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"_model_module": "@jupyter-widgets/controls"
}
},
"7839e64f0bc04af2b2edb4389eb34286": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
"state": {
"_view_name": "LayoutView",
"grid_template_rows": null,
"right": null,
"justify_content": null,
"_view_module": "@jupyter-widgets/base",
"overflow": null,
"_model_module_version": "1.2.0",
"_view_count": null,
"flex_flow": null,
"width": null,
"min_width": null,
"border": null,
"align_items": null,
"bottom": null,
"_model_module": "@jupyter-widgets/base",
"top": null,
"grid_column": null,
"overflow_y": null,
"overflow_x": null,
"grid_auto_flow": null,
"grid_area": null,
"grid_template_columns": null,
"flex": null,
"_model_name": "LayoutModel",
"justify_items": null,
"grid_row": null,
"max_height": null,
"align_content": null,
"visibility": null,
"align_self": null,
"height": null,
"min_height": null,
"padding": null,
"grid_auto_rows": null,
"grid_gap": null,
"max_width": null,
"order": null,
"_view_module_version": "1.2.0",
"grid_template_areas": null,
"object_position": null,
"object_fit": null,
"grid_auto_columns": null,
"margin": null,
"display": null,
"left": null
}
}
}
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "markdown",
"source": [
"#Лабораторная работа №2"
],
"metadata": {
"id": "KQawCXCEhcJa"
}
},
{
"cell_type": "markdown",
"source": [
"# Задание"
],
"metadata": {
"id": "VrOocc6D_O7M"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"По заданию выбрать свои классы и обучить сверточную нейронную сеть до 70% точности на тестовой выборке используя GPU. Провести три обучения для 3 разных тактик пуллинга: пуллинг с помощью шага свёртки stride, макс пуллинг, усредняющий пуллинг. Сравнить достигнутое качество, время обучения и степень переобучения. Выбрать лучшую конфигурацию. Сохранить модель. Перезапустить среду выполнения - теряются все текующие переменные.\n",
"\n",
"Загрузить в colab готовую уже обученную на cifar100 модель. Преобразовать в onnx и сохранить локально.\n",
"\n",
"Скачать [каталог](https://github.com/iu5git/Deep-learning/blob/main/ONNX_script) с html-файлом и встроить в него два файла моделей - обученную на ЛР1 и на ЛР2.\n",
"\n",
"Скачать картинки из интернета согласно варианту и открыть их в html по кнопке. Автоматически в скрипте масштабируется изображение.\n",
"\n",
"Выбрать в js нужные классы для готовой модели. Проверить на устойчивость обе модели, полносвязную и свёрточную, двигая картинку, убедиться в наличии свойства инвариантности сверточного слоя."
],
"metadata": {
"id": "RGiqHSEViis1"
}
},
{
"cell_type": "markdown",
"source": [
"Отчет должен содержать: титульный лист, задание с вариантом, скриншоты и краткие пояснения по каждому этапу лабораторной работы, результаты работы модели ONNX в скрипте в браузере на загруженных изображениях.\n",
"\n",
"Варианты классов использовать из 1 лабораторной работы.\n",
"\n",
"Дополнительно можно ознакомиться с визуализацией функции потерь Loss landscape"
],
"metadata": {
"id": "QUPllPXsqe6H"
}
},
{
"cell_type": "markdown",
"source": [
"#Контрольные вопросы\n",
"1. Сверточная нейронная сеть, архитектура\n",
"2. Ядро свертки, padding, шаг свертки\n",
"3. Пуллинг\n",
"4. Отличие сверточной и полносвязной нейронной сети\n",
"5. Количество нейронов связей и весов в сверточной нейронной сети\n",
"6. Что такое ONNX"
],
"metadata": {
"id": "QCK0mXnxjHVP"
}
},
{
"cell_type": "markdown",
"source": [
"# Часть 1. Классификация изображений CIFAR100"
],
"metadata": {
"id": "OF4X4J8_YlLo"
}
},
{
"cell_type": "markdown",
"source": [
"## Импортирование необходимых библиотек"
],
"metadata": {
"id": "fzjC1ECbdj-Z"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "3O2PElov-nod"
},
"outputs": [],
"source": [
"#!pip install torchsummary\n",
"import numpy as np\n",
"import torch\n",
"import torch.optim as optim\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import TensorDataset, DataLoader\n",
"from torchsummary import summary\n",
"import pickle\n",
"from sklearn.metrics import classification_report\n",
"from PIL import Image\n",
"from tqdm.auto import tqdm\n",
"from IPython.display import clear_output\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"source": [
"## Определяем видеокарту GPU, чтобы на ней учить нейронную сеть"
],
"metadata": {
"id": "CYQeghmzdfH8"
}
},
{
"cell_type": "code",
"source": [
"!nvidia-smi"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5D7sZ4jnLJ_i",
"outputId": "30bd130c-2611-4c60-e829-4366874e3ec9"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Thu Feb 10 21:58:33 2022 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 55C P8 10W / 70W | 0MiB / 15109MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
],
"metadata": {
"id": "sSkRWVdmlktl"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Загрузка и распаковка набора данных CIFAR100"
],
"metadata": {
"id": "4zoT9OgeY7cZ"
}
},
{
"cell_type": "code",
"source": [
"!wget https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
"!tar -xvzf cifar-100-python.tar.gz"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QDPzQmviB8IT",
"outputId": "6aabdc4c-71f7-453d-d4a3-a4a6cb0ba3b4"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-02-10 21:58:44-- https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n",
"Resolving www.cs.toronto.edu (www.cs.toronto.edu)... 128.100.3.30\n",
"Connecting to www.cs.toronto.edu (www.cs.toronto.edu)|128.100.3.30|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 169001437 (161M) [application/x-gzip]\n",
"Saving to: ‘cifar-100-python.tar.gz’\n",
"\n",
"cifar-100-python.ta 100%[===================>] 161.17M 51.2MB/s in 3.5s \n",
"\n",
"2022-02-10 21:58:48 (46.6 MB/s) - ‘cifar-100-python.tar.gz’ saved [169001437/169001437]\n",
"\n",
"cifar-100-python/\n",
"cifar-100-python/file.txt~\n",
"cifar-100-python/train\n",
"cifar-100-python/test\n",
"cifar-100-python/meta\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Чтение тренировочной и тестовой выборки"
],
"metadata": {
"id": "mtz5rqMlZD2x"
}
},
{
"cell_type": "code",
"source": [
"with open('cifar-100-python/train', 'rb') as f:\n",
" data_train = pickle.load(f, encoding='latin1')\n",
"with open('cifar-100-python/test', 'rb') as f:\n",
" data_test = pickle.load(f, encoding='latin1')\n",
"\n",
"# Здесь указать ваши классы по варианту!!!\n",
"CLASSES = [0, 55, 58]\n",
"\n",
"train_X = data_train['data'].reshape(-1, 3, 32, 32)\n",
"train_X = np.transpose(train_X, [0, 2, 3, 1]) # NCWC -> NWHC\n",
"train_y = np.array(data_train['fine_labels'])\n",
"mask = np.isin(train_y, CLASSES)\n",
"train_X = train_X[mask].copy()\n",
"train_y = train_y[mask].copy()\n",
"train_y = np.unique(train_y, return_inverse=1)[1]\n",
"del data_train\n",
"\n",
"test_X = data_test['data'].reshape(-1, 3, 32, 32)\n",
"test_X = np.transpose(test_X, [0, 2, 3, 1])\n",
"test_y = np.array(data_test['fine_labels'])\n",
"mask = np.isin(test_y, CLASSES)\n",
"test_X = test_X[mask].copy()\n",
"test_y = test_y[mask].copy()\n",
"test_y = np.unique(test_y, return_inverse=1)[1]\n",
"del data_test\n",
"Image.fromarray(train_X[50]).resize((256,256))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 273
},
"id": "49KAR3NoDbEp",
"outputId": "d9161676-e384-4757-c2a8-68c2d21478a8"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
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APBaQ9ytrLO7ptukSsCzdIP3X68daL5Efay1ZP9b6sSLH8+WxsKZ+EdIBjwBi/Vzl9AiLteWyG69ppIfvafCAAE5NSjNT9soem/w0OKqW4dej1uH0tBgJCMRg2FbuJxWVRdmU7YFqdxKpCEnjMi4Vsbczg6jQ3DwgFWLCjTAIwiZR4zIxKGxFzqjDJ5A+R1YPoNsohNJRWy8zqApDqKVp5c/Okc2n/AiantrvukGf4wBfYfepAQ4DLWRUHkywVFjn4IbPd/nwFEzY33ohYQJZ4Nct0UQvAnnJWp69E4AnMU8Q/XqttYQvWWt1hKtS4sLlFDONxIeVLtmpDB7BGnIaRk85SjN8eSQgkoMjRRLp/IwgEqyjf4WATnVg4AirqpdFEXFN4B1SI2EKEapWVNiEwqUUEVE64YYlYCKB/lMDKETZTh8P9MpyPUDxdI0YGtdXyQBpwfsIaXDDFPsD8ftHHNN+QiD+Lll/v32yAR4Se7JXOfQB/Arrn2Dl+F0Kry9aKJQHvWKRLs4idxS35Rch+2ORbrgmQ/YvyouvFXA/Ef9rBQNkDFiEL65Y04gyYdtjf/S1nTT+ga9AL9vULD3k4T8Pp0mEIWJYwgTQyq3JWGqWO2k4alPblZ1RU5JFgRD1heg4hUJTMTVRbjFulxFKl/1C0BzkOMaRULGWM0YLUwXhZ1LRSFISz8dLwWNMiONn+wIA128G8XOlNF0DoVL0xyA38f2Cbv5q+y0vUPHvM9dMwHO8f+/S7Auf1z+vMSyAxkeMTP4B+iOHOfLVAv33S16BdgLzSLorJcIFRQPnw39wX5Th5zUTbbwwXlaR3VzP5Vk0u7LItNdLkTiSiBNSDgfYTV5I9FAImJghIwXGHYnQ/sNck2Ipig0wMoLSLshhVIOpqNp6UVX2stQWEGCZM7NamDVAarVoEUjwVApEhukF5P5H2//sOED4U2IRY8t+z81cy8EPZTj408ef1F/reiWXB/xFkRNTnjYyoYTq8FAFGDBV21vrVYlkoJHWQYyU+td+SfxPuDO0yQ5IB1McprKiay5bgVASdyEPUVmcUWFlmam7j7YP7HgZTELXicEMIpLfmogtMVulDwsTGeYqtP90e2QAG+/39oRxbiC/vfJfIDjDJ0FgT7eZ1t9EAemfkRVWr+dyyus1GWCt16olLGs52sloLku+tr1VYAZIFzvaGXuYpJ6pqgTERGBiRtWIDinoONrMhCriBrCvFt97v/d+v/fbbEeFXDFB+3BFCEJUVCAWYY+0hZ68bN7B6LxrmxpimsFWPp2IiZmYCUxsiUKgQhOYUIUmXIK9MFY5Y3CmO7hghmQAc9FjSlPpKLabtVQACtZgfjue9A+2ByO4jfMbEjGngBOfDrfkAVYCFAe9TvMn33gv39kj9mANMB0EeWm/n1OJCNcClyzJRVsL68cqBpAfAXtWmLkF9C3dTGYmIaeUKqCBCo3JtnzK9FhY/dZtXYHRlADVKEJ1R77h7cSh6oDZwcHe+71/7v3e++feb6+ZAABiQl9/DBFxiI6g/7aNycEA0X33Q1vYTUibwAzxEF5bzoxKkqIpsCPIbQbZlAVRVq5HYkJIOYL9z9QAxQ8iTB6QTGgqUGzYiOCAKemesxsPPMz6P9s+QSC7s2eWYIg/ebxfsUOi1DiuiirDbEwd3NcfN09Z26pg8ESmQHtaf1K/B5mWrMVX+Hb4+tEpDx7bSsQfzqLAxFBUWQT3cues+cQnIo+Hokx+TzvPNQDD3lUTtydNIDSK8t2joLbf+733z/f+WQzglmxQ0pKlXMuWMFywUMgKH6tkpG0MTbggHHj5dGlOmXcF5S6j0IzUxFq+GlRpaR9nmhMzuYOY4l8YMMbRmjDAngiXAMutZAAklUSvOLZYIBwLkQc86L+mO5g4TvrD9pGlXh/00BM2eXLRIF1+7S0pGfV42zK9gAKGAO7BwakKqmDf0ADuu/C5ZEj9xbUS7v9YmePpRrDwJfKCLAlVLmWPKSPxjAChYVZ70kuELV3ghrQzWvqIHOUk2UJFKbROg/Zq/Z5NwMQjnsH81vd+OwP8r61vMwUUtDRgRF+iKvYSiUcW0aSrZMmBNGu0tEcwJK0lHQX/5aSl1zjeh+ERlG0hI8J6zq/8IqUVc7KdDUxgxrV8gDRWgpZRZCZG1XA9ncjgIl0nHTxSx0P7CKs+fP6kAT76O3gcsQ9O2T9NATgG4LysDf5KDWAH2+eVUyZY+EOkVsD49hQEhFgCD7G+PLF5yeu1Xq+1fkhqAMoSviirCj1YzqTzQPrfmVE3YeoYRw+TAeAJPVIcb6IQqshICbJa/s58au4gf7OtDv3/1/v9c+v/2vo2KKFeZdEfQXN01iIJgppoOyjOP53GURJ9/G1ZC+U2m4waWKkW8hdMWZZmt7NOMtB4xW9z8UD9UCQLzJP0tTSph9RUTURVhGbZg4PWnmXmb7bvcgueA2Ff/4atluqDiRFuRsD1ctMKsMEAd41Tv4hMixRzLrlloH9f2LWWU38ywEt+OOB5UZZgRfEfSi4yCXUUnYhErhSOCReQxJ7pYUKDSZCe24EBEWxQf7Ap0vOSfhpVdQZ4O/55/9z6c9vbLMJSa4m9HEMvhuwLP1WU+MyhSuTJY8yK3C+DbhVyG/LWjp/iQjt1g9AA54s9xUzIGjywAApppGwfWHjKkqiZbsaXPq9WXW45+QCM/7w9YxFvv7Movt4P7ZsariDQef61+dTk+4BAH04d2ibvDKQu9siqo383bZMN+Mo/q0oPBJRYn+1M1TZjmMCOwccNytSswKtrIu+QUCJdWFLFl2YqjU9D1K8NP3/UEXm/9/u931lP7W1Q0mRBNbV+FNw1z6mMYujh27kIbjTRDwWb599f6Rudn+aPr4Qer4v/B2lUDLiepgI0V8OJkvCPzLiMWymLolB39mqta72ogn+p/c+LA7DS0duD4SHdfnniZTg30ooFUPIl5XFOb1POBwYoNr+C0bJ8wrAQT5OHK4EgLd3Yg/b9bVSPUi/tTDrC553of6NNMa9wy8LyhcvL/n6ac4JPniI8ahbBAel3r9EYgWP8p0IDHxlg4P2EmCnykSSBQwM4aD40wO+OrX3QG4VNpd3PbvvKS1zYDwe/r110P8/UpZ6faMh8dRhiL7kh/cRut+5RCOjE4hUzAuIagKUEgkkZ1K+IkvnmNWudAfZ769uPvFuZX60buhAhZAhMiuX9sgCbcZungVigHhK+g8ujVsl1AwvLU0cxk6kTRigb99fJQFYjlVltMZoCLqNqrEHYi+JBEtAgCstcpvrvH9IED8T1+giOyprNPx3zClleOP9YWLYUe5OqAKlPFvWcttEnw+3U6Xwsv0tWcEvkI6+C/s4JLvvLZR7Snk7/CgNNzaLGFKmBZ92NrqHsLXfdCrAV0CksPAON6uINNCM9Sd4EFJprAMJgXj5tJw944U7dO5RAFG02t+zFqFRxTwqB5Q8aXspgf0HmPVulUWZqktkoz+AUv3PB5O4qor56WNWsDrq2SUSRW/VYGaoXsoyQw4BVRSpBPprA0zy2oBTlWqJV6zrJRKPwRBQsvVHFI4F+/TGfPry2Bw1Q7hu3S4YCSCxw0QDxLUhYbirXTk27ivVPUI/NNRjbKaYZNtzVQfqRyMPXWj9e8sr4rrj3uuxbt792ZOIoYFRFrjUn1S24dGeYZJalEqVD3Dfash+aSTP1ggko8Q7xIgpmZkpVOA949Uydu6eob3lBunCxhHiRN7rGyxPpJN0zJVyS/qsoz04GyILpticDaB84A9T2Fd4b32jAfBcAS9uoazqHrWooHqh3q4hPikUIXEZ4fG2p6Mv3LZBVv3SLItzRfAZ9Fxr+xBm/qTseIZCT4iBtwK16DNovFzSn0vvQ2+pYCYnH2zbbJcWnsYusttZZxW74vkoDZIUfEcuMBocgsIjwqtGjTb6vLgzIpeCxHDCUNi/9ynfjoIbQAIRnfQpAgzDQv/jJplBlaIC3vd+uAbSp39KfEjXYBFjAIhbxIl8SQE8kVIGb3Z62FnIfBXhsVqhS2144UU1N3259BN3vbaqehlQKQfXESwWoShUU6bd2jf+joJ1PMQWxSo4hO+hZKsptFJWlplCDrGAdDZts4GZrmdh7dQ5SupN6USDvn3+iuicNEG8FgRrrOwSaDIDiBABgxeoz0ovhJ7DjDpfDQXeZV1gKJ+4uklJRuDyuJbIiq19emdlGpxAnfDWjWnh71O3A2F0uGEC8FgLEjEKBuY+H6esxwOcvNIq79HzWgO4gIJX8TMeCZnTUVRpgv+0d5ThNDdsZ0f2FShPAFiDAKh53HijxH+jIgo/9yEm/Qf9WjzWEBggGiPKFO5WAWyNDJ/imF46gFG0P+ASFzTE0gBOnM2D7FRjy09389FVE4gsphUtEl2mkyflPVU08dyp+mMRypaAPdP2N9hkC8f4d5788jN2bBsAgW+LKlYYapsun9W/92lVfct5QNuG7z6Iia74WnQdirWGuX0lQqr5WXGFGU1OjbXNkXgzgoGOFZ9/nRKQlSYoCUg2RyTb5lT1MTFEO+KaIkfA8X5abWSS+8EvQqd9T5xch4ePFCsHP5V4At4Ddto4lqGXy7ggxaBUvnwwwCH0X9euEQLHVUrhszcvbAq5u+rnnVKYCKtMbgBGsYJjXofSyEZBFMSylmsT1TGGiUFNqZWE/icpvt7u8/20NkDM7QP6vGeBDS2P34eDhvhlQLAbIAp4HFh4vKVEZ0D/Ef6BvY26cVZsplpU4NUD6LVnTTF8WwjT+B181LQQe8ncpTZ1IzibR97ahQ7Zae43CwR9raFzSD+ePEeaGNd3hgwGCUrarxp51BwNYgf4BgcZmSuUgqmUsZmaHBsheXl4xkzbOxGEiFD35VCoj624ZLR4ZZipGFVJLkPye4/evtP/GOECNb6+hikW1rCK22dI/kg7CbJYp9yV8TU03bOuFAWCsSwTK8DAupBigC1155METhk0oQvOUYKqI+jy6HvIaywmsL3Sf1G+BlMLCUKiKqUAv3s9weppWCpprHhfVo0p/IptkADgDpEGcDODUnrVtVVPYz/jAX21X5wcYqwhi4syuCwxki1VB9n+p3RmgxP+3NIB98umg5aSf1S61gYnGina/d9u+4huQpqR/ibwGA7zKLRKaqK8ee8uFSzDIbyN283yHUyTWHarYoQFisZMFA0TSpVwYoKhUxFMgTX35Oem5SWERGVVLsPrzlTGPHD5NlXGsIzOj6fGnGhk8EEk4BoOamtrWrVvfe2vvyWixkZ2vPYNuj0NDt28q0N7So8JnWrWHtnsS/0MP3OCRqZknjNZZxgwtimCJm1Hizqsts3x25ptjvv1TrRigmbWwzfnVM9IZPtPD1E37xs2ABwyZllXm1rOGyu3JIL9VaWzJAGMtF1fAJQtHAgmYsfYliVnesHJ1vNW22TY1X/QkahQTz3u2WE4mCyQWJfSAZQlEE3r9HFGBeuoOTUSW2BJTMaFuqi+uRXbFgT4pImZ4wVcKaOgpM0Yh0HPlZLOBBkJQRqwFNHp96JD93t4RYY7IstpOgeDg3uMgUdA8XEcdNDi9mXagmD5ghsWTTOzghPDaYG7/1sGtyBiliQhsiZlAly2FeoRYTcMRTTXkvgJJX8kXf2QbPLcXruTeTxoHYeRhcMLDT9yD1c9cFG9A+F7680NYkOl4j7sV7FlMkc/w+bxW+v69zBon9Udyj4XbPXDHzuDP22NPZm8Xkb7Zm9DgIj80QDgsSKzY3AVivdgyiX6LLRGKLaqILKrzgIhK1NmU5M2w7ikiSBmoxI40oRw4K4pXkzx2W5lUHxszprfJnT/uyXzvvSO3SHcxgEV93Xz3iupO6xq+S1jzQBqzSa/eqfRtTKGf7plgg5hqDilosfVS/KQ0YCBKwQIhpmJLZAtENCpI+hoFZNB9gIwOkf19SuETBEpRnKHYtGXmGXjozDDh7fKaIGVexqy0jlM/WdXL6eL/teRHlDPkyzcmlShK60DCnYBApDmk49G2qcK22dtsb32r/VT1Dc23M0BUILeoj+AFPpeQ5Motvrx2mqd9CoW6K9VoqcQWGbrE1jKhrqUO1pibkAGAL2vxIroaZRdCT25LcnbqZ0QPNHjAoT9hMDBMRyUCyL+3r6mMFluzRxgLZaWXRk5fTZJ7yf6HOcMUTBWWSUXdIa+ikpxec18QYFElIIiDjgLh3rTFpaaCJaYiKuYli+KpvUNNXgMP3VRB0eR1WUl99YFnXrcr9EPdLnNgocsFb06eHO2hDfCkARLDu/MsPGaS/v4EP1nMc/Hl1rBv7VUuHzWY5xeammq8q7rbR01jP0N9Vxaa2Tao19DxtY+yQKMonN6XeQ0pWSrbGc4cwYuoZNXD2BFGdImupUuW7HDM5rbx7tZElEs3UAzb7Q5VXVk0wWPGQf3JxLZ3BUbgS9aEjpfC5H17ZunP9/v93hVqdqYxYxGgg00Pz+WchWB6hKeDusK7FiK8rMGEQ60aIryeDKVp30+ATBKRQC5mIirYoTlbAyhNXG8xBX+jhCvJft2+1hYfvECT1HnQvT8MWw3moosU7iEBrG0AOxjj6BDnURlJHQSSWM3CyOp/0RkgDVS/ggt/qpYGYLh6IgkytzXf27bq+21vtbfqNqi7a1xQEQF+PCgcziblEhGjcEnWa8haz3EgIrJ9EYvIWsujczszUy0VgtORQM3XozM3p3dPqgfq3HO6N3zva9mxl66JxwkCFuGSV/1zv9/v98+3er7RWZMIoNNTrWcuFNPuS/hY2JB9jOdt0rem+9DVGMxQYA6AmUlaeTFRgZcoBCA0yzXEQs2XSSbgEYpcn2FmqTH/3va60XdEfu90722aIvGJjfeT0K/q9EP/W6WW03M6+4Wvfnl6fyfEMOBDVMExmHr0N+M3ruQr+dGTcN5b32qq2DRfAxzLk9x4XUaSTqZCUa8Xyy1Wa/wk1pWXi1aW6I4Ci64NloittZYgIa9XMUmZm7Zmpj0bBU7xHpza23c73QQt2N7C/G0GSPjz890MoFoMIJ2T4CtPsoJbzuyh6jMs0TR9UPvwSmdeVrqIybGcLrWMWqYGWBcPcIDvYsS3KUS5lCX9yMgCRkwU9PfZvUf7b4kD8Ez5PR3Es/b4kRaMoYdal38YKrcGZ1B2KzZMQbViAIC+LJjcKJ9Q7oXRej99dpMBbC0V0bXWkrXWiuNlS0zN0+TEiSMdN0ddICoIiPC99L32estacLIRr+BwYQBTDeyv9TLPMYJlYi7BSDJ4HJmCFRJjF/ZmZEKLZSr//T0FRFWI0KuGj3mJRUKrPpW8t/ASChARlTLBc6uyf679UgPMR5n/xNHwFiSeLAg08uzn1abVwaKrQ8BULXtPCuPilQFYCW9tYxcv2KOyLNvDz3ZX4DYYsAMWayyA9WygqBhOqgm9Pq5WMCQOfBWwCIVLdC2h7FiYsERkr9daS1+ia+uW7cX5YVB16PL++fPnTxfbtknABDQhltc5hcercnHPwQAeAXi7Efz2tWVbdYc3R3o2bSy/rSgOUy2ETpcWteZpye6SLaIPr6WYiTlcWUKvsC5qscDLr1dA6ND7bogkXw5RUnkukSxkYT6YuPvLBmv+ze3FGwO0I4cJ8KtZKqSD3J4YIKqV5bOirN26mcNhJCExd12QinAdK8wBcZMuHJ5RWZJJ/SOrHznDuTwxA3iIXBP/DTPkb532S0Wk9BGKrLUYEKjoHqn9PUkunKTcawkzUL1kyXqvtdbaL9nr9VoewAg/ju33dtjy/vn2XBwSqqK2CWOEZJdueb+y4hpBcQYwwm3d0CP7vbfq9iS7mIsacKZDz3dtRaJ3uFOJBiO1nfi5lgcxsVU8KxkAKu4ItiU6sbsIVU3odXBtwmMvCoQ0uKK08Wn4KWnCZYRI+LZjNqxUuw1i/U77lRH8gPXbBdQGkQUbTi8nEnkANhggvk+vQ1F/uTqbLQSkeLqbVP67k/4CF7kK81hMjOOE+HEymmePlJAoLHM6Lspn4R4LZIocMvvL3YYWyHlHirRvG1FQ1wG0BJOhsVDYxb0fxhKHQu+XOCf88KWaznsJ3yOAZVAh9hbVBVPoy2yrrj236sg1C6Ay4sBqqlvfubgmlvTkTAKlXdPEGnjeMXbqCCsDL3C7GywmxIL7yypJVHUtM1VbKirmasGiRrrPRaoWSyoRC9njDBfAKDNRcgCXqx53hma++bZjcj8wwZ8oiVs2KOvZj88tIUQd1AfFANbc4d9Ngh+yGKEGmRrAsfHkgdVZOZma4IlgmTdAKX0zk8VjNJUUyeWOBkok+zvF5rrfGK9hiUbFcnP5FfTtC32tlkOUBgCieiGAXANO+lxyqIL1WvLT32WtAB7mpVE07AA1QIVYW1TF7AXdZktVdpRzjLprYR/m0n4kD3hug2osqOr61iCrAKRImCGUVASCmEUWVg/DNUVKhMKUvnLXk8tV7aVLxdYS9duqb9la/UuRaOU5CVaIhPJczlaJV+4Oci4yiQCNZimaR/IuCrP5waMh+EFr3CPBnC6yCXUOAZ9Y6HB7NsZGJoMzr+mmmIOXouFiABTsCfQf4p+dC5YMQF+d6JaRlA82bILUN65VoyBgeC9FCskkIcdjpdOUuSzWQbOR2EAulQlxOpwkbFeJc4XX8QzSKj3wXr1ywT0o4ZzyJSkamRkmgqVitjz5wRlAiwEWpSq3pXcyEioy1cEnZrBqVvWU2DLBTfZiVuZslMtsagA6LXg41pdKKl4G2PIsq5eKqvvwTcWWUYfkNl9sh4LCKbDKcvM5zYpaqQHEzBwImdIr2HEQcHHBbwGhT61sgFvLbT4GBAIK1mEwQHxcDOCDV1hneNKCVLw8KiYDVNJbQH+myWuMRVYWC9YDAkmIP8yxiZs6PjETX8GM2BIl89CkkUCq0o6JppZ3qUUFug4hkGrsZAAEy7lzL7bEgASprbWE7yWV1UEGAAvZbRH29P0tnG03sYANiO0uZpp7dlhwuA9IOgMkMXx8iyxom0aKSFor8iUDsK0pJ33YwpoQKMGPSr6risBcDcCUGpsW+LRU9D/c1siF9haVZDL1SwijmiwHVEJRakLXksb/hA3w699fDPr6syIAt5tNBB4IAqULEhU1xVMODQAuYGWBEcJLzuZtfO9BOTAZEt4jNYBLG5HA8IP0qwVUDb9RStIQUB7BdcP4VwyQZR2MjLViLmp1++ake7FXrAFEJd9X9TQDYVtMRDbVF4LBKy1rFawlab6Fh0kJewteTvUmp19hHKcGkCsD4KIBcoGjLaBCnJ7ZZ3JjAHHXqwp9tzKJ7XqmF6WpvzB1W2vxExi5hCYS1VXJGmd+Tct/1P7NOEAU/U5EV6vHD+/+L17hDmq2tfMGqKGtZSSxHu/ib+5VBIJO2z0uWnZOrXPEmC6iX0ge8ndfEmZlsYbXFTSzQGCuYS00auGWxLMZI7NwL1qAisSWjvb8MVm7BLhZ1V5FkcJhMjTAlwyA0u0VzpV4T8c/POjxkkr983eDOPf6axf+/+X6gpi4rCY0ij5BBKIeD/hH2pMGsKd/ng4ANNlw/lG+hzkjh9CcdnEvekT6f0oD2L0mTlcoSGLvLsxLhwYgw4tTdF+vHF8J0xmptc1m/MWuooflbPXha6sgYFiCcATccbemEaYtqKvDJQI9ZhydHDQ/nu/sRjxuDGCU/c1cKandMaX5odxWNUGle5IR3TI1h1fmlWDoC4BMqMvXOVBvGsDMXqpxmhux4rvHxGK7kBWdIjPECKP6ux3x5qOnVUz3b9QE4QUaVyyDpayX43YDavjflqZucUJMaZJ0ASB3mk8SJYGsNB5ae13kfX2SQKijYBZWFCw2YWOa3aVinIKEWIYlWFFJt4jsYIM1eubTVhJxCvowCuIJPbmxIxqgSWR/1leO9HArvxzX824H9S8vdYHlL9oSjt6aJLpjWtsLSfpuRPrWAsLY51IyIy9yl0oDFB8OqZVBwFx0A5PA5RmjkWVIq1d1ydqiS14qus3E1Gv+uGPUxNREPGbnMz8IJqMBTKZLmTVw23jS2GqVZfYMauy/vovnmwFun3CcyZGCjcerpyTj+Il/nm7mYoIx72QuwCoN0EDIlQCWYTE0wHIOsYMBpoZqGENzcCzhX4hTfEG7b/pbL194aJo3Tmnr99I061tSIX0rTEzNfFC3TTtXwhB8748dXlM/IfaNQBsSkuVvc38DRnnTigB4jnbYAJT06QTpM2oCLI+YLXco+57wnq+KpChnAGSZm3iuhEAm6t4fZ2NGFMzSRzn0gKqo6muJmeiW1zKYqZc81Ni83gRiYmKiE0E02GIGBiQEDP1kt4lrF4Swc4oef0Hn1/aFxnjdLmaXoxGAgwGs5f7of3xyCRQ4L/CD4oPCvpw5V+EJLtL342WN0QsFsVBQFagZzYGwIJ3ZuarWwol0kP4SrAVTs+UeSGAFAzgv+TKUVtXJuOl+CXNyudlOz/WHL1BINggnO3t0eBmIguIrOSrr+2ItvhZl5cYfccdkAH8tSkp6iboYkZ69wu1TLiAevk+mYyX7V7PpRe48yuKozJRpmJZsFl/+5vJeVV6R7WSqy+IrT9WwZWoWmyOg4mBTrIYFZGkKO/Vbdjsxm5MeOdeaTf9HXgkhuB9o5JEBTvovzszLp2hv/FUDljzAZgCWTih0Q2Y9/SKhAspIavEDiEt9oJXAUA4u0CPLt7ywc/bQuNijYBQYsQxqWAsvxU4GeAliszi/mZHLCG7f1s5iwy6tQWXjnrQuuVaTWC6Xga/UdAZgKeYI/ZSKqGxqBr2GpM/6dgurqH8NrnMiDg0gSfFcskIDMEE/hcWeoYMcUdRsWDJB8Tm9nomXb/DxVaktwIJF3QZYi2ai23wxqKqZ4aVmuiKquGyZmWFZFLGIqhbIcHFSaIWNBW4DTP9PSVAE9bPCaDeA/qv2CIEe1cktkalILJRWaQD/r4YyKbypvaRNof+m/iR9ry9eaT8Jihic4H8yqu5nVwzXJxr2teZ1FwBCLc2AhZeXqZKswtbOm6g86AzgO9u1ByO1l6cr+Y7Ca6WrZZGEdCAD4sgmmbR84aUQh3OmZbwzlQgy9OtfBQMEUWedvMEAlYBUnh/vQsr+1l7JyafhVtDXZ09SR5tADDCaJgSy2ITTVExsLTUTVXmpwaAquu1VykF0qYR7x6tA+4hYm1UWS8IjAyt4QKycFoMTCgURqJXzk7x/Exv9kRu0gEz+0f+hGCCHuSFQMkCQaLt04sPDGeoRgNQApwsIgR172i6MBYBREt0yFLCKAaTZoHJeaJCVN7ozQGGgBC0SQprhZwkyZL07jQ4GSIcn7MIASbjNAKkKsISZJIMU/A1jDgZYsrhkDQZIlLPY0jMeAgP73wim5zWt7bSGPdcNqzxCi2Z8qZiZpgZYKrpmjhBM1N07GlonbmQn2ADK0q2Rfmy/T+Nftr8xDuDO3KDB7/RzoP9vvU4KPxTU4Q516J/vl1DAS6D5Cq+ngC8UAF1Zw8frw+5mgCCLKO0kIl2P8cYA668xQKbvfJcBph4YDDB8ERcGONx+MYCM0TM7JGtls5lRRKyL/aP3A444AHxp7+6tAKIWkBmw1W/0hVO/IjmPBGD/wNqAv0sDjE9yoItihwbo5XGTlOs1njZ8QWJdcLlOTg3A6tH5ulrZ4QlNz89aWMqXRiIkAdmReLABlYMB1tQASAMgNcBaSfQvZwl3vEfINhkAEQtKBkBAGlkZn2ozN3LpHESBxQAYDCDhfXVqv5F+MIAPhJzz46NyXzk+Zjf+ifycdFN4rkTHBFZWK1JfhBoaQFTWsqW2dmgApUWIV2qr5cN8G7MYGTTDbXJtWXIxAPBfDwh8ZABLjD3DMCXZW8APTmCfhrJyOw9qPGwQ8Y36OXxBK7GQo6PCTuNCiakxvg5NwVDcUWTGltg2LIEuvJQqsBX5DQIsT74R7LEvy/YK4R8gUHpsYlNu36Qs2ODV6Qf0bDJPXn3QAEiSDc8mSWlfaiDgUwNIaQB37i8GGzDM3zgRbYe3o4fn9J2yHkhBa4QKBFQg1mcJzTImsCjG5du8xPoYEVHnQ/V1Akv2jhpKEiFtsfL4pN3VpIEohmLTafjACRAvpuRMbX+VB8ILdMvyv3FAy4yyfxPqXDTAIdmfkOZwox5KwGKlb1F/ez9tnN+wPze0S6yfTiKiaiG4KAXMvPYGVOg84GnU3CaAbi5CFcoU/6kBFBEQeLQBfBNiEa6XSO7TGp+sMj8xrBZPyzamPz6dlWxrT1CBISK3upkMICTCgHZfaKoF56lgEp+KCwP40FW5nYElcZwiEE3xJchk3LQEPFltqRiX0kxUVVXMdKnsZbKjWJjS1JdKijtDfdqud710LzU3UuqHS1SdK8wDFUhvzFAo/GwJ8+E5Abw+M9DdyVLXclas4/kMLPRTDq78ynvYHQkZDSC8nOHnicB++oJKrMevWplYaHMfsLISxDWA5SiawRyJVvDLnT9Zdjdhj2uGYIBYWbUB8+2mJSEQyQmBXhThenEtrlfuy73CQ3oY/plAEWxJZ4AS5yn2gmAz2yKFeQVyV7p2nAUqJlYBupKeyPs3WZRPO1zlLUE5LFLG1vKUNJPMCN9NHrJMzQvciugyUVvLI8FYorFh4ZbduUOxEZIQmjtBFZSZEAiVPJejIewBksyWi1k9w7RX4m2QYKcEONqvbIDL9cvZg9amdZTv7HNrTA9nqpX6awjk4aqRoFbZECkLMwKXty33fMqK9NwRXtfExVagRtry6NhC+oYohGxRxi6OlgywATM6qN2dyhsMkLCdS2ID1iW5E/2S9VrrFXuWMZJvEJnSgX+cMsPHkpTvjxl63Ycsf1J04LatSOkC1x+gJ1LUJZHIB3m3I1hU0KOfq+a6tHOQvsHrxnuVf4gHBz1Zf1HCNyomtpeKiWxZoQFsLdNtm75wnlHfznk7J+pcuWJBYdljjoFqDUDnXwdMmAvAq/cfKiM8cMv3jWDW/yla4q8TAV3ueP/UMPAMC+fkMl9mIHVqgHJatB4IFggKAmHBPcasIu48oJEtSREui6rOWAKL1Bpz0lcnemRFqVokaXphAEpoAIk6vcs1wEtW8ACX702f8YGO9p0MEJ+lwTTQXc/WIIJigAzswjUAPXvSfyrHJVMDPJFA0J/NwSUD/BoACnxhNAhIrKywcInKMtgytdz5S8REHfp7kby0AbL0pzoX5DqbG/kffWwVFohxGAasLGtj9//PjIHf9QKNKWu5X/SMDwenyXuR/TG8zxqATRDHYMVk1fwWzEhPU9kAkipj+SwasOJnurkKpPqywNwaazJAuAVbA1SWcYYCggHq5WwQTsrICCm/TJooZOW/9IAAKa6tGQA4NcAaDJBoh/nk5UcpBmjGQpNJYpAPINfFKiNFqixyN4WrcK+ILFF3Cnkhx7Vzw+Yd5a4ih0KHkZPxLD5Z4QdtHEZwgyIjXA38dT/Qf6AuEHHDOd9w/z9ZaiD8JJmYybLArUn4rwGBlKDImvxRXzNWgTUD4KoBAgkUA6AYoHcrcPEvLxl64MYA6dVpVZAPUu/4xACYEKgZoNETJL26PK92jJ0fftObLmm3hX8euUohX5L7NI+lAvb0Qi4cgCnkN935HJNcOyT8Xe0LBuBJdlNOXeTyJw1w/F2Ax5XAA9x/Evzn1RvJDvSQyjIhUNYEjLI2vgmt61zSYSzcZlbLJXoKIBgAwQD0WspaK6EdoKcL3v/zAkbCCAjkLsVpH8uo8bKYVQRlQqCrhxiZJTMZwCAkigHO/Aa24kPK/p6phMvdwj78JDun2k4dX7qrPPqpAVzAW60TCAs497DSfK+EvMTxjfQ+mbFDA7QeSFSU4oNfqLFvtVgQ8wjFDoeSY46zQzE8+fXRjVmKKYk+eCBXdS2gF76Qy6IAFnstfDPAMVJVNs8lYCQNFpDKMhJA7DuaSwccLyso9IJw8PJRtSoLcyuDLJQyxmLi0EQlbuwmxa+IE3edj6D+JNpELgUirUbwIE1LkFsQKC0QqUpi7vByY4hwdi/brJy3NSOYTsJyJM6WMzaxGZgBE5oJaFkHzrAE5jjQsJTm1K/NA9sXORD7cE419cc9w6PDgxCzykFyuk3yq8dwqGZ1raSPWxvfj/Y6keExGLz+eYiWc9hurib3vD3Zu27arszCrLQfKd4A+jEZNGHuGYhhcwjJoEMhlyQD+FZaBtH0gZIObVz+SCSJKmnaKa5xWcsxjfWKyPWBxQAo1dNex0qCkIjjxt4FWe2j+0lKrabhxOY5iDUbmizRGiBhlKQkTJRvafcMNYIYP6trD7YAzNAbsXxoQRvt6MhFdp7dwE6QXkLNFYy1f+GhB0itksZk5QQ0C0aSZwz79OscSkCcEDKU8Jl8Pz3OpWU2aH4ZuZ4oTTyATeKOCzZJv+20r/Ifl06n1ZtxLg4NYH58FD8csh/wh07/AFwkuLJoKgwGYOxx5KaW7+dLH2Ch+Kp3mulIey9lb7GILAkfVnGWQV1+skMi9PrzkbdTiQypLUYiA7PzQ3veIVBObdkAKGvQfZ35SU3PqbJz2NNNcsxwh6KKjj4iInecxruvEDB3J5ibAa4HVkIgre3KWw+wC3oX1zIuXhwwlznVvNeMT9lfAqCXRbXkKCEVn+SS1Do+2mtMaR8Z7h7MtAFKHlQn88SLi+0cxqJ+sjaD7leUAFqR+3B9BSpJQRasSE8X62hqagC4nrYQjrGKLh0PUFEza2HoD5VeVVTRqQkvc6SLljpC7mi8VgWQlcbDmnJ25Swpb0ihSMz3YDWLx61zGGwfRF/ascVNOkbySsVCc3Z4g80TeNg8Na1AF7gDkDgPVDEfEy6B5mqBrb3ybi/KxiJ3z09ZLRguzItDlFErgOigeJH+IX5LY+RayU/a4MPnz0siGRc8F0S2tXB2Ia9/v8UpM+OZK8K1chHwarUAptP8sAHqJiU5UudXZKqWzVIAUQQeTlXvYQETmInvvVDbERYpkg53r4M2KKbweUMjx+FB2UnoDJySfx2ZPE4FHxiAxenBAGPwSw6OQRka4CJ37vi+BzEPnaHtRoHnBVj6oviZgKMatwfS3bkvGzkLlnCLZbCaqRUPHgiuzHGuBJ8ZDkvqZ9ZfGqiexbmH+L88zcFy2b7yAp1XulL8fZhs/huEkpE6jATNTDCRFv+Ff3h3AQ0RB4yE0iStPCgeCIlR/k7/JGCjl8QFLHx7aU+npRWi9koPpQGqBGRWyskUjMBgJfLTai1VMDSAfKEB8MwA+X3/4gLJ+u/r9eJ9yPx53v34+UMCkRbkuhfUrOQTqyUDDumVB4ITdttBLEb6IhpwMwCa+ln5FId2+1a7POnfHwcok+CLHnzH/f/4QyCEge8a4NmUlQWcSgBZSi6cockAbi0WAyRi7NIOzQCT7/ypigFyezkzmGkxZHGglKMysVn1MJz4fw8DDO9REcC/sMduTw27ko9J7v57jQbUqgCZi7x8in+r2o//5O9fD3DJJ31UHl+3gePaLleDpAbIyn95gyQ8diggxP8FWk0IxKxXg6TUQhbV0igGmvQt1a1l2DwEeHvZigGkGYAYtGn9mKEBlOp10hgZjgDYGKz70xCoQ8gHA9gXDBAdzJlpe4vD9DrSRB4ZaqKfOW8DCSWguJjRj9PdaCSkh5FGT4du3//jS44JayVQY3y5Fx80QOn8D/k+327+66sGuNb47DN5GZBOwWBLx6FmvdqCxWogpCu+qd8kgqvt9Y9SfwX1A84QQBQayS8qLWxkU4b5wNSssZtAuB0tnKfOACwbyxlRgncC3hYDNHioSmmmpkLxksg0QeAritRabrQGGCmcgwHSnL2N8hh5R94lL5Kby18ebHIiHxxEb+P4NIdtTOjYymH4Yg6D2BqQVOEFUsxTkym+yTJrUVtsnLzyIPVfu+tc/DkjG2bXitrcg10S72CGB4r8oqXv4n7+VQNgjMPn63GKCktrUJNEAEgSjRpoUcR2GMqG9AZIEPYV/de9IgE2z4iBjFjsbFIaIJRLqoLItgsVav1KndFJyTLcLXH36G/Qg6rSxExJ8Y2m40tWna0096TKSWUim6S3Kjz5xxinlenJGuARzslulJluH0T+OLjElVL6VOY8cximdZAchlYH02pOQ86AWNVAM6FpJKaK2BIssS2ylq0lvkPUElmiy9fNHLMWcjFJ9EJnIGKVdlN/goDvY/9H0g8NcF9S44LlqT89IqWwLAtUaCmAkpRDA1hMqyXMCQGfgr+CXzcI1J8yUEr63ScAkpRNqSfj9wyXaM02HxhgpZV6YQD/N0ejI7M0NSWpSppqEgabAZjreZtVTxtAMBlg1tkwBPC/SKAqqXKbULNEbS3afQRLTRzi1Xn7mPdwvJYXpntSQfIcsIM0UtuFMTCWNcsSbNHQAMvWdjMod8MuX1C6cwhaxfOy+85nTJcr01uRasO/HdMzBcKdeJ/a60rsNnjxElm5XHCoSMs6x4Zw4Qa9ezaBuULwL9ExXQsQyUHnOKfXR6SYowg+fJ+VFtafN8Q0oVdrmwzg5Tng4XMpBDayVb7BAFR6rSe1AWEL22RFkrNmYUXs2ubOh2zYSUbXPs0jbt8kUfL8LkEExo3G3QaOaJ0AC246jYV5ZJV15H7lkksWGzyKxOJS8+WRXjx3lPfKai0hy5kLA+pBbDxUwq2ZTfwdCPRE7hdF6e0BAhXtX86OLhrAVq6W7sCgdksjIojGN10pDdAPVM5cVvSX9UicLMEU/wEEB6Uy3O3xl+9jkaZswMzYYKxXTdTwWvjjgwHg9dnCuBsMwGkDMFS/ElDPHYrPwVrDG6ReXqBMnq6AXS9UNCQAmHLX7+3HD5N86oZnudeadkg3/yP0RQrf0kN5ONF/vDvpZ+/q1OKBDITkvpFcEivCHP9k7uy0BCik1pI+qxsctOhjFFOfGqA8FfNRv8JDn/nhAQL1t5/4qxIEkD7FwQAFgZAawOzoQRVjaJ4+KD41YJI+JunHWH/V6ttkG7/W1AA5jZwMwLQurgxQWg4JwkEl4XsittdgMECu5L3nAcXnkwFu85N/fpyEx4n5BgOEdAnSZWqGYXk/EUMOWNmF806SWxEyV+7XQN49QsNFPFU1apHgIIlxL7bgP15fK4Hvtf/MPsH89jKAa3a0o+vhbijoHemgTFiUUeETAuFgAPl9BojfJqV8wQC5SH1oAHnUALhoAEvM7XfVU75N0Zd9AAvoX4bZ5h/z4K+TTrWKDPiKmXzQSzRgNu3a/4Dtbxmzfwu539uTEcyv7lJz9bUGmOmUsHLHt5nbsv8iDOp9wp5+vwmCq8i/mMdICXNlAPc+BObnZADpjj5oAKSPNQisB7B6NDRAKaXTXctiAD3J1Pt2oPrLzN8ZQPKL65XyHvODJw3gK9O0bj7+uXTKUk5fvjkmiIw6DpkoetUA94DAByXQY3AX/0964DuMdGkPGsB9wg/qt/PHPWE46ClSAjBWCuFQl5d5nLGs67KYSGUYQ3NngPzhyTqUdA8V3kC8NQSqXqJ0q186oYpcNUCNazGOa/uy1A4AX5Ujhhcog9XZt4QLQZwCIPNPkdARSgBWkbCvGWB8cf+kxHOBfVIA3/E7QqvOCBJ3blAW1sWRsmxNBcmkjCBLQM4cSkw97cqvw2GS68iMKr71bZaquJkBeEJHjZnLUAh4ehkKS00437t9gEB2H9KZtDQwo3VWmOUdnKFD8J+k34kzkUV2iOsQoG3oWsamWHrhye8jc51g2sFeO6cyK4oB1H2zCVti1tI1IemWmgxgQLqqFSKmICiixVMJgbKTzQAsP2irq0MDkBjmH50EKpV5DmJT9TOt3z66nerEJIBRTCBqKgZfNIfMpxy/SM92ENik++KJEN6zoJWP5gh+NQ90aghFRYRmYlRWzgqLVadQ73FluQ378eKHY5nApR348Wyv+6Kgy40vFzLfvbvkQiCdgj1X0HmybNlJGXpCIvfMlZUo4V2jWXxysn76a4rcyy2U5mZjpmBHqF7wRGmAvIDzQbDpY5OMy6kpITZiIj4JcUdJI4RpshDps5oM4A5Ry3H3gCIkY4c5guV36/zoa7tmg04IMT8SwEAVMPeEktY1kEOjxbQ3zGseKKav6Fg8U01DO3wiCnawwSJ3OIIoNE8qKWa3fKbMhj7F/zmKfuDbqYaQb7JmOi2nEuj2uvFLpNGzZF7ByHzszoZsCITWCxlPGd3lRQP0+xD849+knCp70DKgBOhx5vCthZEcEKhsAMAgYmaYm5U4O0npbX+lSImHiSSiIkBF6CMUsAqxGOhaUGsY7+0AsMgHqynX3KIj9fkRHMp73BjgUQPUux/qOItCQMRUDb66JVYCRQHbc9FAQn/Lya+FJcW5JZuMyJSoKwriGu8FgcSo5RM/IUwBIg7emLh/6ulH2T+f4jyMH37hBcphyLgJyhAaUn8mxifKOO7RdJ9UOwX2ow1wQUT9PjRAE2wogWlcSccCmMoRZlCvSEAIYi94FHu2B08OBriOG8VghIAQTy0do9nXcwYIJg58dgT9mgEGdQMu+xm53GMQe57/kAG8zxZ1FRgPGCybPSlcUcFj+lutVow8w5uWyEdB8/hB/ZQyA2QaxEZqBgSILudX4ro6cpH6YM1caAA8VhsqwyLF+uBdfN8Naon2za4MMMyiHvr76zHR/1lI3hggbYDns2W86pODb+YEtQRBzv3trvKBprwJEwDYZbBZl8Tod8UvcsrG098YACnvq49/EwPUvxKBjC8ZoD6x8Q+AwsZ36kc/zXjc9q49v4L6a4Rs2o0P17/M4tQGXyiBz+1fjQN8ZxnAdxYG9NUKUl4srNSNNYh2CvM0LRGiKtPUM1k9lXyujhzBvFhO1qpwtGcGSCCMzATIvcSiwJNTm19JLT0toW7zvnLe5E4ePJ0c5ZQYF8vPvYyeiQ0o68fbTZC465/tzstU5nbZmLmLCEngn0yUEIP8fr2gv6X9ggEu2P4LDdDi5K+/Tg2QqMEV5CcNcPiXJamK80k4pUZ8etjbfdeRSXmltFozz/JX9JfIUXCKPDRApGrP4h5klQsdXoc5FvcOfGKAW3s479bXqwZwy9itIKiQ2o88lNCTj+SQ0OeYfqUEYu4qn8IuT/9w/S9fv60B+GGYnfRLgV4gkKJgf+ArN5qeR+Sczfkakr6K3fDxV5dOFnJxyC+51Yr74KWJ30qG8sitRyLMobH9hslyfUcm+K1b13fTqVCgne6scdQdesU5Ae0GZUcDDl515aT3hx/jev/0TxigPD/NAHTrGGHK0gRQ5wB6aYj0cBxGcl++MUlIAEg8d7grjiUyvlhewgyIbQ2Zrp/Hi8/XuN087ebWmYN7bd/VAKlOK9pbloSTfy/MmH0ixlLg+jMt+g9WwTP19wV5UD+vEGhCf1+vMbO3UeSWFyQwxHL+EmAt1GYtzTh/+CgFMUaiLBiQHMgn6/8+2wBG0JfcniRs4ybfYYCHTzk+HDZAvof8D8pVdMgsYgSoorR1PRvXrgG5UGpqgAKD5QXSJcy1wpDOjfuOBghZGXxr88QxmLdOXtrraoFNgWZE+v+K7q8MwMjjHtPCo7vENGta9oc/4bBjjlygIUuuk842XdPN7MmGtSKsObP8VMkUIaGzr4+SBQihB4YDwvJPlMxsjp8dnHQ6SeCgew4bQA5Dp5B6dqS06vh36p1PM/v0BfsOTwwA741RyzmJwm30+E+lgFyvasNn4x92klbw/hEUW2JO/V44qOHhoJl5h6b7HoSroGSLuULuTIR+4YX0Al2H9/RkFtbHBwao27mQRCOJvGLCx1zbdhD9NQGuzonpIOtRu0SG97c8oJID6hlYE+Gb3Ve+5i18b/Q5dhfVVfSdz+b984fEYKAeOvbwBaxw6h91gtI+z7zro8OlcARALfk4JuwyWfXJFMAfz+P4MLonCd0cdyU/EBB6jkIZRjlu/etJUwSYuRI9iWzQVzUDHPxUZGCHGaAdOjEbarH7e6H1+3Gd+aUlcIzKp32CCzVMMr6fUHbCMbhTDFxfX0CgUxskD4whKHyeM5I2VLdiMo3VOVP9xSfZ19DqQYwufay70KJ36o62U+LXZA8g54DER9mlZoMB/YtS8tdsG0AgCo3I7a3x/ufzRN7OaPhSIxFyxjumsNgcLICY66qW0ajqOo+kdgF3CYHOihmnDZAu7CKARqKz/9crP0qCv9MGiJR+4DMEunfhwpcfX3a+8ET9NW35dRPgOJrNk8vgtJmLc+rZeYyBExvjhbkxV47tjRcx7m3fYoAS8yfICtIoBhg/CpZU3KngOsyf/vzw6RBS4XSstD6LvDyYbw8mUH0cYc5Htuvle/YvT1x4lXcv0JHcFXx2yzn9NUV9YMiv239mPcCfNTK9RXK1fVsDXJffDN/yqZ4uyupDk+tfc0rTFpkXvMTCDwYoNvDrdBc+zdoXffzAAP2xXf79j7QAtw8BARlVg3J5wP4qCesfancGmArjydf1O+33NACeUVDI/kgjiOTKD6Ijc8Us7PeRnxd8EdUh4gFJ8/d4+fEpy05lNDRASbb5vDcGQDAApvhPBpi65TpqH3T8uNXDn7crHdr/k9oYRrAPuOvCxxZFEfsqM+8sR+0m+z9qgPsCsaEEHkbly9cftIcFMRfcUFh/3uJU2PPPmzuq6ZtN5Q4+0rna1Ake/JAz0mR3p/v+xE/z/jPubekXaE1vkQWZfUMyYfv6bUTCgiSKWDPJ5zMDHAM6KV4S7TQ7janr3w3b4CMIus2AfT0NPU8PXw0GsBht4HQO+1DHdo8wiZ1aLPyAeV2GYzCfwEk50+N4JGz1AY8pTWphzoX37pIM1/SUL5uHnxTrdTjv6dB+r7ujbV5jJI3PGbr4j47BvrIpcSH+vMLBUEl9Sf0oF/KK7dVlGFAxeB6ps/RhdSrvqBiSKY0zxhnvzZiTZ6s75PBm9oyNQTgG7jCtT+12jl3Z+5Funt/e3KBPf41c6OkLYv/89Cj5J5V7kDeycZAV452EPXFZcjG0ICoMAx5sSfni1G84GH/MHhkaoG1iTh4IQUnCzr4Oih9P/0n8nzzAMSpXan9Kh45/ijXOCfX06kvRpTw24FgTcFBQkpWzjyWt+W47UQUziS+lCFIPMBfa9vqiXmXF0C/j6cOADxTkblpLMskktin450H0PNRViqBS6rH8u4HQHKTLePqXHl4tjydbZMdYkNdhc587L2n+NYVfJEkdvSBHjy4zmVc25q67sfV60g8jxDnki2eLZ/K+5oUyQFCyM40kSyWNI239REFXDcDod6Z3kom0BsVzjGY8aT+5Ne0D0/V3HagnDVDpDWdzYWywKLV/jmnbXEdZsTn4zcDJKShC9M/aBkgYcwGRiE3p3AjOvOcaNtalc8B8UXndLkUr5zAF6TPZM2RgysxgwJ5Wucaz4pR4shsEQpJr8RTGAIbsn4IbofhrvKzeitEfZscevvDAhU0aGF+NH4dGGl7qFOBV+USEChE1ELGdGgFEeKwFFlF9J6quW4V7wUn6MtkDYy5rlRyrg0N6PLzqUS4Dchuno70eFgB92WJiDuXtuPGBF7zG0zUS3E7Pqw2QXBGBp5DsDA2Qsp/FAzl4g/6AXg6UKGjCrEIEnzVAyOdhDXt+aMr+gkAi1ctWQE8aAH1ansE5QR2XtvjmtAA53jiv2+0CgfqkKhh3+dGRG18/TdmfcxzWsOd2uiiWpICSUUzUdiqbqTYmZpTzdbQp4EenDhr9xAfWvf8+Pb9+eepQMDf9MdqTLfFls6f3u+IZo9YrDEtxjoE7r2bh/7GHfhERaML0/JwHOOfiAD9tA1Rm22CAW/+P92aA5+GKyftC0H9ggI+Nn44nRPhEVWmXJM0H3OMUgz4BVhi9xOOpJnMWH5vcvz9kwCep/6gEfqv9D4kDCC41dsLrLzco7Fyk56taDdX64mYl9ZmwXS4fzgSXKwPcLzbfiwFw8c9OSfBf2QQwkQ4umtk2fHfRAOeiv0tMYKwK/PfbRwYYmOJ+8OEnH6rMfdXskXKGIiBOxSkPGqDAlMP9uQTEcBMkJHJd76MGuIiVFPxDA6z557c0QA7QwQADmPVzo4FQvR+XK7T066F96sB3Wuu/utXxdKkBhLlLTNq7X2iAVuYX++mCgQoqflJdZ0//XPZ7e2CAGuPSeF92w2K1dJ7ffXoc7WE8xwin7VtfG1i+ISAXz2cGSTlAKVlNtKjfJ1gBNahR/UJPnS+oUyGwkPdSS1KzsOik9YhCj2PfORFPEPHqhsjvg03ZReNzmX54O/RUG7+Ctcy78enjg44Gmv50yfDk+MzXOid/aZSUcCLPPa+bW8h+Uith02SOc6/fqx9hUAUTBM7+foF5HtngO+x+ZYBEfcGzDwzQAxfEylwM4LN33rZ/O8XdR5Yt13L4aSzScyqNUMQ3ZZ/+/2SAqGcBNSjoW7xruT8shyml1UXkm1Q2LpKoBwQau9gMDRDOawK1YwnHw9Sf6XUyOklGPS3WM/vwjDI8Y9gmOzyMXRF6MUJ+/MQ550k3J0T/zP+3eDJ6Ba0wkI+lK07dZQOkhy2Tp5P+z5VwU+IzXQmDE2Z/rQbg69dHuvqKESYDMP8pv4rTtdR3n3Mj3FYf0QG2vo3RrcJHJwNP9sql5nRXzsAjJLvU5kRBJf4tJshMDWoo6lcW0vJRInjTAJ3lU5WKQAIt7G8vDm2AXEZ4zhnyaQKN4U5wMIVvXlyjQek4RTkDf9V4Oyil0kzFb7kqJu5i/S2lNn1a1Av6ZIb46GfNrVPSyGt7oNu0rh9PwMeHDz5NiYZ4uvxB+fF/KXNfODowNACaQW+/MqC2c/Z6RK2BWXfO6Ff26Y55e1n4vLTFzo1+7BoA5QKKlV8VN/TztLSSwYANqFHNNETtsWgNOZcp74MN2h5LTshNaVLqr7QBJmNMnDtvYjkrk/SdCjVd56XwvO8OinTMcBckqpm8zMcvSbppk1PCnxMT41irGfq0wjTtgbph9gPk5C2J0hDTEphwJwj4IPok6uRDO3au/fIxz2Gx468P7a4Bkuj5BQMcFz7w7bU3N8E0dchQWxd1MOgzTcwjhEgyydTCz2PlVLFAQQ6EUrWMB0mbDUaaTLpHPHt7Jsi7EviCAebzZH8eom4MfBbnaNcF9K4ewzMn72EuUrZfZ4ahSL+UpLNXF9MgFRlTnvUdby3Q/5D9/cmttw/s0H8PbPC0+uy3ml3+fWqvEPnBek3732SAe+sHOCHQTfd/+fupBXucs4bAgf7NdEy0++m8tIem8G3eBgBO8T9U8aXM+jB5i+j/MgOEVa79bbcMonNGbZ8o6MOwXT5IGGD9fdHol3PAL45StH8o0BHwGcRX9u7F9uV1zuv1x6T//fZfHAfwMThWTD7ouXD+dH6uAW0D9BC2k/lSnfE3X1HOdM1aFp3oj08MkJvzjfpC4x23Ty69/+9rNTt2e5Q/Kyf0H2ovuo17aAB80ADfQZtJpcei4e8pgQSEIevvWnbKmeiORfLWSK3pZQBWkjTXtD+8bvR9CH5JxH/RAPNPVtdzDO4MIIDCKumCTwyQS3LDcTWV4BjLZw1wV9NXK7BG+JMOuc/G7e/USo+TUhpA4ub8jhL4IPt/D3X8hfZi19CZECie8jZ+uMBBhDEXQHucGxC01b8dpD8dgXk6mTQZnRDkfgFJDwi6d8PXjpvEhS3Uf3bHmTuXeAy6nxgmS7LPuGQT+voFA+QrEcAtxQ/JA2WyRA7WvRSa18XFyErLC1rT+Z0+bjIlwcpxRnbuAzr/eMHLd8nvzwxAgOm/fuzacampQY5XEt8fQKGLcPnSBhBKPmHJ7/bMfLIAJhNcrK2Dg4stbvbAIQf91DmgnSfV5dJTxmaBrorAaT8lh9nX+J+Z3mMMxw7Sy3nFM4PEmw2uGoBXBhAr6o8RxGn7aj6wFgwvF/91fojUBoWEK3X5ew6RGvs2Juzo2+OM1uq5TwTTvyQjf933C2GJ+1gcEO7RMwvyuNmng1/L/kdA4RzzB5jxxTOdhiUvP5B+9dLOY2L4yRBfp0d/dvRIzhwGWh4V6cvJBscAGHwfevjxrUxLmWJgLEe0CN4c1H9x7V/hUEp655CpDQ7eYIaEU27ERqSTATKx3mq0tOqsJRekmvMV4T3858R+GwK5s6l/Vhrgax6yOUspx4bAy7muJz4VgURc1DxAcPdO3WHPI082WqjSNt+hbkvFMSTuuPW9hQ3Q53D8y9yF+PoInwRF6RCU1k4qsPSAsFTCscqyBxFP1O/smHNjZqYRUjWzjQN/uX+oXAzRgwF+ivpPHrBG/xPkrPGafzJVRwGqnMxDA2jjn0gyHauwcnDmOuVnMMxJkN9pDGXIvOxVNResfCasiOKYpWMfV2gRXWpFnd4hd90zea7Q/mNLSq+U9Iy3paonUjlZi8/niN7ls3y2rwbudRf0/rzXHheOecitaUV2XZp6wJ/56aEDDhx0ackDyCEyM9MeFVMzHWnPPqkeIxZGGD9Ysmb9HtZtKrcT6hhXYp4LA1z8oWk8XTWAj4YGzYUYZvuF5gCyjo0HwdYJU8Qck3Yb5ISYj+Q9Fcy9XX50n8OwdBi+zowH55QhFggEqu0l4R/54FQLDx06//j0Os+3T1eY7YEBvjz/i3bBc9/t9AP1y5X6k6hsgB938xvU03KTp5wcQ6Qa5KIBZEzFY4LDReSvzxrgygBDA9jQAOkT6ck9PbZzxBL3N9XET0oP2Ae6/YZpkN0rZfpXW1ltCKKPKRvM8MCYHxxB/6n2XxoHuIRnoymgw22SDICLBggpmSn/11ozw+P5VLDm6fWCLMiCSB7UsccB/L0ZAMh322ehlMvxyQDffb+3q9b9D7T/LCH/afuaAcYC95bvZwr/FRoV6j+9Pw+QauK6hFUE7zEAgAaawRRKQK3RdWqAZgCBlEA1zrUBUxPLqQHEbYBHqf+6a4D0Ar2GG1TmimZgopKpASaAvDCATg2AZw1w+bsaz+MnHkjVwqcL44/4Jtzl+f9hxZXT7tKFG8g9fvj7ffiL7XXKp2w3sj8/PSne/8gaisgtR4Dh6mct6gd8A6zrDZsypg2c8d/YOQtUy9pXrQHaCGaGhMVN7uxJ3TonhnKuTVomi9c/l6XsZwv+5d+WBuDUAAFma9fRGqKyPVfZZYCBZRm7ZYKTJW7v31zREjwwkyGGJKoXJzYfpReeW/W75jTNgBsD1Pdnn+JGSElXp49TePDnefsnuP9X22NdoFPej08eTrWQ5e3etPxlmcs+vGwqABzUnxrgTBnvYKxTvlngn9hAwUxJ8+TnVDhOi5oOpzQWBjpyX6iEBhDBEvPC9UtsLawD6rAp/oWTByBCWRAJMzDX5mfc9xb0LXnR7lHSkuidsIIBrAt3Xd6rokxNxTGmx+QRNCoMXYeCrUPs/js/x6rHgwaG488OdoqTTwbIkhbx/UHORePF9zdW8K8+EXnTvyVEeUKH3+WRDxoA56zFJ+eNWg8YUOVHhtytWF4PR9pwrB9nud1sLL96pRkYaEY1mCYAax8dDNgW15bcZNcZQ4wZEK5ZZ5aqafG/FkRSAyxng+AHWZBX2ACOggZjWGkAD4SJHRpgBL/8kWt8EMoxmcFm2EvQfu+qMJPvrAG9zNqk6gG0nKY6+EBEOn/L6SJRP4vnJYKJ7bzDuGsU8LA2akvPXoFZfGhJ8idqQnVr0Ix1Hy7N5lGc8WUY7wO6eiiN+HwROw4s58Lv27FZmEP08RwAIiRySiszVuzx0L3hqCcISlA/XHbmpk0kKrPGyzORMGiU7jPHS1HJevS+VcxY2J4va7leEKjkfWIhd5U2G0guiRKTvguBkSPi969qjNp4PKuOjCE2QEqd9YxGXLuc8vcZmkRbk3WQfg1Bn8TLzyfd9pzUnNeflWnCxLkZveND8KtudRB9xjtTMTGdvA9tKvM4Luhwp6DfaL+wAY6LGmx8VIs5cq/BM8g7BjiCGv10BlSoA6kHMAP3SaRIDYDCP6NTjIQIZWt5wK1Jc/7CZZhcYmX+5oPDZ1kePL1OIMTSAIVuUxhO2e/PXjli7ZA1MnJZjxhFQqDJPcf7ZOlnikkboAMoxfylAa42QHz7CRC0qLuewYT8CXPj/5sWyFsePNBbpKJ/9uA/TdpqoTmG5c/brzSA3T8ssdDUOzpmDxx1M+8HYR4xvjh90L1TRxVivWLWILB0OIjCpK5nR9+6L+kIGnlsJw98pPjLq+MA7GcsNpj+fisxzFYFecwj8+f+wkf9/dwGhj+W0x5C/6YB/p7GxJiTuxrnXJHPrf3tHfpV+0/GAeypcs/DGY2qL9InB6sdKau/4bj6pypA3woFvNoMeNIAgwH00MjHY1hmgz5OMDNeVudfRuF+2V+B1P/Tvtd+DwIdH00NQEMbuFfx5R9i4rerRjNNazjEtoe3xHy9S93p7G35HUvrlv/nVAIZKWu9OvxNKZdqI+uydF+/qQGmbM0uXwX5RzHfw3bVBrizzQmCMAH62f5tgTru/CTe07edA3Zb9vGvd/ODG/Q2clfU1aDCUJUsw7wKlIEPV3BsrjZYB2HKHp5RNUNGDDh4Iy5GRtkNwnez4kiUcM+Ruu/I6uo2YVQ5Hw49sCgL4QhaWMvWC+vFFRqAzR6LWOdaMDZTh4V4AT/5iu/nwmfYM2M8YBgUv+Nr2++3VAHLPmpJ1u6M26Wsbp5bZg1nXpEyrxAoymjJWRm0ya3lyO12BZlR7r9hc356WOKo5Xxr9/Lo8wEfnnjItuPE8cCVeBkeYS/zgGcNkKkMQwN4fpvRPPoVP9I6y6ffncX0NJTSBGn+BTP0+uDBTUNaDg1Q9erXCAKsoQ3W8AVJ5wI1cA1e1uYBG0RsHKzBHrle+V3MMKyFudYo/71N8WXKzlO+zwX8cDJ78n0CHlnvToYhYMrcFVBzp4xH/P/QRhjnfqf7V7/fXt+9RlL8UXxhzGQ6gDvpPjwCIR0bwdigFAUUpohaPuqCvxjAXLm47Fd1rBSN8NoN4hZk1DX3+BoI0CA2rmv5ZXEykPNTayCZbtDQAIn72QGBZA+ujNQFP8I0N+VOX6cN6j8G80IvTfd2sAE6ly5K5atLlKtCLhby8EvQDS/EOymmdUippSshkFH5H+nsnN23vuFoTU0xKVMH1AqiCzBiGsy/hEB2Hj9yRt68aPOsvXO5xbcZAPXQpQGu90uGP8AscvZb5MMU1Jys/Dy0qBrUTFUNNHFxr6kXrJPhSlmbQIywXqDtAQNLrhpGhV0sgXEtsnYskYoG3F9M9E+R4PSYPR8WBQWmTVZohJgaHKVJR7CnYMfEQvVZ4ZAxiQ/yNy/gUPSDSvjUirX49OGjgrgS/U0DnAxAoSpB6aUDVwh0vf6vu918fZLkr38J4AsI1JeffWkJ2pIkqrgG9Vs9UMMMe9gPzAzKUcLZ4HogSJ0JLL2MlJmpmkeCo19uSzGvjVkBzUKYXjTAJTLQwhbAnKVeFH84Rk/vUGQqSdu+8W/K/in1m8sPSXxOcHanvg3GsimvUd/kSVZ8xOtXX4KEu6VxsQGK8h+0Q0gs1mEF9C3ZheYawIdKRWgaNoBeSJ8ZBevLpwv75tGow1JEqXU/POWvbIDvaoCJvK73a7V942g7f/PVaypTtUx/SzmjFw0gZmY0SEIqAmO0vnph/nmI3PDnHwr7WDhfr+CQ1nPwLSQO3h8P9rEbD8M8GTMR5NfzdBnfX5z9tzW7tvFA7fUHSRGY58veINAXGuB+w3rr6ftr7d+LA+g3XwbVOI7tiloDBAMQkBDbtaL/9u6vT7f57YGbwvJeq+hfJLr/0/7W9lkDXOf0GV5NevviGppkcryecFHrAc0FzmUZh5cTAMJElnLuHDbAV3K3XsFq+Xo+bSrPMlLv7bSvHu6u+T5ed0h0GfbLFDxy2f3bX7NhuW5nzCEe4LeY+K4BSgWEVehxdtJip7AzNPzR6I0RfIZAaXjY0/j9bnvSAPb0hxXcz9bKGQkcnq+hIYvNa3UqoYbIcc59gctDY2m/moR9FZaBM0BWHTM5sLwRIwvamSLBtIMc328gXkGC9QnsZAbV6TydHHXO2G3+ggLMDtL3mIemFZ6vqOB+SgO7sliMcphedNxbQ2yfqHZOFJr0MNNsLtqyK/E+UsD9DvObgcTTCABm7sM1ZSS6cDBBUfgnhDjl8ZRzee/fZ4PP6dDXO+bxCL0irbO2d/PE8h34B5pRIs3ZnsULjVHGM7e2MAU1Vr6HSwe+tDA0AGtwDGZimAtLmF8fqmBIZTV6jOxK9E8v0Uyx9vtZD/SsFgLUpIWF5mw273JoAO+izl4mUefDNaFbH5zTFQxhowNzvqxPOn70oAEGV5xW711KW//TQhlmvv1B/TLCe+0Lcuq3k+rtduk5IFeMgE9fPAxMuwasqq7eWOT1Abp8Mi/cSRBPhulCgT1LoxRq6fKHQjRzJw4NcJCoKSmEakYCqt5tDELKd34CWLhqAAV8XcEkyu3vG3tjK/aGbCyFbmyn/nynxt56Ib8vQ3QBPP2ypnhniTESt05blQHpWT2ZwfqG9e7LHtgsEAOT1nNO1VcaICTH1/IwLxjuuNR27bNxkotbM1BDMwCsAkMXwmsnUh0bEgM0R9QXt8Gwzv0ej5AC+qOR/QyB7qeXsEvJH1zs/1t/ccyXzyAz3a0qAxqgpPoaR1LBVAIwQDXDpi7yfTxmWcFJMmOzCyfOkwcCLAaAUYMp9RTMe0NXsoFANvaGCGQHh4iTvucmJW/5nViPHBMXNUAvcP/CDFCY5iNqYss5zymBmh5zfXMOTGqJQQbH8UEEdgj1rzQAx3HO5I0nrL7qj04IlEISE/xYxNYeWgn3Zv4Pgv6qzicG+gXzPrVPRvABNAE0gbvH/0TDpdIOJZDUPx6DsWlL0XoS8QGB3FqwLPxsw3WjZrXgFxktHpL36q0x+FnMvKBGPi7vdUOXhQZoBqCIyYZscrf49wOvyaXs1ffxtCm17AA8dkU+bv5qsWYO1Xg1vhtzMEV+z9NAm9cp6z9ODTAv0HPkjPJJ9d/blP7517hb0P80sSf1f5NY7Xb8wBEP5367/Z4btF0G04TnQzcvXcYEKQV7rnbwMDiLNAL5VGJPXHCI/2Heil0lQ79Q17lCIKf7gwHe5hpANujvAm6o53mouzaOVB/c5P2jRTE54Zi/zPc+nr6HobmiPpl/GvrkKTc/0QSbH1pE/7YTqKe5zJbjJq56Jvo/fvWd9nk+n2b4D9q/Fwcokf+tV4UCtDVAHMw25H0bA/W6Bhd+97WPP0sDzPUrLOdX0rf+6uFsBiOeAMb/af9i+20NgKk+n9oEnwGCiIrWHiK/NIB1UvQ4SESYP56WYWoAU+Y2dzRPIKrLZx6EUuWD7C/xL9gb720i4NszHcAFeYMCyUqgZJUBjUEQ+agBnl+NlJ4Y4G/XAHnypznL9JWBVK69+Xvb3yz1p4L/o/78ggF4OeA4cI/xuO3ZAwJZiDj6x8zSpC+G7ReRojXkdTEDFDRrl0lBoBosHUBIw+FPKgQWnCPpQVKomhq2cpeMX7bTCyRvbkIkfEHyxnsBKyAQ3uktmQ9bZcKUtu2XpD+M4CfM/pkBQpg8zbIVG3yCPQ8QgeHuLNDPmt2LnBnGTUOdX1IbO7M1T2/r/SPpj0E4h7mP7emVPY5FIV+L6KNNBnh6qNPUHZ/wcsLo8uWrsrpK8B+q4JqjYEHnz7KyIjVajtSIGSureJZFXCxfVIUSyhD8i6aKvW0Te3MLNvHeELG3gG+syoF7gwvvlP3+IMufysddEfXl1WyjXzfD94gBpw0wya8+eZjgConHaekqhZPlYKUPvhPWh7dpvc63zTk9Llaf3VXX9YoPN0gWyqbehiE9eGxa148M8cwH9vHRbIQFZrt5ga4K8zj/S85qb0R9YkjfAof491zoDAkrqcaS/VoxslwUaYmWCwgZYOKyP9IlAgjtAEwksNO0FoMoNrEE20w3lHTko4qttjc3sd94Z6GU97trpsPz/tEM4OPk/4rAPAtDaRvYOF1AGA+UNoA/RmiAMWohyQgrIZZSsSQB+uCOiILA4o8Enshv0ZeakzQgUPCWpbAenGV1kbrkMRBJN+3qPBxNVpcI6rfJCXctcPS15n0eXFVADY2PaJcRtgxePLZP6dCMH11FxvXf80epQX0e200UToZKuzRGVCkyoieNJPVPhDeTBlhkAKiCNE3HkrMBVH1GeWAs1wOKTWy1rdBtmxDhpm1xDYB32QDvrgUttZ1MS5BItDCBCAxJ3M4DG7afrAKfumEBG9D7ilxoPQ/qgeuxL82S+k8bYABJSxVy0teB+xuuTF65iPur6J9k/0QRRzft1AAP5J+3P29j8xrjatMCuK7yeM5/fvjwaxvAfZ3+M16e0c7j7vNgA8SfHXAZ4AcRmU0S3eUlKZuh+T6N4tKJCiTuLw2AEHsuqYfT02GPk75KMIMbwbJtC/eGEO+qlfvO/Ode+W6uB9y3h1jlQpGq6Wn+BLbZEGg/AaFB4odosttYXgR/nnPRAPM1GOB2nQ+TN91oB/gp4iroVVeegvGR1MJdMs6e8t9U+zVpPwk83OLFv8eT2MPjNZX+BRvgU/vqch8UC9o/eI04Ho6gw1F58VumTZtY0Oq4ntmp35Cr4F0QRPKdXV6x2Gw77Ek9IG4B1x7BbgRLRILlnUthnB+Q6pS5VEFg4mWscnnPsASuSuBU5E3xcyxvGuCQ/X+RAcZpH2bzibaa6gt+DAn92cE0L3uV/bXy9QP6ufTnEPfPPDA7/P32H6sL9M2AwHS8OmxqVg+5q55L0OymJMOv5By2AgURYQ3X+3CJsl47az3Ufhm1IAYgof5uYRh0oavJANrGwPOT+WT9hqz6naHF71PC/6ZtGsFfs3IP6CddCqS891/5Uh9/k3I1d/Zb0ku+DImgWw84DJIr4PP7qqmUdvhCA2inf5YeMGxLDeDZPrtKhXJnGLhfjovQqQTOi64BAITd6bkSO/HPfhD/07r0fw7P6hTSf6YBpoieWmVc/EHaHr+5aoDz1+1S+rRhTcz1cRcbEOjann9+akO7Co+/Lv5xGsE+GYfj8/D8p2WRPrnz2c5Eb4ZXoZc/JO2woKEBG1hO9IZNZwMGV5hJQNNeJNwMYAZKJohprLu3urd07ppbFc4G27BNttkyqJpKWgJKEqIQhewwBpr631jErnpOBgJ7AWalAYiw5KlhAyDYwJcy53sQHy/zZTiN3XNw48DGJ/bMAEXcM3bY/owL9DJgbkk3ZvOEPZd+5bfhMApOmCzd9JOftmmNS3ePZzvIJD5qzGNmRsuLXYk+bU87KHH2yo6/AAwIlOrYbBD9eQ0+eDpDgtX5TD6odNFe/NOpWNaIwbZBwM3UALRtBSYC5khk+lhiSU9v1Br2nlJ6rdxyssbyAgTRF/hUqNhWiHArRI2EaHinpDxCm7LpVgHfcFeQmu0CZNJzFfti+NfDCNaZD5fzbSE0wk98DKl1EZmiV7s8aA3/BwYI0p8/T0kapHSjnitTXlni+OKqAawnAPWOyNFu3hgCrMVZXjsWz6BXCbMYF/k8ZhZpMXZ2JJhp3P94hvPP+v6rRfEc/58XOeybp5+M57gvgMuaDU7iAghsA2/gFTwQzOB7i/oDisUqMaY3jRSDmgIIWA6AhG0hDWrSDMDMwQnqt01sMw8RiJQGMAdCWyCbslip0TuZVxAuZk9XNQLi0sPyc8A63HvNfptpcCX4L27Q89+hAQZWKkIvuvKhOq9/PbBUBfOruo8VD+R5JXm92jYnS3Y/7cY6QQvZw3EPtF/pfM629lxuJkMytfwZPuywEIf6KTlef3A+L3IXhG6v+TWAzmw+9YCFZ9Hssl6on3fsm4wKA1xW/9PJp2z5dIBa2ABkWwK+69yYEQmnQU2OOgqCaY+vD6ASqrpBEr7DzKqSQ2ESIPQAsRXiXk4lFaJ4b5CpB97OABQGU1KNhl3Fbhn1zMtUD+pUwJdBVlJ0QaBw18bIXp3WJapPDWABngJEpaQv18hhCSTd23GQArjn/LAJJvW3LqlTD1pGrlCoQZ+EdBOaw8s/OPggpFpiwsYKyDP97JA6lpZA3a/u6UNzEGaM8oAp39QAaQO03GkAY+OUi70TDOyxqJGFXgtoQAzaCQiEpvtSAkJIro1hbI1dMNA3XVcjIOwcIZJb45YyjOBt2EZpU9hUuQkxL9hn3CDdC8TYQGnH+35jI1xDG7EfXmxHw+IB0oyOwHBEfI9EoDnpQdAnsdym4zLPKbCangIWTpOy6X68curscunx19QtdZs7gRzwiVeVg4sgfmqTEQ6pm2DZKS0XDeWqkInkPj5kNT4d8Pzk99ygl8f8qgXvXDQAhnVfei01AKcvqNFRaVk5p9hiFwlfJJCz7qsWHyMAzgzbZJuKcZtlrUpsTwdwwQ97C+hrAySSJjxQQEAMKtg2yp27BqgCfE4+Feu9aYCS1ulOOEfzS0wfp9QVnk5+gEDnn3nPVEg1qMePj7keX9jl2p9I5YEx7XKvQ2zeWhu1eHrKD0T/RIbpt/vrDPD3Ns33X4YC7v1eyFEhSkfFGSyv//3geKmCyvUOm8R/2Y6I3kVmIhwLF1MXzMoOxg8tNWIR6IgJTIxOnM/09w6tARwICt+RWv+17U4Vv9V4vjBI5rcZIJj/wG4+2k/6/LEvpaEZzK3hNby+SgPUut8UK3mFwqC1fbzHGJ5jwAYx22ZbTQRbncrVLYUemxWV+0TE0yJWoX9AnPQtHaCpAUBEVlkyAENyDw2QNeGHJPY0jmOA/0gDfAmB5s+tPr1qgDmsjxP/oBoeaCOf4qP4P3XUBSsIc7NQcGj2oaEetN1DJ8wv3fIphFjgEMnTXw2/ijdKqvV7jJvd7+EPS+QGnhYX8nNZUm4mRYOd3mvKABPuqtlgWAKRmmCE7bn2vWCEJQMYMrdoMDhz63YHJ0JbhNKUtmmi4WQlBxZJueljJlQhN+Wde4qRFAOX0UAxxB6pMYkyFXd5333q1DQZoIhI6RGLMG5ZvqBvieqk6xMR3CBQG8HWWAaWxvSEF8WXxUH9yuka7/WHzT4NsXTlr/NaTXMxbM0BBwZCT3cVSKjLoa+I443htIFPJiGgJFEgWQIPGoDXw3q8IpH8r89jfuBEbqkU2sVXv+c8jGcbjsOODb9TA/iCL8dCMmbE+S7HU6FiiJQ43xrDxnIwiwQ42qYtmiffOQO40eqbx1nUIyUhmR2k0nsZggs04wpVswkoMmRnkgzAFrXFAOHAKKnlta8tdEZLlxbiQwMM4juYxOonTRs9KlNcj+sEp+Cg0HEvjLvcGPIq/ptz8jlCWF6oP5VT/3hAc1fCx44x8V2mgKkFfswi38X4UzYn2RWDZUwhJtPFVUnjRw0weOCGZ5LQP7AcYkadOFHlQ3gPzs0JMZjC3ZncrgRoEjwQgl+CDYyMVUwZEgACmRvUIho1of/W0AAbENJ5oGB9jFYhihX1dMTgKKiT5JwjAwKBy9zg9oTZVpsIBmphaBV+ayKj0RNK64OH4bH5wU0zpCjo6T71AONL9gXzQqUeUJwGOzDVdeYHtU2GvPJH/lkkfyS9jW40tUWuDAL/SGywEoXOzMko2yVP/qGj+Wxlkfku5kKs2KYDqXCAoQEm1fdcHuNf79ayyBU9AY/SBLSJUAGt88StbIX5R82MwZTclhAINt0zQmxf9c5ZAtTnPm+m2oFm1fDViA5OIMRsGbeakBJQR73Awwp85QEWCkDhekNKFSSC4/JwWEQh6rkq36lAVAqqiD60BYwcpzTMCpO2ii1Nd4x9jdmh9y8gu2jDUkLGDyqtwEo3XTVAcOOQLMEbfbdTCfTjHJ0BjutOIDT0jEsbNvJmYaGkfyIBz4RAh346l9bBWsAzcERsyBQ8MMIML/LszDfs2KOVBigZVXbg0UeHnDGwNJQTxL+OwCkTApm5wN0wSX7I/QQsHZCFstMvX0DI3fzuYaVQajk84cpFaJ3EYI7ES3AaAnth0QQmYsJkg8rmdktgMraYMa3ixEJWDGDDBnCuHRrgEGRJPlPst8A9D8zOcR5KAKfA7k9wfJvguuGZy5XLGpPkBIsCiIOKkxrjHCSJPrUCZ4PZUIGiGTCNjIHsV/dRk6UQtFTkWzzgtJU2IJZgkS+hUFYYdK0Bku7/rnaK91NaTBugZ3bwQLqA2BpAsuiD0JSmWWe4ZFqPixhM6TvFp/OHqqlQMMBMMkAOnRnp9a7ckS8wii33gb4phZpWSRWIYQ8GMEPJlg5k9qSZNbFbzZyNUaoRtEGdd+gCBJM801gKd0wGQJ+fX180QGqOT6/+trpy9r1VzS/bfJLReL7m/R+fEsVCRXdAD78ACyH1l+QrNMAVAv1nmqWn/+LlfecYSL5/ERxYl8u5L7/3n+H5Pv8ECOyIZMGAkOobYkZyXdgGI5vbEDvELJRPAZ8ZoIy4nrQRQ0BdAV8wQP7WxnR/Y4gn+P5CA9SipEfi/JNmnnOrtaz7Dy6RifG9fOPDmYUiY/gJxzxL+BJ5ibyES+RgAP4xA7TfE/icFQ4cE1fCb+C1Q/yXBpBTA+zQAJCMYrEoNnFuqF/klM/1kPyVBrBCcIngN0w23mJCE4GIDb8/2yC+GDVSFpbl3h2WQowtuM6R+aAB0n4YGgBJuGwh3jL1AmPq1midkOeeGkBvV7gam1cNMEWwpbj+vgYY3W42f3p19z4qganIQVYAk5REPq8lL5EfwrUmAxADAj1Qrg2kEmgDkeADy0WxcdNh1Jd4HmOT88cAC0UBFtQvGQvzdBtJG+BNE1a5BhMNHvD0H6m8uuCB3FXV60mo5hU96/NggA7uAhFgGOyAt9sAYu/gPAtPMiBm72SDFt95LRKEicQ+kbA0PM1qk7IaH4yoZs9D0X1rgGE0FRmjaHgQ5Dl/49tmmIMNzCxySY49BaPYZLFPPoL2nUe3q25vi6OvyX4IgZpAjNsU5Qd4PFdPjlFxAmJPgnhwRYr65bX4wxlgiWuAJQcDfKL99OhNcV2At2m+533M5OBJh7vBqWkGj+t6QlkAIUayvWQ4UFBKAIvp2rFQAmkMpGiER8lIM1MNf6UqGezFYoBF295ns9AADO70XjuTvAXNfzDfjrtg2UaUYInkUBDJY6qhoe4iLid/FMnw5y+x4CSiRag5ivmYzNmHjejyiXbyoAm+SKt5Iykw6B5meltw1fEnS+VgaYAN+F0dShY4eWAK8fObSfIP4l+H61PHr6uHSWpJ/e7qETjif4m8XvJDlvPAa3FRpPYp+5UN4FzNZgon/nQwHQyQlM/xQQ9OESgsp7rfCtPvJK2y39+FhcTTluOY7v5k/palnN08RhbJUtsAXHibiWK5p4n+80TrZ6VbAm8alfIOv3RqACvBPyIJGIEVV0ug9OQkLbLHEMdju9e1C+z47LbTr0YyIiBMTh8oZrJWn2/OIZhrMuPaBwQa6sKXtVm6EOb1m4qTNnzYIwaUXfpa/BdfnsrJSsxr0X3UjFCr+hHVBcyRiZkbxq7IgP5Lfqz1fy15iazFl+9T/w0G8DuUQ+cm7yv3YThOM3EiecHaNV94wDpO2Twy6qPYHtJRMju6OGFaAqEBBhBi0gkslACpYCwvoAK7bQBqmksDu9lUF1ExDniLR9PgGiC+Cg92sS/n+ICQJtSohDVINHnOoFOOlvg866e3cnKol9ZAyfIrlJikmLrEiQiEBis0BBqCl5MfTj0Q2iB7eZLL6M/kAR2v5p8rp5SamaRvsTvuWTorNEyCkRxpEunqkdfiEnH0/+MlP5akBpD1NQM4mojrcz5eRBca/MTbCYYa++Tnh8SvJXo94RYQyJCp0U5UrgTegSnsogSYbFAHUh3PtZs0QNXoC8Q00+sEq8yD7GrkMzNMEAKkiVfOIt7ZoXdmicoARULgIAYaIVpPDwMrCWJwPnMUIhjNom6Xkoqm37Ib4gaD+hp2NzgoAZs3rS0Gh/ScEGigrTwjnELJieNqpxuGYyKTrZq2P9qvRwc1af3hdRaOix7WzZMmBRDhi0n9Tu4iP1ZoAOeBtdgMAPJLL5AN7XsQed348buhAXqOLkcXEVKA0+3GXUotRe1mpMdtgVi74MWGJdDr8rLfToTW22V7dlCndk4GmCWSWQhsh79IiHek/AwG8GHcgXZqnaowczgGELTENsMGaPlBNB1ZIoIBgU4GaL+bXa9sByE22Bj/NIW2BqhPhj4xHLR8iODubmK3cWN7/tWXLZWE6qEzmvhnCS0mFvcxdK9OgP4VPp/XGgzwCg3wWwzw7zVLX0jR/YgGmJTXcdAtz+AAhzdlXS9tgPWOA8f+9AMCoSLxdRpjwSQJ+qYYVT2lejOruyVgKEeTDTl/YYAgZfZj43Km9jFwnPZ8/hTqQ6LoBDB6oe+pAU4oVZ/r8dtLlGDYTedNP7XegzNm4GFjhmquBOLzS3OfAwlQlkAoAfrXeMk4EHmJRCBMKu6MV8rhKaXDl2IBT3qmHlrOoFnqoxs6PC8/PuNx79IAObDmz+ni/80D/IRvRtorc4HBSRxx0LhaB/65a4BigK4Vpyn1c53ye4QUiMp7SyoUCLEWa6l2GEyJA8vdOtmDRUIfNMAkNbKWuJYGmHIeLVJPwX3RABo/t1MDHCfY5TK4aICawa8F+xMWan3h32+1rbrV6pUQSEsDoFdAEen2kYVFWe7uPBngx5LXK/0/bh+HBgg/ynVNsPWjHY9Z6vpG+ieHOA/kmvi0Kx/4wJI6LfW7Cz0nlJ1KnpUUDVQakzOwCN5mzA81DfMGiHUnX0Hso+55cq4QNkqMEGJ0s9izSjOIJmoCNz743sEMb/fU1sNIkL4uqGGRapZJJ90VGmsJvOeQ1zUa1lv6f8LezIwhwB1Q/svmXhdTA3Ik5MfDK1XikPdW+uFiAwQDaGYwpU64EUwJme5DA5qS5tci0HliSiQ1Pam/eMCvE08UtBZJbYugAxuR1+Jrrdfij7WGBnB7gEskvEPIhLvUAFfhHEq9pGcquBZnv2zznJRz7by+XcEAmEU4LBeo7LzSuxgAVkHs1ANcNJWo0cOI3iBJww8sCcU1Ls1N6Z19Za4JIE2jpApYaaSKTdTqYYG978kOzgAL6seEeM3QyuxKV2mZBQRcZ/r4R8GbBOXNBvlAHl8L5FvWyjAbisrT4G6hOxzpBz8U8jk9/a0QFFF45iTcnOUcVUlN5F5MF9kBYsqcTUhzLteztM6C4re9t+2tW3XvawFdHxieoF9WMYCcGmC9hK+M/rrsXwRBSazyYANYDCgK1yDHP06wzzxw/bzV9vHNENGlcPxAkVye/EfEwoAmuUH94kt2PTwspMaaxC5j2zTqG0qiM6WZF91iAKikeKHRsA0qk0JifZqvdyG1lkCmxBWJyRSo4CUmRtWsqgv2YrEMGdfoeq9Y4q1kv4VvLNY1MTRAlfMPGyPp4kKkKdptMENDKkN9EpRbjqDpaU8GKPIrDWCF5pguvASZpunHPND/+HwwrOYaVX+91d6qb2cD1Z1M1DzswUyX5S7alywHOS95ifx4NfofCXAS1JIJ6KVHP2qAZANL99wk4sEFvPwsbYALM5QeuHFBfhkkr6jhTcCXzMqMKnSaK7HJLRRgKSiRoZ+kFB1KDqhNVbM4rvfIa5pQjEqVAD+iuYRAoypWFSqS3aydd1I1mPElUKEJl0AFSyHiofkYe5ASA+GEy0DxTvewKfsHBApTxdLuKeZ2sjvxx1UnFPgZOAeDPUIIFwNoGhIOgdLKSLl0IFozmsSnw9um5dPRhjEZ5LrogShP7LLf9m5joH+epocDF2GI9h+La6314kvWy109iz/WWsJgAFLEV9sgLN8kW1/6d9MAQ8Pht1q7Js7AEg5PiWHaBJbUX4PqwxhYoK4quT6IJQEpi1hCMSzYIreAmn6dMjjTNxqLNn0iA+gnw/oyMwFtrArQAkKAlxBlLSEISAckJjaBlhkg8b4EKvSspmVUCca1lATjMRPzYKD/JGF/GOksrDC+MYfRgMQ8cD3a1G8XNsj7NPjR5L4k3NIAGleLkWttjSFIy7dU9u42dQGesl0HntHKEXWUfygBfefv3AUU9DgyoXze15IfnuX2ktdrIh/+8IiviAgXI0rgujdFhxU+eNAA/3i7qZwkV6egwQMVFwPCTRMCAL7GfdGWYCs3sZApEhZaolVBMZ9Pv29rpDB4RVAzZgFeL0idRrDRsHOZJSv3YedqEb/wSkpC42znHzFbvoGS72EcCSuAI4ex1m9ogGCAkrzBzR7dY/r/UEbAQekJVaZWeEjtxO2TUB5HGFbDTXSqkAGBfPI6En/68C9KYLg1j1OGyavvsn1TizRe9uSVAD/h77+6fZa8XnxlPHhJRIjL4TYX3jlH/VfEAWazE25xxATQCsHXOuQrM9xgXa2qmlxuABftsR7RNUC+43wfMQH/8x2dSr6qYTSaBnlm7ihj9yShSCxYKx8WTg0QB5pXPSMAZdW3TXOJv9vJCI4ik2w/jXJFQsIucr7VACWhHxBMfujsvG+BzdLgH93/evKA3lji9rKRJktIZBZSRFZ591/j1f7+YADxTU4QURzcBS+A/wwD3I2OHuZ+WXpFd017hqoODUCHQNguztvWMGaa7qEBinhLoE278UEDaGIeAnOtdV3YAgW5JLxoADWIqRnEpJ24bQM8aIBe+4pDA2S+aTKA1L0vSgCtAXB+jz5++vzqs58agDVgOX41qIRnkeqAQJdXQKDdyOcU/P6y0ABphgCu8AUSyytkyVqR2/Pj0ABcyw0DOO5fRIIfR8K+wvJKfRUHeED8VzP3O22ePLDiAVlZes3OE+OYOU0IUWWbbbazktlOBhAYapG5VWWBQ34lo3HMuoMfmJrSKAZ3iRDYVEAotpXpvkkngownKGKCQRzUxH4cvoGBX1OWLsqdAZIB1fSgSisIhEb/lnFCsmPfc6V7MkBbFwVfsruFnDCQEkYCXDKABgMAZhyX8X57bdaSJP4MA9pv9wKVEay6oap7Y5exuy1sX/VjTeoPlgMpklWnSILiIOe1frzk//ohP9wF9Ao3/xKsqAFhrHzfJMKDDLLddonk+MHkgFxDOQnpcv6l8SD9wWan2pwtqL8LSIeH7fIz94kL8dNTRHMrjXnTMMQTxXgNHtgQAkMDqIHu81OQgliFrCB0J9Fv1hP0wcrOmcG9mhqliZCyn6KioguiVF9mCe9u9yAK3hwMAKACXrDWAAzHQjGFpagP6vflLbg4mStT6dQD49gyXcoqPa0YoGii8RYyVzqA0mAA3WZbdQB83abbdrzr3ra3vlXfW5sHNKzhsMlBitceSwIkxc3cH+v1kh+v9ePFtSLKK+npLwnTZOMTdKzyj2f6DIHYI/1Vs2cV8UFtzLMbmGSHGldMzFnomPQ16MYk+gUViOsBgTI2bYSQNHgFIYk6J9ErG7eOw9ADrjgs31UJcbSz0wjOB7PBC0GoWbSOS8xAJZfLfqPQNytQLy2hOT/51Cns1QGPVbdS6BfiZ/J1/3UooLhaDy+DiQZ4OWZjRhHaVtZMQtN004bkSKrJDhYoitCzW70u+/eTEbx1b/j7OzkheEDt7fHgXJIQT5tRq6DvJevFH2u9XvLjRwd6fWsrInB/6crkghNrhCw1e2SAls0NfX/ZrOajT28CGQcThaUUnD07WQSWmt7GD5kQ6CchtJ9WJU661klSSumQQ+WUCYoU3o7YTQ3iEEhAUzr4EdK4FRX+yC4OuEFYOlLVTIW5LFNEVA1qUkXmSmGF86gEf0rVEFh5o1wu00a+fyItPlKZYCTBZk+t2CD/KDF+oLAMAtSfRfLOXsw6Lv5rTdKHp8xZ4Z/EPK4KyitqB/jZ7vPR9vqnP1ZzhIVCRhBrCUm+mgH4eo0Mn1iMHehgcupxdINBr5usdn4rZ3p/HFJ1zv/xNXDhlse18oPaHzDQUFpECrWwhutTEJGbvACBLeiCVMoQR+qownOE2h44rJG6Rc4+Ldzokf7pGik0AABgX/sJeAIqQ2qbUFygEmKmVDFRVV0euy4GaOweHFQMkJ2ijcX6YTegICGLtmvCExD1STnOB+WesKeNDhQbXCHQOYPHBUr2V+R366MG2On/aaLfmui/QmdFsL5Jg+euyVqRyfNaslaQ/uslL0+FSO8ILBeF+3FR/IC9l/a5NijTpJxpbrff3355wchP7VBHdh7MKbr8qH/GTMpcrgEIoQMWY/obZ+40pkGC692KAWAeDIixEy81h8z135YyxAagHmvanerMTIxeZ0XMLN/NLCqtc+ZRu5NH86AZwG/QjJuwNu7CcG2Esu9f3WaZ44Exr928c7DBbEhXaDNbM4AGXCrw01GwfHfoX699eYXLX5v60VjPvZ9LZIk40BdJb8+S9aIv+3KrN6QBcT7jr9t/XRzg3vTGTLlUwDVARAME6mV6cv0ASxucljEwLNhuRcx5hstjwrpgd9Ihcg+akRJHRCSAELdZ48AEcPQgdmoAIL0pyVRlcyV86Y6O7h+CaSq1m5grqeWUq+nL7BURl5yEsXDChiu+rzZukuayGlLSf93s9u7tuA8JCqKYoa9vfJ0MUGkOgiWZb8Xm8e/SPoABgWo0c/5zhsFiSRzUkWApjJVMMJ3TcodcKd5tCK1PGqDFmZ9RgSJm1cS3BwQyOaDy5J32CBE0kpucgKrb22gDwBCkwzESGoBD9rcGQMsb99LUMoBIwCbFr29jUXN1xVAi3wesNcDY/m1qANcnKzQAUktYKclH1Zs4K1fH37Yuvsr+0gB5/3GpHJ5ETXpogPuyxsPrv/s4Y72hulzkq1IEQCxsXxH3dQagv68VdR9m3e6iuBPl4vbX0Z7rAvHD8fh0MEbKpXl6fY1GM5Nc6pP4c+JTHM/TM4sRot1mJLfh7TnS4TBsG4AAoTIgnMUSLjqHTNzAeh+fh5AT8y0s3LspkQLkGRe56N6fOC4XWcu+8YEtwCzSPi8M4IEspvlwh0BB2VYM4FCKBYEWDIDQYInWcj3SxXdRIuWZ0MvqvbyaeHpuhowam3406TcbDNdn4h8H/Z7q3ImhQVBezGeJ+3h9jUvGfZ3oZQlEIiGU6Q/LZ00M2hKqaPgjIP+oAYYBMG2AIviBCeK/UwNYGCJxjhPBhJ9WfJGMUX4eHoA2r2Ru4RC9nOsNL1PSXiB/FwgZq4drDBZgeXJQUUqLyQMxkLXE2E1bDRisVIJUs6oI1ALH/TOWsavkBBfmy0KkKyN6oKkEkFFK5J+YBnFiNRf8ZhgawDrI6ZOfc90+oBIkGe0KqPNI8R8ZoOdUg8wsF8okG0S7CP5x0BZwgyJ/RGdnoQro2XqsfJ5iABFyuStBSvBfhLsVNT9K/SkW/IQXL6LiLvxLvCczTH4iTgYofiLDJE8irtsOsd/DnFgETXfzsQxHYbQsouien59g7NEaNeMgVFqMUT6rDTREeCFddzpWoaOU/c2ALjiVRvMNe4QwqihdHGdmsiUXmMXlom6RQyBbpEV5R3rdoCDZUF7WnBAaAGaQoR99AowQtcUwNgxg7FIcVS0cidkx1cnYscbXCgJV3