{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [Statoil/C-CORE Iceberg Classifier Challenge](https://www.kaggle.com/c/statoil-iceberg-classifier-challenge)\n", "### Ship or iceberg, can you decide from space?\n", "\n", "Here what this challenge is about, quoted from the Kaggle website:\n", "\n", "*\"Drifting icebergs present threats to navigation and activities in areas such as offshore of the East Coast of Canada.*\n", "\n", "*Currently, many institutions and companies use aerial reconnaissance and shore-based support to monitor environmental conditions and assess risks from icebergs. However, in remote areas with particularly harsh weather, these methods are not feasible, and the only viable monitoring option is via satellite.*\n", "\n", "*Statoil, an international energy company operating worldwide, has worked closely with companies like C-CORE. C-CORE have been using satellite data for over 30 years and have built a computer vision based surveillance system. To keep operations safe and efficient, Statoil is interested in getting a fresh new perspective on how to use machine learning to more accurately detect and discriminate against threatening icebergs as early as possible.*\n", "\n", "*In this competition, you’re challenged to build an algorithm that automatically identifies if a remotely sensed target is a ship or iceberg. Improvements made will help drive the costs down for maintaining safe working conditions.\"*\n", "\n", "I am fairly new to deep learning and this competition was a fantastic playground to try out a bunch of different approaches, technologies and frameworks. I have tested MXNet, Keras and PyTorch and I have to admit that the ease of use of the latter is just striking, hence I decided to go ahead and experiment entirely with this framework. Here the steps I have followed:\n", "\n", "1. Preparing the dataset to be ingested into a Deep Net pipeline\n", "2. Fine-tuning a pre-trained DensNet (a very deep CNN)\n", "3. Repeating step 2 with data augmentation\n", "4. Repeating step 2 with re-initialization of the learning rate every 6 epochs\n", "5. Freezing the first 3 layers of the DensNet and re-training only the last 3\n", "6. Using some data augmentaion on the test set as well. This basically means running the model on different versions of the same image and then averaging probabilities (i.e some sort of ensembling)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from __future__ import print_function, division\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.optim import lr_scheduler\n", "from torch.autograd import Variable\n", "from sklearn.metrics import log_loss\n", "import numpy as np\n", "import torchvision\n", "from torchvision import datasets, models, transforms\n", "%matplotlib inline\n", "import time\n", "import os\n", "from PIL import Image\n", "from scipy.optimize import minimize\n", "\n", "plt.ion() # interactive mode\n", "from decimal import Decimal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper Functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def color_composite(data):\n", " \"\"\"\n", " Transforming a pandas dataframe with 2 flattened 75x75 bands\n", " into a 3-channel-RGB-numpy.ndarray image.\n", " The third channel is calculated by the ratio of the first two.\n", " Code taken from https://www.kaggle.com/keremt/getting-color-composites\n", " \"\"\"\n", " rgb_arrays = []\n", " for i, row in data.iterrows():\n", " band_1 = np.array(row['band_1']).reshape(75, 75)\n", " band_2 = np.array(row['band_2']).reshape(75, 75)\n", " band_3 = band_1 / band_2\n", "\n", " r = (band_1 + abs(band_1.min())) / np.max((band_1 + abs(band_1.min())))\n", " g = (band_2 + abs(band_2.min())) / np.max((band_2 + abs(band_2.min())))\n", " b = (band_3 + abs(band_3.min())) / np.max((band_3 + abs(band_3.min())))\n", "\n", " rgb = np.dstack((r, g, b))\n", " rgb_arrays.append(rgb)\n", " return np.array(rgb_arrays)\n", "\n", "def imshow(inp, title=None):\n", " \"\"\"\n", " Visualize a batch of data with labels\n", " \"\"\"\n", " inp = inp.numpy().transpose((1, 2, 0))\n", " mean = np.array([0.485, 0.456, 0.406])\n", " std = np.array([0.229, 0.224, 0.225])\n", " inp = std * inp + mean\n", " inp = np.clip(inp, 0, 1)\n", " fig, ax = plt.subplots(figsize=(18, 3))\n", " ax.imshow(inp)\n", " plt.tight_layout()\n", " if title is not None:\n", " plt.title(title, fontdict={'fontsize': 20})\n", " plt.pause(0.001) # pause a bit so that plots are updated\n", " \n", "def train_model(model, criterion, optimizer, scheduler, path_to_save, restartlr=False, num_epochs=25):\n", " \"\"\"\n", " Training a PyTorch model.\n", " Returning the model itself plus train/validation loss and learning rate VS epoch\n", " \"\"\"\n", " since = time.time()\n", " train_loss = {}\n", " val_loss = {}\n", " lr_dict = {}\n", "\n", " best_model_wts = model.state_dict()\n", " best_acc = 0.0\n", "\n", " for epoch in range(num_epochs):\n", " print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n", " print('-' * 10)\n", " \n", " # Each epoch has a training and validation phase\n", " for phase in ['train', 'val']:\n", " if phase == 'train':\n", " if restartlr:\n", " if epoch % 6 == 0 and epoch != 0:\n", " print('Resetting LR')\n", " scheduler.step(epoch=-1)\n", " else: scheduler.step()\n", " else: scheduler.step()\n", " model.train(True) # Set model to training mode\n", " else:\n", " model.train(False) # Set model to evaluate mode\n", "\n", " running_loss = 0.0\n", " running_corrects = 0\n", "\n", " # Iterate over data.\n", " for data in dataloaders[phase]:\n", " # get the inputs\n", " inputs, labels = data\n", "\n", " # wrap them in Variable\n", " if use_gpu:\n", " inputs = Variable(inputs.cuda())\n", " labels = Variable(labels.cuda())\n", " else:\n", " inputs, labels = Variable(inputs), Variable(labels)\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs.data, 1)\n", " loss = criterion(outputs, labels)\n", "\n", " # backward + optimize only if in training phase\n", " if phase == 'train':\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # statistics\n", " running_loss += loss.data[0]\n", " running_corrects += torch.sum(preds == labels.data)\n", "\n", " epoch_loss = running_loss / dataset_sizes[phase]\n", " epoch_acc = running_corrects / dataset_sizes[phase]\n", " \n", " if phase == 'train': \n", " train_loss[epoch] = epoch_loss\n", " lr_dict[epoch] = optimizer.param_groups[0]['lr']\n", " else: val_loss[epoch] = epoch_loss\n", " \n", " print('{} Loss: {:.4f} Acc: {:.4f} LR: {:.2E}'.format(phase, epoch_loss, epoch_acc, Decimal(optimizer.param_groups[0]['lr'])))\n", "\n", " # deep copy the model\n", " if phase == 'val' and epoch_acc > best_acc:\n", " best_acc = epoch_acc\n", " best_model_wts = model.state_dict()\n", " torch.save(model, path_to_save)\n", "\n", " time_elapsed = time.time() - since\n", " print('Training complete in {:.0f}m {:.0f}s'.format(\n", " time_elapsed // 60, time_elapsed % 60))\n", " print('Best val Acc: {:4f}'.format(best_acc))\n", "\n", " # load best model weights\n", " model.load_state_dict(best_model_wts)\n", " return model, train_loss, val_loss, lr_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Preparing the dataset\n", "Quoted from Kaggle:\n", "\n", "\"The data (train.json, test.json) is presented in json format. The files consist of a list of images, and for each image, you can find the following fields:\n", "\n", "* **id** - the id of the image\n", "* **band_1, band_2** - the flattened image data. Each band has 75x75 pixel values in the list, so the list has 5625 elements. Note that these values are not the normal non-negative integers in image files since they have physical meanings - these are float numbers with unit being dB. Band 1 and Band 2 are signals characterized by radar backscatter produced from different polarizations at a particular incidence angle. The polarizations correspond to HH (transmit/receive horizontally) and HV (transmit horizontally and receive vertically). More background on the satellite imagery can be found here.\n", "* **inc_angle** - the incidence angle of which the image was taken. Note that this field has missing data marked as \"na\", and those images with \"na\" incidence angles are all in the training data to prevent leakage.\n", "* **is_iceberg** - the target variable, set to 1 if it is an iceberg, and 0 if it is a ship. This field only exists in train.json.\"\n", "\n", "Given the structure of the data, I am going to transform the 2-bands images into more classic 3-channel-RGB pictures, as this is what most DL frameworks, especially pre-trained models, accept by default." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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band_1band_2idinc_angleis_iceberg
0[-27.878360999999998, -27.15416, -28.668615, -...[-27.154118, -29.537888, -31.0306, -32.190483,...dfd5f91343.92390
1[-12.242375, -14.920304999999999, -14.920363, ...[-31.506321, -27.984554, -26.645678, -23.76760...e25388fd38.15620
2[-24.603676, -24.603714, -24.871029, -23.15277...[-24.870956, -24.092632, -20.653963, -19.41104...58b2aaa045.28591
3[-22.454607, -23.082819, -23.998013, -23.99805...[-27.889421, -27.519794, -27.165262, -29.10350...4cfc3a1843.83060
4[-26.006956, -23.164886, -23.164886, -26.89116...[-27.206915, -30.259186, -30.259186, -23.16495...271f93f435.62560
\n", "
" ], "text/plain": [ " band_1 \\\n", "0 [-27.878360999999998, -27.15416, -28.668615, -... \n", "1 [-12.242375, -14.920304999999999, -14.920363, ... \n", "2 [-24.603676, -24.603714, -24.871029, -23.15277... \n", "3 [-22.454607, -23.082819, -23.998013, -23.99805... \n", "4 [-26.006956, -23.164886, -23.164886, -26.89116... \n", "\n", " band_2 id inc_angle \\\n", "0 [-27.154118, -29.537888, -31.0306, -32.190483,... dfd5f913 43.9239 \n", "1 [-31.506321, -27.984554, -26.645678, -23.76760... e25388fd 38.1562 \n", "2 [-24.870956, -24.092632, -20.653963, -19.41104... 58b2aaa0 45.2859 \n", "3 [-27.889421, -27.519794, -27.165262, -29.10350... 4cfc3a18 43.8306 \n", "4 [-27.206915, -30.259186, -30.259186, -23.16495... 271f93f4 35.6256 \n", "\n", " is_iceberg \n", "0 0 \n", "1 0 \n", "2 1 \n", "3 0 \n", "4 0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = pd.read_json('./data/iceberg/train.json')\n", "train.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking class imbalance. Seems like we have a pretty balanced dataset. Some actual numbers later on.\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Checking class imbalance. Seems like we have a pretty balanced dataset. Some actual numbers later on.')\n", "\n", "f,ax = plt.subplots(1,1,figsize=(10,6))\n", "sns.barplot(x=['not an iceberg','iceberg'],y=train.groupby(['is_iceberg'],as_index=False).count()['id'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we transform the original pandas dataframe into a numpy array containing 75x75x3 images" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1604, 75, 75, 3) \n" ] } ], "source": [ "train_rgb = color_composite(train)\n", "\n", "print(train_rgb.shape, type(train_rgb))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Splitting original dataset into train and validation set" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "X_train, X_validation, y_train, y_validation = train_test_split(train, train.is_iceberg, test_size = 0.2, random_state=42, stratify= train.is_iceberg)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train shape: (1283, 5)\n", "Validation shape (321, 5)\n", "% of Icebergs in Train: 0.469212782541\n", "% of Icebergs in Validation: 0.470404984424\n" ] } ], "source": [ "print('Train shape:', X_train.shape)\n", "print('Validation shape', X_validation.shape)\n", "print('% of Icebergs in Train:', sum(X_train.is_iceberg)/X_train.shape[0])\n", "print('% of Icebergs in Validation:', sum(X_validation.is_iceberg)/X_validation.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Splitting Train and Validation into *yes_iceberg, no_iceberg* to easily access images and save them as JPGs into their specific folders" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(681, 5) (602, 5)\n", "(170, 5) (151, 5)\n" ] } ], "source": [ "no_iceberg_t = X_train[X_train.is_iceberg == 0]\n", "yes_iceberg_t = X_train[X_train.is_iceberg == 1]\n", "no_iceberg_v = X_validation[X_validation.is_iceberg == 0]\n", "yes_iceberg_v = X_validation[X_validation.is_iceberg == 1]\n", "\n", "print(no_iceberg_t.shape, yes_iceberg_t.shape)\n", "print(no_iceberg_v.shape, yes_iceberg_v.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Saving numpy arrays as JPGs. Take a second to notice the folder structure.\n", "\n", "*train* and *validation* are 2 separate directories split into 2 subdirectories containing icebergs and ships.\n", "\n", "PyTorch is going to access these folders (in alphabetical order, so *no_iceberg* first and *yes_iceberg* second) and assign a label to the images, 1 for icebergs and 0 for ships." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "no_path_t = './data/iceberg/pytorch_icebergs/train/no_iceberg/'\n", "for idx in no_iceberg_t.index:\n", " img = train_rgb[idx]\n", " plt.imsave(no_path_t + train.iloc[idx, 2] + '.jpg', img)\n", "\n", "yes_path_t = './data/iceberg/pytorch_icebergs/train/yes_iceberg/'\n", "for idx in yes_iceberg_t.index:\n", " img = train_rgb[idx]\n", " plt.imsave(yes_path_t + train.iloc[idx, 2] + '.jpg', img)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "no_path_v = './data/iceberg/pytorch_icebergs/val/no_iceberg/'\n", "for idx in no_iceberg_v.index:\n", " img = train_rgb[idx]\n", " plt.imsave(no_path_v + train.iloc[idx, 2] + '.jpg', img)\n", "\n", "yes_path_v = './data/iceberg/pytorch_icebergs/val/yes_iceberg/'\n", "for idx in yes_iceberg_v.index:\n", " img = train_rgb[idx]\n", " plt.imsave(yes_path_v + train.iloc[idx, 2] + '.jpg', img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same treatment for test set too." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " band_1 \\\n", "0 [-15.863251, -15.201077, -17.887735, -19.17248... \n", "1 [-26.058969497680664, -26.058969497680664, -26... \n", "2 [-14.14109992980957, -15.064241409301758, -17.... \n", "3 [-12.167478, -13.706167, -16.54837, -13.572674... \n", "4 [-23.37459373474121, -26.02718162536621, -28.1... \n", "\n", " band_2 id inc_angle \n", "0 [-21.629612, -21.142353, -23.908337, -28.34524... 5941774d 34.966400 \n", "1 [-25.754207611083984, -25.754207611083984, -25... 4023181e 32.615072 \n", "2 [-14.74563980102539, -14.590410232543945, -14.... b20200e4 37.505433 \n", "3 [-24.32222, -26.375538, -24.096739, -23.8769, ... e7f018bb 34.473900 \n", "4 [-25.72234344482422, -27.011577606201172, -23.... 4371c8c3 43.918874 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test = pd.read_json('./data/iceberg/test.json')\n", "test.head()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(8424, 75, 75, 3) \n" ] } ], "source": [ "test_rgb = color_composite(test)\n", "\n", "print(test_rgb.shape, type(test_rgb))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "test_path = './data/iceberg/test_images/test/'\n", "for idx in test.index:\n", " img = test_rgb[idx]\n", " plt.imsave(test_path + test.iloc[idx, 2] + '.jpg', img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Learning Pipeline\n", "\n", "A couple of clarifications here before getting started.\n", "\n", "a. Why a **pre-trained** model? Quoting some [CNN notes](http://cs231n.github.io/transfer-learning/) from Stanford: \"In practice, very few people train an entire Convolutional Network from scratch (with random initialization), because it is relatively rare to have a dataset of sufficient size. Instead, it is common to pretrain a ConvNet on a very large dataset (e.g. [ImageNet](http://www.image-net.org/index), which contains 1.2 million images with 1000 categories), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest.\" This whole thing is also called **transfer-learning**.\n", "\n", "b. How does **transfer-learning** look like in practice? The idea here is that we re-use a pre-trained network and we adapt (finetune) it to our new task. There are two main transfer-learning scenarios. Again quoting Stanford: \"**ConvNet as fixed feature extractor**. Take a ConvNet pretrained on ImageNet, remove the last fully-connected layer (this layer’s outputs are the 1000 class scores for a different task like ImageNet), then treat the rest of the ConvNet as a fixed feature extractor for the new dataset. In an AlexNet, this would compute a 4096-D vector for every image that contains the activations of the hidden layer immediately before the classifier. We call these features CNN codes. It is important for performance that these codes are ReLUd (i.e. thresholded at zero) if they were also thresholded during the training of the ConvNet on ImageNet (as is usually the case). Once you extract the 4096-D codes for all images, train a linear classifier (e.g. Linear SVM or Softmax classifier) for the new dataset. **Fine-tuning the ConvNet**. The second strategy is to not only replace and retrain the classifier on top of the ConvNet on the new dataset, but to also fine-tune the weights of the pretrained network by continuing the backpropagation. It is possible to fine-tune all the layers of the ConvNet, or it’s possible to keep some of the earlier layers fixed (due to overfitting concerns) and only fine-tune some higher-level portion of the network. This is motivated by the observation that the earlier features of a ConvNet contain more generic features (e.g. edge detectors or color blob detectors) that should be useful to many tasks, but later layers of the ConvNet becomes progressively more specific to the details of the classes contained in the original dataset. In case of ImageNet for example, which contains many dog breeds, a significant portion of the representational power of the ConvNet may be devoted to features that are specific to differentiating between dog breeds.\" We'll try both approaches and see which one works best and in which conditions.\n", "\n", "c. What is a **DensNet** and why this one specifically? Let's start from the why. Because I have tried all the other available pre-trained architectures (take a look [here](http://pytorch.org/docs/master/torchvision/models.html)) and this one works the best. As for what a DensNet is, the best way to find out is to go through the original paper [\"Densely Connected Convolutional Networks\"](https://arxiv.org/pdf/1608.06993.pdf). In a nutshell, quoting the article, \"Whereas traditional convolutional networks with L layers have L\n", "connections—one between each layer and its subsequent layer, our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. [...] As CNNs become increasingly deep, a new research problem emerges: as information about the input or gradient passes through many layers, it can vanish and “wash out” by the time it reaches the end (or beginning) of the network. Many recent publications address this or related problems. [...] Although these different approaches vary in network topology and training procedure, they all share a key characteristic: they create short paths from early layers to later layers.\"\n", "\n", "Btw, below you can see how a DensNet looks like.\n", "\n", "Quite deep indeed!" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DenseNet(\n", " (features): Sequential(\n", " (conv0): Conv2d (3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", " (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n", " (relu0): ReLU(inplace)\n", " (pool0): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))\n", " (denseblock1): _DenseBlock(\n", " (denselayer1): _DenseLayer(\n", " (norm.1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer2): _DenseLayer(\n", " (norm.1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer3): _DenseLayer(\n", " (norm.1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer4): _DenseLayer(\n", " (norm.1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer5): _DenseLayer(\n", " (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer6): _DenseLayer(\n", " (norm.1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " )\n", " (transition1): _Transition(\n", " (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n", " (relu): ReLU(inplace)\n", " (conv): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)\n", " )\n", " (denseblock2): _DenseBlock(\n", " (denselayer1): _DenseLayer(\n", " (norm.1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer2): _DenseLayer(\n", " (norm.1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer3): _DenseLayer(\n", " (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer4): _DenseLayer(\n", " (norm.1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer5): _DenseLayer(\n", " (norm.1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer6): _DenseLayer(\n", " (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer7): _DenseLayer(\n", " (norm.1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer8): _DenseLayer(\n", " (norm.1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer9): _DenseLayer(\n", " (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer10): _DenseLayer(\n", " (norm.1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer11): _DenseLayer(\n", " (norm.1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer12): _DenseLayer(\n", " (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " )\n", " (transition2): _Transition(\n", " (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n", " (relu): ReLU(inplace)\n", " (conv): Conv2d (512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)\n", " )\n", " (denseblock3): _DenseBlock(\n", " (denselayer1): _DenseLayer(\n", " (norm.1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer2): _DenseLayer(\n", " (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer3): _DenseLayer(\n", " (norm.1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer4): _DenseLayer(\n", " (norm.1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer5): _DenseLayer(\n", " (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer6): _DenseLayer(\n", " (norm.1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer7): _DenseLayer(\n", " (norm.1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer8): _DenseLayer(\n", " (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer9): _DenseLayer(\n", " (norm.1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer10): _DenseLayer(\n", " (norm.1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer11): _DenseLayer(\n", " (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer12): _DenseLayer(\n", " (norm.1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer13): _DenseLayer(\n", " (norm.1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer14): _DenseLayer(\n", " (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer15): _DenseLayer(\n", " (norm.1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer16): _DenseLayer(\n", " (norm.1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer17): _DenseLayer(\n", " (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer18): _DenseLayer(\n", " (norm.1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer19): _DenseLayer(\n", " (norm.1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer20): _DenseLayer(\n", " (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer21): _DenseLayer(\n", " (norm.1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer22): _DenseLayer(\n", " (norm.1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer23): _DenseLayer(\n", " (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer24): _DenseLayer(\n", " (norm.1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " )\n", " (transition3): _Transition(\n", " (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True)\n", " (relu): ReLU(inplace)\n", " (conv): Conv2d (1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (pool): AvgPool2d(kernel_size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True)\n", " )\n", " (denseblock4): _DenseBlock(\n", " (denselayer1): _DenseLayer(\n", " (norm.1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer2): _DenseLayer(\n", " (norm.1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer3): _DenseLayer(\n", " (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer4): _DenseLayer(\n", " (norm.1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer5): _DenseLayer(\n", " (norm.1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer6): _DenseLayer(\n", " (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer7): _DenseLayer(\n", " (norm.1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer8): _DenseLayer(\n", " (norm.1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer9): _DenseLayer(\n", " (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer10): _DenseLayer(\n", " (norm.1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer11): _DenseLayer(\n", " (norm.1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer12): _DenseLayer(\n", " (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer13): _DenseLayer(\n", " (norm.1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer14): _DenseLayer(\n", " (norm.1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer15): _DenseLayer(\n", " (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " (denselayer16): _DenseLayer(\n", " (norm.1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.1): ReLU(inplace)\n", " (conv.1): Conv2d (992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", " (norm.2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)\n", " (relu.2): ReLU(inplace)\n", " (conv.2): Conv2d (128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", " )\n", " )\n", " (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True)\n", " )\n", " (classifier): Linear(in_features=1024, out_features=1000)\n", ")" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DensNet = models.densenet121(pretrained=True)\n", "DensNet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Fine-tuning a pre-trained DensNet without Data Augmentation\n", "\n", "This is the simplest of the solutions to implement. We just load the pre-trained DensNet and we re-train it on our raw data.