZ87VB7AsSD8o/jjhSkqw60YUlIobdPQwvw6joo1qGjkVoI9weQDC7gSGHnJwfPbHfHkY4AglLGHEEgqGXPXpFc+ZvOfXVsqaVdFctBrK3xS1UVa90uJokpwi5iljijVjKrsCuNyyAMhIUbsaZchJua5N3hAGWeum9+UIHJD1NaQ5nPfdPax0JeOS3Mu2ui3a0VM0deUldQY8yRgshcZ2rIpLIFkDlJEJudIKA6S4PtC1pSFYDfVQYkWl578i9ZtR09n1jYNUU6SjpTRwzS0zzBtrKtQ0Yi6kZyrqG+UnaxUkKUqkTEiog0zAKJJHevP4cRDHMHlz6cLve5rXfNKaBGpaqmkqUSOva2Vz0iFUm6zl1kNLuiVX6yvHujzhpBkNlKmYXluIFSmalHszjj97Yjun9Mvph05R/trT/KDT99hmUKtTU1c94erhYkfuhLM9Mkfgl9oVFXuR2HGr/lTiGUrpCUHGoEAEANb3D9hWHJWemH0r3K3vBqfl1Z9NptAaoprrNbJYFO0YUQyLEowkYI2FGVFDblA4o+Km3Sov1ieVRoOzd2B1lEL84PTb6bdM2LQjPz01rb7G9yhio0oLZR324SBoJJKenWqplWpihjSOWWIFXVjlgsrgMuiT+UnHEDffTtUe8WfDg1lJcijbXrV8tue+EbS90q7Aak6U1tV3i3aYppLjXadqXhgvtDa4GZlq6eraKEShC7yGVlJjkROqqqFPFmYF0A8xN6M++vZ4rAMTKDNS3Vr23BmiZOdHLp/VDc0prHZ9U827RDY5Wjgtt4oqTU2lKp5Yj1oLZUq0VdH0umk0VPVGApl12uVIV4H8gnw7KcJL7CQX27GtURapSikoSMQIOfcZcu0U7oqnWunVqrbrjWdTbxT2mC009i1tpsTxXh6cuksUdFVxyU8clOrxCCVUYjrs0bg9Yt3CtCwMCcyXSah94Y3vGREuYhNSx2fy42R0R5R2qza20FZ75TcmbFVfs81K1pt1PdLVSiZup8TJbQKgwCeQrES1LIEMsjlGUEZ8/47xWCaXWXfjyNLce8akSMYwTAMXBhxFPjdD0l0nV/uanTmv+dEdw6WKa3XO5fFiJUJRw4mi3u4Jwes7uWVcMxOWGX4hBLTAnD338OUInAqTiSCVZf2rxTTXumtX6bvIvFPfb3q6sp1rYoor2s1UacgRNO0XX3F5TGHXwYmAkAKBSOOz4aZLIIUwdrWvSFHw65aklyNm3cQX2vemVIQ10jPfb3p+S93WphuD00b/C0SxRAo4l2wTVj9xVSNKYumG2IQ26R+yqSpwCVKSKb/AFA2RQS6cJ4i+3OvzEzcp+TFNeqa3mmv1sqFpqqS33dRdwlwtBaSbo0tVSeZI3QRvHBM0Yy5MbgRbG5vj/yKkqKsLOA2/mLcn3w1MoKOFTML7QdtWPDKJV5t6Z0vozl+89NNU09so4vhhucQVUrF9ywJKq7H3kljlDnDsR2YjJ4XxZXPALF+mhGqYykYkBiN2y1avxyigFfZNc630Ta9X3e201VplGQ0UcdYpejoGkaRXij3CSaJ1iLlgJJ5du8Lt2A91fipcpZAP96XjKiUSfNUVAypEhaet9Zoq4WWPU+itRR22pM1vWkrrcaoQboZMmmY43NICiMr9kVSqhSxDc5c9KwQku1e+e73yjTJ8ORVmxCw+vgUhm6r01K+oaWmptPWKe718opbfbhpRKiT4Td0C9NSIyQowDRo8DyRnAGfIPD5HiU4WqANimHN3PMPCJ3hzc0fdRt9d9QN1IcMnI1bHUVNrorFarjJG+al5pKm1FZmAYqIIFaLADLho3kX23ttPGQ/nFbT/wD3fJeGnwyCXZPXD2Y+sM3SPrS/C6sFk13beVkWguXV9lpJTVWBLH/wDcVlp6ZpEWY1AjIqW7pFPC9XOzOuAwCq3DlTSEvKPTOPQT5S5h/9orRyanhv7CKlXb162TQvObQuntZcyahuW90tE9XFcrhZKWjkoJS6PTxXfpUsdNLM0UgEk9DIY4WIZki3NEvV8P8AmEkfrm3Odaa2Rjm/hCEmZLAplk+4/PIxJk3rPj1It6HLG/8ALvXU1PmOYWujlle3SAfMyQzSJPMnzI6yPEFdMMoZS5T0v4+VJnpxIU445xyfFSFyj/ln579n8ziQtIeqFJ7fcTqnTEupaekfoR3ixRdOOvbYG2y0srBYJvniVo0kYBn8AYJZN8D5sKFNxyhQnUGJIr3blBrU3qz0/TpNR6W0LqOqvK7TUvW0YaK2CRDsaWGGRpJWX5S0YKHHYsCRwCPw8xbFSnBsz123+IJXicPlw82FOWftCJTeqe+SGqu1JpXR+obZSGN7lb6SaSCampXKKtXFK+4sNziNo5UBR9qsI94cuV+IbyFRBLsTuuOUGmconGkBswa50o0Tdpv1AcvdQ09tCQ19ruFQGdaWtpBEyhXWMkTE9FgGYDs+c5G3OM86Z+MUC+K2tkGickUCXPDPrsMOfU/OTl/pamc3q/6Wt1YAWallr4xUEgqCOmpZ9+XjHTx1CXTCnJwjw/gph/7GJNnoTRq7z8UigPqI58ag1kNTcsaKngl05UR0tyWieMUVddqPYZIxFh2DU5ISbpbnlqFijZQFLoPQeEkSUspBrYm+uNo5ykzFEy1MXNtjWtQxEXKHlldObtolqW5j6Ms1shuwoqrTZSZbrqp+l8Q8LqgSNFCxTRCStkVYmDhMgHN+M8SZZOFNxQmw+Y2hILTFdL+to6kU+veTmiLppflvbr5YdO03wklRbrLChRIaZH6QabCbus7KURXHUlKOWDlSRxkyZhTj77YcpbgsaNWzaz3bIOUnOGyV9Frqo/aduv1FpqqK1dXDTVMNBGFiV5KaKdUZJWijdN5I2I0iDc2QoJPhVBWEuCdaELPikgJWC4q1uVNjxHGouYNDQzXG/JcZ6W1xQVM9VWVdElUbZTpksVZAC6qchCpG4sqYzhTpHhsQwqZz3jMnxAFK96PXZfZHLW9VtFz65s6yu8Fg1FMblI37HoKOiBuF3m6O2KT5AVFQ4jmq5FY7Jdkse5DiQ9ozP1Jwvx/kMlIVMdwWOTvrhFnPTjrTVml7NdOW9Pd79X6ZuES6jtVRPTPKtvl6rU1QrM6oRFOVo3LZ2iSOT/EXUcuehCl4zeoNdkMOIy8+uXtDvfXdXqrUupdP0Fm/aOoaejeivP7MuAeloKabERlCSRMjVKBXkiSnU5fO91VOLMkJYve1L9PeBTiWi1Nvx/WiWOQnLyyaOk1nqepulVddMVdUIJLwk9OaIU8KFhKalyvWCl2DFUjG9TGi9vmz+ImGYoJSKilPbVIYp0NiDPp+WVekK+o/VBy6sGmtNano+W1yk0rLQTKb1XzGipLpX5eFIrfRlHqKqBpYWSSp6aqvzbFkb5eFS/CKLhJcg6rDVzgGIzs7nruiiGuPVFz5r7xqfTd81tpiHT1RG1tq7LbYTFa6GmeLoyfBSRBZ1SIsytI0h/eBmxJgcdUeDSzgVGe+Mqpsz/s5z1uhmX+3Wm58s11Zp2/cv61Xq3p4dNUNTFRxaLpWldwIo6xkmkjBckOgKF5FO50HBSkkLKSKC2/7gZheoLvy45G3F90KHLS4XLl7qW2UutpNNc19GVpMElDSPFerhFlVcz0ON00ADMgaPcFnMTKV+VZAnxARMSSl0qHJxsMa5IIVhuBrMX6xZ3W3KGHmKKzX3LObWs1gu9MRV0FvC0sTSgRn4h1m2QrH0ghxM6Rq+2RztBXjJK8WASCffTRczw6iTlxLNrOLD8trPPp/TSWu0aCtPMHXllrDTVVDoq5QyWqy5H70117qo445q4+JFgSRg0ysyRIdwwzPEJUogqZ8rntbhSNqPDOnEa0uaCmwCrbzCxeNXXHU9RDyqqOSunuZM5lt171TpGzalZaSz0hqR0aisuUj0y1UoMSKQiSwBWkXKBjJImbK8ruQS4G08dnaBTNxUSxA5Dlq+y0dLtB6pslbYYKWaWmsdBSIY6ZWlWOjp4cnbHB4VYsZYthFJbIUZ45a/DkUTGz/AJIbznXCG1qbnDobSMdfc4r1Za5IXRJko6j4ppnYnYm9Mxp4Y5JOADnHbjRJ8CpQsRGY+MQQ4qNbYpvzU/EMoNAGZJrFB+05cG3ww0JqmiQEiWWecsMCMbX6SFHYZCsCMcdCX+Jepy366xmV46Zib4P9hM9O/Nn1T+odbJedQJpaHQqVNXULebhRV9qk1HDtkMTUlpZgxgjkMasxlKtFEQGMm5jl8aZMslCa86DaH26MaESZq6q9n2ORVhFyKmuh04LVYNU6mqLnqNKF5SsazSO8abiXeSQssSeVBkcdl84HZEsqwuBSGFISSpT7N3OIF1h6s9G2MV9j0Fpptf8AMNzFHa6CpqTDSyySzJTxvUyJ+8cGR+1PTB5SApPTVty6x4F2M0ls9ZRkTPD0SDvuOLX523xx31rDp7nHfeYOuOZOvI73zKiNZS266Vtc9Qaq4RCSSNKaggBp6OzQLAcRxttIEeWmZnU9qWZiSECieTD3ffCCqUpJVc7c9tzSGJp298zueF35V8v7GlTNb6M0tZ+y46CkSW0dCZUqauskjlWWIRNJLFktAJGSLYT1DwM4IlgqFy/PW6BkomTGFaC1Kdr1jolqflbzusugLLyw5S6gorZE1QloiuVHBDQXD4Ixu8lY7iQQJMyRonRRC6kNIzygll5UmakutQ+zGlSFUANK8P7thMTmrp6h1Ponk7b9T3bmpWLeqO1rVXO4umjorB8WkApaqkjSKCd5pBCDWtHLG1RLCI2AkV+Bm+HUEGYotSwuTqweDxEqswfn9b2EdB7Zf7w9poLdp+O2WykEfwtHCaXKUrAAINquibB2GFUAnGCPbEpKQQ+UMM4swPHWUVA1f6mrFX2TWdHqHVFxuGm7JSw3eurrY1LFR1aSTMlFT0ppjuaaq6ckixsxYIsJ3DrEx75PhSlQIDO+i8ZVKDVJI2+wbKKK/wD7QdZY7/zH5g3HSlZbNbV0YstJ8HGwp9N2wdoqeCoLpBWOscJdQYmjMx6ysoXL6/1JLIxU2b83ilHCogCuZFh8UhncsOZsOkNAVFZXrdabU2ppaizT3WKnWeuFqjp06y00r1CZ2PPsjbMKxtK0j9RhEqOnyiVFmp6nT74oTFYQ2+gz5s3B7x0C5NUVSms6HTmkP+I9OUNwtrXp9NWDUC28W6nzsNRcI33Vdzn6yxL1KiVol6UfSJjVk4400pQnGS+/brYPWNRlliM8gRT06d46ZaLtCWWkS2VN5qRXzTPI0s85nnqG8tMWcHahZu2SANwAx2HHKmqqSeX1FICXZ4C1NrLSOm6v4DUmsrhaX6DVb7d4jipkwTPUVLIIaaIA+ZXTcA4TcwI4tKCUukW0wETca7Wc8LcI0ob5omjRbnT3WuKtUuAZp6dIEcIJDCrxopk+Vz2AkOc/N7mBCqj00YFaQEF707eo4XiJub/qi0hybWnttwt1nGp6ynd6KKtq2p6aqhWTpuWnZWUrGzxF4y8e0SbyyjsX+H8IFHF/fWErm4B9vr2zeKx8sfVNd9dXin01zm042mobxPUUNjuNoEsRp9oqIzJGjdVaoAflhqIuohSOUxssjBDneGCXCC7XgpcxWHGoV36Dd4nvmH6ZeRnMSW3aq1Hap5NSJTxUUd9oStBXXCBVzsmwghrI8qJTE0ZjYoG2FABwhHiVJog17d/aDl5AVBPQ+3CIuh9HNmsMNLdOVms77RVFWsLV73SaSqju8axkGRacGKKnqiDJhwuxRLMBGA/Z/wDylXPxClAKSMSR2EWK5e8s6mKGst98vlVfaWopIaWus1XVT1dDUNFsCTIKndscJDGmxQqkZ3FyEYInksyTvf2gQAVOL7t+0W9odN7sFXabhHGtQ1homTcFp6YwvtXBZDV90TcSMhcMQDjwWApW6cL01veBUVAY1HmWz4B7RRnnxqHW2lGuGq+X9q05q3RnwzWq8R19L8XPS4V3St/e7DJTwM0LhVk2fMzGN+xTdKYAYj37atABLppZ+o9PmIK0RzCvejbxpHVFbrFItWU9xaojut8MVZT3Orw8kcrVOUVvkqGligRQvSJb92QhXQvw6VAjDlbdk0RKgmhJY7O9GY846GcwbJyg556C0tzCrtF81odU26uBoJtBn4qts14jUB+ltZoJQGCxl6oPTmOVS7FHaQciUVIdIVQ57eAvyjSgAuVAkjaMuL078IUdK0nLky2wXHQ+ir7rVaZbxFbLVAGq7WBliyrRVDRAqB8hpZMBSdgGctMZKSxOHiNc4FSSlzMbmPqLOaX1amtmoa/Uq22+QtGz2upqkaR6qJTt6EweFGTpksjRyl5W3Ou4dMjjLOlqljADxyHx23wqbNfzKodu7P8Arw5Ql2tdW9bW3C10OlKenkeWf9rvGbdEsO53j63yLAgBCq4HTCli2GCrnWBMDEF9Ze+yHImAOAc+fvTvaFmOK0VsNuvry0dbBLRo8NVFUpVwzI2GR4qkLtljIO5XBKkN8vbB4YhVCk22fX08KE3CMW3fs37H00Fqu1acqaO2W5OjTUUU4Zabpr0ypDAqdysdpLP2/KQ5yPGFDEkBVBW+zh85RpZLE53y1WEC82WlsNuli0nY4bK6orBLfaIzAQuMxmNCgYMAF8MwBztPbikzCpRBLnfqvpFAqRYU5sO1e0Mes1DrG3agkqItF2PUN+pV/Z1FVydHdCsrIrQPIjdcRLJGkzlsqqdwhdVDaSgYaFqxlE1YBH+ulNtCwO1+oh7US3y6/EU+qqu1VNr+GWlqqZbIViqZjku4ed5CYxkKsZXwGLZJAVc5k2ENxlIBej7s91O1N0ZptJaXNfFc4KeSuumGVKp3VpUBxlY5O5Vewyi4XPt2HC/+QtRZNW9tnCDRQUDXfbX000Od7ZHRQUdLS2qkmSSUCbNQ6MQFJJGMfOdoGW7Yz9uBSTiBUbbtavFBTJciue4xD/N7li3Mmwz6Mo7xYtKTTbKlIqyghr55AsqMHEDOhZVBIVlKnewbecbSaJwQcYsNmhF/8cFFqG2bHc4pEQ2HR/KjRNNV6Yp7hzwlipKqWLNmvd9r4QwbDBnWdljkDBgYgx2YAbDblXSqZMP+W1y2RokpOEY773eIKr/TboL1n0+hOYPqp5Q847FT22kgltuh9aSUw+Hk2bJpZK6nhWrr4qgRI8i/G1FLOrQzBY5XJWzLxsCHjT+z9blCgXyD3EQrzZ/Bd9I+u7dTx8ttHRciDSVSzrXUFTdL81ziMRBpJ6K41hp4o0kaAxsgkbb8jBdyEHNkAsAW+dbIafyCgf2TvMrLI7r9uEc/ubv4MNl09p86y5T6v5z6Ysgq5J5LjruosGi4tLyR0yoZauoaGWrBkl2rBFGjopmcmendiSpEliyaEZ58dzaEaErSkYqF8val/aKY6F036odE80+X/JT0+c3qb1M6s1BZP2q9gtlJQ0F0oaePfvVr1XRVMQSJ6YSyzRTGCqphkSNTyu66z+X8ThCVLJDU17wP/AlEGYBgtyLFqX6Wvuhx1/Nn1K6Uqa+38+7Jy3v2vtPVo01qzSTvSWLVem5GjmlhnkokK0tZRzbR06umdkMkwp2kTqIh6P4782uWSiZbdfixYGOcr8RLmnGgjc5Khb/7bXcR0W9OPLfkl6rNKVGt9Ic76mh008fxz2K8UtGbnaZgoLxS1MkiVDgbMJNDCAybVZQS27qTP/IiAChL76jtkem145038Xh/2aXFdvUxHvNbXPKvlnDX6d5K6ytmurtUU8M812lt07UdtpkcmOKMTqJJpCH3OhUQxI2CZN5EfVkS5k0gzRhGwn12CMkwoAwIPlepo3AV6l9zRU23yX7UcVZNf9Vfs+63OpNRLV3OV4aetlVxG5rJdgpwQlQQJZCFiCMrtCrKTvmzQl2Bps9oYgAp8yuD7+TubmJtuvKnXdVqSy6PssFgoNRRVFNSU9HW1EJlpaypqmEaI8io8STj5kiqiY1dyDNtnhkfnnxssB1OQe+s4cvwiwsA0Az2a3PWLPaG0c/JTe8tj1jrjmyiG73jVFjtEttkp7dK8RELXeqRoIoUWnlZ/ilVWEnTGFMWMM2eZii5GE2Br6fyBcGqRxNn5ivrEi6i1XpalotYcwLzo6zUGt5q+nts9R+14me5VslDNA0qVFSEp4JYlMlMxRGaTPT2AEKakheIJKqDQEGsOA1C7Z7KvlqsUw1DzziWgsOhdJcutJXCnoqxbXaaeimrxSVVYk8kgnWvWencsJXikUIgaZ1EryIrlT1ZUihWpVO8c9M5iDLqTQUvo5UYbYgLmDzv1/zMslTpi/GmtNm/aEz/AAlugeCmjZcFqYRISJTG2T+9aR497f8AWTx0JUhEs4hbtzhalrKsKgd40w99rxHCq1rtdO1bRMlwqJHEcyq3VjhQYZEVflAZmDBwSy7ThQCpJKIKyAd0PUnD51A1Ozt1q/KkTNyzvPPa7ant920FTas1PLHSpZauSCilqYTQy/KaSsVBsFO+4dpSNxRHDbo1ZcE8SQlifMKgc7jTQ+XKmFRJHlzL9yX7R0B0zoyk5d6l1zPqTUtdNVrUddbRLFN8NDU1NMgqJKSDduqCyNGjTS4Ru4CrtJHNmzDMAYa3xZCUpOE01stzENL1E807npnSFtprhaqq86Yq6qGkFycGTfSOXkNJHGzCFWZIDHgK4RHY57rlsqTifDT2MIC0kgqHXPXCKh3K6UevLnBqrlvpW68vNOUSvRy001dNc4KOmeV41qfi6zqthacRqzrEpDA9NEGGGpCSgfrPmPDt/BD/ANaT5nYDVvuGfZ+VV7rbRbdS5tdj0SYmkp626Kbcb6TuIegp2frXAfIv7wBY13oWIAJB/wDITiKRUPqvxFCUQz01sy4ws6b03oO1aj07Ta61Jy91BbDAa14qqludVS0ymP8AcRn4YRrW75HWELBUGJC4LF1yUVNVMUny3fcOuYjQjCP9qG23pX54RLeuJNF3LTFoJ5iafqRQV8auNK0FDYbBa6GWnE8f7QxUS1nXKIjO/wC+l+YxrvZXCollYJoz7b9dmcXRgFElQscuTe0SJUcsdOXDQ9vvnPnXXqwW5ak3QWfTcdTEf2jEXMdPFEldM0xmcPG7xvBGED7hGgU4xp8TMCsSCnCm9D83OUOlyZYZOFzcW7U6k84tBeOVGsrtadNVPOPmiLLyYs1oZP8AhOsanp4qhN0kUH7chSpp7dHN1nTcIZZoJqkdUsCzHjNJ8R5cMpNzz5NX3gvEADzzDVttuvswhnUWv+U/ps0pVahrbTpmmprkUobbJpmho7jXagmjiYPPJHTNDQQ0ysvSWniYwK4G93lfph8vw6pqnOV3gZ0xOG/BvnN+EV20lzX15rv1LaH1fzY1zqblUfiblR0MN2uZ+H09CKeVoaXomGOOCN2gaN5JEAkkJclEjjBb4oJEsplAEBvu1TCfDpTclyeL8LdhHR/SOsLXLo+2axvdusMiXuOH/lLnJRUtGJ5u4NMYpnWohY52zopEvRklChtsXHHmVUXy2aEOMlBzqdrfTPspEec7OX+jpr1T6jp9BXO+8zHiNpWutNq+PgttRIUc1tFDOywVC07TtItZsZgYwrFGVgmjw89x5zQVavTju5QM2WP+o3bDx3cqwT5PvdeX2krPeNY8y9PW5tNaZqGraWnvcd2/ZtdRyGS6T3a8IA25BOIY0MiwDqPGkkrs5mVNV+xeFCak7GJ2NejbIYgAJJSXAv6bn5jlDAW06s5qVH7O5Y10d2iStt9ZV3579L+yLduR2gqqCnnjnnqv3iVMgklMsC1LSNvZ0yjVzQipvu3bT7MKboTJ8O5BtzYngGLczDq19pGx8uX5X8vrLBYtW6lqamSy0FkudbT279uQSMFqZLpc8mbpIrGWSKIq1RLGok6zS9GUUqcFSrbd+7ZDQxZCWfZl0z59IrDYtGaSqNSvrGlo9C3W1xanp9PWa2WWkktNrvSg1EFeFjq536Ma4qac1DN1AXuCZjVDJFsWothrQcS+VekLQoFlKzs4Ap0tox0G0Fy8u3KPSPMSJqnlVNa4d9TZ7tUUUFoqa4Kjs/7Vlp0KmlgcwwwCPrskUbHcxdUXlGYJiwwNaUft7k/14KglyQQHIcDXB45m3b1EX7mBzb198NBa9U6QqUudv01+2R+7tsCUxMi0yNtYGrCLIyOuYkMAyoWo39pfh0iWAb5gUfWW28JlqViUrbtv0h8cudGV9frmwc/dd6Kq9GcprZbY7lpharVEVCZ3hkWnkuMNM6yyy0mYZ1hp3VM7o06gVAz4VTgUmSgkmxLdj7msRMvCf3UYWB4VIaI15g8y+cnqY11e9Dcp6692vQC00016oYq40FBFFJLsNTdHaQiqqGRQWB7jd00UiNn42pTLkgKmCuVHf4EIBKjQ0425esENac8NDaB13pvlJo/ltXc09PacrZam5ftC4SRTX+/qiLNPIaYN1TCsTQ+DEE6yBFiRGC5Xh3lmYtTKUw5ZDdrODMzzYkWD7Dz2njDnsUXMPnLUWnlfzU1dZ+VWh66F9QVtrqelbKi7B5nlMipMiPUs0qGRmqGiiUbHzIemppc1MvzS04jZ7trdziKQWwKVQZZ9GI68oZ/ODTOqbJVar11oq8Ul0tmn6eljs1RZrXst9gpusk8bxTyZaSJVmFQ1ZNHmqmhDs4CpEwSZwUAlr5Zk7NWi1oZNKk52pz+InHkf6j+V+mtR6m5j6x5k635r6+po9l5usZrphcoJJzDTrS00KRr+0JWQN1ZG+GiXBUqIUXhfjfBrXQBhlbmSdm4QqVOGFjXfU+9eNeEdU/T7zErub+gbVruOkXSk9XDVw2ujevhuVLb5kZ0d6hUSN5KuNiMxSHCln+XIfHG8WjCrC3s8bkElPmq+9+EKs+jdb6Ds931bdr1oSO20lVTTftT4O4XW41VOyj4ozwyMsZqHd5IonVmEULKBghkdAJWrCxPp88s7wa/Dpc5Zs3QjnDTut55E3y5T8z9VWJjd6Wrp9P2yfU1AbRFTGSpLRLTyXMU8cZEjfNKrBSfl3FsIWIKj5QelerekJCRl1c+znTw2bnLZ9X0BkudHbLK5rJA2n6hKerhrWppD03bJIRo5CxNRE6rG5ZWYSBgrUsKGo6aOmgJkt62OWy+biKyahj0TY9EHTFx0rR6qtdbda6vtkVlrau7VFVVPTtLLCJOi61NLIMQqVxInySxEhZQG12+Xk0LUHViuBs27amnCH5y/5mVui+aNFYOcHL3TVxtkPQobHeTd0nuNsr5IkZlWVJnpDJNlkjVhEyvFhOgJpIlGbIKxiB1rrECkihVRq5HcH2AcNtc+gFPrjTdgtVfdntsGlLKtN8VPNXQLblVAoLyTRgBQwHlj5wNpbIPGAS3YAu2yLpjp666vWIv1x6mbHp66aZXTdFZ9RWOor/h7lf5roKe3WtMZJMu3bLKC6fuw2VGQ20leNaPDFqnXHXWFkt5nD7c+1W30iOuf83MHWtJyxqaO/wBqjpYNRxN06+NKqzVDTxtTx01fT5Bmp5TLt3BkaLq7gXYKpUlRQcI9IYiWVuEvxN++3lzh53KOh1rZdYW2zVMFHWUVT8HXQXaFaqag3MHjM9O5Qsjr2jc7N2CQFZMKSZiQp7vr+wsqKFBg1aN3IuIpXzU5GXq0Wip1DZrPygmgSskqayCvtRgjp4iqGaSCqWSOaDqrTQRvE7sp2Ao0Y/dnXKmPWtD0gyColTsKvasWX5M87dG0NNHpiw8opNItfqietoq/4tDbtRziNPiSKldrVtyjlhKOAEZ4kWZZGUFBjneGP/Y0z1rfFu5C3cZsag5M3qekWxtmlNKV1FPT19ouCMxd0+A1DcpqiGU7VYx7grZZFRWdXBYIEYOgB4yrmKJz7cIBlOFu3YQ7NO0WnrLZZKLQtosFHU7p3VayPPVq8Ksk1QIdheV+km9yQ7EZznzZlqSog2Oe34gkhKksqo1tjXV+hrFr6zT2TUdu61JJ0+rHHJtDBXWVQGxkhXjHbHzbRkeRwCVkP31v7QARhyY62W9YeOjUsGlrNDpy36YoNN2iCSSSGjtdNHBTKWYs3Tpowqx/MS21Ao3Fm8lspmoeqTtvX+xYCnbnqrDd6Ri4ftm41ttrIZDbaaIOWpoqGMpUMSCpkmcFl24f5Y9u7dhiduSSZiUFznvv7P6WiJQ9Bw1Vj2hg6o5j6V5cw/t3XF1jjstdWLRV9dW1LiloIQG6ZdUUiMMyttcrjewDMN44FUsqIULwUmQyiBcUyPEMw4xC+hfWByX5hXC1aPtJk0hqS5UQqaaC8O0VEXbIEE9XEB0mbZIAXRYz2XqFmUF8/wAKsDELWprt2i3NcZY7iH+g1t+cWESWssMdXVpXPU15UGVhPtpKf2dlEzMVjUAPsLEZX5SCeMWHElq0tteCTNVLNaPfRpCOOaVNFR1slbW2qokV2knloZGFNBTjIMssvdY0Xac7SzA+R3zw0yiaNr42xUmWpZcjk+3PlDUrecdmqaequc9TFZ9I2yM196ud4jelpbXQpGz9QzuwDM42sPKqp7qGZV4MygFAJqd3xwgEoNEAAb3D9XMNflPrXRnMSyXCLR9JadM1l1eW4zU/wNRQ3K40Bk2088hlbqskiBMSMUZlchYolyDJqXSyi+usV+k4iUtrZkIcaW/lhanqLTPqi0QS0r9EwSV7oKXsD00CEKFGfbPcnJJzxaJqyHY9I3foahYjK1BsvHJb0yXvllyl5ncuNM8v+aPMC11VzeWS8cqtG3Causl2u8xnmaot1BUu88dqU1pZYoae3xIKWNk65cxRxJKThI4sQc6A/wAjWoFTkDqKim/rc84pt+JHzVvt39ZVh1ny51fqixc3tLUq6SoLdRSsty0xVdbd8k/xs9FTVlRDVs7rDmNwoFXBFJmMRRWVDCH634U7Ro/HypYQQaihox72bjThBzTv4lkvMLkVy/5faw5a8sdXziroDfOYHPOjqL5pentxuxqYUr46H4j4ioiKQiCXp/IVCx0+0FTapoIIHHPT6aNM3wICiuwfIV4vd9oI65Wo5iaItNb6fKHmt6H+Zvpu1rZ9NV98vmreaWiuYsul7vpO+1DpI9yoZ6i409rQtT1NTFJSV5aGaOaFYz1ESR7dRrc1O7dp452PCr9ZAagtVhlax4NueFv026s9VnrstOiofUV6QuW3OflHTaV/Yds1dftOaY13Q1N1ZExX3Oio6qmutkjkli3VUFE0kI9qVXZJ1UlSl1ZwKbfTk52bYfNSmWSlK2VsezOwrutbtHBf1TWq4elLnBqei1ByxpfSz6nrfe46m3af0tVXWgsw0+3UlhulFbLitXULb6g/L8JJWh1LF1RMywR2lSpa3l+U+u2jFx0fKGSJip6A5JSbuQ9hc4gCd4Bhr8s/VnoK9UdZbdcT3vR2oOo0tPVU2Z6V5SwAZXG6WldAO8g3gKjDvuCD2vgv/JUrA/f5aXALfI5xxfFfhsBH6XodoB7sDyMdaOWWianVOlKbVslqtd+sd7ucNxsldTVc0tAAkr0qPJDE3wgdzC3TUL1FjgnLvtZYyyb4suVosQ3HXSMUuSgKbMPvIfffKOg2mKHSNl0neKa33PUtNfa+21VgsUNuVXr62yQdGcUVJM7RwCRzWRyK8jYQGBQsiU5Q8uXiVM/YQMIe523tvjbMOJOEuH69a05cWiENRx3zTtBTUmm7zdJI7TR0Zr7jqq8stFp2qCoBRGnkhWmMwMryzfvR1WUTTySxBIj05bYWUCHqw1rhGebMSSwVR24/XB+sVX9T2qdV2aspNOVtRXoLfLLDLHHKZqC3xzhlbLtu3VM+QFlXpAKDiGAuiJ1fABkONGMRCs65fW7ft2xTq5XC3mtpKqgS6o9OiytBJHiKOVchmDo6vtAWNk7mRQNpcqqluphUE4V216wDgUcnM2rW3XlD81lqfWmpq2o1JJb47DTxwUlDNNQss0Uk0ADAiVQV3SSFpG2Ha3ykghQeFy0oT5XdvSM5XmKAk600M+aR6FpmuorbhWM4mhknqVijG4E7wVJwzMV+bJXscg+wrlOQU0aHy5gcMHfWTx010ZquTkFpjSmhuU3LXUV41TrBLNJVJqGfryT3KpgkMSTUUTIlIFTrP0n8w4cs5/eDzSpYmOuYr+ZM99sdVE1WL9WGj+z9YsKeVd0r9Xf8Z3jSsxkNXBPZ7NbZJJFpqaMFnjlSA7Z2O1iqsGQbEJ3s23gf2+VjbXaBTIIXiF762xFvNL0WXzXtlrL5qHmVpbTQp7lSVU1VdYGWGKNYZIJo+0hCK2+F0jZ1VD1cEtMeGj8okEpSl+8AZUsgCYr7f3/kMPUnLn0zcjko9PczedE2tayn3XGOxWC2UlwkUohXrVcKkU5nULtQVUixoETNM+0liSmfOOJIYb84t5Y3ndlwOXrwjn1c+YkXMnWVyvz6Ot1qiHVq6qKe4VE1beIMbVauuk87yyVKLsA6SxxKVzHEoBU9eZJEtOEFztjLLu6gw1ra14aemUqdTaitNFY7JV6ivKzmWmmSimrJAhYOxaBCwWFW3YkJ3Es5aRck8B4malNFEdb8M34RolpUpJNgN3qeP3F1/Szyy5haQ1hdeY1t9Let+dev7XVLDpG52u6xUVFS3SItHUvUKZRDMuKhEZULAGOTbks0icr8j4iVN8ipjJzADk5jKNfh0TJaHCBiOZpTn6xYi2Wqp0rzLs2t/wDh7l/rHmVD0auo0ppiGtutHocLCxYQ3aqrFp6Wok/e/ETPvaMIxQCML1MUxZmJwrcbAbneQBfZ2gZYwLJl0Bzamy59r5xDqf8AE/MbWx1Mf+GNZVEUFTHXXqSUTae0bIr4hjovyQ1KxlNiwRCKCbqI0ssoOG1qmBKMDNuz56eBkpUV4ncHjV+p1R4jXnDWaNsenbLqS0aQ5bXbTVNqiutkda90+Kj1BNDDITNUwvLDPWTrO5DzrSdB5YzJH0IFjgd0uWo4gSp2G3o7Nyd4pU5iDSvDgKVLHoYO6A0a1Zq2PmLoXmteuX2mLBZ6e63yOeM0NPbblUQTUsluoZCDBHVrAFLKTvWKolKlumq8InTRSWtIfbs3n5g0gsS5w2arV6cDlSLrcvdE0uobzWas0/qGzSaW+PiUpLTXCuq7zEY1hgoZ6isdREkcbVCfIZhGWMjyFo4mGDxE1hgIq26nQZ94OSqjvQHXxnCVzQ1hV110uPKDlrZJJLxS0gt0xp6MV1LPR/vADUQLIhVB1E6ckzsCqTRlZABIzpSMIC1ls+vfj6wiYpS1YQ5qa06X77IgDWnMiqltFZyat/MKORq5a2k1SLYa251UcUQzU081U6iWas+IjqDVTJBHDS08PTjjbpnGwScA/apNd+/6sObxMYW0tJ6Dtt9N0K9s9QHMPlXo/S2jdFS3G7ajgmlt90udwt+ytpKZtvVkr6UJvjn+KcCmpIQOmphaZ56qsZUyp8LKUrErOze3zf0hilrVR65vW3anrZoK8muVnL/Uc+g9W84NR3zmAZIaelobZShafTlhlp6edEtqzzvE07wTyLtmpY+lFKY8yyl3di8ROmVCPKN1Sd5OXCsJkypWJlBz24Dd/BSGh6gdYXHl5bavWPL+5aborXTx2+l0pZ6G00Fxp6KmEIjjesqA8okimWnnDvKGEiGFFG7cxb4SWlTINGd8i/UarFeInMaKrTh6f2kA659VfMe96ssFfqm/aktfLWa4U9RTWypihkqKi2xlXZqqSN4pT1o3dmCNGJCiKoKxsRafBpEtkAOzfy9PSFqmKxHIcb7H2mEywarpaK0PdbBBpq2apvxTTdDPStc4Wttr2CaqqLlSrM6M9TI8jpHEJCVPUlKpEqGYKsXpXIud1Mv5D1FZG+2ynvtpQQ29N2XlvpzRU9FpeHVfM7mff6majkra+rtq09EIkVTUU9UHlhhpGqJAPiquNAUinf5Am42SvFjXQDIA+j3gpmFLiWwJ1sDesTZpLlhZfTNpyhrrXfr/AH7VlQ8EV0udHT1G2KslTaksNM4j3xQyfERxGokAaadJXAAUx4v2Gc+NgNmr77QcyQlKgEvEHc3NR26CisvKnlnytoaWeqhFXqL/AIf009Q9XRRx71oq0qgkkiCy0s1RThAgaNRKZt23jVISMJmqNd57iobdUwtSSVhIBAe9vV6DZ0rCPoq184qaWJLxa+auktOBFrJp9IabsaVFeYy6CWaSeWn6Y6bTxJI+5ooiqiNlKKFmbJAdLc8Te/2YJaHTiDngwHF2p0h+885NYai0zy/oDq250OijULQUlDdKyK5VF3qIkCiWaRnXvEoVyGwu+ZipA6ZeSClIISH7QgoS4UTTjWIspuWb2ey1dBaUqYYoqlP2XU16Qg1VfkNNOkO7dsVe3WAKISrbWLbGsrc+a52PTZX1hpSElh66vF0vTnzfuc+irvaeUGv9L6M1hdKKKGx0d7gialeKmmqosUDyZkkESmM7Ckib2nYrgSEYp8sJLrS4F/uKKsJBJw+rDW2JC1FrPmIvK+9VmtTqDUd2jaSxUoq9QyUE9VWPNsU1dTTtHEsKxOz9RIleQTxbUQnAThCqD/PCCUkBWFQB1sc9dsTNy05uXzUnLSi0LLcqusrmponptMU8tKl5rtMdR4kpqGXbDT1VTB8E9JNGYswsirIS0iyjGWcqV1y57t71hoW5wFm2UfrRtND85UVXLfmbf9TaOkudxsuumL6ig01XXWZq6307HDpUW9pJqej6ewLFAjEmETSgAhzGK1rQMQNLO1/fjAJQCpylqcWp05xYu1ctLPR1stxksVludw+HFCy3COGeGSISJKsQO09NEZFKRLhF7ELnDcJmKCgEm0UQq5DPnqsOWv5U8ttXaSrtH3rRel7fYpE6VTQQUK0VMrEFsxdMJs7sSrrgqxJUgk8LcpLh/eDHiCXYkG3zso9rxXTWXpTrauKz6YsWs9S/8D0lM8dJTS3SSpngqt7MrdYt1HVizq4JLFT2YHGNkvxrKcit9ZQrCAtw8E7T6dZDp60w6wntt0raAwwI0VvEdPJRQmQpS1EbtIZYy0zySEMhkcI2EA28UfEOS2t7wtKnDG5y29LHrE36TtlFa7V/wteoxqi0VME1HWi4xQ1Ed0gbys0JUpJGRhdmCMKvuOwTSSaWg5cwJSyTTLb9bhthwa5r+XK1o1FqWSptt1ttIEatp6F6yrgR5Y3EJjUlpuq6RfuGy0hVGBUjfwtMtZ8uufzBTFABlE7dHL3gnovTLa3tzW+8Q222X5KKKW4UU5icEtH+9D0weR4oyd2EcuMFlLNjcSVPwmtdcoXKQ4C+lKj5hL0Tya0/y2julh0xoqw6Ygnc1taKCpLvIxZwsrgZJQsQgf8AKAQgA2qvFK8QSyVV6/2L/YFElSsT02HnWJw01bKuigg6xjCjycgbjuIIwPPfsR37j6jhWDrtgUKyZ/rgWh5UcVPBW1eyagjcnLgnvn3LZx37AeM9vtwkgEMReB/Ymj1HHT8DAr6ss0cLiAu9TvMcZ6IG85x2Ud8dvJ9sfwBMhlF861+IiluGXQ8Gd8yB7UiFb1z803pHUcNPr2+aX0xY6uOX4epuVVIauvnTIKU9LGjEQrlW34ZnbcNqYVm0GQcJw+2vSCIALJHBge2zpEhVGrbXdqKOst14onoZI+pG4Q9VhjO0oRujcZGVYBgcggYxwoSyMmML/Ykh6vYU9c6bqQkx6it9kKNbKSsrawoQSTsLeMgufbP6+M8HcYTBrnkliHGq5/MRRzA17y3slw0jW6j5d2e7agWsj+Fp7cEe4rBI7ieSGPau+DBmMsLMsU+WVu7cUmQSCE/XCLRMKyCTS2nh/wBJZo9Y6Roa/lhe46rStRE1EkMyOFhMTFDHIDtlQD5UKr3aMJgMh3OgnB5V3FuHKGqlpBxEUrqofVIi4+mm/VGr59Zag1Td7sgnWqpLXXxxVdrtbjaN1PHIoKSECQs+5cs+QF6YzpT4wAsAH7127NwEZ/IpgKA0atdlLViSb/yntOqktdFdYKe86cineskt6RvJRVFR22vOoUrMFbeRG2Yt+1mVike3KvxVcLsTw9MvWDUwLqFfjhQ6ENy/cm4Wt0tPaL7Lo+ConaepeO7mnWsdgqyGSTcJeoYogm5WVwoIBHfLVTCCA3Y6PCNQT+yh42b3yG3tD0temtI0NBS0tGbTT0aRqsIEBwUCgDBOCRgYz9vfzwr9rUHqBAGfKQW9o8nXrjt+qfUnqrk/6jNHcnLRqXTOvdXx6N0ncJ7saNtSSxoBHRChkpId9P8AES1cEVZ0I5FmNXHUNM22RBWv9nnOXEty2R6HwchMpeDGylVNdm3Y+3llD89ZeqrLyjq+VPL/AJm2HV+pmsWgbJNq7Rs2pqiw19PdLq/WengutPBNR3ywu6yxVJrYt0eBD8S8h6htIIlgKuRtL/EZpWEzStLM7As4pWvPMXyERh6XefPqJ5WetjmNXcqOQlR6ka5bVWRa30np7Ut0uFtmot8DTyWeCtVZqz4WOYRxUsqVEiD4gRIYUMgiZoCgXc8zlr4jarwxPlDpFKm4NGs5Z6vvjsH65fS7+H7bLAmpdecs6XQFZqOunrW1ZaL/AGewyW9qeOkoow1FdVNrlhd3tlK9KtPGiMiSSvEyvM26alLB9a2Rx8cwKIDEi2Y7Oa7xyjmPzL5Bei7SXN7mFq/mR6iOY3IXnJoSnorLrPl/zT5Xx3yXVDtCxWvsNHBdqGa4W54+rNDTRKkVNEitFTxx9NDn/VRnYgDQzjVJm+Q4UEhVKNd+FOP2IfPqL1ByRP4cGsbPUepLlT6oLYus6q86QumkLmkcunWnrIKwWmaw311unSpUHT/5Z5hTx3RxPG8ClnaJyVBRcN1Nd3e53sICZ4ZYWhKAQTcGmLe9uZFWpennBvumdZ1FDQc09Rcute2HQF2lSaC/Utmq2oJ5SjBTTVMkccEyM8TAKkgwEKq3yEHIhbpLOBvc+h/kdP8AY/8A61Hj9PlE4Wjkp6ouV9w0TfrLoTnbaTqSga/advWjKK4st+pFkxJJBUW+MszxsrRyq6ienkQiaJSAGL/lC6TS1CRTffpFqlhbJWBwNDuYH5taO03JXnZz0tXL+TnBzr6HNLkVeLo1tm1/c6aojuGidS2mVIRY9d2unjaKnkSOTbHcxAsah4neULLIkPc/HfkFFkzT5douOMef8d4AISDKd77H4aMTlbtdaWu9ltuu7ZrLTho56Olaht2iaW3Xqe8dQkPXRW1v3JJemnkFR80cZVmUfusD0UqYhYaWQd51blujmzEFJq/YdyLxD9it+v8AmHzHqpNCW3XdkrXqqmhuNxvNPBV08VNIuFNzjkfFQ6qhhdZFYyui9w52ruWtEtDKIObA+kAhL/5tmW7OHiyGrPTryo0fQSak1DquwcsLvXRKkNxqq6itVvDqixv8NTzOm2N2Vz01kbbkqp+UYyD8mqgJp0vq8GqQcdnO6/Roi+j1v6fdF3a+2u1a1sPNCBLc5SK0pR1NJZqeMFKiqp40eQ1U2+T5pI8RIW2Fu5VTlTcbeYdebnhEEtRUwBFdZQ8+WcXJ7SXMrTeg7Do6quHNhxJVU1NqCqxdJWKuEmpKIRRSI29BnCSMUV2VgqE8J8VPJS61Bjw1vhkmUf8AoCacabjs5iOgujdWWioaaLSemNaXotIGnrm03cYzcJWbBnhSaFJKp2KZMr/I2FO6Qd+OatbBlK7w/wDSSXwmvYxYSLQms7hQLX0PLLWera6dk3wK4pRLnc8bMal4unEuSW2xs2STsPyjjN/yEtUg+mu8MT4UtSmt0VS9Q/pf52c66ighvOsuR/JrSNsIqLfQ3K+SdCB8fPUVJhj+d2DttZpI+mAoHd2fjbI8eiT5UpJOuMLVIKiCpd8vug+4rHb/AMIuv1HbHuVn9ZPIm426ERy1C22yyzUUEbqSrCeCsdV6mHAkZRvCsu8lWxoR+fNkyn5/UUn8cE+YqI5fcKcf4S1DZ7rW9D1Vcp36dIxeCo0jW1DCFmeMyyxGsXaM4UOuACjN3IINK/NEjzyy/wD/ADfUPHg0pNFE8nPrE56K9MVx0LJBY9Y+p3nVfLfca2onp9P6M0hdbVHdJmlWPAkiqTO8Ilqer0n2IxLtjcSxweJ/IKJdKEhs3y4ttzvDEeFw/wCiSbB7dK93ifNS8gbrqXS2nVunM+812hrPRJILU2njZoaiCLAhjr6anK1bU8aY3KhyERi1M54SnxjOEJoc3rydxBzJQfE7kevYe0VB9ReleeGvtF8vOXY0bquktmpC8dLpvQ9I01DVxRKjPSLOkhp5jJUKZHUybOig9iX42eD8VISCpx776CvY74y+MkzFFlhz2fLdTjHPu1at01yn5ZQaPpNI63PqFrNRRi/NcauCkpqaelpg8cIhcSj4WCeZ32MkSyrHFJIwjdAekEKWszVq8tgwenTPOMk7ClISkAG9du34rFdtMcr9ea4vlvPL/l/qLVtbbqekpp3WnaeKgPVaRFnnI2LHvDvvmK7vnJUdgdczx8sJwzFXBptGbDTRUnw68QVLAYevp1j0E8obVzEuNkt6c06uz3fUlCTT19bDDHTyV1bUzPNPVSRJHHTmbDxruEZLEMzEnO7yczCPLLBAu3oNsdKWFNU21fjBum5dWS2alt14vl51DUNTVNXHbKczGpp4pp0w7QxD5XYoGyQPDv3+Y8aF+ILYQ1anLrCESw4Uq5pXX9ipnqF1joDRfL218rdB6+v1VrK2y0/7SroKuSnnoVAjVY+rCUZ3DmnjeRg7w42hk8DV4aVMxlawA9h65RmnKCgEIdk7bcnit+nbdy+fXlkr9Ox2u+2r4Oq1JbKWuQipSsZwka9OECNxG+ZSTKiOwglcsRtDllQl7MunevWBlp8zCp1oRaXlrpO7Uuo5+Xt25J6kk5aXC6vQ19ymmqJqemqasSvbaqT4HovXR5p42mY1ESxxSuGAVt8vNnTknzpUyvbjka0YZRqlS14QCPLwfazZUz+IlHW/Jq08uL02rrfpfTNxooYKasWCurqqeCnqaWOMQyUcbs8iRqsCYjkZ1AihQjEalbkTlTEYVHceG+KVJwjy+kV05jcoqTX/ADQ0betdatjiobfZ4a6O0SqSbpWhppBEzIAi9SOGqYIU7xW6VUJ3Yj2/vKQUIF8939pugAkO7U6/xoUr16fdL83rpbbnU6Uut31RUs6tTV1PNHbrSSpVaq4fDssheVthWnEp2ovZV2leM6vFlALGnc+rdoclCVOyanWqxYWu0jo/k5YEr1N5tdi0/TLTUUlvo6SKGqVgcinhVZI46lsFSzRyzMzYjUtgtmR/7FMWJNTQ0416WEFMWAHBoKXOhwhmcqeUV81bqKPV/MW22Sgs80EtTW2m8Bqx6ueSTqh50GwOadEigiEqMFHVZY0duxzfEIZkdbbg3vFJkj/Xs+nhF533Kz1t+06txt16RaelnuHSpwQVtsW1hDmN8uskqhnVU3bKZ1GBkl0lDJIBZ9fVzAqwg7G267RTmv8AT3pHUaS8xDrOoSK9JFVU08FZIxZHjMn/ADUNPGEBAVQI4umi9I5X5d3Dk+KmJOFIZuWu8Z5khEzzBuh+YNJy01kmiY9I8uL3SayM7M1spWov2eba6TI09dUJPK0c8LpHFCA6uAvSEaphg1q8QknEoN7uKC1M9GGCSQABUb6NwOeqQt3y3auF90ZV8yrNc+ZNZWUJS8xxUZaOsRyE6vSRUi2w7ocOFiwDtYYkDKrGkghNBq0EpKnYOe3WGFzrv1roLNWWfTf/ABBHLQUctLDcpW2n4aNx1o2LBBIKqSXtIgd4wsAYgSMsbpJH/a/zyy3wBBtY8Xh6aPumpudceoapbLHy/u9PbLPS2Jo456cTSRyyMZCoiZgEdYHQyNEgb5e5fvnmYZagBW+v5WFAYg6qD22xaDU0Kaw0hqCbXtFJR08NKL1X0tK8Urpdi8FSWjyqCIkQq7RD8gmkUn92MpSWPl4561aNWJQOLb16RU7VUWkNOa00j/8AbfllzC09FC6NNWU9vramW7XGOCLfHT1ctY7wwRx1ZV41kBkO2UTSQmGUXjcHEoE7y/tfTREy1HylLC/w9e1I6N8vde3rU9tiqPUxdbnoe4Udyjq6Wx0dVU25Li1NWJNT1Mkm+KpqGiVMmlkeRdkbTFZN7KuBUp1PKD77mKxAsgF9zn0OehFmbRqOk1F8fHom6xMYbh+zpaqRRUiOdRE5jk3zDLuKiMhgxJDgqMlSErBSXMChTgE9fv2hYipeZ10qIKbU1Fb7FArRJHJT6hqKmItvJ3xqlPERLs6hBlcZcKo7DJFM1LEpq+tvtBLJTQEA9f48H7hXtYvj6quqEUtGvU31O2KUx7isewBhFHhmLfKzMTkjOOLSSRSEpSVgp1117QBZtQ1GoxXrTzWe70IeemnuFJMHpxtCMkGNxZpU6sm/KqoIAKhsjig4vrW+HKU3mxOcsu3uOcHbndZ7fRzVFJJHbKWJFV66YgbB2AxnIUdgBnLMewBOOCBSSM4yKUGKQPMTu16xS3nXzC5mWa2VlLoPTmprTYo8SC70FVSpXVe+YLIwaWRXpldW/MpSdw4/fRZZF0yVIfErWv5DBJOIi57u2b094hhebevbnzo0xy90zrK5Wg2OsZ7kRSlK+S5bZAUleFnjjEMVNKcPL88kkm4uAqq1SBhxKz9IJC6sK92562RZTmRaOV/PPV2l6fUt5m0DzVt9Ustu1dbadVrYpY0MtNJGSwCokyQSlJB8POpkB6TSRMcaMUtzLqnrFGYQgJXbfd4uBR8peX8tPDe9Q0VgSp67TyUFgoYYKKnqWyjzUwhRamNW2klGlbYWZWZzmR8iJ6mYdz2hISXcmo5cuOUL1XqLTtpfT+n7DpXXt9lrCaWmgtVllMVJFFtDPV1Um2KnjTso3tuY4CLISSCDqqT6waJRCcTOH1XPVIEu1qgr4bhp7bUUtRU08iTQU88lPWNCfkYq6MssY+bHUQqy57MpweLQ+HZCyp6CvEW4xG2n+QGhrbrOq5i2zSNktus5Ifh1ukpZ54afYE6MEkrsIIyqquyPbkZBzlsnNmkhlFwIJRJSwFX2X5xI89JS01unr4a6lgomYwTVOQu1g205ZgMEN2ye+eFfuctmYikmjn412jWSKeOJ5WaeZsH5sflOfYfbiVAIyhhUkEKzG6u54rZNyjutZztqOZlQ1ZUaeqLXHQiCKo6bU1SrECYLjJBSSTLhu+dpjIAbilTyElOcKXLxANlv+r9e8WP0bSXLR1zqam3X28CiqJFlqqeZYpklCqRhU2A5bcuXJLDaMeTxlmIBlgEcL11shwKVHGX3bG2VqxyaJ7W6W5KeKWS0RTID1B0gRhx37AYP17d88ZlKKkuXpw+faBwEgq9daziDNS8x9UXenaflLV6YqaiCsmpK41AeOWleCSRKhGSWORFkQqQWIAV1XIYOcapMpJHnqDofzrDMCQXFCx1rrDH0FzbuvNG1VFDNpzWFHqenkE9VNdY6WFXZHG5YE6syOVWRwXVgrfNsO1geJMkpTrbnVqxCpwFEX329ehpEkWy5W+7Nc4KC7GJaGqaikULsUMFVxgTID+WRPy5UHIB7HhqGUMT67+sPTNSB5wX4RSb1jeiCi9R+i+T2iKK827SOjNO6std2uNqmtamG7W+JTBOIjGFMNdFFJNLBIuFM+eoDkFNKkqIZJtw99vXfF+Hn4ZmMlxa7a4RzH1Nr/mXyJ9SXq95scvJINZck+X1dZLJNprW8dXqOnrYzRLSVU8dwqEnmevhIarPUq0qJYpYyVmDOseSZL/8AY6RYDhr1jfI8QhUkJUogkmoAHJi1Kc45X6K1VzL1fz21NzP5R6goLbznm1TBq2yWTSuiYoozKnxEoajp6aN6eiaCCWf9yY5VmQnBZuruGilUpytrOojZ4dIQkhf+Reor3z3GPTh6Mtf+mbnZ+3K7UdypLV6mLhdrW+q9F64t8FDXf8SJbjRJdae0VclaKW41tGRT1Hw1Q0FQ8JJpqebqxhiFBnmGm/b8xzZkvCQJVL2qwetRk++OT34t3Jjk56buXOn4LjzYu+oNeX/XWodWWjQ01FS0dNWx3KsZqioeomSrnegoKcihhpesF6tW08XRZnXhviwEArIqbCj8HOQrSx3RPxc2YpQlg0CWJ6uaNU7eUecCs1PfZaGgsrXa726xQVprqW309dNJTUczZUSxJK7ZlAwgkkJfaNpYgnjnrUHoKeu3hHdShLHEWt/P5Dl1v6hOfHMrQWmeV2uOaOpdU6Hshq6e326VII4zDU1RqpVn6SK8u6dzLslaREdsqoIB41zV4AXtfR2RnEhK5uIFj8W0Ykrkb65/VH6ddX3LU2iubGrrmtfN8RdrJqOuq7nar1IafoF6ileZQ0yxKsa1MTx1CJheptJVgWsllJOuuyCVIlq8i2pnvPL6MelTkvz91b+JF6dtM2n0yc/OXXp/9VGlbNdLbqDlrqWruN7t2r9OTtLRS2u4Vtc/x1XZnozAY6gCpNDK8cbz02xJUZImqbCkMdP/ADZnHKn+HwTHWSU2ow5jXKONVz9Bnqf5BWOtuV8/b2jfUJZb4KvTHLWGSS5XCsjijaqe5WjoiWG70EsIRTU2952jdXp66FRJFUcQSmJCSzW+adDuaNi/Ey2Sq4zNO5IvoARdDnFzv0XrrlXy79S/Ij1b0nIyhrNJaWsV2sOnb1cHrLprfEn7QjvtPQMam2OkMZRLnMkyVqviJJn3FtSp8yZLGNTEBuPQHqd16xiMhCJqjgxXZrAdhXZ1MOj0jerDkrW+p7VFZzOg5jaT1RzItFPptr/rTUVPcbfqKlnK1tNBXTTioprRc6FamE0lRSyxW+Xd0aimoBMZo1pWoKE1bvrp6wUzw6jLKUsybtl6EvsLjfDo1voX05egTnzR1tv5s8+OSPMzR90pqi0Wm/Wy3X6ya8hlMv7PqqqS2pSXOut9SKitIp5DiML8LLJTpFSyMxQSlTgu3Bzn69YpJmLQEioLi5DZHYHbZaprHOD1G+h268kOeWtrlrPn5y1sNom1I01yvFma73SbT1+qZUrJLRXPDFLcKe6rHUK9NUtA6VRTKyoYpulU0GtANtdvKHeB8QD5BW4FqtnUty2Xj1w+krV+lKmy0nKZPVPzA9T3MVtPxXq63eqvlxmNTEgigkrYqKeWaa0M+aOSWnkm7zzyvGqgyqjPDpIBBU5NXy1xjFNWXCglkimsjfKm+Kyeszllz1Wet0J6fvTxbOY8t4ur3esut919qF5KGjkCRzvbKRq2gpUrad3ncQvcEkQzQzQIytIaV3iFllUJJ3lu0MlysVSQAA1qnn8V2w8NA8m9Zag5TcveVXrW5NUnNS9xTwVtRf7NXG6Wu4VVMoWmqNUUK1UArKtmjaeRlpZ6El4RJ+9EzuqWCUeYFjleJNSnEcBAPQubs4fmQ+yKZ6W5Pckvw4vUxc9RaU1Vze59an1rZq6KPlfpjRlDUVF9tytLLcHvQhgnFRb6aZfikhpIPjkdTgOC5mGfIwKE5RbcM912A6kwyX4oYDKlJtty+eo4xBGr/wAaKg0PzVt+ldQekGx6O5cUVG1PdqbT/MKC83C3XiPpJvpZCsVB8JEI1zSOsBkyrtJTSROsmgTAPLUc9Nzrui1SVE3c6HM7CKNYvHZ7ROpbXzm5aDUuhtQwV9irbvHUUWoKNDfGpZBinrI2ts0kk1uljElRSSUs8eacxOxikWZZGSJwUSrby96wvCkkHIbL9+r1PCHXd9UXZbLLZrq1Pqa72euplqK+rp5ooeklQWNRTxQshqURowqQbgm+LtvjaJmZLQDe2r5gCIpJagfjf+7yYja2c/7FrbV+o+V2oorxQ6DvlnpLZJZLjaUo3p7gsBqpY6uGtiRKnMUU0Ww9Vt0Dncv7t+K/UAQtFS/V6Bvg0MOU7AH6pkzfUN2W38kuZxu1l5kaZ5f3HmfaT8Fea296WivcNaqTGMCOqnhepeESQGnTq7ZC8RjWKPKxqaUrRtGdC304zaBSsTOJ3QS0tyot2jetbtD6OoNL6Yramas/YNiilmjheSVurVyVUshmqS2+FY6cKGhUBELKNsZKmk1dzv0AISoUY5chrmIjzVepuYHK/T+mf27y+5jXapvVRF8FU0tkuMFFbkaJdlPWyuk8qzhXaVy6B1yYxFuT5tkpSVk1AbJw/GMviEsyUh33H0+HrnFQtTXDm3qe71+nb3q3mFDb62J53Nut1Zb6OnpzsEUEUhDPNSiQqI6cyIGJ6s4fKjjWidLDYWB3M+t4vtaEL8OtyZgLa2e+UV4hay3j/j6xX61JPYpjUU9VVGOVqiFY63NIzVJUfuykMAjp+oinYAFH5uNqSAUsa8RzaFoQWcjtR+kb8rYNXahtjSWlKSO5wQpOL5RUbB6CmaIqEgyTGsJkUbBGrbHy8fyvuiXPUgfFxTVYOQhf7MLZ7tnKL0enttJ6/wBXapufMXUsdx5t0M9PeDa7ZfqmtTTsEEEcO+ukNQaeWQ7qYExh9rwF2VO/HM8YpaQEhwg2ej58ufeNMlEuq6FW3TxJfqL50aauGlaqwaa1tpKo1ObfJLGsFfBLNDTEr/zBgZlIU5Ce/wCcN9jfgpBSXKaGE+JUFBibbH28KRX3Ql81Xdrvo+p5c1+m7fpdL7+2tU3erlWY1NrgSRZovin+YxyJJLCYwyBN24EBXyfiMJBCwSTQcevtBJl+ZxRI1oROr6lbmXqCGwaK5oU2ltH25qhb1TJTwzy3FBGOnuq0bZEuGV2iYpLg5bsAAuYoyk1S5227QRSlyXe/bfsiWbJbdI2iy6Ylk0jmhsxmks1zuMRNbT7lIeeDed67kdx8Q21iGIXCsS2OYSos786fcaErWC/rs7ERXLn5zTooNH3+klvb2/TdKwrLpJQTSx1Zp0GQqNG64U5iATcGd8DcpIPGgL/UMashupC0pM2alKTew16RyLufNfmJrXXN1p6qGh5eaIqJoaWS2uvxhFPCNsbVUgcPNUDu+U2KrM4XyWbxPjP/ADZUtX/pZsnFexDPH1Hwf/44SsBU13O9u2Y4xeLSnNHSkFI9k1TZ9Kal1dHP0Y0pqGREuFrCNILjGJS7BVIaOSlkdikwU7pVcOPSfhfyqfHSytIwkXG+PEf+Q/gV+AnfrUrEk1BaAtacxbRV36fUlbQR11XR26proKOkyXQrMhjjVYvlK7yhO33ZicE8d1MsgYRSPOpcuM4a8NZBYtERXXVFFWC73EiSihlYJLgo6l26jFljcYjz3wiFQVzwTYiQLCIVMWNuUR5pCBNRtVG5VaXCxLFNUXMyKBUK7RBMbincBTlZAwDCMjYTs4uaQkDVOHtFybuo5bKvlautkK3L3QsVfqGXWVs1bdJ6WOU01JTGAwMkX7vcIVWQo2I4JSJXZdzASBQxxxJk0AAa5wqWo4HJr6e3zFkda3fVUdptkdDOY7tNVyQ1NwfbGyq3VkRUkh6ZeBtrJgnqBmfIYCQjMhIxXbTaMNIGJj61HD3o8Bcn+Ztz+N0zdoKnQt21sZ47dcLzOZKKqkjRYV2P0aYpJ0/hsQVCq5WNYgjRl2YJnyGqxY5fDwSZiiSkKFKMzPzF46Bzch9K8wqKS/6w07p7VN0jmmgo7hUStW1EtukcSxRPO8ayAI0k0TU4LQoUGM9R+OfL8UUBgfQekWqTTCC/Xpyy2xC9VywisV2utNV1FqsdqetjelMStNWBGiRAUJbERQDavysRnJLbhjSmZiFITgGJy4NviJwornq2Cslgq6uzW6npXiYV0tO9NDHTtsIRakl+vMzblCHG7em4A4ylYBLNXW3Iwx6YdbNPG9PbdQ60tNfqAS/sCgqJ56aglppFkqZaZJyqb5NoXY3RVgYwjgMQcMoZbBS9S7DXOFAFnPt9RKenYDpaz2+0Vb03SjBCQ0UKLHFuOQMJ2HdiT2JyzMSSSSvBiqItJs53fVQ3RoJ3era5GJK1aYqmdkUQYKh8ZwScn7n+njgwkg2inYtlrO0Rlq/SVk1dbqqyX+O4PanidJlgqpYMo4wQXiZWBAGQQQQcEEEZ4JNLQl02JY8IaWnuXmnOWUtDDy50PYtO2GqjlNZRW21pC1XUEArPPICDtRFcYbc8jyqowScEqcX81SN8NZ04sQDc/bW2CWkOVFbG+odZ6gWitHMe9SCSaopYA37JpgFWOjiLFgxVEBkkH55Xc91SPFGaksCaBoGZMIq7xP2mrxq6w06U1z1LV3yIykrCY12KhXbsViCwGDjOc9u+4Eg5lpB3RUqcPLWkOC/NzM1nUaIpOWtItg07SPFSaltz3BY5p7f86KtKzlBJGoHUjljcEHMcsPymIZyEpqaGDkIFydcviA9YaZ5g2WWmFr1JV/8ABFtpwWiNQHglByAk0EVRDUoV7bnUNG24dvOGSyGYiurVhJl4iCeY9HzpCFZ7jHqipqI73WUkb0kjPNVw1M9nko1eMMUZTEkThQyqvV3MV3nKsMmKSUGzHr6xAkEYb6bKJkSislnq7Ba7hSfG1iK707MJGEaooz1JT+7DbmQje2SclQSDhVyTs1xhqFEeUqcHWu8Oae+fDxxC3Ik07f8A40O4N3//AIiAg9h/hzg/Xxwn9ZIKiKCEK8tjXa1OvtCkKujlkdIjEKv/AAxzTfO6+f7A/l54AsDR6+vtFy0kUVbi3DjAcr00DI01RRW+fbkq7bMfTs2O3Y9/GOLIGWes6xEpSKA1GWhXjCnaLnOoQRxxTxPgRmNck5GQMdj34rBXESez+9IJGEVVQ82fVjEd6u5jUkGoa6lruT9NqynjgjoqmriCVU7oSuIJl2kxp+QiFidw8AEKjEiWB/osd7WhuJagEqFKb+x1zioTUUPMCoOr+XVDrrk9rGjqJKC3XSvRKWi1RakEYD1NNCY3NF1JHQSiA9CTbIDtk6bbDN/Wz+bXrnf1hhS4KQbVcFss6Gm3tFi9EXHWVotNRT6p5H3iru71UkrVNtuFqrYKpDjYwmM8TNhQEyyKSEBwAQBnZv8AKyBwMbJakYaoxb3/AJ6QLdrLqSm1XrSo0FfLVqbUNdJS3OnSO50x+HnMTxbKimFwSSSlMNPS/KsO3/1HiDEMqEp3cFxTZ/K5QCVpV5SKvreK6aOMX4yHqv0daNKaC5Owapt1TU3ZZLhcbVbL9AtyhjK5gXegdAvZ+onUCzDIMcqHIYufhSUvy2/XbnDfABSllRsl63Yg9H998eablxebBf77pvTWqNXaq0PyrlrY3vNwskDXNrBGNwNWsKFOr0ndWdEZGddqBtwRhzAkhy7jPLtTpHfnzEp8xAJqA7VcZZB+Jh4aYsGltO80rFFp31CWrTBkqYnotbW613SghssoY7JWeMxVNLs2Ic07yhchRLlc8UzqCgb2qff7gDNARhwvuYfz52Ex3uH4pHInmtyP1N6YfWNJzC5h6ar7HNpWPmnDaLZqlqevWEUkt0npaP8AZ9xMauYZ1q46dKp8lerOWWU7Zc0I8ig4zz73jlTfDLmATEUNCHpTjb23R5tOanLi4aH1fq7Q9RqLSep/2XcquCG7Wi4xVdtvdPDL00r6CqTEc0MyplNmScMjIhQ4xzkpQS9W4ct3LbeOomamYkKVQ+/TmKMYE5S8j9Z84NYWrQWjb5oGDWFaskFuob3fqe1m6y9BpYqamnm/dPPIVMUaM67ppIomKNIm4CCotfmNdIaJyEjEsYgNg2HZrdDM0rZbZd9V0Ol9YX+Dl9RzVT0NTcbhbqioWyz90PxVLCDUIqyIiSLGjToC37uRl6ZkxNcJ50+nh6v1lOJCamxcMfjc8WY5OaV9Q/Jv1iWK0clqepqef2mbwa6xUuk9TUd1qayeOkLtTWusilCXB5oJHQQRbppo5HiMMjboixCFBSRfgR6+14xTZiTJJIfsQPfjbYY7y+mj10+mf8QP000nKb8Uq86Ss1+0bq213bTesq2qqbJRXitLdCnrZqijkhSK4I7vDV9IQxPA6zSfCsDMnSRPCgUrNBx779tH4GOPN8IZcxKpNyDcZZs+Ra+3bEE+tT07669PemNWXHmRb6vmb6aKvadM8+eXTUtS+mWuMNQqU+tbHFVVCXanlSYCC49aOokjhppTVXGSBKNMxThNLa1VucP8PPCw3ru2HI9o5kct9Va10pzvtGvqzXHKzTVsvs5oqTWv/Ddpvul6CnqJ4Y5a6mt0kHRaihPaelhgSeKjNRHJTIGeNgWU0pbp6daPk8bpaUpRmM66fuUx7Y+SHIewvynqeTUWs+WXPr05XXTMFPo22U09NdYqe3mkWnutLSSxl462yyVMnWp1iJFDHIKc7R0QnS8GnCAWBJ9c9elo5k9YWGBKT0pk+/fnHk69Zn4e3qN9M/NLXlbc+XPNXUvp3lkjtVr13CjXmBbEBHHTLcrhT08ktPJAI6YTQSxxNIKcBXcOspT4kFBaW7avG/wC8SQmxPDsDHZjnloDk36H+R3p10LJq2wcy+Utt1JSav05Dr3XdzlpLlenpFolqaeCltVRSfDfCb6pYpno6eWokg2okpIaMkSgSX21AdhQZnN8ss4woQtU0qSgBQez0BqblIfIXzeE/Qf4pmmKa53o6Q518hNA8qo7ZdKiLR3MeLV2pdTw3r4mpSlpKW52a3vQRW5wkFTFTq9Rsiq0iBiAQJoT4lRNttzXo1PiClygQ5uMgA28kk1I22iRtY/iW3nWeuOXnI/kLyufm5eNcWSjl07qqUXzSlJUXFo+pVRU9PJQyVLtTxpLUOBsGyGSNsiRHY1+LSEhSaqOzbs++cEnwalk4gwHOmRIfLftGYip3qL5U6R9Vaa31j6ZqvVnOL1JWenpdGa30BBGsVzjuYiNFDqB66kjkEkMfxVXA9VSrIRPVx9SSKTqxEJ5wnzFiKGmfM0bPpDJUwKqgOk1oaAcdh41OUebbUWpdQ3ZbpJdYq6prerDS1H7alaS5iWFpEQzPOS5mVC1M2AcrTxq6jaOM65ZHlyh6JuEMLPbjemUdPvw7fVpzX0L6mrlzFuOoNGU2iv2bVz6rsdfWNaaG6QyQUsKCB+lUlLoxgovh36ZREo3icxU7OARnCgb+M31ygjKATattp2/ZLx6b9CevDktzIo6KoodS6q0nLII+nHeaXoAblGf3kckihQcqXJCkDcCUIbif/JeHCsKldfnP0jGPDrIpFx7Xqm+Tx09JNcrgKXYHVGqmkiZDggqASuCNpBHY9iM8aikMSKwAH/U39YedFRpVRlzOamViWePIKMexxtx5+VT82e4B7EDCQkAnEK63PDAQzJNPeI5rLoadp4v+Y6IYja6vgf+0jIxjx38cbEoDsIxliBjvG9VdLzRUK11jnr6NHQq4jHTIA7t3Byy474P04WACcKvmBmSzZNtsJVFq3UUlW7TahvcsIywRal1BPj5iDk+/wDPzxoEqWwcDoIGXNq41zh4xa1vsyiP9tXWWEeIzVyuAD57FiPHGSYhDPhvDv2KNdm+AoitWGc2WzSZwGLUMJzjsM/L3AHjPjwMcLKE2NIP9iwcLsd0CW+h0xRVULwaW0bb7gilEkgtNNTyRpjG0OkasBg4wDjHbgP1jKC/YXNX1sjS8Q19Kk0tCtNHQTJtmWKnVGxjBDMoG4Y7d/HBolyzlXVRFAqYOd+yOdnOr0i/8diSWxcwKq12kGGWWlFEjdVFZt0VVSQyQLWEh4wlTvSb92omZtvWbRK8SuWSMufrVvSFhGNQY12Uc7tnvFbNZaU5i8qtKS3PViUt90fSTyzxXqyU70tgs9PC4ZI62nl21EdwxgMlWY4Qdz5nP719UmdLUqmeVyeeY4V2whUstiG2+XSjHjSKY83PWPd7V+zXZo7w1RTS1tHSUl/hm6LEbEWaOPBi+fcSrgyMNrKojOT15H4+jig3iMK/EpBahPHQiBtQeoup5r8tbvZ7ppGltV6gqrbapqulxHTz7neaN54HVujOiULZlQrvG3Pzbwef+d8GE+GmeahBNdzfPV46v/ja1TPGyxKSVHEAALnhv6RWOq1nfJ9RVdrpdJ1dHXM1QkTVjESJIjMFd1AKmM43eckMuCO+PjPiPAeDly0zFTcTmqRszbN4/Qf4/wDJfk/EzFyZcj9aQCErU7OLcRwdq3aJL5a6pvyWuC63m81E92ppZYISFj69JG67HjTpnLtISihPnyCc9snj0X4P8nK/56UeAThlqDKfNgTV7NHmP/I/wHiJf4yZO/Kr/ZO/6sf8lxwd+HWHra9Xw3K56w1BJJc5auGaOCgtTZYhNvztPPtVFi/chiMBxhhhQGbj6thDJa2Z28Nd4+GLWKg313hZtuoJ9UX24VF5qLaI4KCOWrEzqFoqdSGI2vkOoGz5FIILIRnupFQAAw11rjDCpJS4ApXZ6jtAz6vp9UXiCsq9T0tNXs0iQ260qs09tdGVaZnDYjY7pgpZRjvhO7DMWgpTbr9RJcxKls/S/e+mixFTQa8sOndISWHUa1lTe62KShrKZy4gYwyArMVYfP1nkkZwxXeHPlRnASkkuLatBqQxqKdIK1WqNWXO6Wyz3C1WXV+nLlchRNQzV6K9bE24puRn3OG2sFWIdRAX+ZHZSYrCKl3HKK/ZWrHbU96w2eZdwl5X2yiv3Lk1lPQWianvDCILNUfDNLtl21CqGOBIRtnVySpbdgcX4dOIsvPXCCmTWDDq+tUizPI/1LaW5VU9qu981VfrjEKUXKmgt1D1nudCZAzSmfegAUrVJL1BvjLMjpIApXJ4jwqluSNx1X4hap4CQ2uWqRLusPVZyX1hJbbjY7xr2mvEdypa27UdVao4JIKJ5RBIJWUyIhVOnMhjyW2qWxv3FMrwqhQtW2vXKNCrOhLdDEGy+t305aNrLhb+aHLTmlrnV1oukyW2w0tTQ09vpaeWJN8k0o2r1pjPKzQiFsAQ7yJQw41o/GzFJcEYT69Tq0Z1+J83lDjj6516RYXSn4lfLvWdottZRcsdV6cp3lWCWKunhkVCQm2dDHtSRBuZdhZZFO07GAcpkmfj1IJq+tVtAp8QCHB7e+3c4i1envUjya1jVS0tk1tapQFQqqwzZkQl1342Hau6NsM23cpVx8rqSgyFgBxET4hIzodb+kSNX19slpi1LUwVBK79sWCX74GBnJJ7D754oEkYtkQqTc3PDvEJcx+dehuV0EZvlxpJr7Om+jtqSqJ6nuqlm87UBli7d3fOEV+GSEqWHg5UxJBD9wIiiwetLQGo7jbrVa9J6pglqokqaNmkpphPAzP++kUZ6EYRdyklnlbciL8hcs/4qi5e3GBWQpiG2b+9Hi0mlNX2LVawyWipF1SVWZJI4/kJwOzbchD4+VsEHtgHjMtKhUmFjJJvx1bq+6HNWUiQxNujb8uMA5JPYePfgQuriDMsBgBSG9Z7tRW26tXxT1VumkchJEAV0wfzD8yMpA7ggggYIBAxU9GJjugQUYg1G21OuMTHU829G1MdpgqtR22WWsZqSlmld41lmERdo3kYFYX25IaRkx7Me2ecqQRQjXaNGGyEnmwMRJf7L/w+1De9G6cpKmz08EFEtPTxVE9bRxRj/wBJqTqqHeHKnpLudk2mME4DP8Ot/K/x2hKpAofr1p1hWs2vI9UWS3Xiyay0jDa62E11LV1FXBLT1lLtUmWGLMbSIBLGcsUYbhu79joWljUVtCSQSEi1b98vqFzVOodH2m2/HVmtrHo/ZIesW6Mq1IUb3jTLgrIUBPbwDnGccKXLW7Nz2QDAviD8D8+kKdDddP1VvtTG8G5STdIwsY2HxAZdyNE+Bksh3dsfXIB4GZi2Q1Ex/KjOu7XSA6cJp+tqI0ttzSSpkLU8NRUgrVOwZmCmXO2TbHu2k42/lx3HCghi6Q/vq0HhRQv0vC/dNYaEkp3tGqr1RxtUwyJPRSVTrNJH2VgrREMT8wGFIcbhjuRxSUqNtHdFEYQyBfMCzXu/b7hnXy++nXS9lqrzNYaioo4QrA2uKaSpmLAQosaFhNIWJWJQAQzYCknJBCTNVQHRrGhazhCWNOB4U+bRF82s/SRpK0Cq5g6ZPL6iuMa1Lx32eM1VxLgRMKsQM21l2oAjPjeTgBhw0y5+XbbxhqpiQoMrzb7bKcecSbReoDlqsYodESXc2SljgiVXscylN0EcijDxs/8A6csR+fDd+4/xHL/xwarOE7NCKX4pYLMB776e9Y4r2f8ADk9Rth1n/wDfzkhrPlL66LLdrRSz2q91+sRYxY7itX1+rGkdRCamWmdUmhkEtIYjNIywgmSKUlIKlULja711wa0dP/koCMJGGoFANmnu8Vp1ryK9d3IH1Eah5g6R1/cdV+qq62iTUeqrRX2ZLpLqNEhkkqHtFZVUKUV4jhp6eEyU9HLHXxNKIoY6lVeUh/7kqxP5tWg5E+Q2AghHGvP+Rz49ROvuZPqu1poSn503Tl7y21xTUNPboayostwtyXeWWX9zUVNHFHPLA5DoolghdXXplYVEjsV/8xwy2SeY7NzjfK8KhDzJZUxuHd+FXL2rnS0UqrrXc46GFKmhp4qf4hqeC40VYtRG0sbMGU9+opJjk+UojAggoCMcD+tTuohtt/qHpmIJY0OyzA7Wo4hTqtW611HYNOaXr9SagvGnbYaqG00EbP8AC0S1EommjiQYx1ZQkjg53OA2BwtRSlg5HVnPvFhMpS3Zmz3DJ9mgSYsXyQ5I6f1br3llpqstV95iaku9ZJ+0tD2mmuNXXU0cXSRxJNbIXlp6mp60wp+zikeAGrE0UmOLlABIUbnWVt2cZx4lxgT/AIG2nr1vUWeLNeqn8Jrn3yQs8fM20aVuWuuUl5q4qGCgtliq577ZhURsWpbnaqc1EStFIj0hniqpkkkEbB062BomSZmEzFAhNOI431sjP4fxstJMsFyQ+5uNKtWlOkQdzD5y87LvqPRXP++0OiLhqnlxJp6yVerf+EEp4Lhd1NZV0LalpaiFYKy7OsUySu69Sb4UuWZyaiRSlpWkBLEjVd+V4eZQScRd1U5D/wCu7YRX0iMdU82bZzT1fRav5l6Ut3MahtbLVUq09RJapZqVZTMtBPUxndNTBpJRFIGWqhEiqlRsSONCJUT5w/D6fvUQf6sAJ20Oz+77GPU16b/w+NX84PT5aeV/rypvT9zpstLYOjoHX+mTWUuvNPW2sZaiOI3CppgRC0MxZBKplhO+CValT1I98iWotisd9Rw3bH7xxfFTkJUVpUR6Fr9MwIhDUq+o78LTUHLb09cj9E1fqJ5UakiqrTy51Hf9LUlXLFWTVUtVW6Lq1obhG1ZQVEO+ojheOGaOoaSalppUkqIxFpwMpIerW+CHfO25oIzxPBU7Fn5bQ9mP3HPrRFHftS6D1trXnv6UtM84LHqWaelueotDWyXT92NxrKuCXT8t1sUnwy1NurJ6qKjobyKSmqqWacRyVEipJBOqUupZt+wPs42GjGlQCcILuwvd281iz8yN0KMXpzuVpotJXz0a8xzY+dFhvFdTzcs7zp67WI3G/S0TK60VHdCUoNRS0NVEk1jnnQ1Twda1VlSwFHTrKVAMm3rs5t1jSSgeZdXFDuzawPqM49MXIz1r8qabkXyr1dz95h6E9NHNcQU2n9SaW1Zq/wCGvOnb0kr0nw9Sa5o69lcwLMKuoQK6S75J2w0rb5M84QVjWW+ORPkMshJBPCnx3eLDap0hcNUT6kGjeaNsu9DWRo990Pq1KfU2lL2JIF6Jmo2b4mhVlxKJqWUxzFxI1POQp4ep1Z/Gu++HYQkhxe3vf+cIrxW+jj8PzmLdblb9Q+nf0YXzmBp6mCXugs1Dbt1hEsxqj8RSwmKSCN5WeQNUxKW3MAdhKkDIS+FYY7Nx9d8EnxBwlQ/yTxqOtokvQ3IPT3LHUfM29aQuF8rrHqatorpJaqjo1kFnrIaSCjeShqSGqo45oaC3h6dpWiR6RXiVC75NEtGMqFNzbmpxDRnC1MUdWer7dVeKe81/wz7XzM19pHmby95h8w+Qt/s9vq7FTTaRsEcDJaKuoae4QQSQSwJR1NS9RWh6ynjWVlqn6nWYKeJOBphLM/e52OdtY0pIJLhzsPYWJbmBFD/UD+DHynvzzWPRtV6vBrn9kStDqKaz2m72unr4GPw0VdTvDRXBIXjYx5oDUoQse9ozGquj9AxXBB3buee2sGrxSsLBLDeW4ljemdo5Tag9OGuPRrH/APZ7XlooX5rO02o2Sgo6ipq5bXUM0NHItJHEahQVoarI2fKwmGezccHxUuYFAgPirrvtyjWZiVeQG3R7wzNDa15xjU81FpTk3zTuMceyplnj0ZeSZImZwsuBTBhu6cmC3YlGGcgjhS/CYwCQ5MaZEyWl8SgA20d9+3dHpt/D05sX3XPIU2LV1BXQah01qCrs9bRVMEyy2umeOOppUkhlUTQRuk0rRxyeyyBGZIxju/i5akyihThrZUjmeJmIStzmNCOhVJrWxwyyU9JVXasiiLrNPQ2qsq6amdAC6yzwxPGjgMvykjv289uHzThoq+soz4wTlftEL86PVn6WOWcdfT8x/UjyV0LqmkHUkopb/T1NxYKN3TagpWlqGZh4HT3E4ABzjjQiUsDzhhtLD1aAM4L/AMVG6uusQc/rt9K2qNPz3Gw+pPkc9upwoqYa3U9PSVcBZQx6lBIwqS4GC0SxtIoHdM+XSAGJFYXMStWXr237ojvU/rk9PGhb9YbHqrmfp/T0t2t0V3tk10mitVLWUbsyLKstc8Gz5o5FMcqxyqykNGp4bLwqYIuX+9BwcopeIqOM23jO1q9WhCg/Ec9OCsK+08wdB3O3Pd6KzRyJrGwu7PUY/wCYFPHcGmaliLDrzKjdAfM6hPn4s4R/pw241fZSGp8OtwwJBfPZy+YszZvU29ZcqCg09yl54cy6Ophknjumh7TbdT2rCRCXabhb7lNTh2UnpoWDSlWVAzDBxzkpTRSm5Ee0T9RKXvwIf+b6com064ttzpRVao0jzd5dQfD/ABhrdQ6KuVshhiwTvkmmg6OCAfDnwRkEY4TjBLIIPP5aHqlqA81hzPb+wztA86+W+saPSly0fzM0jqyz36letsktJdAou9Ou7dJTwybJm27H3KYwybTkAAnhv6lKqRnCStIYE5W18Q/tRaYq6uBr5p+jrZYlBaVI0YgYGSyOBgYHkfpwUueUjCuJNAI3a2Qy7NVxQXWluFZUVMFzRDFHWQ1LRVCocZjLqfnjOFJjkDJlQduQCJPlJNG+IpDE4s9uv5FD/U3+GLyQ583NNd8s79Z+SXM56kzXKekt2206hD/+oamjhYinqT3PxFOoDZIkiYkOr/B/kp8lOH/SdmY4H55NCJkiUtWOxzZmPKnW/GKXaj/Ct9Udlv1ifRJ5Ha50bQpKogpbrT0EtT1ch5TS1NPEk0+2OJFaSXIMj5JUMTqP5WSqWRNSXN892RduUDK8GtCkrQoU5EexbLdnHLPnpoPXPJ3mJQ6C5yaSXltqyhokvNRp6urIalloZ2k+FkeWiqn2RyCMkESk4B7bSrH5D+c/DpkzyZJKpZsWblUXHC0ffv8Axj/yz/lSRKmqwrF2ZQfbdq3vQmsEtKaJ1HNXWnVVbTporSb1dFdEpXpstdJFlV0p6SllctBARFGTLNuyo7JI0pK5fAMhZmtcEX2hnfOOf/5R+TlzW8PKU4pXOlWow6BodOp7/eNba71hXaR1Jp6vtVTWStMs1EsRqnYASyiUYEj9UOOq7BmzuLNu4+tfhvy0qV4dEuckhSQKu9vmPknj/wAatcxU2Wq9SGbXaA4LodJrb0uuhpq+60tO0tDLNSl6WlnAG+4TSCOVZEjJBEZJGckdiQfQSpiJtZcwN67mjhzZcyUyVoZuY4gw6p9eaeutVbayymlguRj6VRT25HR7gZVUSOZ02h6ooXVMOmFBICsVwRlECp+vWnrAApJc8beux9sC6h5rwzUsFVeai5x1khiqhT1LNILOkIkUQbPkJJ64jIxuAVVZiwDcSV4ck+Ue0KC6Oo3399/CGtV8w7PqeuoNLads981tGxjgohsNKzS9RZGMzIDkttdACQu1c7F3hRZ8OQ6pimaLTOphSHGfDKLL661PRabq7deZ7xRz6efT61b2ullqQqwBGEsTB+zuVYrFv3fKwGNwLcY5KFrOEbdZQ+csJLq/vARXzT95twl0xpbTkDXerpJI3F5o6aF8RFAHVxLiOOVQY1Zg4aQhS+1lXOlaM5nT+e9oSkuBhHt3iVLhr251FruehKHmBVT19dHDHA9DHT9G2YdDF0JAMPmPfG5Vyrlyz7yqrxmMurgUHeHJUP8AL3G8/wBiBK7QGhtMXGvvepuZ1ZS2ythrpIK5oHkr6ubqyRIUkBCBJ2Ds0i75FTqBsHuvQ/5E1flQKjVRobIxEIQQVq28e/p1ht6RulmqdO3OvvV91NZ7jbpVnpltsp2JEqZdDECqQRD5fnO4s8hC7QpD14gEK8rcxD5QlrDNttq2+3rFw+XvL+36L1pcI9Zamp642moilnpIdy01tqyizHfBEcSmNekXd41xuOWCgluZ4ibiTQNrf7QKUoIL6bImLuvzBrOWPLutvtm0zV655nVhW30VPUTGCatqFDM9QzvuVEp45Ip2hBXorIOoIXdgMQdS2dhujSsUcAmlNjZfNX4xyy5l6851cwNZ0c+uRf7Fc66seWONrbKppXkJG5ZmiVXAUnAjPZCe+FO3rS/1pSSip2/UZ5ZV/mre5u3GFGgmuejo0tNy15LJZneKaBLpVfCCpnhnQxEK3eNDuLMfCord27ArUTMOJIf7ggUYql+vItuiY9ac+9QaD17eNUcstZ26HRliu9Tb9MWay3SjggqlqHZpa2amhRmrBIyo7yuoeR5IwW2RlSCPBlY8wblpoqbNAqlVdPz7tHRrkr63JecyVr19lfRjJmAUNYdzJhS5dJ+kqTFfyLEoVm7uyKBheVN8KUVBeDR5SztsvDWvHqc07Tcjrlqv/iKj1HeI7XcFqKenqoqebrMwDI9HKC8DxlpFZF3LGHhKtIj4ET4YlWHDfWqCFzQELYu+/T1yyhO0b6pr0y2qVLrdIrrQ3Y2bUVIZfhDS0MySyRVAmUFizovSCHILx4KhXVuCV4XJqGoett0XMkh2sHcdPe5i1np+57dSiq0uHMKLVGhacrFW098keebTFSWJjmirpgJzQSgOVjqhMYZEAScK3THO8TLTiwqz9eEWgkBwajvuD/MKHOzWGn+WcU1XW26y8trnGtXU2ueLSYq7bcEeReqHCguhiMks0tLSSx1BINTGrR9YxggEl063w1IqxdqEWDNa9jxrFZrN6grDzEv8semuXPK7ROp6OaWz3Svu1qpa2CasiDuIoJo+nVwNNECUkeNcsQq57kbz4LCjFkeX0W1aFBWJRBVXg3vEkWCk5X6LvVv1vqvVjaQnqY6p6Gw2vUkrUNSUdqeYdSSNN01OqiOWSVyiZG4R7VIWULWCEkkDQhCFJSDhAD0i1uhqGqgvdvkjotH3DQzWxY6KupK2Zqmir3k6tShDSSR1MLEQSK5+dSruHG/YcMytSK63PDZcxQDUINdh5Nl2NdkI/PDWnMXl1pm66k5eafpNXPG22903TFMyxupHxqFnWJgm0Fxljkq3b5uCkJCyyje3xD1IBAC1CmgOJ0I5U3fXPqp5rXu+6r1xr+x0cH7MEE6WC8uJtF0EqujUi0QDTLK5SN3lCvMzfuxKomwvZaUhOFIf3+IZIAUkE3G29Dw9oimx8o9UXqrg03yp0xrDSdmiaqhivVymNfHTJOjxtJCvyrQrKv8A6jRpuBVyhKkAmvxSEjEoj016Qt0zVg1d8q/2t98WKg0By9sL1NmTUnOrV1TSOKWpnsVUsNvp541WN4IEEDbVQpggncG3BvmBPGRHiZrVbqYWtE8FvDny/wD8pNc8qcMjHQmH0T+r7kDzm5oc3vSn6hOUWt9PamMtTXaE5g2WptsImMm6Ix1Vt3UztEGkjSV4qdhEEjJwCx5RQvGXz4+0dH98tYwEWbZ2rHSan0tUap0npxeYOnbL+2BFBXVtuEwqqS31w2yHoscqWikACTrhiYw6kduHpJriz3+sInFKFEyywHG0b6l0DpXWVN8HrLTenNY04Qov7ct1PcdiZJwpqUkK92Y4BABJPkk8MmISXeKlzSCCl3Gb9h8Rzg9UP4V3Lz1J6oumtn5vcz+X+rKktMxpLXZq2jlmJUk1MIpaaerQmOI7aiokbMSMJMjtmneHSour0jYj8j+tDYaMbvZ3O1uQDRxb55/gXerGyWnV+r9JcwtEc7bzHPBNS2eymW1VtzSRmFQyrWstOjxnpv0N5WRHcI8RiVJMx8HMSL/J4aMdCV+SkzCASXNDkOe314xBfp4/CR9aOoda6rgmssXIqCx1NRaZ7vqu33mz0V5UqI5Y6H4fp1VRA6sSWTMMsTLuyHMfASvBKxYQK9MuW1rdI2eK/IS1NifbcPSz1NvWPWlyF0TfOU+gdNWbUt5gvOr0o4orzc4rtVVZuEyRhOoKiqSOfZtAVY2AES4RfkAHHVkyVJSHd9ZsG6UjzXjCmYq1KMGf0eKLfiXctdQKugvVlp+482EotB0V0W6Vmna2G4QWGjqaSRTdLhp+qjeG70SzLQLXIwqXNBCUWkl7TU4eLDjECaXqPjXaG+FWzpp5hR3vSjuwfaWrlnHLn8Ozml+Gt6kOeqWjnZ6TuXHK31Malp6uJVlqzdOXWqq6RGkqJKKzXB5IrNXyokpSAmSnbfIkDxOY4zikfqUvzXfl9PxaOj4yVPlowyzy/wCwbqVd49M9dYNJ6EsFHaf2VcNM6cgt/wCzVq7dBLTrYaGGF9jvWRjdQ08Ee9kqJHSOEjO5D57EoBNG5bOOxu0cJUwqBxElvMa7c2e+3M5xyT9Vvpz5X/iTek/TunfS/wCqiz+pLWWma6eu03W3rX0dW1zqmEayJfJUp/iJWp6Zp3jmnphJhgXl2O8xyzFpmyylKsTbxfK39jZLeROxKThBvTK5YE+lDsjnlyguXrU/DP53cvNH2L0k+rjmjoC5aeNRJpPUNdTalhmq2ike61eir5aaSaCCJgq74l6ZnUbK6ndzC8eNav1tTzdn3Z2jqP8AvSoJLB8gxb/9nIq8egD1B8obV+IB6SYtF11/11yvq9QWq36gsMt4hn+M0nc+iWjS4W1Z4xP0hPUU1RQSO0THflWMacdNBExFmB2/UcYgyppTdujbuPaOc2kPQR6uOfdLpvkF+I7oHlJzf5X6csEmm9Kc5bDqdP8A7gWMQmb4eeluQjkmraOSIwwyW+8QdNhHHKJFmLxnAJSwRTnTXVo6qpkkjFLNdm/Mb9r2ENDRfp69a/4Z1/rdI6Uk1nzi9G12totra00FakqLnytr5HMSXyo01MZ9lDAJI6mso1jraSYxyvHLTgrFGSZCpRqHGt99NAoUialyWWLZDbl0p1iL4/xL/X7yZ9Q2utO8xNF6O9SlhorNcKagoLPbqWKCvqaSFXivWn6indpGiqI5knktUFRVyNASEjpZUIjbOmgTKWsaMKC/9zeCky//AEuCa2fLdVjStco6b+hn8VzkD61dUwcqo9K6h5P880SskTTt3CTQXY0gLTG2VkYxI6R5kamlEcyLkr8QoaXh37KgPX3gv1FKCo231HWLaak53+jtNfaa0jqXnP6e5+aF+MKWm0Talpqm4XLO5IzFTxyPsQ7WG9giHb8zdgQ5RWkAkt2jGVJJKU02uNe0SnZLxy+pbNTJovUnLussxed6dIdV03w8mzJn6czSygrEFdmVcrHtO7p4JCio837+0NCg3mt7dHPKPIf+LfzZ0h6kucV81j6eZrjeZBY7Xbqevrqx6OmvMtAK5FMAL4hpnjrhJEkwjVpY5pWWN6l2PnfzYE4CXKWUq3UfdcH5j1P/AInPTJnmbPlCYg3DAsKV/lY46NpLWc2lKSCqNluOo0PR/ZddeaaS6mUwluq9PO5TaSTkly5GW2dPD8eAX+K8aqaZilm9sbEDbQ25x95//qH8WEYUS2F8WCj7LVO0tufKPRF+FZ6cbva/S3qi8vX0sur4rnT2CZ9G26kvV20nAZHlkrPhqqURwwiapCRsIJGcRSyR70DxH6R+ECpUkpxktvJ1TaW2R+ff/KJwn+MKsAQTsAAZrlxQ9DaK/aw0veLxzk0Pyl1Bpfm1679SJcL1cotK6u57QW+gu1HbZJdklTaUpS1vpAsE7yh2hPTWWNOmrKW7E4qcBSiAT/1DPuHO/DMx5/w0lGEqQACA+1nGZs+z2igOoF9N2koOZF9oZdV3XWluq1qLVpTTcFpl03ZaKqq5SKW81tyjaetqYop4KWajpqbqxSxzt8SiIxizq8PiOJYbPaa7chuvwjXKnqSAhKnJzsOjF9/vFXL1rSq1W+rdRV9dpmlhjngqLBp+K21ccdmiarlleO0KsjwUVHFGN0jyF3kWONWLSM85s4Vf9QB6+5O8vFYgkhWIub377d1uEMmouupNR60v2p9e3m8XvWN3rP2pcrlXTSSVd2nm2uaiolc9SSRt4fc5J7jBxjhU+cpVcjqkF4cBJwANChYtMXm63uoslnSatuL1ENOlOpBeoM5CRoqAklyxCgj37Zz24BUwAimrRalJPluLbdcqwckpL1ya1TebGbdqDl/r5VnpLk3w8ttuFMTmN6eX/wBOUDyCGwRgOuD3GgTpqTiSotx0IUSmYAPt9kX25U/iRfiO+ka/Wi2aR5m6m0dT3qkt9/ptM6jstK9lqoqymVoa2mt8yxw0y1CPFO8sJgaWctLI29nZoZ4VQpBA3n1uXuHgv+MnHRXmI1lrOLSwfjU+vPmubxU3zmLyo5XXSGlaqsdZadBW6M3G4Juk6LV1wd44BtIjlWSRhIkxJHneX7JQtLA6vrpAq8OoscSiO3960gvY/wAZX1Hs1DrPXmlvSdzE1GGarppbzo17MuKTfI8QqbbX0jmok3KIsRyFpG6ZKLwSZ4ZiCANij7kjfAf8NJdYPm4DLpXYBSF2w/i/6v1RfQ195UcwdP8AULURn0RzQqbbRU+9AUzb7/SXqgWVFSYq+6MvsUkoN4Lf2IzJD7gfZJHWBVLTdIBzuU9a8PqLfcv/AFncnuZt3/4JvXrq9WHpo1pMgqaak17QaepbWxCF4m/4w02ojgppdrIJJaRYWkMaOkv5GpSppLy8Kt2FieSnHQuYD9UpKfO4Lt/p67KNTtvi4nNj1mcqPSB6d9F3S4eoy1+pvXN6uVXZbPd5NVW69zvdCnxDxVhtNNGj0NHGe8xCzzFoIz0zIXiWZc7CpQS6wHZm7PnstGkS5algJoguHvltpttlHmU5qUlfzk9aFq5/645s0vMmyanrWlvsxjijWZaWiCRUcZpQsaUwFPR06xKgZVWMHLYkPmfynjpk+V+mYhlpt/8A8nq7x6DwMsSaylEpVQ36U+InrmHHqG/zSWe1TLbrjIcmunVpIqOJUZAsaqwLOpw/cgMxQkkKRx5yTNGLERTWrRtTKBASKe3C0OLQ3IPSNhs1FT1NZqCt6MMQyKswBSI1zhUAOMjtnPbHnjpf/LTv9JYHhGY+FSB5nPGHXe+U0cNqudLp25LLFVq0FTRXHaYquFuzo00aBx2OMsG/h5Gzwn51aFpUrLMX9YRP8IFIYhwb7Ir1qLTeruVUNfR2S11cUs4iamoZJ0dpYZFxN0p1AWpUhVOFIckksrMWbj3v438/J8UofuUxs+/J9nfjHk/F/hlygf1jy3b2yf1ioV5v1Vdp56qeskqG3MsImO2SNSfylDkJ2wNoI7AZHHt5KAQwpq8edSlRdrNCnpevq6KsWtW5V1CadhNtVXDnIIBGBt98fMV7HsckcLnJuBWBlzCA7260izdNf6pLDNWawkpIqYaUaGOtqYY5ZGlMQjRQBh1Yx1UigsBvk2+cEtw1IBmNLu/T2jomYn9ZSTRn0MupiKLtzLuKW2DS+k2mp9MUzybKVQ7dV2IJY7j+ctjGfmYkk4KgDpI8ICoqXUnWhlGRM1jSzap8loKWvUWqrbV28W6gi300IqmSQNinMqbusZDgJGCxcE424UDIAPFTJaCKwCCcIIPHRNeTtGl+0xq+Cz6a1jriiuNJbrgVpbes7IstXtjV+p8O3eNCrIFJUZcgkMzHME8ElEupHQc4JUnH5lmh7/UD6eplguVRJV2epudZVyCn6MNX1K2k3dgsgYEkOXUFSNzsCvyt2FTV4mTkIGWBizPruzZ99aXi1XL71EVmgbLqlL9pu8aoq64sXFXWpSrJNNhZZaWNzPLNUBUVxPtRI9i78yGMjleI8K5cW69bCNiZwSokCvxzL6pEO3jnLW110kvN/wBR819QXlKanpof2vOUp6OjSYPHGYo3AYID1FbaE3t1dgY542o8PQJ8oG7+ekIXhUMQFta2ZQ846rVHMbSeqKKlr9I10k9JSIYfi1VaiUTuWkWA7i056cm0bYzKAz4fIU5jNwFyD3jVLZ2JAUN/9iE6K02uoq57VS9XVet2yYLVG0qvVzyHG1ZURmqZFyXZQ6KWAAZiGXjSlSgQo0G2kYlIDkVxC9AANmVCeUP618uNdm9i50GjLBpO7ZXpWuVYxLQYh6hLw1jySJkB3VpyT3bAVckKXPQoYRXOHJBNDU7PZ6ju4hSvGr9daxvqUd9vbvNSvE1uqae709MkEww8brPQRClZmDMVdUVFAOCT5AS0oBUkU3h6c68tkCsuAgWOdRuruPHpaHCttvdxmi0/ctaz6xmr7jV1rQVlxnqpmrxSBI4KiQKUaZggYyq5bpRROURDgAsglwGy19wSEskEqIGeZ4PnupthZtGq5OX+i4tXC93GTWN2qoqWkSFlAoLXC6SyzrKi46s0tPHEmDt3JUMBlc8LXKxzcIDAd/5DCgJOEGh1QZPsMXy9F1yruaGno4+TlxntfNTTa1VyvGl7xRTGzzRz1s83xVBXj/louvHURwT09XiTdTCeJ2IwOR49Blrc1BtthyGd6u9afGeebwi/iG87uclu0JYuUev+Xk2lbTe6mCuesrHpaqC5iniQk0UsZYh46lpCs4IMsDpjzOoP8ShC1FQd+h7+xjPPdCSD0r14xXzQ/qXvlLpC1ajlu0dm5nWN4UpbxBHCKlGI2u1agVZKmmqI9kcpmMzGYLKGiYoRoneHAWwqDlbjnt3WglJBSBUDWwhvQ57Iv/pznhFzBstg+Nt2kZrdX6gp5KSqrbjJcKS33WnhhmkepwiyRUmcRrWbgqEypJGAw38+b4YpYfwj5ziJCgokX2+gFbc3grZvV/YeVtBfanTXJUWC2y1tXLU0dZKaaeilecFWnaGN3Znd+nDFGBFhshnZZHBjwilhsRbWjALKVPhYded8u5iFOcfqh1NzekisFzuOmrTp1bdNVVtorLRPLhw5kjMjU8rYDRmCQRTFVR8gs22PD5fgEo82ft8w1IqQa4nu5G/g2UNPl7rjQl81boinp6cLNQTR1FI1VeorZCVHTEkiVMUcy1GXaUDMaM37kMyANAZMkrCVHbpqkdbRZWoTK0ZqM+tzRP8AzZ17ScstKUN+0Dq2nk+PphBFdYmWhoRMw6kxpUUCpu1yk2KW6AZkYrlY2jbOWX4crXgWGap2/XvlFY2quzNyfi/9jmjcfU/ps1LJcNHa111VIAhq11PJY6aEDxDS26kieOngUYwrSSOSWZmy21fQf/GL/wCjNwfvnGmR4dE9P7F4nO4fEemTmd6lbdqzVdn1J6efXJyyrtG1tsmFTTWXUek7sbPPCss0VQmn7kkdXWtKHhpXSGrWXqmAvTwr1JOPHLmJcJQaem+tdWh6QQ5Wkhu5GQawerkRC0344PpXslPT2Wu01z45i61px8LWS2jS9HaKOsqEBEskUVbcerTqGGzpSruVvPy4bhZ/Iy0oLH0yjSn8LOIJsDtO56HOOmHJz1Jcg/UM1DScpuaek9W6imty3QWBKjp3qCnKIzO9ukxOVj3qkjxrJFG4ZTJleNEqcmY2E3DxhX4RSHKxVL1D32HKu9uETzU0FRRSLFX08tFJ4CTIY2Hfv2bGeNAUE2P38QCXUh2DapGhoEIcFRk43HH9Ptj/AH4p3NLQZTUPmd3vaA/2TSOHU08O0rhk2gg/bB/v6cXiZzCZ2IBrDZFafVRyl54a/wCUWobN6YObNq5K82GdXp7jXUa1EFXTbJFlpVlZZDQyydSNkrURzC8SnCgl1qbMVhZH+trQ2RMlghUwEoF7sfrn2jzzcwvVB+MT6fNRWjQmqdJa2j1LLajaLDWVfK2C41F4uZDRRV1Ld7cz0lZUIzQncS8c5J60EuWJ56fGTpLBadcdcI6Q/F+GnAqQabizDOmQaOFVFX1d21e2tqOmqk5iR1cmp56uwVUUDUEvWE8lXKkKrT0caSSZ7JFFHlVIRTt45czxC8asKtb93SPRSPBy0ISAGGzjYWqcxmY7r+hv8V/1J37mjyW9MfM/QXLPm1pe73Kn09PJTWiFbqaHoOJjHDHUU9unl6aSO+SkUgR9kTblhboS/wAlMDSyLMD/AB9GON4j8VLwKWhTZtWm+jdw8elO8+nnkVqefSd1ruUPKmS6WO5Q3qx1kGnqSlqrNXRlWSejqKeKOWBvlAIQqrgbXVgSvHYMlBIZIpu10jhJmLcsol71u+4vypFHubXo79U1w5s3/WPps9Wmn/SbYKuqkuNT+xaS53SXVFVIKfqC+2aokW0S9NoqhoZqeNXImYTrLIzVHCp6JqnVLOHWyHyJst3nDF0ytW/HZkIuhyoi54W+0V9v5+37lFrPUEEsaW++aOobhbjc4QmHeut9UDHTVBYbsUsrwbW2hU2BnbKEwByQ0KmFDkoBG12bt7xKkN8qYz0qG3XCfA2hWQ7yc+wB7/XGfb278PyD3iglgCe9Pf1iofM713cp+S2q6DR3qNvlw9NMtzncaQ1VqSm36S1eoK7ZrXe6UvEXj6kJnp6r4SWEl8GSNPiChagnh2+YfLJmEhBc7Lkcvd4ot62vTbruq0hy99YPpd9T/p/5Y8zdA3Oq1JdtS36psVDpjU9wmSALcaq4BZqKivBNNFF8RKwFVEUiaSLpxcLXLKqtUV/vSLlrSksoHCQzZtsA2bRzEeYX0zeuO08j/UPyuuWtOSHI/mjbOX9bUrPTaXoEaqqIWacVNcLlQzSR3Goihqqh0l3up6UWJIyOsuGa4OMhs73OdT8R3QgKJEtV25DKmWjHbr1YV3oj0b6wL1zaoOS3PPntzvp6ezXSStv+tG/4VNcsNLWUF7Mkc5ra6plp5aV+iJYkV4NxhPUaV547xCARiOItawY1qd2wc4z+BlTlyzYB67aUtv23jkFr+o0hzE5h1fMWhSKHX8lRNNDU4FXBDG1QWlSlpZgVoViMrosECxinEg2KA7Z5s/x8yYMJsY7MrwyQArvt5mECuhNrrXqKA1FNTSBt8VMAQwLbhKkbdt6kse2N2WB8545s0ftQUKqDGzwq1SlBcuhvrlyiuFx1Hdo447XpOitOsL086z74pp/2LQEuW/aFUJSqwR/mJgZgRKWAyw38ZD+PBoo+XZRzuplvj0s//wAl/wDUyEtMzNWG+vxFvuRPqCuWhavSzcmtRX7UnOaz1VXbaK79SSll/aEs0lRV3O4NFhYqN+tIqwODG0cXTw/cca1rmyvOCRag7cvWPNrQJv8Au1XfZn12RdfmX6zK31KVlw0pqGfQ1Ny6uHRkSPTtgis1BqRrfKjrSVzdSWpuFnaWnn/cyMtO8kWFjXpvjer8xOKTgABsWz21yfNu8c1P4iSGKyTmHLdvQl45g81eTlxludZqvR1updU76uWokoZY5ZqqwwPM0yk9laojLVEzSVTM7gRR79iugG+V4pMwkKAc93vXf/IzzfBqlAlJLHVrRWO8awiuMipatLaftiQ0a0MUiRyPPUzZkMlwlmbDNOzSS/IcRIphj6bGEE9IrCQ2umyOalCjQ9stzvnmavCfS0lR1Xtxt8kkHXZuuuOoMIQoXPyhQSpbznacMO2c8shYAzjQry8B6RMX/wBybxXaWs/L2uttmrOV9qmr66WGkpDFO09WViluk1QhWWWaEKophIxjWPERQiaTJmSmrX9aekJUTcudm7Oz3Od+DRIXNrTXppHLyxU3LDXuo9Wc3aGueGruL2q40sWpKWalhni61NV5FDUwTLLTyQQvMgHz9WYOpjtas5h9246vDUuSQlLZts4RW/UM+o/2ncaHmJJeL1f6EpTy2+81lTFV0xRY4jTkNiWHCdJNo2lViXKjaONBIUwU5bVN8JNHCTe9NCDmn7PdrjYqRopbrDbBFFWI1XULFCy7JVVoldgpyI51Xb3O1lH04xhdSkDj/YaqUwBdn1f6g3NoKehq6iLUtNeKS5xrTkUkdBLGD1T8g3OoCDbtPhsklAN3filksySGERKiFOQed+jV+IcFv05TV11slPfKu2WwT0j14mpCskFLEkLOkk6Lv2qR0t5G58s21GYCPgUnLI9+veDm+VOI5HhutBy5Uln05fhS3GnuQtk0EjwVNMnwHx9O0rCKojyj5SWEK5LLks7BlJQg0KFk31qkCnxH7EjZQMDWl8mB6xcb0w8stM3myc1qG/6cpLjY6ueO3QW6vPxEdNHE3WmZS2Cs2XogZVCuTAhyMccb8r41SJif1liP50pHW8H4UKQoqDg89NtiYuW/J7SOhJLve9OWOktF1d6inkruo8tSYS5+XquSduwRqFGAcHIJJJ4f5Hxs7xKv/cp23Rv8LIRJGGWPuH3QW2Gou0wJDGNViXPc5PzE/wD9wH8OMqU5gRoU+VIlhIUBWBY2G4ZGR/f0HBJFKZQmpGJqwTWpFW0wVhG8dVLAyn2247/pgj+XEQp7RFV3QvV8Fq1DZobRXUkVxt00a9SKojV4ioJIJz3z3JGMEZPf34Z4aYUKcGAIBZOveGfX+mb05XG+6O1/d46vUWnbdX0tZebcb48H7bt6kdSM1bJuZkHzdNmQy9PpCeISHj0/4v8A8n8TLH6Fr8p2XH1w5xx/G/ipK/8A2AAKD7n+/aLh8uvw9fShqXmm/LbQ9x0hrrSF105V6v5Nc5LFf5LzRXughqzTy2/UNtpbjTj9o0NT1IoKqP4eKtjjeOZUrKcqfTj8h4tIKVqPNmPAsdWjzniZEshwlhxNO9d2yF/nL+Dhy21dddaWzlpr/mTa9ZS0sk+lI9UXQVVquUscEs01JFUwoGM/zxllnw1PBTiYQ1sbS1EWlP5rxUsYUsSNzU1nnui1eHlGpBY1vQP0PLLaY4X87fS3zg9OMMv/ANzdD6+5fwrYLHdL9U3yBJaGOWqqY45B8RR9WBIaeWQIrSOzTlV6HXdwnGmR/wCSKWrzIBa7UOtsLnfi0hsBVV94H9vuhhyaGn5fcx7jyX5lVFtpNQU1yRqKokSWpoauqAlSOCUIpkME0kRVEZAZWiWRFaKTfJtP5yWsUDHVsiedoxHwSwUlNRxruod92hKqeaUlfUUWqrVf4L/fPgmiSUxS7KQSruklDSSFTLI5KMkaJGERQoYeNPh5kpacOR319IVORMS2IV1TXJ4Kadq9U2ywXPUVJc54GhmEMwBVJIeptBzghy0quwyAF2KRu+ZV41TAgqCSPtsoVLKiCHoN8TXavUC1fQ51D/xVaL7HWQTtVafkioqeSNJd2J0iCzIyhpAohOwGWZ2UyP1OMJ/HgFkltjiGjxIUl9mxhrlCbr/mhpq9Xu237SlrrN4lSrqjX0+ylnqIxIu2MFC8mFlXCyDszSL+ULlkrw2Eec0gJniXYt19fiIJirwUmnqNP2uotsWyGm60Z6UC93dEZj85baP/AFeo+Adu0nI2swwgtrPXGM+AhRUkuS2zLlF3uSmpaTkxpSPU9807Kkc9vNxlaoU756iQhIx8UvzwmONQ3TZWG55Tt3Ksh404iZMIfd/NsbRiRcddU5CGXzh1HQ2kVcOvqS3ai1NeKhKu60tsk7U2IRJHSVfxIkRvzU5YRoA8YV3Xe6JHo8PKeqaAWhE7MgOTybvs4xGPKvVV6qLtrBDHVR3WttqRyTQUw300MM8WdsEURwyt0doVMrgkZYrk/GSk521v7w2TOI/0ztX4hRveo6Z+Y9h0XougvV4oKVXo6Y1dfKs0tc7uXQymKHpKpKxhBGgRjIxUl24KWj/1OWD8CG3Qo+IV+2nK4bg7l4auutaSXPVtbWUN0tsqU0sVjo6e306RUZp6ddruke0bY3llnZWI3MGJJ+Y5uR4YBGECBUCVY1ltOx18w++RXMbmjy7qq6y6S1Ff9O6Xnu9uq72LZcnpZ2lhZhT75UZfyu4+Rjtdlw2ACQvxcpCy9i1DsiioqtQHnfpTb7xd/R/4h9JpnREmkNe8ubfznt6gTzwaj6clFR9mCO3WjmmrWcv87TkOGQMm1AynkTvxBmFwwrz1z5RtJQCFBTHT3p2iCbfz35J3e7XhKX03VnKPUs9fDMzaW1rcKa3QwIGJppbZIs8UkYYBk2bOnnDCRUzxo/4M2XTG4G0e7QpXiCVHGAOFxtfc0MC801bcGe7aDuVVqcUglppFaop6WaOnLYWGISETzyh5HGyMMwU4jJXIVqZgBwzabKE+lucEpCkv+uo4s3W/tFzOW3pH9XvOK6aZuN05Saj0jbZZY6ylmuiU1ggm6SBF6FFcjTvJFHE6qJQpiUbV6g7A5pviJEsKCDUPrbAutQFWs9RfeQ7tenOHtF+Hf6wtD1lRcItB8m/hkMjR6grtaWdaRJmxiV5I5mljO6RS28BsOCPzABMzxyCMRcboN0ubNlwGywbQEPixfhUeqfTGlq25XTmN6YNL3LtLXXat1PcquegLMQyxfC2sliS2wrvaMswIDHGAn/kZZIWEnDsYV7mCMyWo4Aa73biG+IhTU/4aXrL1b8brSm1lys5g3WATUf7bt2oK+eKOk2lJUjlagRaVO8gZmSFTgYADb5NUn834eXRSSkcm9feA8TJxtiOLgX6+49xEE3b8OT1cacrZbC/ITmPdamlJilbTpp7tTRtkkK1TSO8Rk2lWK53KGUMFbKjWfzslXmd33H4i/DeBxIcze4+fWsS9d/wx/UbyS1BYOa/Pzlpb7nyD0w1XfbzJpa+2jVE1ripqYzLNU0FbJDFJRSVEcfVhAZmh6iYJKBvnZ8EpJZdr9I9Sj8pLIGE4RUDKvG77ISuUOr+V2j7XT3vmvyV1Rza5e6goqY0Nu1FyiutqtlurDUTSJS0NxoK2QCL9wYqaeKnqXCvUZjj6DQykmSk+ZQtuO8io94qdMBUpIociCm+dCxO+1Yvr6V/V/wDhhU8dx03d/Q5rXkLX/GOop59Ky65RJ0mIj2VUUb3SmqVDSZTogRjqJG+CAW+FnSMRSUNxz94V42X4kkNMCjfZ/epjoZarR6ZLRYLjzA5H8/PUl6a9O/DR1ctxqqrmNS6coIFZXYzWy/xfsyCn+YB2OEAdlynnjUUh2lkgmuZ4094wmfNHlnoBSBuHCou2y75xZGT1iemDTGl9K3q9epjlRqe33Bmpqa7UF+t8tNcpYiiTSRMkojKRsw39NpOiPz4AzwwzkAO44wKPDTVkjCas4PxeJn5b82eWXN6nu1Zyv13YdeUtvkjhrpbZM0yUbuCUWSRV2EsFYgKzZAyMjg0zEKBIU8ImImgMBU8PoxJQp92cKWwf+vz2/wDPDgkZwn9rmlzx4W00aLEI4Z4YZ6mlhlOZVWUxrL7fOoIB9/PBAEOBFYQQzV94jPWvKXQ2tLFdrLe9DaB1NRVTGd7derZFVW6qqAdyvUQGNw5DgNv2M4PcZPCpgGHCR8c40odRBQ4br9RFem/R56bNIagsurtJennkPpjWlvDPR3az6St9HVUTvG8chhmjhR03JLIhIIJVyD2OOJLkS/8A6gNs/nSCX42etyVEjjEvNbp6SB6GFzbm6bRxM0ZkVGIIVtvbcAcfLkZxjIz21DF/1EYCkgFvmEaltWpy9ugvGp97wVL1M1Tb6KKlgrIdrItNLTzGodV+cSF4pVfdCnzhWZGsAsA9d2jDJQGddZWh9x2ecK/VAC4z9j7gEfz7/rxMdHgkBJcj07xHPMzlFojm5oTWPK/mVp6DV+gdQUD2u82uaSSOKspnIJQvE6SRkFVZZI2V0ZVKsCOLJA3E9dGDOEBzkX38zFV+SH4d/pq9Pmh9XcqeW2jdR6l5W6inWa6aS1ZdRqW0VlUHGyZKCujeKKYD91viVGdNokMjRxOmceFYuByjUrxq5lFU3tbbVxHgz/Fy1fyKp/Wnzc0n6WuXGh+WHJyw1yWSGm03RCkpbpWUm6KpuCx7mVTLP8RsZcAxCLCoCQXTiiW6Rsr9aEO/HrWoY1O7327vc94p76Z9bat0Zzv0pzH01GbZOa2YS00EQEMlI8ci1CJGTkwohy6A7gi5DAgHjjfksCpRI2vw4R2PAjCsd47w6h1/bbpbY9SK08AFNFUy5l6jmkmcbCXJ+d48mJww7iNGBBJHHnpZdPmrHbUCDhIzaKu86bzZNBzRXnVGobUsU1Q1TSUtRG6rLFGyLNGrLuyrR1NSjZCFeqmM7FIbhxCgfXKELWBRXPhpoK6dpNRagtV0t8WsajVWjKqnjltdfX0jR3F6N2YbjMQjOcKU3OpbIJ3EEcCVB3SGO60T9RUMJd9ao0OOw8tJTcGpJJGqYKYJUQSylWCxmLYpQnwylSgOMjBPfAPCpkxIdStvDXWNBmL3v1hu3jlBbpWlv1OejdUq2JljwsjptCOrY7FW+bKkFWz3B7jizby+sKmJBUKQparvdo5fWi06bt94hrdWT3Wk/ZdvkpmiK0rUs8QSH5BEsKRqqrEhwuzsM+WhFCsikDjDiWavfPfu1SJcu1VBQLJUws0lUrl4GQgMSBhWDdsZ+vFpAYEmCClDy9PaGbrml0BGKOv5h6N0teayiiR5YaO3LSyTgMBuq5aZo2qGZyEVCcsudzZLk3I8TNFEqZ9coUuXLVVnYaG34imnqd17p/lIlhqtFaQ03o/V9bOlRBZnpnr6KKkRGDzgTzTI8fVAgVHALPDUuUEbQKvU8DOKhWos9j7RzfEowlmbPhEK8v8A1LVOpboRzFipqWKGFJUqIYk6Uk4qXkMlRF2eZmeaT5z1HBc5IjUBOoA/+BfWztGQPiBNRwiz1ijrLTetB680bd6LSWq0lS/2qtFWsccVRTSRSU7B3K4lzCcpnLdRFyx3LxEAk1B6a5QqYQAXsNm922vvharZ7zzt5qTrzI1dpu08w51gtlFcq9YrVa4alkVKelqp0i6VKmxIo4pW2QRH5HMabpI2KQoKtS+tPFsUgBy/NvkREWlXvNj1DT2l7XWPXU1zWM0UtIJZaSqSRF6bo3+LfGI2GcAKwPYZ4zeIQ5IPxD0EGpHDP4izlNpXnI1aY75pjmne75MzVdFc7nQVcsDmBY0atp6rvHIY4+iDOrPEEZArEMmVTFhgpWeQvwvBgBaWwv29fYiEnSvp+1TduZ2mtM1eudK8vNMT0U9z01qbWDS01ku9FFSvUU0sdS8W00dR8NUQ08zjZKTGhwJhjQpBU5RU57d/2xaFJUQnCosRspnfgYm7VXoV9TvL3Q6ar15yW1FofThpZrutzWl+OtVLRKtPM9ZTy0BqcwbZ0mEjggwM5XeVdlxzlAqJAqNrj1HvD5KWJSc+B+aRa3Q3KLX/ACI1FQaC11ZK2mu1dNc4Z55IpBDLK8SSLURM0MBMEnw1REjFAA8Eke59uT5r8m37X3COt+OlYpZZ2etdkOuh6sdnubboDkBFVYsbTuI75JJJ7fTH8+OYVtfONyHsIbmnbJqGK/2S8Ud2rHss1bVG4UpWEosQpJliZGKdRQKhIyQrZPUOflGAYWnCQb5QqYkggg0r3F+uyJcaneSoWdWkwqn/ABnB9u49/rwhbMwhgSSLPrKGzTSw2Cpv0l7udNS0NXI0kRORtdsYzjOD5H3yD2wciJpN6xFBT2pDP1pe7nVxVWj7HG9EkT07VVTTzJLNW0LIwZYEIDK7SKkIYjBLdmxuwwKYxahQveHFy7ttCtbFdLpS2e+x0E8E1K1VAtRTU9Sj7wlLHJlQkYRQZiDLIWDFhgKGpIPCM6ZzcdavBrnpcRfOYPKC+6L5n23lLq5LpVRWu7PUzUcWkK6uqrfHc7m9RTAfDuI6WklZpcQs/VdtsjK6+g/BT14f15JoN4u1b+scv8jLRVYFTemdn2jjxi9HMH1SHlta+R1o9W945raU1V+zpLze6XXHLuoguFzoEv09Bb6qqvVBItZZ73aqmjaaWempqgu1dOYJJqepCv30zf2VDcizE7mYjJidsefmylMcIJHX37sT3jlb6pfVhbfURqe+c9eVs1p5Sc69Lw1doutrtFzqq3S3MPTEtXK009HRVUWEbE7Cutc6innpqgVC/vYJl4FSgC4D2u1NjbtjMY3ypKiQkF0irh88jZvfOKEnmhetQWrUWpp+Zeq7TrimudPVUX/6QrlqJKmSJYpK+kqYWaICFImpEikMckdLJCYpZmSXgZih/wBRTh1preBFfrmAYQKfOzQiLKeC51l1pqugDUdTFJuglcpTFnj/AHhKyfIu9R3GCCFC474BiFkshB4cdsNmqYEEUOVLHcXh1WnTmqxdqe13Wzap+OqaL/8ARsU9OBJMu7EbOKlov+UYiReoCQCAAPzFev4L81OQ6D5hrPIxzvFeDQwJAB1lEx605G82NGyQ1et9AUPL/Tr1Vvpmu09whno6UVVMs8Mss0c00hgaFml3orr+7nRSzwSRp3ZH52S1XcZU+o4sz8dMWrzEEbbNuz7GI7slqvd0tNbVUlPfKuiqN1IEolfdUuqNKyttVsqixNMxI+VEL9vI0yPyPh5iQUEc4k/wUxJKSm9bFueUTCPTZz+03dBqDmF6d+Z9PS01rqb5JZZ7NNT1zW2AwxtVPblHxlPQrLVUaPVyxpEvWXMhJYiL/KSE+QLrty6xnHh5x8yQ+tppziSuYOobDoOx6SorhetOQ6nWrK2ylpaYVCvRwMI56mZJFRnqhPG6Koz0tshAjkdjxh8LNTMmMk31lGqb4ZTOqwzamhFKLne7pfa+a6X+pqbhdp5DLLPK/wD60jNl2P1LHJJHk5Pnx3kICR5bCMplUw4XfWWtsW65GWK8WDl/zH5hmirKWvqaV7Pa6xoSUopI2hqZ5WnOWi/cdj8pLn5QQSDxyPFzEqmplZZ/DQ9ALWtfi1Bttd+RiObvd57Da49YSJTNdrrT1dNZXRmaYlpiKuqkJLASZYQKy4HyOOzI3GkIxEJNk32buUIcy0uEuTrVzwiIbC8kdYh+KS3Rx7Q7J2m25xsi7H5jnAH0z37cbFsARC0LmABINOuW/wBIdUDtSWyghhipIZKemJmiRhIJGMhCs43h45GLFfoVCkqfzFAAIKiPbjxG6LwH/sKC9B/eZgO5iavqXprZXotMo3s7ShS27G/AAycHGe/5fbtxaFYRiw9oMIWXFeN6Ntz4vFsfSB6LebPqi1A0OirfRab0TT1cSXLXNbTTS0NuQEkrShMCsqW29oo2GF7yPChLHF438gmX/m+z5gZKVpASlI3Nsz5x6TuRvot5R+km5WDX/JuwC+a1t1I1LcNQ6mq56y4T08kapUvQxU6baRmXqyLTxIQ0rqGeTpxbfP8AivELmApUYKZNIU4y1Xl3FotJZ+Xt0ui3e86k1rcNUR3i6Nc9prKyhqoaMxxLHAVUq3SRok/cN2QzS72ZnZW54TkR8wCpqpibMDu2bCO45w5Y+UmlaS5y3ik0xTahnkq1qZIp4oAjy/EPKJSxQNmNppHXByMADPvapwbCKN25xKk1A5b73boTA99qZLfUgmZbY7VAAkaMskbZyGYKO23AO5sAd8+2TlEGodoYtSicKgR76/kEKG+WfSegodRa9F6r7JZlM0NyloYaI9FdzRyP8OQFQYyZCkancrGPPYglVWBZ4ZMKleZItwZoPJbfUFdVFzpebdws1uqMz0dFb/iIoqSmY7oozsmQPIEKb5Nq733MFUEKBBCaMOn3G5EvEP8ARQ1G948/3p3/ABEfXlzm5lT6E1hyTr/VvyvuNsqaLUeirPo63RLPbpwyyNLNJSuiKUMkQFQ6wzKTHmNwJBxPDfkZ0wgkE7adax6vxP4vwwBXiwgMxd+xJ1eO++qvS36ZOaeiaLRervTvoGq0gbpS6hk05VUvSpobilPHCDNHTyiGZhHGkLj54pdhZhITvPeMpBuKtoZWjy0vxCkzMSFuoP8AeXpbZCJSeif0c2aGrio/SB6YaeKaRJJetoyin6kiJsR8yxsVdVJAKkHuT578EmRK/wAs0BN8VMLuTXMH3zjiD6kfTZzSt/qW1Dyp9HZ1LzO5O1VtttXqXQVt5jpV0kUQqenV0VUJ7tLURu9Qhk6U0QcLI6qhWIB+J4+SUlpQOWfrVxyju/jVy1oxTyAXLEpFsiKepN4kqt5N+tqsuV75MXz8MT0N88dKwLIKbXN55fDRcV6g6YSKcVduuMX7PrTGEjOBBOrREMzEiR2BM1I/WZYbXcb6xQ8T4dacaphfNyDobGHKLMcjPSbJybh09zjoeR/OX0V8yv2dcKDUNo5OX+LmBb5olUNHUSU1wrZ5JdzIshg+GrHchkEkTSKRqleFY4sJSdxdxtqc+BbbHOn/AJA4Skrxp3pwnhQVHEjhCpzD/FXtvLvTOjbjoLmt6dvUNc+iJr/Y79pjUGidXQUzOqxyrbCz0ZrAQ7S0Rkp5NhjeGMoTwpfj0pYBQzJcEUfiz/2NI8CtZIUCkFswa3FGf4i+3Kb1gaU1bySo+avNc8s+Vl7jNZFcLLR8xLJeoTLAhlVKGsjqUSpaWLYywuIpVYlGX8sj7fBTTMS6vLzp/DvjHP8ADpRMwpJVQVYji/Dc++JW0Pz25dczNEXPXnL5dT6poaGA1Ffbo7LUwXWkIiMvRejcBmqCg3JDGXLnaqkk8OKwoODvbNuEZ5krCQDSrXfOv9irVh/Eu9LdbzCXlZzHh53+m3WJjgkiXmhox7DTOZY1kiVqpJ546d3VgV+JMS5ypdXBTjN/zZYIckOMwRrVo0//ABywnFLY3sRTPYKjoduUdBTTTyUNDXDZVWqriWopKhQGgrIyAyywyjKSoQynehIIIIJB42yyxeMaSEjrrjCIkT1bziSzXK0yIxAaToukq5IDIY5HGD5wdrAeVB7cFLWQGYxapRuKa2/EIGmqXW9JPeItT3XRVypv2jUvbltVFU0zx0LS7oUnE0kgNQiHY7IdkrYkVYctHxFYjUimtsGQmYkZnpzbfp4ZXMbn7yF5Q1BoubfN3l7ymnaWOn3akrv2bTRSSKXiSSpnVaeIuqOyb5F3hHKbgjbamzQkOS0MwqWR+lzw0/K+6JMs9XRapsi6g05WW/U+nZIlmS5WmojrKV4mXKyCpp2aMow+YMG7g5B4pCg2JJ16RYdLg0rnTq948xv4pv4At99XfP8AuPPT0val5Tco5LnpozagtN8q6iGmuV/po46emS3wU9MyUkctLTwmWaWQRLKhIVd7Yiio/wCKk+1G4mNEuelIJXYHnW8cIvTn6YY9IUVvulr0zp2tv1fTQJdKua6QL0AkSyrFLCT16eGZjBKyCMvJG0eT02DN+cP/ADv/APN343wE2b4ScmYJqHCQJZYm2JKrEA552DmPvX/if/4v8V4pKPEIUgyyxLmoFCxGR+yYjzmJrfWluuF00Hy7o6u4x2uy/si51Mlv6kk1UKlpHeEMDsRQ20M2Vk7naARx7v8A8U8evxH4+VOnkY1CpBcF7Fw1TmBYuI8f/wCQSBL8bNRLdCHoCGI3cNnUxVDXFy1ZzY1va7NXX25aku1YEtKNK3y0yNNk7FUBVUbmJwPIGc449OFhKH/+vvHCMpCiMQe1fmOw+m7BbaOj0/boiaWhpKWOkhIGP3KqF2E+wIUEH2b7E8YMQSkNeHYQomN6GlWg1nV26ugSWIW6aF43AO8JOGXse3brlhwjxfhZU8JTNQFB3DgGoqCxzBqDlGrwnjZvhyTIWUqZixIoaEU29IGvMdJR3vS0SuopLl+0Lay/4hM1OtZF2z3waCdcYyeoce4OtPmBUdojGSXAVw94rb6wNPT3fkFc7vb45DfNPT0d9pJo3Ikg6M2yVo5BgqenKzHHnpg+QMafCNiKciIzeKUxSsZNFOLfzz5v2iCO4GtrNXU4pMQxXODqRFmQhJGkUI+EJD/mIbABBycWhKHcNDlylBxt7RNPJ+8UHMTT1p0zWxzx6nsdRaurWPI7reKdRgoSclKgOUn2ntJgBTkbRwPzX5uV4GYiWpJKpxZIDGr1eturR3vxf4Wb4yWubLKQJVVOa7sq9oH9Yfpr1xzVtug71y3slTqvVVBM1khtlujM897iqKpFpkokTJmqevP0fh0BlkaZAiue3HpPAz0BZlqO/wCaR5nxvh1kfuby/PbVo5g6s5P8zOXVVb6PXugda8vrxNGZKekvlqmoJ6oCR4maGOoWNpNrxOmVBw6OM5U8etlCWWUku3N247OkcWrsLx6N/Sd+FXzX5wenrlBzJk5ncuqG13ehnraYVC1JcwCtZYm/dUxEkbrFK6ODkb1G10CyPkWjEcSRnu+Ytc3CCmttfPCLgTfgsSXa2222Xnn7bRGlTVSVApNNzMamJlSSJCxqY/n66yGRgi7oyu3bJkjQnw6ySTnqlDCBMSghn1ziFtc/gu+pG16oku/LSblLryyfs6jkkHx8liMlYYxHUwRUtQJRHGjqJklacB42IwJQY+BXKIOI33ZehhkuYwYGg3trn1juB6ZPTD6j+SehvRty8TmtyvWTQddqS36gC2SquNPfdL15FQbZTzzNA9PKssaukixKF2QqepHHJ1FTEOn/ADWFrlqKyX7U7n0iXvVr6AtBerKaySVV5pOXl2gjdK2vgssdXVVxgSJ7bE4lk6ElHT1NLTySUMsTRTRl9phkAkYVoqMFtbPWkMlkB3vwz582vErVfpE5PXXkRbuQt75W6IqtJJRQ0CW+w/G2iktjiqeoEtnBnqJqAwTzz1FMiyP0XIjBETFeKRJITQ25jrR+kCuYCXIZ+va3KOGnrI1xpm4+pTT9o0DqG/ag0/ount/LyW6Vt2qLpPe3pGlaqkkqKiWWeoKzVs0fXkllkJp2f5kYceP/AC8wGawy12j0/wCPln9RcNXtrnEEVEbRw1NOodpfjGYgDttV3Gf55/lxxFjOOniQ1bw6rRTLT0VFTEKxESg579z3P9Twwg/5hJU+cKUtZDGHjhxUVJ9gff6E8JrhiihKRS8MO80nx1wtlJI6zzl+rKPIRV8KD79+5Pvj7cApQuRFpVQ7eMQ76i+Zmt+WNVoObRNRHEjUdfJVLUJTTxSNHUUxU9KQFmYipKNs7lMYZCoZez+J8LLmpUCxNK1jH4yeUYQDTdu4veG7or1BW24cvLzBonT91vHMG2w1a2uww1y1bVKNuqGqIJmVev0E3s1Mc1HShAXrFs8Pnfi/1nEosja1oWnxWIABNTYPf0bnHDCl5+cyl11eNbyax6dHcyYaqqVZa+OipmcOyRwOVO5AdxUldxZyc72B9dL8KhEsJSmo7844MyaSp1H0jojzn9fGtOZNLb+RPN5qSsrdJ0FNPo7Ulvhjt8lPBWLBVy0FZBTyNBPb6jbbX6cheSlnpYpYmB68MooWmaP2PXPlxq8KwYU4bg8vrtyh4Uls0df+WvKzRWkq3R0vOS/60qKu9XGroxburRTRRx0KSVplMcMcMy3N6imZY0k69K4eYxlUrC9RbLd8cYtE0AFQsnbuufgwzudPJu/6Gu9TX1l10vq01tpsepGqbPWCqVqO9W5LlTyzFVCqweaaldctiogkjDNhQMa1ArsRGtUzESVm7l37N6ZcIZekLnWaTuFLbNU09zvGjhcZJGo6W6MirUpDteeDpydJ22mJWO4qwUYKkKwckUIIY1y0IiwUqFj7e9YnXlboTVGrrLSaf01atMc6tA0WnLjq2/WtqaOlntNrsoNdVQzVJAqbaJQ23dBJsqBOBG0rMVU0zXLqLHj637wp2ISHr6m9+zNxyjv96fPQt6ctd+mXm/oXlvRWjVN5vL01PqXT1TrW61lm0zeE/wD0ja1pDNS0VdRJtq1nhqjG8rJLKxkq13q+xSVJDF69ff1aOMkgGwDaY59I5+cqvSlrT05cx+U/qcsTctKbRJtlXcrFadTUkc09dLSwtNWWS4vnMd8khW7Clmo4pInkoYfEMjwutWFmVXVX4ZjKNil40sbnZkdrt/k8XDxYDSXqR5Mc5OfVq5282T6leXdruFeugqC4vquCwW6yUFVBPd7LJd5LJ0amencdWFa1JQiqancroWgJS5oWWSX1rjGSbJKZOBFwXu/rWmWYyikPNDSd6uOqpeXfK69waq0Yuoai0X+1ajkt+sLfS1dNBNVG701MjCSa3V9GOrHUxRhpDSyRmffSrMQ/YkqGKh4ap62jUFpR5izt22PmRYg+8V95r8tOQetuVmleevpi1a+ntW1lK1Tr/kzWV71dZo6WNlD1NhaQGprLQGdJEikeapgpXWR5JEgqGieiepJCnL9OsLQhS1YFihtsPwd8IGn+ddKnKmy6E1tYaWCywWGOe0TMtLLJWyrdDI9ZRuxZo6mQ7IF3jZtjaR0kij2N2vBTkzJmNRrck0HKvrnaMHjPCpQfIQa2cnlTKISq66uv1fPd6yJnWNEp6anp95ho4QTsiiJJO1csdxyzFndizyMx9ASGCWYxiASpYCz3b6f7jaqgoaaWAXaqht0LFVzMBuYnHcAlS30xnJ4ATSkHVIzpJdhYZAvu2Q+OX/KrmBzSulDpvlly911r+8TORBDYrHPWEscAkSRoUUD3d3RF7klRk8BM8RLAcmkOTLUllTLbS/tbmI9A/pD/AAs9FadsU+tOeFm/+63MeaGRqKjpDEbDp2cMyOix1C//AKSq1ZW21cqNQKf/AE4qgbKlvO+K/JKWpkUT3gpRspC3rm/A1+3jsnoLTOo6LTtBR6m61JT00UdHR0TTRTCggjRVAKwosSOXV5CsCrFlwVRPyjms1c9WhSqlh7dR83yhVvFiSuiWCKplio0JbqIE+cnP5XKttG1mB2kHue+D3CaxqBTWnhaUh2AYjbs6c4alv0fSUN6rLqKe0l5KVKZpI5KgTLtZtkYRiURFUr+XazN3YEKmGlKcNXY6LGDM7/oraHps9uDHbDxe932heiotM25rnUsQWfrxIlKNrYdzKy78kY/d72BI3BQQeF0JrrgPikMCkk32ih2bnvthdkq6sSdW6ywLMi7mfufl+mR5HkDtnH8uAQUgUtrK8TEyhVtz66RvQRS0lSta8kq3Z+/7iVxEvkdvmO3IHnHcgn34sqozdbQ7GCWs1v7DwgvFJb4xDJDVM7EyErTzyZyfJZBjP9eKQFNaCYigPrXfFG/Tb6ltR82NL6AtPp79DmveVfJiW3LcIbzrGa16a03LbxGRTJaUtfxslbNI20KRTLFHGJHeXuoOWQsEAy0s+npQ8BHZ8X4YoUUlVRxJG27NxtHQulp7lX09NJBZLuJiql4KYGo6LEZK7kBDY8ZGAe5wONiS7PfW6OetRCiFksO78Y4J/inerz1K8i9YNpbkJ6iquno6i3wxak0rZ9P0Ul60m1QJBTTftOKi69ItUqnpL8SKxZULIojZXGLx01SQQlbUqA3XVY6n4vw8uaQVI4M7UyI3Xe2UWI/DA016n6ugvevObmgOf9FabxT9Sjq9d6ghmnq1aQzCSKglppLpOXV6cCtrJ4VMVNCqxzbBKyfxpnLH7CCxfZo7Mod+V/48tWFDOKU96hhuvHXKmo7w++pn0xeaOk+URVMlLhJgU3fIy5yB4OQPGRle/HZQNtBHn/3OaUO6v8gUlC1LISsgWRWAkwynBDd/qD9PpngkUUwryi1kkFOKu7P+bjFIbl+Hp6OtTz3C680+WNXzk1TUVUstVqjWeorlcr5dC7fItVcEnhaoES7KeN3BdYo41Z3ILnDM/Hyl/wCw77SfnLKHyPyU5ICQWAdmAbRvxhjVn4e/oa5V3iu5h6003p+bltDRTxUOltZJba6w2E1AhVpqBqmm+OErGCMqZaqc7gGX5gCDHgpQDkOkZM441hqvy04nCCcZzS70plpoptzJ176evQndrxP6RfTTfublt089BbdTacg1PzEdLUbhJF04o6OSKpsjSyxzrLGD1JcGSUKV2g5yZUkn9YcitFKz3VHAExqQZ84JTOVhCnYsnLN6K43akO+4+sn8OXndqfk/ResL0jaV5bap1tHVT26+avp7XW0lBF8QIEqa+5U701XQJUTq0YmmhRo3UNM0SuHAL8XLUE/sH+vSnMV3CDleB8TLCv0rcJZ79K0dq+wjrroT07creTdt1RScgtK2b0/Vl0PWqP2PQvUUBmVncSG0VExpB80shPRWBn3H94M546CZMsOUUJ0/8jFM8QrFhmOQ1rd6HkYXaKk9QdLPZxddV+nLU9Gs8YuQj03e7RUzQZIc0ri41sUcu0gqsiyJkEFgDlbQF2IBJ4iAUuUaVAGRb6pw4RJ0lFROlTHPR04Wc7pQYlYSnaF3P9ThVXv3woHgDgwkAOBrWcWFJJdzS3CK8ay9MXLi/cwbbza0xRVHL7mvS0JtQvNnqa2lavogxZaSvip6uCOrpV3TKsE4lijFRMUjDSFuFTZQWMRNYejxWEFJseFezPEKc2PSnzC1jrrT/NOzcwr9W3Sz2y501BZK3Q8FVUWoS9Iiott301NbLklwQwRCKZkqyQhTpISyy0qQogFJbdlrvD/DzwkGlNFqwyuZvOzWFr5XXqxXqh9QXJCCrs9Xpa8VvMHlJrBrbZ454fgmvv8AxC1viaOki+K68stSpQZjA+HbuSleJAYmpGuV35QC5DrfKlm6EPmKDN2ekcGPTv6QOb+rPVdzM5c6Yv8Ayf5tC/6R0nzHS80mo1uduNsFJS29nq5KJqGaSJi6U/Up+ixqIZkRd6BH/Nv/AJb/APj9P5n/AMh8IPEIUZUn9hUSDhUgnEkBVnxHCoUIuKR90/8AHP8AzVH4v8DPMlSUzV4QkA+ZKmwqdJcswCga3a8dvbl+Ed6Tb5pG56cfkZqTSPM6spJJ49SUNTfZmp6zo7I2iZulFWUsJjjb4d0EmFJYoz7uPt/4f/xnwPgfDjwfh04UCwcluBJJvv4R8h/I/wDk3i/Fzj4uYvEo3cAcyBm1O0eQOm9NdVys9YXPXl5f6emS+aLu90oa/wCHdnh/aC1JpmKOyqWTKzlchW27cqpBAxeLBTNwLPHlnHV8MsGTjNNnPjFkoITBsRgdynBH+3CSsMA0OSAoF7a1eAblTNJdrHdkA3pHNRVPcZZHj+R/vhkRSPONp9jwFOcE6AGy7PEe87pZrdoyk1bbgVrNPXS3aiwGxhKaoTrAH2zA9Qv6MeNMlILg7DrOFLX50uWPz7/MPfWFhtl6sGqLLcYZqyyVNDWU1WIgpeaneJ0cpnsXKFiue27GffgZZwsrYxg5kk/40Nf2O6OnvwQ+T2guXGhI05g6Y5l223QV9TNqG66Ir6Ca72OopoXoTOlkrOoKukRRIs+yQzdWUSgjbs9FJ8DLXLteoru3jb6RwT42d+wgmtrdWrDJ9Pn4Q9Z6c+Y9019rXlryM9YPLSvkp51l0hO9kqrejz5We4WG6u0Vwji3RmKGmqkM0kjdaOUKjLyPF/8AjPhJ/iZfi5oJVKcpDnCCQxNLlsyC2UdWX/5J4qX4dfhZagBMbEW8xa23s0dI+aGnNGcqeYtJzY5iaU1Fp6Gjp1vN1l0/Yai40tPW01NNRW+ruXwhklMlLSXC9rE8NOsah+o3VFPDJF6RPhpILoSMsqkCoy214iPP/uWE4FEk7HLVuXfPaW+BNWyemfn1cKblNryPlXzXu91tJuMWntQ2iOulqrcCj9YUtfCWWLIiIO1SSF9xw5QSR6QAQpJOKhHUdYkYaZtdJK1KtHSsIE2GIBV6EaDbjYCBGqAAeAFCgdgBw9KsKfLAzADcxEFZz25OWrVWhdJaej1dzFu+oLpNa4ZNEaen1BBamheNJqi4y0ufhaWJ5kWSfDrH8xbAQ8DMOCq6c6xUtWMsl2HJt9YlqHWOgLnfrhoq16wsFfquC3vdZKGGfqFqEVUlI06TKDDJGKiKSJgjsyMBvVQQTSXcbe8EkgeawEELFzF5Y6o13Fy6odZ0d91tSWKHVBt9Gs0kMdEK16VKsV0K9Ak1EckJi67HdEd0QK54uYkP2hBU6wf51ziQeYl75o0eiqiq5MUPKe66tpa+3rINZXSrpLTDQCeM1rS1NKryRzrS9V492F3YZ8jsyZpalSaWbXS8ahMcWpCZeefM2hNK6B1xzH5HeoLTWm7qKlb1crHpxtU0Ggp4jhXu9VazIy0U25XhroYpIyjK8wpgW2qIcMFD0fW9osAXNG9uHtHjot+orBrumtWquUWrrZrG29Okd/2ZdsAKsasIalRHMd0eWCrNEssanaJAgXj594lK0KaYGfc/x9x66UuWvzO8WvuNOFuMsahegATgeSWdyT79t2Mf68ZVukgmGhQAvCi7dKIOA3zAIq/lJ/TgFlLsYNChifKEGa5VMMU0tPb0FOmSZshWfH5jGp7kfT3bBPuM2lqAQLMCQYL2Z6apaS+I5q6eQBYmjXd1F+o+x+vBgBmOUUp8Jcd4tvy5/Ditnq/0xoXmDcdfXDR90tGpp7Pd6eOFZGewz0/Xqnot8MkXx+56fAnIhaGCRAYpJUmX0n4OWcJUMznuFN7bY4f5LxATMCUvb11wiAOZH4Nnq85VpS6v0nqax89rpbzSikj0+xttXRwRyzOtVQw1kq4lVYKZjTqJnM9SzRtMsbM3VnIOHCpLDq+6OSiaC6U0evMW/kcW/UB6StbWfU10v/MrlnrjlbPVySWta6u0vNYorvWxLuk2089NTiSZVf5iFDEDLdxwUtAQAE2HE9IcokqrfKzxUDn/AMttZ0XNSmv8+nLxb6W62yyhKqSAxxRqlDGkiA4Ch8074jGSEQnLZBLPx8xASQakHZvg5j0Y7r34/RiY9OUM6aS0jcK+Qw1b25Kho2jaNjTqHiD7yAgOV2kjONoyTnjUslyLvEwhScJoRlou/s0TLfdH3TQtZpG/Xy6TXCkrv+aoJqGgrVWtplDolXb56+mSnrqbqRugeMsrSRSphAFkKFApNc8gzc/mBRMlFIFiM+PDMbIfOpuW3KjU8el7vy61rS6dpaq1xrf7PdKavml09fER1+EpUSn61dSVAgSWKohjcwvKKecKRHNKExD3OuejutAy0sMIFPbv9bIkfkvyZ55WXVOvrpy70BqrmtYLFpyao1vS6fppqmWbS1RBi4A08nSeqp2gaQDapkYvCyoA+VnlU+JvrpFzSKAEjY1Lc69I9PXLzk9feRuoeTtdyV5X1NbZJ9UUvKTW9zqqI3iDmLoKSg36a1XWV8VNC+KWP4KJpVCpTPUVsEkdQDDu0pUcVb55V75RyVrCnJLtVPO4HPjDQ/En9O3I1uUuj6+o5k2j0zrW80Lbeqa/vbJ62joqqakagnaCmppIxRKwWhkmeIpCksPWkUgyEUUl/LfWqwXhHTjKkgsA7WNbN8Rww9YvKebkH6jbnyy1lpiqsHKBdP2/Rtv+GWm+Mv2laOmi+HvNFUmJIWqRNTQzdMM2yamanDspPAlLnC2hu9I1eHmlKQtDGt2YVumrlsrvFFrHzG1jZGtF2ri0tXbbdNbaOOWOaGCOGdXMkMXSdfytVSOsL7k2zE9iqlQmeaXiAL6vGoAvgTYl7ds+EJOk9R605d6sodbcvLzDQ6ltNE1bT11B+/aKGVPhZGkLKHjO2oEZ3BXUvlCpKtxbsoONZ208LVICxhTzuK8afyJO5jc+NHc8Z71RcwNE3jQVFC9zksA0/DHc57ZDLUNVxU+KtlZ4/iZ7hPL8M8DyvVyEIqiNViUJAJSevvaEBC0EOHKtpYjpd9h6wgaXv2puSGsJbJbdBenXnrCtdSVrTXDS9HrKgr41jeUfBmQLJFTyRuomiURTZRQ/RkTaHSPyS3wpUep1zi5soLFGAoNA06x6s/RJScqObnKjlf6geVvIvll6e6+5WmppLu+kdP22hkS5xSvTVtHHVrTitVIpIVkR1lG+OSNXL5cM8ErDE9/YxwPEKIVhW5btyGR2PSOgtfZqy/W8267T3vVlt3LJ07jcJZ0UqrKCInYrkKz99owWJHc8DLCQXaM5CXZTO+0DoYLRQLpahhp5LY01HCQ9LRWumZ5IkAJActIA2WDd+y5IBycsRd+EDNU6n++bZ942mvaV6CeicQ1CRCZ6OQlKxO4LBxkqygsgIHueKC/+hq8UhBIdBtyptY+jw37Xd6i6zTyVGn2oelO0JkZJgsqA7g6dT869x3B253AAYxwSAoUVAgOHyOr+hdoXzND8J1Y6eOGB1+dWG10yezED37jz9frxCKOTBF7dfrTw2aa80j3aSl/Z4p7xuWjhnpIGnlmURdRmmYRbadBhhhpCCQM4dlj4Jdy2vuGGWoVUadzrgIdokqmiEcsdYGQlBIy7N/8A7h47D64HjsPfgEkXtArCgML2yq8GKFoKeRisEKkjLSH5pD7ncxyT/E/6cRQziJSkKKcr59tsDVEjV0nWt921DHAMoVoa6KONWBOQVfvu/p44SsuSWjegyxRYL8Ybl8v2qNF6CtlRaNL6IpNUxwUlKaaG5RWyxUk7OkIUVMqqY4d8gWKIqGkYpECrMrFkpJCbN6DflTW+NVFLuS+WZya27pEcc5vT3oHXVXQ679QVLzButqtdtrBVUEur7vbNNPTZ6rNU0EMlLFUNGoO2QgOw/MJCIwozfCJUoKW/ByBDEeLUhxKsW2GosXLkRxe9YP4ki6E5n3HT/oh5laH5caSvFtpL3qLVeidP0MVVqm6yfERymvnqbaJpaiCNaLpy9UsmWOS2THx/F+OLn/itXgT7ZR1vAfjMbjxLkCz0v6Vzzjj9qj1O8zOatTLS605s82tcUq1BqWp7lq66Vy1Uit1Q6meZvm3hmDFiUYsykbieOF4uZPWQ5JA2vT+ZR6aR+iWkywkJpsA9YsJ6e5vU1za52WXVWlNLcy+eHMuemuF1tkd2u87vKrCVWq1uNdVK9GIpI5WFZ1cwyRlssQcl4ZU1U0EgqfK/PgDwhfiPDyZUhlKa2XOoAu1wD2j13ciG5vTctLTSc9dU8vdZc3KOQU9+rtJDbS0dQ6JJFDVBZGVawRSRvIypBG/UDxwrEVZ/Z+GExh+wgq3e+Tx4TxM5AUTLcIL3HpowJoTmLb9dz6tituoLdd4LZcZrfNEtBWUVXTbZpqeQVUFSqt/69LUokseY5Vj3rgEZciYlZIGUZJ0vCApjWu7c2yl3irPPn0V1POTUWrKqP1I829BctdQ1NHdb7pGms9rulE92gQIlwpKirQVFDIyqM9NmCMX6fSSZ4uFeL8D+x1YmCr2qdvsGjX4P8kmT5ggKIsTShyLCo522xRjWf4SS6Nqpbpye9TnMLS2uqi3vNZbaaC4aZtNasFTAJPjK+zVHV6A6qZjjMc8tQ4MckW5unz//AIRKbKbMBtm2rtHUR+eUzKTu4vspFGPWF6tfVVzNulw5Dc+INLaSu1t1BVW66XvS1BUW59SinkMdNNPXSx1VXUxQmGbZPHNtk6kplSR9x4w+KnzQDLLUzAuOOnsY2eA8PIpNQ+E5O5B2iwD5hmj0a+gvlJcuUfp40ha6PVfO7XHx9NFdZl1Tc6y6/so9GOMxUD1dHSzxW/asLRqyBPnVk2iTHHb/AB8kplMSXIFPilt0cH8lOEybQAcPepcjOLh1GoGp5DDO80T4AZSmG+zAHHbt/Q8dAyiK4YyJu1gNU+IZV65x6H0xq6waH1Nq/TWntWXi33C7Wigr6hqd7nRUUayVs8TuojKwIyyOS42qd3gEgVFLh7nfTXGClpMxJWmuG+d9m/f1iPNH+o3lbzgumoLbyq5hQ6+S2vT0V1NnpaxqW3STwCppzLVmFERpYHWSMiU9RCGUds8VLnJW6UlyPf1hkySqWkHCwLs+bUNtliNsUO/EK9HnNz1Jacstbya5o6n0zqen6VDcdP3DW13t2nL7SK29KiqpIXkRq6EgKsgVTMjlZGJiidA8Z4IzUBKCQQ25xvjX4DxokTC4flUHdx+2jynV9o5w8jOc1Rq/X2i5rPrKir5KCqtevNGrcKOK6JHulhNHc0np5lUPuGGlQjoyqW+Xjy3iP2OCof5NnLcN4j1MjxIUhSUln2X9KQ5bxerHqxGGnPTZa6LUdfVVdVWU1JcK001ZNIWYx09ppUiEeP8ABCnUCYTsAoAyy5zrxWWSa1A5MKdY2rUU+XEcI2gEtvJ+ItnpDUGtOb1jtXp75k8zofSrq+TTsFs0/abnpm4Wmm5gxQSrFHT3i5UkjGgnpxRSj9qy0fSOZPi6g7nYdCVMn4RLJILUqS+4vtjmeJlyUzDMSAqrm1DkaXvbpEUaa0qNO1WoDBddPahvnxstvr77bqs1cF4lpZXi6qVJOJ03dUCQdnxuBYMDxzp8tYmELLm137xqSpLOi2xm7QvXGmkG+YFWQsSwXtg/TtwAWxY3iwTU67w2LlUVsEdA0SiSH42AStj8qFgvcfqR/Diwg1fWu8EoJChA9ytlBfqO42K7U7VNrraSWiqoiSN8UiFHGR3GVZvHg8EkkEF6h4CZLVhAMCVVuuUdoeWC93KmEEE8kjqyq2EiJAO1QfGD7ePvwtbMwbpD5ZZQMezq++oP088kILTozUetbVylslqtlnShlvFsrrRZqahmp80iw3qrijtshWKLukdU8i7MFC/yn2Xh1JCEsMgctN0jyE0KKiSHrx+Ijz1e6M9T+rtE2rXXpPvvKzVGrqfoVUdi1DSW2vserra6TNMslZ0HqZZXV6cUz09ZSRIS8jSMHPGgqThJFDFJUQcKrQ7vRXzM55cyeWN1qOeHpl1J6UOZdsuRoqu2VUtQabUICAC4UMlS71ckZEQiZqiSU5RNssiFdmOVOBUpxbvGlclgMB1rbFgOYOidLcydLX/RfNqzWjX2hK+NUq7TepneiaNXjlxs3LjEkEcgfIdGQFWUDhwCXcX3dDxpCycw1K9IoDz9l/DK5K8sJLH6h+ZHLXTPL7VN8qKdZ7tq+5Xue43ZFRX3SxtWVLy0yU8UYlqN5gjjWEvs/dln6FAFn9PjoIFJSQAhid2mpDL5f/h0emXUVmquaXJPmxqLVWgtZU1tntt8otS18rLZ40hQwWq9W+up5UheKjp0VJFkSFoSDEyvLGUylKuDXXHr3MGpAWlsLnry274knkR6QLF6YdO6s07R6y15c9IVFVVXmrqb3qCpqrfTRMzuaeFpYENPAiuWZQ5ZmG8v+bJhTqBLudZRFkkAM27ftANLdqGI95S+mXTeh9cU+ruUVs576thvlIauOrqrvNV216GYCT4i3QQQyR1e5npAsY2CmiaMp0kXpsZUAplHW/f6wBlqw4U01cVvBXU/I3VVp1lUak0FdNWafvs9cqa8qNf2O5XRNZ2ea3hEpbdXVWaeOWm+DoFVIlWBFiMNSjgLHxFzAoli56Nyp68IBCVYQoDyj12mntHj3/GAu3OPXPrS5tWqp1pdf2BpGxWubTlhpTXwxUdngpYIKyS0087uBClYtbLvSQiSMbo2ZEAQTMCAEk0O/VTGiTgPmSKZUrXl1cVim/4cfK+HnD6q9K3Gq1BY44tPU1bretSqmeKW7LQIsq0sCBR1Jnlkhwmdu2OVm+VTxh/Kr/8AQWDZZaEavCS//aCba3x6CbpMYdQLTysemtNFE7k53M249/5Z/jx4Fdco9ShyCIxX18sYhhoEWa4TYCFu4iXHdj9e38+F15QP7CS5MEYKaoYRtuk6EXeLqOFd295GJ/xE9/t2+nDAgxRomMaW01VUl0mt8UrxWSRmniAOEh/xSRjHhc5YfYke3BqchsxAFJKnTc749E/oA01UPyUuN9t1VWUtfJep3p4vjZIaOuPwVORHWxKsgeLc0fziMyxgMYz3ZH9N+DSf1E3rrVjm8cD8yr/2YXbWvaL7WG3VM9itk920/btOXuphSquVvpqwVsNNWSKrVEaVIjiFQokLKJ+nH1AA5RC20dpdo4+FXMREHqC9OmhPUfyl1byg1/aoLjp+6RRqjPLNG9FNHIskU8MkLpJHJGU3KVbBztYOhZGFSU1BgpJCHpQ3jz32f8PP0w0nqi016QPUtruu5w6WtFHcr7p2eOe4WisobjMtNUG0XWqgAh+HFKs1Wk8bQ05WQpIsBZM7pfgknwavFJLKSoJNmYihAu70PaOIv85NR+Tl+DUkBMxBWkh/9JUAoE2/yQWptaJg9QX4OfL2xc0NK8z+WXOrT/J3Sbavs9dNpfVWxbXaokqKaOKltFQzArllp6eGkrC0f/MhFmXcqPgku9Q54e2Y5R21TSlBSKBuPWn8hZ/EJ/DH1lz3u2iNbenHQ2lqHU1fdaij1haq64LQQUdNJ05Y6tYp3WEdKWAJN0YxUyxmAAMI3DnMQoKBNR2+vSFeHmM4XX39Ifl+/B55T6s5c2zR9o0zprlPqa5WzT931PdrSPiZIdR22CGnentdbJiqoLfVrNdpphAijrtSTKo2NTFolgf50PSAT4nDLIVw4XjpAOR/MTSnP/lPzM5f65juGiTa7jp/mEl+xJdr9RQUiNYqv4mNFFRcaeoi6M1XIetLTyJvMhhDcAQSWNPnXKMk6aDRbu/UbvZt52xZestNHcaesoq+CKrp50kinik79ZHUq6nJ7hlYg/Yng8RDmDXLFucRLV8k9H1V/wCY9zvVBDrHSmqYLaLvpy9wJcbSayjV4lrIqWpEkccssHwcMqqoV/gKZyN6liIDkiATOVYBmpv69bNEZc9vTHpbnld7TU6v05oO/wBhj07d9Nz1E1FOl/tcFe0BqHtlYrPThJFpYVeGemkw8ccsbr+9jdapYcE+zPrMUgZS1JRhFB2+c6+kUI5n/g7en3WPJ3UGjOXi0th1ObqbrQXuUzxQ/HpSiBIKxaRxEsWVIlaCFJe5Owup3ClID8e2+NJ8YRMCldv5Y7o83HqR9MHMLkfusHMXklqDk3eY6vpwLV1s13W5ZRpnlo7vDClHURICgJDh1j7MJHLlBKWScStfyOjLWhbCjbM/r12xU+lshrKS0C3XG4VF3w7VkkgiWCniMjBWRlLOMfndyBtG7CsELGChAZhb+xaUJWMTB0k6f2zhZNDV6X1HNTMixX2yV7QzQz74TDLDMRtAOJYhkAbhhlypBBweBluC4sO/TKKStKgyQMrHKz2joT6Gef2vPTTzX5c3mm19T0np9umooY9WWuW9LUqlDUmkhkqqujVvkq4YniljqEi63/JTr+QSxHbiZTCz7tcow+N8NillKct3bFHrpXVdsggtqGraVqmBJaaspU+IgrYnXck8csWUMLg7kkB2MCCCRwdaNV44ExJBrrWezYIJte7chmEl3tVMEijmnmadlkJ7KpeMA/KQANzY74x27iqitofKKmCQOFPpoH6Cv/8Avix0zgE9mZiRjyCPI8n34okgsYUQQd1dZU4tueEiWo2ALVw0MVWMlZEDCM4PYt3JzgdyPf2A4YoFnekLSAWIru1nAVK8TMogq7hUyjKszM4LE5OAzD5h83sTjx9uKw0p/IYEhKWRQdIVqKGmooHZ4lSFQcqrDavuWJHnvk5+v8eKO479PBoS4G/Z66aD5mhq4m+GgqIV27lWRflH9e3t78AEYqHWtCFocKah1rfCLNJcwC1NJQRVG8ZJD7EXd32nJJOM4HjOPA4IKDw8S0lRD1yps1tg7T1twp1eOpulbUtuLKZKeT5FPhRtTGB4HEdSqw6Sqbhp6Ry75heoj0KetXRPNbkbzb5zaE5fQ0NyqYpYTfZIXMdFUyba+C4zQCgqEZYknAjMyIrsrGXG/jIuchTy1++Udnw3hpiCJsq/ptDP3Mc/Z/Rd6adff/dvmToz1bUPP/03aNoZ7hV2qmvs1TdaGvhp0ZoY7hJRVNLHTGMUcZlpqeSTvFF8p2FOb/8AHy1rCUrdIG08bRpX+SmSpbqSATY296nZlnHEql1THp69XG6C26fqoKsSzfDVrSGOKN+/TQiTqRSj5NkgkO0qVJdWIbkrUFTGIca2COynwa0SvMb3zLi+deELugoNJ6/u626XUE2m6mYmpq4qxaenhpsSESNG89TCk67FZ/lZXLYjKjbv4nifDKSAUmnFmrxi5PiMLEV3XPo7PDnpdTV2gtbUdRyq1XLb6vT9ygu1g1Bbomoq+mqI33RVkYkYyU9QNiN+ZgMlcuFYNmUEoUSkvvbXTKOpLxTAUzw256Hp6tHT30/+trllrj1mReor1g0GpNL3g2enpbXqPRA6VHTSU9H8KXvNqp6Z5K9J6eSbqTx4kh+ULEYUXpdZHj0KnJmzaMLhn50rHH8V+MMuSqVILgl2IpfKo2W6x6leXtX+1NN0LDVty11p0hnsl1qq2mrZK62yOZKaohrKZ+nUUrQSQCKQxpKY1j6pkcNK/ppDm5pVn2csu+2PEkBIbMUOtvrDhkpkhYK5QqewfJIz9wf1H19/rw8pc0pCCigxWG3W2K+H0ycopuelw9Sdytd01BzHloYbZTG6Tw1dtswjjCLLQ0rQjo1JUSZn3tIRJIBjd2QfAy1TP3LDt0G9q9bxtl+JmJlGTLoCXpfRiLL96Z+SvPjmpHzZ5saQturtYafqaWjt9qeppzHZo4lEsMFwalUJcGLO9StPUTVEUKzhNoBK8LmeEROOOZdNhRxmxIu92f1jRL8cvw6SJamCs8uh9SBCR60ab0maV5T6m5i+oGutnL5qQ0QpL9Ya1rZqR6ilaSWCktdXTPFULKRLUxgI6oiTuztGo3q3xKEYApdBtzHDPgIDwc9ZITKzNqsePzHDr0t/iC+obSNRrrlzoug50c19Y6vuLR6I00NfQVcOlI5lYhaBqunqKu4XhFEMcbNI0Mhi6ssFTNM3HC8P45SJqsNQbAe5L13NxePQT/xqFy0iaWKbmnMUZhFsGtmtbP6TtUXfnd6bvUl6itTah5i22gXTPN653G2x3mhjmK0DzCkEdTc5OpUuq1NVADFJJHHHDTxHjSEf+pX7Ao1o9Kcb9mGyMs+egTUplqSk4S+EdwDQZWu94pLpn8RfnPovXutdb8u//t/yus1RcKiWDlzTWJqvS9ikIELUkdtppKf4dhJEZHm/9WeWIrIrLGijDN/I/qWVYcI2ZcNr5vyjqDwMudLCCX//AGsbuLu4EXY5W/iAevrnTzA03y05c6O9P3OlLXXxVOo9TcrbFU11sr7fIsTiF6i6tDT0DoJGQyEwAyBshUgaSXpD8hMmFP62Jpll8xz5n4uQlzMdIaj0rub0NdkdYvUnyZ5d8+fTNftJ+oq36sTR1Lb4tRXOOlu1NRVun5aRTMZaWeOdqJJ4FaZPztTSqZFIZJOOuQZkplC+VNU3c44ktQlzwpKmFhv9/eOeHo25DeknW95W3+layGpoqakm/wCIL5rvRFdqG93K3S00ltaktdyrKiO2UHWapqGkioYqx2jYLJLGsiLxxvDeDk4wmW2O73pmwZgS+3lHa8X4rxCkFU+oIZgyd9c+vOOuOgeVvKr0iaDki5acsLVyZ0Va6cTVFPpfSUsE1SquGYGGhiepqWZzkxYlwWOFCr268iUhBoI5MycpZZRqcnoDlwpHkevAaoqKsUFNb6aGWqnqmCEFQ0srSuVCnBBeRzuHYkk+/Hhpigolao9h4YEBIyoLwyLn0aRJYa03C4VbKVSnpoi8j/oowAP/AHMQB9eKADsLQ4yq74L04rDp5hcWHxUcP7zce5ZcHJ2/oDwtiK6/sRRoz1vDhAHXjEYZScjPj+vt34iENSCCXMGdCxRR3DU1q1BWPbDcGqqmmqn27PhzAkTLHuIV3hALtGcFkYMOwcqc9QAKlUFPSNHh0KKgkAlR1SPYFys5587ZNE8tK2++mLUFu0neNOUVYtZo7mLZ71BFRvTwmnBt1W1tq5upA0UhSNJdrfIOoQOPU+DUlUpJybZu5x5OcnDNUh6i9wxeJ/jv+mNO2mniW1XCwW2Ck+IjoodO1cJp4R5VKempyAQTjpIC2SflPnjWlYt2tCFKZqueN/WKYJ+Jt6C4dVDQNv551s+sJrlJSVFDHo/UQnt84Zld6hJqFGihVonDFVYJtZiiqGbgJ3iEyyDMLV1q0P8ADfj5s0H9Ydt4+a+sTlzW0xfebOlKO9aK5maqrNOzUTVdlj0ZzHrtJxagfpzKBLfrY1R16WSX4dT04l6Yjly8h/dBoIIpbgDCSjCT+y50bnvfK0RlzZ1hNyj9LeornrblrJy3sqyKt8t1kuVbe0j6kgU3Geo0/azUTneVlmqRSLNEivKXMiruHAk1ID7x8/yKX+wkAF+384RXjR/Pv1B6q5ZXDVFp5Bz8i9U6X2WXSM2oubd3jsXMekeM7ZHiqKGOtqXl+GJRqiNrj1UkEiwTSbZBASU4UkkDOl9xzG3KLVKTLU5FDlUtyEXZ5Yc1tR3muuNh1OdPVNSKWO52W4UFFco3rKOTLdGapqIxTz1dOCscpgk6jbRNLBSs8lPAUt1JD9NekCZgA4Uhh8wuUPKLmfV19Hzu5Jcq+adI9XPV01zuVgpai4U7yjEzCs2fEI7Z7uJAzZBJYqpGhXh0q3DZClTDVJDtn9vEG6j0Hz2tnOPlJyo5Bc4OYVl9OddS3TUmuI7q9FHJYrWslNBTW2yXilporhFO7iran3yTNRFVeQyQzELnMs1D11telrRolzpdiHDU4vfJ86Wjmr6yfSfN6xuZNr1vBrbU3Pb1Mcm7zU8vNUWSwaFrb3+3rHX1clVRWXVN1FroBTsaSQwvWCSoWkZ5nlQZklfKuUPKlTAbr5VbaNG8MWFB1yySSKAkM4DXFgefCDPpM9IWkvRt+HFddH8sdBLzK5ma41DqO781r5eCtPU8u3sDT089FTMisJ5qGaGKkWNnVqtblVVYVYtqIr834XDjF6UsKUY7a34wj8B+SVOCJh8pNxeoLEU2EG94pnqxXj1Nc7bFTxzb403ZOVDZwB+gAzx4JW6ke3QlhwhNt9sFEY6mpeWpqtmcY2quTkZzxUAlYqYXUElTKpWNpVUg7WRf6Nj/AL8Qsaxaic4WYrabtSy2X4udfiF6YWBSsyE9gRIPB9vGe5HvwbZmDC2vrvHov9GdHrOxcj4Da+XTagsctyrJ6SejvtHDNNIDFE8T01R0umq9LKyrJLu8FEwCfY/g6SAM6+seW/JrAmkZBotndxZ9P1lq1RqS9vpBZmhoBDX3ZKajqqmXKQxOHbpNOGd0jKEM5Yg9TEYXrBid9+kctLiuvgw6paaviq6RquY0S9NzJSyUpzOTja6uxVlC4IwAwbPtgcCs7IAoLM9TFB/XZyb5Z84IfT6Oa1ou1foij1s2nr7TK9TSpdLJdqN6WphNZSukkESypRTKxdMvDgK3zAuk+LMlC5YYYwOoLgj7jD4v8emdNk+JfzSiTuqGKSbbDyi09juuirfVDlXZ7TqSD9l2yKJqaTT1w/Z4p0lFOIviZ4ejKyuqkorPhCshwnzDMoCu+NiUhNbdOO2HPbUsi0ssFoekWngnmgkEbkJFMJW6iEsezBy2V9icAAY4j5kOIUuXQVc7tUOhCmYQxO9ds69j98cMSogXvATCQXUGztA0R24Rxtk257j6e/BlJbfFlBYOeW/WcKBpWl7ARgD2+n8eM5szQkEguGgo1BKxJVEB+58/39fPAKmUAJiFKiOGriCxop0PzRM0WSudue3nH3H+nDv2ApZ211hSUqz++uyGzR6C0pbb/dNW2rTlotuo63etZV0cfQesD9Ms06oQk0mYYz1HUuNuA3dtywqoEMBoDzy32+3hnc0+SHK7nRpi46K5raA0zr/SVSV69DcoOpG7K25HUqQ8cqN88cyMskTfMjIe/EVUsDXXKLlz8JpUjhrP4jlFTfgp8p20hrPQ1+5q6m1LTVuqor9Yr9V6epJNQabpG6hraOKuMjQztVE07SSNTpGWp2foh6iR1X+lqWfXLVY1j8mpJ8obc+razizfND8Mb0p84+XXL/ltq7S2oqKr0xaqGz2vVFtrhT3+WkpqaKlj+MrOmUrHEFPFGJKiKUIqgKqgYEXLJDJNdd4zeG8WuWWFjemm9I5R83/wVucmidFarr+UV80nzcv9hvkd80rSQU8lsv8AqW2SKDVW+csTTxVsEkNPLTCmmCVPUqVKxSinRgAICQa8I3D8iFLtcN01XOOlf4eS8tOcvpgsMfLCgqeV2qtM3qv07q/TVxsooPhL5HHDLOoo4mVKORhPA5MMUKPJ1zJR08pkCaJc3CN0YPGIH7CVls9vpcca8Ynx+W7aXus4vFfdLYzARyI7F0r1DMYyXYuHRTJJ+QjLPlj3VeNf7HD7Y58xDEhItv26rDkkq1SggpYLxX0CSSrD15aMVaZLZw8bFWXIz824bcgjPjhcwFQMWlIIKQH68tNANXbyI5ay0XkVSklhFK+Ah8f/AC8+fPjgip+PX7ilgkeRhTOnvGOmrxdQ1MC1BVcuA23dj29x4B+nBJYFjUQQL1Pzpo+pYJapnhqrxVGVmJEUCGMqhz8oIO769wQT28Y4gmMcQEIUxVhWXOt/eFG2VFOtN011Dp65SKzAinkdXyGPyBCzYI/KRkknJx3xwnEcTnT+8NKwVBmbdlxvzjWa4RSTvHTJ8RKmzqxTQyIFz37OQA3bt2zjsDjiKRVjCySqobse2zZGiITudXuK7juIMjnaceB37D7cAyhRNo3yJYSnCTbefakeGN7rpCotdmtNwtdbp2sElTPd9Q1ha8VV1qS7GE9NYkkghSNUiZA8gdv30gBRY18zPXMUyHL7a13avnHuJSSFFRuzWAzqbdS1Mrw2KjnVqasti8vaO/cwqPTNVc57hdtM2afZR3CVlC9amo6eNIBtTqRBGR1XdvRY1MicPBUBcpJ4MeO/dbjCpkhC1goYgA7HDe2xqjhEQav0gkVylrLyyW8mngNH1JI+u8Ui/I0jL3cN2BYfKrL2++UsnyVw6yH2/psHmNcuu+laQ34b+Wo1tFDankvNPA4mmrawES4JcpAhCiNMqTsB3ySP3LkoA6WlP/Z34+gyjKChCKN2enFn6NCbSlbVVzomoWoZ42pzS0kzrtieRVbY9SzFIwqSZKvkhlKttIYrY8OkpwqpTLQ998PTNKQVJOem2kVBEXC9P/MX02w2eO384bfzY09q15p6JdYU1HT1Vr03BPRyxQ1sdCtZDUVM1PM8VWI0w0qJLCoJeNkCX4IBRoW3VA1tzi5/ipkwB2dns5d8vRiaZx6DtL+tLT8/4f3OTVHpLl9QFwquW0lPb6ev1u1FHcIKbeJmr7elCs9JJDDEHlNsdAkcYkSVVjPUPf8ACzZSZapcp3SKZDldu++PN+JkLM8GbhdWy3A7eIdsiIs3oD1R+rW41kOgeYHK70qa55s1OjJNb0FLoPVd3ipLnTVB6dvhpqqamnoJ4DINk9fS1sywmaFPhgxG98jxKhhCwHNePsS96xl8R4NBJwk4RQ0B+CC1gbteJVptAeoXTOteaHM/TsXLDVVx1N+y5rHb9earvfxOhqX4amFbZ+rS0s8L0YqYpJ4xSiMGRiWO0jh6Za8Rms5bM27ekLmFDfqcs7ml9hLkV/oinfrY/Evuvp1m1JpHlzZ9G6617aVhs9VcLrQ1QslmvpeOaehldqiP4mojpZIXFBAzPmrR5Zk2GncPG+MEpJSbpHQ73bjti/xv44TDiTYmm8Vtz6ZPFCuZ3PrndDyWsF79WGtte6W9Td/juFfoCzHRtrlqtL6aq0hp6yulohSl6OorgKmnt6tNFLFFSyTTd2gBxK8U6cfiCxJcU7tcbBbbWOl/w0LXh8OHDMqp3MHeu812CIh9PPpIPN3ldpPW1yvtl9N/M2prVuenb3c9M3Z7RcrTSwVE9Tc1zBLQ7oZKOVI2EiGU08maYkrUPkl+DVOSMVC4Z+73e23lHQneLEpRDOlvMzO9qOxq9q0zi4XJb10+pz1FrNprSFy9MXLTmpRVNtjqay4EU2ptUzxxztTRWqzXGskoq2smk3JLTmKhFMJ0RalVC9PQnxrpMs0VSp40Z7k9oRP8GmW04E4S9KluJFWajijCGnyfoZPXJz006vOz0ivrS7rbVOotV6Iu1ZoS0Pa5pZ4jcLxbJKeWKrnM61pgqrbUo9dJTsrCRIi8QEKmTU4kOqueW+4vm94s/wDqlEoW24ua3p7g5XjvxbuWmkdL2GH/AIB0hpKku1utMNqtNRU0nXkEcMglhFTJ1EknbrgSmSSQu0jNI0m5mJ76fDIQAkBwNvWvPjHnpqyokkvm2XAZDpEIc/8AmdyQ5f8AJK12T1wRWeDltqiFbVc0udue509NWfDmeaKsmtsSiNYSkrx3CmghWEQo5eOTpyuM2cRLxTrG/MG+njQiSTM//wAf/QqMs3pX6OV4x6gdbXrl16KdVX3kFbOX3Me22zRqy6fgrJStqq7O1NmSWGnhAFSPhWknSkVoviCoVpUyzEPDJSiU0suAKb/Utx6xS3VNebQk1LZ8OPKPLZyB9YPrD9Neq4Ndaf1zriq0Pd6ijhu9o1NTV12p9Q2wuXcUVPVwypFH0W+appp6dlWogbqOpHHlU+NnIJng0Tuo2wVb33x7Gf8AjZSk/pKbi7hxxuW2e0F77UVkOoq6keWZLf1JYafMvjZIVB3AgNkYP8fvxgmsVYodIcAgmsbWu3pG8kpuYjaRsvsbBcj3J/gBwoqBIBgsOG2UG74Go7Vc2XE+acuuTneQQCDn3I78MYO+cAotSw5+0CSVK7nKDDhZGBII74ODwuXLpBmgb3iT9K6cS7aUdZgjr8UskaDGQY0AxkgkFgzDI7gH78K8QcXlZwY2SSzKBIO4seWyErS/Ov8AEQ5Vauh5U8h/UJ6otPaCluhtdgipLpW3G3u3QaWjoFgIqoqZiESFIAtP1mJPeOUEeikeJWZYwbNlsre8cVcmVjP7SCoPQkgn57jbWOzHp/8AVp+IbprSVFzO1/Jpn1R8oahLFa6e3TUUFh1VfdT1FWlLU6atVP8AA0dTRXenkmZWiulPLBLHTtNHUqsgI3yZk25LgCu47CN+VY5viJEkEsGJJZnIba+e+0RH6ivWH6I+atu1hpPWXoq1jzH5wXKFq2+1uqLNbOUlZYqdZVRK5tSVnVmAgqYoaP4oSu7zJHsjljmaAaQskeYVOV+lRaEDwpQoJSoAbe/EZ5PlnFO/S56pOWmkeTTyP+JB6sPRPzKKtK0FRpWa+aEuVdSsJaeouMopqis6jUzwRVe6KIShlyksUa064JZNTbm/Lj03PHUmycKgAMT8ukdSOW34qepa/QH7F9W9FqH0S6/uskVPo7m7RaLa7aEvEdSvUprlIerNSrDiGRWkhq5aZ45S+aYKRFqE0sQQDvjMfDpcYOmfKOrHKrXGhfUBy5uN3tuv/Tn6jLF8RNY9QV+h6yO7WKarXBaknhnmqSj7djmCd3z2ZMptbjQFgnfGZSFIOzOoaJWa3wpFDTLE8CRqEjjYYVAF2gAe2B8owOw7dhw3EGZJpGdb0KoZE2h6IX+u1UlTqOO5z0cVBLElznajMMbu6FaNmMSSbpJMyIqvJkby4VAsTteFYQPLBZrNVW2VKm2TKiqwJgIXZN3zgEglSPIxj3z9jxB8MMUSASnWrRGd609ZtHvrvmlyd9PXLDVHP+4UNFb62Ge60+mKjU1FC+UhqLqIJUcxIZGiEyEbvk6kQOeFzEYQ411i5ZCiEGg5RU30/wBn5Nat9YPPCqvXLV+UHP8A15ZVbVWnf+JaG91lNWQSR0d3tE600slJNFcKQ2e4w1CwQzswq+m6mB14RN8QqYQJhdg3LZkT3ipHhJXhsZl/9jiN6qYVD2oKtTO8ciebOhrZy+19eLLbda6P1/YomEdBe7FcorhR3OJRtVlniLKsqhNssBIkhk3q4yAT4Lxcky1lCso9fJmCYgL1yiF79qKg09aP2veaimobUjCJq2urIaKmQ9/lapnZIw3Y/KCW+3CUeY4U3hrhN6DbDBoOf/KSYrEOcHI+H5iu1dZ2t2U/QjrqM/z4Z/xZt8J6H4gTOl5KtEraV518o6G9WeSq5u8ommaZEhiXVdueSodmEaqiJMWJLOFAUElmAGSccWrw00+UJL8DFDxSLgiPUf6dJ6DkTyPMvOi+UHJqqpay4T3CHVV5prbDQQoygSstRMI4ztB3n5X7fOPlU8es/EyFS5ACwQY8z+TmIVOdIG7b2Hr7xbuP4m42lq96mivujrtAq00sNLDVUFZAyndiUM0NVG4/w/MhxjuPHSKHGw93jFitvzpWK18veQGptNXKira/mvVaZs1NB8PT6f0Tao7TaQBKWU1EcnWjq8KAis9Okyp+7aWbaJmJRLgqqd7n7rxgVpSAWtuuegbs8Ob1M8rqfX3IDnBp+hq6qWuawz3CjiZzk1dEVrYfnQhgd9IoB9s+/ATQUi1YZJL6p8Q77DX6i1/oXQ+t9LvbqulvtppLwJZKsAxCeBZDiORdjEO7jJyBsbs2Rw1YLloyILjtzhR1DEunbTUXHWV0pINJ0NB8Rd7pc6ighoaaniBaSoqeocJGEQlgqiNFJIK7cqOAbIpihi0KH/Cd1msFp1Bpa9PqGKthjmoq+pqEqKC5QSOknXiaAwRSnpkdOSNgu1xkSAjIJcmz5RFMOEBWy1vq0UFzsN7tdTpyopepT1NqudvuLXB8kn4epjkeNgmx42xG6tksHXawFpIG6F/sKTX1vrKBK2264tsVZQWjSstXU01OXjrLze6Slpq2QBQElNLFPNF5bLCBQNng7hgWcAkQKCB5X5A8+HKCumIdbV1ut8upLfo2C4RU+67LaqirqI2l6ZbbQCWFHZSwAxNh++PmOOKWKX3xML0D+zbKZ9ojiqv2tZaGfUtl0BcUvy0lHBJT11HcKlm3vulWK3UkrlzGWJ/wTjuGyF28UqgBUaazpC0SQ+u1Dzf7iL7vzf5m6m5h03LPlBQ8tqbW9LUwy1tl1rpzVdtqLxQvF5tsktNSQPKHD5ZZqgLiNGQNJkAtNQC+8uPSp1aGBMsIJmq3DYHOd2i0lPRakkuiW+76duNpkVJiSaaVopSCgUdcLsjfBf8Ads+Tt7EgdlGbhDbdd4QsIAd3ERvzb5raG5J6L1brjmBV1dls9noTcZ0emdZKhN6xKkBYLFI7ySwxgbwoaVA5QNng5s04SBdoLw/hzMASi5vs00E+U3Njl7z60HauYXLi4Vd0tNTOaWSDbHFV26dWCzR1EZfaGiYkNsd1ODsLn5eDTQuc90XOSoDCb8cvvjEoCFyrROtPWoPlkUdmU/Rl8g4YdvuPrxYINMhCrFwGHH6PrEQ6n08bbfr3fdHaT0HSazvlTRzXCtqg1B/xY1PCYaeCrukcDla5Ed4oJpizqpVds8W+NEpWQBhoT03C0M/YlQGKwGs9bo5q8y/XRqjkZcrRpn1I6F1BfuXd4vN1tlPd6mijo7nZKqJEdoJZFYQNJTO7Db0g0sC7xNUKI3kSfEkKxJz3Z2jV/wAP9iXTk52/PV4b/JT1E6L9Rdwq57HQaq01q3TVSJZ7E9XTGoeBgaeWR5KVniqqaOUyJIYXCM0UZxslReOhJ8SJgpQ6rGPxMtUk+UuPXlSLWWjUBp6usgltUhoSipArMHqKgqCsjBMAJH+TBY/MWZvlGBw0gGmhGNZdJBLjfa8OJp0hE7rR1VY8RJWOGJnZiMn6nuPYZ4YSogExUxOMMA3Po39hP1JAtbarhar9Y6est9UBS1NJUCVetHJkNH8pBy65A7gHPftxQANTpoogy7Fjp61rugxR3taczChtlttkkRARKht4mUDGe3zAE9gO5yMcKVYvBNiAIJJ0K5DTQorzF09VVJtVTcKR7tGhm6MYIBUTNCWXcSCOojpgEnKn78J/W7k26QShiLioF6ZxmTUFodzvraTcOxEdYAF+2A3Y/bzwdRlDZcpZD4h2jw/ajj5I0txutj0tza1xqrR8czQUtbdNItZ66ZXlbolUepnjpgoQSSZckgsiI57p5pYS7KJPAfL6aPdqQvAwAAycne9r7n5mHD6ZuTnJbmjrfWFZ6gedtn5FcrtPWaa7S1cFbSR1N0kWWNFpqGOaOd6qRkdyY4Y5Jl3R7dzE5d4NAUXyz28xntpE8asy5SUpbFmLcDYNu97xv6kJPStcLnLpD0r8uZNP8trfOFqNS6juNbXX7VbB90k6LPMBS0QClREsKTSAb5OmGEKM8T4iWVj9YfaS/vYcoyeCE8IxzlXdqW5ij7BlFP6K8U+j7/Raji0Vaq2gNYOtDLHK6SqrHfFHUSFyDhQBLG++NlyD2YNmKcKbij6psjaEksx3GzPtrX7ic6LlpozV94tuptd6f1N6eOT8xkvMMqpU11ZXWt6iZhRW345jJc6+oaN4YZzJHBEI5JahljjLnYDjFf8AIN9m0bzsAjNjSN6tlB/AP5DPo6vlnaZNRUOleWkFxoKymkpKCqvlxW4V9ieWSIdT4iJIaZnXoyRq/RB21D5O4LtzTEAGlQNZNDpSAQlK1VDUAIBztVrVMdkvTh6KfUp6mvStoq58pfWdyZ0voSOavrrZpC16Vmt9VbL+hWJkul0oo4JmmzCpNW6VDLC0bQbxLtHS/HyVFPlIA997NHP/ACXi0iacSSXuTUEHcSeh+I6++lr8OLkt6Y7xo7mRp2p1rNzgorJJbbvX0V6norRf5JEdJ6ia0r+6ZpA0eS2AXgSbYkhYcdDwvgEpZTlxW/X+WsWDRyfE/kCtKghsO8AnrcMbUjpbDHHVKaeRAVPYr9Pbsf041Jf/AFGYgKsG+4YN45e6ad2rKjTGlKuU1jXFpZbVTO4qykaGpLMjbZ9sECGcfPthjBbCIARCSq2tu8tFKxJsSdmtCPPvqL0/c+/WZ6+blXc29F6dtXKbS97e0V8VZbIrTdblpunuNwaleS2NVNV1sU0tNPD8YJdjpMkigIVibj/kpKps8JZgKO2XWtc65R2vA+JT4aSVDMcankG4R095keoD0m8jOW3MfSGq6FbRobRRp9MVmljo2daWpZ2jSnoLXDVQpRXGEu0cX7qR4oip6pjVNx6Sp0mUFJLMnLc1uMcyX4adOKV1K1Wrs7imfKOaevPw99F8r+cnL/1GUc2p9I8sTZK7Ut9q9bXaqqH0BfYIUrKGpurW5pXuCSVZjhniUSplJU6mwxyDBO/HBa0Tf+pqXyaruMo6En8gUoXLDBTMGeosQAbbcniTdFah19+HP6fdKrcuXmteYnNfmZqmF9TpUU0dsp9GXirpTEKdDQ0hlExqJYooINpR2SborEFVJCkpHh0/tVUqVUN/l7tkz5Z3i1q/5E39KSwQKG7tz5coD9Gf4tWgaXla+hPWfrbWM/NqhvM1B/xELLUVMNyoWcmN62YYEFRTus0ThyXdI0cZfcnF+F/IYHlz3Ch15xfi/wAcJmE+GsQN39GdLR0YpvUxyh5tU/LfXHKnROs/UZy/fVzaUl1JpywVdZBpytqKKJy81PJGjmlK1MMVRLOscUIEmd7IqtrV4mVMTiR5mI22PKMa/DTJby5hwuHq2Xffvyh1epblTNzr5T3XkvBpusu1q1JNHBVXZK+lgj0u8MqVUNykglzJUsk8MbJFDG56iqz7FG46fEf5KQKGn3yjP4ckKC3ZmNi53aZo4Vc9/Tjrb07aB0Lrn1Y+tu5W7Xdip6+LkjZNO6Ar9QNbZqCsNcYGrnnEUInMkXWldo4iZGZ2m+DC8cOZ4EIQDMLJTba93tnsHCwjtp8epZWJKcSlVVcEi1Pe+2OSDeoLTsdFDRXa01NtAUZerD1yPIVBbEkTiXs2/BMbMRjPfJPnVTgo4rER0/8AkFIw66xta+e/K6bAqdTaAtUwA/d1V0qaPwPrVQJ34t3GLPWt0PM9DMDrjnD3uWutOXzSl6rLLUWi80aUtRHIbbcYK9GJTOwFCcsRjCeTuH14gTeHGaBXIxIyXGGst8dfBDVx0p6kXXmhaFQwJU95No8g9+wOO3bB4QHAZWXxDJoQxA6whPz40bpOWSgPMKw00hAD0dpH7Uq3I+iRlo42BJ+aQqB9fbh61AJ83xCv3gJYK947T+mL1oWPT3pk1JrTVXpiqE0JV6ptOjdKLo2K0W6r5oV0VItTNTXKavqRNW18VRBJUVESRyxmOtiWlicAhe5+InBaHAoAxOb86DrHE8c4mgB8RJO4DluzyiR73y19L/4r3KOy3Sxc4+dmm6rR0kVqnorPzArLjFaJZXato2rqW8BmlkEUkjUtRM8E0ERePfGsAROujDNH6q7W+Y5pmKkKExQBB1Ydd8cXdbevb1pNpam1Dz/5vXf1A8s6PVdLZaDTGrK1rja7ldqOjguYlqKJPhqmllp4KilaOvo6mmqlqJ4SsjIS3GJYCQUqNAw9aFxuvHb8PLlMCE3qbvso1LmxFoefor9WPoNu3NG96V58+mqjsF9rLg+o7TfL/wAzVltOlrpSLNXfCR1V0gerp6arldBsnqq9ur00cTRzOOMcoIKwop/1rO0NXi/XhCrbqn5IEKnND1S8vtNc3NVepH0VT8zuUdl5hUgvt4tOqdKad/4d+IlKpLaKG3mCSimpopFk6sz7yrzTxiIZ/eTxU9CSk55XbZTfB+F8PMXLwqYtnUH+bM4t16VfxSeXllv9vt3qE5cPyyd9NW+06f5n8m9N/sm7oyvMmbhQCZ4aykzJsWjaOqpoJqdulRqtSURkrxSQoJv2hPi/DLSCX4g1HW/OlI7zt6ztA6R0JYtYa7uti5haTvFqFw0nqjQUE1ypdbwwRS/HVK26NZJ7eKWWnxWRgzpRddDIRGrmPdiAYqOvmOTOGfp8xYnlxq6m5n6F0nrWGyVelKi6WyC5G3vcaW5JSrLu2qlyonkoqsYXdvp5GADKHWNsoGomPk0WsKSoi8LlZaZacudpB3Z7jCn7Z4cC94zgUBMNi+abpLza6+13AXGOmqYpIZfhqyalnVWyCYp4GSWJsHs6OrDGQw4ogMwggk1Uk6+I4/epn8NzROmtF83eZnJi3c2b1ejQTXm4aea1y6lguE1OyV1ROtUMVcFbMKV5WmqZpoqqWNIpQm5JYch8Oxd21rlDU+IUUhIDl/79FrtHnH/FZ0XzK9C2tdCcyfT7qXV1q5Wc056/Vtt1JabalusN4+LigrDQw2uSprEQ061yyxvOOsYKinw79EsMsr8fKWpX7A459s/nZHQPjVKQEg1FLepf+R59uaHMXmJzKuceoOZWs9Ta11Ay7YprpVPUmGIdsRhvljXI8IFHbxx1ZHhkoB/Ww1q0ZzNK04j3hlWanOTNMsHSztJx3Ax38jH8+/HQkuKwhcwB1PrjHaz8E3klYudv4hXp0h1DHaqPQOlK6XmXfnq5o6eL4CyqKpC2cFt9YtvgHfJEj47KTwjxCqcdGLJDA8I/RR5u+mbk/wCp7QdNpjnFym05zd0nMWqKKS4WxpWp6h0KmpoKxAJaaowx/fU8iOO2T24wqQDuhaVkEl9CKzenH8Njkj6T+Zy695Gat5/aGoIrfPQS6TbWVTPYatpWYvPWULjNTMAYwpkbCtDG+GkXeYJbAOX1lpopfiFqHmGu0dB1SocGOeLbIe4KjIP8v8uCwWYwpTMSqEPUN1vlltc1ws+k/wDjSpiRpBbVucNuNaAM9ETzgxpv7oWchVByc4xwKqUF4rA4wm3t8xVX0J3K52nkZUcpr9FdLTqTl/qy/aJmpa6eKWaKGnrDPTKzxM8bbYayOMbWZSIsqWUqxPBUkZwfiVsoKDVAttFK76V5xaBJqi9XG9vU0vMizrSu9Ehrqh4bdckeEB5KeFJnjmhKuYzJJGp3dQAHBPAKAVcONh+4xjykN/o8dr5du8V95X8ibR6eBre5ennkHyw5V0l9uFFPebJR1LiGoMaLC09vWnk+GjQRP2pSlIC8czEZdOpFB711vy0ItKqljTVafIiSjyE0RTatoL7YNJcnNK0Etxq7nqahi0RRltRTvRzU8UkdSNjUTI87ySSxRmSpVRG5VWkDp/UQQASx3n7hK5mKq216xI9TBJVfA6a0zQzR2+BEpqSho42ZSiKFVEjUdo1VQFUDsq+wHDgaOqFTVFwHhIvnNLldygttc3M7m3ys0PT0TyPVPddQUtG1LhTvM6SSBowoU5Lgbcd8HhKVg0EEmSpms/U8N0baO5t8jOcdoW/8t9daC5oWkypTCtsExrEV2lkiG6SJDtTq086GQ/uw0LgsCvBAdT31si58plFwRrtB/mHyw09zS5eau5WapqL/AHDl/fqJqC6W+lutVQPLAxViEnppI5Ymyi/MrA9ip3KWUrwkxSlFCnzvTTP7QkckuV9FyU0xJoix615n6wsKTvNRjV9/mvdZQIxP/LxV056rUyjYscTZ6YXy2TxJj5QE5ePzLoWq2tZQ+dV1umaeKgs+oqXR62W81k0FygugpTTV8K0FTJL1kqMRyDbTxBi4YbBg4XxnnpOIWNeNhGvwqUrQtSqKADM4q4Dlr0jGn+Xuk4Ki/wB40taLzZ6eqq5Za1qKqaagrJjhpJxErywKzHJcqsTs29nBY7yaZaQphGJWKj+hpk5t3hv0nKG0UGubzrO36w5mlboqtW2Se+PcrJLKM4ngoKhJPgHDYJlppIlY7VdT8mA/UX1qsCJgCMCmIGbNBK708z3a96ehm0XqJobU1XW2unqRPclh34Lz207pGpnBjxIBgSAqQdyMGIU5ZRf+QtaCEfsFMn1t00cpfVr6qeW3LfnFpDlHrTkhe9Z6iqLjEtTa6m3olLqHTzUjI1yoa+saNluVCztEkkUmyZwaY1FM7RSQpMtyUmjjfy+NkdGT4VKkBaS2dGObN1yPVoZ1v/Dw5acwNL2zm3yc5l3628xKikt155dcwI6kxfG24wokdJdoqaCnar60KRg15RqyQMDO08sNR8bSAQpxftwpBzPHBJ/WoeWvL7iYK03i2XqD9u8vajTGqIttDW0axF1piux2FJJkxSUjExtHLFhXUBSS4wvQlzHBbLlHMWk+Uuz6y27Iqr6jrpf+vS67t+vuWuiaag07eJ3tlZZKCtq7r0IviVAqKydVaESx0yyQRqjhXk2yHIwZxKNLwaZSVkE1DtnThv4Qw9D6gtPJbU+j7DrTn9oGGe+2elqrXYP+FqCym3wTFJkSknMj5CSysgVRG0iRpGXYoQWKUokg317QBmOgKWlgKc89leLxOl+5+8n9N6tk5c6hv1jqtVUfXNysduusL1MU8kuEp0hX55JhipLRuI8ecKBu4CXMN0/3hXrAeMQv/Xzyd6B8iS0fXzmNy6serLZp9qa46QvANVOlDV2GS6TSM5cL1Fouq0EjSRTfuZI2bJY9yCeFCaxpk/DRhipPkxFyno+1t26HHeeZEGn7jLR3nmVy80/VSJFUCiq9QW6gemDRqSghmRXADbhk7snOSDlVvFsAjTLkKI8oO+mccceYn4JfPvSDakflpzG5c8wKG5VUjQLcop7PUW+BOo8TmqRZgZWyIGCIqDcHJCoSObN/HFvIY78vxgxALTbc/rR9/eK0c3Pw7+b3p59N83NzmppCwaouNbIbRW0DXtmruXTDqJSVcNbTtNS1sTdNWcu0CBJmhaMEwyxmfClKcZFPga+GgR4p1BANXJcB7/8A61ypFHbjoOmk01YrpyxrqPV1O1r/AG9e4bJNPP8A8NQCdoDTXWor44lp6pCKZ+n8ybKiHDytJHtxzloJplXg2ZI9o6S0EOBYls3fJrdz3pByi0m+nrZUftW3w1VbVSwxfFUVzhqxRwK6O/TjoZWWSoHgmVljRVcRpJIcx81SwTicvx03OOgmUsKO2hOjflF1fQp6DKT1ma91bc7zctPaB0xoujts5rBpmlu9Nf6yWr3C3VltL08Esfw9MzSuZOo6PArgh9y9Pwyf3U9sstXjk+L8Z+hGIByT3OYv24R3b5selv0W8sNOX71F+qvk/wAnaqKxaepbXcKvTum66hskcAC0sT01n+InEdRiZadZ3lZkiEYLq0ay8dlclASFLH+QbWjz6J03EZaCanm/HIbQwffEIaD/ABPvRHykp77oLkzyX5hnRcGoZ6G0f8J0tLJT6gd0UmthSoqI3jeeZSiwPmZz02wXmKDIr8pKlghIPEPr5jbN/HT5hCpqqm75NvH8EdWbZzUt70GmTf8ASeutO3+spaCqntX7Pa4zWT4lpFVayoohNTKqNBNHJKshjidQHZd6FuqpZUM3pT5aOUpGFRq168L7/nKIb1l+Ij6KOVV/1DpnmB6gtPad1Baa00Fyov2PdquS3zgAssjU1JKgA/KzBiquQpIIICp/ipaXCjXOh+Ib4f8AHTpwCkJobWb1EWS5S88eTXqA0ZYddcmuY2l9f6buk0tJTNSTdOoFZHEJpaOWklCTxVccREj07RiVE+cqF+bg5c1CvMkvq3HvBTJEyUyF39t27jFYfVx+H5yc9Xdysuq9UXjVmkNdWy1/sq33O2TI8XQEss8aTU7j5kSecy5jeJ22IN4CjhPiZInJwktr5hkrxipaqGh073tyinHJv0488/QJDa9L6K0drvnPoi9cyJKyvtGho6e5i5WeSymKGle23CspnhrY6tgIqqlj3zNTySVCyB6eJM3/AB1SASLEh6PStG2u1faNniJ6PEgJNPKQ/MZ16ekdRrrTXB9UU9hqtOavp7VFb56mavxTi21TlvhzQVMbkz9cK5nEYRU2g7nY/uuOwSSwI4+jH6eOQgFIKgzjjxcM0RN6j6PQqckOZz8x9d2/lfy8ktLLqG+VFN8R8JbWdBOgjDAtJKmYFADsWmXakjYXi/EJdBeg27s4nhVuoFSavRnd483usfQ9p3mnJq7mRyD1fzQoLrc77SwDTnNDT9t5d3aP9oV7mGpgqK9pIrpTyrMqxxwNFMWiVnhk3pDxwygTMRQS2y2dL5bucdtBmSyBMZxmC9nDFs99dkdlfQL6BE9GVbzDv9+1nV6r5g3xza557ddqw2mWzwTl6MtQ1ESMtYN8m5mkmVA22PZmXdu8P+P/AFr/AGHOzWb3MZvF+ME8BAGs+W6H1zk9ZWsLdzB5ken3kjyb1ZzH5sab01TXzVF/gv8AYrfZdEU9bEfhZGkubvHU1uWVo6GqSnjmfpxs4FREX1zVscCQ5YFvnY+qRkTVGNSsKXve3tHl/wDUDNdeaNZzS562/wBY0fqgopNRU1htuo7rmjuOpaDoGoFTT2OeVZ6Slp3qZ6ff0TRBmJpny0kfHmPypJSVqU4DBuOzhnHa8OoAfrSnCav/AHfvjnjeS01dK82ZJBuUMfIye+D9e3HnVrAqI0itoZdxpWCkqihQTu+bGP8AMY4LECXMCtCgABVrwtacoKB+VXNC0VMcdLTzzM7qVXax6ERyexB/J9sY42+HnMDiOW3L6iTn8uHI8oaslrN0FK1yeuvhRBHH8dVST7EUAKAJC2FCqAB4AAA8Y4xKm4ixJpstyh+Ml4eFlhSjmpkTpQRgr8iDC/r/AH54YUuHN9ZQApTbHoq/C89N+i/Wl6ePUByl1nqjVej73pvUunNX6Uv1mp6aWs0zdDFWwCsjjnUpUKRSwo1O5VW2IwdXVSO1/wCOAYly3Z2P3q8J8Sopwzheors5a5x6KfTJ6YNF+l/llRcttGVcuqKWGWdnvFxo6aK7VFOZXeGmqJ4hunhp+rKsQdjsWR8YLNn0kuQE8d+uccif4hSziIrm2fwfWKy/iEfh46b9Z2j9LVdruo0lzZ0rVyVlgq5KaOairIZ5oHq6OvpmwsySLAJFG+NTPGgkcRvKQc9CZiMBD3I4657ouRMKZgW9Df6z4RyP1h+Gly4rOaHP25c46L1TaR5M6VtdVHouks+ha9r3ZLdRos0FRQ1NNDLp+amfp12VmqXXFVCGaGdWYYliv6SaHb37743TfF4U4wxU7nflxdsw0crbXzA9JfKqq5kTaB5Gaj9Q+n9SW6kazam19rlLfV2WSQTrUVUdtsMfw3WkkRNpeomZYY5QjRyFphgXhCmbE1LaMdOVOmFhiwinyIst6TvW1yI5U6601pn1C+iXkNrDk/CtzkudxTTU+ob3YqN0nqfh7dR1siRvSCrYSNJXtUyojVA+JRcBAlS0hTLqd9GGzeYXNVMKGSoirXoWHbl2jp9yD9S/oN9cnqe0JpS0+j3m7yxobBpO+aW01dIVp207XWISvEKastNLEHt/SkuM8vxtMztQ1Mn7ySXdHIuuYoEgNQa1sjH+pcsMSCDy710/GKwXb1aeuD0KRWDlFyX5r6C1ByhrqyqvOmdXWm12+/aevUElN1amggutUsizx0K7Jt1SKethCStPDGrlHSCUKZI723CNS5ctYxrTZtz7H1WO2fpV/ED526poINN+rPkPZ7DqOFerVXPQzzT1rUgrWonubaXVZ5p7fFNFMairt08/wkSpNNSxwzQSzukeIUakfPOMq0pD4SX2H52R1FoqqC92qK70FLdqamd5I+ncLXU26pR45GjdXpqmOKVCGRsFlCsMMpZWVjrQrEmmcYytTscoojzzsXKjW3N+7WHn9y3rbfp/S2kW1hpbX9Q11TT9jp0bqXBrlcaY0Ytk8UtvgLqtY61VI6o5pQ8izUXKyDaGpLJZBrqz/EcKPxoeRjU/4cuveWL1lXdJuRvNjTd/0k5ZGhptAahhqKSjjpCcv8LBUSCgxvl+agiIkKkDi0ADCTV+W7KM4nqmKViAB58avxrHiZvjF7lURIQ0QlaNQMYGDj+Xc/z46skqJbZrONJJw4j10O0L9tqKC2rG9TEZzGQwOwEF/YKPc+eGlSgGGnjN+2YtVWYcvftHuO/+nY5M8veSPpV1nz59Q03J+zT86rrSWi3f8YXWGgV9PUctZBR00IrovhKpayvprk4hEqSH4CJ9rgpxyvEuZlKga9YNSxVDcNe8drfVlyT9A2hrpS649QXLO9cnLnqi8C4VmutN2m8WNa64U1PKyrdb1ZwhBMcbuEmP7xo0JJdVBUm7C3G/eDStQTiDOG77OMWZ5D6O05ZNMWGXlLz/AL5zf5b/ALNjprSlZrNNT05iz1Elir3lmmd9gYAtISULFtwVBHSUhJrTtGeaVKUxFtHQifqBrlT00EN8Aq67JDSxU/w6uNx2/IHcAhdoJB7kbsLnaCbY33EUSksA0K0aU1YqhJFZj5XGDjgsJcgwucli+doqJy+o10X6zvUjoWWTZS600vp/mRQRdJVEtRSlrRcGzuyzFzRsRtIAP5vK8NEoGVjSGYsa7nHK49ozJ/IrM79ExTgJcDZXzVzctQ8jFuEpHi3HPZvIwBnt9v5cZyoDKo5wa8Wv7G4gii3ysY4EUZyewA+/0HEU4IJgSo4STXuekM67XoTA0tKJIqYHAHZS5+v2Hjt9u/DEh6iFrK8xrr2FYY2q+U2l+bui9WaA1tHcLhpy4wJR1sNHWVFDUKhdJQI6mFo5I3OxfnicMuSMgkjhc8BmF4uVjSrHY9/5xiD+UnIXlh6abDq3lRy05Fc86zRd21FKVe+V1PqGiqXraWGKplFQ01TVw2+Xox9daqKNS6OzDaVLZU+UhNxBFS1D9hPHL0/pzh0RejvQUV8sXMHRL370881oTeDUXjQV0YpWx3OqkrKynnirYmhnppayQ13TanTbUFmUqCcsQkCgs28608UuaSGNSdXvlzi2Vuhu60tOt4ko57mqATS06MkTv4LKjElQfO3JwTjJAB4UUjDZ9evaMpWDeh2afsTAlVRioTbJGuc9iB5/7/7cFiY0eIUAByKExXrnnokauo9FaP1Jy5quYuirvcay13gBoF/Z9JLa6xXn/eSwuR8wDGGQTRgiSNZCgTgVEFg7Rs8MQgLUk1AFWeritPSKf82Pw2eS99or1f8AkVUX/wBL2vjPNd/jOXNyTTD3SuWlSOCOpmEU8VPTq9NSyExU+Q0bSd2klLhMSAxo+r+0FI8aUOlQdOwvtvcesPXlhzE5/UfLx9L6YvXKj1qc0LTYmq/irzrL4+SPUMiQTG2vqi02xLcaP4eWoaGarioK2YIkbJLkyqC0qah+YOYoP50lI3UDZA535RLHNqz+nGz0Wjeb/OzRNkj5p6U1BRUVDerM11e92S5VD5hgtdVCq3OahqC4K0B3QSpO6tCyhwKVMJsXeM6FKUSxp2bad/fdDWsVLyP9TFv1ZpTVGjr5zOio5WgrqjmPpajvtK9PKJtlJHNUULGqpAm4LJC8bBKoKZN/UCmEAgqAfW+0CpU1I81BYVPqDq0csuVOqfXfyA50am0Po/01+pXnp6Z6nVM1TVWefSdWJ7NSyRohexXFI4qRaBTKhSkJaPMToxYL8XwEtcwFrne8alSErSCtgSKVfbvbL+5dMuYuk+asT2eipOUHNTmpoG6xz1EdRpfbaNSaNrv3L080yXe4rTCNoA1LMiKsqOD1YZELGOwChTW9O/sw2xiSlChiVlTiOViOsVT5n+k71P8APHlfXW6x6b1Lyh1oss7y0NVfNPRR3yPoMy0Neiz1qLTydaaE7Jswk9VC4ICaAtSkDGGPv1aGqQmWv/YUMqGnLbt61iylm9Heubfb/wBoPS6KodUx0lPSrPcL9PUKWCoXD9NW2xqyDaqjblUIAAA4cZ6cte2rRhASrymozZvr5hLpPRvq+s1TSag1JfOTVHWUkJpaa42ekqo62UzIhrXnaSAFW6kEDqYZRuCqX+Ze9fuYFTGut14tSzVJNL2I1p4dOlPQ7oi0ambXlW3L6TmcYjEb9R6PE9VgblVlnlQSb1EkgBJO3eQMLtAUZ5AcjWsu8KUoqSWLpJfdxIy3RIdy9FmkrvU/G1N7g6zD5hVaYpJWByScGSRGAJJOCD3J7nPCFqSov8QcmbMSlg/SF2rt4wxSFJFOCVK9n/UHx+oxx08daR2wCQ9xxinXqq9FfKn1a2KzW/V61undTWgVUtju9JDBPLbppkTO+mqEeGpp90UMjQuFYtCm2WPBJVMSVhlW26EXKmfqUTLBYjkY5Z1X4J98sV50H/wT6l6l6OyUAtNe94sVX8fV0s4dar4J3mq6CkzFLPDHFHTOqq+6UyyZkPPm/jgSCFV26y4846SPy4SFIWgkHf0fMkbi+cSJRfgx8ro9UWi4Qc2eYNn0pMYWv2ko5RXJUU4ILUlPdmip2aBmiCFqiikkZQQChw/Cx+HloLJJHxrbAo/NqS/7Egg0enGtL8OmcdR+THp+5Xen7Tlw0dyk0zDpHTNRX1FxakURv05JZXkMaTCNZXhjMjrGszytGm2NXCKFHW8P4ZEtykUOuUcnxU4zGMwu2qg6ETM9rguNPPR1NNTVdDMjRTRTRLJHKjDujowKsjDOVIIIPvw0TrNGNaSgXp783ii3Pj0J8secWq9IUi8juQdu0DOqxaqvFujksOpoaenmE1PT0D0lK8E1LMz1EdQjmml6TEJOwcxjJNkBTJwhvt6N8c42eG8WqW4UovlmDTPfpouzQaatNptdtsFttVDb7DRQRU1DRU6kQUUEahUjgHlVRVjUDOQFXucZ40ykBACUUwi2yEEhasZuc8/aORHqH/Bx5Y69g5kcwOTestYaU543O4yahtz3269axQ1TytJJTfDx0zSUsLmSTbInUaNxGcFd+7LP8ElYKn8x5DRjpeG/LFGEUKGzvxvftviB+QX4PvNmw6te7c7+cmp6LQVNdf2jUaa0nf6lazU88Dh6SprrlTvEsLxzHrKVSWf5AoeDqSZSj8eoXUcPrxjTN/JggKCQFkXyAO4ubR3+o7hWVMjzVkrTVTsXkfZguxJJJAxg+TgDH+XHXUgNXKOEcJIfW/dypDR5p8p+WHPLRdfy85u6G09zG0XUvHUNQ10T/uaiMkx1EEsbLNT1CFiUmidHXcwDYZgaWgEModbQ2RNIPlLatdu0RNzbul09Mvp7qLlyr5W8yueVRpuioLVZ9OU1ZcbvdayNp4oFM9WUqqySOKN3keodJnURrkEeAxlEolIdoOWAuYnGWc1oB8D2eHfa6Gzc5OWVn/8AuRyxr9ONdbfFJedI6miglqrNUsvz09QImaMSxksVkjbI+R16bZVGy1KWllJY7Nf2EzJKUqxJU7G411p1itPLn8Nj0bcotcf/AHF0Byat0epYan461m8XWrvFNp6YoqK9tpq2SVKV1WNVWb5p1GAJQNuES/DI/wBEP6d42L8TMUkSUqYC+30iROeF+5+6E09FfeSPLDS3OS+rcqKKq0/cb0bZNVUbVKJUGnqGxDHOkJkdXnYRhhlg4yjPmKUzpqRXWqm8Y5SQZgChfO+VN+68ebP1yQfiAay9Wtl5r0/pT1Pyq5m6Dkp7zo3UHLzQFdforlc6WVZaWu/a4o5TWsDFCu+ZCifCIgp/l2Dn+PxqLtrt0tHb/HS5dS96VLUzYMd/zFJ+dXI7nZpDXuv+Z3MP0/c2+TWjtRVM0lJWag0VPp2kul3qEWuq0pIXhijjQSNVNFSgF6eBY42PyMeOB+bSVISrrTONshaAyHcA2vQWPo8Uhv0QWsqGU5DsSMfUHx+vHEQQaGDKsX+a/EI7UzdmyxJHlvlx4B8DPAq8tSIpKQM7a08Wt9KfoS5s+tOh9Q9FyNv16XWWjNLUep4dP0tRNHDrOWS4pB+ymYERrUSwJXPCZBiSSBYSVEhder+OkLmqXKQGobgX1Zs4V4haZaQo1qBmcoqMUMUaCFHVWG7BUqx/+QIBB9iCMg5BGcjjDMlFKsJp9QSSk3LvA9H+UFs/UnvgfYcNlGlKxFAXOuMeif8AAE5lf8P87+bugJazZLqPR6tQQtBLKlRW0lwgeNGMSMUGypqcyEbVUNkjwdv4lX6/FVo4PavThB+LT+2R5akEbsjwpuj1Rx2jWNskmuklbWX63ihw1rSwu9Q1eZAVljqIGQfDAZUxGnLp/wCp1SP3XHskk0STTVo4SA6bOYYtk1Hz3vmu6jRV+9Ot50rpBrBHOuu6bVVuNDFdygEtLFQSOtwMSszNHUtCrYUCSBc9QrCl0BFDeHLlCqwajjX2HpviD+WXKf1ga6tVy0x649E+nXX2moZZLjY6rROoK613yzXKGZ2pphU08saxs0JRVqqepp54ZlO9GjcmMQ5OF6Hd0f8AlItRSPPLuO+jk5Db4qrYPSDT641dy05Q+pP0Sc4/UByQaN7zb9f67GlKLVnL2v3yda2XSqsssZvVNO8NMfjTJO5DzTO1SZ1SHMEKdhZ+euMaMTglJZQs33s3RSz/APwP839Jc4FpdPc1OUPMfk7W0VSJdX6ua4w3m2ydGaFVrrVA2K3fT1KUxjgqVgnjpiZjTtKNpzZAUXG02a243/sGfFjCcTk7++QvzjvHqD0e8nub3/DWq/Unyo5Nc3+bEmlaXTeqalrKZ7Pf40qI6xlFFW9V1gWtSSpgZiZ4DPKplkDsSUuSWH7MukZZir4Sz1jlLWfhx+un04c8tV639FGquQOqeXWqFU6j05qCrWzWa/wxGaCK13yw1PxdLcYRSPFG9yVlrZWLgGESOXpcgMaVyOzXWGImISliCOHer/O5o606t9M1l9Q2jeUGqPU5o5rHzysFnJYaF1C8H/Dd4wnSqrDdiIZaWqpGgRqStUxPGjyQyCSGSWKRapRKa5bNapCZcwh0g32/A9Tzi1GlqPUFv07Q2vWOpaXV1+h3QvdIbctEK5FOI5Xp0JWKZlCmRUJTqlymEKIpy0EBiX1ui1LsDnvgzU0tI8bwPLFNCwKvHIu4PlSpUgjuMEgggggkeCeGLBGUCHBpbWt9447/AIx/LHTN39H3OnmRdVazaepeXl70dqfFOdsNqnlp6611rRxqzA0F+tdmddg7Q3CrHYHsCioABswfaJL8OBMC7UL7D97I/NBuEbvdHl2/DGSUkgEZTJ7qfuPHb6cdjw6gWJtDVJd8JiQNEaRuuo76Zrfb3uwoovip1VlRIIlYBpG3sM4z4GTk+Dw+dMSlIctAypb1d4/TM/D45cag1V+Gx+HjBT63qrBTU3J+zJVWOttkFysl/hqKaQvBdaFzG9TA8UqK0YlVcYIAcCQcNUqrbIWpbKJLt6xczlP6XuQ3Im762uXJfl3auWMGooY4LrZrPVVKWKpWNzIClkaY0FM7Mx3NTwxbxgEEDHBIQwrWLmTFHjq5+YeXLnlHpblzJav+GrfpqjeN5KquqqS0UNsqLxU9PoRS1Mdvp4KVzDTnoIVhRlWOPaw3TdQcGGgyhalEkE567dYlahGo6M3FG1RWXqlkllemSqtdMzUgZsqgkiEfVRDuC713FdoZ2K7i3DdzWDIsW1uhHFi1lVaansdRr67DUL4Md9orHbqariw6t/8Auzwy0hyAyd4fyscYYBwlCTATgkcNjn1LmIE19Sak0v6gvR/r6731LjWudRcvrvUQUy0Md0FdSJUUjS0ql1jxPSSsApwr9l2hmXgwsOpGamrw1oxkn+GUtSZr0SDTa/xFuxeiY0daJpH7fIFLHBPbGSP7PABJqSaQ1yf9ev8AIRbwslc8CzfEUygqxRVGEPfvk+SPp7e3fhlKwvEmiAfTKM0djttPKk7yTVpBVlL/AC/fwPbtke/ngFBqiEqWzC3vwJv1hwxxvT08MEckkkaLtV5pS5cexZu2fOc+/k44WlKR5chEWorUTkeX1GKiIk5cxRZbABT8pz7HP9f7Edg7dIBSSCzEmAKeGOE1MsMojeSUzy5LvuZgO43EgdlHZQFGOwB7lOA7Ka1whRQ7J2bRoQfjrHBRkmEcZwchWbb7ePcffi1Jz/kEU5l7ctb+rRgVp2HMgiZSC2yMjcN3fORxeFNQda2WMKKHD94Zy6T0jNeqbVU9lao1JTCZKe4TzTyzUwk3GQRiRj0w24jCgLg4Ax24MpKQcnjQJ7jASeFrbfl4c9Ra4li+JqFp23ggfKjOwAwe/wBf1/lxRAICRCFBw7ndBKw6D0jaHr79pzSGj7HcpiVrKu22alo6qsAP5Z5Io0kkGc9pCfqO/CUCrARZXiYv116iF2k6lM71FJVVdDI4IaaB9sjLjGGZcE/T5vbiISXoNcoMIKKqPz9QPJHvgklrblPM/Zmadzu9xnLHGP4DH+Vt5aC+uMLQATTXWBf2ZBWxyvWTwO35gjAMAMA9znsfHC1KHA6pBolqclLUzaBo6ajgAiMCT9wu1c/TAJx2Hgff27cEpnrrW6ASkBWFNX40jMseRHshihXydy5JI98k9vp+nBYgCHNdaFotSE5F9707x8YHRgxwj+3fGPHbP098eOAVMQz6+oH9Qp66r1gzC8xRSZaiTsRkH8pJz7frnx9OBSUjzWzi1ySKKr8cID3VLOEMbOrEDaGBYY//AFsAeOAmOkYkl/aELKFIw12ZseFacKDOMSIgYs1kkkZvmJangck/qzZPAebZG2UPKGGusVle1VbqzLCekPqf+/HTIy/j8I7SpZNHrq+TQmSWSoZ41308TMSAeuME/bGe/wBuLLZ17ekZ1k4thFmJ+Nv1G0VorJFnR/hXCAGQbz2J+oxgePPFEikOIw0aja4b4LPpW5S1cM8Bpmp+k8bL4YtvQqd3nsFcYxg78/4e6VBpgVkzfFPiMpZ/KGesCDSV1fOaqkUAZdWy2Bn6+/D0rSKikBKAegcnVYRtTaI1RdbLV0Gjtb0OitRsqmnuNRp+O7wwEYyGpJKiASKRkf8AqKRnIORxFBRT5KE2Or9RCkBN1B3rx4G4bde0KNh0derdDINSaui1NVNKWVqazrb44kAGFWPrTkkncxJkOdwAChfmtCSaEh+fyYJTYypL6Gt8LD2SkIZQkpkA+UlgO/12gePb+fDZbAvCcICi9NutCNorPTSd1SpKn/E3y4GPABIOPAyRj7duKS+vWHoSkpxX5abrCn+yqBG6ppaL5gNyld2zHjDDHFAZ5wEtQAJFeOUANa7WWDPb6GSbOCxjY7z/AD7f68MBYsIMIANaHWhAT2u3qoi/ZtvSQ7mX5TkgY7gZGcdv6cCDnFooK3yjZ6CiO0LbqRWPYBIyWPbt9/5cE5dyYJRu5odbhAYs9ISkrU9K77QiHpr3A8Dx83nt/QcEFZZQWEMxci+vaBvgaQKWipqUquCW2A5/p2/8cCzgnKFEAKY8n+IFEEaSRK0RgU4CjCjA9hn+8Y/lYVVzDEoUVCld0aF5qd3Qz1FPG5P5ZGB7DHcds+PP24FQemzWhFOHw313EcWvx2onHo00NU0qJWRQ80rM1Y3SyIIDbLupYyYwnZj5/McDueOZ+YST4ZRO5of4IgTaVBB1nHikqZTWU8sjFlk+JYtj2BB/v+PHj0BV47qioMD9/UFpQhhlmqFRYY1Lu7gAIB3JOfAABOT4Gc8OEsEs0WiWpRwiPcV+Ch6YdY+mP0bJceYForNNa/5hXxNdy29oujWWe3Gjgp7ZDU9twqDDHJVmM4MPxqocOJBx6X8T4Yoll8/SOd4qaCsJyFI81n4ynIm08hfxAOdNLp2horXpHVsVFzGttLDgJRm6LI9XEqj8qivp7kyj2R0x2xxyvzSMEwLT/wBg/N2MN8BMdBSoGhOgY5dwjdAskrEIct9T/ff3+3GKUPKLxqSTjoGbn1juV+AhbrxW+uzTt1tlrrq2htuiNXVNfLCW20UTUUVPG0pXA2tNVwxgP8pMo7EgY3/jgFeJTzPaKnKeUskXbfmKbo9riPXs5KwSRrnaW6iAJ9u/n/Tzx61gQ+yOIog0NzSMziUxyNJJGpB7b5fc/Qj7DzxCUgNBoObwH+/UbhHRTqDgt18lfqM4Hf8AXgpZYUigBYmggSnqJHch6MUU3fBwHEhPYHI7sft24Ipe1oMgpYQeWrO6NY6ed2QASFhtG8H6eB78KCRnaLUkvWDqmEkSdVctjaWi8dseffP/AG4igUhmiJZSXPtBxJ4D/wCjHCHI/MoPg/cH+/fgQ1jFzFD/AEzvGssgZlURTMh+V2J/Iv174Pnt2ye/8eIxFH7QLg1+ddeEAl0diBAxjwfPn2yO+MfX+HFggisVh8xbKNJ4IXwjvIwySBkHGPGNv9+eBU4FIpa64DeI65qcn9Ic8+WXMTkrzApXn0NrOxV2lbr8+xxSVsD08jrJg7GTqiQEj5WjU+2OFzA9IJHlFI/Is5q8vdT8reYesdBarpqq2atsN2rLJdqWaLbJT11LO8E6OucAiWKQY/THbjpS5xIBeNswlgWoNZQn2AQTVNI1TiZOovzMATGcgb8HtkA5zxtxPUhhGLEXZIfaXz4a3x+pd+FVzo0fzz/D39LGp9JWt6ZrDpSg0FeLbGyZt13s0EVvqYweww4ggq1JxmKsjPcnJ47MSkazgFOFEm5jooywqrCGISylScBTggffBx9v6cUSQItRzN9b/iBqaeJ5wiRAZ2J83Ylj+UbgpI+ncY9s+/EmAMYFKkgu9OzwYSZDPJTyShapcF4nBjdfsCchh288UMIDmAmqSu+VI+jCGN6iSKWGFTtU4JXx4JXt9RnsPb9YCDQQMssCk5a5RXf1WUYbkZf9Y2tRU3HSF0tWtIDGcMjUFZG8mMf/AMiWozn2HCZxzVkYKUpb4Q9RnVhFlErqCVzV0c9PJQTAyxsF7dNx1F757fIwPccPKRlnAurC/s+vaDmIqmnjlp5CYsnYI8kkDGR7YHzDz5BB4UgwjEw39IytWpmMSUdQyhieo0Xyk4PZQPft/L3zwK2ZibQpZsVUPp/Y1eWQBMQR+SQST4/TGQfvjGPbikhyEjPV4IIcAD1gtJUPEGQRsIypKhTt7/QNgD6HGc8CGHCAUpg1BrjAbVDiaQvVQxCME/KRvGMeP9e3sPrxeEKrAJSGZLMeXdm9Y1SeNk6D1LyMuULpIxZT7fp5Pj6fpwIUaExGSAQRr49YHaacOrNHVfNuWMmIld3bBYqBnP6fUfqOEWEWMRGFNzrL0gygq0kMtO9OZNoKqqHI7d+xPv2/XP8AHhYUzPC7Lt/YMwyNv6csFRI6qrMFwGA8fNjt9/JPFgKu94JClMxrfj6wbEkiHMaFmzjDDBKeSQ3ft4/lwZWDqmt0AQoq8lNcYGSVoGlV0i3YAZgu1WJ8dyO/sfv9uBCnbdaCUFBL7Iy1RIGcSULbVZRv7YHbz/8AHwMjPjxwKbOrWzV4EirCp16wYXaVYbVikyezdifGCcd8eO48fw4spzgEoSxoBm1u5f1jHWjFSYt4arA2sjA4Jz7kjJ8+3ED0D03RoQSLDv7aeCzT7TE6RVHUYgMndME+AxPYn2A8+fPC2Zwa+0CmrYRTcfjLZlBeK4tJKUaAjBAYiYbpf4djj9cHtwJJYnhAg1a78NmTQoNO7MgjeSQAAjKBwMY+UHz/AD4s5dtGCAL4gac+X2ILyXWln6ht8i1pVgr9FoyYe3hsnz28eeFFPmAbh9wK0lq31bcM+1IUFqo3A/5kQuOzKzxKQfPcZ7diOFLC0EpRaDQqW3nvuJ+D6xCM1vqpoiJa2NXB/Kx+Yds/Lgj6Zxx1wRdJeO2pJxEEbd/1CcLOjQFGNO30Bztdv0Pgfp/lxQW9IQULCiXcUZzm1GgBVVIYZqyoeKdVEbLEcYTz2bznAAxnB9/Y8WVYtkB4hgMWdNcoEpqatloo6eWGar2ht9VLtpywJyCVztBAwMr5xnAxwClsQBAKFcQYPSnp/IUGMQDmN6KGbyEjd2Zjjzn/AD4YE7qQKiB5Rf55wnPUPGQ0sqKAVHj82fp3JA7cAEhqCIJpUWHtr2gNnJaRuoZGwGBRfAwB2J7fX2Pv/E0g5QvEAcJv2/vrA0MpdCwjJViRhWJ8/bHftwwli2tc4YVOxYDrrvAbxyKivKWA8szZx9j2/lxMXmYXhKj5nNeMbxAxP+86UowfHue3bz/eOKKgxSL5wSaOR303PtBaScO0ZZHYAjsU8nxjIOP6+M/bgquNpgkqALpH89oI1VTPHHKYaal3KhjV+puIzjsCfY+59z7fQynI89PFIWDY+7b7NqsYp3qRCF6caf4iAu4ZAOBuGNh/gf8AXi1bD9QwJr5qkcup1ug9H1XLsqsT/wBW3AXOcZxkBvoT9+ABJMVjaj27a4NGEj6kZikxGpIJAcDA+mfOcnPY+/BBR/0n350hkxL+ZeetsCw5/eFWaIj3ZBkkE9xj2/T+vFnJxEXtBccMo+khTKHfNvPgZYgfqPOe3ALtrXvCkOkA51aPNX/9QN6gKOl05yq9JdBSU1RX101JzDuNcJXEtL0jX0NFSCIYVlcvW1DliT+6pdoGXJ4/5vxWGUJTf69o2+DSDMKjcZR5R4aJ4qSr65bYZUQN/wDxCoIJAPj8wGP1+nHlJbPT5jqkA1JYRPnpR5F3j1M+o3khyB0zPHHcNU6loqCaaaMNHRUMbfEVs8g/xJHR01W+332hf8XGqSSpQCc4cCgDGKgX5CP0hqpjU19bWRnoRz1ElUI3YboUeQnZ37DAb+n8/clBAw8o82ku2XJ48dn/ANRNyVvWmfUzyp58rUvWaQ1to6DTwlDEx0V0skjrJTr7KJKW4UVQozklak9tvbh/mZZITMSNo99Xjo+CnNiSo62R57KpZ46imoTvSSUKyBhjKsAwbH0IOR9RxwpUshMbUMKPWO2n4JNDrb/9uDlZXaQp7w9DBU1SaglpqhY4qezNbK41JqlJy8TFIVVQrfvjC3YoDxo8BKJnpKT6c33NDvEkCQoK5ceke4RIFfY1UUZjF3BAGTjPj6+e3188e1KmuaiOEAk2HtHwFLE/w6GJYu4DMAQX7H2/j/eeCQ8DQX2tt92gV443jUlZJnPzd2Krg9+/1+uR9eL/AGUZotKgbuPSEyH4glTHSyyRA5WQMjMc5zk7gc+2QMdx7cUphQa9oiU0prk8KAo45tszpK5BBBGQv28H7/oe/ngcYiBJL612jenobckxc0NEZM7kG0ZBx3xxFTS7wwAO2cbLuheVkiBgBJ2gZcMTnBJbz38eO48Y4pJakCUuWtstG4q2i2PWUk8KbSwYEkA5GFPg5OT7EfL3Pjilu2tdIpIS/lpB5DbpJm6M0Tu2MqZsHcMYAUn/ACHFY1M2UEUjK8GzH8zR9F2fHcSLsZR9R9P1H8eAtA0suCzRRmklEaLIOm2TvOxsDJGTjhbhqmDbLLrH5kv48tXbLt+Kz61TQaZt2j5aTU8FvqoqfJFzqYLbRxy3KTJIE1WympfaAu6Q9idzNr8EWTbX8h9QHzjlLp8r0emhXej4XuCST/Z7cdRKiwDV9oQvCaqDnZod49mn/wBLh6g7wl/9THplvdT/AMhV2W38wbPDOwRhV0tRHbq0Ip7kNT1ttZsDxSKSccYPEhlPy+IOaUkYg1Q/3HsZE4kTY6NNnsVHzbx3z/Hz/n+mRSaVhSJgVY65bYPb3ZWDowdckpuBIOPOPrxSiDClLSDia2uMABYZm67YIIUK7AklM5x57jJ4tSqRCHJSmh1/YMxvNGziokNTG6+Qm07vHzY9h5/T24HC9rxSQCWHf7hA1Lpu1aysF/0XUUplpLxQVVonARcBKiJoTnOD2MgP8BwSknCYFNCAk59ew+YiL0y6ruOpeQnKi43BW/a8FsWyXOZqk4SeiZqWTcDnBzTg4+/kcAgukEWyipiQFYd7HT25cIniknrnUTymGkp9vULmbs3kZbcuc/5ZPkg5NZ2VhAFHNG3+2UKoomhputSKXpWO/EcgC9/+kjt9/bxngGLNA2Ic17+lPX0gtI86TxxrA7MM5/fFAQBnztx5PjPfvwCjVjC0pDBba7doxDDKGVailcO7GSMwSsFdScHduxj/ACPtwJJJpBompwhLt094OvTmFwSJFUAocS98keQM4J7nhYUHw+0JU2cASQSFB/zFYkaBf/T/AMYHjue3vwxTPX/URQFtnCPmnigdGlqEEmWc7mA3DGMEkdj5Ayfp9+FXrlEKWfY+hGFrJl6JiqklyxMpjBdWUDtvYkEN9SAft44i6C2uYgyU0wn73btsHYgszRzqssNSqsIxuO0A4OMD5c+2SCR9uIE1YWMVjoEnv3b1gT4lGlaiT4qKr2MyRsSj4+zHAx47/THCSnMGCSsG9RataDXHlBuOdppzCGQTdLfKC6ZifxtKr3fOD3HuMcNCDyz1/YStKVecU1vgWWJS0e43CIsMLJH3Ve4OMkHPgdvH9Mhjehvwy1aLVLSlRc1FRByKOMdRZSiMQrSEds92wAG7YU5PnyfvwyiQ4FooFQqHYdBBYzoSSylQH2ptBkGe4BIB+Ue/cdjnvxU1ga66Ralji2rQIenFA0rSQyY3bJC6HA8ED9PGffzwAThICbar9RQW4Czc8NWhLFnmmufxEt4rpoQSopikRQdvKjA8Y8j+PbisZKSUj19IEgChz2bN+cHVgqIZRFEKFAwIz0jukYdjnsPOCO3sPPFLSWxD69IfKULq1v8A60GRbY9iSRGKFsdk6XyZznMYBxg/UfThC1kMDTXEQhEtLsAKPl3p98YGWCcDpNa7qxT5cxom1vfI3Nn39+FJlJIesdPw0xQQxUekRFJI3cERMgxkjtu9/wDbjroURV6xsmqsFB36wWnnbqFumpzgYX3P+o4tRuCYXiVjCbEFoIuzb0KmMYGCdwwmT4+3f/T78LJqwtEWrE9SRa2vSApIHdN8ikqDtBVN3c+5x289z7cNIzEIBxHFny5PsMFngqsPHHDvIGU3k9z48jwP6j6cQpcUigt1MbZikarS1LU7itiUTE5Co+VdiR+Y9iBj28ni1BJNmaFGpJ1y2wYgppZIVVI5KVB3wcFR75BH6Z8niw3PXCLXiSlzaNpIC0QSVC3b85wp8n7++fpg/wCclgAsYESHG4dddOcbxQpHIGigEI37mjXOCPcKT3H6+O3FlFaHdr+QUstUAgHWm5wGVUSsEDvhgoxkEZPkZ/T+WOGYD0iOxwk03/D+0JMxczxCamE1Kqtt2NtZmOc5yBhR47ec+3FqCTzgi66iw04pBaod5A0vxlYox22FAR/DacnxwYVYM8CUlJZJ3wFBLDUPLGK2qmKOrlXAAIA/LuwOxxnA+vbiAXJEEFWWCw2QdjkrHTpTun5iQqA4GR4OD3/24AgAOmGEu4Vfbr+Ru9UIemrKZ2OSoVTjPucfp9P8+CTUViAUAdo3ikYovyK0R7jDYDfQfc+3fP04XjY0zi0kJAIPxH0UL1lVSW+ljHxU8scSjG1C7MFUbiMEZYZ/yPEI2wKzkKDpfvHgi/Eg55XX1aerjmNzNl1RS1dgstxqNJ6dtq2+GmWksFHVVaU6dVF6tQ03XkqGknZyXlwvTRUjXxv5fx2KcUmwLDPOsdfwEghG8h/7HPCuVUr5bdJIYZUCk4JbYGH5s+47Dv8Az7njIUMBSkbkMc8+ftHoU/8Ap7fTLbb7zi5t+p2/X1JKvQdKmmbNSQRHqS1t5oalZ5pvfoR0sMqRAAGSWZySohKv2fxEoFWPIV1XRjL4kqRLbbma2INDlHrbCGQOzPE8ZJw6ZPy/zPfzn+HHpUk02RxlpuSK6sI4S/8A1DGgdSa99CejrvpqFaq56d5oWa61cQgDyxUUttusM7gYJXYhV5D2ARGJ7KcYvyMsKkLBDikaJZ/9gVamXzHi009a5klr0utPOLsD06WIqQ6Puw24D/EThcfb248ogpKQEikdkEm/TXpHXr8HrnpaeTXrs5UUSrQXG3X6ok0PqKtkMhgscN0lgiglWZfl+LFVFRfuskCJ5t+ztxp8OtUqalas6cjR+UNb9qCkVArSle8e6IJVp0xNO0SsV3Bg5KMT3GQfOe3j3zx7dSnq0ecCQ7kvrtCi0Tggx09VOV/JgNIVx5wD79/P24WaljBuUk67/UBiaWnMsdHRyVRZGwDgbjnuwz+vv3HfHELf6ESWkJBO3WmglA90Zi1faqmkpz3j6dQrg98MCoACjwc58n+HFqAuDBYFUULdoMpWVsDNJLQ1LRgkL8y4QePIIz+o8ZHAqXsgCSHxB9W3wcFdK0azPTSl+xZYmVlUg47qSPIIJHn3PtxAAC2cHMLXFBDkgWjXZTSLLLNhQTsYIewyST2Pjz9uFWEMZ2GXOE+5fEVDrBbqq3wqrAyPIrP2wQUC5Csc4Pf2Pg+eDQAf9QMwFLHKNgtLPMsxp7eAhfbHDTjYxLAh2yNwPbHY4/oAtyKGCASokg1EGHiSJUjKL05DtZRkCHP+Ig98efr7Y9+LDipgHSVFh8QQ+EkEqTQw4gkQCQJgZ8+fP37ee/AKL0i1EirGPz8v/qcOQa8s/X/T83aOmrobZzM0dbdRTMwYq90oSbVWYZgBlkoaCYgFsGcntuA4d4e5EMkS0q5PaPOJaqyWn6sYK9PcGDH6+/HT8OAaQqbLriSGrm0d3fwF+cdRoH8Sn0tT3C6PSWnUl4q9AVUgbAlS60FRSoG7gbfiTQnJyAVBx2HGefLGEkWHs3tFKCiCkPz1lnSP0fYVnRoZp7ZN8LMWRJwNi71wCit/iYZGR7ZH14xlIygAupIH9g+RGnTVd88cYDruQ5CnsSpb379/YDiiHDQCUgEU3wp00lNIzmBIwUOSQ4LE/YeRgYP14hHIa94HAVE7usfTsYZ9ooK2odtqM8S5IwD4GR27+314GsKJB8oNNtIN9RIk3RyiJwQu/ONrnuO57efbhaEsHAaGAmqXrXW7rFbORMEVh1n6m+WqSNS0tv1iNQ2+MYDLQ3imSqG3yNomjnHjzn34GSkWNYk2aSkE5jdFl0jV0lKtTEMch1GCVwMk9sAkg5x2Ix7nhlMozhScT5jVMo+o4DQS1M1EXjgZw4ji7qWJ7k5yAfHjyPpxRWYpJBFDz+oWjV0zwrWTSpTEpgB8lAe+SGAOPYd/r9+Esbi3CK/WWvTf7QQSqpd7/D0ryrE7iR4V3RxOMht+Dj6n3/14PDSFkbanXpBiOSFgsx60bsvYMpxj6gn27f14WpVaj4eK/WT5bQf+CjczxtP8PKBgMkmPI9/I9x24tSnZRyiwAEkDht1zgKnhrBUCKZqnqZAIb5gT2GMjH29vbgZsoMWqIqZ/rEee3rfpHy03XNT8dTxEt8pSVVYKuMH5SB5+njt54hDNWJVrt663xqKS2LHFAtDCQS2woNnbHkBCMHB8e3Cj5jiz94BAAAL0zrl0fV40o6SmSQVlIZg4BPyyyBmOCCcMftk5A+meGKBuLD5haBUs4fVa625QopaKSqhIlmYlgWLpUNGWYDJY47e/8M4x44AlR/00EhWEO9S9f7AAotrIlPdapCDtDvOJC2R5UZH0Ixj7+OLUoJLjXGGqOE+Y0OqNrdH0VqlLrNLWKWMmPlV1G7GNuN5Gew9u3bxxSVUIEKnsFg+t/T2gYUNRghYqNpdzfPKAoLj2OR3HYE+D7+RxcrCbQRBAYn1+fVxBOpoGkRog1vSqDYgUHCoTkDJGGBIA/wAuwxwUpQKcQtrKDl4gRJHHP6DwFSC4LhhQTVYBIV46hunNg4+UP47j/L9eFYQ9t8WQAkkmu/3FbQJJcqiSdBPp+qhMrBJSzIQDjOCQysQDkY7+D2754EymPm38/mFTJoAw57a32DPjlCgZ5BHIstruRXJJO/O7t3IAJPt44FVWAbt75QKikP5jhu+/bT0j6OpjaNNlHOigYxKsu7+Pfi8KtCGypCQlsJVvcRCbvAKlFEIqmPZpDMVA+uFIAxgee3G4FyS7CO4VAClDu2+vzaDSttjVo3VKYMcMB/6n28dj/EngVE1HPbCwkk4cjl9VgvOZWeMxTRh1ILbox9O/jHFqGYzgzLwuP+nNn45QLUGocovSrEJyRiQHfg4z38j2z7/bgSgCprrtGdTWa+Wr+kfU8ZqY1aeoFND3Hf5XJB8DGe/b+R4NKfM0AtACasAdcu0bRyrPLg9GONeyNjJH1GMEj2P8eLCS22KSFVUBT1fZAMe8PmoQJCckYJIPv3x37+PPFuKARUoJDralo1MVOnVd4WaJu7bh8pyAe+cgfoAPP14paSaO0ASgELIe9X27/iC8j9NWCCKniDE4B8Ent57/AG4aFAwuUk0Cr73grPUMxkghnip2xhu3zd8Dx27f6578GAM4tClEUB1xpH0cM08LVDgJTA4Mo+X6Advf9PtwKiYNWT57fnfBd4R1xLMZqche2Scnv3JHb6jhhJIteAWgHzC7a2xrFJGUWKSSbag+QshzI2R3A9/r3z2Hn24slgw18xZdROJ35a7QXScSywU0UEwdTvAfZtcY7krnOM4/mOKUk3iD/QKaa3RiqmZnKrT0qsASV7KOx84we/n68XLiEsQAK64QHDK4EqKimRvmKMx7HPcHxtwD/XgKkuYNRDEXOs6RFvOHm5Q8iOVXMnnXf6cV1n0hYavUc9NSuomqWpk3xQRFnA3PL0oxnwZAT2Hev81PHpEONSG2+9G5R+bHq6+Xq0Xm5XSKCo1J1YmSuCVAjmapJXc7g4KgOGwVzkEd+PDqOJeJRYn3vSPRSgjCA3QxFdi1bWQaqqLvV0NTOlVE8E8Uo2hRj5WUE99pH69z54NKXTSkUFO4I3vHr4/+nV0Fdk5QeqHnZ8bdrfDeNYWjTCQLIzxlLdb5KozK2QrOTfBGV2/KsY75Pbs/gkNiXlQW9oX+SmAoQkC7nhYCPRvJHXS1dJOiVtQpjYOFU9CVAfcA53HIwx7qAwGePQNSsckrYuKWv7RB/rLtWjb16MfWDT81ZLaNIS8sNUNUK0cg6e20VDxO7KCwK1C07AgFs47DueEl/wDJtnwav9i1YcVjuNo/NQjrL4KOS31cBkaSIxTZnbqwu67ZUSXvle7gN3+U+c8eKQsJDvaO4ohZxD+8ouh6KNUUWh+eHIaqr5rXpixUOudMypDTL+7o447zSSGUsxznAJ2+OxJJJyEzUur9jk7SddI1y1FmN/qP0e5qKT9q3GNoTRSipmjlwxADhzn5Rlvbt9M4/T6FNKRSPNJFN3P4jNTS1yyyKsBZmBxlsNtHjt2Hf3+uSDniJWBBHETgYV2QUNLcIlzmkqGK5V+svzDxnuc4I+h8jgFNYQZCmtbVo1ltVxfJimtqVARY0eeHf8gbO07Dg9s4H8++eGJXZqxZSQXsNkL1MZopGKVSRITtLuwcj7IMAA/r+g4CYoC8AxNNdYzmZVqGlMFVKWfa0knkE9kx4H6n6kcAoki+tcoJgfM1oHaSUMFaqnjj+juvz9uwCj384GM4+vF/5DvAKZVvmMFEmj2TU1JsyD2GP5f7cUVkVJ17xEkq/wAnXtBdTHSSydCoIdwflf51Xv3OOx3d8Hx5HntwQL5wS2H+o0kSPDpTywRRswJK9tp/xYH6Zx/nwKiBcRSFkU9cvaN89UxGkaGWUlV6Z+YgnA7YGS3gDt3zxLmACMI3a1SPHF/9UFrrljzR5XcipdN3LTt21DorVtwtaXeK4o73KOuo42qqOljRSJI6eWgpneQuEDMxUHcGYZSj+wE2MPQCl6a2R4tuu+JJA0Q8gEnuf17/AMOOmhb+ZJcWhpUp63i+fowub0nPLktrCiroLRQ6X1NZ9X1dVJWClSjht9ZDPK71DKwgCqCxlYFUTc+G2bTyfyfjhIAcPiLBq1OfDtG38V+Ll+IKwVBASCp65C12cnKPaR+EN6/dMc1Od3qjv3qp57aZtXqi5l6xsdg0/py51U1JDNDBBdamG2WukcGno6SETJTUy7gah0lYvJLUAOKEJTU556yEczxEsFGFFQ3T727Y9J0TENFBGXRgAwRSB2PfcPpndnOe+ffi0zBlGaX5nGyC9TWU9K6rVS0oYkRDqlFO9uwX+JPYY7/z4JZasAJaiHZ3hJn1Xpykq5aKouNIZ0//AAxsxMWB3B2jt9cZz59uItRsDWGCViOI0EJVbzl0bZ6Gerq7rQw0ilkVquuhpkBA7jqSuqjx5/24BJLWgAnC5N+FIrroLmvonXfqF0pzE0Nd471pHWOlbtpySqoapJ6ea62SrjqgIamN3hqU+HrJ4xLEzoTE6ggowVKFecgFvqLmy1gALGee+31TjF2BUd0jVnWTswHyglj4HzeT44atNHAjON9ta3Rl+mYTNLRVSKQF2qgJ/wD7f4EH/LgUrBL5wtWEkPQ7vv8AsDQCCnCvFDIXZiqgOQduQCfAyO/v9fHFKQ4tAgCoakGmgSpjZXZJYkJGFGQzZHt7eCcj6cQf6qOsWoYPKm/q/CB4qWmqXhkmLTBXyqBfP648Hz/XtwLmwrALIoDr2gRmtaHY8VNOBkfvPnOSe+T5+h78SW70ihLbzZwYg6ZIeCFTGThcSkYXHfG79P1+o4CYSFML74LA6SEuDqogCIyPiaWjqo53wJFJO9ME9h7Hzjt5zntwxmG/XOEiY48z01a8DxzTTiCWrgnpRIwIEo2hW7Ha5U9iCe/sfH24FKCaa53hyVALYX35fyDjNHSyPJSUENTVL3z1lU5PfG72B/r2P04FBD8YzE0Ci751glIaKueGW4WWneWOX908u2RRIQMlcDsR43H6fbHFS0Bqm/KH42DAffH7g+08PxC9WiE8keDuQ52HAJ7ZGDwOAV9dsRIW4YX3HvtjENa675Uoa8R46g3uoIx2OEySDk9zjHn654kwEtr3gRNCHKrayEfLPTypG70tRDTsMKu8EL3GflH1JHc9uKURlnv1SJLAJLcM2274FkjhIhSSi+H3ZZndwjDsDgEH9Mjt+nBpJY4mG+2uMQTKM7nfXtfvvg/vy707EAyfLIOtjHbG0HI8j9PH34SEMGVrhDZZu+/MhtdILCaNyEKSqQpVQqqW3eO+RnwOxz79weIpJSGSNa2Qin+03JFR22h9thARaFt0PXqhJEoaRMokkOR8uR/1YJH0/l2Qybk11rOCWVM/1TODQWlKojrUlkAQ/Myf/wBo7DzxEYmv3Ma5MlKkutNYgqooBDupKmGNZH+Qu2A0gz2Ax/kB2+x46hUC6kmus46U4F2eotqnrAkVPNAQVlMRQKib/JXuP59h58fxJ4mF+Bij5RiMau1AkjvIaiZFwfBUBv1H8Bg8RbmFgup1fz7geI1UsYkWhho4h4My4Pg/MuSM5z5/88ApZ/6GuuMLXNYhw/YN8wUKoXdjI0xZlYgYHj3Ax575z7/w4ehSjUjp3i/2Jc4ak5HZugdYz8xZQF39lBIb9CT2z5PvwtCAUsTcZxQCQXDctZwVBWXazSKsgZh2UuFzjGPG7/ThspSsITshZTRyNdfaPnkLKIliWA9ly5wx7+SSTjHFJJBs+tsBMVjU/wBfzhADSQqJIYwZhnLMCSx9sDsAR+v9TwVaVr8RZwE4SYJ7Y1Q9QQRxqd2X7kee+f59uCxKgcEpnPrqkFJFqalC9JGoTHkN2Re2fp+ufHYcGADeCNAA1bb93KDPw1PGmEJm+XJ6hywYd+x/r5/TiBWyCQ5B35QXkDbGMaqFJIc/T/49/PvxQO2BmFJ81WggsiR1SRLG/UUhcyYJOV7jIOT2x3+n9IqkCl8TDuMt8HKqrpFBjllKoSFCBScnb2AI9+3A4qPA4BmXNtZ94LGshhKv8iqTtx0WXC+ceP5H3x9uCKn4waiADiHx9xzc/Fz1PNp78PL1E1dL8OXqRYbVI2/aI4Z79b0ds/oFXv2+fjP42YRJWRZonhwkzUdeFzbN9CPBtf6wVs71qEJOrEOPDD65/wAjx4tEsVFWj0QBJZxSEeyWiuvd3ttps9qqL1e62qgoaGhpY8z19XLIsUNPEg7tLJI8cSqB3Z1HvwRSSP8ANeMMSpIItU63DnWP0QvRX6PLf6RPTNys5C9eGq1PaYpK/UtwpJGRK+/1M3XrpI1BIaJJD8NGxBJhpojnuePZfj5apcrCbmOP4mfjVfdp2i6VTcDTRMqNFRKHBYqoYr4wB2JH6ffh7qJbOASoFqMN+mhjcx+XNs5taB1/yo1RShtO6rsVx0xVxzkx9SOtpHpsrjLdjOrAgZDKD7cWJqQajX8gC6ktY36R+WNrO6y6E1fceXFZqjRNdqC1V09iqa1auVaOunpqhqWSZJWVUMTSQyMrsVG0gnAzjz078cv9hNS2YZvqOqiYMIV23Z732QFy45w1sGuNMVmlbxoSo1JQ3u1TUUd7hjFvnnFbFtEiTo8csAKgyCRen08l967kJI8ASCpdBXe/Jso0pmkjGkCm9jXMVyzj9OzkDz3sHNXlXpbVtHzz5Yc7L8Hahv1+0tXxGiqbqMyTpiNIzGQJU274oOsgWdYo1lCr2/DKSoOFAkdt2XVuZjnTpeFbpDDrrrm0TK1ymZp5YZY6dcl06syMkjk5yD9fynJyQfbhkZgSb+kMLUvO3lvpSSjtGpOZWj7DeKpxTUtI0wkqKuZgTsiSLezv8rYXAPyk4wCeBVM8zwxCcQODI6aOX/rD/GT5f+mG5WrSmkuS2uuZmtrhTSVFF+1rlHYbbFEjbA8qlJ62TLJKQBFF8sMh3hdrG0gqYim+CTJJUxvsFWiivpn/ABsvWHzZ9ZfIzkXrrldyLOjNX6moNOVmnbNY66nu1sgqnK/GQVc1ZNIzwL/zDLNH03jikBERIdBUry/tSXGyjX02Ubp/gzLV+taSk7X3bqcrx6dX1HFIqSxVNX3G47admBbHbsRlR7/y/QmU3Ya9I50xSSARnGLjdqiRTHRdOVnXqRyvEzLGw9zgjO0hSR5IyBjzxamvFlYTUiMUmoaaWF5Iaqam3p5btIHwFI2HAXvgBfAPfJ88AMQFbwOIhWEWgf8A4kpKZmiqq+mtrdUgGSVQH+wycsD3XP2PFFzeKAAqm+tkMjUfOLQGkIol1Hq63UsoZASgaR85AYdOLJYgHO1fYAZyRxX7CLQTFQex1q0eaH1KfjBep3mjcr/oflDaOTPJ/Q1Tc57b/wA/Wx3ap1FZo52inlS4xS5AqYGDI1L0GQSAiQBHZsfiJwRRagCcnAfr9R0PCeDmKH7EpJCWcgFTdM92do88nqZ5A8yuduh9P837XHqC8R1F+vlqltpuD11ZZbhExlekcs/SmAj+HEZp0USI1P0w5cgcTw3iZyUCfJVjxEhQuabCMxYAUPGPoHiZHglLV4KfLEspSkoWkAYgRdSSxAN3NQXFqxT3Tnoh5kan07VXa33bSdpvluuBpb9bb1cY7W9spmC9CsDStmaJ260bBF3xvEAVbqxlupJ/8o8MsKKi2EsQXBB4UJ4RwPH/APi3i5UxKZacaVh0qTUEcbDfuzziwujeR2qfT1TW/UV1a13OtvBnElBTowjuNJQmOr7mZY+pGJFiqJYmCMYYHBA3EB8nxs3xCicBSjIk1J4ZDvtjk/kfx/h5CUSxNC5meFyAM2VtB5Reb0i+j/nL6xJNWwaB5tcs+Y2irXeLXV6tu8ItUly6zxLPFVvJNSvcFTDPEs2UhWSAxfmiCjozP1AeQk9BrfHDE+a3nQNlz/H3Uj2jcndL87tD8u7VosapsJsFrp44LZX3S/3TUVeih5JJVmqKl+rKWaWT55JyEAjRFVI1UYFFRLgep0ecRCgC9ezDW6HLPoPSF1Wnk5g68vDyLGyR01FSQ0xwHbvEzNUSMg3uB3KgMR28A38168B9wta06r9Ns+IBtdY0kd0g5f8AKHmXcretuMcd+1UWKiYSFFRbaX6jwhU6hkWIAKVxvyQLlqU5cMN/t8QycU4WJc7rddveJ90tTXGpgtjan5caPEZiETdW30helO078RujdRHP/wDD29jkjB7GU3cGM3+XYVHpDW523KlsWneXHMS2221UFJofV1nue6mEcUEVrndrZVxKsShEjWG4BtqjCrGMAYxwpSgKiut8EhLCljsu+rxa1aapiklpaqmQTRyGHbvDAsvy9sgHPyk589+GLW1nAhboItfj762xmGqkCvUJJIVVigcgoNo8jBXLd/p5GO+DwsoKrh4X+wdchq8G/joYXWB5KRaiTcUVmGZPrtXOe31/Xi1CuI0MCVAABRd9lvqNuvWKC8dOkkWNu1jliSRgBs9uwP1OT9BxSQxLh4ElIdKs7bILvUTyq9ZUyxwUSkDpqrB2JGAU2t+Xv7r37HtgHiDZnEKciG36aMW2WorJlodlXSSs8gR5lJRyBkMD/wBPcnzkkEEDgM3AiYwBha+312Q46U11TTBaqOUVJBILbQSVbaTtJIGR33fQj34HCEikAQAMNHy1/IEqKhUpZWnmljWVcxgM0cioQBj5u47g9/8AzxeEtrtC64vMNb4JTXECV5KwSQxxqVcl+y+MDB7d+3YZ7d88CutRTXrBEgKOLK+fzA9LJLVx11LSz0cdSg/dkYJj7DJaMke5P2weJMOJT66RApUsYjXqBrbSFWSOd2Q0r06BV/KwKsW8Yz/Xx9uKxE2ziBS6FqQNGtS7FagoyL3K9Q/J27jwf6kj+XBktV9fejAEKoh95badZQI0sECGZ2RcdmwGIUY7t2GfBPb6E/XhCg4prWyKSgpAwJrx6741hqIHO+OtpUUoDsUqWwc+3Ygdgcfw+/DAlgxpttFiSlwoZbowktRTlmeWilgUdMrt7se/cnxkqR2wO59/eppwqILjWni1t/SGpBSXfOVENPFJFtZmQKNytnC7QAADkHyRjbnhQIBIaohpxXtzMCxU9WzySThXTOQdxV2+bJGFxjtnHv8AwzwQlqSGsNcYPC6cTenKsDvTmKoinhlqXRHLAZYogye2z38+ff8AzBaHTw3X5QuYt8SS2zX9gBBUgN0qavgQkkKhKgfzP9eKQJLecAnVI0SZswOMWesojCSllcJUMI0VvmEjSA7z2H5CfH64H243qDV23jpAlRJNM6/W/wDsJlRNRQxI9RMHHYsm3Jz927LnOB2yD7cEE1rCjMUBSo458dnGsEJlSV4ZGUwK2WXvhlUDuTjz79+3ngkg2isRBrc5bvmA5KkTKWaaBpMdNOqSdp9jjJGCMdu2PPjipzpV5hAoxFk+2hANK8si7axhO2cqIE2hie/cHsPfHfAx54JSTFS1FsJNN3wLRobj1xQJSQvOjfkKjK9v8Wfp57fx4JaQXftCwVPhT7dY3lSdWlZ0jWNR8xQqQwz9M+O57/8AbgQ5G8wBIJD+metCC7xo29Qs+QSxKxEA9u5++fb+PFJTeLLLJc03t6PBmOcqzpUy00rHbtITYVI7jz2+nbz24YUt5gHigyvNptc4Dkjj3RuB1CMOAXXue/YAdx5/rw01MW5DYBTVL2gQ1CAMGheWRlO4hfzYGAQB2Hk9vb+nAYdsCsINAB79d3eCgcof3aQSgDJDKd+ce3jv7nt24sAKvAVJI1rnACVayS9PpLP8mWUFvk7e+PmB8duCIpXKCYG1821ThBfr00bB4qqaodECDHZiu7GW9/I9/IwffikuA5i1TXJzgpUTxJOJzKjzJGzojKGCjHZgGwM+Tnt2PtjPFpIUWdhEwuanWtzRuldLV7elDWBiDlmUblTAwcA+D3IPcYzj2wJDU2wYWBR6jm3PPfHNL8XrRGpOYf4enqD0ppiiulwv0T2K6CkgpnnqK6Glu9K8kUEEaPJPKVYMsSrucp5Hc8B4hClSloF294pDfsQoMQNdtr0j8+yrr9SVsFwuenzLqCzUnW+Kq6WidoIViiEsm+dQy/JFh2P+FSGPYjjzI8IQPMDrdHfCifKO57ZR1Y/Au0ZrTnV+INyS1dy8tNLdbRy5rzrjUtxnWRaegt8SSUq9N9m01M01VHFAv+JxI2UETMp+H8CpEwA7fTrE8SQmX5vbXpHvoi6UENHTVNxqap40VRFSnCkjttJ+nY9/P8uPRkliR3jkOz4r6pCXU3+hsMjSrdILJHIoTbU1aKARnvH1CCCffHnHj3JAAVMWVFn+Ne8VS5oesbkby6kp6i583rdTVkbNLObfbpbpNlcnGYG/dNkEA92BKkbSMjRK8PNmHCgRl8R4iWhLzFMOjR+cZ+IByLs2medeu9U8p9T6o1lytlvVTSafS/mSK9i1K2aEzI6KJ3+GMUbSRPMzvFI7nczYvxPhZiFuXPr9xr8J4kLTQU7a5COdtOlTTVLRrKYJO6yLIdn6qQeM0pJxOpTZ/wB2RuQMTAR6cvwm+ZP4mWneXGquW/p7s1dp/Rdzraa/S3W82ylpiVSnjpURLtXESPB0gmynTeFVZ2jC7nViShMtRJFSOFtboXPIUPKqvDa2tsdP9LcvPxStc1990fzN5hW/lvBLFNVxV12u9rmnZ0BKy0ENvkPxG8MW3SqIhshP5i0ZYtMk/wCAQeLg9ge8ZcRBDFxwKT7h4t3yz9NnOLlppO66WsXNPXWq66OILYv2lp+SstZjErSslV0pInlaVp5g0k0kxAZCA3T2DKJRdlEDWz2jQqdiLmoA2sd7HKIXuXJjllqTmddLh6wvTvatM6+ktEdBNd7/AK8udrs1/pQ4/wCXoSEgopVdVAkiNSZcIA0bo4kKzPWkArAG9yG3nLvG6X+hSD+hanNwQLNtcHtEx6Qm9MXI/UtXqXlBdvTPy+q0h3WnSumLxZ6Smt80u5KipmqaaSaqrKmaL91ufqdOEFIkXqSljV4gbRTg3aMJlEqcP783Ii4mn/ULBqCot0Wn7hrbUdFIenIbVpi9zwRElfneqNME2YbyxUKdxOcZBKWwLmu4H4tAYC52byO975w87pzb1dLWGhhtV4tVPA5Sdq9BGakqchowkhLLkKd5257EEjg0AqcNALADEl+H8ERjqvW2v7kVprdDaqnrh+tNVXZqRYJAPlcIyPu+4HnJ7D83FTpaz/kA8yPaCSpApXWtvxEM6ntPNrVE1ur7vqy2aGWNmDNSUtVcjVJ2OMSwpGoB7ZEgPc5yQM5xLnj/AOvc/EGZyQHFuQ9Hin3q4mqeXXJjU2p7lzbslmphX0UED3d3tFHLK8nZJq3qsKdmVJNrybY9yhWYb+GGTMF1dA3zz9YATAVBDUzq+23LnHnMuvMXlpbuaVmvmmKPlnzU5Y0y1FZqq2RW6mrKu9XOQTiSdUt8WyKd1MKwzmVFyjdUSEZ4R4n8fJ8TSdLC22gnvTq8df8AHfn/ABngMX/EmqlPdi1rba12G8W35PepDmBrGxS8u9A8ida6N0vWXCGripZdIuYaqcLg1f7s9FQoGSVkBZgpwSAeD/8Ajj4dGGSkJTsDexjOvxqvEzDN8TNUtRo7uW2OaxPF95Ec89UVN2utu5c6+5z6zqqKeksTwUc9or7HWmMdBaWQO1MacSF2cNTmR0kcB0Kq3CB+KRMmJnTqqGZNuDW6Pvh6vy02RKV4WQcKFXCf+zVq9TwtsEZ07+Gl6/8AnJrDSGotRry+5Z0VhqWrrZHqa9R3w0FUoQF5bdPDWQyhjFGCFpkDfMh+TcD0RKlJS+J+Hs7+0cOXNLthcU3P0Z468+ib8J/Qnpu1hS85ddcy9Q83+d8SKaC5JaorLQ2AlyzfCUkDqH3FnyJg0QAQLErAMFrwuw1rfWCHiCoF89mzY/3vjsVb9LadaOugrbdJXVU46sizyTFamZVUB2jBGBtG3PvjvnPClAOFDvCcS2Bdjut9Q7bRo/Rljhqrrpiw23TE1UP+eEKsiz5woDBs98EbQu0A5OO/DVmKUtWIknnD+0vb5JIaSoqphb6MD5qdFQM4x2D7Bhj2J3Ajz5PnhQQM6wKpWRNd2vbjDoorLbKd+qyyVsylmVpCCyn6bjkg47ZBH+nDCoM14TQAnJ9bLwz+alhfV/LfWugqKzQO1+s9TaZUjXHw4qImjVs+CyuVbsfK5+nC1h0km0aJMxQUzU3wg8neYdu5h8udF6r1JV01bd6u2UZqfiMU0kFYECSruLAZ64kHUUjOPY8WA58vvGaYVA4SqoPpEmXWrvTU4q7LTWiedRseGtqGBY7skowJQk5JJOP178GBSpiLQCk4aHaD24bLiFK1VySxQTXeOx0t+RCrGGdZNnjwSAVJz4BI7djxnmqNMP8AYVLSWdIvk8OAtT1SzSIyyTQkKogkDAHGCGz+U+x+o+vfiwaAGmyCmIw+UDvBujoZHoTPNUO1aSColQSRKQc+AAWGPIJ7j+HESACdsClTEPQHfrr2g2YVO94JKiFJi/UKSHL/AEJ7YPgDGR2z24AKZ3tvglAUDsBqmyCbJUU4p6iOqdbhEkiiZ1QGQOTlHwACAO2B9j7DhkwDM7ov9S7gVF6v0gN66pbc00lul9m3VO4AfquT/X/fiFABYAQujv6a940FZRdNY+pQQYwqqQXTP/UFCZzjt3P8PfgFiytGDQDjq3PfAr3eo2gx1IYYwBHTGQpjPcHsO+cfb/MBKDgi8TzMDc62n+QZjulU6RkVNZtcDvHSsO2B2yGyPB7jiXNM9dYsJKUsKnZrLMnKD8VW7oBJW3EsrK3zUzKM+cggd/1PniAA5bYBSyKjL219woU9VTSQtUNO4YA78uqFT3ONo8/wz34QoUbKLONwDThG9PW2yoCO1X+/UM+yQ7XjAbGSD474/p+nDVqriBFKfMUErDuGgzDfrNMwWK70crkYC9dVbH1wT/T/ALcCkZtbV4WQope/ofhtzwqxVUMiFgQ8ZG/O4HA8ec/68UoEpGuNIrEMVTU5/wB9oFjeHbuURtHg4K+/kdvqP9+AmO2Ko2RFYDV3bNgRrZAvVCZK9aSVc52r3x9ce/txeBWHY/b6hrAoxAi2/vWDQqWXs7vu/X/vwg4dsb0y5oDAdyPfvFYUqZEkkyqRSeS6MCx7+C3+g462BI8rxpmEj/X11FWgJ3cBmnY7w4AZsse/jd2xjv24pKVEBvuKE0Xant0MZkijjQyCWOVImKsCfJI7/wCv1xj+PES5De9emUGuWioFjXY0AinDhl6qJJtDKVUEuCCCWB8ZOO4+g88Et7gwkIL7+vTTwSlkpqWCqrJ3ho6SBSWlkfYFGe7EHwD9fft9+IkmiYVKTgBIOsny5RinrEljirxLJVUk6h0bsu4NnyT2wuPm9/t9bUt6pyilSxWWBWvD2fc8GompTDHUQCnYYHTCsQp79/fx75PtwouSG9/WBBEsskcrCuuEG9kVVmaZtnjHbuwz5xjwcf8AfhsskF8+sGpv+pdzwy1YQWgLCWpiyZIGyykvuC58lc4wPHbvjP8AHi1WYxaJuKmY2/OZ5OIL1e2NWaOmirZMYVDgBjjPc+Rnzn7efHFlzRWR3/yM1yCKgayq++C1S0UjtHTzZBA79LJx9gT3xg+fpw4KAYtrWhGhMt1VpSEeKpnq4leM08hDMB8pQR7SQV+Ve/8AgOQPqfoOL8rsYzZPlw94Tqi33qrDx1F0joqdoyjbMb+4IPzH27kjtkEDizMw2vDUkm4pr17QZpaWgh6ghqOtXNt6kkZDvKF7KHxgeMD+WSeEjabRQQVNi5WpGlTV2ahnEVfDb6WeZWXbUTJunVcE4H+IAYz5wODUl6nXpEws6La3e8Mi682+XVqoxHLrHTlKUbb0oJQ4i2nBDJGDhAQQC2AfAPAJBcgdoNSEtQcjFX9dfiBckeXFwoo6S4ag1VXq6MUtKKCY8ncC8jr5Hy4AJIfufI4bLlOQIFaDhLa6Wj8zbmDfOZN909QWOt1BqAaDtpelpbVTu5orY00rTtG+MIZnd2yzAscBckIqhRSn/Yu/rqu2OvKTiXR3b0z4cI74/gj85NZch9N6yv2mdc6pt+jbzGuj7nZKmkp5YZ6+CqhrKSopqgRh4GVLrdSYZSdyNOyv2CDMx/Ya9tvv7QzxcoWIq4a4t2+90egXUnqo1Re7c1vv3MWrhXsjfAyzo85G1sYpip3d0JA9j4IOONstBZjHNWof9aHpERXDmhoTUOoYNMXe/U2pNSTQSSLRSuklZNHEnzEwnNRIAo7naQPcjPDxLOB9toR+7ElwXb2iu1+ttx5sVsx5Y8quZWsLfT4pYRZtGVsNIWJOWlrq4UdLuGCvaUqpABPcnjXJ8UEJZ4ymSFKBU2vWC0noI5gcxIo6HUnJXQdJQyLEtWNVa6h6Evy5bNus9NXg7X24DVCt/wDzARuKVeP8p3w/9F92s7Rty6/BN5d2zmlbua951Npnl1doUhRrdobR0b2tdmfmUXqWpzKTg9UwKVI7D2GNZdy2veNJL+VReuf1HVaxclNC8v6VLXPNr/X8AjMY/b+oJmWUe7PTUhgiUEDwEA+g4ACh12gipkki2y0PKgentlXQnT9psemaOI/LTWuhjhWTPhjtXc58gZPngzJBZMCqYHY3HWI7vXPPVPKl7HaOfFp1DprTTsY6DUdiqY2sVREn5Yax5B8VQSkYyJgYiSNkzA8Z1JIUxTTbs4vbvyjTLKSAX82zPlt9YkJrny91BDOlRyot+raGVlmeS/xQ3CBSAQGb4oS5OCfA7Z/jxolKzTrtCFCgxF9X3dIGfmrS6fu1o05ZdWaY0VVVKtBb7TZaiGilm2xlisdPFsZ1VVZiFGFUE4AHBOkHC/rASpIZwKcBClLdLzeUg/bd8v8AdaiNipE87kMp9gHYkexz744Z+ogWgSMNPqC37CFXKqpHPAPCrA2AfuO2c/p9eDUk5xYV58vqzQfg5X3G5JHAtFeEG0rl6uSINnz3yuT5wfbv488KASATWLE48e/8jer9LMWo3oqu53TUFJJEw+SC91i9ceyyDed4PbwQce/FeTZbV4WuZMJ8hpwA9BcaEPir9HFkv1rqbDqWy6du1lq6OS31dNcbTHWJWUku1pIJll3iWNmiiLI+4ZRDjKg8EmcoEYb7vaCwuHLnVNnpCrZ/SJpDRtko9P6eg/4YsVNSRQfB2qlgoohFEvTjjCwoCyKnygNnsT9TxF+JUr/Rd4XhaiQGG7tCjbvTRoeKOGEQXGaiWMRiMTYCIvgABR8o7dh24Upb3zi1k4XTD9tPJXTlDHHTUFvjjgTGCwJ6RAIyrE9vJ9+/Cmq+tdoLGpN/r5h30+g6eFBC8qvGyBSAv5lH1x2x9gP4cU1C14J1Z+0OCm0fTRukhiVWXuCG+YYGewHccEEMGEAcKiVKqD7avAy2IRMTHJPMVYHu+8AZ7du/fzwJWx3QsDy4VZ/1oetBY5ZqaSZY3ZlGZXKgq3sM/wAT5PALUHdUWfKXNtWgP9nxUDkU71EPYApGoIUfpjAH6cQWqb94WApgQDRz1z6Xg+k00MRlqZ7fFHnKs42qR98nt/X/AD4mJjBAsLa2/wBg9Hc5YJSscU9dEV3RGjiaR1YHtgDsfI75HFg51iB1016RDnKK06gtVw1fpnUPKaosOlJ9R3u+WStuNVRySw0NXVioWkkpo3kaOVJp604LbekYgCxLKgIBA+SPaIspzPJvlonitNNQgslsS5RsoOxIgAR3GRuIIP8ADt9+/BIFGELTKAZWfv3bhBeG3WbEszW2lRiu0R9LLSf/ACAHbucZ8e/bigVPWKMtkFnSOV+N4Ni12mlenemt9HJVPGXePac7fGGIOGHb7DvwdTQQOEYyAPX1uOXOFmCw0khkjWiWniiGdylvkx3GB9/b24Egp4doNIKgxrrOFGeggMipEcwgAKD8x8f1P2PC1Oo+YU1bZAISFEJAYnZnu3wRFtDIyJTpIpBKkj5ic/U/Xt754sgYnMWcSqj+NBpAIFhLUjRrtwzbMBf179j9CfHAYXAAodjwKFB29tdoNQ0khSJGj6XzEKwl3MPlJ3Mcn7Dv3yRjzxUxyWVpoooCh/8At88GpBymWMdSKpGGPyEq7jz7efB+o9+IVEJvT2i1NYP9793Ac4VIoaNsPDFISRuGHfB8HPnHAnE4UBAKlqszEafP1gwtCU3ss0sRYH8jnuD59+36gZ/TgZahiY5w1BY0H86evaDIpkxkvUMAxYZA9/J7jt7f3jiLWA5O7h2hS0B6BiHbpA6U8crRNLFDIUIKM0aMy47DuR24BZAcj6g0yQSR9e8fPDCoDBaWIDsT01+X9ew/scWpanD37/UWUpFV7LfdPSmcCq9MhjUSRvJ+XKxj+mBjz/l/Kv8AVczraIJmJQQ4ts9PnrGOtTgqTFkH5juIB7E/U9/08+OFrBsS2zZAzinGSa8ya5aygaOdMFljRVB2s20YX657/wB9+ACmSybmM6QFXvr+weSYsDhqfscfm/8A+eCOIUDa5xslpLf5HMh/SKuxSiWOMSxsVB3DbmNUwT2bI7dgO2Pf78dIkuda5x0AFYQE+XrTXGNHhXZh3aUHKs6yui5OMkYOR24DCTU2iKUoKx3PGNaiKOZYIy6KA6FSF7Z74VsDuMe3g478EgJADQM44QxJe7+1DAMYmepnkaZY4zJkogxg9u7f9QPb7fyzxMQw7oBIBoovs3/G4GBHWnlXZJCjfMxAAACZyAEBGMDPj+vBggPSkNSAASL2+tdYTqVa4Gsgq4aJVOGhZZGd8ZwQ7FRuO3uPbPb78Cwo1oUSCMJPKvq5+YIVBmoq6CnoIbjNHMWBdEysBx5bwuPPYeDnxjg1B1PmNcoFCKlILAfG2noYO0cM/UrHrJrakAKGBFLlvowkyR7jt2IOeFKVTA3WBlurzOzvp78soOAQMysKmL5WYrtTdlO2G/8Aaf549/PFjE7ZRAsEMB9nlAMyRbtzTYI8EJkgA9+3f6n+f24dhq+cNmIA/wBn+dBCLPLsykcIkibLkbAWOO/Y/p+nji0tfZCFS0gsk105379kAT3aktphkqpozSIBJKFxGOjnv85GATgjI8HHApKiGMUkFJZNzz55v2jjlddQesPT3NPmKvOX1h6l0VoVLrNNpL/hWx6fpqLUFvcvLDHFNWUT/C1EUWI3p5GknDxO2XRkkktOJy1AeHR9GNAwqRRAKrXN+R+t0Txp3m/eLXpqOD/jLmdrKomhR/j9T3n4ytlRhkfu44qeBDhhnpxJ/Hzw1ElgGOeeqwpdS4YNy/vzDO1LrKa7SqbjXBxTw5DzzZ6ALEt5zj6nvgdu/GhqUHrGdT3vEFaw5o8q9GXG1W7XOtNGaXqa2FpKCnu9wippa5TnMsUMhBmUf9SqQM49xxaamtSRq8GnEAa+43avFWdVah5PavudzqLDyf15zlrXqJV+JtOhKlqQFSoYQ19UtNS4J+bqLM24dw2BxCwplEKTmwPGvzSKJVPoFt2rtVXO92r0c6U0daysNLQi98xpKWOKmjJCIaC0x1X5QTgmoLt33OCRgFpQFOBYZfVI0WRhDvwrxr37Rb/lj6B+b1qH7Xs1VoHlrbZ44aeqptCcvKZfi1RW+SauuMlRI+QSWPRViQO/jhRAP+cs4NU1QGEgPvO3OLKWb8P3TNyo1oOYNPr7Xse4STRaq1bcaiGY4AybfRvTUYHYkIYQBk4HBJCgkga3QEw4VBRPJtHm8Wn5c+kLlToLpw6c0ToXSVPKnUla2WKmoA+W7q5gjDyE4JO5jn34Wpzxiwk3SSw55c+rxY2DRVMTS73kqZVQMizqcqgH5ckgquPcE4AHESXtFnGrKnf1gC4SWuzMqUMcDTncNsTGQqn/AE7gcD/5H34IIChWsGEEKYiG/U6lr3JhpEgo4tv5h3cY+/jd2GTjiMbwAIxM1R3httFPIzs/WnLHe245LN9cnvweEf8AS0RS1Ah6vrhAyNqGIpJZ4qGhk7gMrBf1+bDN/rweE/5NoEzKuGJ10hn3jQGs9Sxz00vMKu03O6lJHt9ujqGZT5G6oDZHn29/HBYW28m+4okG7f3ZUOYham9BXKG73Rrhre88zuYlSQSaWS5xWukmznvJBaoaYynHbLsTwv8A46S9L3z9ItU6aGLsO/Uu0Wc5T+lXlLynqrneOWPIbRmkbtXQiCtrKakKVFZFuDdOWeZmlkTdhipfBPcgkZFoCUlwN1oCYVG7k8S2t8WFi5cXi5rChtFgtKZD7ViAII7AEr47Y+nBKmJJd7Q1BZPlDE6rDsh0hebSVSgs2mM5I3oZFfH0LHwM+3jHCk1o94o/5AV6RitpNWxQrI9FbGRdqydNepg4GV7HP1GPbikqYOT2hcshamG3Rg9Z7pqRZmlpdMVM02doliR92Ppk9v5fpwWACphuI2YQ9aS936q2pJpvUG4nB7Kff/3EcIUlItC1qU7m1tNnDkhpqwmM1tHLRk92BkTt2/8AbniFItF+Y1Atb5gwKAK7IkcMjnv87MST+gXHAAuaRSQKMHgysFRJHGjyQCNfAO4AdvAH1/hwQYVgFE1c14a1aFKKi27kU58fMITj6E9z34gDWEWsqYhVIPpQzbl2xQKnsDGM/cjvwDCxgQwq1OPXrshVipahSvTqIlAx2UKP6Y/14vGWEUkOpgGGyDdPTj546iWtmhP+GPBz279vp2/rwCgAARF4CyqX01ue3fA1TZqWaiCwQLFUbhsMjY9/GD9fH8vbhLJIwiogkzCKzPng2+Ccdoo6ZmApBSVOGyQqnbjuQe//AH4YtRLMHgFS1FyoNq33eMw0viWOgcTe4yAWJ8Yxxa6h/eFKFALPXb8wqUZqoUQ17LK88h6EcKdT5cd1k8EMhB9u+ff2rClt3Axf7FBLGnDPWhCzTUc8gqpnp54UVyrHpMDkdskE/lI758Y88QhqesEFJfA3p3hPNDTQq2+YUsg3bo+0ZXuO4z4XDD5u/sPvxFoNiH10hOAKoksdrH51vgKO1ITVQR1NXGYxktgAee/fbnOPYngFLIIYa3wIceUcTcvwB9oVaSJKF5Y6dunMCQXIUO2QMEkjB7+/j9ODVMwlyK62xZxFOTbBaDsdO800s4b4l2BCuflwxGCce5/Q9v58T9hUXh6JTULVO8B98fdFooWmng6M5A+Tf3lf2A3Y7dz3+304oywE4QIWUlyFW94UJokoEVZnTsm5FyemDnzn7ec+OFFBbEgwtKFIUQbDImAXFPNLToJ5Is7umO4VmX7+3kefPtngggkAbb/yNCFKJKgK/XpA08Uix/NFSzzlsBTggnA9/fgUqSA+QhS0rSWN2y/pgyqLHUR9NopoWIGFfJ+xH8e3biSkhnNhATRhFqb9XhQJlGNlK5OPdgO+cEfr78UGIxKNDx03pBzCwxW1X6DRos0+0ZSFEByd2O4z9vb+/fgQirWhRCinCxrrKkDdGokj3yfnz8u3ts+vfIyP5eeAK0AsT9wxSElLqAr33e8Fkt87KBugeZU/NIuSfuGwSMDx/HggtP8AIskvSgOsr7oOrTloukSEyACzr8re2Nqnx3/vtxQmkeYW61hU9QAGZ47tkDfC7D0QIzHgLt2sQP5H6nhQQG3g5w8qugnY7UtlsgOnjpzOzyfDRSKuJNqlGJ/U+Pr/AH3AzWF+kAlSVVIp6bHPeDXQqCF2Cj2493I/pt4JCksIbVHOv+X7vFVZ55dxaE08jkASMVI+b32/9WOw7/6Y42oKn1bjHQXKDMN+tkbTVRi6bziCSM43Nu+UZGOwP5s4A9vrxUspZxeLxKVRu72zb+bhAc1eu1IpmKQIu9yVyPpjIOAw3A/QjP04ckAbn10gZyiUt2t8g8H7wTjlp6iNgnWqSuf8JwVJOF743Y89+/f7AcWVKLC0LKziJy1t/saiZy0INLGGJy7OwBjPb+uc+D7e/EUoZ594P9aXBF8jAUtxaQvA5RVHzdvmXOewAB+2fp/lwMsMAQYAIYNshv1moKekV0uVfQ0cIf5IxKThc5AZcd/JzjA4NKHuXgVknKvxDFruY1uhqZEpLdUV6AgB1wiv2OcbhnAwBg/w8cEQTc0y3QYIDLHpy5QTPMu2yUm+qprnRVByGgp8MqnOQd2QT7fT9OGYATC/ETPMC9uWngxTc0LGqszw3qOU7SpkG4Fv0B+58/XhSEmjetIiJmAUo2uEJFdzghVTHSW2vlUkqSZAM5Pntnscfwzjh2ADzHKB/aHxKtTd1b1zhg3/AF5+3qKemqrdWqrElehc5IcfbKbW+nuODLO8BMS6gSbb9uTRV3mDyQ5Q8z4aum1dy4grUlYSSlLtWxCVx3G7bLlsdz392J84IsKzOUKlygzPw17QkUHp+0XbrfBbKe8831plIZnk15dqiWZg2f3ss07yN3znDLkEjx4tnBGfOHCcXIDXeo72t3iYJOQ+hdWQ0FDqPROjaywxNHPDGkEUuyRVZY3ZO+7Ad1BbOQzZz54DClsPa8WStiavx7UiXbdoilpaC02b9nWv9i0CRwUEC0w6dMiRiMJEO/SQIsahQQMIox2HFEBmFIUCC7jWyFyTSNqkhaja02ySDDBlnijKyJnPzLjB8/5cBjOUCUD/AEzb9dNkNOn5OaWoiVRbi0O8t05Z90SZ79vl7Zz4/Tg0zXDPSItsjS+t5h70OmqKjhWlo4YKamjU7VjyFXPjOPGSB3/s0yiHpDlDMhtcYBnNspHMlVc4Iajs5y3dsE9iEzk574P+vAlHmDWgn8zhyN/3CZLfLVFMVpKapq5EyfmxFvz2/wAR7+58fXhmAmsEUjlx13hLmkvd5Xo0Nok+HIOQiMxb7FuwH6ffg8LViJWAzh4J0HLa6Vrj4qEULFgMPkEjBOQPGOCKht2RFLODMg7NkOeDlTSKVWSrmVgoIZcBV/h/Ed8/fgSqjbINCagU66tCpR8oDPUpFFM4Zu64hyx9ye5Ht78TGBYRGxFnprnCsnKujpwHevrXGDj92ifN9SPIz/vxZm0gMIJGvmFSl0LZ6Q7hTI8inCtIu8j6fm7fx4gWSQBrX9gUuHTR9W+IU0tstJHtWr6SZG1AmCR9lXz3OMY9+AUTnFoLjEu3Y66QvU9mlZYS0iwZ7kyl8/8A9PYj+X68DizzgkpGGzAaaHNTCjhgZLesc1TI2HkDDCD/AKVz37/3ni1ObxSmSCU7dD5g2KCURrIzB2bOE3bex8nIHbgEGrCsEVVemuMHVoIwwkMcJkJ7E9znx3xjgQ5rAlVElg8LECQIIzVsJpW2oiElA7kkbe3bGPqe/wBvcQnlBGYcW2BxIZEqFgggo4Aoy8YByTjIB8dh3Pc8NUXo8JcJFLduH8hGFxkglWl+BzNhEkmTxE3uGkOQzdx3XsOKBrh2wBluhwXEGqUxPVtHJRzPIyh+7BWx37nPc+RwJX1hyp1C2Vx8QsRwFqmQmOnpmG3fsJARD4Kr7nvxGY1hZSogAf3r7wZhzPPPTFRGkce8u5GPPscjv/DH34olxFialmUG1vhZp7bLUQxyuoQHDEEbTEoGd2M9/Hge36cR2U8DMVitr2gxLSRUqxmQxtFuyWVj8qnwM4yxzjsMDgFIc1gZYJPmFYDgiMpAxTREqcBwSWI/xNgAH6/U8BibQilDEDW2t8Yt0dUwWC7dKVACBNG4zk5wu3z/ABx9PPDVAAxf7Xoq+tUhwJDUPNHTESTxqgYySKu9D42gDGPfuR78UFZnltgAlg6U01rKkGJ4jGzIaWRPBZ2kULjPjOM5x3GQM8Iw1oPbpBApwkGPloaevKCQwiTdvOA2FIPbxg/bPtxC/TV4kwpKqn+d4Ca13Mxw00VXVOu8hpQ67tvcr2Ixkdlx/qM8AVAKKSK/POAQSFMG9eVMoMUduroGWOslSujcHs0KI0aD2yCRjHbB89+/jhpxcIJSsQfX3ACU1IDKsayxBlHdWALEDGQcn+/fvxZJZj9ekKl4SDhJ1k0DizxCcyxt1DlpEDxqWjbBycjz+bODnv8AfvwMtZxe8WFAeUUPHo8E5VuAbpzTJBI7dlTcy4wf8JAPtnGe3f68EFCws2q0+IIuaPx49coxWUd0kpqnoYmqs43yB5AjYyThmOEOAMYGD9feAF/T+xEJOLFchz98OHSEsXm5R0LU91ss5iUnqzRjI3D8qgKSMdgQcfXtnhoAah13i5Etsq8PQZa2wrWHUVuuTtDJ16WtZwU+IhdVmGSTjsCD5OP8+FKkbbDlADCQSkUhdjWGpkMVOOsN7AOhVwQAflOO4J7se30+p4UpTJfnf5imH+bnXrAMNXT0slQkszSFFBcbMuif9WBnt/QY4hYDE767w9eHDSh4Nr1gsurLPUVNRRwS1NU0e0syR7kA7d8jwBkEk9/5cT9blzre8RADuAGF/WtH5C8ORo6hlWeCl+Ih3MGeJ1ABGCQckZ9u334VRmNIBypBFhcfdM4FhWZTFLIkTDdkg9jF7eBkA/Yn34U5Sopf742rBCUGKk33n0Ab1gzDURyiaT4ecwq+Mlgdvv38Y9vHnginBTr/AGzwpScRBVfaakwYNLKYviVJ6bduwYZx2zgd/wDzwJSbi3H+QKgxYNrdAoVYyCjJEcEnuSB/Lv4+nBfrYAEXhpPmBzvU3yjV6eR5IpI1TaGZixzlh9f9e+eErXict6/cVLcB8NPf3hRjpkdQz00Uje56QOf48L/cp/IKRtkJUEgAvzimjU9WiSdSaSWHALMcR7R9c/p+vc8dJCgS4NOv84xqmyCLhzls9xSCcsSLkPUVchBUMd/cN+g7hv8Ax78NC61pAzFpISDmaGmWbAQ3LlebXb5NtVV1XWKH92m4smf+oKfH698/bvwaUk1GUI/WwDAuRl3hnVGs6QErQW6ollIKlpGPb2wFBYniHabZZQyUCkFKO9fS8Is9fquvimjgoGp4Cuf3NOEAT6bic+59/fh2b566QoA5hjvp9H2zjUaK1JVFWqKqNyCFKtMW/lgY+v8ALhYJN+UEmWpZKgXA3aFL16QZfltGpCT3WUsWziKAZKk/rkee/wD34t2yeFzCopJvrjA68u7Yrss1TcpFUDvuUA/pgefr/wB+BlrJtaNMwgsXyf1ffvtAiaA08GjZYK2QAeOqfp3LY8fp/Di0rf8AkZUSwbZa1lG9VojT0UKPNbFCkEgJId8uMdgNw78UhZfhBLWQpwXz363wjVth05baC4GWgtjVMOwSRGRmEO9vlDN+bOAc/Q8MSMJcUhZUGCsvXWmhzwW/T8NrpaqkpaGKBso69EFkA7nOe4AJPY5+vvwKkkuDeLKXUDtOhuggNI6avtZV2yOnqaWpDN0Zol2QyKMEkkdiPIyPfg1lTWiSQSohIrsyhCu3LW17nFqmaArIyytUTOcY847dx38/Y/TiIIet92yKAKqtrXF4SKXl1Uw5kS4sqk9ui/dwPf2GP4/w4MLDsPYekKTRTu2tUFoeFt0zHT9GRnrKmLeSyuzjtj8xLN2Ge/8AeOKSGOucaF1Zz2hdgtUDb48YVDtZ0XBbuMlcj8v+f088CoOITjfzKNN2qb2g0lskELRqVqJi2eooAZB9vb3/APHEUoJUx7wxKnPm62+ukffsgSUuypCNEMxknsSD7ZwDj7f9uK8prlCVh3S5YQlppOyqQpoKRgTkBkZwoB898/fgyoO5z4Q40IxV1R9GFSmsVqgZDT0NIpZR3SBV7ePOPH3+p9uJAgYiCs11tEKht0UAQn9zEcgnPY49h57/AG+nAAMS0OmMipuel9t4Ep7Y0+zp0zOpYIWCY+bBPzD+x/A8GTcRTk0f5hO1LbqK0U7/ALUjRlYqHQytgYJIwP1zkfbxwUpz5jAqKQwJqdXbqIDslbp4XT9mCY0ohVGUIXZXlIzgE+4wP14taRhY84tJIUGD+uucLt5qJLXE0tdNGqEoQzZGELAHbjzj/q4pCXLQ2YTTFCqttNVbqe4w1BoJXXbhdpKLnsQDkEEeCP8AvwCUVqHiEFiTz1m0bR0M8YcRvTHuSJDuy36kYx3+h4Iud0GhwQ1SI1cfDoJKs26MY7gRO5J+oB9vvxYA/wApMIWpanBhRttNNcnlWiqCrJ/hFOilu+Mg/TOB34FiTUGKKEmhG/8Am7bBqgjjeMzGpZplfb8xYFG3Y7LgAkY8EdifbHFqNYpJU5LtthQqqd6NQ809MkYZjI8ild64yD74I87ffI8eeKwuXEU/nw6HSNVike34aqAinlZSwVUlpxuK5ZclRkbjjuDgEY4FCmpFlaSDR+sbijipD1mbr7QdkKjai9u+Vz5JGf444JRFtsJl1Hpr5hJqrslyrdtbOzFht6RkYqvf8w25Ix9PH8uAA/6jbDFhTPo7tdYCNJPTQxSU0Nsp4mb95NUsUf3/ACncWIPn28fQ8Gz3tCkFSaAO+tXhRt6V/VpppZ1NKFdUjwrLIT2DE57KMZAGffJ4FTKoMomFyWHvy0Yc9uqhHPHBWAVm3bgOh2oc47HPb6A8U2zKLlzkrGJNhxLc98OiuuVPQLTRzSKMbmkdhtjUDHfP6kAdySeBAN8+MWVP5c9l6Qk07vV1ojgZiqkqVkj2qMd92Pp38/x+/C1AMxhCVF3zuDeHEFgpdwlnlXdIsaNEgYAn389hjyeKCaBoaoG511yjSphioyQN/WdsBo2JAyPJPfHYf2OLYu7Q3EomhpBuhY0tlT4eCsgMaNmOUM8rgHO4EZz7YwPbx7cLwgqy12jHMWEmtN5LQqiup5pDSvNTPX9MSvTIwkOGHfz5XIPfHvngVAgONekaFTCQxFT6bY2stvjuEk1ekMUKiUxgDt8ykgg/yIwRwKlM6hl0hYKil2r6PBiOgq6FWapqKetRu6Mi7GUj64Jz/f24sZ4ctdYalBLFmHprQgzTzu7EqhdfOcFj4x59/wCeOAYt5tcoilJAIz3h+OWcJsdqanus9WhV4JCFICjB7f4QR8pz9z4HFy1gJYwv9B/0r6PYwM9HPEGaMhVG47GO4/bv2Jz2/ThudK6ytDZcpKUlQ3fW46tGktR8HE5qIHiQEENEjyKBg+cA4HY9/rj68WCx362wslILlXeo4F67+kGmij+SogBRj8oePCO32J/XJIPuPbgBQ14xCjG2APxH3TlAr0YSZHIhdiGzknLbjk5+vcj+zxZUzgZQspSkMe+W4w1qrS9M1a80VprYpiS++KuO1WHf/wBMt2Pc9x2AHbhgWRf1higUpJFu46bYDlgrKTbDBRXencOQxmjVwR32jcGz38hse38OFqZbt7a9IFUsAlIL21T73tChRXqndEF6RoKqORmUxszBT7EZGR/HIz2+g4FUogFhWsFLIUcOzr2pzg3Taf00wWot9ElGP+qD5EOfPyH5RnPc4zxWOgKg8SZKCvI+4cIX0p6IQJSOkQT/AKYowuSP/j44ViP+tmtPFzkG689Z23WjX4BXJanqpYYsgbUfAzjHcf75/rjgg2dTqkRSCQyqDLWiM4Bmp67CRSb2pwGyOqGL+MHBwfr5z54UgulmvrOFpSQQts9ppBuWrjp1L9GKNlUZkK7dozgZJ+2RxSFUDChjStYWCu5424/MBPcJopqdZpEET42sqE9jnvuyFGPBGc+cZxxEIblrjET5JflO7tlRjbvWFiOro3hSoilSWIqpLBtqg+MYOPJGB2B4UEKChcQIT5CSCAN/V3zfnGxniYnFTEhBwQ4OQfvniAE1JHUfEdDw6SpAPsDrhFD73runikampVepJyd0jbf4gD+H046gS2WtvDlAEjEUr2PR9HfDImuWprxK4pg1HTAM8jRHpDv5JfwT9e/fhtCGtrV4VjIqoU25dctlI2TRU7srVdfHDIz5IUb93jtk+/37+eBCxnlDCCmrAb8trarDgtGnrMZulEFqWRtrNK7fvD/7TgA+Mds9+IVBnGvfpEcLYB9cSxhyihgp4ZFiOAIl3gRglWP1we+Mk9wMcKytr5i/14KUA1lrfA0cMgeL4qZeiVbJSTqe+FOAP8Xc5x2HFMAKf3+Rc1KRdXc64xuFURs0sMfyNh8se3f2P8j+vEQC9oAzSNg1xrutBU04PZBE7DKqoxnd/l9eBUMyPqATLOIBdn4lzrrAi0TCOKdkK07ZJJU/x7EAdiPBxn7cEkAmjHhESooUSpTgduO+CK2ikqaiKpqqGkq66MEiQR5MKnAB7k4xjOfqM8aCpqRPDoIOMhsjvPJ/iPqqijSEIKeOOpcESfIp3k/9R7d+/wBOLCwFVMJXTy+zNrdBKBiruTJHvP8AhMbAjHgEgnOP9uBUodYIXOLv8fEHZURldZ6j5z7L27j278QKGysVMwsBYPBZqKVcRskqMc43HsPtjv39/f8Az4ahTF4D9TBzrQzoOcBLQyRQ71kSrcZwZQTt8AgHHbzxAaiKSgS2AFxSFmkstZWSSiNKaT5sgImw+3jucj7cUlhTXvDEhKHqxb/r6Vy5QQaljpTOJoBPIG3KioMouBjJPnv5Pt4wcdzCSQHeAlIdOIcNarA8CRymKKRI4qmYxPFGJFYykjOQgPbPtnB7dhxSQYYglAIApv7wnQ1butwYWwwVyDElLIDGzxKD3BbIA8DufPFJG2sAJy3JAFBuNO0LUcfxMYqpUlh+TaYnYEq/t4GO5Pt44ENaDSXS70OnJtG5iaH93TrEDgfLHF3B/nj+X34InfrdAFyW2d+EZihlfrQvI7RgB3EqDZnPjbjv7H+PvwRlPoRMRw3sYUKaurIiYFNNDTxyfulATJIz4Y+3j+fbi1AWJhqlkHBwv7CPquI32qlpLhS0VziZ8BZCCpb/ABDHkEY7Hyf8hlhqiCUgEMoV16/yDElms0REVxejWnkCy9A46iAdgUYDH1xnv7578TErCxgShlAg9WhHudDRVcsa0N4qFp0IMC1Sqdvf3kw2Pf2H68EgmKCiCS8L9vpI1q4qh6/qUYiK9HChDJkHcfHY9+4yc5+3AOdkRExgSKjh1z+GhWeGCpZXghmaLGMFfDd8MufA/p9PHAgf9TSJ+z/s19DTQAtohaRJPhkaRWLkmQBcf+4HKt3PvwaSLwM3CACFV36eMafs09PWCNHq6K3Yf9yrq3UZj48nx7eAPoeLWkgYkiKQs3doWZ46UCSBzDUluxZ5O6jxlWBBB89xj+OOBKqVMX+pVFBNN3tBWorKVqkU1fXUizbguw7VCn6E+B7dz/pxa5SWDxQTRhTrocIEYRNCZKZRUOowqI/ynI7jtlT2H+2PPAlOyASAmzvtF65QDNG0zxPLGaeH/FJIU39/OAcjPv3I8/pxT5AxFspI2Ft31Bfbb5ahaIVdROYjhkWQMu4jyflJwQR7+w4tSUiog5IdwzCBp6OCjanNPaIKieNenGzrGojTyTubvjv7cEdtoz+V2OeucGJrrHBJTo9wphVIwaSGniaaTx2/L/me3vwCVb/aLmsrK2u0KltjqdpLw3T4QS9Y/GNtaRvY7AMkDuO+OBJam2BCgpgmrQZuE9VVyNLMPnOFBwM7fZcfQcWFVoYJRSN2XHbnlCaLZ1QzU11qKUh9m10Yr+hOTkfX7f0oLahiFBUHbf8AEBUsN2raoUEtfVSIo2EqnTDJ5+VsBivYePtnt34vEwB9oFGKoVUceO2HtaxDviit8c0keRJKJQxZFI9iSe5I9yRwsAuyr66mKThsLd9cYUJZ6mjDNUpGmD1A8Z+ZTjAycYJ+pOe3YcJCntbZ7wZBqx+OO6ESpjrTVRSDTkdVcj3V6SojWQIfy5LYbPY4wcePGeDSGDEwDhR49NbqQ+6GVqmmt8Ukj1cqKyGSSMx9JiO4lUMcNkfzxg9uAUku+zvAgoKv/WQANb+UHI4oLjQzNLVvJ8wLqp+dWU/4u/nPfv8A14ElTuwfifSCSHdqa2iNRWRxI4RYqZRIoDMy5m7ZPyjx37Ht7ZwM8GoG9XipbM7V22/sG6SX9pRKGE0Mh+d17Bs/lOV8gYHv7cJSw13h5WQAoirNG/7MZX6spjqEHfsmCwJzk59/PcfbPFJxYMAhbnC6R7cm9N+6MmGdFQU8kaOGJ2p/iXOQQCMA+3f6HhmFJsfeCKibUpw7gX9YzEfiFYEzLGw2gRsg25/6SR57gn3B+vFPcjnspBOMTt5bX7P7mNqahp66CnSFZZqiMAhpI/n+vfwFP8vHFEtVOULUCUhVOptweE47aXrB42qfnLfKC2wg/l7HyDk9h588UpZJZm1eGy5ZBIUwJ4vzqYM0tKC7pCGihViUdpcq31wSSTj7/bipbYcRHH63bIpVmN9z+1YPV9FTV1JPTlnpYWRQskap1FA8bSRj3Gc+3j68WtzUhwK694ilskAgAau0Iken6gMzx3S4qxyFDEBgRk43DIb+IPsf0iWu2tc4BBdJWqw1a3ttgOCCuMhgMlVUwKe8ucEDA7EDuG8nxj+nFFYUDXt8wcpIPlBr0vve0KH/ADKR1MUJniqixCLIxDgewJPnt48+AMfVZTckVzigGZTtlrlbfA1KZBTCeqMiOf8A8isoyD27bffGf59+AVMLG+uVID9wC3UanQvny6Qfo2LxQmqgLysxRShBDDxu3KducdyMjH04p3Lhm5fENmErOFVdV/sGJniErUgp0ilkIUk/KpxnsGH07+fGcDGeAAoBca3xomABgg7SHN9aMCQwBBHBKg3Feyu4IJ9/qQO/bPfv7cEt1Ak0asLSgVO7PP0flCkbTHMepUpB1D/1RZOP1zxETVJDItx+o6KPChQBWOz04xzuqNBxQyhZrpEkm0Hb0WLEeOxP68b5M3F5QIFQD4lPffw3U4UhbpLN8HA8afEBZcOBK+Fb7gf6D+fvxctyqtAdbIROXq/rTvB9aL4cs86HruysismcnHfPkAfXH8OBJJDp7Xg5qHvfu27Qg4YzGsslQqHABwCRjB9gP19vqO3DJpslEWZicTEV1Wmt8JdUJI46cimqp13dH5EyT3BGcdz+YnJJ4MpwgjLWvWAkzcSQ41c69YGmkpnRwu+OpO/YH+ZWXPytnHk+ce3jhADjNh0/kTCXcCgfVKwDbaR6GlML7JJphltqgFj74wPk7Ejt9PPBkC41yiKlkEYfj6gz8BAWAqqVJW3BlOcHapz3PkgHv3+3DCnI1gFlIq1e/MXrvg5IyyBhUPLNMxHeRiZACfbz3xj6cRgBSzQMguoF7cdGAqRDDmTOdq4UkYOM/lY/Y+wPA3VaIlYKQGv175QWSOOmneeaqqJi4G8SNhO5PjPj82QO/gd+DXVjFfsITiBpTZ2OngWjjheanjiZKt8HaI1BUEHxk+SP58WXPl2a2wEtDqYGvt3AME6qmp6msfZUxB1Abpr0wrDOOzHGWPbtnOMfThiXCW/sQkmmzWUJZkFNcYbdVR3KBHkUK7w9pSR324yWAx5HbPFVLqfWs4pS2BLWy2+nK5gvUUNtevke4VF0t1VCi71ad4UjB7g5C/x857j9OGTC43ReNNgCSTrhG9K8VqqjQ1txrZamQ74YfmEiewXfuxnBBJJ8d+JMlgpcwIJKq1fdr7h01Fdaz1JVef4vbuJkpi67x2X95kBlzkfUD28cLFr9/mBdnprn7d4Co5KKWv6VItHLd4zuEm0ZGPm7MwBI8kDPELl3qIMBJdr7q/MAy0n/ABK0lHVG6RUTsDNIkqfvz5O0lTlCRnKkDsR9RxbhNovAXxpodUz7QGKQUFNBSQtDEkb7tkIDs53YGA5yCe/g98/YcQLBtc6rCSChlE63NnCrFS1LzyrNKklEE2AMcEk5wQ3YuTgDv+X68TAP+phqEpKWHrpgISp6qSCluFPUrT6fZkLRzdQTOoP5WK+xJz757cDgJrs6RP2JKWULavCeL5aqS2x3A16yQxU60lRJgr8TKMFGiyflkHc/cZB9uGpBonVb84SkAMkWvrbAH/EFBdXUmW40+9wC8RNOjdwO+M57DGFI754mEtWJiIJA++mXGHG9JFVUyvRUtZVyhhtyO20n/wBzDsAO/wCnEO60NRhJZj3r2aCFqi1JHVRpcNH0k1GHAaUVHzEEnwoLYOPt7cWohnEWohiQD313h7pQHpySx0H7vG5YviG8fTHyAf8Ab24Wo1peC/WkeevI+jwm09dQPK0S06WidnAEklGzkdxkn3z5Hk/Xg1K2wCV1OK/SmtrtBijqfjauWl/ad1o54mMTiW2hombJxskX5V/Q59uImW9c4FCxnbv6wrzUFtjq0qKueiq65GB3BcMUA/KPmIBHnsP58UUiGYCXOfMdYCmpaOOVm+O6Awdw6m4FTntgYHuce/j6cQKrEGJJoNbYGfpvHSrLDPtQYp+m4BbGBuKnuo89yck/TzxDiD5RcyWwZJcP3PKBYqRREJanqrChPcSEDsPLDOD+vvwAYnzG0KwkHFnnx9oLtUUKK3ThmKvnqGjiaRpPu74P0HjzgfTixLSOFYAS6MS31ue/WPurWikElFbBCEBO6skEHb+GW+vfA4sitTeInCS44wHbqiunWpqqpadYoVilT4dvlqA3ssrdyRg5AAPjv9aWS9IqYSVYOr6p8wrvcILdTVEkNJFbqAMFzBERvLEbXAAxt+YEk+PfOeFlxV3iOlKnTxjeCGIUqXaqrJa2kp5vlqaacBQOwbKsASe4AwPf34JQ/wCogUCh+jrlCJZZbpDqC7Q3y010tLHM5iLOqK6HPygAnORtOQRxFIBog0gZqSU4jnxGvSPoLHFebjFX6XrOnSU85FTBUbgN3uVBxvOQRjgFqcMz6ziv3gMxp8RIHVuy11NUUlJbVgeRWqC8jF1j7/lAHZyCCD98cKUpwBsgsf8A2Id2P3U/MKcdEzxRnfJGqMxRvdl8j9cAgn9fpwRDhlNDBN/+1jmfTVY0qRc2jjNJNEzB+xkQkOfGAR59sdv59uBKwCpStfcKGJ8KDfPVYOCOoNEjMY6WMJmXpxFd7jAZkBzgee3fP34oJrQ+jQ0BIDapBpXnp5oZtlPWqqBnIfbLEMdyMnBH5Tj+nAFTVenH+xnSlwoKo9oO1MFRXVcVXTydKAoUYxlUcnGMnIw3tj6ffiYkthBtDJaKBRsPjW6E40MNwnkp2p/iGjAV06oWVAfDAHsO3cfXcR744kxJH+fqCQDhU9r2dtdoP0MNSY6Noq16TbM0bImwnAONrk928eF85HFIUQd3WBmEkOSx3+3GFxaimkcpC1MjDG4dTd1BnHt4P6g8BgLuC49vqDBSoPZ9vtTWcbMrNBFUvBK4AYMqFniPfuMDuQCM9vp2HEmL/wCoLxZV5XZ3Pt0+4MqyLLFDJ8LDVsCArADePH5vY+P9uDMstd4mBaA5qc9ZekHZGlSBlqEmfI/xHd29wD7++B54UlS3Jdx6dIaouACKjiabsoTzBR1CxT07wbd4Z8DZsOCPC+G7Bckd+3vwZWpRZnG6vxC1JOJkZtrOz7oBpEZ561VFWkETAqTF2YHuCP8ArH08eOLQtvMm+7buij4dL4f+vYkcNbYHxK21pKbd8ip1BuTJJx2XJ+x74I4X5QfNfWq0O+BmElFTbd3v0eFJYH6aCME/+1R2/wBvr24WQxa57f2GySQkUYad897ZwC9CQUlOCXZnyqLmQgAd/qcA9+LDHykW1cQosFG4a4+t+4wQlsxqlZY5jGgJCE9yo9vPce/bOPHfiUSaOSNc4MABRIsLDfrnCctPS0Be23WOKrp5Ym2rIMq+O4LY+fsff9PPkWlYZ98NMkjaDz1xDNSDdvlkttqo4DSy1FuZcNIoXdTEDC4IHfB7Anv478LWKeVXLbx2xRGEMacjnrZBwyXKLdHbelMpG6PLE4Hkf4M4z/1D+PBTFYaHXSDlKo5Cjqmja0fCu3APcqaoVt21H+WT5sE+FPfA/j24XMSAWTDpKHcrqnpyHu1DnCnFPRwRpCtBJNGg2q0JXYR7bcjOODxp2iNKEhKQn9mFt7Z6HKKZxVLxQORBPIpOA8cbSNEPqx9yT4wPPvxqwgeXR1/YoLdwjWe2kGbbUUNxqnoaOcCcQkzrJGwanPsCT5HcZx3/AF4BQo6tcoOYDY9D6D5zhSjpoY6otIH3/LvJ+ZwcE5AP5VOO2fB4NEwMK308KBUU1FMt/Lfv5QVlo1NZJlXYlRsk35QKCSFUDBzkHv8ApwRmjBBSwSog61stBOrorg1VAlNHRGhR8zElm3A/4VAXHv3ORwCcQBbXzAhZCq5bLHv6vugnW0lLVSTpFSwsD8zd9zIpbwcYIB85+3BvW3aEqSXGGxGWmglTJirEdRbKykijc9SochC5yAAo8sPPnHgY4JSABfWvuABU2Iiz0HZvuN5HpUqV+FkSGHqr1Piy+9ox2yAuPJIUE9u3vxSVuCda9NsWQQWUW5nQ2QeZpWEj0wippfmQkSh9gH+HJ7EjHk/Ttw1NQ7vBrJKmsBfT+0EZIXdYVjdqasRtscincAGxuJXcFB7kdwR5PuBxc4YklJz6wkSypwm3SmWX3lA88QaspaqCoIoEKrJCYwY5zgjawyCvceQO+PYcHUguGNjz1SLUoWpX23+3WC9wttJUQI1DaaOtrHdQGj/ddNzny4PjvjB4FL5lhWKUGLgdiPQQWmtUFLbg1LbLbHJt3mVEWX953GUZvp3GR9cgcGRR+8GnEmqEtu1/N0HafrT0yt0oqepc7kMnlGDHwU7gDv4z9TwVgQYFADOkdOD5wrUxkhaplqp5Kisbe29ZS4Y+B5H0x2x3x38niinIB4AmoOV8/WG8TeGrlqLjS0tfTLgxtPVCIr2HcoO2e/n+nBDYk0hQVjVQb8usD1sz1JqRWx2e30j7SwSUfMV9ztUDv79x27e3AsQXvBS6f5+OG48YMUKQy/8AMwxwXNof3YywUKGzkE5OV7kHHfueC/Wp8Jr27xXlTRY9YFijvPRMdJT2ShogT8gklbaPoF2jaPtnHv78UC5Bd4hmi6v72gSkjqIHUVyCSHO1nRFQA9sY2ksOCUSTEMxku4HF+H8gZ6KmYtOqguuAzPIW6R8Abc9+7D29/wCHAq2n3iwry7TSD9qsq0UdPthh+IClWlSAK7j2YkZAxjO3x4PFjMCACsPm2vo79O8fSta2memrnt0ssZBCSkExsRnIU9/BA4BKy7QYUDLxEuOnPdGaSSSlpqWnKvIgJMUUMDoinOchWOFGD54JTmoMIIZLGo46PRzChRVq0qvTyUVMspBVV3Jkp3yPlGPYfpgd+BWqrQYWoVI5ufl4GEq5g+EomcMpZnJCoo/6Scdz/ucnGMkBTZfWuURNF+Zu/K1ISa2elc73ipDgiQF60RNGQO/bJz37d/PEo+wRoKQg4CfUDvfrDgoVaUMs0iU+zapiWpaQqx8KMqO5OCe/FhWfzr2hJUQMRD9O7PCsk1PHJNLXFZWjJjjplQqgYf4j9QMn9eAS1AMomAgYmGtXhOVHEKxRQQhPICABe/0wMDzwSmFRBzgQrCk002/sIKG2PEzy0opYKgsC7lM5/l/54tIeoMUkoV/oVz+rxutLXsSKq4Q4IyTDCVJ//WYn/LgVpSS2t0RRw/5py122QPDT0O6RcmskR13NM5dkJ7DJbwfsBwGJQELUQa17fHrAdVdaSMwxmaFZGfpgNIAwb/pKee3v27e/Fqpca9YiUY6mp1c0hu1kl3vFX8KlNaqq0henNTzGROq+QcnAyGGAfp3PbvjhiEnE51x3Rcxrln2vrWcO2109TQ05p5rbHTW1ssIzMAUcd/lx2K47+3ngLmKmBQBKWYNw5QHOgamq6J3r1eqlMzukmZGPbcMk7gvb/D5X34pawBiIhEyY9XfWVoESlsVNBVCK0dSaUNnpU52qSc7m8Y7k/L38e/gRKqRFKOEPUjj/AAvClPdoYZCK2op6eTYAFDlnIH1znP27cLSC0UqZ5Qwb546aEqe9wGN0prlUQq+MZJbJ9u+c/wAvfiAF2glr/wBAl/nkaiAxaayoWnlkuN0oZsHqQpMEjJ9mBOck+c4HFYgC7c/v4ijQNYv1pdxth1pRz00IUyhIRl8rlyc+S2O+fH9jisQdyKQK1kJdN27Qfip98qr1aiRu2cnbjI7Z/mD78CJhSWasUUF8R9j3r8QpTrIjsYhO5Hy7thYFfYgjHbzxQUVbIi0l3z1y7wBBcZJZ+hU0s8bRAtumpmMZwQQdw7g92AOcfb24iUgkVjQFuSzEDeO/3CgkNvq/ivg3giqt+AxiOUk7HPsDjH17HOfuBmUAc84QtQKAoBk0pr5jT9kWyWqqJaZYIK5sB5UUCX+eRkYHuM9vftwzGRX2ilAOU5+3sdkGKn9pvBL0adbhIsW2MqwUk9j/AIhgN47nt/HgVGrKz16RoSgf/wAIV9Gt/YLxiopzFO61gZZO9PMiOzD6h1JOQT5+/wCnFfr84tq0Xjo4F+N884dFNsrIVRZpqaVly+VKlhkdj9AckeP5e6FpHLVt8MALYb7dcY1rbpbDPLb6mlraOHIXrNRs8MmSOwkHbPcA5IPnvwZZIvApYM9dcIP0sFxppI43ctAFLHqEso7eFOTg5PYnt/HiEuYKUpOFg3U/DN7wpGKKrdnWbbUIwJGQe32+3vwozagq1zaIlSlMAWA00CmKnaL4dnSPbGTskTsD7bcEdgcD75/nFKLtnbLnFoUlIoNbj8xs1FTqhZfh1lznsxKe2ezeMHAxjyf14WACWVBLBCSpw1aue/8AICkjalJMojaLO5TGWG7H1+mO32+3BKDNv13ipM1WEkJsduuVY0jmjaCOaOORVOXDdtvt3UjyDkfy+vBLS9IJIwpxEbuH1AoeNzKw3STggMPBGT7+4P2/TheGlABrKGKwgYlUfdQfcb9BXDCQoXLBj27A59xxFgOFUI1shcoOMVeRzoNzDPfBeoo2hpyIQ0aMQ5dX7Kc5Hb+/9OLlhTvrWzbDhJyVlm7a4dDBAUS1VbEBvNTHGXRwgZXG4ZUEncAe/fsCfv24WtWEEu+tzQpEpKj+wMM9vF9/bfB+rooYjSimV3KDe0ckZft42dsAnOPuPI4Fatovs197o1mSDhALgvTP3PPPKEaltiQRdIWqiuSAnbImYVA87QnsB34vE1H9ITLLBks3AxUS7rW2qhoauGmmq7mI2jQrF1XGfDFV7ZXJ7+PtxsljJ7H1vG4k4nSHbp8R8gtVJBTVhipo5+juMUsQj6bkjLM6992e4/ieGomKIZN+GztAFSUlib0z7fEHHf4BaOlpo3lkkBkjDSLulOM7gWPb9Tn+fFhlB3dotSsBanQ6MHaZKqC51ctZEgpZKZO0WC0cmP3ik5O5u2Bg4I7+eAVNFAM9aMJEuhJqW1w5DjCs0dLUIwkarqMg7UYbZMY7kf8ASMf58WpBelIaR5cRFBuy4esFDPCKRZ5IYKKFFKdZkIOzGR2HfHgfwPvxX/ZjXVoUVJKStP8AlztMITfApSwJDHCkbZkTqBkAA7HKnJ3d+2RknhwW9BWFTkjFUsD6Z1eCUttkhlGHty0+0bo2piXYj5fz7v0G39Sffi0pVie3GFIlYUgoHD+/2A0oy/wsQQRFSQRjLMFJI892wPHv+g7BmMEAgxSQCskDWwv/ADgY2hglpHrZZKkVKyyBQDGQsGO/ysSc/mbJ+vjiFIZ2/sXJUiqh6f08oEWljAYi4QLGN249PcHx3AI85I7dhn6Y4D9indta/sGsgpw5Z3vw+Y+67UrfE3OenprR0z5JJcn/AAMhGVABx3yTnx7cOUgm223x9wgJ8pAPMmh6RvR08WoViqqr/laUMHhhTA3Ffcrk5APf24sKbfr4g5iHLps4yYUpSFmtmoKAIZpFV1jYhIwWbAGCVA8n/wAeOIEkikGJiQS4c8D3OzlCXUT2FoqWaoq6YJUYiBMrJu7ZwcHz/vxAoh2pCFSgSCqCR/4Snq2oJVsM0ioWkMuD0/8A25PbPfx5xni0pL+asCoPYsej66RtK+krRA6QvaKGQjuxWPaDk5+U9yPA/jxMIJcCIpn8x/mucGbDc7fWwUNtpaqOdh1A9UlHhZSxz5HyqoJIA++PbiJQXoYFgFOWD7/X4harJKKkpHrCleYEI+YITub2G0AeOKAe8HJUoIc0Pz6Dq+cJD11urswilJL9w0kb5Q9++BnOfpn2zwaksbU717QSnSThsNlK/EHqW2U7lzSVFVaFbaWEamP4kgY+bCk47k/b+PExMKh/Xl9nhCEoxCnZ9u/ODQocSERSikCjv1JDKGPb3YfXBxjzwtIIqBrXCDXISGAtt0/XKAJbjQ24gV19tlLKexJILd/qT3x/TPDHcOkxBJLEk15/y2+sbm4UVXG0lPHcLuVJ7wIxV+3s5+UnvnB7d+BIOGhiYiBQV1po1b4yYMgst4IztAkrIYQAfA+XuB2PfgmCg5NN8V+ulQz7oytrM0U/xFstcLMpGZZ5JmBxkHLAj9eLIFAYsOCyiW4huH9hSoqClXZvpKPr+M08Aj8fRgNx8fb37cAVG9jEANQC2uOW28LPWWnY1B3wBRuZnyABn6+PfiwK7TFSgas21zq558owlXBUSFYZ4nIBYtnsvfxn75Pb39uIXbFDEqDOWf25d4Taa/UlTd57aPiz0j880ce8A9x2Hvjwf6eOKWRhBiy48oBr63hckpqxevTxyLERE5EzRlkjP0CKcE/x7d/PjiA3BrCiQVtri0JUFonpRKtRWy1MVRtEqEABx5OG7t5z9OBxAUbfAGqThtxo0APoy0lppKY1lFM4ZpJusH7lgQux+xOcYx7cDjUA+vnjFypRKn22d+2yMy086UgoupQ1kYBUdfs4ZsD5M7gPYbPGT+gBgvVJg5iQ7Z3z13gtX3LUVLDItPaHrogCI5IvnPjbhlBznIx479/4WJZYEVjOpk+QggHd9mFSG73qSghNRYbupCxrkooKjwTIMkKfsPoe3AEMSCaiHCcgEE58e1XEDxVMlNOZJTWy0PTbH7jrCNtpwMnuvj9OKuHBeAWL4zQ7Y2lSK4pF8LqdYl3ES9OBZH9vD9gp7nvgkcAZfeIp2tWnH5HodkEKjT2ladxJU2t6okEGWWZiG+74J84xnHvxeImgpEYhFTXa3xXt0gzartYKK4PSxWmgt8C7QJYIu0jf4jkAnGMeT54FipwD300UU1It1B/kPKOSedzLGsL0LYJJBbt7fKckk4PYdh2xngglqi2vSAIe921/SYL0zSdeRJFURoymFKeVgD7nep9wMeAfGeELQUlzeLTMJSzNB+Sdad4zUzpLDuy0gydgJ7Z7H3HnyBwwgmm2FgJunh/TrdDrWOknpmjmbas6F8hgQw85Hfx/H+XCEg2UOWvsQyUSEhqV5d/UtCHTpS2GpliqqqVZ5ZF2jqNIrA9uwJJXx9vpw0ALGIa55RFAipyOqBoNV1bO880McJaRVDEAK/VXOCQAe/8AP9fHAMQQDbWcUiXiBU3Gh7xpTV1u6plaGRZzGmWCnt5IBHtgs+cfc54spSd2XvDVzAl//qw5jWyHBTOxloWR+rExbqbw3yrjsVHgjORntwpJJJfPZr3gf2NtO3XZ4zcFoY5krfiwilT0y4+RifJ79iOw+nbvxaipgTBqahIZ7sPfJo+2SQwwTI8MkkgUq0eBkkY3AE9l7+PftgeOAcqBAFtP8QYATU2G/wBa96QNT07xVDSQSPG7K4WNZi0T4zghSOxGO4HjPjgmJesLUlSQw1nl6+0OUK6RszlZsDwzHJHbuCPPkfbxwJW4dIz2PFpUwYip10gCYYeCOKEbzneWGFYAeNw7qfofH8eAXiuNbd0aBLAdwxN9/v68YGp5kWFKetiE0asNrSZYMfuR4b/fiwggkX2ZNFlChRnHXo8HIqSWkUrTVc9RH2wJcMce+T7nBxk9/Gc9+BWUnzZU18RQSozMLCj/AMr6QJimhbpmONOwYADspHYkAjyMrn+HAqCleb+wYl4QABU61SA58rCEKx1casFHS7YbsQR/U4/p54FSv/rRxrjoQiXhUpmbqK63Qm1Qp2kRY54IqnYJERnUl1B/NtPce4z/AK8WGt3/AJBfqU+JPM5b7t6QoUZC9SGokEoxgMI8FGIz3/vv9eKnTKhaQ2tb4ZKS48hf03V/ogfKwLt+SpiIOdj5C/6j6/04qUlWJwL613iykBkgOTXKneMPSlleSLbIoU/u3YqwJ90YeCO3088A52Me2uUOWMSSa1664wYiZpY4knRyHJjV5ArbceFcePrhgc+xz2JiiAXegiGUKpQm+68AwWSWqjWaRGjH5VCAEADt/i7+QeFdY63hUjAHw9R8xTyCCStopq5qaqhZGyUCZRl9wXH2z2+3243zJgJqHEJRMTiJNGufSESaorcL+z7aopSzfvN4Yxgn8v1bOc/U47kZ40KSqr8vuK/YtJBNtzdeO9qbawoxfs/pxRGCJbqZAOsjlAHPYgD2Ydj747D6cAsKScQqwvrKAEqWHRRwdXtCxU0Bp7fLbbbPDLNGojIcFimTli5xk+59s/w4DEbi0UVKbysQOAf41aERRLBWy01MgV87pWLHcq4x4HhvGASMgHHjHDkKDvblTRhCkDH5i7cQ3N2pA8ZpLg6TxThqRN6sw+WUHGMhiPHnv4/z4KY6W26owggAXPSujANRTzvI6oGlnIyRHLtwcdv3h7A4A7/ThaWVY294BZW9bkbNm+3vCPSwz0Z2CqnqmUB1LoPpgqfmOQPsPsMjhmFDgM9IWlakp8xan8hWp1dz1WiQyIdqttGNxBxgeBjvkH78EXCqdxCiMTVvBUCbpxU5iRnSMBd/dQ/j5gvdu5z2H+fagUgYXvq8UqgYF2z0xb0zjMm0bFgjd58szrjwe2GAI7Z74yR7duHyiSk7Bxi0SjULvxenR+0Ma6ajYXQUf7Lnr4Y8CV1QKv1A3H6eSQc8MlpZqU1qkAhSFE7Rv+c4dks1gkVLfR1jUFWsbOsiKC2HHj7DsDgecfrwpJWHxW3Q0AMAacNf2BZ7LR1C2vNXXrNThSWQgAsB+YY7qfv/AJe0SfK5v6wCiSQpFWGn/jQMkVut8DCrfrw5Yt8Uw2Pk985XBycE/pwYmeVrRAKg2FqUPLLpDNmoNC1/xSvJbKkSOJCkRlckj/8AWJ8nwMY+h9rU2J84UJGJLk22GDlJDardNHFDpW7ZTOwR25DG3uQCFzjv7/fxjggHZvfRgklL2ftb1vugafX0lvJp6q03qmhL4KPTupORnsxyD48dvPFiWQCoW+YUFBw6ag7faDlFrGOsnpI4Lbc6WnlcR9WoHTQdvPfz2PjHj9OIU7bwwuBW14N3C56rpJXgWw0VGe4ikmqlRJz9UyRuBx28eR+nECQ0ZkTFpxAWYb+2cA09TrC4KGp6SwU0aOY5wZzJsb3Hy9u3f34EkWEMdLYWpt7tSFVLNJU08kGopaeryysop1eJEGD2Y5yw7+/388MD3EHMVQEnflY0ybV4P09lskLRU1Na6COY7nXMPdgBk+e/bP8AXisZtlFhKCaHo8a3K6U9uQhiJqrarLAq5J3Ehfft4/y+vAvthKkplkl9a5ZQFXG+y0FuuGn6Ce4yRu3xMIZS8hC9gsRwWUD6d/8AQUkf9g2t0OWsq8zGvHQ5Qt6YqVWWpkvlvkpwp60MZlBmQjycbsgZBwPP647hMVirsvrOFFgWeuUK12q7ZTaemqbXaql5YSEVpI5CSoIJJK98Y3AFuxx3xxagTakPlhxu92hsW2+aa1FHUQGrSCoG2No55MRhRuwQD28/X9eGLQQXOukCQ3lyPM60IcYg/ZIgp95ilnI6RwSnUA/McAjb8ox4P8scKKUkvnrpFIBB8pZuLCEW7VFwiaaooppMRh2NOy/M77cBlPcADzgDP1zkjglpJZz21qsIC0s4T8+2cK9urqmp0/b6Spxa79j96rgAkYznaGHY9/4e2McUcWLdD5JexfflBd6K5RSotTX1eyRvmUKkZTscnABLdz4z78WpSQdWgCk4yRdwOPevryhTgpkjp1qaisM8AXaTt+fAbIJYDP1GO/6cRVbiIuWyXNvn34tCTV1FnWYySzU2+J93+KVgB37L/hPYef14vHkb6vD/ANbJdddp1nBqlr7e1QmKgyDp9UEIQSGI/MCO+Mf+eLAfOuukUpQoocK+8CftynhkeKvinjD9g0ZdUAI7ZBHtjx9/PCykkUNISAkANfT2eDlb1IemY4a2ryfzQqfHfyCD8pwe48Y4UoOW11btBjACTf26x9PebLRNFHLWUNDKAS0ZfuZDjuu7APbOR5zwKkqc4Ne3aM4SQa92+X7w2rvrWjhlhBtdVWAh5Imjj+Rgo74J+g+2OGoQ9H2RcxLss1bh22c2hTsmqJ65AwntFGxYp0A3Wlx27lQVx59z2+nClu7bc9GrQkWLglVBrVYd81TU1FVHCLdcyYwCshPTUdj7r3I/n54lGZ6isMluotVtdoS7ytwr2iSt+CaEKoUmmbLfbMhUA9+LlqGFhbXKLDBLgsOLOdrbd0OSgpZPhKOCC4VEMikh2FOjEA47b8AA/cZ7cDvasLmLZOE+1uMOWdYZqKpirFlQRMJVcgFyM4xj3B+v34ViBL5cj/IoqDvv3aMMaee3RVpqKu5VMcy7olnST5UzkAAYGDg9+2D9c44flhy137wyWVgkEuke+UPG1Q0tXTU8k00Uix5ZJSMdlGSGxjGc8JOLiBrOmrRUyaFpJy9RGJZIrlSUdQaSOCdwQ3TmICjJ8MvkHzjtjgZaCwU8VgQg1p3ga00dNQD9zBIZChG9yC5B7n5j3wf778EsedndjqkMUaMLcHy3WELE1GtVtifdHtY4J9jjvhhkqD2HjuM54EPRSh6wCah7gatzvA1ygWc0dvWStpaieQpD0lJwR327gCB2wBnt7dxxRBDqZ/SNKVADdyPsKbIVKtEnjaONvhZnQ7c/MFbHc9v9+/FTBkWbn8QIWnFiBbf/AD3glPRNVsZ4q6spCkwcwlPlOMjBPv5zgee/8ACmLk+8MNaJLA3pb25+kHkhjTMlOg7KIwA2VUZ9s/z+vfglKOJvQbYoJlK8iAXPPv67ILwTTTLLUyskA3FFEq7cAf4g3uDnGSMePvipkxw7W5w4Sk/7d05361hTp62KSKOWJ+xlEbFDuCtkZHbx+buOFBKgCAHELmEqABGy9QYU5EWdY5VGSgIGScMfBzg/rj7/AM+ElQHk29tnHrDJikm9N2+A4uhJSGKogb5U2hgdqt2GBk+/bz2x9fPDAc7vw/sOwYg5oBvcfXaCItOyaKFI5p6dRkxyAMVPk9yPy/l7jJyM5+tlZbCYWmXiFT0c07n2gGZJ5ZxTR3Fo32EdNodwB9sMSP0+n8+D/Y6WbPdr3iKSrJwbmun78oOzV81uRZJKSZQ+N00ERIHbPzEE7cZPY+OFrSVOw+ekGVp/0qzvThd4K0d5sEvw00VXFF1iwjy21JcHBHfycj6f9wWgUBuNXvFJmebET3DHed0LkYnkV5acPJTqcyOAp9vHt3BHjx27+3FLLsNnKHSZYVU2O5we0F4Lgrx5eWSnYMylZAVPYkZxj7fb9OFoUGqI0FeE4Xtw+YqdFWCitEFDRnpzSkKiB9zdznH09vbjSl8RUTTVt4pGuYKhgSSNUH1DYns9bWw1cLV9ShkbMjARkU4yT27HC9gPY58Hzxqlhjd347L1hS/DKSCQH+PXu8Awu9HWw1M9Wk9JCpMjJD8omOAULd2LeDjH34IqUEsRTeRqkLJlJ86iXNH9WDP3hfleQ1ZuMk4MxQpky4Qg5HcD6DHn6d+GKmMGMClLObq4jrz0YSLnBWUNBCtvtk9ydqhZFlbBMfzZJL5GezHBAPkDtwANAZppakZ0TP8Aqhmfa1OApqjPG6BIp5oaOKdY+mS8m4I24n8oGfIye/t+pPDVsTS/LXCKlKSgFKgztUufjjkBxgCt2UyVkNW705Y7JQGZOoWz+XHZcDsTkDhIJDE3MCsAgpB5C1+B6VjNBRU3wXTp6eWGkGMb2L7Dg5bJPYf5n+PDlEu5DwuZU0r67twgWstxgSm6ccqFO4y43Ht3wM9z79v4cWpRoCbavBlAKAATer11tNOMFlhkjZWb4qkiIKlhUbS4GSCB9u3yjz74A4MlQDCsJWoFdXGyw5g25ZZxukqKTT0LK0aLmXqLjdu8YPgn3P8AZ4sZm9YhlpzDmn3l1yO0QYWmeGF6uYJWyQRkmGNR1JAe2FTOM4PY/QZ+3FBy6XprfBpEsgMK8vuHBQafhS2i5MzuJG3vmU/KxAGB3+g/rwIbEQRWM04EEFwz5+/wHhMulnpJAscwrtwJw0dWynOBg/LjOfHDE4gHPx8w9UhCiSaNeue4mCAtVtoqM3A0Zkpo1ZmnqSZSgHcgM+e/Y9vrxMRzuDrOEiWCjHcHXXiY1tlWKq10VelG1PBLlGCY/dEHwQO48/0J7cR1OST99YpMpP8AkVHU+0H55hGshiRqxgTuSJSxTGe7Ad+/6e3DZYIUABXVosIFwPR68K6zhp6juN9p7RVNbqeOCd03I0YZiAMZ2qBk9sn6j6eOKSgNiFDxiY5eJia9e2uMRk9nvtTdaBa6uZqWnRZlkSL95EMd1YNjHceT9BxrlkMwMCVEGgrrZnE9V9qpr9p2nrZZUq7e9I8kVSzM2x84UhM9sH6d/PGR8K2GvaCSolYCPf3bns3xEMdRqDSN1+HQftC3xTJGWiZX6qedsgXvnz589uGKSFuX9Ii1sQW1np4nWhudiujVdTS09YKY5VN6EGM57YAHnx/TzwlaSCzQ1C282R5/zcYQb7Jqast9RLp947i2FXbUKY5ImK7flVhgjxjOMY+nbhoRV1RncqLDMvCXaYb1dqRbfqS208c9LCqyVFMFd3z+RWIxuwc+M4PFmaUjGLVfV4YkHEXDDV4Uq2kqNLJUvUA1MMgCipWZYuk5wV3gd85DE4BJHCgBdtHXtCiUpIQoVfa0OW4WaWutctwhqZyrQxtHMydYsfLe2Svde5HgntjvwBXTEYLAAXF9lKa3RpYlq7fQ1FGxSqaNBviVm7eTgHznuRj6fy4a9WVrW2KXNS71fdrtDVXTuma2+1FwqbVdLeBEsk7RbljkfuSyrjAYjyDn+fkStRoIFSczny6Nt38qw+1Friraauh/5alp6X4cAxKtNN8xIAVR3YZLHPn7HgA4oBFJTLGwFs8tsEpynw9VFQKHqXYSxAb9ik9yc+TnA8HGFAwM8UFlnJ1xhhCFJatNbKc4FFAsksVelBHNW7c9SQJHtcgAgZGQO368OCtVgVeap9PeBaSHVDzMakWaPcNgInd5GP8A8toB88CCCHTQcfS8XTCEjLXWFqLr0zqEgEMCjCMMHefcgdu/gHB75z24SqlDnfW/lFlgWSWF4atfcYKaeujZqSASOsQyjFix8Y7dsAkYx/twYlgUBgHcPv0w+YOyqIaeCnSunpIkG6NomYHG45YntkHvn/IcUsbtbool6qf4b0PCkJE3x1DEkxv8sdNk5aRV2SgkeWL/AEyO/t/Q0hy5pzMClRUAFFw9dnPf2hRt8V6lZ5jVpVyKd6qZYkBXHkKrnt29z7eOFLUDyi/2BlKD7Nr6z9YVKiiqa4UdXDSCkql/9Vii7m+U9s/MMjPkYz3H24tamT5oUlCJagpNjqmucB1V0aK4W+lakeruSZkDJGR0QTj5iMIPHcfy4FIJBUNceMGoJCtvt8e8GIa++Qs60lssTxDsFp6gKQffJ2gY7DxnzwsF1XP2NsUpKCCSO+jB8x32rgkDi00kjEbQXkm2/XdtC9+3nI4jgNr4i1KSSoAu+mcV7VgxSW24WsJWu8FU6jLJTUmN3j/E5ZiMj6gffgVTw5A29vaFJSoslVdtNNwhZp5JJZIqmZIqUEgbWjy2457BlbH07fw+/FiYDQdzt1eDNapy3NB2ou9tiKQQhgu3dsbID4wDgE5KnPtwKEkVxcTr1MDKWzrFvk9IRbrazElZW01wEUMkDYTpKyxKMZwv5jjtj2H9OLQSHT1ikpSU4kDW+BKCokNHAJKhK4E7UdIsDA9tvf6f04AJFa8b/wBiLSooCSK7f4fqFRZFILyAGTt5bz/njvj+fFrL+UHy8+0CEhKgpN+LZZfNoOw/L2ZXI8/QAcSzJN9UENOFIdqjlDokUmFTHG87YJKdgcgfU4+nccZ0F3aGFA/639fQ86R9FNUlyu35T2x8oYD2/X9MfXvw5ISxq57RSEkOQOlCN+rwG0zFRURnrxljv2EuCPHtkE5+vBy00c21nDVr/wD9dC+1xwpnxgaIs0O1Xjdtx2qPKrnyAcZOD/U44WggjfD1zcLN5gOH95wjXPbTwyzRyoxUABZZsYk+/nGe/sM44ICl6+v2IqXgsS4bg20Nnzg5GKlbdJRUUK2mUJmlleESIN2S3yqfmwQc4/6vtwDJDtbWWXC8ElNBTXJ/iNYI7otTsubxBNv7qWjkYoHA2n5SCMjJI/7HhSsFh8a4RCwDAUO23xutCsUlCwNArssQPWVl7yk+WA7eP1P6d88Ugi4MaJ0tJBKwS1wz8GbZwhXiqKScfllpsKCQx8E9skY98fy4UqWUu7ExbIDklgdtRz1ucQoJTQ0qokdLCse7dhBgAn7dh9P+/FBTkVtn/faBUC9E0bIjr/TB2Wm3CN0IBPYfQgeQe3tg+PtwQo4UdcbQSZacOIZWZtHfBGOKWnjd3ldo95LSKQgCfU9/HkZHfuOCISXY+mhzhctIxErBxO7WEIFbT0VXFU1j2+jlVHVmUquxySQd2A279ex8ffgQaUp3+fnhDEBLlJDappjCh+yI6BJntUCyNgdSnWQpkY7DaSUIHYZ+h9+KBLOTQ79MYqYjzYdj2Nyd+W+26EqpvFckpD6eRmxnPxUff+YzxaAoBn7j5jYjzB/14t9PiKg2imq6iWpYmUyY/dRzSBdyDyC2Py59h2BPnvwcy43d+mjG9SHJKO5rX1O53hcmcyPRSTIGqEiYNAiYy4XxuznA7d/H+XGpNAoW1whGCoCBz92u8JFPJTxVkVurKaJKh0O5lLCMnBwXbGF8+PJx3xxEofbwgUKLkgve2s7XgaehjFTTLEamPqRPmVWJVgB/iByNvvk+TwwlQBKRyEZyhSiGenLjSCdRHUzujQ1dVNGmCqlcs5UD5R3B2DI7AYOR9McWlVHprs8LUMTEFwLUy51DZQMyXFzTvTyPvSQEBPlj7gnuvfuT5Gc+e+eEzACkKNjq7QS5RNchff3f2huVsV0lira4vHUqKja6SBkBxjwoA+XGR9/rxqBYNU67xmKwSKU5M3s3CFCguCdH4Sl6RkWLe4dwgQZ8fMfPjx7DhanuD9vFIViSQD79oKTvWRRw1s2JRLGWYZLBM9yB4J7dvIz9PbiJFwBaAJWgYVKNa6eDtJXy3DpQTSGO4KGn+cBhCRn5uxx7kY7fbiyQDrnaGLIKSFENvox5QUtte81RWUNRW751cHrPHiNIgSCWJxtz47dgASOCqKtTZxgZZWBievT4hz0lwWyU0M8nwkBmnOH3AoxIO3aW79x7ntwJSLDI6tC0fs/7lg1298xtgtXm60JX/mBUwSoCAz7d7fmLHvgZ8knx/ThyVYucElKwC5rTJvrpzgQxTSVENLSgrVkEK0wLYbb3PjGBj3wOBCwzgO+yFYiUBiwG/PpBiqtdzuUdRQ3SdWoGCKGjkImyAMErgr5B+vjg1EAYjlArUVf6LDNnvxjS4WyqS21CwvJVVL5VKhh8mMZG5F7YGMBux+x7cVKbECDeKQ3+y+32fPvsjfSNbUSwS0t2e2UdQY2/fK5Co3bsG7Dv+n+/DC9hVoKZ5QCb7/qje0bajsNbd6TZapnKbHUN8y9M/wCFjt7kZBU4z5Bx54vw6nYa3wf7EnMv317Qwqi0VcqUc14qrZV1cOXRoom3Ig7PG+Dk5Pf/AE4iBUnhCVku5Ary5V9yYkLRgrHqLglLQ0ws25wsUc5ZkmH+IIV7Bu4xnsQe3fipqXsecGhASkhV72f5gxXWy1VjsK20tPFIGCdWhO6JzgAK3kD3x/Dtjik4rZdoWgeZkiuWXb5hIprZWrSUi2yrpCImNOsjyNMm4N+XG7sRgePB8cWQw1r3gz5qK7/LW4Q4J4vhIujDVQSSSRkLE/diwP5QSQe/btnHFgtcZw5Ep1gptx9tnrGLfS9OpuEa0i7pU6jVDt2RQR8itns30HBYq0NRCP0m5t29GOwQVuNjpdTtBRXGGtgjiKnaoXqHPuCQR4U/MPY/bheIg4s4okgsmpLNT5ue0ENcUVyFBbrXYdQNR0lNtQQb8lyxwNw7EsT/AAwD2HEQWvDZgCiSKkXr8U4w/qN6K12OgoJaB4poYAHmfcFlc9ycZYZP6/544BEsFz3ijQBqNv7Q21YSdGomt0VO6d1IfcGAHhsZyMkdvfsPbg0pDlrRmXbXfZ6wd/aApKGSrlKx7kDlI0fqKSB8hRR5z7d/6cAU1ZN9XhqirEB8Nt0TCpFHG8FNNukCtHvZJo23qfcY/X7cCpagKjWtsCsk27apygQWzqIZTJOijuQihlPfsMEe/wBjwSyFHy0MXgc4SG6xpLTTGVK+lkiZSmFV2YKe57DOVJB/0/XisQAYZ84n7CkFKj3vtyy2ekH6uCiraExVdPFHOS4cMxG7Hgg5yc9u4/nwTKBxHPrDFJqBicafkesMmdaYVdLbborrDID8zFtg9gNx7ZOOx9sDiyAd+tXgCou4oo8xy274Bnhqw8VI9VLNK20FXAMYjBJOQMfMQFGQe+PbPFIlJ/60giXISOenjMdkEatLBSTQsTkmkjTDMT37S+fftjiBbUMLKGd65V0PasKH7FhnCh0ulPUKQVkFPAgOO/cKMYPfIJx5xwAAbRi0pLVooZa9RAtu07V2u4Ce33yrio2HelySmdxbPb9fr/njgQzsAzat8QtCiTe+5ocKw1ESSzV1f8SjAuTKuxV/gOxx34WEA2pbPjtpBTSCph2GucBFagqooI2Rc9mkBjTH/tAGT3+mB9+GGZyNt8KSQLa+oOUNDd3bqy3SJPmyRDTYP/8AUzHA748cRK8VE1aHlKm33z97cS8KFM9NLI60809SSgWSRZd5BOexXIC+PoB3HEFU7eMJnrQeNNXHau+FYdRaFqNliiO4EPEcED6eCP1P04QggnzQDKBce/V78MgYJGDqBoaoq8LH/owRg58Di0lN0VFrxcxSibdK5vnrnAr0NNUR0xkJ+KhkWSKRCQyjv9Sf554sKuHrqsLUpQTdjlo2j5bdGlS9RCTGXwXXsIz9GUDwe/8AeOIpFg/W8RIBqnWesoOQRp2Dxrg9h/r9f48AJiiWOuEGZbkYQ4I2fTQcjpIJJEbKAKG7YOd3bBBHDEqUWBygkeGd6U3bdb4XIJ5NgjqWE7Y7M2Mn9Djx4/vOUYg7iGftdIJvlf4y/kbvNHHBLUFnVVIAIQ5U7sdsd+5/34NaiwKbaaG+UnFdtbRBhULzsOtHgAIqBNmGA79x2Oe3t27ffiyCnynp8uLxUsMHfbXY3sOYhPjhZndZcIvU37T22MM+/wBeAWqgUnlEWvzUPFjv27I2646gSWDrjDKW2HaD2Pv9j2xkdv5UqWFOfb+w1K0yzUcH19+x1KKlQSIJHiicDfHuOQuABjPcdwf5DA4GUhhbXcQaUpDAu1aM2uF4Gd6gylAYQH+SRTkFu/Y5z2+uP48BLIVRRtDkBVUk0O7R942WnkRnQyieIn5YxhlQDPv79wfOe+f4BMSkgbfrZp4CWCGALgPbfSNGuMVO0cjztFSpEzbXVgD3wPnPYe/b6fp3ZMllTnbBOgFiQXtlTjCtQz1U0fUQpTANgBxvDr7FWBx389/4jHCZgIIGY1wgElRLKpxcHvCzTVVLMYqbqokmW2xk/OceQBn9f6HgWSFGl9l6xumu5CKNTWXeDYjUt0GSLoh1YfLkMw8H7Y+o4K4cxSyP8tfWyMCiFPUSVdOhhyhDJGNiEg5zgDyc4x/54WE010FYYJlXTyA3Xu/zsjM4p6WWAhZp1aTb5JMRP6ZO04757cDMdtctCFpUU+UMWs+q94+lt9sklkert1NM5JIYK/cffAxnOeImSlQeOhJGFLY8O6kUmpqOCaV2knjgNO3TchgY9+CdigdyfB8j/Xh6VKs2X8ig1Tau1++UEq+mq6wQ0dtq6KBAAZ3kLM64bwq++T2yT2HbHD0Lu4vrVIWULIYmmdu0F474stO1mrLqZQSqK5iz1FPkKO3y5/n378NQEGgFcx7xU1bkM9qfDt29Y0mSomvCBo4qmgkiaBYentEWT3dz7+RgAY7e+eClgADL3hfiFqUpjo58fSHBLFSUVVSUvxcVVVLmNSqgjGMnfgY3Y75OAvY4zxCSq9Nnv1iTlpS6UqL/ADwcNshJoaF7nHdKJ7lTQUu4vBVxbVaOQNjZ38AeAfc9+LUtgCByy1wvCkyVEs9QKPw1sg7R3CJrVWQR1VLWNDmOMuQchfLEkDOM+f17eOLGGj6bhFLokuM/7qsN2koUtT1NfKsNYauTKuy5bdswdrAflzjsf5cWFKVU31vjKyg4Abj/AHOE6spad2jTr0jEBN6ICzfbP075AGO/fxwaZvmrnDJiQhyjW+BKW4T1EUVI7iKrYMx3kQqfnwEDEnJ+49/pxCgAuB1727Whcu2M/fOBa+3SxN8MlqppzO4BM0wTbjvuAUdwMD6jt3zniGY4cmhhNAoMQS19vM7IEmtFuqaKOO7UsL10Y6kspZpEdx2yAT2x3+2Se/bHBpWzuf57e8DNWhQASKZjVdwg5TU/xQ6qxqJFkBihdyxdT4LOMYznx+n6cBYXYHp0zPGLWASCsN2A+2hfsMTyUxro6aakn3tmORiMnOexJ7jsMYOOCWQwD9La00SWAA6Q2zXHlB+Zoaq3SVVP06dGDIpbuDt+nfsO3954gZyNmuEVLDMCeWzexv3hChq66KtkWMsIW2O6HGAffvn8pPf7+OCUl6/9oYuahKRiqD1+uwhL69rr66rMlPbl3lBhY9r7x2DN38HuAcdv5Hg1KNIWm5pXZTkDX55Q+6WoMTpDCKhfnGSpwMHPdsdyhJBz/tjgVu9qfO6ClqTiZNKbtP0glBdYXobjVGqp6uSPImkjiCdJz7d/f2yftntwUxsVIBcteEAV7j65xpuprXLHHb7O9tnnAkL00S5Y+f4jH+nngh5i99h3dopUljhH+dbKdOsHHqIKiMO1WrEArgg5x27AD/McUMJYjWtkAQokkuaDX8qdka/CUkfwS0lHJDTyHarQnasXY4ZsYOOwGe5/rxFTACHruhicL1sdA/V8o1WGjUq07wVaL33MN2cHuMnIJB9z9ftxRSM6fMMXiFQHyp68uphQipaeeJoI5+mH8EkEN9QCPP09jwVhCSsCoBFdP7wHJS1MkXRoJEldf3aMJdoJz9e+BkY8ZGOBLMEC2qQUxKSQZuu0HL7b45bfIs1MrXCZGwiS7U34JxvA7L2Pn6dhnPElBnCfuKAUSEB2am/W6EiOaumqagSpXpRhYiOtiSJiRgqj9iQvY5Pv58Y4FYxAJSYFU3EXZyDx1x94NVMF0mulFJTJbKa0R/NIsnzyO39ML38g+QeIgvR4ifNbXLdCjBmoPxCdWmVZmiVJRtMq/wDVg/4T7E+QPvwDJIJFoFay4me3rBiOiihmSMdJxht+AcZ8gZ98n2/y4ry2ZsuUUVKUxe+9oW+mJACsEMUxXJxjZuxj5se4/wAu/Am9IvAQUvdt46arthty0D0kdLbaG30dHEH3MiAkAkg5A9h3P8+GGcnFhjQlBq5O58znx4CDYlFP1kmgkRwRlWY7ST2JXPt4AHueIQR/hoSEhypBJ5tbMvmNkNW8O9U0xpahqCphVHATa6nJBYGMkd+3c/Tisav9Z6ygETHIUc66F+MYp6Gpqrs9d1ZWp3iwrbAVYn3H0Hbx9v50zB001qkESf1hKhbodzbOEL0VucrIq3Oug7ntGkYAOff5e/FJJB83xThCzO8he2mgmaC+xyov7ctssRBCmWkxKMf/AAYAn9cfp34PGwYxWLEyqtx9DomPmFdGop6nUa5BwvRp1LE/zIHv7cAUJ2c/iGSkAMkAAU6vQvs2jtCxRh03S08dxqp8kdaqyp/gWAA//VXvjisZYcIjM+IWvbr9CkDR014mldpq63U0ZBH7uNpJPH/UxAHk9sY/XgFIx2GqcyacNkJS+INltvXl2vwjeKhtdPOssr1NwqlwwWWV5G+v5F7DH/xwM/Tgnc0qdboNCWqj02+nIw51mWSPaKaWLBHyyYUH3J2+RnH24WsBJBH2dbYiCknzF+jc9VjRXg3HM0e7uNue4yPcD34lFukNEMlmUPSnXIb4DdSjpuI7du7gYx58/wC3txEsAK0rWAS1cIruNhx37Yx11JX8hP8A7Tu9z78CJb+XZ6/Jy25mKQVAghwO/p7QdV3Chdrq2dxLHuPt/HilKo+z1O717Q6WlVGypa2y3rGA8iMNxZQe/YYye/b6e54NGFmSx9uuUZ1LBLqPGrOYGR5VYlUkC5O3GSH/AKf+OFKSMQIFdco0O6SE+/8AIHWoqIQjtIIyD2ycee+fv/f04ZMAJqb7dbYtIKEktfrTaAYNQ1gWUukqOpbuVU/P28ZA7cDLSmhIgjPxOBlV/bbTe0H4IY6urpWiqZokjVlkGMiYZ7HA7fXvg58Z4WmWRUsx1p4jpSN71LVhNqbPDSW+40k1dPJBVyhZP3x7MTt+Vj4J9xnH8ODEwqHmDa9YBS2eWK7d3b1gxBDuo1tcsrP0h04jL2IAHcd/rjz9vvkBMZyoH2h0qYMATnV69N0GJaaqWNaglomMhE3Y/OBkAb++Mdj/AOeKTMFwba4fUUiakHCsMQ23nX3eNBM0ql5GTrL7ghgoGDn2ODgn24JCTdmPrt/sPJcs/E5Acb+ohahrIGeKnhrYZK4jLRKe65GQT9jkHHCMeA1NuGt0GhCU0NQng/XuLRuIapJDFLNAtJ4woy2c9hg/KQcHt2PfgU7cvg8IgmFJwlw9203Njxgw0BSGVZVkimGQGCDDHxnaM4Hn+I8eOIoOp0WjUlkeYA0eztz943go5OmImkndWcEnKkDx2HbOD3/gTgjglrBBa2rQlAoGoNx1whxR1Jm6iyOGkJ29MeVJGcZ7H+nbjMk4g53cba4w1JBOECld3qP5BpaepC9SMoQO7IDnBIHYnvkDuQcZP24sKU7m18/qJMlOMRvnvgwsFQ9SwLqwAG1WBDA4PfPcHOP4ffijNSagbaXhn6gSMeV97wYj2xqFlQI3nHfxwKEKAoI2okgBg3SKMUcVNSBY8kKSzNGucMxxklvv29/4Y404VLFC8MCTLccuD5bC+0QlUktovF3qaCgh31meg7R5zH9/sO+O3b7+/GgqCAUE21z9oQhKCxItuHrb2gHUYm0vthtztC6xNDDTUlEHd2/6nfByAewPbGSe/bgwsGoz6wlaalVtza7d4VdNbo7BKt3luNJXkOWH+MDH+ItkrnBx58fpkFqcvff8becFKLhhsqPndBSlqrfdayoSlppKOaEhZHjz037dzjwW/Mcn7nH1dMKgovX212hcolKHQpqi4uOzd4P9O2Rgpa63bk9GGTYGWRjg4wfC+Dn2z+g4hmlTAWz17QKUALw037tCEKe20EKrAlvoLle5d3TgpyFjOJDnBOA3juPqPuODxEg1pATnDsqorsYaygWOpt6tcaAtR19bIdtthSTG5VJDDGAoywY4Pk+3FENWsB/xxcpqb5djc6aEoW+Wtp6iqoZKaSJYZFM0ZG4EYDALjOAcDP1PnseGBVGI72hc1Dpxj/PC3CGxT6OaG4JXVlRdKmeUkU1O5RUeNf8A3ZyQN3g+Tn9OCKkm+taEJShQdxV+vdgN3Uw/BHXS1aPMaipkVDMV3bmEY7YwPbt44pKgaPeDVJUhRKQxPTfAVYjy1NGZo/hat+pGkUvhSDlRkee3fyPtk8QTA5SMoVN8O5dYcWzPS0ExFcKOF6W9UKCojOwy42rOuMgkd8ds9vp/AgkEUU8AC6iwoKZjfX13Qp2J+iitHTtFKqDbEZCFSPJwMbslc+APpwtCgS+tDRhpR5cSfvmcxChUJFUxGOsgENKQqiEYQzOMHBUdx3Ht2xknglMKpP8ANb4mIiw9uH8ajwetlFLn4hgqSMDnLZGe2cE47Ejx9hw1TtTh9iBSVGqv9enzCLcrNZWuNPXJRUlNXswxNtCMxIyQSO+cBv8AvxDiFoi8IHmLAb6a22gS1VsENQahpUEkhSCPc/dlGTsfccnByR2/04Appr1gpimT35ayhZaOhkdFio/hGik6gkhKhC5z2ZRjP5j2J/lwQUTcWvAgOkJBZtW+YHNtqZ5BM1WBTR4xC0Sn5h2yGIyD9lPjtwwFleUX99cIWkKKQEjpt+IDaEQR1kKwdOqwZOpt7klewGME5wTgecffgktti/1gCt9lOW3pBpY6Yk/808aEL01TA2DHg+xyfIH0OOFpDUz132wtKEoFDo2glFQUC3CWsiciXpbCkkmYgCe+S3j+H1I9hxJihhc69oNCXJJIO3Xp6CE+KGz3G6vRvS1ltenRZVqIptkVR3wSFQ9gP4Zx44FMxgSSGOm+4KWnEkISL3rruOkP+22GipxNBCpCEiRwSAExkhseAO5OR5zngpi6urO0aESSmr24U4ZQi3CNkuVTPBUO88yL8pJ6cyHDZTb47+f5DiZPny94yfr8xKWY65wmQiR+yy9BgGBCAbQSRg5+nnt9/txYLmoisFfJl1HyIV6eOZxsmkkcZ7FAAdvftj/b/twpZBoaQYKkthN99uvfOFSqklip6NaBKiXNQjOoQOwXwWyT8uAc5HfHgd+KmLa5bnqneBlo84Kj2gG7VlptIButxFDPO7JA6KXBxj5iPp7Z+ueBG3LWyAQ6Bicve3rvhVEqskZYKJFQrIEZSCU9z7geO/8AtxeMJLbL8PrbFzElOEO5PCp+9sF6qncRSVNNG9Y6oJAqNg7/AGG72AIxkeB5xxSZwIp1HpApFHYj01ugjVXI00dDT1tJWJSyoyyxQndKpDfnVu+FBO365OTgdjQcHQilSCQBTbrZvaGTe7pbxeayjZKtiiFopVXajZ79h5yW7dvAJznhqQSGPWGqSShyxPoNm4daQ6KCE01JSwnMRSMZUMSM47+T9cjz44BKgXS+vXtEUWLqGddvpQc3hVR1GFXeSewByRjzj/L78JJFUqtw9vikLTidhY2pm+2orvAzjMqMwBS3JUxjvtkcZbHuO2Pb6+3DZkxhdvSAURMIJHG1Dk7X2AxmH9obkVLY0Ab/ABLIihf1+/b2zwsumigW4wQ/yXsOzcPYERgC4AlqiS3UUeBnBaVh+oIUfT68W6Vf5uM769oueXqzHPbz9rnhGkDU2aiGsrVumRjpy7FGPf5Rt7Dz3J/nxSyoIA+9aaFhALOKDZZ89FzBmmqqyXbTxTWehjBG3pSh3P8A+rhVz2Hv/Pi1KSKnM8j8wKQSPKPjnWmVoMCJ2dxLHd2OCGM42Rg+Pydvpnxg4+nC1AEEX2fz1hq5IVctvp32dINRU06oU6yZycLFCigdvp3yfvxSFhnPL4gRJSVhRO6lOfyc4yxTLO8lKFBwDKP8X2yc/wAuDxs4GvUUgUpZ1OCu/tt7wHHcRJIYVmWqlA3L8PA7A57ZBPYds+fPAJSMBOWevWKTIAV5mz5bcnf1gxHWI8oK0ucd8dVM5x9ASf4duKTNSKbNW9+saCMIuK+u19HdAnxFW7KtGaZf8Ld8spyT4x/txQbO45jvthcyYA4BZtO1PQkwahjrnz1GwuM4Lhcfyycfx4YZwY4btBJRMYIDPyqcsheBnSOAI3wdVPF56sMRfZ7D7kdj3H04XixAJSKZ/PHV4WJQxEqZ+Gi2yDsbBlMiU80hAwm4NG47eSGx2/18e/A43S+/vBLUVDy3a1uxb1MH6aISljG7gofn3jI8+B9R34orxFgH1xtDBLJAK6cv5bdeFSErSmVHVFjY9RmYE+wGT/Idvt+mSmqY011NIeFYk+csOGXK57QUraaCSYkUgkABkVwCN3YjCn3YDGR9+CE3CHrsOt5hiQSwKr6t7d4Rayeva4UdWLTNX9Fz8O8cm3Z2AwwzkeAe3CcQBwliR2hS5ZxYlBt9s9O0Kgq22vVNQSRADOIGBIB79vc+GOQf9OCXND1Ov7G5EoI8zEEFqbd9c4PpPBE1POs0cVQ35TIgU5P3GCB9e/b6duFoW9Retcu/Yd4r9aRRQa2WfHOMs1PU1ckNXQzyjaFV5VBR8H8obx2Izu/N3zxYcpZJG0V7+0VLCnIOeYtTPb7QoQ1sbyROJjGygBkK5VG/6WPfB9h378LUiiqAmDlrUQ5cC7tcfG/M0hSMlNQwLM0kNHGz9LLLtLsT47fcg59sngWSDWj7/mBnDz4M9PlB+npJQMSXB5cMGdTtfvjBVHwCBnuP8/rDMerP9RollLtXDlTV4VNyxlVMrxztgY85OARkfXGP/PCAoO5EGJBJxAM3DvQV0INCSCUxCSBJWjIZXxhd32J9+4H2znxxRWnE4MEhRSkAFn2B2hTVGcZEAT/4r2P34AFKhipBgqTSkUOnr6I96B554SFyuMbft836H6/6caxL8oOeUasBysQ/Kz327awnWWrpqioqJ1galniiaWNkVd0o9gzecHzjt+vBplebC1YSiUogYb11U3hTpaiucT1lfG8NNOcQtFL8/b3bGPp9/twzzMAkXFNZQ9PhsZOK/S3D1gyyKRPGHBbBx5yyk47n+I7ePPFrcK8xcH2y5coAyyU0Ds42PrpCFcaqeioqUGopaeRum7PsZj7MxA8AY29vse3DQAoFKSb/ANjHMGEJO3tC1TQUdS1GYxHT00kYnRkUrllLfO2PJySQMdi36ni1kqJbVYFUspSGF6/3XGEYR0ax9GiiE0LHpr1gWMG9ss+Sc5ww8cMTIViNfMK6MUqSuY6Wt9b/AE6iCj6O05QSFZYPia2aQxxLHJIgI7nsc/KNpPfz24BJJTjej87O0X+liCDV9+3jGlCKAWaKktonkpwxeRTM+35mKk/N8zEbSck/pjzxpTiJxCp3xnShvMBc0462wkS1dDaqSS8G3tDFGEiiCkMfzkKDn2Gc48ZJPfgUpL1LiFIlkgC1dfBhwiagoTQ31Flhkqj0O3cjByW//wBh988UlH7DQuNloqWkIRU9rbhuga901opLrQzVtElfcyFEHc/Kc5wAflB98/X68HhegpGaZ4cpKSzk7z31ygatoVrql5Bccy9cNUdSIuZMpnZnIwNp8Dt2x7nKkLBcijRsXImAgJZ/TgYxTWs0NbRLbLeWpfljYiQf8qigkMpY7i2WUe/b6Y4sKUoYjGdaQMQXnzy1eAIUa5O/RVJS0fUSpJ2sq5yGUY7Hb7fw78HLdJGHbDFyVBOJVX69dGHGKSohzudHfbvkQnOFOR2OB38eMe/FFgrAnlr5gVIUfP8A9RR83EF0hlrITRU0sizymQRq2CIwAAdoPbwcgHtwSJTqAIqYVKlkJDGkbTqkkBpQgVimw498rgsM5wR34oqLUFLQ6X4dSlFIAcEfV6dO0FY4J6cdMPUSUsdP1HkmZSNxIAUgDJJAyTjHF4VAO94sSJrmWWJ9yNvvCiB1JsTIqTu2BjuEXzgfqB3/ANeCUhYDG2t8Tw6SqpTd89m7nByqZqnapRnXwTkDco9gPbvji1Ai5jNLIfACfLspncZboINb6WZoTPTU71CZ2Db2U+MfxHv+v14MJYEAW1SNc3wy6g1ID3y2tmdz84Vqe300TNTrSRBSfyMAy4Puc5yRjiEkB89UjNMkkKQkJrxy9IGtVrtEMkP7PpKClnlYgdOAL3z3Gcf+4cRaJhDjQ63jR4VKkAuAOHbV98Gb3UV1ka47mpqX51FP0mbuB3ck4yDgKceDnH14tCC5OqwlMpSgVNUHv17WhpzdC4JJWBDBTI2VXc2UAAwRg9gcjsPt9OImWSh05wONZeYf82bN9mqGD1Md6skLydRcAggFse3c9vbhZlqYxFyCEiYmwHR90LERSR2BwSSWPbOPqTn3/TgESVFmLA67QcqUUkkpcWZ8/iFW4mO10UNxkdI4llQyBk3B0PbAx3BJPn+Y4JIKTvgDLUkHXz8bYEuVtpaiiVBEkMIJIHlRk9zj+J8Y79/twtJViwmCmKWzimzg3bhaGSUUJMBJU0rbDC5Z9yyMRksAPpgecZz78RSFKLGuuPeKVIKEEM/x1+90Oa3SxCCmV5Qpl2rAU3AybgcAn6YHvjHjiySWSDCJCnLJF78sq7NsELjNcEoZUaKjuFKX2gP5XA3FiCMZGMjH6d+FqANxBmUplKFm22++sMK+2i7XeEUEYpRKGAlDSHsGbO7xjPnOOHTizNbh9wuYmpSRb1h9U0EVNDBTFkwqCMkL74x2H/f6/rxBLViqmvL5glJqQ/y/p6c43mmp4eokk80cgPkAHb29u3jt44WPDKIdAZxt3tFeGSVk/qqTSuhBFKy/GBjb47dcBnG6V2iJ/UAEfUcCiSVV1zi0YkpJsPXXIQXmq9ZQhQ2nKCZWI2mOuAPtjOVH6cMTLq78daMJmJSGCiXNO+75hWt1Pfa2UfHWuit1MAVcNMJpFb3A7bffz34VNkq/y9/SHLlrxBJDqDbGqaf2NrdpayWuaSegpnp5pQcydZ2Yr992f5fbiySA4Nr62bs4ESlEYkmtasNtaHPoIWo4aaIkuXY+4JyFI9x2z7/Xgv8AjkFx7RclCljHLFDlQRme6W9WWHqsHfCqNp7/AGPb7fXhCkrcE1JtaBUlio5cTe0YmS6nqpU0dPRoC2CagyE9/O0ADBHtn+vBkFH/ALCeUWmowkVrXIctvbfBeWx0dNFE7xRSbe7mm/5bAx3wQGY9j9fbiChZeesouZLKKHLW/lB+Wz26UqyisuKNjatTUvJg/o3y/bOM8DMRVhoQl0hONJIAgae3XCneCS3QrIryBOmahh0xj2UbVx2+v8OEynU5Sqp3Zc9kMTLVhcDhXZ8/3KNKa6xU9XH8ZFtMrbNiIuFfII8Y9+2c/wDcikg4c2pqsKSHSVH/ACGD5jnUndCzUVlugQfFRwKXJVV2lgzY7+3btk8ApK1AMctapGgSUkPLoAQTzygjV3MKiTRS1SwgBEVQCAT4wCRgH+mODMnC2IUPeu7fEHhZlfNTZu0ekCCnq5FNQ9fVRhPm6IRSzqR5U5wCMHye+O48cCoJcNnbTQyWQp1A2ftf6N/WFGlFXSyLO3SeMt+8VnYyZ+n/AE48HtgfbgVioJPaATMLFaVOlO1+w25QuRlpsOkYUbiCQcbiD37frwyZLV/2NSO0OlyFqbB5TtffG0cUm3ehLDd8pBx5Hbt/P+n04iZR/wBJFBVt5i1SiVVAJdt/xAD3iCjRqefbkjI+U7tuSCQR+ng8ChKsshAI8Oo//wArX4lvq1oUYI6C4zRboEU4YHGVcbe/lcZ8fXiTELsTWmneNS5VhXIZZ309IGkEcBNSH6izBSkR9sqTknHntn+OO3CgpR8ohsrw2H/I2RsVpDIkFIJ1qEUlWDf+7uO/v2Hck+eC/Uv/AH/9e8PKFBF7bM+LwHJbaiePdZ6mGMmcpK06ltwCgfKPH8x4/qCEgVPDrC0oGF2om+3dqwhwzzVENA6JTyzzFWaNA6hZiAM+fHnye54iJeIFzbXPVY1GTMSplD0uPXnBigi6zpXGI01cE6boGyoHsM+/6+eM5kEHzGKIICpjlgdNG9FKqipoIndoUUCNpFBznLY+pwQfOPPFrQ5FKmKlyChNKdIEIuPViSelpY5BKBE6zsSY++T484Hg9jj9OEywhTKQ/pBICzUBtce0L9MWEe1CHUEgEMV/pxchJKaGGqABZo//2Q==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(\"/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_img_val/ILSVRC2012_val_00000002.JPEG\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(u'n04228054', u'ski', 0.6965568),\n",
" (u'n09193705', u'alp', 0.29820144),\n",
" (u'n04208210', u'shovel', 0.0016679094),\n",
" (u'n03792972', u'mountain_tent', 0.0007202051),\n",
" (u'n03218198', u'dogsled', 0.00038468122)]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label[1]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(\"/Users/fmcquillan/Documents/Product/MADlib/Demos/data/ImageNet/ILSVRC2012_img_val/ILSVRC2012_val_00000003.JPEG\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(u'n02106030', u'collie', 0.5772368),\n",
" (u'n02105855', u'Shetland_sheepdog', 0.40733615),\n",
" (u'n02090622', u'borzoi', 0.0075451825),\n",
" (u'n02088094', u'Afghan_hound', 0.0007474787),\n",
" (u'n02096294', u'Australian_terrier', 0.00054024416)]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id=\"resnet50_predict\"></a>\n",
"## 4b. ResNet50"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <td>50000</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(50000L,)]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS imagenet_predict_resnet50;\n",
"\n",
"SELECT madlib.madlib_keras_predict_byom('model_arch_library_imagenet', -- model arch for ImageNet\n",
" 2, -- model arch id = 2 for ResNet50\n",
" 'imagenet_validation_data', -- validation data\n",
" 'id', -- id column\n",
" 'x', -- independent var\n",
" 'imagenet_predict_resnet50', -- output table\n",
" 'response', -- prediction type\n",
" FALSE, -- use gpus\n",
" NULL, -- class values\n",
" NULL -- normalizing const\n",
" );\n",
"SELECT COUNT(*) FROM imagenet_predict_resnet50;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Missclassification count:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <td>15867</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(15867L,)]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT COUNT(*) FROM imagenet_predict_resnet50 JOIN imagenet_validation_data USING (id)\n",
"WHERE imagenet_predict_resnet50.estimated_dependent_var != imagenet_validation_data.y;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predict accuracy. From https://keras.io/applications/ top-1 accuracy claim is 0.749. Difference in accuracy is likely due to differences in image cropping and pre-processing."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>test_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
" <td>68.27</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('68.27'),)]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT round(count(*)*100.0/50000,2) as test_accuracy_percent from\n",
" (select imagenet_validation_data.y as actual, imagenet_predict_resnet50.estimated_dependent_var as estimated\n",
" from imagenet_predict_resnet50 inner join imagenet_validation_data\n",
" on imagenet_validation_data.id=imagenet_predict_resnet50.id) q\n",
"WHERE q.actual=q.estimated;"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
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