tslSf9ggLNZPWackD/KdYsN8h+zHix5QCvY4A8nAgFfC+5AjvpWgfi5RNZyqwAH7DnmN/nVAkTYhVgbjDR1v66e7aL6Bw1QFJ7oHwV00hhsDVCed0v1bBMN5QBfIBCupO9SdDym1SKB2kp+BIs8ziS+xWouF65evwZmC8ljiT+ivhHb+2i5IsUr0Xn/BVBYVEhH6AEX5vR0FTPJ8Qkx7kW5EtNolnxx5ZUDbBG/GUilMpFXKiUnfT8ITWKwXBwjck5lDVmOcSwBO3KeO/XttAQOBrBx4VaMCf0zfjASnm2HvLfzvUlfLZbaELm4TwAg9lzIKlcSuc1RyqHWhTNAKf0STWuDwhOAD3l9i2rd4wCXIWzPf7n1G/ogZf9NA/iJdg5aErAVKV9FfY+3Xf92Yso/M1sHKfsjKBQQiBI1e8AMI03dZScb+DXHfrd29liR+8iYqgMNOzWAP24GjF00JgOkDZAvJvYi3M0UV3BTQvxZHfGVmEUUb7dcjJ8aIFyuhlhnU8r/ouxdv8Z1w8k/M5+fNMCYhImDklNQv6iVLiOf+p7xlhECzxIaqc40j3YtgLEvPOOTiP5WLZPwgNdElvFvl16iiPiIXNmN2j5rAFw1AAfeR6J9ti/wch0cmmm8l/joQ9xPfGj1xcxZ3j6Cni+d7v8FrWIqhLKfx5JVm/oDG03bt7uTQ6sppgkMBuiVzhZXNFbSkceGGR+VBggG4JUBQl4NUXwwQPK/pa8rdJc1a3U2YM9DdW8wwJnyOdD77ZMcDOshCQF2++Wzz+eCf/weBX2CVZ3cLbVm0FW4QT0Q5n8nFbbO/812UwD/E+IAj+1SR+idw1FLBSSXCsQ5YzG6jh/OAQkAYSP0cGdqBzycuG8Ki6BMIAa7tx1W5KJUopboRS3j0ZVyAtlwtlZBLsskkIwzRNjvVXQ/D7I15U4IFEuzRgQggwOf2tCNYzXjo2v/3o4C/xekIhJmEEOLBwNkJBjC0OY9Tr8kke+1uwYYxx9ucqGNPmvqoSFHnzTAl+3+NY+jkraGEO9ErAfw7SV9X4tlWGNVcSzoxSjUwF44loJtQMmLZgKA9NYnKINb7i6GXX2XcuGQ+kcu5xVCNiJCZVg+awA/I7Be+/9dq6QGyI0Jjnkpa7VAUIKWQ/Jf7ICYOx4a4JT+l/WNl9f7Lv5j+Bhj4BJAQjcj2B2smj+xis5nLOoB9IBZP+IjuXygp253DTDIu+6WH4WIe+CZ6NQBx8qNkmMWz25FPQ+d/QCkrh+7vnTFrlnxNupn6cwUqp5nNf9RFVeQC4Mt3ffqZm6Z9AAMJDS5gXn/romfaIRJqeH/bAgUMN2PSRgkIVBpiBGucCLUZoDYF801AMIGWBnW9WIVi/CoM8IUaUkZMvu0AXTA9jvysVYYiCmMyKEas5xKL1xMqzdXAm/dW99bI/O5QgQBewo3B/LJFJ4aboBsURUJoVeY3h/ZSUU0G7VL3FZjPMW1vfrESztt3gtVHr9Jme/Sopky5cbQAMUgfZ3Ln/fWD+62YN/WDFRHBFnkSoC3YFluM0zQTMAp+zfd7qKm3U6UcIeqz0oBoZEEWA8RuieWjYXjKOh+ZO9YsEcxANMG8M3oU5SFiYuhAVJua1gQlqjGEgC7VlnuByIgvovsRQM4j6bZ6n3XBuK1/Ld5oeyQ9AMNS6BUpQ7Zf6TyR5JPJPqn+ZvLXGqSPew1nsWQboOc4vSu1G8SHt2Jp35RwvqJQB/bwzap1cX5wyfStPrGUv4PskcNXg//pTvF9dd74zwlPubU6wQyQ9qY+QoS20u+neKJZW3y+ppFITcgsUlAKAeFEVT3wcA8+/Sw/9MTms8cFAozTRKPKDKjTAikflxlTErne+IeZWiACYFcsQUb+K99UU9pgIRANLXljK+JuPK2A7GguOowXE2tEj8HpKm1OYMH/C2jZ2ajgoSW7N+hARL5xELHzHrI5O9w55Q/h5ltcpDAoIQUKTcS5HliLx/94vSzPWmAL/HUw+cWmLzkBCYEmv/NXxRxt64a+AIHqfvfBh+soREAkLVqjBkgC0Rk+EmkkPFUtLYQ6vzykzGxRqsyL2Xo+Wr5PxrFkRG4stIAHZpNIT6zhyxjInCTQSRu3CH0s0IVMkXCKXw5iRsxVkbHjQRiWEhhEAMU6qrVipmqTka4LAGwlP0ZLK6xjg+j/E9ogDPJJ4uaOARSzYWOzi2Bf+BezgAZEQAPA7go6yAjln7+RIPZxwBLN7AzFPjZXrfrNii4X4PnQXY0ybOQ9J0BRi9Tvz88wU0N+aWYNmBTv2OTvAuM7RWV2F04qvnTwh+aGx1hR8Yo1Yu8se8hEfA1F7IOHN2HE6rOu5QbgIX5a0Cu44TUArTWAOiwYrnxnAPKS5QLt8LqTXbBSM9gDrhlf90mtB6p6DRjcEoDhA5ORjDEsvOm/ErpKfQfmmOs4O7M0aZ+9+4HoXeec9Q2rPI+oTqS+mscxJL0L6jFYsKdHU84kmPLy0eT8D/J7Wt71ADf/XF1Noc2SALFAG344mso9kXj8e/sm1VnfaZdnG/tZDNfPr/BN7CAzdyF8rJaYFCgxPQHP19vOFKmmTx+ML8FU8A+vNhXrhyeIbMHHU4NYCnYpFKDckTbYHBhWgl2+dLUUskAXTorYP/llnmQnyM1wKxl24I/jN35qmW+2qscY4GK80A4xdRRod0YYMqbED53qf4FoX6X2P7eOEChPP8roe8/38ayFKh0KSEmJ9CdQuz1A5UPHxjkgzUSedBT/cW7KeYDVjn0X2iAzDyK5MEsVV/jVKScmwpfsbGfxRESTkqvkDcv9OQkHOwSwsnMkCvgEdsAdEhgQKAbAwzQr7Gw/XipvnsBwKjzcyWEMAEqsjp7m71sNv6H2i0ZDkgD6vmmd31R8r0UKjC6n7HSdkvYjda+0e5w73oQOMXUfAs8EIV54j01AB0FRSKFJQzJfgONOw4A50dzhc7olrVpnPbNEM0dbE4NwHTlf6EBUhSW6kj8OOnilxpguHRSFSTSyQnrxPz6LruiyT4Nfra9Xfa/9f2+MkAvdOzl9RVJYGb0MkUER/z3cao/k0S5xc5Z+q12tQH6MoxRnLi/j1k3jt9NaIkmpLyq0aae+8BdT60ckAcEmsQWqwrTg6+Infa8BLqQ72IA4QZ3lFwHfZMl4y43BKOj6vI0sc0g/cyxrkoVNQPuanUi47UkXfCFJaFHOMvzKYYXKObSWrIcOaxDJPbzK4xmetEAHnjIm9czWDGMNc074XPS/bSDLVfrOp7xnP4m+re+937v/X7rW60coOEn7bKRbAdxPvDAtzdJ/E1GCDja5fcBXI7npS8S/7ogJtTzIz+V4XHD4TVmZqZq549inUnyhB0/vmsDe/yTPG88NEAyU2kAYpMeFWYavm9gkcs3GgM3uI0ExQgFFTudQQN7GkCxq2FlOh2bTZ3WOq73zuiqIoVSYojNo2Ht008NUO/xm8kA0YPBI37NpTQJ8yU1QDJ0PU/ri+MahwIv1dAlEnOtuke7sNXUcY6L//f++d7v9/7Z+Me2IjRAX5DpyGAuWiiv/xMIGatDbl/3NwDG/NSXNzL7zEyPECg75x3Jqb6d0xJleNQ6yS/vO5g8Prfzz+sVOTtwnPpFi3mM6tC1wVelzJAbeAsX6HbwBkMD1AobtX7OcMWagWqxgiweyQykSbiCwiCgeSTKR6S6HE4madItCITYziBuVokOSImSDDCGIZKfe5ZzYadBvN4v0+ihqzOEcdIp3kMSFWhlcEI6XYoT1CytAmvbN2T//vnWn8ED+nPvLOpvb8NWeHW8IYvZkMI69B1a0Q5KdJAYg2MH9V0kZJ1UP0yCGAjEDADJK7c8uUGPa1/udqCQunjcwXIxXEtMDu67sbF9xZgfINIEivNZSsdn6o2pF0x3NkDscbFze9+FgD2ixeFxlZ09c7pGrCAL8Y2QsfEf4XvuRg2WEvytAXzKC5+zCSDec3R0DhNHStp0g0qGSPqBk5JVsSR6d6+HcUiwVgKIR8sYcF0MTfoFZraZbrw10hyS9J0NdhgAHhI27qxvpQBq5b8lwrjM6sN0B/Exldgnn09Q/IgdlSGaT9u89bAe4ND5X7YxQU26YwzHa1wwwv1PyKl+frnFyaWVeHTtZ5hApW/yPItFMfAqbFsi52eTSm6WBgAtS+0jlww77bGp7mjTELHkBH9EzRUJs7REnjoVAkoqZFZbv/rZXGQNZVI/lHEFJ1XP+yNgmgWoU12UdXGl/sPUdjClJyhKj1BV8Uc5PRv6z9fWn9uXBGObKai+5TbrMTjQf/bpA2G7wWhINfYEQSaZe7xyTJjdhX2cebMBjvn61O53rxvlYFZI/XLOp2tPYDo++tJF9MBDB6mVEqCRKpSs8amklhJwk8AKLBg8V85iceolEFICF/PdUAAnuqKRhlN/nj/IgylI2pa43CBQYjFApcy4djoYKSBQddSXsXFSf+GfNIGHy6fjA1Q44aI0QBXrHHt43fyeYQdr7XbhW7aV9wuGCzFOTf6F9HV98WvqRGLC329/dxyg3/ENtvq7m8/3CIMZMyzA8fIoQWLogi6WXZ+O9TWO8UTPx/3rEleCPnuYn2hQqaIh8fFu85P7a145tVCPwOP5eW7I+HiPde3uQOtwb2qAtz4xwPSBZtpPeEtz4wEbYO9fp4ZvtY8McINpLfPjrYDk/TV/c33uEpkTiI6WBPQ4XgdMmEmyvBBKblLhB6CRmglwG/aGSaXMhEC18soLm+gteUC96pbfLem16aowKEuJ2WVhjV362X/aw+dx1UMDpNv0SQNwrOixGoehAXjVAOndMRcCvSTGsAFfuLgtqT9Lmbv5G4Wdt85Kbx0oQGSId3rXOc2/1gA8T/ueHng0c79ut/Lo9/4dh5aKGQlYym/WPFFdfhJAh5fojoDqsK9yFbg1NtbfMuQlnUgJiMSeikKEJ8R5wDYgZm/m40QuD5SmhJGSWmIBRmouLbAMglWmkIdiOQaqExeMHTFoLgn/XqAsaZxTTpEeB0+rOyDQoP4p+5sTkG7QSrJ8YgCL/aCMIfjDgwHu5ARngLcl6asOBtCte78r8tVGgpq54Wvp/C5rr2Ek+/gTbum0zpjdA+7fmaEgEB99o5/b6wGn9/g/SO/SABnynYLfcOBnuyj2s8Px86ee2jBVKnZUKQQhWCqf3hEvMtPAFxx2/YDUAJbpD54yneVEYLDCRY5chVCIZlhtgZo5pGGCHikNrd9b55UZwCR6Hw+x2pE+Z8yft434IUeIdJxkvOwMnE0FG8v2C/dHhmmaGTFQOU8h+zXfU3KbwnZpgO1Z/rl3y444V2Ce967UtzKU3ZJWR/81OwOgN4OfNBHDcCEUa/k2iKOlTb+51/RX1P9UFeJXrDIEjaFzA9PbM+MOdvsVrwc+HBl8yUs83ux48qPfpHuNw8tRC0oRBO/bBMTumJSmCHPbjraRvsxcA2zDThDgRQt8TCpMQM08Inb93TONJROA7sE9IEtLIARzn3tgIQwz4lCnR8aEpHsHcb3w9Hl8INnDc4jdBVg1tlxWFei3gP7qgr9If+fOje9Y2RilzEMDjBSgvXUr9kiUqPGskXBV72uCcACAy7wOQzbEjKGG7EJRrUMMN8q+E/qHeBdeN68lOk0DF+Uz5iNpeAYSL3foOT07XRqkQwjnE+Vztr6Zn0YfkZn1mWxGmsXq6VhMTXFIFP9ZY1xT853onRkD3njqxIus3PsXoTAlF3yLFioYy7BGqJCEpFzKye6+Vy6d5/QjlZW7dDjgXv++/zgYgJn+zB7ccVBYaHzY5eXCW5v7XkfswBnABX/ygNlGoP93bV26gw3ecVDLX471LgcDsIM9TGIJt9ttaktZ9KOzRm9+0YQyAJJTVJqFv2MJvC5h2Z6Ij7Ffl/jl7w8dUBPZ2GXmOFY3e07jMmMcAsQdN6MLgKvCd+p3sQfEFuqR4ObbJfia6ki8RYlWd53v6Izbf1jwav705SUShOJqwZSmQwO4TbySlSWWYwUPFDBMeTZEiDqj1LMzxYaNiNhgIzuHr/HM6TZ90CGxLIGpsP3Z1Z+LSfT5vhHV/Ldxm73N4n3s8/tOPRDZPqbvAEhp+FpL+NP7ac3jSTvVzTH3dsQ2B2y80mHxxoRAKTK/IPxRyKfbx32CHxRGx3xhFW17Rv9xhTk7h2Bry+GDBugHSyx869KRRZwWt5sBJHxzwVx8UU8QAKDCapYE7ajGQf8TA3jesdVPkMLY8ucYDzvk+CnWyuFaTv0r1rHknIaWNZrHItkvDmqyOj1pMIALe1hJ+p0fXl61oPFdDFBZ/hbv7i1tT4jRbjAuHj2e2KwI6ZxtO0nIbgfj3A8tylTxEwT6ng3w+fr/hU0A87qSjE0BmBvmyNOjWMAQnZ/E0hmPhoESPhPC0XPWTewaKqm+1nlxPr1/Gs4LldjlCztnns3kOCEQb5/M98EAWgygAX402SC2Mnob3toaIBjABgMkP9yLCR2+/9sTPM7Fh1G5DOc/2540QNTou35snzRAavpoCcl4TL+NqTg0wPWR7dB6F3xU10+8UCx/aIAuH3ms/AoPoMugivwXeNaMAIgL/kMDqPn2kbeptREvC02VdgWaPo+xqR8eDGDnd9fn//LbehIk0XcsOYccVgzwvmmAZgCzt2owgBXF29zE12HPLpd/RSuiI7XyqztZePlJA3x62ktWyXO7Ao/f1QAXBmiFfbt7qLim2oZ1uRYKxLQNEwleu3m5Y/4gb08rD3Zx08CMFkA5V5ZG/SQpDdDUH8tOBKWYo/ddmTzXmyqjBm7CoUTPNM3MHMl9ASYbBPWbF17vRQ/xVF6qp5KxrqPAHsd+VD/ImbHrD2FpBNS4ZKjB97oEInyQ0ahmgH2DQG+zN0rkO93b23Sb/ez6Vvo2OCdEqk9GfHs0OqUtjsoTmpPs7tALAxx8/0Ql/u9NHteoXrw45Mx+G+cOSD3asw1gGAOM+cMyeqfIsujM9FzaSb+cGqA7/Sjgzp7EZQoue8fCH5TUz9hLEHM7I0/eDGZyMZWap4hC8x6EKek1s3zDJaUYoNTlDrz81swUvqc2ACyDAUKs1IauxmKPpohaZOSigUowaA1TDAVrbIqNeoLd4GcWCe7fDEdh6pNgWk0G0A8aIBhA7W36NrxNf+qAQJkF5AywzWW/Uz/j4ie1dYmk2cckHkvSNysR20m0pxacYG4uw6vf3TQoiSfqd1KxTrHu9vLs5WYgnKHNU4/lYzQDHIAzyL3ZoB+sTPya0fSj3rt6/HVYRt3NBEpB/yKSsCddhbkUJDg5bbT0AXqfCinUs4fzWSBep94owwiuJcTIPTNmnNi8LDsNi5FhFM7YgeA7NFikPwfR2i13f3Q9XNQX6YniKEWCH4/ywiwt3YMBSvBDW/YHJ8RxVbx6p4fUjmyfWuR1Tpw5EMxvm1U9QOZdk/MZ7gpBBsVktZohMDiSxq354QnoIODJ3aX0CIFCcYn3t0nZzqep43p6fxuyeij0vkVnqt55wKP/qNhH688SiohxBArxs3cNYcujTDLIX9uxQinYoKUo68mXxwESYkjSuqT7f75WO1JtAc4DFnAsHJGeQsRSCskSzQaX9T9TQ1qeXzK+IBDim0lEqWLDw2vp+A/wg4H+y/ZFgh/Vt9lPS9vXsM3jXLYVG+Y7ZW6XJGREfIks6jbo6MyUremuSNgvbQBjlPoyAtAsNDYv58/P9DpPOnqOe/3aBphNz86XtIobF7+hpeiU+xZsGTx0C/t+AX+aHFrLHD+p+4QNLORKU9h1XYTfk9OY+dq0NOAHCGVCJWsBbUZ3d6ok2hkaIHfIAK18R4mFpN1KIKnTLGGGgTJufRNJ+cincChN5Vrkcv54UDO2N0aZu+EFA3jE16m/bd+fKOifSkBtm/2M4ADU8O7U0fYfWMsbHtMUM14PkTYAmwQmBBqTO+mD9eiGXDHkMs8OWrYMwoZWD9z5yALX9hoLuGqwxztwG+17u7rgrq3gB1Kh3dXdr9pBDePGHE1aydUUeaxp+p06hFdXDiHbNvsBAo0eG0DtwTG7ZCOQbO1OtWSAeB+voQE+jJZ33uoRxvBGEJnHoJwagCn4UVFenRrgxgBDA/T7Dj+PbUMkO6ACvcGPlmRqPAVwD+RdA1z6fPngQUDacZHbcOEQq94+JT7c2//U/QFm45S4J12Nifj1VSocO9b6tjvRtdIuDYCbBsj32psjgxMNdi6S4nGWHgFBCcO4iB48Yb2CDBYO3DZ5taNdrQH0gEA6IVDYvi71nzz9elLrd2ntY/PL6PHQWkyVlWsq3lEezf73z7vwFQTCB/18fvrFzQ/Bf0CgCT6+365Y4iApDPpIP7+vyh4S9aCpIbQSiB8dyvRJzRsNCWbFAJ4d1O/kgEAW+RkU77DUPNYdTpBXFUGznPnQAMZGeScrmVETNSUE0v1tBtjQt6LjXy71zeIKxsEG7QLG0AMYSOzyamJ4bFet0ONbf8RADAYAkBui5Rn5ZzFD/jPG9qkLr7v66Pm4JSHNr3sKb4INp+i1IsJ8YKsrXaScjfdLb9LxGWGvaUaah2vrzlmNYAIeOy/Z9gz7BlHVZ5JoPJ5FxWgnsKA5D/svQzIA3ZHqPEBwgQmBvASgeH10IDyiaSkWOBx9TjM9u4AaZ46ZKdPF5aUnG6Tsx8XqLQbIz5sBtiGSf4B3LOq1bQyTl+H3jGUVMWDtjwU4fMsH3aeWmCZCCqn4Ip/cqSQHIwcgEyumBiChGnssuHNYvdY9wx4oPvlVe4RAN3jW9PC1yB4xCae6ibfnNBeHXPBK80CcV/GLjPlOX2dFnDikexJQVf0DnE7yAZhO1BD4SV/FYfdnzQ2qM5ZkBgVXpFJTovQ0q+yu80AVJmIcKPuB/P0UdPBU+um0jaFKs49OdxFQ8LFirD0PBpg5PxX5SopXmKptmtpkBn1ncDfSoVHLAwr20OgaoMdyhntb5J/utTvdRD5rj0PSCjJmgXh6BRCe3CsD0Ld3IEEtWmedVvf/FRu8Ljo4b1JCfcCEpNcHFPsIBkub2VQA8x//0YPyGB238gAm0bOGAoUALJG7NQPUbkOXOyWW6g8tGYCVvGznAw2HhkaKrplBfEvWgPsyqD/eI0znB5i3TbJGE86Qexmiq74X2nDyTwY2wB2FakSL/5L9KNI/NIDlMoCwkpG50AF7tq8cKkBludNNloofs+nSJGPsOdc95U0iHAfjT/NLmJnr2mQAtEqoDEGkr8McVk4gNO92AULdiSve+XJBzAcIdGsZ+hog24pm5gv1POjzZt/vEKie4AL985Y+LQFnDJGQW0JUw3it0oxW4j7ZyJDKIDmqVEMsXGm1HNTJ7Kalh4ixOXFRv5T/p7xAvv4xTQ237OYQJTlV58f4eDd8MHxXofwMAM2gMQytAfaTBiipb2YbsWvRRixn2ck/O/sVoL9RVgC3GNMxVU9TPcmBiLqgpbvvlJdBypBjiakMWg8PmobPzwRQRbJBkf4Ynmt7CIQ99WP+4ECflwMcd7Hj3+bdiwmAFH/X31/GonpcCgAo7TcIuCBQ/O6EQKlxmqaZUj4ZIEHPOThBjky6K7Ues8IQSkUFvmCAAhFCujRRQqD0isa42dQAA/AU9C+d0OPEYp40JTK9mtCIttfyzpMBZiTYbMPMVGmqyQAwh/ua2CPdqR7wIrI8z1yc5SMfPY0Ry4m/cvAkyctxSBbr9WOwDNdkMDtzs5ieNahpkr53IcNNk2B+2f6Jsijjz0ex8Eft6v8ZfpDTjr+7/IuXWkq0kdQXukuH7G6K5nEzCx7K75lcaIgECoZ6qpyNOi07cx0iGwdPGuB4/6gB0mNzQKDUAMc6mNy2MVCQzQSHMWm9vIvl/C32vFL6Zba/3XJsE7JOBgiPQIOaqHwWJfsQvBcs8psu0f8/xAH+5Vazy5Grw9ZrB/r8Wn+OS2LQzAPxfNC6/cvcTcD0ZgRfGcBjw8dBO/inp1+vvfiPtjkqv0fkX7X/YQzAQUPXNunH7EEQXQyijxrgdAwfyK4VmT2dov1XF3MAO1fzQQPgCoHq4BEn20cGwJ0BZixMTw1gqQcs1771/UeE5KK8L4r88v73kOWzBjhHouR+6+M/bP8KA9xH7veHKgzffF05wc4lGcddY2pOw+iLHoR3tZHTMIIxZsKun1jftTTCCJQYLtN4Xu57Q2NPf45HtXFQOuH6usHEc6GPGXh+cozNZN4rWf4R/umfDp/dr578/s0fscH/MA3wH2/JRFbcNOc70fkfXHUeByd870IWlqg74XgovxYGPPrZ8t7Cz3V69x/Y7O8DHb/f7pLjzyX+tf1/UKGTvrwelfkAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"source": [