\n", "As we'll see from both accuracy levels during training and from the learning curves, we are clearly overfitting (as expected, due to the small dataset size).\n", "\n", "Something to note is the fact that we adjust the learning rate during training, reducing it by 1/10 every 6 epochs. It is important to keep this in mind as we are going to play around with the learning rate later on." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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LTdjjvAZ7jmKMGRF7Q+xG4Fciciv26WDjwl2IfV03VGdTdwIPVpXzL8Uek/uA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AXtALYd2LVAU8VToYcF/GuAswa5Z+AUEd0UshsQZSJvME1gWxGLoMV9GP8hLFQwTZFT2CvE6x2K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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_transforms = {\n", " 'train': transforms.Compose([\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n", " ]),\n", " 'val': transforms.Compose([\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n", " ]),\n", "}\n", "\n", "data_dir = './data/iceberg/pytorch_icebergs'\n", "\n", "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n", " data_transforms[x])\n", " for x in ['train', 'val']}\n", "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=8,\n", " shuffle=True, num_workers=4)\n", " for x in ['train', 'val']}\n", "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n", "class_names = image_datasets['train'].classes\n", "\n", "use_gpu = torch.cuda.is_available()\n", "\n", "inputs, classes = next(iter(dataloaders['train']))\n", "out = torchvision.utils.make_grid(inputs)\n", "imshow(out, title=[class_names[x] for x in classes])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model_ft4 = models.densenet121(pretrained=True)\n", "num_ftrs = model_ft4.classifier.in_features\n", "model_ft4.classifier = nn.Linear(num_ftrs, 2)\n", "\n", "if use_gpu:\n", " model_ft4 = model_ft4.cuda()\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "optimizer_ft4 = optim.SGD(model_ft4.parameters(), lr=0.001, momentum=0.9)\n", "\n", "exp_lr_scheduler4 = lr_scheduler.StepLR(optimizer_ft4, step_size=6, gamma=0.1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/23\n", "----------\n", "train Loss: 0.0637 Acc: 0.7475 LR: 1.00E-3\n", "val Loss: 0.0474 Acc: 0.8442 LR: 1.00E-3\n", "Epoch 1/23\n", "----------\n", "train Loss: 0.0627 Acc: 0.7654 LR: 1.00E-3\n", "val Loss: 0.0348 Acc: 0.8723 LR: 1.00E-3\n", "Epoch 2/23\n", "----------\n", "train Loss: 0.0462 Acc: 0.8309 LR: 1.00E-3\n", "val Loss: 0.0371 Acc: 0.8723 LR: 1.00E-3\n", "Epoch 3/23\n", "----------\n", "train Loss: 0.0365 Acc: 0.8839 LR: 1.00E-3\n", "val Loss: 0.0319 Acc: 0.8847 LR: 1.00E-3\n", "Epoch 4/23\n", "----------\n", "train Loss: 0.0366 Acc: 0.8730 LR: 1.00E-3\n", "val Loss: 0.0553 Acc: 0.7850 LR: 1.00E-3\n", "Epoch 5/23\n", "----------\n", "train Loss: 0.0367 Acc: 0.8776 LR: 1.00E-3\n", "val Loss: 0.0300 Acc: 0.8847 LR: 1.00E-3\n", "Epoch 6/23\n", "----------\n", "train Loss: 0.0198 Acc: 0.9447 LR: 1.00E-4\n", "val Loss: 0.0253 Acc: 0.9065 LR: 1.00E-4\n", "Epoch 7/23\n", "----------\n", "train Loss: 0.0135 Acc: 0.9665 LR: 1.00E-4\n", "val Loss: 0.0266 Acc: 0.9065 LR: 1.00E-4\n", "Epoch 8/23\n", "----------\n", "train Loss: 0.0096 Acc: 0.9774 LR: 1.00E-4\n", "val Loss: 0.0267 Acc: 0.9003 LR: 1.00E-4\n", "Epoch 9/23\n", "----------\n", "train Loss: 0.0104 Acc: 0.9727 LR: 1.00E-4\n", "val Loss: 0.0310 Acc: 0.9065 LR: 1.00E-4\n", "Epoch 10/23\n", "----------\n", "train Loss: 0.0066 Acc: 0.9867 LR: 1.00E-4\n", "val Loss: 0.0315 Acc: 0.8910 LR: 1.00E-4\n", "Epoch 11/23\n", "----------\n", "train Loss: 0.0053 Acc: 0.9922 LR: 1.00E-4\n", "val Loss: 0.0340 Acc: 0.8941 LR: 1.00E-4\n", "Epoch 12/23\n", "----------\n", "train Loss: 0.0084 Acc: 0.9852 LR: 1.00E-5\n", "val Loss: 0.0323 Acc: 0.9034 LR: 1.00E-5\n", "Epoch 13/23\n", "----------\n", "train Loss: 0.0053 Acc: 0.9899 LR: 1.00E-5\n", "val Loss: 0.0337 Acc: 0.9003 LR: 1.00E-5\n", "Epoch 14/23\n", "----------\n", "train Loss: 0.0066 Acc: 0.9860 LR: 1.00E-5\n", "val Loss: 0.0332 Acc: 0.8972 LR: 1.00E-5\n", "Epoch 15/23\n", "----------\n", "train Loss: 0.0037 Acc: 0.9945 LR: 1.00E-5\n", "val Loss: 0.0328 Acc: 0.9003 LR: 1.00E-5\n", "Epoch 16/23\n", "----------\n", "train Loss: 0.0033 Acc: 0.9969 LR: 1.00E-5\n", "val Loss: 0.0335 Acc: 0.9003 LR: 1.00E-5\n", "Epoch 17/23\n", "----------\n", "train Loss: 0.0047 Acc: 0.9945 LR: 1.00E-5\n", "val Loss: 0.0393 Acc: 0.8910 LR: 1.00E-5\n", "Epoch 18/23\n", "----------\n", "train Loss: 0.0040 Acc: 0.9922 LR: 1.00E-6\n", "val Loss: 0.0328 Acc: 0.8972 LR: 1.00E-6\n", "Epoch 19/23\n", "----------\n", "train Loss: 0.0056 Acc: 0.9922 LR: 1.00E-6\n", "val Loss: 0.0332 Acc: 0.8972 LR: 1.00E-6\n", "Epoch 20/23\n", "----------\n", "train Loss: 0.0063 Acc: 0.9875 LR: 1.00E-6\n", "val Loss: 0.0331 Acc: 0.9003 LR: 1.00E-6\n", "Epoch 21/23\n", "----------\n", "train Loss: 0.0067 Acc: 0.9867 LR: 1.00E-6\n", "val Loss: 0.0339 Acc: 0.9003 LR: 1.00E-6\n", "Epoch 22/23\n", "----------\n", "train Loss: 0.0055 Acc: 0.9914 LR: 1.00E-6\n", "val Loss: 0.0336 Acc: 0.9003 LR: 1.00E-6\n", "Epoch 23/23\n", "----------\n", "train Loss: 0.0055 Acc: 0.9922 LR: 1.00E-6\n", "val Loss: 0.0350 Acc: 0.8972 LR: 1.00E-6\n", "Training complete in 13m 11s\n", "Best val Acc: 0.906542\n" ] } ], "source": [ "model_ft4, train_loss, val_loss, lr_dict = train_model(model_ft4, criterion, optimizer_ft4, \n", " exp_lr_scheduler4, path_to_save='./densnet_noaug.pth' ,num_epochs=24)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There is a huge delta between training and validation loss during training, with the latter being significantly higher that the former.\n", "That's a clear overftting scenario\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MWWCtXd1it9OBotDPVODu0C3AH4EXrLXnGWN8QHoUx98s3FqueHslU4YPiMUpRNylnZng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\n7jgvj0lz1jP5i1Luu6hLgybLAPdf0pWNO/bwu3dX0CYpFo8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('There is a huge delta between training and validation loss during training, with the latter being significantly higher that the former.')\n", "print(\"That's a clear overftting scenario\")\n", "\n", "tl = pd.DataFrame.from_dict(train_loss, orient='index').reset_index()\n", "tl.columns = ['epoch', 'train_loss']\n", "vl = pd.DataFrame.from_dict(val_loss, orient='index').reset_index()\n", "vl.columns = ['epoch', 'val_loss']\n", "l = pd.DataFrame.from_dict(lr_dict, orient='index').reset_index()\n", "l.columns = ['epoch', 'learning_rate']\n", "\n", "tl = tl.merge(vl, on='epoch')\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2)\n", "tl.plot(x='epoch', y=['train_loss', 'val_loss'], ax=ax1)\n", "l.plot(x='epoch', y='learning_rate', figsize=(12,6), ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Fine-tuning a pre-trained DensNet with Data Augmentation (to combat overfitting)\n", "\n", "A nice and clean way to fight overfitting is to perform augmentations on raw images.\n", "\n", "The idea is that, as we have few data inputs, we are going to artificially create more, applying random transformations (resizing, cropping, flipping, rotating) to them. We basically feed the network images which are always slightly different, epoch after epoch, hence we don't let the model \"remember\" pictures and eventually we manage to reduce variance.\n", "\n", "The approach works very well indeed. We get to ~90% validation accuracy, but most importantly the learning curves look much better. There is almost no gap between training and validatrion loss." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_transforms = {\n", " 'train': transforms.Compose([\n", " transforms.RandomResizedCrop(224, scale=(0.7, 1.0)),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.RandomVerticalFlip(),\n", " transforms.RandomRotation(180),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n", " ]),\n", " 'val': transforms.Compose([\n", " transforms.RandomResizedCrop(224, scale=(0.7, 1.0)),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.RandomVerticalFlip(),\n", " transforms.RandomRotation(180),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n", " ]),\n", "}\n", "\n", "data_dir = './data/iceberg/pytorch_icebergs'\n", "\n", "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n", " data_transforms[x])\n", " for x in ['train', 'val']}\n", "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=8,\n", " shuffle=True, num_workers=4)\n", " for x in ['train', 'val']}\n", "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n", "class_names = image_datasets['train'].classes\n", "\n", "use_gpu = torch.cuda.is_available()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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2oXw/8eAcNrm8DjhkLG+BTYJfTTw0wBSjs7AdVQdPaj+m8H0BM/DmMMfAPRah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IHW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YTT95xeQGWuMgPM1PFt60QaA9jGyZIycQ2cJhxDXQRwVPSsxNKIDP89bWDFN2Ke5zfX1ToI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\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tEC/RD+LHB088fV5Z/vACBfpILifWwFgvxNroUYi9ITkS1ki3AjkxjoJcFiQu2A8rFgv2\nZozdOGKhHDuP4pLH0AdSO4HB/vtBYiBbp6zizKjHhaUaMXQw5bK41wE5kbK5bNECQ7qH/MggWCeP\n4abzEUpuBNwgO4zKkjpxVMY5XP5Y4Vw8VEBE6CHQh6dNbYcXi1GEUQpR3Ax2vb1QqMQxiPdANKdv\nA+wjI21QpDGice9C+dE9Ik2c7ZdT8qJom1SR2zRnDgtRB28pUcRlPsV2WD0ldKwr4IzD0NXp4U2I\ncxFY0wBRxHbMArtcuJyvBFGKVWIovNqKpUC1lSaJS7/z0N+49htbv7tP4Gvj+vUrr+sTYLSt8PKS\nuUZhxjaQV//5sHaO+2C9KI9bp7iQg/FaGZfNWUNycwli726UpYZ1Y5QAa2G8giQj9o7eO+FtJ/60\n8HC7kUclLULZYFsrzxe/OZ/Wio2GSKeMk/1IHIfy8r9Vl97+djAuyTfcVqk58hgPT4a8FPLTMm9g\nYwyXYolBPjrhdYclER6BYjwsfuO+/T8NycKpwmu88rYn8r/4mZiNH8odyyc/rj+zoqx2su53tv1g\nvXTWT8r694TwpmxFCL+PeDRCxE7lPIVv+8rdFr7lC//s7/99AI6w8tB21JSugcMK90kxuu6v7rvy\n8o0WC4mdY3TG52eWdmJN2fDglos6wjaGso3B0k5Gd788EWis/GCvDDMe6o1FIJZADIboINVG3A9G\nKfSnRw+sShlrDiLGNaP3k/Hgk3R/VuL95OfPv6LsO+n1TtGDfBwQE0de6NnlRsfTs0v0cqLiC/0S\nvOvaNmGkCBMg7OLsWHnOSIzkxUirIikw/uXhLPqpBYxbIl+F8NPii2sUrq8dGca6KOdFqT9+osdC\n+zs/IGWwhH/JZi5PXtZGDJVjCHSjPj2Q4qA/XCEENBfoECTRbMFCpETjod95up4O/sZCPafMx8Ql\n2ilQe6Qebqa+5k7OBkOcBaXG/rNyvgqX2ChxkJ8DIfoxHddnyrLzVRdsScRV6LFgI3qwyHmiMaM5\nEaoDwXH6gKbRCNqJh4OHaQy6rOztwj1s1OdnaipU23gKf8lTP4j3OilngT4SKg5S04W+u8m9dJcf\nf60P3A6FcrKHC+vnRAvGMR5Y5XT7i7c3xiGkl5PcTrIZNV9AX6hxJnuoUE/fUFzCSWJHo5A2xTSw\nJqNV4T5Wxuo+sVF9TgqtozlT+kklMSxQyTywe2MtirPKA2hwZsKIhSbCz/X5Q4c6mOt483X8Jr6m\n39WBvvd6RVb/ym3aoyxz/Q/MNbw62FfMazUmkPS6O3hUZz0nV/+ccMLCBAgnG+29UbnHqQpRZzTO\nXBsPNAiTOTibvOHmAJVdJiB5+qGlAumzFzz9gFW/N5wNr90Y7qU8ZtOz3OFXdQKhGW7mtZsOqGL0\nCPfcuYSBRuEt+qFqg2HCe/BaCkaJxiKKjsDXJjTg4QGeTlgOeM0Ovv3BvEa6mDeE46yhR/dx78tU\npTSXYRtQ3sNUJqj60QwPXoPplIevszaTd6z8Ng9+8XEZdx8vw5mCFegPzgIc+JhUvCY8L/69RSDs\nE4Dyvp7bkIR5DoFvHQ5XqvIc+GvWMOE5oRchbML6JNxR2lPmftl47Ss2hLQ39DRab8R9IKpIUFig\nFA8aC9VDvMLixeOxLXy5PhH6oFagLHQWNhrxzwXeOv0PsMrg8zh4W1e+Zl9j7D1k6aa0U2n3jn3p\nxB+SS/JD4K+ePvMYb2z3HQ4jpUh6DtSQeAuFO4VUb4xt5fn1lVWbA4UUbI1YFmz1miiIYpeMivDD\n34Pj9JTgdoA+J3pK2EPjx3CwxkGKykHmy5kYeUGTb1LjcMBuS3A7AuMw0qMxujBUkGas22Bl0Esk\nFmNMKWDKkA7F7oNEoEpiT4X9snnNuSV6CDwcB6Gob877YJxGWvwG7N3DQR6uU5prSg4n2/0L2gKp\nR5op4aLwey/ex+5po2OJgLJfE6wrpTVPGU5uy3PhJKsHdLR751gLgcTjD77xtqOz9EbXwfKm0CLS\nIzlPgDDD//GXjx7IIkbWznbbKVulDKNshfy0Qci0kTEd8Hoj9Dv9pdHuLpvMw1yZYH6Ngp7AJAAA\nIABJREFUrKuRVl+7k3j9nXJ0Lzb1wCjJEQvDQ14W4fjpgUMLuxa4NbZ+53N8QwqsyaB3xq0TVQl9\nuAUSQrwIsQgjJ+RxElvOSuluXHoT9+3e88ojlY3B023np/aVxwdhWQLcG3YbaDVOChoCsU6w71rQ\nSyY9RJYklL0Tx0kelaDdZdZVsRjQS2aQGTWjpI9grLIMrs+V8iDoJXAgZH1Fg9HHYFmVT58HyQK5\nF2RAP4Xj5mCarAPJglwGxnDAR2F8Wgh3Zd0VuXm4WJLIGjvJbuTa+Hy+oubBZseIDHVf+KucbJyE\n1umnsEpn0ca6KOXidlQyZsjFMCT5NXItjbANPpXGNQ5CCKxSncV16cTcuO4nS220t8F4/esS478Y\nn1xFl0736LZvlNHoWyFk+FYeafFKPwOlQFVhq3fQhSCd1eakL97wtRCpMZMejFCU3Ctng9Ia/UV5\niQUpPkfJEmmSqcUZO+P5AvdK3AKlGBLcfqmcd2orvPTNbU5CZoj79Jere/mGRb1GG4beXI2T5EQi\n3MQD/sY+6GrUGMldiBUQIWKkzTiOyOvPsJQx78GVn/t/yG4/AyDLimlkBGffjp7J7SDrwe/KP+eH\nz3B5e2Ssws9/yPRt4UgX+uXK0EC6G8voaI+oBBoJs0A8Tu4tcouZt5bdI/o0LrwxciT3jg5hxMSu\nmV0Kxx64jgVE2PqN8TIocZCmR4Ys0Uk1IRKTX59HSx9hSzF0UhRnD+aAHg07PPAyDqX0ilikmXit\ncHpg5miBt1E4NHHmjMVIjL5YXvJBQjnXzRO3pXNhJ6b/nwHC/4/Hr83sL+bf/xL49fz774D/5Rev\n++fzub/5EYDpM2hjFnTBizsZkFbvBsc2MbxZ1MzAV09gW7wgqdMMe8F/NzYvNsJkeqX3A/fmAN1c\nIpsiH6w1R3P99/rqxSU4WCbqAFY4+fAzFLx7FwRC9uInRtyfcP7OJLR4h7x7d5zuHYg4pR/vDMTQ\nHaAM6ijw8PsADQ78NZxZ9u7/AnwApkuCx4u/7unin/HlDxMom2mN2ARFcbDODJbNj3OG9NCH+4SE\n5J9hkXcP8Q/2ob2DduLHaMyuPF4Yj/G9+2xhjv87CDs7+vIuZTEIx3z9u1djdxBXJhA4DL6Z+w6u\n3dkJy/s5nN85zPeKNlU2Cls19vjL603IYTDKgnWlNSWYU+ZyGoQYGNEXduviVXhx+oGYA2WFSkyN\nyMleIDbvNo4Qv+ulSkBKJiyBYcVJpBYJErzDnJ0yqj88wZtHTAdrUAdlA4mJhBfz8dvpvgZ3XwhG\nhJi7e7kVZ/olBsNk+iAaI3pXpiSlY0R1Q9g4OhElqKFB3QciBFQGNi+oIDhltEzaQXQaqFqgqk+o\noXdQIS3Bb0JTYnf6RWwuR6XB0MFYFnoX5DQ27d/R3CFY9cIuB/fyGkQigXaZZvgpEM6GKjMpdF7A\nzGIlJUIKvmAEkOYeXWOIdy2je8OMGUeepGMmLO0gd++oukDCz18IkFGCuHdgQBE8GYwOqoPSdy74\n5H9GIw7oeyGK+Xy1d0g4CLUOtsXYFgf54t4ZV99gjLvTXdLZ3AtwdrDaw+ZUkcMTtfuHif+Aq4+B\nzHNo3chZ6BWu8USisISKlIFYRw4jPCTaLEq0DXipcEn0aox145renJo+01M1zdS7AzCjXRaXpb56\nKIR0o//m0c91MM5XZx7Zi3JeLrzI5jfn1Y20moX3YGeKVgcHTwdhyqjYPZB/ysRfZ/jzHfvdAp8S\n403gr3bW3Pg0bvyeT84a1sHt+kjuw7uSEjg0s1tGhxKCd1Ev580/477zMF64Pf2Kou79sUj3Sygn\nqha2dhClI+LSkzIG1+POvm08JA+LeRg3N5legBTdV06VhLNq037Sz0p/fsBuBzCZdwPGOaAOgsD+\n+IgsFzftXgNlH+T+Ro+JmuYilrx7XC8XzLyAKuOkl4WkJ82UZI2zrB9TmzwVn2hV3d8IdaB4Sm74\ndqBv3QvnHAgh+Jq6BaR48px0o+WVWh7gWoDMiJkxGX6Id7yXftC6TF+ZRCMzSIyRnQ2SEiNGVJJL\nMsW7twApu7S454ziAR/BFMnemReBZINBJ5gHPUn1zcftNTLUgeilm/NrnyGkQH6IJFXa+oBNA+0R\n/PuJ+fydtTHEC3tyxMQToe0j4UyhecLkqBkV8/Uh+LwTHjMxLshfRfSEtiW4ZA5ZicGzL2129PX0\nBbWbcDsK3+6R/DCIR2VJRjihbwnJ8MYDZgEbd/c6XTK0k0UPZ4xrAoRDk3sS7l4oJhF6MvKDIGMQ\no6cSakrUdz0u4WMNFx2EIeiU4C9pUNTna4ni6/n7uo6hIdGssI9Cs+QsxxncJvhaPWZgiZp7BUr1\nOkvnWqKLr9frfE3yqRrBG3o2m4xelMEp08Nwrtsd/7zZgHf57Alc/DUS/LkCHBna/G4yZk0J7jsV\n+LBdiY0Pb2eUD0nye6Ejk4FXZw1iv2DXeWKgXypxzs1t4yP9+NbgpwGPz7AfDnqpKmU90Jg55ge9\nZ5oV+LDYeVe+NPPjrsPD2K7J+5zt8Po3zR5OO92TtG4zoKR5TUmE3SBOmfG7R7m0D1LPnCv4UHfw\nXoPOW72J13LGbNyLH7vfV16zvatGavo+BpZ8XFr0WlYSvJvoS+A7sOu9CmT196Z6Qxu+15bvDy2e\ndi9b4Ai+yYqqrBeId2fcRQIhRtIfd6jmPtlByJs3QAzB3umOuM/Ulz/5kT0W1l5hg3gGZ/F0iH9S\nCK+NJTTiMYi3gSq8PG1+wS4BeU/2/ab0FNFXpWWDz4UejBYy/fKI3Bs9+Zq67i4ZvIvP9Xcyl3YS\ng0KHthS/DJdE18A9LuRgFBRdE2oBNUWboVdBige/xbmJKAw2a2DC1yroAUsY7PM2O0L2EIPpf00K\n7KeQHyNdvN7IZlgO/gtJyMn3HGXx86SrYAcMESwH1gUGym6BRZR1U5YC/RCCClRlDKGsxn1qy2Ve\nT2XSgEtyC5xTIuFornjZIvqHE7sNTmBUZ+bEnw9o7i+cWsPW6Oy1aKzqye3hObFUJRUjR3VgJwtl\nDNrpLCV2V3h4OI4DhG97JiUHL1c1Hh4E8qwpm1vupDCIo/oNcDZGU+TE1Rrm7C+V4Kz5lAiLM7tj\ncB/NMOdZl2+6t3YM3hSTyQTSkZBm5ON089HaaUkI027KumHV01NtiKuAumHBRb9tzR5Apw078dAR\nX2I9VCFFig0Kg2vbKY9CvgpphXHMhN4ojJznsTgjngABJU1T+TAG0hyQf9/DW/dj0RhR4vROFEQU\nCS4/X8rASEQ6ZRgP3dBhNDydeFk8/CJGQ05vbrYeMYUgDqz0lJAYGMUnE31eIVTCqMS7ooSPsU24\nrUiIhk1GVRiKjO7nIgmyBmKMzszTQSqGrBFdAmNREMNUfG845YMlKkRli41r6G7zLcGZadkIDGop\nhHNQI2j4PgcBvMpKG8JjgmtpNDdzcL9TYAzh7JFmnpIcm7ABjcAGoMZoDl7oPD/kQJhKsaUocVHS\n2mmLc0cN5biunMvF7XiKs3lDO6gEt7NSQbo3UixBTEKqiobIw2WwPEQe8s5DqRjBrw1xgNBVRZP9\nH923u6pba9UGWzqR0VzxVdw/tp1CI/D6DbgMrA+0Kuf5AyzClr6w979LtBsqhqyddOsQK5/XrzRJ\n/Dk/8JsfXznPTHtz8Pm2PXhg15cvlOi2WWqNiPKiGyriAXABxlSbZVN0GIXGMOcSWs5weiqxZCfg\nvOmC5uQqlOF7mt6FlHCrheABiIlBJ00PZWFUUDE4FUnT0gXDontkajW0y9yzDlc+iO+1h/q63ybz\nVHQQTlhpPEjzvAr4qC8khe//+Fs+/m0Bwo+HmZnIvyFvERCRfwD8A4Cnx4cPkI3gE2Z8ddlh2/CN\nMXwUre9UNht+wYbk7L9evWMK01uPCS5Of0MNDrwZfEhSg3z/mcVfF2dhZuKAW9RJIxZ/P2nffwYT\nKoZVL3BiccPrAF7Z2WQaMpmDUwpsEzizBCOJS6DnKOqUcaCTBWle7C3RCzJmJzvO0I+++meoTlBx\n7gd+eIBvb14M3w7xzkrF3zzMcRP5uImTOR70/j0+r17wDabEJsHucyPv84/OMWsVSvExT4ufs+N0\nQC+oj6mYj+EFB/QGDvqaehc5DUiHf/hoky15evFuDeKTf/VzXg8nvHvVe7fc93TA9MkJEIczPPPg\nI1AFoFhjBKNZYnQvWuo0aZXojI9gykiZxCDfD8gQmhGeFuZyR8pGRhnavFDp3dk80cdWxWnB2txw\nvEukLgvdCufIng5ZPG2qP650DZwY25jv3TuQKGqkIfRX3+COQwlrZAwjilMp09yZWBfOHtzT6LMS\nc+AyTk8El0FzZI3cKmKdLlM8Zw5e6IARk4M77f1CdXnLQAgm6HB59tES6zI4anA5c/DrTNosltQQ\n8w3yUJ2TbKKOThyBRSpRIWlwY+gVgg2W4XKEZoka1ylz8ESpYYYSIBUPG8hzcgiTkpGF+B4oM893\nHwFj/h7vOibI09NMmgv7I0qOmxuVF4io+1eIusRQXHYHPhflfjqVvwUv8nYm2BgRUUjwtHYInZKU\n/HojZ0NTZuyKjkYjUi0z4saiN3S4XD1iyNOCFIUvjbhGR9cNRonomhi9I6O5TK7Cw2cvFJcwaC2w\nqctI3/oF1JBfX0jnAf+iYyk4M2RdKXTu5YqoeDNlrgxNnYIvQ8lf78jdTZvr89XnqoqzYI+K7Qp3\nB5vjUVnTnZd4JS3Fi0ibN6oZD+lg7Sdrq/7cAvmzD2x6Fka4ELIxDuO4BeLSud8SL7rxebnzv56/\nZQ+rS5+tYibcrXBqdjo+EFvlcry5ubUa7IOn/pW6LbzIJzBYk7Nk32PlR3AmYLngIMuhbNN/ZQT3\n6iwbrAh5BQk6593u3b+SCbWRXm50IqOsjPoGD2OiJkBXB+6Btm3eIDg7uyzkdSWflR4SGv0+DZfF\nu/4huGRIOqIymQC+oSn3+8fclrKhEpDggSAihlmbTang92h0P6TwvKFPgm2CFZ8H0oN3/vNdKaGx\njJ2xrqwcrKGCCcI0PfbZzaUOEnmNj5TooSNt+LwtiwNOSQfNEtd456J36vLgidkGVjKqEcZAotHV\nu8iYeaPE3hdDPihhX79m0lkpdpCfBHYjXjJM6UjmZJQFohBsIAhjBG+4qPvDSMM3+BjRKlkquXqB\nJ2pIH4QmRGl8syuv8cq+PHCU61QORKw6+5sVNAdqWFDz7pY25V2qLANiF1I7kXvgEjzJOsfs99G6\nOftTIjkXZ7IFdaP9pbC2nYNHkp4EDV51j8CmBw/lBt396qQPhAAhuzyYwMnigJ/5XI4IosOBsfcm\na0xIysRszj43+/CXmxcaAHvLnmqP11TvIJyGWavNWq4ygTSZ0tz5Fmrec9LmvQ6i1wHp9EPK4s8J\n/tkVbwjG5HXawBOS14p7pU6wMKrXCsfdv0OfYWcyP1NnLWINwkxAlhNsBqVY8jqH2cB9P94xaYP1\n/fs3IE3Z8tz8RPXv3MVr0WzOULzfYL/B7/4ULHfK5R2w7igrpoGmgYx8jPcSZ3PbXBK2RgcGv9xg\n7P75ccHrm5uPWypzE/Zu0XmdDM3hku9ucHkH6MIEN8XHaTQ8IRvmppaPGnG6dPije50W5zIbZyJc\nU6//+oUP2x3dvM5Nr/NXJ+AfEu4tyHc2pgS3U6rmr8vZ36PMmvSXEuOYhSjilkICLUS3AWmQNvE3\nAZecBldEvK+Z7RSv6avR94kBq3H7Oxe+svH6/MTndqP0hlwDQQZ2CNwr65OQQ/DwhNtATbicBzwm\nSnfvMC5Cf47EM1BLhDflePAGb8qBcDTusRBVsTQ3whF/7Sy6X2tkG4l4Dk85Ht74rRNEvImHNj31\nNtPSI/Fi9Ls541eEx9i98QVs0olD2TWwt0daCoQ+aCmS8RADSkCWQG0RSvBMD40s2RjB6OIN04jN\ncygeQFhdYlVMGV3Qs6IXNwB9GJ2L+kYVhK+2Uqyx1AOy0N4c7N/vXqtWFXJQtuzzSsuZsDeO64YO\nD9sYE1AXjLtl4luH4rYcSz/Rkki9sxSjMIiLy0fHF0U3Z0GMSbqgexhgrz4GXQLjhHEavDR49OCp\ny+p1+GJKWbyuH+obxFJPLFSkOZNFzo4O9bCKGTA4oie9WkqQI2ENxKQuu2Sy0JrOQDoPr/HD9HM7\nojPv0EY6OnY/oSltFcIppOyhGlLNLS2GEBqEpl6PJEdfRxH064CbYEfAloAFJw48UmnNgc9lKs7y\nBdJijOhBbkPEQek4J4TgVkWCkXTAaEifFi3Tt8vmNX1+E/YQOCTQTmMEJZZOiEIaHrKGjtm8G8RW\n0QEdIYXAshTCcKC2N5dbtvGdxCDBw/QkG/3iVOjDsu/tk2GpORg5HDSQoOSgBOMjGEuGuhR5MW+Q\nxYAkJXUjt0EsII8JfQiM0bAxJ6DhbxDEiLW59cKp9DIBoAgqQo8JIVJTIaTBGWDIL6jRwJEW+mzW\nSo7U4OzDHhNf0yNf9MqYoPqJUHpgDN+PDYkMGagND3O9Jgd+k7KWwRI8vTcvSl4Hx+YhgxWItZHl\n5Hz6RBSXVufTvTmE5nvii5MnYnClZFIjz25aHt2Za1Hcg3yuZSrBx1LFrbZmzTksMiqMUxlZiTb3\nVcnBxbat6Jq5vd5JCFZdhszZMO2MYtTtk9dxyxfa/QomRBqFnbLArz4l+ibczpXb2wJbRR9ekQj9\nHrxZfVM2Oi0kKpnWB7usDBECnRiNop2hsNDpZg4iVr/evKIUcjFGciXRTRayuqKldw8hkuiqTQ8s\nhToiVYN7lgYHr6meLB4mu2mAgywWHNDH62cMDhV0KmnEJomFRBhGiEqQRgydmIytmINhKfg+533z\n+rd8/NsChH8lIr81s78Qkd8Cv5/P/wvg7/7idf/OfO5fe5jZPwH+CcBvf/N3TMP3xq0Ctszi8ZvL\nE2r2xNoxvOgIs+CT1Qu8WB0oWvEudUkTHDyB6J0WZhdb3iaQEgETCp4MV5hdUP2Y4z7YcH2d5IGK\nA0UDCO7vVRFGgEvx7+6TAoRH8U1BBr2Zp8W90/bmfkeTy0F69/2jzf3yGvxPNFgu/n1XvHC6I+zR\nF9geXeJrA8bikpdyuDz7/3qD0oXXF5eVJPwG3+amoFxn92Qa+Ryn///1J1hO+Cr+vkvyojBGB/Na\nmIWbzo3B8PfOgERnLpZZdO/3+d2Sj+cS/ZiT+XHLLGjj4gXnSA58jg66e+HdbsDFw7ZgnvsBL4qn\nVhdPC6zBzXDj8HFsuEQ6AW1xSfL7o0T3olqk8bJe6Bo5zkkzrj6gwQ5SeyNG87CRP35Bfv1AvEN8\nzkhtZK3wVilvLu2tVZCUMAnELTJKJknzrlnvRDE2hyJpEtwnzBpgFA6+xmfk04VdI5dw8NAbyziI\nZ2X53OjZkMPQOgHW185OooXi6XoVQu+8fnM5g76e5E+eqLRcjKQd2zt6Gy4zI6CPwaW5Y8JnIUBJ\nZB3wafGOUAmQIqkNElDyyd4iMcLbmdGRuZ+JS2w0E6JE6ojumjcGSGRUowWnWDdWP+l2o6iyvBzE\nBUKOE+dTrCmpvuGOBR7UYWnhPla6JB77nRQDIxXffZwDRBm7s0RUhZicjr2k7rHww5O7lO9zSDCD\nEJH7SbTOyosb9+fVwZe+k86Tst9mkYqDKRbQGdeY6tSmvSgkIYxAmAmVD1//iOZEeTlYbze/Lr5F\n3v70iXFO5Pxs7HFF189kDmoTyInr73cu9xshLph2mmZGikQ1alnoy8pYFy94ngy9Vr71yB9PuITK\nYS5JOiRxl4X+U2Lngv5GOKRwlpVog/tSMG54IEZj4WQ0w2zQzkSsStsNRuC0zBlW9PNGGgH+4kTG\nIAR4VAdXjryw9ZPYO9vxwuf+ypPdeC4nT+VAO2ycxDiIT0L8k0L5s0z60ZCrd/BVlXY32hBeeOTn\ncOX3+Zn/e/yW2+UzoVxQNfaZFr0f0Tt/5p2CyPDAlD7JryboTD3+qf6Rp3FjyYqV5CDdp41wJJ4+\nF6Q10iHstdA7lMUnf8GwJJS5csZpxmwhc+YVpDJCZP+5c+8Q10bsO+nPf0aKe+DFz8W9f7LTZ0Jt\nUCJpNFgTIwUWGZTNOH4IhLCznIM4GvGcHZ4ZsNEsMN4OwrcXj6AH8qIOTr9v7Iejxz0v2FZg64QH\nB4jtx5V+SUgcc/PdqbdAOg1NyQOWtPF4/MyZnljGyba+eYOiDd6OBa3G3jIjF4ZGXllZDp/r4l4p\n94OwJFI7WR9X8mNktAdMO/XyQLsbvbsVwLUcJGmcr868LdIZWrG8kY6dst/pXSiqfErdY2Nn8yDl\nSKqGSEaWTFsWtw7Qg9I80fuxfXFPr3Zwrwsh+yYlZ1/os1UkdLJ18nHQRsBuyi0WvuUHarogWcib\ne09ZEkYUXh5/ZNmUI19peFKYpIS9nUhX7FZ9DVwiP5VvDJsSk+OFej4jiJtTR3OPPMTDpLbIiFek\nn9iSPXDIAvVVOfbAccK6eeFy48J2VkrEGz1BfYOYA2vodHXfTVNPIzYzhnq6qEzPyLhG70bHTjg7\nCtTuDJQ2kxM/yevHtZXe/feYjJHhYF4dXt8Uc3xG8ndwKc36R8wbtnkHuTugnBdfsxu+nofFXT1y\ndAW3BdA75BPGxkczI04waVQIb/Cpew3Ysktsx7QbSbNhLDN47hC/PcLpNWCctZQyG7u+ZPDNZi02\n05f7PhvZ8r02itElVo9pNlrxOqwGb6xCoBAmkrky4oZVJX00fJWsRgruf6Z4g6ZVl5LXQ7AbXMTH\nyNnZ3ii15oBoX/371gRvTkbgrwRo8Nzgag7upTQbq9nrsfcmfBqTYTkZRrE7I9EqH2zKOcUj71Li\nDSheW/e5FLsp8wQv+Y7tC77vYTb7dV4joc96MvqxlYkeRvx8vT/UhOPm0jOxgJwDOQesQl8yicoo\nkTic+daXRBwuczxT5BxC6J3WlLZljotLz0aILLUxtkKN2T1/q7NL+GHlrB19OVjoSIlcQ2NZBUnN\nwSUzgir2HLjp8qEOAEhvJw83ZwmsrUISlmCk3jkq9CWjx6AG4e35EX1LPO6v5L+o2G83bHcvxR4y\nxR3jqOoD2k9FT5x5HWQqQYSf1gol8Ace+EluPHPyvDVqNL6MwE9yQhQuqbNr5Fi8uH7JK4PAUzKW\ny+CexJsMayRkRdvw5FEblKq0JuxnYL8ZsnXyOcjS2WYIQz+96P5iKzpWngeT4GHELkiJ6LR/uVtm\nXBbSozMs9N2r02bI2yVx/OqBdjqDkCBsbzvxdFuX2AaxCKkEB40jxFXIPwhZDAmOUhtQqzdJ9sMb\nxXqJ9BiIXZkCCq658fl5sF0cRLSQ6dU90aIql6+vRJQVD5+qsdBFkJAIJWJBaIswyoJcV4IIOQhB\nBEJwj++qyE3R4UCSZBBxz9hWFoYldl39noxKUiWelTCUERJVjbx38h/u2BLpD4UeFvqa4POGPCaW\n4imz63lQ/xAJPcNjRp4DD6nyvJ28fo3UGnj4NaxPgfgQ/J4qRs3GCAH5VIgpQPNAt9VOpAnjq2CW\nCK8Hsjdir0it6Nm5vRn7Dj/viTNE7h0sKWs6yMXI1onWIRpx787kq90L82FwJuzL5qy+IDRN7vuI\nYHGuVafRdtAsDAq6JI68kVOlLEK+GKEr5VSSeeJwToNOpi2FthQIgYhNOxZhyVDMSEnJWXhh41va\nuKeVx/7GMirbBC05HdQs+0Du7k/aEGhCvwg9Bc6yuSoyNUpJ9E8NLX+dQfhl+eRe4PLKmRbf35qD\nyL0LyoB7h+42TJexE6LPJyShjUjRhi2RSGcskRI8tbe06hY4vSNDuZbBmRKpdqx26iZc316IZyVm\nIdWT5SFRk3D2RH+njF9g1ITGwG/iKyUaP9STkAL9ANNAEJyZmZ3koeLhJ6dm7qz0auy/9w7c+FVh\nlwyxEpM3KEa6UMPGD7bD3sn97gnw+43UTuI4qXtmjAcPsCSTUiWIsjzcQYwS7lRdOLUQPifKQ6Cm\nyv018/vDveTsL09i6xxtsKVGlYDtSg+BszgpJ5+VqJ1tc6JMj4k8hBqUFwuuVBRhCx0LkXsM6A8X\n9FRGTpTUkWykOK0L1O0K9l3Qi6cO5+nLps2VYNrNbSBSRochk3gVD3Xyh3QCAR2RoH0mGfu6u1Zl\nSYPt3IkdVDI9ZVop8DA9Y/4NHv+2AOF/B/ynwD+eP//bXzz/X4vIf4aHlPwZ8E//Nm/4LiMI048E\ngdzdf24MLwL1XfKBF4LWvIiT7JIEZYJM713t2T0m4gnGAchQV++AtSAfHnkpeMfXxH8mc2lG4x1E\n8O/0IdGY45wVQja6uHm0LEzq4nfG3CgOeJ3Bwcgs5n6H0zJAx5T/Ni8q8zILqimtCUncQH4WbtUJ\nFM7im/LSmQmB7pCmR+NbdEBzC14QPm7AgEsy9PTC3jK8ODmFMYSIA6exeHM+vPv5MFmZs6ijzSbz\nHJsxAbkyJUDRcNPcDi14J0XmOdjwRndSPpig7/4zIwqYF+HvDIMle0GedpAHL/bPd2IbTv7Jc6xS\n+YUsaeCpkUzA6Bf3xnsHr0pGcvQkORW6BrQPxAZl312+EYTUbkg00su8GW0hjo4kw/ogaSPcG3q9\nYFmgJFpKsCxoKs6SPPfpDwLglPNgiqpwtTcnWE15Vy7OAruMg4WDnoQ8DBZjHH7Ni/oFdqj7S1n3\nCSUNMDPaCaEI4VulPNqMym5+3o7maTbXhfjmgRbalZECl9KdsYNvHkeIDhIml0vI2UgX4dKGm++H\nwZs6s7ERydFvqKJeYOvh3lsRJZsvUOe7tmiNhKiepLrhJtAEpEw6d1jgNmhS2Jcn9uWRAy+++14R\nPIEaM1gz/dbpKtjpDKek7wbZDhSW4DqrYOaG/VP6YMKHX2U4vJszxvArrCnxOBgydWgCmiYbUd2I\nXMKUMIt3cmmKLvOCWzKXry/k86Tcd7QXzqdHysudVIweEjuF0g5C79Qh7C2y7ScqAki2AAAgAElE\nQVRJ74y9EZ8T/d0xvg/ao29qRs7EroyceY0Z2s6zuKT2S0t8jm/UUdAQOTVTNdLWQDPfKe7blaKd\nIo0Wsid6992bxGreJFVnXhXgeLyyvznjK43O+Obm2svof+3eiiitZOyEmB1IfYoO9OarkIZTg0aM\n0JXlx0D7eXD9MwdX1kulj+jsodfEV9n4grMW22VjGYPXPCt5a9Qe0CX4hr0qbEJrhWQ7wYywRdq2\ngkJKyg/HF/eWodOlUMtKrI3w4GDh9Wq0dWEchdEdzAk2GRe4AXaSGdWgSguezhceEuyNfl0Zdy/o\nip6El53wmIgPye8BMYYkFhohuoRi+X+Ze5cdSbYsPe9ba1/MzN0jIzPPpbpa3Wg2KEGABA30DHoH\nAZpKQ830AhrxDfQOAvg4EkRBYAMS2WSzqs45eYnwi9m+LQ3WjjyHA6ILggZ0oICqioxwc3OzbWv/\nVyvkNNA4UBls2dOOi2ZiK04yHNO+fyv0LbMfSr8Ghm7fzn8MPnhax1GXAUUSzQIWFDkljAe6BUTn\n51oiqNHvYOYPzG6uMljrzYHys5HV1SrtYcSgruQx6MPt430o8hZE22fZSr3D6MS2YwlkSdh5oQ5X\n2lk83CoowwtLlGm/MnoOxDjQh1/T2jtRoJPZzp1ejW0ZKG6v5WhIragOkhpBGgTlYjdkdJ77Z6wb\nO5kt7NQSCbG6Lc08h3ZpO9EaJuLlRoAejdgO+ojsf/GD2xS/7tSnM3I7KPFEf+tiCzqPX6C4/XBU\nnQ3hhZCMAycAiK7YRQajVbeDS2BMpr1ZJCXD1KZw0n6zlg4eI1BJFFlIJ4Ms1JmHogzfyAe3GKn4\nsdW5XLrPRRg22Ia3JgqwxuIWzFJpkmCSmG4n64g0En4dSvBnkVV/3oY5RxXxcav9RpGm0clDM58p\nFB9Ay+5/v053iDfLuyPgDYhXd+yRizssxm9UbiHyLW+YyWWZug3X8PccU8W4ToVaV77FxozhqkUx\nV9Ppm+21+HJfxf//Oh8HM4LLbbYTJIzTOv329+ap5fw9yHt4NEGqstWFrCsiiaBCSwG6Edtwp0oX\nqukkpo3WhS93+ffm+nJ3oHIuBaRp766rnz99KyMZcO8+R4fu82x6m+XUiXUmCW0u4vl2/G+zJkA7\nXCkeJqH21jhtEzxlXkphOEdm+Hc88HOn09nyTYk6X7+N1MH4VmwTjW8FKQbfigAB9lcnmcEtXDRj\nRLd5nV5uNPNnb2yeRTRycBJ6i9j8gpoqpQ2ONVOWjExLSki4BbE1B50xLEcep5WR4flLoW0nkh1+\nLUd8NpiRM30IJUSyDO7D4JJIczOfj8KbZ3vZD6IpMSqxd57ud8Y58tPI8IDbXzxDVj7ur4xzYgQl\nlcqinSRC7G41m+1qSO1cZoXEQxLvcmVbB1UCsQ8sK5LheTR+LpH3VBjwHA5vER6JngJltgBd0+qN\nnqOy1UFejZSUghAVtDakw26RYsKjzmu1gdw7+ew51KiwWOVrz+wWIPrfXmohJ0M2V05aUkY3GoqF\nwKZeXnXEWU0OxFIhGmE+5nqd93wZnqWYwXASujfg5MSoIJyCA5mty8wiNSQJ9TpIZ2Uv4o3T2RVi\n4+431mkZ5MWI2QkbL2UOlDIIh7HtB1gnpgFROZ4SIwXSAKK4QyOJX4Nb+KbeVhzgapKIw4jWGUkZ\na0IWRYIrRm9jndmwEY0Z2SDkB3E3Uq1YE2wX7N6RrxUuhp0yPQbKkrFtRdeEaoVmtOKlZkSFJaDn\nQJROVON0r+RmrCGSomLRQaEaBj04oR6zIckwc2dQNFfFaymz3NAVhNI6enTYB/0F4uIlISIKe0PT\nYDsOMoYOFy/AIBpT6j2QMQitM+6Do4kzL9uc1Yd/H0MEET+XbggQelQsRF+j1J8bSQa0RilCM48/\n6SaMFKk507aFEZI/I7PbYlPyMrhgRlpg9MSDlRfOhFnmEkZjGQ6YqzlREa4d0YCJYoZbnPNARqKG\nzLG+I1kmfDiwy+C//5/+F+B/93VBGl0Ce1qx2DnXnTwaexNaNUaa57Y2Eo00DlKqiAk9e1Y+uOsp\nLyDsLKO6AwbcWVAnm5cU0TmnBCMfu6+1+yB8rQ5UVWdsZPOM4vBmJ4iCZiUEZRkPUq9YiX4Pd3/W\njApx9XvNoiuAHRfobu2NvoE/NBJOgSqBsfk5cxK0cEjgKEJ5MZp2xmvDcoNeaWHxvXQRrApK9fzl\n1NDQucQXGpkumT0+w7oSSagYn59W5FEZ0Qs3BbAGKQ5WdUfXINKDMJIi4mBCCoO+KqELWY2zdUQ7\nZoFszRWFa6LmSNNKyUYWI0y3hTLYb0opoLUTOzQVUpjPXsPPzVsMXACSIMN85plirDHEiWQZrNa4\nSSCOjs3C0Z4yp+Cg+zAv9qwkFoWQ/n1Q+h97/aMAoYj8r3ghyfci8m+A/xkHBv+5iPwPwL8C/lsA\nM/s/ROSfA/8CJyr/xz+nwRimZaIxkaPJPg8Hnb4cDkLZaYZbgw8PA2wCR6lMJvnsBNXjzDc5ooZf\n1XoNKE++mMjMR1FXwXNyJ6Dn7+HDFMccLl3dSe9uh4gnkCDE4BbWJfmGLEz3Yuywjomify