"## Создание Pytorch DataLoader'a"
],
"metadata": {
"id": "VJHI8GhtZO8F"
}
},
{
"cell_type": "code",
"source": [
"batch_size = 128\n",
"dataloader = {}\n",
"for (X, y), part in zip([(train_X, train_y), (test_X, test_y)],\n",
" ['train', 'test']):\n",
" tensor_x = torch.Tensor(X)\n",
" tensor_y = F.one_hot(torch.Tensor(y).to(torch.int64),\n",
" num_classes=len(CLASSES))/1.\n",
" dataset = TensorDataset(tensor_x, tensor_y) # создание объекта датасета\n",
" dataloader[part] = DataLoader(dataset, batch_size=batch_size, shuffle=True) # создание экземпляра класса DataLoader\n",
"dataloader"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a77Fex1TIhGE",
"outputId": "4ad7cce0-26cd-4ba0-ee44-4ab2aa5f21a6"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'test': ,\n",
" 'train': }"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"## Создание Pytorch модели сверточной нейронной сети"
],
"metadata": {
"id": "FxcEeFaHZV-G"
}
},
{
"cell_type": "code",
"source": [
"class Normalize(nn.Module):\n",
" def __init__(self, mean, std):\n",
" super(Normalize, self).__init__()\n",
" self.mean = torch.tensor(mean).to(device)\n",
" self.std = torch.tensor(std).to(device)\n",
"\n",
" def forward(self, input):\n",
" x = input / 255.0\n",
" x = x - self.mean\n",
" x = x / self.std\n",
" return x.permute(0, 3, 1, 2) # nhwc -> nm\n",
"\n",
"class GlobalMaxPool2d(nn.Module):\n",
" def __init__(self):\n",
" super(GlobalMaxPool2d, self).__init__()\n",
"\n",
" def forward(self, input):\n",
" out = F.adaptive_max_pool2d(input, output_size=1)\n",
" return out.flatten(start_dim=1)\n",
"\n",
"class Cifar100_MLP(nn.Module):\n",
" def __init__(self, hidden_size=32, classes=100):\n",
" super(Cifar100_MLP, self).__init__()\n",
" # https://blog.jovian.ai/image-classification-of-cifar100-dataset-using-pytorch-8b7145242df1\n",
" self.seq = nn.Sequential(\n",
" Normalize([0.5074,0.4867,0.4411],[0.2011,0.1987,0.2025]),\n",
" # первый способ уменьшения размерности картинки - через stride\n",
" nn.Conv2d(3, HIDDEN_SIZE, 5, stride=4, padding=2),\n",
" nn.ReLU(),\n",
" # второй способ уменьшения размерности картинки - через слой пуллинг\n",
" nn.Conv2d(HIDDEN_SIZE, HIDDEN_SIZE*2, 3, stride=1, padding=1),\n",
" nn.ReLU(),\n",
" nn.AvgPool2d(4),#nn.MaxPool2d(4),\n",
" nn.Flatten(),\n",
" nn.Linear(HIDDEN_SIZE*8, classes),\n",
" )\n",
"\n",
" def forward(self, input):\n",
" return self.seq(input)\n",
"\n",
"HIDDEN_SIZE = 32\n",
"model = Cifar100_MLP(hidden_size=HIDDEN_SIZE, classes=len(CLASSES))\n",
"# NEW\n",
"model.to(device)\n",
"print(model(torch.rand(1, 32, 32, 3).to(device)))\n",
"summary(model, input_size=(32, 32, 3))\n",
"model"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jxfiec1w_bLr",
"outputId": "83e60984-3f5c-456d-aba7-0ab39f3b5bd5"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tensor([[-0.1872, -0.3005, 0.0117]], device='cuda:0',\n",
" grad_fn=)\n",
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
" Normalize-1 [-1, 3, 32, 32] 0\n",
" Conv2d-2 [-1, 32, 8, 8] 2,432\n",
" ReLU-3 [-1, 32, 8, 8] 0\n",
" Conv2d-4 [-1, 64, 8, 8] 18,496\n",
" ReLU-5 [-1, 64, 8, 8] 0\n",
" AvgPool2d-6 [-1, 64, 2, 2] 0\n",
" Flatten-7 [-1, 256] 0\n",
" Linear-8 [-1, 3] 771\n",
"================================================================\n",
"Total params: 21,699\n",
"Trainable params: 21,699\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.01\n",
"Forward/backward pass size (MB): 0.12\n",
"Params size (MB): 0.08\n",
"Estimated Total Size (MB): 0.22\n",
"----------------------------------------------------------------\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Cifar100_MLP(\n",
" (seq): Sequential(\n",
" (0): Normalize()\n",
" (1): Conv2d(3, 32, kernel_size=(5, 5), stride=(4, 4), padding=(2, 2))\n",
" (2): ReLU()\n",
" (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (4): ReLU()\n",
" (5): AvgPool2d(kernel_size=4, stride=4, padding=0)\n",
" (6): Flatten(start_dim=1, end_dim=-1)\n",
" (7): Linear(in_features=256, out_features=3, bias=True)\n",
" )\n",
")"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"source": [
"## Выбор функции потерь и оптимизатора градиентного спуска"
],
"metadata": {
"id": "raKMPtc4ZgsZ"
}
},
{
"cell_type": "code",
"source": [
"criterion = nn.CrossEntropyLoss()\n",
"# используется SGD c momentum\n",
"optimizer = optim.SGD(model.parameters(), lr=5e-3, momentum=0.9)"
],
"metadata": {
"id": "-sRf5LGwHIZB"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Обучение модели по эпохам"
],
"metadata": {
"id": "hFtkRYFQZ0xb"
}
},
{
"cell_type": "code",
"source": [
"EPOCHS = 500\n",
"REDRAW_EVERY = 20\n",
"steps_per_epoch = len(dataloader['train'])\n",
"steps_per_epoch_val = len(dataloader['test'])\n",
"# NEW\n",
"pbar = tqdm(total=EPOCHS*steps_per_epoch)\n",
"losses = []\n",
"losses_val = []\n",
"passed = 0\n",
"for epoch in range(EPOCHS): # проход по набору данных несколько раз\n",
" #running_loss = 0.0\n",
" tmp = []\n",
" model.train()\n",
" for i, batch in enumerate(dataloader['train'], 0):\n",
" # получение одного минибатча; batch это двуэлементный список из [inputs, labels]\n",
" inputs, labels = batch\n",
" # на GPU\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
"\n",
" # очищение прошлых градиентов с прошлой итерации\n",
" optimizer.zero_grad()\n",
"\n",
" # прямой + обратный проходы + оптимизация\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, labels)\n",
" #loss = F.cross_entropy(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # для подсчёта статистик\n",
" #running_loss += loss.item()\n",
" accuracy = (labels.detach().argmax(dim=-1)==outputs.detach().argmax(dim=-1)).\\\n",
" to(torch.float32).mean().cpu()*100\n",
" tmp.append((loss.item(), accuracy.item()))\n",
" pbar.update(1)\n",
" #print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / steps_per_epoch:.3f}')\n",
" losses.append((np.mean(tmp, axis=0),\n",
" np.percentile(tmp, 25, axis=0),\n",
" np.percentile(tmp, 75, axis=0)))\n",
" #running_loss = 0.0\n",
" tmp = []\n",
" model.eval()\n",
" with torch.no_grad(): # отключение автоматического дифференцирования\n",
" for i, data in enumerate(dataloader['test'], 0):\n",
" inputs, labels = data\n",
" # на GPU\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
"\n",
" outputs = model(inputs)\n",
" loss = criterion(outputs, labels)\n",
" #running_loss += loss.item()\n",
" accuracy = (labels.argmax(dim=-1)==outputs.argmax(dim=-1)).\\\n",
" to(torch.float32).mean().cpu()*100\n",
" tmp.append((loss.item(), accuracy.item()))\n",
" #print(f'[{epoch + 1}, {i + 1:5d}] val loss: {running_loss / steps_per_epoch_val:.3f}')\n",
" losses_val.append((np.mean(tmp, axis=0),\n",
" np.percentile(tmp, 25, axis=0),\n",
" np.percentile(tmp, 75, axis=0)))\n",
" if (epoch+1) % REDRAW_EVERY != 0:\n",
" continue\n",
" clear_output(wait=False)\n",
" passed += pbar.format_dict['elapsed']\n",
" pbar = tqdm(total=EPOCHS*steps_per_epoch, miniters=5)\n",
" pbar.update((epoch+1)*steps_per_epoch)\n",
" x_vals = np.arange(epoch+1)\n",
" _, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
" stats = np.array(losses)\n",
" stats_val = np.array(losses_val)\n",
" ax[1].set_ylim(stats_val[:, 0, 1].min()-5, 100)\n",
" ax[1].grid(axis='y')\n",
" for i, title in enumerate(['CCE', 'Accuracy']):\n",
" ax[i].plot(x_vals, stats[:, 0, i], label='train')\n",
" ax[i].fill_between(x_vals, stats[:, 1, i],\n",
" stats[:, 2, i], alpha=0.4)\n",
" ax[i].plot(x_vals, stats_val[:, 0, i], label='val')\n",
" ax[i].fill_between(x_vals,\n",
" stats_val[:, 1, i],\n",
" stats_val[:, 2, i], alpha=0.4)\n",
" ax[i].legend()\n",
" ax[i].set_title(title)\n",
" plt.show()\n",
"print('Обучение закончено за %s секунд' % passed)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363,
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]
},
"id": "j3N4gdE9KKd1",
"outputId": "3c020fe0-a0f6-460f-dd3c-b3b1fba9c862"
},
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7f20c914b3534bed895c22434e3a17dd",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/6000 [00:00"
]
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Обучение закончено за 38.8170702457428 секунд\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Проверка качества модели по классам на обучающей и тестовой выборках"
],
"metadata": {
"id": "pM3jjyu2Z6cf"
}
},
{
"cell_type": "code",
"source": [
"for part in ['train', 'test']:\n",
" y_pred = []\n",
" y_true = []\n",
" with torch.no_grad(): # отключение автоматического дифференцирования\n",
" for i, data in enumerate(dataloader[part], 0):\n",
" inputs, labels = data\n",
" # на GPU\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
"\n",
" outputs = model(inputs).detach().cpu().numpy()\n",
" y_pred.append(outputs)\n",
" y_true.append(labels.cpu().numpy())\n",
" y_true = np.concatenate(y_true)\n",
" y_pred = np.concatenate(y_pred)\n",
" print(part)\n",
" print(classification_report(y_true.argmax(axis=-1), y_pred.argmax(axis=-1),\n",
" digits=4, target_names=list(map(str, CLASSES))))\n",
" print('-'*50)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5RlNWKIRM8Hj",
"outputId": "508f13a7-79f5-4ed6-a2fc-30ff16df537a"
},
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train\n",
" precision recall f1-score support\n",
"\n",
" 0 1.0000 1.0000 1.0000 500\n",
" 55 1.0000 1.0000 1.0000 500\n",
" 58 1.0000 1.0000 1.0000 500\n",
"\n",
" accuracy 1.0000 1500\n",
" macro avg 1.0000 1.0000 1.0000 1500\n",
"weighted avg 1.0000 1.0000 1.0000 1500\n",
"\n",
"--------------------------------------------------\n",
"test\n",
" precision recall f1-score support\n",
"\n",
" 0 0.9238 0.9700 0.9463 100\n",
" 55 0.8515 0.8600 0.8557 100\n",
" 58 0.9043 0.8500 0.8763 100\n",
"\n",
" accuracy 0.8933 300\n",
" macro avg 0.8932 0.8933 0.8928 300\n",
"weighted avg 0.8932 0.8933 0.8928 300\n",
"\n",
"--------------------------------------------------\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Часть 2. Сохранение модели в ONNX"
],
"metadata": {
"id": "ko077UrraTKW"
}
},
{
"cell_type": "code",
"source": [
"# сохраниение модели\n",
"# ПЕРВЫЙ СПОСОБ: сохранение параметров\n",
"PATH = 'cifar_cnn.pth'\n",
"torch.save(model.state_dict(), PATH)\n",
"# загрузка\n",
"new_model = Cifar100_MLP(hidden_size=HIDDEN_SIZE, classes=len(CLASSES))\n",
"new_model.load_state_dict(torch.load(PATH))\n",
"new_model.eval()\n",
"\n",
"# ВТОРОЙ СПОСОБ: сохранение всей архитектуры\n",
"PATH2 = 'cifar_cnn.pt'\n",
"torch.save(model, PATH2)\n",
"# загрузка\n",
"new_model_2 = torch.load(PATH2)\n",
"new_model_2.eval()"
],
"metadata": {
"id": "pE_EqoQ6aYo8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "47b4f020-18f5-4027-93b1-590ad811bdf2"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Cifar100_MLP(\n",
" (seq): Sequential(\n",
" (0): Normalize()\n",
" (1): Conv2d(3, 32, kernel_size=(5, 5), stride=(4, 4), padding=(2, 2))\n",
" (2): ReLU()\n",
" (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (4): ReLU()\n",
" (5): AvgPool2d(kernel_size=4, stride=4, padding=0)\n",
" (6): Flatten(start_dim=1, end_dim=-1)\n",
" (7): Linear(in_features=256, out_features=3, bias=True)\n",
" )\n",
")"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# входной тензор для модели\n",
"x = torch.randn(1, 32, 32, 3, requires_grad=True).to(device)\n",
"torch_out = model(x)\n",
"\n",
"# экспорт модели\n",
"torch.onnx.export(model, # модель\n",
" x, # входной тензор (или кортеж нескольких тензоров)\n",
" \"cifar100_CNN.onnx\", # куда сохранить (либо путь к файлу либо fileObject)\n",
" export_params=True, # сохраняет веса обученных параметров внутри файла модели\n",
" opset_version=9, # версия ONNX\n",
" do_constant_folding=True, # следует ли выполнять укорачивание констант для оптимизации\n",
" input_names = ['input'], # имя входного слоя\n",
" output_names = ['output'], # имя выходного слоя\n",
" dynamic_axes={'input' : {0 : 'batch_size'}, # динамичные оси, в данном случае только размер пакета\n",
" 'output' : {0 : 'batch_size'}})"
],
"metadata": {
"id": "jDBxut1Ixa3H"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Loss landscape"
],
"metadata": {
"id": "qYN9U-JxJAqE"
}
},
{
"cell_type": "markdown",
"source": [
"## идея https://arxiv.org/abs/1712.09913"
],
"metadata": {
"id": "PZhPD_s5JHas"
}
},
{
"cell_type": "code",
"source": [
"from copy import deepcopy\n",
"state_dict_backup = deepcopy(model.state_dict())"
],
"metadata": {
"id": "KT57w593Honn"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def generate_theta(seed=None):\n",
" model.eval()\n",
" model.load_state_dict(state_dict_backup)\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" params = []\n",
" with torch.no_grad():\n",
" for w in model.parameters():\n",
" params.append(w.detach().cpu().numpy())\n",
" params_n = np.concatenate([p.flatten() for p in params]).size\n",
" random_theta_flat = np.random.normal(size=params_n)\n",
" random_theta = []\n",
" offset = 0\n",
" for p in params:\n",
" p_weights = p.flatten().size\n",
" random_theta.append(random_theta_flat[offset:offset+p_weights].\\\n",
" reshape(*p.shape))\n",
" # normalization\n",
" rank = random_theta[-1].shape.__len__()\n",
" if rank == 4: # convolution\n",
" #print('Conv')\n",
" # Frobenius norm\n",
" norm_r = np.sqrt((random_theta[-1]**2).sum(axis=-1).sum(axis=-1))\n",
" norm_p = np.sqrt((p**2).sum(axis=-1).sum(axis=-1))\n",
" norm = (norm_p / norm_r).reshape(*norm_p.shape, 1, 1)\n",
" elif rank == 2: # fully connected\n",
" #print('FC')\n",
" norm_r = np.sqrt((random_theta[-1]**2).sum(axis=-1))\n",
" norm_p = np.sqrt((p**2).sum(axis=-1))\n",
" norm = (norm_p / norm_r).reshape(-1, 1)\n",
" elif rank == 1: # bias\n",
" #print('bias')\n",
" norm_r = np.sqrt((random_theta[-1]**2).sum())\n",
" norm_p = np.sqrt((p**2).sum())\n",
" norm = norm_p / norm_r\n",
" random_theta[-1] = random_theta[-1]*norm\n",
" offset += p_weights\n",
" assert offset==params_n, \\\n",
" \"Not all params are utilized. Expected %d, found %d\"%(params_n, offset)\n",
" return random_theta\n",
"\n",
"theta1 = generate_theta(seed=0)\n",
"theta2 = generate_theta(seed=1011)"
],
"metadata": {
"id": "yHXOawGP5EZk"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"criterion2 = nn.CrossEntropyLoss(reduction='none')\n",
"loss_curve = []\n",
"alphas = np.arange(-1500, 1500, step=5)/1000\n",
"for alpha in tqdm(alphas):\n",
" state_dict = model.state_dict()\n",
" for (k, v), v_new in zip(state_dict_backup.items(), theta1):\n",
" tensor = v.clone().detach() + \\\n",
" alpha*(torch.tensor(v_new, device=device) - v.clone().detach())\n",
" state_dict[k] = tensor\n",
" model.load_state_dict(state_dict)\n",
" loss = []\n",
"\n",
" with torch.no_grad(): # отключение автоматического дифференцирования\n",
" for i, data in enumerate(dataloader['test'], 0):\n",
" inputs, labels = data\n",
" # на GPU\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
" outputs = model(inputs).detach()\n",
" loss.append(criterion2(outputs, labels).detach().cpu().numpy())\n",
" loss_curve.append(np.concatenate(loss).mean())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"6a6459f421354118a2fcb08191d5563a",
"3cae12d958084863a80c4d3c5fff7a68",
"21e3f55a31fd4beba318d21881e1b555",
"593d640a7c9148b48763cfd4dc5a6298",
"420cc6be1e13419589a33e4578b9d1c7",
"83c184d6b9234325a2604b3c747547cc",
"4fda3fcc311a4dca95408143453fd6bb",
"9de4c7c9af72460f89597dd4c8b1bec2",
"1195785d8ee1459cab26d3c096d7e7fd",
"037f10b9689c4d6089b887b08e6ae880",
"b45f113e768f4332a27b99fef85a33f1"
]
},
"id": "vicvS8mD4XOk",
"outputId": "c67a854a-5615-492c-f38b-b7cac55f0e2e"
},
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a6459f421354118a2fcb08191d5563a",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
" 0%| | 0/600 [00:00"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"## 2D"
],
"metadata": {
"id": "T3X-3QqAdn3N"
}
},
{
"cell_type": "code",
"source": [
"# увеличив step, можно значительно ускорить вычисления\n",
"# однако вместе с этим теряется разрешение\n",
"alphas = []\n",
"vals = np.arange(-150, 151, step=5)/100\n",
"size = vals.size\n",
"\n",
"# создаём заданный растр\n",
"for a1 in vals:\n",
" for a2 in vals:\n",
" alphas.append((a1, a2))\n",
"\n",
"alphas = np.array(alphas)"
],
"metadata": {
"id": "mlg8QTVSc-Fy"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"Z = []\n",
"for a1, a2 in tqdm(alphas):\n",
" state_dict = model.state_dict()\n",
" for (k, v), v_new, v_new2 in zip(state_dict_backup.items(), theta1, theta2):\n",
" # линейная интерполяция\n",
" tensor = v.clone().detach() + \\\n",
" a1*(torch.tensor(v_new, device=device) - \\\n",
" v.clone().detach())\n",
" # ещё раз со вторым вектором\n",
" tensor = tensor + a2*(torch.tensor(v_new2, device=device) - tensor)\n",
" state_dict[k] = tensor\n",
" #print(k, tensor, v_new)\n",
" model.load_state_dict(state_dict)\n",
" loss = []\n",
" with torch.no_grad(): # отключение автоматического дифференцирования\n",
" for i, data in enumerate(dataloader['test'], 0):\n",
" inputs, labels = data\n",
" # на GPU\n",
" inputs, labels = inputs.to(device), labels.to(device)\n",
" outputs = model(inputs).detach()#.cpu().numpy()\n",
" loss.append(criterion2(outputs, labels).detach().cpu().numpy())\n",
" Z.append(np.concatenate(loss).mean())\n",
"ZZ = np.array(Z)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"422e6c01742d455f9b5bb5befc8744ae",
"780a461a262044e4b26302848cfc2713",
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},
"id": "Ur9ZUxgfdW_S",
"outputId": "ee438e95-e726-453c-c672-43f799535afb"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "422e6c01742d455f9b5bb5befc8744ae",
"version_minor": 0,
"version_major": 2
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"text/plain": [
" 0%| | 0/3721 [00:00"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"source": [
"## 3D"
],
"metadata": {
"id": "xywuE-eHk2Et"
}
},
{
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={\"projection\": \"3d\"})\n",
"# установка названий осям X, Y и Z\n",
"ax.set_xlabel('α', fontsize=20)\n",
"ax.set_ylabel('β', fontsize=20)\n",
"ax.set_zlabel('CCE, log10', fontsize=20)\n",
"# выставление прозрачности сетки, для красоты\n",
"ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n",
"ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n",
"ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n",
"# отрисовка 3D поверхности, данные для отрисовки аналогичны contourf\n",
"surf = ax.plot_surface(alphas[:,0].reshape(size, size),\n",
" alphas[:,1].reshape(size, size),\n",
" np.log10(ZZ.reshape(size, size)),\n",
" cmap=plt.cm.coolwarm,\n",
" linewidth=0.1,\n",
" edgecolors='k',\n",
" alpha=0.8,\n",
" antialiased=True)\n",
"# первый агрумент - вращение вокруг XY, второрй аргумент - вокруг YZ\n",
"ax.view_init(40, -30)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 466
},
"id": "UJSRo09pk0xL",
"outputId": "df7a94b7-586b-44e4-a299-bc8823eae991"
},
"execution_count": 20,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
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