/su7Ds\nPgCFqWhpEWoQ30Qfk0l9eL/p+b14+6q5ejKIcJ8qvJfm77NMwE0m22q4UnA00M/wuw1Y4cfViN97\nEcjjDl9enJD78sWVj5+771U+nhx0fFqMEsUbi4cPhn0O5OfkYGuJfq7s7XyoM/0K3wbBy1nIw3gM\nIwVB3xqLA+Q5zI+Aqz5egSbYE9+UlmUDdj+XS5vMvxOfbuOuEzWflqEUfWhepsVI/dfRqUj4LXau\nObCPRBP3jofgqrZikf5aWfvhwbZTebJXPwFPWtAE4XH3wPcYvFnXFtI5wZaRLWLbQlgCugQedqKX\nQXtE+uGWAuikvLtdqHY/gQoflk6LK4/4jgPllp7omngunz1XcPHz0rtiD6NKnNmHgwoEG5SqpCfz\njWpShkXa681dU/MhIkMIa0Rvuysrb5Wyrj6PPcO6dPK50zVxGxnTMNsRM5yHP3jrQWqF57WTyw5B\n6SESojO8IQXsEIIo8uiEoxA1oM049Y6uQlqUvHh+TQidp+8ar8cKEhjbymO853i3setG3Y0jPfFY\nn4l1p9hOGHd/OAaj50xf/PKr1+bM2+1goDx65rIUWjHO6UCt+wZehLou2BDK6qFSbQz6mE2mtfPY\nTvQYvEl0ZuZZGJ5VE+eC27tvWrNAUEaKjOjLq9RGPIpvINJCrJXl9crzeafXwFJ37tuFooljNpvF\na8UGHFGJy0I6BTQF6lUoT0+QAxrdOun5WYO13QkyuJM4WiD1iuiKqnGczogMVx8u5m3OqxJGp2ik\nELkcNxS3RVowWsrsZFhhSYNwusC9k9JgPa5QoSyZ0QOmXnvxdX3muj5xl5V9ZEQbOjrvx5UQlXjx\njJO4GnveZu5Z5/hFWP+zRF4Km70Sh+dN7reF99z5d/Wd50suiqrwKf0IKGKeDXdeumf29MCrbMRR\nWMJAiIxLoqtvPtI+6Ptg/dmbtFsS4tkzGw233IQt8amdeRBhHewvnUQnqLcuqwrb/dUz65rbrk7t\nINZG+3jBonCyQfxyYH//St6AIIw7jHvj6SmiEsjaqU2pCovdIUPsBVMhp+GAtPluWef6YKeFvHvc\nQW+dc+jclsCa9VsJ08GKxUhuN7f8vT6wKIyRaKNjDyM9JTQYMRrWG+M23G4zhP4yMM1Y8PtXuqtT\nlvbwbCzNHLu65f8AOnSdu/kxsJicjQ2Z1A9e7cLz62dS9uYrO52xXxq2rry1KUgpZBss7eEq+pJQ\nM5a+IwMfiIYjNiJCXjukQU8QXp2900chrEIcO8tROI+DKA1CYLMbsRcWKSzx4AJo7Ix3gZw6ZVfP\nh3mIzwtBOGqkE3i9Z641Y1Kwmhifdn/E5ZXH0zNXNqgDS5m4BiQZF3HLd1zdEjeu0S28tdNqAome\ngzQ69SGMnmlLohdvCeZR6cGjEdrIJFG3sdHpzZDbTnwMLpvClinLhawHj5hIj6u3JKaO9MJpfSAq\n9CWz95VAY++R0RXG4DRe+dC/EK4H8sdOM8XebZADNhq3mGmSZiQJnPX6DSAcs7FQJxnJGyGpriIs\nyZVtPfwakbLj88vEV9xxcfZ/H8UBwrfyijiJ0CX4THCr8+dP+D2wTXXgmwrPhUbsBqfNZ5KIgwkW\nnEzkDbwsUB8+K4z9N66VOV/qFWR34K2KC/asw7Y5eClzjnkS/08IUx3XfZazZ7iffS4pAu8TmEVP\n11Vv2lwiSDBC9cZDAfYiPHa4FeXlBl9vQplgnB2eRdgCyNWPu7yArU6u6/ycNqDvPrt9rNN5az5D\n5Q757PuPKq4OtEmqzxg43vIKS3EwJpbp0ukQnv3c1z7/LQ42vmWp9UkYvxXDxAOk8S1L0q0sDg4G\npnIwTmJa+BbhQ/dj/fZ7wP0KjxDhQ+IhiTwEPQZ62wlDydWtdJIcpLJu9KcM54iYOZgQoKh6WH1t\nDqok5enllUWblwKYEsMgjs41B44U+Xo+00zZYmYdhQ/1BYCHZgqzDXMIr2l1T7h6IQpJyN03c6EP\nigTPDN4bz71S1jOtG+8/ZhiBVjuX7wA7efyRGSE0B8+v6grQYdijk64H+lL5y/c7eYUff1f47qnw\neo38cPF82MvaeByBdR98R0HNY0gODeznjVCUzTzbsEkmZZCk3HtkPwbaBW7wdDzIOjhvvn8QAk2F\nfg5whvG0wBYZNijR12+js9WD39sLr7rQg5KCIhHC+4h1iEPgomh3EK7MZ2qfuUFRzZt4raM63MmE\nE9K5NQdYT4k+/EKxdSGEX+3KC40UlevhjfLl8BzKRwscKtRNnaiMgm2J9UOAHZ6fKstFCavQTTmK\nA0t53JFS4NPu2cfngG2KfK+EkxJGIOQMtVPuvlnbbHeQUQJdAi/xHW0YaShpwLgs8MNGEP/sd1v4\nVC8AlPsgWCRF4RQDyTqn653R/MaOn3f0Dzvtb87Yf7qi58xyXugfsmfY7n02CA2/Oc8B3mfS98qC\nsVjj6f5ASqXlM7Zk/vRYubcTvbncOoRODhC0I9p4y1MTM/hs2E2xBEQlPDqnn1+deMyuuoqPQmiG\nvHh8xcfznfXUGUNoI2CHsmh3Ykz8XrKotOqKYZOOhEH50w5PieXDiaMLj7uys7D/cqCloRfQtWEn\nIfztSghG4uF76j+98NlO/N/yA1EGF21edNED+pxYW/cC0ihIVhBlXYxSla0dWFCWOGgExj1Sdlch\nalJQJT0b4bXS/687432E94nxveMWBXikQKqVo7o7iWVuTj/72vYxXKe7XCZR5sDUU71zea3kenOQ\nrw/GJbPG7gRAEJRAkI4mdx7EbkQrRHxfYvfBuBqHRvLJGGvwjNPLQIYSit8r/UulPjyN3b409PtE\nUkVqoWrCDNq6Ec7C89o4WfMCyD5VcE2ouyJtcHwdjKBwUs+6BE7jQTPl8l0jnCKPcKBb5DEWWvD9\nVrgebO2VlG5I2OG7ndEO0ocvrOeHR5xREQp7PVNLYN12DOW0GJbb7DqIWIg8vzcOFvLjzrt1J48r\nlM4vIdMPQf6QWYKLg9Zt0EeiN5+9xxLRS2RE8UgzCcTF7fXr2B2QOTq6D/747kdajGiEljdEqtux\nAavieZzDkL1h1Uuc5DnQGBTxeAS6RzrIcHByiOMhmDuXTCEz956js7XmtuXTwp2F1+d3aFbeRbeP\nr6O4NX84kRSxb06jP+f1jwKEZvbf/Qd+9N/8B/79PwP+2Z9/CPMleMkG075r0CYL2nC77VteoOD/\ntnungw8f7mL8llHzTfWnfu3KZLhHB5lWYBO8AtycztbGt2zCNIHCekyW/I0pnezyYJJbqw8xMJVD\n+PD7BtSGeTwxgJyBCloEfZgr2qayTiLE6mBYsGmTDT5cj+DHus/Psjiu4cP02/mY51CHf+6U/Bg/\nmvEugq7w5eZA2UOEpvC1OvC3nBzkO2UjD1i6D/LbtFJX+NYWrDoVl+aW7xJ/Yxl6Y4mDf3+ioBnU\nvPlJo/9/p7lhsAGPOXRqd3AXmZuL6urGmJzBxoUgvGVLhzEVlDbVjwNs51u2TZjXDeJh502/Ebjf\nvkdtzlQWVSQqMQhhdPYiHLdIFEGSS3w7Xm8ucmDNK8X78IwgElNCXl1qvyyeX7C6RDLiTE3VQMdo\nj2ljmLkDPrgGP1/RWYnxBmdOtUcdgaB+0iQDTRjFA3+LRpoEsnpWXjOZjJ8wtgUbDqKIDcJs4Amz\nNjroIMRBvzbaoyJBOP1gbJtbGCKGjYAReAsYshgJ2rC8MA4/vkxzxj74Q6rj1zFm5L1Dqwy8+AP1\n/EaN+k2J+/T9IEdHm/MYHNWtALoKIbgt41W/4yErVhpLOTh0IVp1Fmwmq4dFZ/OyemBv9GG4d+Gl\nrGzd0N5IgltRRDEmsq3qBGYUzxmI7hsbW6bfDZlZKWrGMJcbG4Nuv96LhADiWUdj+qrasrKfL/TS\nSbXQYuSy+gZ7S5W2OCCaGeym2NFcMVJ9Fz22zCGR48XXs/IYhBSJNm8GMzbb2Y6KNPgSn3ltG09y\nx1Ii6qCTXO0pHTYl7Q+u3YOhbeZkFEss5UGUTltXqib6thF788yR68Pzvz4I/ISrwkpjRIM1cEji\nSP70yY8di8ZXPfMjv9AvC4WF87YzRHzzpK6eTKeAfAzk1Ij/+oXz+pUeI7w2nl93PnNxwF6UL/LE\nJoWzPbjGC7E38tK9Tc5cZQU+yN9ZeU47Ud3C8xb0GhZh3wEbWPGQbhaIUWn3wf50cravBWwoQqcU\nIVwyI2e0HZS0kvc74+hTTSSEaMT2QNZI/blx2TrHd4msjd4FbJBXsL0Tk1ERtHROtaLlcNKqD6KZ\nh0vboIv6Zje6Wlj2TgwdzkpujaaRbasso38DCCsJ04wsA41uvTpYOdiwlx0QpLlaPAYfWuiGTRRi\nBFcQeMmGuF1WxKvkFcor1NfBWJNnls2/o3G4jTYJB5lCJIu6Ap5B7r8gzVy9XAcc5vlJAyIdi8ED\nzhFUhge8D1d9nNuVZF7i41SNYUFccXEIoVZSNH/OFkFjI2ohvuVgiJMjrgIdPEWPhLA1EJdBSJ5Z\nhIB1t6gVi66ey968yDHtT/dCPV9omqE2JAYf+iUyQiYEY4SKqjHUlcx6UXh0bEkMyVRJ5PvhmU1n\nt0v13YeMMfy74d6oIfqmzhyxCbXTb51AR63TZFDiQtCB5Iq1ij06FM/dFO1wFMQctQsyqKg38XWf\nfbQPku3IUeCLy9O7RMqWqGHhmBkkEnUq9wI2s1fKJBBP4jOKzUsm4WuVzKrhQ2e5xcSRbc4UBAfm\nmGUhTf3fvAkEV5mEIP5gj8Gf7yoTlOpzjpM5zM7jSHF2YKm//zcHQ52KtAk8xQlmjjHVg/Nvs8/P\nIw6iNYHn7Aeepz03AFuGpzkbJnF+SIfPVK07cUmFS/AZawnD39tm3AyCTeWU1caYs8+9Co8D7tPO\nWw7YH3wrB3mbb/tnn7GiOqGt5gDsG0Ga1S8fGU6k5wLhNO9Z8fm2Vz9XKfKtGLDNfOs6iVyLoA8g\nT85iioQHU3EZfU59Ux32w0HCJ5wwtzpVEs03zJOb+zYv2oyxQee1Mxx81UnMv736OVGvhtVAOKlb\n/LswRic+KhYVmheBjTg32h2kzPKj4UUQa/M1uRJIL3cuP3We+oPxcWV5rbTndWZjCQvevnvPK2Gv\n1C0RBjzCytYrLXhcRLo3DiLhFN3O1bxAJclwFdkYrvPrXqqwi984cgoEhXdyUEQZfZDMcw4Fn1+G\n+YBxP4QlDGLtjM8V/eJr8t/83tez//Kf3qg7bERSND6+q/RmXB+Ry+q5xAOhzpzevg9CDkgbJAY/\nyp3YDWJ2PEmhVCXPfOvjNlhbI2Sob7LWYd+kvmk/0EVRdQW4roIRkK+dqI1cOtkqcgpA9DibIC6s\n6MPBBnBgd7aatg6nqVrT1tkiSGv0apz3HR3+fUsxeo6Eo5IjLK36RS5A64TDKEUZ10EXoe/q1smZ\nLSYIp804XwbsMLbIsa7IFryEqxsSBptWRKqXw011Phrd3rwK0dxVYWaMCpqM1BtDfd5sIlQiPA6u\nttHXMzkZl5nKKeafJ5edZkq9BSzgYDO4em+vWG3Ia0d/KcjXDsWwJSLrvDeWQM/RN0AD6EbJmbBF\nOC3EYORSXPl2FEKt9MdCj4FbjHzOG6Ed5DHIYq4qF3O1v3lOuwDsHbsNeBd8bsHIe/ECsFOgrIFT\n7C6+KYMgRvsyoDZOyUFC24WonR6VIpkuyvG00RdX09cW4N7pf3dDLoH1v1ZCzuxxo90GdvUCE1Uh\nIOSLEHQCzZ4oT8cbvlkjhtHzVHnO6ziosaROTq4K1anWHyjVPJ9e1UhiBOkO2qAMdeBh/3DCxorZ\ngpjnOEsZ6DpYKZ7F1wqtuTL810XQX9mawyDdlfqZRh6Ffj8gwPPXF886Hcb97DEhpOnuGs62BPXi\nm1ArOjo6OrZO9WxRRlB6FcYIxMXdYaO7cq3coB3CIBLKVFVXIY2KVDDxwozUd8c6VpCsni+uRkeR\nLugitN3/rYErLHWQzOZ/DyQdNCKXZ6Nbp49Gv3khCHuDDPU2KAL3T4rugfsPmVGNd/pKCHeOunF9\nXKgjM7LvO2I6CBoIodE1YmE+/BHWdSfHF2R/QDXK04UaI/WilEO9xCl39moOEAYIqz/XWvQSz2oR\nZF7/wx+y2rur1mcWo0mgSKIQfS86BA7PGX3cAseukPzcDPNnkAqgY6qMoZvShiJqdBNseEyGMNDN\n90/SvRSyS0PWRDOj6JtCM3LWglVXZWZtaLFfrZZ/5uv/q8X4//dXlQkozeFp4M+d6OQUpc/93cw6\nobnqMAUId1yF8AAuQjL/e8fqto/s35/jG4IPiQnKJlT1wY3dc2LiHBTbMUExXC77Zhs2ZhB1cGR3\nWt59iJnRCTn4wBq6D0mS3L4QJ9M+DFoXz99JbkEOXrrkzXMG7TIHq+jH0Jktd+o/X/C8PQOuU2GZ\nzFV1SeHd2YhzsL7dXQHXgZ9uE3wb8P69/93TBx/Cf3dyIDW8QqrGVxEfnJuft2HwNC/g08WH4S/q\nioF29/dt87sKwGUBjULs8/jV0dfz3DDcm7As8zPfIA8jTIsxK/DwbJxtGCRv1WoG9jIVAM2tuEn9\nu9rfznfx7+IQeCxuXVLmADpfLWWXTI/hm1wbnLfKMGVbBnvGVWzNF+A2NqoKVh4ILsmOo/AIGwxl\nxETR7A3zXwYhG0k9oyKNMRudT5QqPPrKubll7h2P2a6bweCVM9UiUhvJDo4WXAUlZ6xW4lF41Qt7\nXtF2oHTWtrP2nYaHMi3rIL+LhE149EhpygMljIbseB7J2lkXXJY+hPxDIN0gb96uFpKxZJdo9l5o\nFnhUT0IfbXCXacVcXbUYs6DmykgRcwbPjMGAzZtS071ia6SfTsSTEXvl9B0k7fzwt5V2gNLJF7j+\nCRYrXB5faLwSOPNzeuIIT1iFYk/EroTQOOmDIIOl3BC6szR64hYWbzzuA7NBL4bVxtEG+gTczBeD\nJ7cHeZYh6IIDM+tCE094KCGg1VmDvQnEQTeh9EC0SlBjk4NoXmIiCPUt3POHE3wIlE+VtkZOttPX\nisjgMbMTLCS+9jPHrdKvhYcE6lMihcHzeNC/eqh2OYySBmKd86nz8fqZTQ4ylfY6eDw98WkYn384\nE6TAkulEcsDtiye/cUrzFutWIWhjOQphHNSU2E/OXFsMbiN5ylgVRsvkxdCj0F4Ddh3Ua0c2OCyj\nmyDWWW8Fve0cmvl++Zkf1itRBz8ufs3H4NdY18jSDoIa715e4AXeffgJfW+k9zt9KOOx0D936tX4\nHE7YAn+v39ERLqsHXC2tg3SOXSiPwOvhchRReBqJsxyMe/ON7ZJ5jEilo30QnxP6bgUV2q7IU6b1\nSG9ui293t8gnFR7bE7Zlmp5Jn/7E8Ui06gTFuhrWPC5Aq3H5q4XxqqSn4GpIE1oTtlRJ67Tp3AsJ\nV+KOOrCf7uimxE3Ij8FIkUvYKXFzddtQRrKZZTRYvgvkDYYF+uNXJvqaPjJy5HlZSPerr/c/VTIv\n7A+lPxwkRqGW4WBCqw4QApYitmTGkukaCLcD9gP7+U6/d8KIBIu+KbskdBOWy/D8xLxR8sr18QzA\n7Xhm4eB6+sjyWjiXK/x0xYqh6ptayREJgqZGV5ntnEKzQO2RaIWDhJmx2OHKY20crGhsqFZCaITm\n7ZBrO1gShE1QxBUBxYfhRzyhzRXP+dTJz0BSHtuZ++sC28Lj1ZvOmkDtgU/XM/eROdgomtl1o53f\n0deVGO/kx05Yhls2Fx/GWBYvcEkdqY2Wnhh5eAnVEom3O+F29/w/8cyf2pU6IqM0nv7NP7B++kr5\np9/DopR1w1eVCY/eG/20EU/KsSXA10/bB1aDZy5ZQcQIT5GxbR6s7qIEB2ARLvfPPB2fCbdXODrH\nTThyZP9kjMvAlkoMh9t4xS06Gh2oZoGj+HxGc6I2zKgXmhOZTxscwXOPu0wCdzj5+Y10vUwy9zGJ\nXuNbpnEQvpXVv9lQ32bcVmaXyIBx4PbDt5mpT8xiAoTyxixHn3GGX/YseYJX4rNYZirjgs8gok5U\nnvFyNT9g2E7A5q6UvcDF/H3Cw8+vJD/+dwpPK5xO5s2qubNEI4ohEjETmrjSgTHozahN3HJdoVTh\n69VnMJhgaofyM+yf/VgkeoQMHQ5zojsPOKl/D33OcOE6Z+wvPqOauQKxHT4v38obqTfn7/irqtAS\njG3axyZp/MBB3SgTfJ2/0yuUxwR353Fczb+v/IZ1H07+kvjWrM3wzVgOvxaVtMqvNmTgmhfiXwi2\nBAjmjZqjOsl13d2O9X6lf3eBe0NfC7JG9CkQ9hncDKTRsDZcLfgUeWqv5L1Qbq4gqubqtrYP+sM3\n16UnVKcV8RT5KS6zUUYJ10I5Q+8BciYcnRAmMquBe/FQe0suUBCF9XnQkyveOvwAACAASURBVHiB\nicFnVnYSa7vTZG4UJjq6z9K5/QX6a6G9FlIc/GV+EEfn8cfBf/5f7Syp8cN3nfbDwehCzoP7i6+n\n1RSy8Ok1uzLcjPCo7C+G1AQCizV+tBuP7gDJVZIXKphwVGErg/5aKOdAXZQjJKI18ucr+hRJdyN/\nCHBJaBSW1WfzdT+I5XCFq4EtQume3/qWeZAcCSUe3hL9mD/PtwNbGpoFefiNkKxxuR+kqxOtelZi\nhLgaaTN3sXQh9AHDuFnmtTnZUpsXi5kKx2WBbpyWwbtT5933zds+fwJ7v3F9/khbV6R0wriyhTvn\nD8AiVF3pXbkuZ3QN/MVzYzsVJ7mKMl4K7f+8kZ+F9a/cfr7rBlLYZOd6rPzp/CO/vP+R36WvPOsf\nPQ6lNfLeOL8c3MZCkM2ttEFoMTJyQG0Qbo3wqcBLp35c6VE4XqZKVtXFCItwXb8ntSuXj42xdtJ1\nh5J4/Pg95fNnRvnEx/JC7J3zv/7Mftr47rtAWndeT0+s74xNOuvSiYugJycYl1VR67xPkdc9c20r\nqPLLD8+kbFy+fEUx6pJ4d658yJWPK9Q7nMuD7aWSpRHiAPVZuuXICMYIidtl5TFnPpKhe+fjecdy\noNaF1Avpywt9N4oNWhD2GOkpQBJSHEiHQvS4Awb7uqF/scEwaqtYECxFQvb9+SqDEMGyu8vuLLxw\novzSObaNY9voi5HkQcgVO0V4XpGkHO+f4J+A/vWd8HdfCH/3hTVX8hmy3Rj9wfUh8BDQiCRF0q/0\nRy7FZ6oAYXRydtWsnKYcP/ni3E+J9D4ytkjYjJDEbbnA2B0sHF93b/Msja/nCzddiNagGClCD8Gz\nf833qaMZfRglJBdYjYAu4r0KO64+m+Ux5y8V3idCzOy2kGSQQvf29BioBETUI8/KgKhobWzdWaIu\nwYFb6ejXg3I5odLZm3G8Ap925GyMOAjnweOLYi3z+suJx9eFckssywlZ4A+fv+ewE++ywJqo+oxK\nJ4RB1Y2Vr+zynqhXtvgzT+e/50McaG0s9jcc15Xbl/+El8+Bn/+QuF3O7OvqD6Co9JopJtTu+8Cg\nTiZg8N5e6ENpMaG9sV1vLOHgp4+/Q6isVKjGzowVGkYXpaVA3ARLgrYJ8nWmgsqFRM0CMhworNHb\ntFsInGJhORv1aoQl0C2Ra6BnJauwpUYMbtF34jTQDXJtyO4gPe/4s1//0QCEDZwhZrKS3dnkKoD6\nkDidCf76DZMan6fqUGFk/1kNHg4dxFvcbPgCEJqr6d5UgOCAkjZ/cB1TQShzGCrFGUiYrLZO4H/K\n3PtbBXt120e3qXKboD5MVv3NwiLA6vaPPhlq1MFOjRDfGIXFP5s0/7nOQTnj7//G1NfmP3tMG8n3\nc8D67nd+Um+vQi1wm+qSzabIKRjHEE4e68WHJ/jxCfoDrjc4zybi6KSQg3UGC85QnINQh4OA4ANz\nrkYZ8HVqGnPweayrfSteAR/Io8Im9m3wDcnZ8TDZ6d6m8nO4shIMqV76kqrRgFH9pDT/MW8iGlP/\nvRrc2mNT9fjbAp+iK0GmmmkMmibP6IieJ7NcjOPmAKdvLFz2XmRjOe6UnF03HtRzLSyQZt39otXz\nnAaMvRO0M0ImjcojbJRpE8jaUVNMxIN67Y0V6NAGxpul1ZuUehUe8R1VIns4oSQWO9geV3YycV77\nYVE0mMvrk1Bb4igJtUFuhW7Kdj5IwSWfouJDUXalZ4xGTEKMrrY5y8Ex3MLRhzowdog/vKwSrHqj\nIaBMGYO4VEQXQe8DjcZ4l+b0H4hrI6qyPnUvMgnw4cfK/ohkg/dtp/bA+ORNqTfOrPXK2jPy2Lmx\nctadYhnsjgw/ljQqoxtaAtrditOGoNjMVfSbuJcG5sNA3ztjWkJIvluR5NlgQ4w+QMZAe2PvPki3\n7u2ixSLVgqdW6CCGSg+RHtzSBLBtgyVH+vDPUmIi9Ic3wgI9r9xr4oUL+9IJl52Xu1+s53H19UH9\nnighc7sLUQa/6y8gsP70BTtn8v/zyjgV+Nsf+K5+4Vy/ousZLoGOEhBk94UgROOxe8hzH4pYZAve\nqFlESXGgrbnVg0QLkZbyN4v12JLbCa7Dd4orEISn8UCsUUsls9NY+H55ITKwAe+WnRS89Tb05hmc\nNxCtnJeD/kUQBvE9UOBRMj9/OvPL/Yl/OH+HVYXFw/tjH6yhoeot5LXAUg9OI3EXX5iutmDXRviy\neyPgc+ReAmM7EUshvl9cdZgTesrYmqDYVJ9mV9nOEKywP0AHW+7EBUYKDGv0KgjejltGJPS5+Y2K\nfNzoL8IhGYJy1Ifnz87vgNLQLRBGx56DKxKrTK8irOMgDqFoIu47tOF5vPar9S48Z1h/HTQfPcFI\nHL0RThs8OvG4MvaO7sGZ/ZZAhBHf7g1vO5be6ecLdlmo0SGR0RWORroW0ID98iB8PKOLwsWvU4sB\nC4GwCiEHxlg5diFllyKd6pVqmVEHoTWsguk85uiZoyLOyNtsfxDz71lMCTR4NKQc0CMWlPDOrykJ\nICbEbRAz5LOiZ4FgDFU0iSvezDjtn7iUK9t2kE4dnQ+GJJV4ycgutCN7a2YzXuvGSJHWM9adJbNm\n9DLoauTHge47evEAuC6eX+sS6kAJGx2hsPhDX2G07qhy9fVVR6OsJ9g7UuHyp1+Iv7yy/OEz+uOC\nSfIMpiD0e6dfK3ERsEYcO7QbYTi4Shfi4TK5N23CmGVYJoqEgYpgTQjt4On4zHl/majY8IflGFBt\nxlEoWdskJn0wHuZNsuDxHmHOOtJ85mBMhf/iYNwSfY4bYf6ciXvIVPO5GNvnkin4/G278reXTAWd\nC14dbCwTPLNZvjFRvC6/Ks/ixFlsktADn6EUBxPNHLh6e3+d81WPcz6bRLF26CdXRY75+wB7djCw\nTiI1G1iBkF1heD5BG0I1Y5gQQyCmCDJo8yC1NkYxV1jO45QwSc/pBmn48RXg9Yar+e782hw8pnrR\n/H/X5jPcW55fX+Ystbr1t705PtS7fnp3IFfnd/UWyyPmn3eoz6htnu+3ubjaJHmjk89l+O8NdZC4\nFnj4peTg75h8+5xJZxG653HPi+M3wppvBSoA+Skw3pjx7ORBat1jiQIOMLxL9CXQtoV4ziwnY2yR\nXtymOMq05Z0SYY3u0FizY8mPQn9aiF8ejDViD6OV5E6I0TimTLJo4Hra2EYhDFfgHEk55C0/EuRo\nWDW3iHYIQZAZZp6iocmbQa0OJAi14QV2e3H19CxOeLuO2w5yLZSfGqfobpGuyodz4+MPnee/MN69\n7/z+9zt7Cdy+BMourO+MZ2n86SZeQBCN3GF/iBd4mRdtvb1RxMi90UTJ0sm4jLNU/2C1K/3VKMFn\nRjXB1gh/2NG/dfUXDQRH+nsDXWaG2epRNRogHNXb69986ntHxmxtB+LngaVAbJVR31qJoR/D21SD\nEe9zAXo4aDyqK5mGCUf3LGSZN/TXfCLSCLF49ieufM8bvFs77y6dxYxR5/tHI6s7eoidkAYxCPFd\ngBXasjB6oPcTlmHJB6dUncgtghUYn6cS66OrqEyzzzdUJCRyLSzHnTAKnUFHWPdGboNSlK13RnJB\ng1ngyw8/0EPk9PpKvh/kh2dbSjmwz8L+x5tf38WoeaNFpY8IwzOsT+ywCD0Gjp7Iu1K/Qv08YHcF\njSVjoxK4comNJU+w6ikgOXj2W1VS7tOFpWiO0BdX1IlSzwu1LGjv7E8n7NiJjw63RtiNRCOHSraO\nfu0TNE7+WRTM3NHyYGVNbpWNeXDK07HSG6ELSzlc3YtSJHKoYCpo7fA6sN6pQz0u57vF27FtsiBp\n7nuSeDzDXOctKCVFCMqIgb0v9LhT0sKRN4YYYx8ElJEztm5IdpWjiSKyEONCXBfiMtAcEfxeo4xv\nwqAgiobfbErd/kLcD4J5/mE3359ZCpAGw8yPKygWlCG+nuRVZu5sYAx1YcQpoalz1MBjWQgooTZs\n7/7ASuoqSDPGboxDGG9ukgmCteYqNi8g9fgV7T4rNgKL1plL7MrsIF6kMc6RMQT5WqcTBbfRYt/a\neKU6gCatEUNgCe5+60DfjboqpQfuI7Oy8/qyEOIgpc7X6xMtRf7w+IGP3xVerhEpkXz63pXf3Sjj\nhKm32QcZfL/+Cy7x39LrhobK6/jA6fTC5fKBx2asWbnOYssQfP01G4Q2fH4387gYPFZMw0DUiOkg\nPQ6ery8MVcq6gBk1C6F7jFMqxmtdeKmenx+mqwVcLT3EBxTDFYV1BN+ninJYRBkM9S6KYbCeq88o\nr44dRVzBuPQDToGNQu+BYhGxTsf3qDat5H/u6z8agNDaHNiaD1f2ZieJ8BEfVrZpa+jJB5ysbuU9\nPYy++qa+J9iTg+1N4JF9QArD91AqbrXQHcIwV4p1vze7+NzOhj8sA4SDb61tGp39swkmhuiA2ttw\n05ysouzTdtHEa9KjL0YjzPKNt4F1+IDbFuDilt3shbZINTBxW8Zk3dBp6WHm+wx4zLn+43c+iP+T\nvzJOGf7mrw0x+OUP8Mc/CuuLUR4+qO8Cl/d+Uf7l730g/asfgGb87gn+ZRB6jXDtxGxcH/BdhK8v\nPnzq8DydsOLW2Qrjizf3hfnw1+ylKpfFiYw+AVoVD+w+xgQgm8v048yOC80IwGMXWnOmOahbEPRu\nZPNzFqqz/PsDjpc5tBvsN3+f1+ID/8ieIZTm4P32Kpp9KBqddhhHEMwadUneWowf0+i4VYyBEMhW\nKNtK6pW2RdJo5NDZm34L15dWCaV64UVUxqOwckNHoNs7anpCY8JC5JWIiZDbgY5GHZEHibU93DK8\nKKVmUHhcNqpEiiX2sPLj8gtbfXA6+3XWY6OQPccnuk9LRmeRg5fzByrCL3nj/PjKfYX38cqSO82i\nF0u8FPja4Cz0IuiTomHQCTR8g2rThpVG4czhD3x2wgFROmTFtoyKKwfFPK0EIvF2INK4PN/58f0r\nYVV+//tPvLZnlhO8v3xCnzunfGc9/TWPfeUxNqiB5X7woX4m1jv3sXJuX9yeKv7+dlTs8aBUR7Xz\n/Sty3igjUoKrj1LwzA7iZAdKZ7ztwmrxcoBivybjZzwItkzl29g5EF7qyvXuTYfSffE9b4OmkRbm\nTlN/o6p4LRxLcGs2oMdBr51DnL2TBEdbOPNKVKEswgb0R6cfLrOP36/0uKJX4+lj4LgOxtGhHvSH\nt1ULXpzxV/yJMNsDy8i+iFqmpBP9tBBuO0eOvKaVo6qfO3Nb5UkO1rojZZBP+Abla0NVXGWG+BCZ\nA713RIT9UNIxsHpw+ZtAD8YiRrlD5uAeVn5Yb3w3XsmziQ8Rt/5qQHundSVKQ1un3SKf/+WFqI1/\n9+k7HmHlf/vdf8F9j7ykJ6Ial7WRrflGREFUGMfA2mDZvf48RN+E1CI81icog8fh6Fy5bIR0YSSI\ncRCSIsUIVuEfXgjnjZ4HQ6NbHm2Qbw9S82y5GgzTQD8t0Ac1ROqS6JopKYMG5DyIo7GfHKzsGjyj\nKbqVZesH6TJIzdtwWwiMu1uZehAsiJfdjIa06kDu4oygC2MNC4moET39BiDUJ5ZReOzByzVuDS0F\nmrEVJ0VGMH/ATl+oBWXtD9plnUPeoKaVI564/PHfEv/4iny+oTmxPGfGE0CDTbE10daFOjK3vrGX\nM3c5k7fCIHK5fyKFg6SFFhfGtWEd9KiwKNbE7ShRCZeECbQRaF1YbSdZhXtz1cfPB2MftCXTxUHs\nZB1ZhXAJGFDXhMTB+lSxEBxALJnYD04/31geV0Q7ITTWZ28DV5rfR3fjqJHSAuuT0WThT5+f/Tlh\nQokrBwu3smDXSrcEh3FJA4nCKjc0GMex0KNwtYUafCgceUWPg7V9wczY3z2TtNM1EsqBmKCi5FPn\nff2E/GUgLjsjGSN3D7W/BMQiaXixSFp2Hq8HoxolLs7+5wTV71U7b/TnJ4ZGmmaaJX8W930SIYo1\nhSUix84jP/Eo0ZVh1wea72g4vEzp3YUWVjgao3Q4MYPl+Zaj3GZm3bL5bCYN9sPJ0zGjRwLudAhM\nIGpmC4/Ot2iVI8yonEmEfiN0Zc5WEzEbweczM58D6gF68h+/kY74ckydQNdNfF5bgGV15eEUN9N3\npsd12pN1qvG6zxhhWnB79aKQt/Dwm/hs9P8y92ZNcmXJnd/Pz3bvjSUzgUJVNbs5pM3QZmSzSN//\nWU/6BpKMIiWqyWY3ugrIJeIuZ3M9+ElU8U186zCDIQ3IzIi7neP+9/8Sqr1H2uF0hnSC7Q2eHsaB\nyILqR3oBSa+EmKm6U5t5BoVqINvWrE7aA/YnWl25NWP6vQiEHfzwYmQAhMMth4hZxfhu9TLAdh3q\nEbFjBzsPZbBnfLbrpYwafPhql2BqEaxnwYtdv9qtxu7Vzi3dfLUrZrEVnf17SwPYPKzWdR6WUVPr\nyvA1HXVw4ZcwOTUGIb/qaZYf4/CWEVJU4hu2JyVHiSdUPOtiKHE+zejDiadjpb9W4g6IyS7lJETp\n+PNIL20VNwnsmBdn8LS3xosupjKKJu3xRya+VvLDQpuF58sDU83mj5ug7o23VTjlSjig/NzMG905\nNufN49aZmsbdzQsyHmY677/cEQexFFoMxnwezNfjprSvDb8W86yc4OOHzuPUwTuu/y3RH4T8fSQ/\nCLNUOAln4Kd/EmqMPDwpD08bP32+sm/mudWCJ/dAx3zDdPQ951ipKnznD86p8rJHvpyTMZTwtC74\nk4cIy2IMhiDegJ6fM/XTiewMTHJzw39yyGHo+zuwnF7uHHOidc/lyxurBrZl4vWrfUPdGskd6CS0\nqMTcYBGqOpI20qTIp2TD6adgDVQXaoN7DdzczOIqL8sFac0kwSKUjza8VQcnp1xPjcepjpKvEoaf\n95Iaqb/R2kGbLJ779LLDKSCTI12FMCw4avfsq2PeKic5cGGi/eeExA+Ul8ZryXhtPCwrEoR7TzzV\nV8KtUHOnJOH16ln6wfLTG+5o1LzQJDL/TYNw0NST94qvhewiPnX8Y0BPSo+epMJ3P32h3iPlYWaj\noB9m5PuFtN9Ztq9EaWxPJ+oceD1m5Isn/Ity+sdKrZ1wdWjsXJaDab7z8FBI1066KDwl2mS+vFqU\nH+cvlMPx0+2R13Lma6sUAlUi/frEnh+on3d2F5n/95+Y/nxnWTNJG+fLzjkWUq/0f8noDvW/PQKW\n2Ju746ieo3qWqCRXSbHin6yRa68V3Tvpn+84FdxvHnHThHM20XFrQf6407zniJH9tPDy4wfcVkhf\nzX9aX6sF5YBJPpsRWRBHl4k2TeTrmcxEXyplh7orbAV1DibzJe9bpzbPW5xoLtJkIp2U6UPHPeXh\nr9noW6NPAU/HDxnzr/HBu0xc6oo/qgHhpdFCoM2JGhz9lNCL+XfqaRAV1JkCMjICWs0/7SbJgk+b\n5+sxsZP47vmPTPdKfvBGnFptYy3CmOBZjySiJDEJudsdbrK9VL0gXohnsXDONSM1Q2o41whnIdBY\n20TtQqtGODB7H2+Bad3C9txecbmje2ORjiyBlzoxTcLtmsh7Q33Cew8t4Q7h/oeJxw+Zl31hEzMY\n/uPxkZejkq6eh+8arz9/QsSxPAYmv+JOnu/i/8kD/w9eLSX7FP+V4A7k+0heH/h8+kj4+MDrbSE9\nRHpKWHfnmNY3EGi5QVNe4pUcIvewUGPk7DY+7SvJN+Z2R1NAPv+B11vilmaemydMgY8fhFuYeI4L\nIXTS5SBGZeRwU7dOb8aiveX3AmSmJduEW3A0HDUkRIRFsu3F12r2E8+NdFRS22gt4WOjpQTiUYSu\nynxkUi38e15/MQDhezPd3tNxMZBP2ihOxmS2dKsX3PuEeXikBIw9lgPcg5A9dKcmmehWxDWseAvV\nQK5q0nnCYOcxQCQXRl3q7JeKYJ515i1uCHkbv7eDw5D1Ye9mPivKt1Q97sA8Ug0H07G9eyIG+6f5\nXV6czTdhxiZ7GaFmzEdnyK/fZTVO4SJQZ7jMynyGH38LpwmuVwMI9ze4nkGife1rZ1fht3/XuRfH\nb3+AVoXffVRahqen3/Ff/7tSiyf8/e8JSbht8PlP5kl4mux8nJMVKcnbOX97GXLvDfBqi9avQDnX\nR92tMIkOw94xha6YV2EaE5xmYSMeS1gWw8dM4tPtd7hfsQJKB7ZR0G7G+hS181LfQcH3RuNXr+ac\n+WiUiq9CiwnZM7rYJMs5/ealol2pCLFVQq9MQ8vtUbwfeqUhkaAYintsFshQW7LJncAjN4pEUIeq\nhXookNMMbch7W8X3bn6XCj6ZT9EukY6Z6tohCT0486N0RjWnW+GDCK3AEux9d0m86IUWI84HK1QD\nZI3GSMWSf/tLQ1+GyXea0CXYFAKbMr8zMZM3n4wHeSPKmADi4WjGkkVwQ7LLKTLVjZR3Qq8ElKiF\nh9koBD8+/IkSzlzmZz49/Znr6ZXb/QQn+Pn1yr4HjuJt0i6FkHdonTd3oSmsRFJthNygNCQ34lot\nqex8NS8ub5M4Lx7f1cKHUFxVehJUhC4OmtKypZJ5sS4m1II/dly3QjA0zzk29jym0L2ipVMd9DnY\nveMd+k4hBuroesK6Eo4DXwtxEoI2aIVE5CDiUR69eVlOodDujfQpwDkSTok2Q3uuPPzg0S8Rv905\nYgIPIXj0cWLgEbRlgmCixFqVEpxNZsPCfg24o8LRkOjZM5wkW1p6rkiEVh2ilhKtLjDnja6C5oY/\nOq0288aM0O4Nd3XkHeazNbNhEqYnx2kunKQg3ZqDMMzDW3DEVklzw50EfxL2uOBz46VeSfvOMhd+\nf/5r2KBcTrBbsEarUPeKi0LZOu5im2rbuhWdUzQfz2QcjFqdbRKKdf7eUdLEEjItmsw7HJn3JrBV\noX6caUfFd2wjLwaM+NlArTz8Cd3kbfqNGHtUPc55azQkIBRUbIqZfWTyB9ErYXJQPa4ccHI4HNth\nwJ062NxMloQGb2x0ByVMBCrOKTkrPjl869/kwQDxuFOZKOqoBFwM+Nlo9AbUBGMRvstVvedwCYfS\nfKItiSOeWcPDCCaamYKZzbraDHyRDh9n+jkg80BhOvSiSK+kvhOkcqqvnPQOYqnhhw/G9Lttdiz7\nQZ0FafZ8OltIhsyGX/gzTvB7pp0nmniqT0jWYc0n9ObIVZgfgaeZ6DbO807uaXhd2oak4tAuJJ+Z\nlwbd4XshSCBqJrRA2zrHFvj55cztPrGXiO9iKId2wrET404vjT3DPCh0sdQRmmPy0V5HzaFmku1b\ngR6ILtKCEKbZkJiO+R01uGzPnOob/qOVZn3sWdK6ARjYPhd7Jw7TvdALrQ8kxgs9mDVBfbxYMhnQ\nXbA/6kFMBTDlG7Ouxo73zn7+xhiQCG4/CLUzP3b6tACV3o0+9u6Rq/oLm671AbDBQInMSqSMYe0g\nIuGLeTz/WgWigIw93KbABkzNYiqAOL69vdcAIwQj2CM0ki2NFSfZQEaGV6Aq3AcrsWBD2k3NnkTf\nmW3OQK/SsG86fbvtWLopN9yoPRAgGTBG41swW5VBAlH7PaUYe2652P08pcC6d5Z5wvsDXKHVbkzX\nOAbfh3DvsDf55k2tCs8FbuP9s4N+snMr3urGNB4TEauNchtgZjf5cfXm6XeMhrQ5U2sMMqAFJQwG\npCqwGftTwBKIZ/6N57MECIO9+c4CBb75gIfBCMXZ13M3gFWi/XxzEFfgbKBuj7/cC/Ar5ua4Pt/W\ntlmQU4DWzW+4BPNH7Yp/FPoBlE6+zOw+soYZ2TtXDBQo1RhmPjlccjTnEe1cyBaOEIRQK63Bi9q+\nejRPUyH2RiiVuB0W7vWbB8QLrVkasVZl3WHuFiTgj0ounVbAXwzUxA2GjLOGpgPrJgPYgJLVBgEC\nIgZiosqMJWhOaonm6h31MFvY+ACvt8DDJ2W57DB7MnZc9z/DbY/c10CtwjZugCV11gN2H3nTRA4e\nkcYpHwgwU5EAc7RU+eCUU2rU6GkSKCnAJeGdAtlYQZPHFQNBmnPU6nCqpOFP64Ond6HvHZer+Qd/\nyTaMfN1RjbSls/VEvim+dsJkDZPz5kW45YhLYqmd6mg/TMZiTNZbuZMBdl4gx0gm0hFcCvjjIEyC\n3u1G7yqECFvxPDgz80+hf/NnTPtOb5XWCq1YCMZpvY37wbyBg1fOrtC0Mb1l2DK1Vvgu4hZwjyYJ\nrl87bYOzNKJr7Bpo54RzHv+uTioOVx2tOUCRq6E+PoLQ8b3h9048DrRiLLIPyfoNJ/ijM79ttFbY\n6fS3hMyN0BqxrsxlZz88NZpHnN4yWjsaoD8lSo/0qyAPkWXuLEvhw/mN5VJZHivu0dOjMbJ6g++m\nF/IRURfwWehtppDIAm/ziV0jpQbS1xsxmneaj87sFWZj5/Wu6HeRnhU9ebuvqyM3hxZFSsNREVfN\n920y0kzfO9w74dnSa2VycPG4wxb8rlBu0KJQzsECMLqiRyf/3HDSiffDmtdq6U7aBkNP1cJT8OQ4\nc8QFTZ32NdPXjJRugUTDu1BLt9Rkn2mtU3LDa0Wj0Lyn4HDR0xc1Nn9uuF5xvg82o72mfSflgygN\nRIxF7Uwm2p0jLwZMRG1IruhkUhLpSlw8wXV8V3pV6ngeFCXHRG7xl4wEbfgu5BIsdEdNiu26mt2W\nF3wzz3XfLMRE/JgMDWWHqBLeVqaciUvDh46EAcp4ht2UMx/zal6MOgQKvjZYbYPKTnDF1BRuUVxy\ncIfiIqU5fIHelFgaQYS2R7R4fqqPSDBvyBCUqoKmE40L311vHHfBJ6VFG3pI7Fz5fwm1MLc3ynEh\n+AONOx+e3nAEvmwbX3tnB85xN2WEFKZttVC+ojx/eGTqB90ZYcaJeX32GtAlGJO6e2iOL39yxAdP\nCo4/u5nNzdQJ1CvNVeurrIij5UpvWB+qQo+RfTpTnUdqG4PKwtbM99tNY38dtRPJ1ggnSqjH8JZU\nQj9Q73H9ne7/S5/w/+f1FwMQBg99hbIPH0DM8xQdgSFqhR3yi/Q1dFTIaQAAIABJREFUOJNMHE2Y\nnpUyKbezsIlyD8K0m/yXYgXe5f1Z9FboTGGAhWUUPAsm52vGJvQFGNTjUkwu4Rd732P43L0n6arA\n+WwF07Eby+3YYA7C4tXMtKsFk+gBOOEo0DO0A9bZ6qCzWnG7tXdWpeJfQU/QzkJ3wwhbYSpKi8LT\nA/zt7+zQHh+UZVY+fq/4Dt8/KM+fhPKmlNz54a8LudnC9uGvPPkuxP4PXB+/Qv9f+PgxcX/579xe\n/5H1Dve7cl7gWIXzSAWUbmmA0uE4oNyhfrXpcwOmbFNgmYec570orb/ISGq1or+JoCjbOlKrg5l3\naoVtH1IYU+eRRWhOTSajMDWlz2LMgGpszvaHcT+drHGJfXjs/Bt9up3XJm7I2Rtzy+iLFZHtnMxA\n/MNsBZAXJjKlB0LssB7mCdvBT0ZHOGFU6paBW6GNFNg2GGwHibNuKMLJZTMxFU+tNiVu6onBkjQf\n/E7ADJ37eRqZLx0JjaPaNCcdhVO7c5LNpGBOaIMNJE053kbS0aQsqTL3jS6O0DMn2SnqOYoxB8Vh\npvV3h/y5oF2JF6hFiB86XTv6eKJrw0lAvXCaMovLPPUdesc5o6NvMtPvBs4FNSlcSo25r0xp55RX\npDlOkpnywX/98f/i+vTG6emFJb0yz0blcP+pcOwnnr+c+Yftd0yYWdJD/cqhnoowVfOpq9WT2Izd\nKZ56WCqktIOQARfx10AMnZQUyQ3XjVHVG2gI1lHNwZJjj4LkwvT2gtsb5ILfCv06syCEpfLSZrwT\njmLFq5YROjTo7RJgGtQHUZDXnfm+Mq/30Wk5ppEEus0XGmL+pVrxEb7/WChrx3+aDaC+CiEpp4cA\nn0A/3wlvb1TvSe9N9eLNtF2FNs00dTZw0MPkYWS2h49whnavtJrxYSPUAkF5iAaCGsgFog7VZklY\nNeO6FZt42LIVP8fjib0n5ttKdw5pjkjj0982QoKjCrhE6xMhwyKFJI3JFZa5EC82QVuihV7c/MRb\nvPLn+0c+z9/x0/mRn+QD5bZQ12gg7V5JFPpbob0VWnXs2tgvJzgnaprs3veetoM6h+sGgi+1cvhg\nso9gIKrvDXKlHZ26VlqfyMuZdTda/sU8DvC9IbWhz4UuYnKjdyrOYXKQ2BSWhXp5pPuZuK9Ef4CY\nobd2IbRCaTAvnZR3XKuUe+d+9+wlEFzn9/FHFjVGcBLw80gs1073gQyc8oq+FMqXA3f+ZSsPr68w\nRxIZLRtaDrIGammUNON65fDmHeNnTw4niiT208O3zTiHK00C1XueLg73MUJN5n30fYIPCS4RUqd5\nDDjsHrfvNBe4uK/MujPpjtdqCcSTsZ/zU4SQ2WvifLyah2cXODDZb3KEaGvZ5HYzmA/WFKuPyDUR\nHib63qCLmT4jNAnm8bvB6SQkX1jCTqmBVWdCzWh0pIfO+XKgryPN+xp5Pc70W6feG5//kPjp9Yri\nCBel50aLkeY9vplu8/z1sz0rcTCJVw9RmfOdPk3U8yPVJ0qd2VpCxbO7mUCmOMfSX2F5YKqrJcC3\nxlwOk5iEifB4sgTCFKgE8/LRAt4xnSBIwKnAUfGtUiWi54mGRx4VcZ4ugTYvlOlqn8XNuHXFl0yS\nzOQLZTnhW0FQ9tOFY15obUi07oWYGqfyhqwrh15wcx+WBMa4mZPVZKVBW+3r96A0qtU3DLC3uPe6\nxf5uAwDywYaCMkAiigFGxcNt1Dq1D2nqYA76nW8SexGzV1FbSixoSkzm37x5Nd+H/Hkdw9tTMJnX\nNLyhY7LP7z+Du47P/2iYjqTBPowDUJwMS027DbBb41tYT34YLLkGcTJgcs3gdyEN6eQy/4xGR6RT\nEIoTKo3qoU7Ktjm2cd5uHt5MLcd9AGs9wX61PiRtMHUjgKQhs5UMLts1Cd5kzkczsHDRkVkwQM6A\n1aNhgPJVR+3WYFiEMg8VizfVHcmZWmR4tFu4y8w338lv0ikHOjyiL9MAWcUAxxahXKy5OV4H2PoO\nco4eRtVYMZJ/qdt2n/DBM8VqdeRgrXmnyGGBB8d14ad4YfWTgYfpAs8Hy8tInZyFqkLNAichXSJv\nMuN9R+mUI3C8KTmaLzmLkLz51jFHpBUm37nsd+b1Kz15ShXYKtdJCalTWyVPMH2w4VJzlj68fLBi\nOLkORQfTBmpRjuZZ1RiSEx0RR9KOqvK93JknkKRMvSP/dDe/0Qfl5Rle/qeJ354g98KmiXUNXGXj\n61vg80+RssJt9ey74zoX9s0Rzsqqlbe48KfwgdO+8yCZ6DpBunmGY56wc+pUhDwH9t8t+L2jQVhC\nZf6cSd7Wgy7mcdvfqrGya6WchHixc1RHUlEqBX+rXH6+WUhWUWLu9GcDQhbfOCVLfXbRsb1Z0vB6\nCkiI6EOguZlFM6F15LuEVPMHqz9nXOvGyqwGsE50dFiFuNYJrZG9Z84GgHZRuhO+VlvPJ+Dy8nXs\nOfbHl0J7aagLpga4Jpg8cdhO6N64r5Fb83x++YS+OKbPr4T14PrkmT6Iyf0byCUSPiSST8wEjux4\n+6lRjs7lEJJ38Jsz/jQR9w1fmwFRa8X1RrnOuBjZzgEtnZfXQEiNh/NqANau+H+80fdOjh6+FPwf\nClmFe3Tko1GOO20v6MlR/uOF6jzu4piWTqubqYVyY6kHD23jKgchNtxkBI/XzzPba+TtFtk00Gg4\n2ZjY0bYzN89RKk53/FlxKqQ5kCbzLtQq1ObgGulN2PpsDKpb5J49nAvR7biSEan02tnmmaMHXuIV\nTcruJrrzfPnwA3WKhCnjaqVuyu1fO/Us7G4CgtXor438DwdBGzp15F6RFfTeIGdCPZDFISv0R0dN\nHb9+tX3xXzfaP6/4n2/4qZE+CnqJ+A9ibPN8UDPkW6f3hk6eIwUDE0+BfnH4o+CS+TC0zr8hEex+\nIkomeU/SRkuBJmZF4bta+MagiacjAw0fFd8cp2xhfObN2nHFFHgvZSK+rGaz0YFL5OxWvBpLEGdp\n3t4r4hSXhDk1+teOVLUQh9nZ3976TFc7rjfksOFHuxeYIbZGbp4jNN564NYSRU32f3q9kci0aOEe\nTtXIJLmyb47dJ7ZH8+HXqoiYrPe5RloASYkgjWPP1muqcBzGxrwVOL4Ujs3j/Cv6m8jT/Mynv3ll\nKV/Qpuz5gS/lv/A4/TOxF4LbCdo4VNCY6JdI+tFzXg1M/+F64+vngE4F3Y123xSe+o3jcqEujlNb\nSVoJpRBapX6Y0ei47wt+htgjsnjqKZj1hQscUyK0SibjpSLJSBNZg22mDpapo33HvVmI0pf0YOvG\n4aErHx8yuQdwjrZE6EpYVvzijZk5g/OKtDoIajuhVELCgPR/x+svBiD0ebD6gL0My50BCAaUBpRu\n6ax+TGejw5gzGAjEZH1yLMY+9NkmdLVYwlwboKIOb7yAsfxGQBd+FK7vMorWxyTTWLRmLo35YHQL\nrjKpyfBQi8nYOnkwC9tgFW47xANkARIjGWxIb8Mo5LACuIsVl63ZpESyyXDrSN/zaudDm4Gj2pSn\nSbi/CH/zt53LAssCU9JvhtOXE0wXmyz89d8VGuaVpa6R/SuJvwf9xONTZZmF8yI8Xs/cXiZurzt/\n/rMdVxxrhA9mz9UFUrQisA5fLGnmHTmUCgyPYOo7+7MLUQzEqirm2+hAm3wzKEdMooS38966sTe7\nDgmSWJHcujEOvB+ylA7+E7BBuNq57t4m+075N2bXcVIrbINDknUUWt4X3w0XFN06Pjmaj8QIdNvU\nOCdSydaMdsVpNz+tccKrWmN0zBPNJcxZQE2mO/y+HLZQSu8UDWa03Ts+GhUitIy0ho+Ac9Y0SkOD\nGDrdik2KRAjRfCTA/m7qqerwXtkO66Kagg/vBlDK3HayRBr+l7H9KcGjNYg971A8+rWhj4n2XGiu\nI6dgnkQjTrLExNTNxL56Rw4XRDKNiOrdivdmRc0pr8YqrZDvnnw/cfu64LTw3cc/kGRjDs8ocJ5/\nJvgbP3z4I396/sC6Rqp6YisE3Wk4SrNkMYm26Jn3BLQQyNFTvSVJOW+BJ0GNMuFaw/dOa4rWhlLo\nwdMmSzTzXWhFaXujNSUenXYo3mXyZYHdEqGjgyzOmE+ObyE406S40EiHdVh9HUkWW7W0abWuUj3k\n5cQeZqpGe0hGg5tbAN+tOL8ITJEQHSqKvh7IOVhq7v0w1kMwNtUhkZqF5hWZoK7VmKWXCal2HZq3\nqTVBcFp4nDZjRCtMrhqVXQJBlKyOLpVYD5NfN8VFISzC13bi1i60o5unn4fLbEnY06y0LlyWwpJX\nPuozc8hMzqarIXa4ONQL07WhMbH7SHYmyfySn/h8fOQmC57GvU74BHo0oqv4UuGotC8HpUc8Ffd0\nGf5iijhL+NYQaPq+sGAS/NbIzeGHz58cFuPZto6+Fpo6HAf75cLsKmtLPMTdGHT6ztZzaDMWtTqH\ndw1XlINEahkVxXfrbIMf4SWjGCxNmE5C7Z7YhX7vbK+KuE7JnhIDqWd+n75nScosncVlQjZvU3UR\n8Vja7bbBIuw//cqoS4Aj02iQlX445HB4Gq07ip9x54QmRYPRsPZwQXrD92oMW4wWdM43pvxG/+4M\n9xWpw/Ppndl2SmZIvRVcFuZWjM0XJjwFwaQXU98RAa8J8crzcmGvM1ELYUxI/SzIJKg3w3lUCFtD\nex3vFeCakMdBXf9ijI0WEr12enXUonyMK9f+ylzuY3hQWMVS7o7LidZn3A8B/3zQEXRTQi3E6ojV\nk8TeT0tne/bs5wWCx2ulhkg6NvMORGmu2/7QM74pLQRjgIsOHz0LmfKtUntkZ8L1hndxDNvt2Xqf\nBIcBvG2XB4I0+ghHktLM3wnzCXJS0dLQ3HHO/IpseudpQWgB6nKhzQtHOJHdQuiZSkSkmMeZN4mU\npkhpYmvQORraVBrFB/Mi2xQJjhYTQQs4A9fB6oA6htOawL0NUE0N9HNtsPj8AOL6GJh0AwCbG4EU\n8O0cuB3aAnmE0c3Vho9aR5MD4AYQ6a3OcmpAnX0o+3c3mIXeW39Tqr3v+8Cyt8HAwwbTzKDzYLml\nXwDB6oZ8WmwwGj2sWM2RBoNPOsaGm01qHB+gPcCbM0/wLwekkbL9nd8gOkYgJYdXeuwWGOSw6xgs\nfO5WYNWhYBnsvtbG53V8+x3TYD3KkF+rNyAuju1d3kHBdyCTYQfphl/qbufN0t/G+Yl23JhfuwW7\niG1T73WYk6G46RBmu3UiBth2hbQMVQjGZHwnMngGW3O8T2t2jV0DplF3KyYL/oVkw9Yc6VDS1cNR\n7Zl4OwAdQ9+A7NUsEoI3AFOE6uxkpaspV3pp1B3SdTA4vMlm66GU7Khd6UfHT57kCosWYxaqEJ0F\nzoTbG/k0kXImYbWoE6VkISagKrWY8qIWNc9ZIElnqoXg1Dydq8Pljm9KcMKrWzgU3o6ZR3/wMRot\nN9GMnfycSV9MfRG1c/kfMw+/c3x5mwgJiivUQ1hXWB4yXz4HWlbWNyHvdm0Qew4+uszOwb8MD9o9\nJpIcgIxgVGMsNjyajPG/LR6XLHwrLB7XEv1Fmal23bxQ1mzM96r01sneGxlgMN0rjrDZ/nh8d4a3\narYp18Bl7pQqpNHDhEVph7JvZmuQC+w/LjjNXLbDpI/VZLYm93UgMK/78HH0VnNulj6ahqnl1M3g\n/STFjnf4rvXumIBU87C6CDQPfVP6CkEyyStlguonC787OqWaT3ONnq/7Qi3w4U2YVuX8wcEi6GZM\nU0m2/rYQqG5CuzK3zNSOoa4ytQdqg0nfzYe8d4HgaU8LNTTy5Oh7Z10DyZs3pV8reqvIW0bPibZ2\n2r3R3xqvl5lnfzHwuReC66bcCg7vzCNTm3LcHJIjz2HBScdL5bKsBDKJinrhbQ+UGxx3Z95/Q5bq\n6SRt+O7R3umt05dgCbpR6LFSW6K3gGvdpO0N8pE4muc4nKmvSicOg9ku1juqD2QXDRgMI4zFee6n\nMy0ELq6bRUrzlMvCPs28yhmKIM8OuTuiWEd2qMcVQe5K3AtuK/T9sHCz2vEZ/HmhH51OsoH0l0z4\n844sjTB5dBLbf7EQPBGBoLTQ6VEHY9iZegYDg7RhDFS1/3t/ufe6Yfb0Yn22dOtrGmKWOuPrrgbI\nnadKDHBqBd86tdp6VtTj1Eg3817YphO3hwe07zwdG4gy+WrqSO2I2JqG2LDlPQyiA6gF9r0zEGNv\nuNogd7SqAWCYyqF1h26N1pXSYY/BfGtTpLVOyKt5k3al9EHx3zvETt4teXqSamFo2Dny3XGQiBjB\nht4JWglUdpnQYiSW/PNhXoiuc4uN2wxu9nSE68NB7wtdZ3Y+Ev3G0S2McToZ2eWSdz6wcrTAdVkp\ncWK9FUqF1kZSPRV0p8hEGhulDk9Ipx1tpla71Yj7kOhTQKdgg3WvzFoN6Mye1Jp5fw/ZtnmXgOSK\ny43WAz1ZirQTRxbDYUp1eNdR7y1E0Cl+KkhrhBm8WGp1iEBXejRCijqlTf8+yO8vByAc4OCojxHB\nYrExwk1Sk0f49++pmCfKu9zEvNA5vUA+A9Viz1ex4jAO0E8GUIgMluAwYXbVvnajgG0yPFMEKPaQ\nhhXSSLZro0cKXplGkfT+rHtvhZyKSVnmHegwPTJGtmIJdXVMnzsjoth+XrHP1zHQ8OmTHUczBadR\nTIEffmNF6qcfFDfBdxYcyTQbHd/NncUbU3JRM7iczomGPXTer2T/Z+b49/T290zpTNdICj9yAKep\nGnvPG0D3S6qL+fOpwtvrSNsTK6bfDa0945owpsXBTHtbt9+zqX1/FuhOyNXkOHMcxWyyYtdHAx19\nsGuEgz5BQwjF2OHHAAup4M72WePVvteNe6v0b4xwwDxy+r0aEyLaQbXuQAVX8/AhLPgQaMHRveCj\nNV94wW3FZHZ5XDBVM0iutinwbryM2KL13nSLUC3KA68Np4Kog9KMnaAdp5XYsxlaZwMx8Q7FCh+V\nhmonloNWFZnG7/5VUEEXkwFU8fTcKO9pRtLRrjQnFHE2gRdPqJmjT/jFjP3bagy3IzvSyw7icSHQ\n3AROibHTXKSECdcZi5DtKPv5YTxExSr+/svnElVEG8fq+fj0StLM0g/y24TMhXCq4BsfP/4T//qn\n/8KyvNJ7x1NYNgPcfM1UF7i4je4EYiJGZXWBViJVIjlB89EK0QjChpeG7HUYwne70WqnHcU616Xi\no8L9wK2ZenRrOySYr2Eu+D3jnRB8oqrJa93wCjLWuRVIQzFkx3zb6YPu0uZI3xqShON0oSyzAQt1\nUHK/3Uud6hPLpOzNsyRPHQtMy92m2WczeloO2zh3DeYpeAqUPOSa07AzQKlpsmLNBbTr8MSpUOAh\nbExSqN3T1MymDhVmV8lZcD4w7Xc6cFTHl33hRU/c1Tb+s+uUDM4b2066MqWGV2MLTr6ySCaNzb93\nMfB7FvRqTMWaHJ3Ats9075lC4eVYAAufOL420n0lUvFeKa8Zf3L4f92p14Wg1Vi7QGwGaLf3NWug\nt3t9X8xAeoe3glP7zOsqJBzNBbJEahOe2wwTnHxn1oLWDslTXhsxiRmJX2wtdMkRW6amC9NxIzhj\nJQDGBgMzak+evCzM5YWbnojPd5vkvtm6UYry9XRiqQfMJzR25L4yrTezP9jMqzHmjZALx92Tv9Gv\nGIuhoveDumeQzvEsTNGbwsBZ49xDQJdIC4mpF4Q6ZDbBhjFqUhgAnw9LG7xOyNhIdWxWzimeRtKD\nW5lxZeU07AOiFMIA3GZ2C5TxkYTSjp08LSx9tQGLk8GGdhAC8/2VtN5o5xnFIcGbhGMJED16siHa\nWiOKsmin1mbPtm+EcOC7oArn8Er2kXo50dyM/Ohxk8O9VZwK+ffCvgZet5nXdSa5agEWI0ghu4kk\nzlKBteLEIzkbM7Lauq4iEOBIC2VeyO6CNI+4AFvlnJ+5+ytOK4efuepmyItzNJfwLQ8z6UZOM006\nMluifc9iAJiA9pEO360GqXGmxpl2PQ9Pm0r1yXySRIYvUjRw6SgjPTgRfaFLNFsBxnwrK7FXsgpI\nsAZ5BxcasmU0RvzJAmVsPTeW1/5me2yMNnQFq3/Eg9qsw2S/Y108gm2RRe13hMn+T8bPfYOonclj\n30G9qQ+2nvCtiQGrE9oY/n57DIY/qQsGTgZvwFrFBtBhPCrqsNCBA+Rp1HJYvSU6wu489GKA4FKH\nrLfbkLaLHbNW2D9YTbIPwAyFZw8/evhShR/GNLrWTvdC7pY6qgE0WPpp6cZ0dH6AoN1scl2Hb37U\n3urTc4apDlVGNhbhnkfPxVB7DG128eP8lCEHd/a3VwMJuz02lmAsWPrzAPFaNvDUe6BCjgboFcbw\n0Ui8FtQERNsyad3Ymbhxucb11lGvVbW6T4u9N4IF+qUxsB/n+v2lYkVmyUr0nnLL+Ndqsi0FTcL+\nLttqcOqZ63pnKpnwfYSjkk6wr+BnT9sV7xrqzTM3l0CTjg/vz0QndbVaUpSlZANFJ4ej2PBwCigG\nrggQp8662cGmYMOhWoQQwO+Vcz/oIxQu5ErYG1qUzZ2o3lmitEZ0gmvPlMN9Y9npWtHgkAhTqXz4\nD54PDwd5m1mWTr0rf8gzLQvTS8P7hJvg5WfP8Wr3TA/gkh2TDpXWb/MLL35m0kZXx1txzKExD6VN\nG/tBSebhW3CE5O3m8Y7+cQHN8GqgnzRDskVscGFppoLuDYfZmKzXEy2aL/MxRfhBCJdIV4dsjS5C\nGnYpm09wgbU4eIyEYMO+1iOxWBpt72aB4ycob530uqHeUWQG75AG26YE54jemINe7abroznYxBiE\nFyDVYn64YoCd3KGtyhQyU6hojbQe0AP0XtllIceJzU+El4we1nxqmtBQ0N7oGeRQujq68/QQKT6h\npTG1Qqp2/ro4G/p3NXuJ1uhNvtmmtKunSuNwQqNTzhGRSp0VmTPiiwWETUq7F9qu3Fripide5Moi\nhUddCb7hHDiRb2EuVLNHqnsgupkolZkK1070B5PPqBe2548cr4GyCd3ZsMwyfGzA5FrDZUF7p80B\nnZ15H7sxVBqBGHKYl/TeEkdx7Nn8HKfcCLUgvaHS6SJkl9glsfnZBhS+od4htVnwEQpejHl/dWxu\n4SsXpHT8vRFXx8U5q7HV4Yriq+KOSr0X2lvGLdgwvSjhYSbHCd0aVR3HPFGuF3Rq5vvnA14F6Yrv\nzc5jhFY7rXa6eLq+U7/FwlAH0KujD/z163CRU7B1w6FEp0grVHVmxaFifuFdjchROqE0gmQ0BVLe\naYciR0KzwA1OzrGr8nZ9RLKn1a/G5PZGbJpo1nOrsEzNQn6ip0/RFDPWdBoAOv6GIXHvgjijlXfp\nVO95zjaM7QpH9/jkmJZEa51+d9AtpEZ6J5fIMQWyJLx0FmfAn+VOG0AIk4GnzgYQjs5JNxsqOxts\n9Aptt41w/9OB+sZLElqKnP8D/Onr99wkkR7uFB4IbuWWvycfE6/9IyS4nA8+sLLVwGXZ2M/CcfGU\no498gY4/CT42yDdiy+af7Ayr0DzYjx58NJspoqkUYwTnGlOreGmcT1ZYCvb98eTMmq522DriOtOx\n00RwJztfMSp+timqLQ1C8TPBKSntWBi4hQ7GQcOv4sz2Y3K2D/87X38xAOEJG2K+S0EPsUThWW1D\nO6JRPKuOiWW17+0B/EcrHJf78F4ZU8cGXMeat4wCxvsBXI2iyA+pyXtfImrS4Nps2rmPouUpwrt3\noNYxdfXgF2Edutn8ZnhIHb6FOlmRW89iUubhRXOeIf8RpmTF6mVWksCjWf/w4XtYi/L0YEXupw+W\nQvz52SS9//k/Gjvhr//a/ApPHywBTS9j6h3Anzred0IXro8zDuXhO093lXSOhtnIB3oNHK8J0cY5\n/c94mQj8H0xPwv4DnBaYTp3Pr56jYB5/HcqrLXTbzQrLGoY3TrNzF6qx//BweLt22dv/fe3CXu06\nygUI0F6xyfdgBaBWYAt88xHqMiZo3qb6e4PbBM0L2wLhApdq0urksOr11a7rdRTA768pNvy506TT\nRiqenDzOdUIIxBHZ3idvi9VpMno1DqmV2I1mnW9C2W3Rfkd0+zoW2a3BthMXjJkT7QNMaj442U0o\nnlg2/Hrgc8GdzauM2RiN0ooBC1fzovDRgJheO+sR8UU4/fyGL5V2nUGF9nShBmFdLhSxpOOopgnS\ndeMolZdlonqPTomjR+gzzcF0Ngliny+4+4GOYAOiMwbemtEp8rJPLMmz1ZmH5SDGxuYm3tqJ9whC\n75Wl3XHSbfrlHNIroTc+XL/yNN0pz57SI212JnVYFuJ5I/rKX336v4nO8cPTF76+feR/+1//B+tz\npLzZdKRPndocfrJp9XUyAL9q4+t+Qr1w6xOXVHhMSqDTnwulW8Jz3kCLQnD4SfDS6K3TJk+PC/65\n41vjUIupPGQyfzs80VunGnqnVA8p4KSxJIuZRy0FGoyxKKcA310Rp+RjQS6Jkia7jnfH2hyUTnk9\nOG7dktUuSu6BNAvHHxvQ2F7VTH1jwJ0eSGFHqr3Pa7ekn6aB90Ss1Dt+dtziTMOxVivI23LBnc/I\n153vjjfzQCs2wcvFJpS0TulCyZ1wX+lZuMUzz5x5frWCur/uNPV8rYGHh4agxFpxt4xPSj8n/sr/\nTHKFq9+JoRk93hsb3KQZSpttK6rNpKGxHMgt8qFWbnkm/eEz7vcWENJPAZmEOCvHDdxTID56vBRS\nb0hd2XuguEguEfaCzpHDJXYCVMWv5gNRJ0fzjteaKDlw6cpDyixeWd2FdE1cLjaB0D8+22d8q1QJ\nHG62JkkqKXTb7KMntd2Khy641sx7ZaRpo4pOM4jnD/zA1G8s+1dqdxznM9wy4Xbnt/mP3B4eWZlp\nBeK6Md/ekDejf8T7blJy77j5M86o7PYSW0ubOnqYyWshE8mvdk1EOzGa9k/OkRQdlIprlT4Cl6rC\n6faVdH8j9N3WpMcJjcEMsifzHjRJWace0HNnzm+koyNd8K4ogJY3AAAgAElEQVTjk/DoXwjSOJ0K\nrXu+Tt/zPTfe0oQr2QBGpzA7jumCRk8qu53P5Ign8H91psbIlidL9jsyTZUczvQJ9ix8ON2Yl8qH\n/Sc+6FfO7ZUohT2dOPmVu79w/rTz3Xc3Th8r8e867U/K9uLhOfKmF0LvpNSZ6sbhAnWe2S4PtBgJ\nDqIbTYAXpDp2JqRYcnFrHrxHxFmwUVDuywOP96/E7Y3Ty2du4YHNL8TYjdlL4tCzFcDHQW8VnU/o\neaZOE7M7UBL+dcftlX5UHI3aQVOEKNTg6T7iW0WCo14fUK8GwI9pvW8b2+Hpmjg6PLobGwvBVTyN\nECteYWkZzHoNdy/4tdIOpeVGC2qN6DQZkxADpRQL0VCP/awRuoiTDeLe/aVFDHB6sxrYvO/CUBzU\nYRnTgdnANyn2s1lGbTHUFguwzKMw6KYSaDLAvj7AKrWC3Y/E3mmEaqQCi9jQWQcYlh3UzUCwq5FF\n7dii1Zjh1YC22cPkIR0Gjh2/6u6WAO0ElzHsLVcDOpMzO5U/jBCU9UH42DrTIahakm1oENW8Mg+g\nNuV0gpdXIWfzY6Za/aPdjmPDhuZyGGsvV7jdLWylNGMPztiS0NSUHp5f7Hnu1ert2If/4ABg2wBf\n23szUaDewU3gTubzXYOBp9tq1zN423djgHUM4rWNgWy390zDbuZdVdIwSXd9scLfNb75DfrZzrFX\nON8hvPtaAnsRGsJ66+zVI7fIWRJ+L7QU8NWSTec58Ru5MVF5lB1/ESYR4sdgfl6TXeR1FVrt7MWY\n4elsPmk0xQcbfs9SuLTMkg/EQX2cBoBqIR8EZwekcKgzexWXOU+VS6i2nu4Qa+dpO/CqzMdBi4Fw\nGCvx8IEQCvPR+XqZ6LPnN37lwPPJbey7R6tYeN5WIEVOn2BJmfNRcH+fST8IZYKjOXQK3P8/5t6k\nR5IsyfP7ibxF1Rb3WDKzsqp6mt0ADyQv/HTkjVeeyK9HgEOC4PRSM1VZGZu7manq24QHeR5ZvPXc\n2oBERAbcbVFVeyrvv34Tvv6a+PaL8PirsRclLqDP7iAIAR434R13jj7IWuhB6BKoXSktYDTaUMxg\nT4kvXLg/MiUknrSz1kq6Jk71gC48hYOeIdWB3ZrHngyh3txlM6aYv5Iol8z945XLxeeXUv0z5m8H\n6xWWR4fs1uKuC7e0steIIk5WAQcLZc3fs9xi74To363RBm0fWO5066gaz3Hea0TIaXBeBibCXrwU\nrHRhiyd+AEoRWvB77AgK94p8Ksg/RPjdwvJ3F+J1Zb8L7d65vBqnVjiXyrfHYL8r/G5hXE/0/kob\nB9wHdu+037u9TLsTp1jjbDuJgxC81TrXgmkn9erk2RLpeaHkTHm6EGvl8bhBNLbfrdTUOT+foe8s\n9wfnX1/Yj0795cHxEL7VJy8+e3TCqXO+NmIQLGSKmFs/t4odg7g1JAhlUR458xIHn/76RL0rS/LZ\n5tM/nfl8W7gVoWin5u7PH5Vl2wi1EXtihEw7r9ScSV3QLjzGSrNIH+L24Wr8Py8f2ZryKUSOrPyh\nbzztjfgckQW6eUHgg4VP8T1qHtsSrBP2CloJp4EkIZUCp+iqyBioR+BbSVyt8+6qCALvVurwc15u\nhXO4e3FlL6QyyF83xl9v6LWyPwRipv584S+Xn1m08S4dLNb5UApZO6fDxQOyZi+VSFAfnfHAo6SC\nYDFRR8Cay0TCW2MP8JouVO2c5OZN1X3nSTcH0oZyWOLA86OTdHoRVttYpBP3Qly9aKWZcimNrBE5\nG1/OV6IFPtoL69iJ2YirEM/ynTwDsGMgQVET+s8nmkTaDqiQRvF8/NYIvdNSouRI14gWL4PLoTFE\nkBB5kOm2wHqmrYlw24lVafdAqI1afN63fYB4fFawTtDOJbpSMprvO0VhqZVzrv79lqkSRdGodAmU\n7s3lXQP94SKUz/9n50s885+//veMHPkf/8N/5M/bHzkvO39c/xONEzFUuETKI6In5XfrZ0YP3O+J\nnIc7ZB7CYoNWYLm/Ihusq5cTmihLOKjM3GEix2mhxUwIXlaYWvXPZkaOfQKh3ZWSOmcWvBiQMYi1\nYFvnJa2Eo5JqJcfB8uQnyoZ4AdTsGehmXHKCAQlXprkC39AgjOQuunXx+Ij/mse/G4CQ5rK9702z\nU0F3mLhtNQBxEtDVWSLOPhwibgsJk6KUJt/VYsK0QczhwybFqRMkJM/nmjYRmwBXLT4ctGntuIu3\nEJ+mPJ/qYOMG3HHKux3OJC+ztS2pZ6C+5RieLg5+je4Nd/sDrifjuviX9P2P/lo//GhsB3x8dtXA\n83tj2eDjR+PTV+HDezidjB9/5yqomtySzNkZAVmgDW/YkiUR8sJ5HTx/FNoQD2zvQi+Z0T6g+g/Y\nqPTxkRBf2B4bIAzz7KcQjJCN7RAPzy8+LDd3eGHDbQt1qgjLkGkrNgcJ1c9NMx+q93mNNnHrtynI\nGZrH2aHVWW9xMsjLNscUxohvRCKuTAzd8xuZagWd1iPEz6lm0LsrCfVtAw3zyzXoDc91GG479daa\nefEoPnhkfzMihkWdX0BlTKCviVNiirNSneG/+yh+HCQxzKaC1Y1i3cB0oNJdNXhUdC8I6g3AGr2d\nqLgcQyXAU3CZhngjroTqb7IPOop+3T2U3IR16RRN1DVxiGeySe3ICLRhyG6uEAgDYVCJ2BjsupDc\nOw0hMsbgwTLFrZ2RMqaBEk9Yr5z14Ou2cpXGMSKl+0lYtFLU1UFDgtspenDlWu/0prSuPK83sMGo\nynLaOG4XJHYsQhu+S4kqlC1zjIW7nYnpcIWxdCQ6qzVMMFVidqbnGh1Qk6bk2GerOOSLoK3zCItb\niIM68x9cCTPMMHNZeqYy2mCxzhZXDKGQ3K3avcikDaWaEkUYGtls4coGorRJ2ej7Ba4z5b1UHqcL\neQzK7naWUsRLWEzpLSIUKIOxNcKi3gacjOMOx+YWX+lCFtjiCnIgza0YNpVyuvhmIIXhmf+vB9Yi\nPEOprt7rEqjLhb3dOLe736TeVIrDmahQCuPotCbUcOY1XQk4+9i3Tpzqo12U11el3OC63ln3Vy7X\nRmeBM7yTGzk0TAKzL8JVbMfclK7QHkLvwlaE+qVRXzpHWvh29x2tngL9a/GMxCEEAvuaGE+JZRlk\nbTQCR/Mnt8eUqOCKBpl184nmlhBcYduHso3kIN/1RFYfdH+Kd+7VWIoPcaaKvRwoRk5wC5FWdSpK\nKiKCSXQwDZA2GN1mxABI6VgKjK87djZYrtRbpz1/ABG0VLhkxujINnh6+Up/fyGPwvq4UYkso6K1\nMKKyh5OrrUSpb/72t0dQl0mZeGbbbPylNg/s8GkGay7pyVQClTJbMcJ+YMPBTWfTBMsLI7rVolsg\n1TfRvH/CoU5UhSQkKSyhkaUSpBPnYLIsnfNaacuJ03FAc/WPjgZ1II8Hj/cfyezeCPfjCa4LbZ1N\nwCl5mQIdkUZOnce3wPt3G5dzmSST26QIA6Rz0lf69UJPhZ//8Fc+/PiNqI0YKvoTtNjJz4PYhf/8\n8gFOCamVpTZK6FMtqd/VS7IKGoVypAnGD2g72vx1Q2sUcY/r8/1X4v7qpQTAtX5zJbMJqslt32EQ\neqNq8CD2ObAnKrJE/5m9IhMYcEuLQIyuWizm9pJR0S6MHn2eSQEdRmajd6HZwjbDvocE+jCKJKIJ\n2huMTgzq90T1776ODioIxtg6PQlYnJEWuIvDPItw26dT4+LgzpizVxK/V/epHGvFQaOgU6FWHV95\nyxfUw0GiN84t4POFtu95/B4hMyZoqN9Fs9/nR2ESjd1fR9XXmpx8Vnwb4zA4HnwvV3krA+rJyVY1\nn/NS8rkkTgKY4evWHqaC7vSbki/uPmvESSpLc7DynOAawDD2w0vrInCVRARqqEjqbAn2h5fTbYd/\nfpmW3r25QvBv2y+36jmLrfr7eiNUfZ2eIsvptnlzU1T5bfgXfI76bj0V3C77lik55yadkUbdXIlW\nbAK7w7Mc534IZbpjypznzGdqDX6emznR3h5+LuTzfG/R57VQ/By/OWr63ygfXkrk9Ki+nmbYLie0\nD9Ic+F3MK+RoIJ11NHIYBDGSGQG/COMaOF6NsHdKUFKfa/QwljywkzePJoxL6qx1EHf/EDYGdkoc\nIdGy34tUjWOCl6F1lr9pi1xC5+fnAcOIpROax3zE4hvhEmcJXDeOmBhLIvZOP0WudrjDJidXLpeG\nqbDnxLfb4PcnY0NgM7R29J3f1wmD+g3KV+Hxq7+PPSSCGUtS2ghQfOW+7cGFFrjnvvV5D8hCj4He\n/HNVU4oFXvXEQuMzZy6j8JE7Ujw2pYSIWWccg3g0ShOsdTirW3FFIQVfT5LHs9TB941riu5OWR8V\nVUfDTYT+vEAVnq5GCYFDlEzlSNlLHVtxBReGXgLR4PFqNFV6N3KGqMNLGbKf6yUbyyTt+xAer8KR\nBX3dIcCjZUaIjBiR7KAxe/PF4CkTPmT0KVEXZeRBGgeyNVIbPAY0AkjCxEs3aIEcs5ecAVIqaSin\n+AqlE7UTw1SNiaF1xywQZHjmYRCIiZ4XtvVC4KCPiqohSQhZnOjuQhqwPhlhHVgYxEWoDAgH9fUb\nJ4V0EkQjTYVjQOjdbdcdtPksOJpQa2ArkX3LpN7o6pbT233l2KOv5yKzKMLl1tF80zZUGdEB1i7e\naqs9UMxVY5XAsM4w+GRXthH4JIEjKD+MyqjN567krdv3kjlI02oKPJo7Rtrmdt19EPJgPTYWEc/b\nS/B4OvGLXng9PvKIB8/lFT37vNZNuS0r5bR4DnDrkPyYCjhw2h2UygxOayfqlE1H85K0ZrRtQFR6\nnlEgKu726sPbhLJiwV+vd7+v/2bLg0c40SehQR205uIXTMl4PFGQOcca5ODKP46B6Pz7lN6P4OTt\nEgcrjVOvjAZZ+neFvERXO3rBqgMkj5B9LtZIk0hd3CkSW8MYjBCokty5cMo0iX4MJdC0U0UJ8j3A\nzdWuOdNPCyMNxhKdv947ZSj1kunnjJ3dyfBW+inDiU2GMY7OCEaIzefYWdJ0hIWDhbaDMH67d4tS\nm1JMHYx+WRlL5p9v/y3L2nmWb6x6UFnJejCIhNRYY+G+raTkzkGNRlqgLmDfGipC2I2WI9qaZ/rT\nCNF8Dpn3Txs+d2gwbLrJcitIEG+wxvsBTBRGw8ywIVB9XRoF7DCCDm5pZZXKEjph2ih6UAJKH4Ne\nBn0IryOxinBWtxgoHYtuvUjBrxWdKvj/mse/G4AwHP7mjyxvZYHc2sy+EwcCNfjwspwdJGrTPjIC\nbH8QylQQjubKO3BgMUwA8U0KGrpn5yzijLDKtKhMtrOKK/X24vkvCTgtDrz1Gdx8/5Mh72D/gxeH\naIB8gasZeUBvwnKFC5CzY0zvPxjr4qHRrcKnL78BYb/7yZ/39793Cenfn+F0HYwOT+984C678fMN\nfvqDy9B//P1BtcgRF3YTtuEgJhgRQ5tyzme2HZ7OF16+7Hz4naHdh8RSlFZ+x+ObgmUe/U8ASPuG\nAJ8/CRoEQbk8w788hMfduB9w6p6Rkwdsh4A6E295DpAdMv7vb2BdTJOBBma7N71Nx9/h6kTUNwa9\n+DD7phaVOexrmBsC9ed6GOTq8sIkhkUHG8fhQ3v4bOgGssj/72qvLx1yIltxsLd22IvL/Xeh5Egx\nLwjgGKzDrZEWHDzrDfa28oUTY3imWraZpn2CvhujdSy53ZOQ0FKJ2T8HgqtGTOga6euCLYl091r1\nKt4APH68IreD0RW7DaRVtBlaB+Pp5OH36w+oDfKXO9gg3R4EOqfPO1/e/4hp4J6vIMLQFU0dQkOi\nkHunz7y2MGsmTYUoRuqV1HbqaYElMjS4dS67ZaPYwstrpje43g63jy6NEYTH6QQx8ghn6v5nvvLM\nh/ZXeoG/v/7Vm5/WhZf2zMke1F/+wL5fEB08/fCJ9NR57E983X/Pv/znn/jX//IT3YQaIkEraVSG\nVUZUWloQnHVNwXeKcbxCh3wadDzQVaKXHGiKhEdEnq8MFpbt5vLt4PZErRWOSqYRxw7V2a0WEyXM\n3croiATWVB186krviY0VNBLCIE9v1OOnH8naOe7G1s6EKOwM7Bi0W6OLkspOi4mukWwFYaDWSd9e\nSfTZmBs5A71EwoAuRrbqJTj5ieX2CikQLp77kU4CbVDuRm87j3Wh1Y3xdIatcrp9Y5VvjNl8pn2g\nrXvoMYXUK6FW5Khun1sXcitspzNhGT4f/aDYknjaO49X4+ni4O9/uHwiSeOHuHHtG3E0unhOQW0e\nxP44EnGB0oV+4NlPBPau/OnPV+5l4U/ho6/j2RUIZTlTe4BzoKWFXo1TathohNiIrbDXzCiVQ1ZC\ndjsJFyV3Q2KHNggmnmeQ3EJ+LoOn33mQqpWI1M5Tu3HmQD53TvsdqY00NxT7EhjsHGOhl0GJ0fPn\nRFGEsnVic3UczZDN8xWGBKxD/etBe+9sh7x78pu6wuX1K7l0do3cZWG5vWAGf0k/cuo7IXbO4UHu\nlRYjPWdkDV5Y8eVv7qcyPE9uKOO6Er5shIsgLxua5ro8bT6jDIpEV1WpYd8KtErUglJdHpWNapGB\nD7c2jP2hhEdndKNKoo5IWCtRqscm5IiEwWnprOfB+dLoS6ZlaNsOz0rbBXnd0MfhjfTvhKfPf4Xn\njJwSNhYsutX14MQ+EloLoRqXXDg/Ff549pvyVe5c7caH26+c7c7CnZwb47wwVuH3P/2F82Vjfdo5\nrS+ktPH6T+8JEvlsz3za3vHP9e9IuWFPmdoix7jAUBY5XJGXAuGMD/2nyHGckVCJr3fEGimAViW+\n70j0qIgRBmt9QDuQvfKUBkIgnFZi6PRVCL0xpFKjkRYjrQU9R3SNDkovflMtefGG7uSWcjPg2JHW\nWO/fAOhj5kR2Z7lzKtz1glQjTyC56kK3QGiFXjppL4j17wrI2DusihEJpRIYlOySPakVnYUbwfcA\n5NVnJCu/Ea3BPCqkT3fBLk6YVnFV2nOH5E4hwgTf4gPSK/BHd2CMiyvnYpzz2dxTxeLEbh9ziyVu\nARZ1kAnn/CY773uNdbo+YBLAw2cLbZMXDL7nswAkWKa445r9qxK7v8YoPi+uC9zFga19+Kx53X2+\n7B1KBjn5rHIe8JxgvXupm2ZAldwXTmtGxp1M5RIqJSgalC8Ix6u/B5ngZy1+bHMF2b0Msk1V4Tim\nvVdcuVnU1ZdBJnBXpqtvtkhL9M8U+lRbDj/WdTjJ+pZ7mPF9Z3/LzxL/ey0T/J3qzFF8RgYHgSX5\nsPeWG6nRZ78+phX9vf/7coC9ThLX/D+Z+8vHya+Rt4d+Pag6I1vCYBmNVDzLJtwL8VHZUiKVyvqm\nllCwEFjLRjBXco//+0H4TzvllJCfz/AhccqN57VjUalB0SCccue8d/JRZpP9oPbEfWTueSUMY/vk\nPrOOugKJwOkw3u13zu8hi1EX4WjKa48cmugDJy7H8PxjhEMWkOBKyomeB4UqkZiHZ1L/kAgP4dQr\naYdyA3ZlvQzqPFF1UT4fmfLiJNVf+8xfNW9D5THV/3gZSguKqtBVnXgNgyU7yVglYmKMJmySaEP4\nP+IfeSc7T/XBSw9YLxiVry1weijltRNuhw/Cr519C6wizh5knU4ucZCF2WI9Zb+9wbUUogzivHDG\nXlleHq686IG0CMfmSs3zMkjZ45PS6CCQXg7GPsgRwiXwdHU3Tg7mNuTsAHIAb1sdhtwHbIHwMq+Z\nZ2jmTKZNxfiog2aBIyQsZoIGZIgTYUujyWww3junAYJS5ETTyK1kQhHWUyBcBmcz2i8dwoPr4kSc\nYpCVJVViMIbsDAIvTx/ZdGXrkS/HyuhKPzq5dZajQRLCORJPkJ4CiymXM7w7+wL2rnTur5HwX5T7\nDZ5/+cKSEks+0SywWWSzRKJx1kEC7pOICkH4irF041+//MylPvhQvhCC8el+pVelPwbDGjsBuybs\notTThajFiyBqZAuB0gMv/QP52MnbRk2JmrIr9WdWn3U4R1c3lnXl8ZzI1zEz/4VmK497YjSjx8Rx\nBNavr66gA07nTl4H62JEmueDnwN7KGwh0qKyPMNqSju56qgexvpyeBTK358xzYzkxX2aDGmN2AtR\nPWrm57Mr1UpMLhox6MXY+uLCke5xHr5IDng0wn3HLomyrmySeJEnpA/kaPzjXNt+Si4zfy2ZZXgO\nfevCSStVFnpw989JK9k6Sidb93KpkLxsLMJizZuPzUHZpRROfefaHqgNlndCXAQ5B4/0CIJV6CIU\nc9CvRL+5jgTrqIQDjEANkV1X1Oz7a+ySqeYZxU0jxSK7LJzZsTWzSCM/B7CE9BWpFWIjVCM0nAiQ\nSl4gp4YEB4S1eRkOj8aRIq/J1zFdhJXG0RKHLpRjUL9WSAG9RqJ0ljI4xCOg0r5DLfz65xNhEewH\nOLb/jhwr1/XBEg9y3BAzllPFTLg9Vra6sDz7LGGnQPlLRW5O6pgoffXSwNKU2oVbWGi4SIQhjL05\n6WQOEOs6MYjsA0vtSj3E27pbZzQnrOxPlZ4DYpUlC3aGKIP1BCG6K866eGzY1ugVbqdIj8J5PQNw\nER+sNLrgSA0WqeT+G3H1b3n8uwEItyEcwW2o4W2ow12idQKGZ/VBRdWHOlv4XgVuCdp70NtkkW/O\nAPfg4NKYLDLz96O4rUHfFGpv1or5M/sc+hg+nFH9OcLJbRHlhocp22RgfT/kjZMNnp+N/RDOz3jG\nQYKnZ3eqnjP04czDjPTg+aML156eAIPTxUsHrh9cZagGzxfj6ck4vXcb4/mpUDpsx+LvXyFEZ0tR\nY1T48rojNXFKhXxqYEKrxhhwv+/0OnhsBRuBet9Ycufx1RWAv/6inM8CJmxVPDAb4XUYW4GjCMtw\nC0yc9miZTD7mDHNXyG8A37z3SvRBU+Yxl/qb0nNs89+f+V7MQp+2lTcLyvy9qzh73QYgRpqD8PfX\n6TCeBO2G7r/lgYHbBOXs9P+pH3BUrLjtq9dOkTOlK129lCZK9T89KRN9e64olLQChTCcHejd81A8\nlV+wJWExUBDCKNSc3IIskTF8ka7Xkyuq1JPUNamzLh+vXlWogm3FAfPXG1Ibx2mmh2cPpOU5wreC\nXKKDEwiX49WLGjS4HVBBg8ywemZg/Jh5O51cD+JohDl0x94YOiXzU7YgZhTN6JTJa3RQ3hQHlJp/\nyfrR2VLiU3/PUYTNnGU8tUKtC8/8hXu7+MlvwF14evrC66cfCKWwHVd2MnWPtD3wl1/ekULnaG4n\n7UM97wQ8nFwFxiC0StoLW7qQ6o6lFRvqSkYUjZ16vdBkIbeGjpk7cPiFGlqFMfPutp3X8zMjBa8I\nEP/Cq18ULLFjqkh3JoiHOdtrc2c2H1/vib6bh8faIIhie0OtI6VSJBGag0qxec5ROAohQ7BBVA+S\n6gTUOqMJIQwGwmN9pnVlXJXTcUOviSCecbW/Do6HUY7OqDvj+Rled7IUv25udyT6pik0b0s74yUw\na6g+mH3zm8q5PnhcnkCF+C6jUYlndQC1bSzPyku68n7dYcDT+eCsO9oHA2XcOpa9TY7Nd8yHZEpN\ntLaRtRFwoBLgiF6iU5sQLwaqhHuneWioAyU6aOIkjJQD2wexFB74jTKs86aBYauHtQugKUJQb/YM\nw5ugEcLo9Aa5+m5YeiXRCdp83UJYtKPjoIZMw1XcjcDQwDG8bS21TiMTRyfWA7k3rHVvSzTBbCD5\nwNZMz3mSBVMx+uHK9hrZw4KY29xv6cItXfj9/guC0UPEZoZguqoXLL0BhOaqRZUB0bOx9OwgzGgT\nIRkwujkxolBTwqqX9iBGXgdWBdPAEE/LsTZcAdYatUV6M+J5qruTg7tM8ianQk6DvCpDMi0NNHnz\n5Zobx1Dsm3jZTK0OygN6f8ViYH//M2VZyRFSMl77hV1OoAOleMHG8LXo3WXjOl5Zy52TbejSCGbf\nbWfSO4FGtMbHD19Y0oP15MhLWit9DeTomTBr3xkfz+hZ2T8nEp3Tvk/ltxJEICjdFjrJh7+p4Gao\nS76GzGbgyGjVG8D3A8ZAaoP9QIY3RirGmgfBGv3a2dW/18GjzQkpemzIXrBFPZZDBk393DCmKseM\nvvlmV9eKWYC9YCki+0HKERudrucJHvoMUCyBdfq8h0t3pnngasionTjb2EOtJNnpLc0h1+/zMhxk\nSuqOizEFqhZ9BgNfDt8UgTVBXhxs60D+5vNBanyfSOWYhOz8XZ3uD9HfsuysTeBq3usFJyFlukpm\nBwGmPuv1MC2uOCH9HbgyJ4xD5C2elGA+o9AcEAtvZLRNQFM8R3EF3kZuNX+dt5mxDZ9R1fkM2uGk\nJgVSNi4hc0kRJSIakQ7HWL77fU8nI5+Fx81J1975ri7VmcP9VhDDVOm1wxUPy/BjFTozu4fvjoy3\nx6nOg/Gm4DSPbakG6wQLw5wRFoH78HMR55x8XqYduzhYGHEVYbffLMtUz5cczcHKWSqLTcBWiqs6\n+/IbEaxzvgsu+PbYoflYRvWLKLkqaSmNNfhGXJvfw/Ofb7Q+6P/Nmdg797BSQ2Tplee+I/dO3gr5\n84P+P3x0m75Lsb3YRoUow7P3UF+jcECta6CEREWRR6O/zR8NsO4lgxipN9p1IduGCFSCk91B2EYg\nj84ekys6hlGGA2Rv13+eM0xYu5+30jy/thq2BtZWuEr1a/aqLGdnne9H5EVWXkqiTfW8npSXLeLZ\nb8pWM0m7A2ZihD4Y2edDiZ6npjMr8zDPG32sC7/mJ25h5TQKR4EPvbJL4NtIwMK1HmyHwqKU2ghj\nUNpUWHZxm/v8fFGG21sV+oEXaZwT8VHYDwUZBOG7mpG7z0bHuzO9w3jtjAQ/LE6yxzeVuv32/JcT\njKdBuPoF1TffIIQwaMPFJ30oYbjVds2DUnzDDpBi81ijLvQijOmM6hLcWjn180H93oZ0rHffP6QT\nB4FtOdHSyvli5Fx49ZQ8xnbHXg6CDORUEBW6eJ5bWD3Rra0AACAASURBVDyexqLR1ahLZtMTW4nc\ntgUZrhgPwwFrCcpSDydxbCqFSCDPZCtkLaznTnoWTBLyubD/UyH/qCy/j4we+VZOJIm0s5AkE8qB\nZ2FHbg8lmLKeOuctcbwMYhzoMHfQaKD2QHljgd7OQ3e7+oPEQxd2SZQaSI/B+miMk8/jhUAzofRA\nOVytF9Mgr0a8KvakeJYu6DmQwm8Ksc0iY2RWvIV3nW28qM9N2o3YnDg+7Q/q3lnOlZQNy/691+o/\nE+jwLjKSepM7bmE2MzRMW+t0koXRSYcXhFk2BoF+SpgIowfoA22C7AN9NORbYXQo3UUGj3XB2qD/\nzeL2pDupFfow4rQe93l8ggioA+ExGpmO1MEwYYiSYsM0YGGq0Ww6gOYX4jp2L9gAdwkFsLmf692b\n1HuDlqILSUQ961SBo6LBswcHnm3eNVCGEx17C97ibR0z4/HumU0W4hicRoMgvr5ZZFwz2gReut+v\nszCSEYNnQasZIwgWAqZ8V4pKgG1kwio+I++NWpX+1e++ZROfu06euWc5MsToUYilYsG8wKEN6joY\nGsjvKvd9RZZByHWeX/+MITvJ1hHyO6UeHRXz+CmNvk+Yi7WJ0EOgpUTv6vfS7pZ9qU4CUd3KMLoR\n1HMJVaFXwYphTRgVQOGasNdGPymazCOXAm65NCHm2QI9II/O6B09lNA6dQnkt1zOoIS3Uo9hvq7/\nbc7av+Hx7wYgfDWguFVlLECFx2xWu2dvj4vN/8w3I+5QP/iCPcAHwQlSmO95sBcHDgueMaPBh+B4\n8mEr4c9v8/dpE5icM/RyQHh4jsuP+ttkGN7Bh3/0Yo1bcyXjU4Z3T/CHZ2EV44f3sN38zz//C6zP\ncH42/u4fjBQcQPz2Wbh9c0btw3s/ce+eZzHLCWfDgvvKF3U7wMcfK3qZX3R11VdKjdEjQ+HehKxG\n2ZRSha8vfmB//cvBu3fCsMB6hm/fOrdPfqP85V99YPv8ix+I118dtPrLL8LlAu9/NA4zThH+r1dB\nmlAefozObeb9zWPuCK7/WQu+EZ3XaJ6D3xYm2Ge/DdV282HRZu7Q44v/bsx+joLNTCM89Np0/j3A\nsfvrx7fhN/gAO8wZcpnM6t8WR4yXA70LRKNY9kanPljvr9yWZ1ptPK7XGQwuVPOvytO6s8RGtcmw\nJfEK+aocLDAG+QKyGnbyYaJdzgwV9vUDpWwwB8iugfAoaGkEnVaB5+xD1CUSThmRRv/hAoCeIunY\nyduARenW6CRGit4OeM5uDat+Q9JmXLdXB+ui73xelme0d1arNHRmX1XCqKTRSK243aybsx5m5BjY\nrmdv6rXKum/05nlwPQphNA9wNc9hiyLo9uAYJ2offA4L1o2/lH+kFuGWrpy3wr5feA4Hf1x/4Sm9\n8q19YOk/0YmEvxwcdeGXz0/89fMzn+5X6h2viydzD2e3R+BtNjZcBRpskgeqnI9XHnIiNDjS2SX6\nCSxErqFwpdC6UhrEe8VeKlo7HJ2AEYu3FoekpJNSlsyput1WzhGLgXhNhHuHbpSbh2L3AxDjOAE/\nwC8vJ8bWiHc/ZnkxOEXfkNeOHpWl3FjqwXjxTKK+ZgJGFg8J1hgQNTKNjtAt8OgJgvLr088O6uqD\nslzI2ah1oEf39QU4ThffMPz1K6eLEo6Ds+xwMVfEmiu3ATKeR4cNNCjyPsMw1rO3aq0cvMR3sDpr\nme4bud9Z2sF7M34ML1iKNBJH8XPTKuxflfuWKZcTtx8/0HWhsXC1O1o7j7pAgF+/rtxumU+ceYgQ\nrdM1oEnQp0QypcWMhgQpEm439lvjuEWWbnyxM4RA0YUUXOLfk2dFeXpAINYDs06uO10yt5rYfx3Y\n1vjw5D8zujoochIOMot6kdCrrmzBMx0dZRdqym5BH4GvduZE4awHXT27hPcL4fMDqY2eAyNEbNqV\nLGfa6Zmw3/iynpFysOfgOZAv3jae1BvuRgi8nj+QWuXr6YOvW+Vw9cR8LOYN65ojIShCc9nOGnw4\nbK7UoHfs7kHmVPM2WDHWc5/2VVezmBTao9DvDeheyIBvzOq+YCHwKheOtPIhvxKpjOcTbVR0hfNV\nCetw5YUo5RDqrp57WRp2Toh1ojV0bOznDzx+/D1P+c5TevHzd2zI4fEI3SCakcpO3B5c5DOX+sr5\nqZBjZX3asd2QoIyzwjXy8f0nnp5uhH0ncBBC8SziGxwvmfufIo9/Vt49PbifT0AnnTLHEXgXXr30\naVk9e1GZNjlBT5k9LLQiRGsEe0XPmeWsSDu8kCe4AkVfHtC6W5u7osXQnCdRJqSnQM4HtE6LC8aA\nl4crzxdX+YbjTpUTQZRiiVFcvcBLo30tlHVFENKzkh47kYYuaWbzvWV1KAdu1S6bEI/K8RBacotG\nTIMhkRyKSwSLX+ZpFLpm3xTNnX4MnjVcfMlzIGsq0Fp1i6ksDqJt4lbYMPOHEZAH6DEdHrNkzTKQ\nHIwak0x8czSrOOjVot/TDQcTqZDPrkzDHdbe5BwcWEzR5wm3z/l7OE0QawiwupV2GRMQNCh3bz4u\ncRa9TYDxPq22b/PGJfprWfDPnP2jscz5JM5onNb9c+dsnJJwWTsSDopVFmCEhcHgNRm9wzaU+G6q\nGl/8OeqYYOibU2bnu3qyz4ba1mD76scwD3dzRPPj8PZ538/3VcQBwVEcvCzqqkA1Xxa0ulqxzViI\nIP5ZF4H1Mj/bnNuSuDitZ1immcKmupAMHHzPi6TM2XqqN/Xs14r5EkMofk7Dm0J0Pj6kwpMWL5Po\ng5UDFLJUDjWOBPXTxujC48OVLS2oCsnMwdNunOhc+kH9+4icKss5ELXwpI2hkRbEG+51eARIiFQS\n9nDHghVYy+7Wut1gBI/4WBRdI+ESuP3wkYtU9m/eVNnUW2m388p9JP6kZ+LovLvfON83RoCojTyE\n9HhgQL0YO8Jp8c3tdsCleOxJHcoWI9cfhLAYf1VH07/Zyr16iGgVeLTInhSpzbOKh6FqjA8rrXf6\nRQh7RS+RviRGHWgbHClgZtSQ+LpcaUN40QtdA3/3+gs/fPv6/Ut5e7qivXGg5AGj2IymkEmCMAFd\nYZFBmij3KIO2Q3+pLoVdKyUq3A0Zg3BRthHYJ+BiomwjwgE/9Ts0IeXOUgfr8Jy+KIMRnUzMHyOm\nTvL3LvQ1cBRhn4rUNGAMJVjkvs/yhWH+JQNyHLTaaNtgdGW8NORro70MxotxX5WRI2KRcBRiaQ4U\nXAadlV4SNzlxyIXXH99xxtf2rSX+/Hlj0Rvnl1fOWyFGI52FliKbRVSNJQyiGO+2b4RYyeHE6bz7\nZ/m0e4TKGd9z3A8WqTx+EV678DmdeP7DyilUftg/Qem82olHWtiDMsJgKYrcjb3DoygpCXYWIort\nXq5ztIW2ZJqthBH48PUT+ssLS4YlNUpUHueVWpTHFjyG4DFo2e9pLS/cLHNfE/tIHLsSH4m1ZMII\nDpZkt1u+P+2UfdC6QYCxCdsvMEYkvIuE50j+XSaJcvyaqPfIPi7IUJ5CZEmd0+JCU0sQbp1YOum5\nMoi840aWnaelcloHab+zVG983T8EJCfCzwsaB1I8a95e/XuTtSOhcaiyA6FU4rcNXZTxnGkx0d5f\nnEA6GtK9hXS5HYSvO+HXnVI6klbexRs/8eJxOsfcyMJ38O/SNhC8MFCVTc/0kDjHwzNH82D9ujPu\nXuQD0H4KDgzrwFQoLVIteOv6aB43MapH3KwLLQk0QZNNCHF+52xQNE0FlZJqIcaOdcGtqwPZG4TA\nUitbWAiHcTTFPvugsOxKUoHrQg+NdRkU9dLB7XwhSycVJz30W3Wb7VAWG1hXysgek3By51eWhqg5\nuTSEegjtiE6IvlakdfLiueYE0CTYeeVO5pDMYZ4fcrodiARquRKk0+6wpMr558HeM3tbCNHY28JY\nEzIiVoRDAgTDngS7dZb9mMRsxJJSjkxJka6ZW0lO4oyBmNHVSw97TCz3h+fSt0aQ5oVdXws1Z4/m\nOUcsCfGHM/a1okv24hURugnLVj3Dfk9Yb0QVLmWnHJC2jjAwy7RFqE8RMUXppOMgbjvJHl7e+J5/\n8+PfDUDYJkubJojU8GHS2VYH4SIze2YCTt8nh/qbOnAIxDOUxxzaALr/+4DvGYAbDmwt4FPfBBpr\n998b0YGrtyGyRRdrqTg7mq/+O2efn/mY4MMTvLsalxV+/MltNGzw9B4uPzrQ9rvf2VSkwekPxrdl\nsB/C9eoD4Po2XU47dVIjAyl41le5R3JuaHpjA/yxVT9mrbqaJXdDDF4f0+rzSeAQXj82aoX7V/f4\nfv2LU82//kn4yy9CUHi8+rB/e/jw2BP8eUrg70No5vFWoc/zZf6e34p7+zymZfzGTAeZDPI81iKT\n4QbIIKsfW+mAupLQwpxBZDLX6tk2i/n7q+pAb5zXTLS5/xE/hAQYJ2hByLdpW5kPfcvmaTBQpPtA\nlFojjIMQlJ5nToooBeGkx/f2oPE9LBMP820zF8Omv0rAsvhgM8yzJyRwLGeW404L0Rfk2tyyNQrj\nsmJHw0JEyiAubapEPBMt9uIH4RIJRyfvO1tKnLaNsUQinSCdFNpUbij58Mac0/KgxshTvIPAahuH\nZVQGIp0onoMl5tlI03/hIO3twZqcgeaciN13DUKnS/wuN8fsu6JPg3nWzjB6NeJorLWxAi/1RCPy\n/778gZ/jjSU1Wki04eBo78LFXiiWKHXxJs0pxTaEkZU6Atpm4+7wATClaRvR6OGsI1AtId2Zv8q0\n0QzhHAutB3pVqkagMtZAKHdXciK0NWM50q8LY80sUxoiYhAGLQZCEuQktG+dvBhl72/4OKMP+AEv\nm5nBzai/r2NEnmNF64AlcNk2B2wXobepSjDxQVtxdZkNP84ogcaBK/BiOUgXJUYhqBcC0Tr1GGgU\nesqEIHAKzj5qJcdKjH7MooJMtUEczdnpvbo6KsxFLzlAt2pljyfWZdDCIHzb0KOQXh6EM+hL4X6N\nhKVxaxmlgRiPLfJ6T7z2M/3dM1T4unzkye6UfGIrCyg8bOHbOPG1rt4St1c6oOfkDJvi7X8N6mUl\n9Ma+C0Ej3Rw8QoSv48SSgG5kbexEUjs4NLtFwCBvU678qPRD2L56BMStRs6rEntxhZ8dpGTUWcMq\nOLM78Gw2m8pVsodwLQwvS1FvKOW8EraDcc4wHHjrohAz4/0FLguosp2fya8v7Od3jL7Bo/IWoKYi\nhCD0uBJqpSwnVxU3I1A9X/BtbWvNiQLt9LPbxsiCjk6XjohnNQzke8Ypah6CrglZoyMb8zruAyQY\nTYVQD2jD2c+UsOyLapCpgtPE0EoPkFbxJoVc6aoM9WHUZk2tiWApIGYk7SRze0ekE/pB7IV88Ta/\naEYcc1BdFNHEeIV+DAaDXgfhWp1JR9HYpuXP18HRhLELi95I/UAfncd2dcXccHByHRulKxvGLivb\nWKkxuvW2upLO5vkPNITBvU/WOGQqmdwrJ2Z5Et3Vb48DuW1I79+VbaaC1oY0b7KUAEefhVia0O5E\nAEFmtq5nDw1zhWHtfn6HvWUjelnMwDjsxLVWFvHXZxhxHARxpYcNRaph1dB9h32nNH/tlI2jJ7p4\nlENsnrlpIRBHIbWDuiV6nireOf+8td3u8/YX36R9h89LDW++5U1Z1iDtv9lHh8/N7siYpOCMi/Pr\nt7nTwKY4xSZwJBPwYyrd/IY8lXbTOVIF/+E5txig65w/mr9/G26VRRz8sg5pFlQTPTt5Ce5YMHw2\n7X0+z99al5lukgBh/e391AHLjKkZGW7VWEZlCQsmg7t1nJb1xyeELwYPceAyJD8Op+7vabof3f7U\nHVgL6uPBMWcr2eb7Ejimhfjt/4f66UnBVZ5Hc3BR5myW8Vlbmr/G0DlXBb4X+72Ju3TucQ/149O7\nv483S3YfflzEXOEZqwOANkFO61Nhanyf6Uebl8qAsX4/LHxcC8tRON939pQJSViOgmFcr4MAvN4S\nj3cX7sXVTT+wszwK+mUn9p2+qDu9wiCunZPshOANsbF1z1mNrk7WAFtOjJfu61aBET2LunejdvEy\nuqyQIv39AufIkApb5dvlynLsWDNvQU7dv18hkG8VKYPlKEhMlNUlqmerPPo8UcDtFVIJLN8VQJ7V\nvmrHAhxNsAB3ydxJlA5U4ahKHYJ2V26HbgyFk/o9oL/PUCv9wwmeF0KDXnzNkGFoFkrK3E5OUFuP\n5FJ43u+MJfH85SvHsvDUNs5UX6+AsDv6njO+4a5Krzgoo/YdWOxNqDOYXAG2DnT0aA50HH4/rtU3\nGXkvPG136uLxO+e2M+5KFeMp1u/3K5kqPSuDseh09EDbvdTx5TVwyh2tg0cXwrQ3l92t8Tbbh/63\n/+V/4n/+X/93qAPbff8zxGj/8e5NxOtKi0LTTDyEdBe/z4zZNiwQ1ejB8+yQ8Nt+dIhbzcOJIYFM\n5zQaubuilDelLbgaTQdBOqcwFf/lgW3V25pmQURuBb17YZgmQc6KpU4rAt3VsE0j5eTHbO2G7VD4\nm3v9lFz3GjyX0RKHrBz5wggJ3R/sL4qcBvmDYFGnWkuRrjAGVox6+D2urEIdRreB9Qa7Yoe7NOQt\n2kEH/x9179YjuZXkef7MzoWke0TkRVJNFXZ6BgPsLjDf//MMBrtAd3WrJGVmhLuTPDfbBzuRqXnr\neesNQBCUCk+nk3QeO/9rCAapEeLgDDBEsOI5rGze8moa/XmBoUch3r0RVjYhSXBhxRK8DT4L4zTs\n4XelZiFfBcvMfYKhrRBPoxKplwuyKvoS0TCQR/AH4t2/c2JTgWU+56Ryshx3TJPnK+ZM2Ra/tRWf\nKWSQayG2io5GH35fLf3gF71RWuDW03eAMIbOop0kPk/tp8upTxKPsNJDAj1QVda0I3kg944+OVgw\ngGF+Lfu7hFuEEXy/WnGlYwS6qWf5mTC6uwXa95rb9w26zAbzgPVOtO4q727EWmhVvezoELQFxvA5\nYb3dODRTNWBZPAMvDZomLxdrw9d4FAk+4+gYhOFxB9Y8CmBkj//J2mYmrQNl4zT60R1gEJtlWB0b\n8r0XoFiiTcfYIStJKh13dklpVAJvb4maoLxEQjQebaPUzFEzSTrHiLztQnuA3mAUb0WOM7pEcAAw\nJCPbgN1JhZ0Ie6esTp6PGNlub5TLCtpJ5XAsqjT0UYlBYUnoApYU+TCzbSUij+DE/ah0czdk6M3n\nkACLNkJ05xM26OL7o4Y7H1sPUJSMk4D/mwLC/zgA4S34UPnc/KC2BcLFGVmbVuFuzkZqEKQb4w3G\nCS25TUFlssEB4jO02cbHmHaFNpUs3QeucvHhrk7rRp2MpY25J372YYYCTP94noPop09zIBPj290z\nC//rB7g8w9/+D3+yx9UYN1ifheVF+PAzXJM5gxymivEnpXbPKXm3K889LhKN8xSSGvshzt4CI0RI\niubOt0fk1pU/XgNNvPUuBnj96sf59V8E67D/q3/G3/9u/PQ34/6HL2a//j9KEeH3V1fl3W2SvSf0\nAn//A/RXQT9OUPWYar7hIFsffr3KHOj6AC7+3Z2lnbx1H8TFSQni4tfTAuxxMvpjCjQ/At2vaTv9\nGt6CP0PD4dfZ7kZPoCL0xPeTNoBcnKWvLgLxZ12G8eGHvQFcGNEv22zG9ibdlO6Ua2RopEdX42mA\nmhfqGlGJ9GBsttO7hxCbOoB4tmW+mVFOYZHKtjRnv9SowweHSvZQfhsE7YTNLcvlboS3QjUhnYPw\nlKjnQfq8Etj9oqpv6PsIUArx68Hnr99Y+0l9vtA/XQlxEDO0daNopu2w7HeWvhMkcTkORkqk2Mg0\nvyABzNzy3CUi0l3VeFnhXoi9kb5+8aycPaCijpwLpKXMcgahxoyl7KqjAVuoZBHOoah0nrh5g5xF\nggitBlDlt/Ezj28eXpTfGqPC27rQq7DrQlsjQzO0wTgG5IEVONJCt0F9ulCHsqy+6LUFwNtwrQtn\njzyORLGEmL/nozvwuMTKjUx+iuRvN+r1hdXeUBvUyxWNnmuYtNAJHkafO3XLrsxsw8Gmq7/npp7A\nX4oSbi6jiF9vpPsDauPf4mf+3n4iVuP/2n6FmMm1soREiAPt5udZ1Vtog9sNFqnTdFhJ6i3ZuyVi\nvaEJtr2zPfYp550M/cerM2FJeQmdFPp3j9kyPX8hKlIqeTSC+eCmt0Zo08KdBnEV1BqN+B0klC5o\nrbRSPLtkXbHHwaHKFiu/tmd+4o17SNzMB49fnz5hQbldPtOuG9fcsHVDcuMePxFa46u9UKoxflfP\nAAqCpEgzRcZABozTfWn6utOKMR6dfQTaZWPPG3arztYDie6lNe1G14DtjXFvpOMgKPR1oeQFK7Dv\nwr4rzx8NXQLrp0zWTn9r1LeGdSFuQr8snLp4hlNoHip7WbGUIC6E1lkfJwzcMhIzJXprsAP8C6Iw\nnp7QqJ4LNSXO+4dPWOnIOGDN6MuKlY5GZ7TVEulD9E38/QF9sO33+Xf4z7a/EoZgYSHdC6i61bQ3\n8qXT905K3rorfdD3Qnn+TI8bIyeyVDQwQ7QdPHBHg5IUNHbGBIgkK4Yiw7iEwiUUJKnnpS0XunVk\n94bwdQykdQ/fFuWUjMVI2gZtUdROMGFsib/wrzylxrO9stvKxe7oNvhjPshDNeR5QT+A/hE9IywZ\nIwW6ZiQOUjoJC8gG29hZjwfh3wrp4wHfjCSFt3954o9/3vj9fz653W9vnG/CPa+0bfMypacVrYWn\n5US107qx0uldeAmdS1T2pLSqtOcn9quyafJmvroj0eDzhn3bGWa0HtCcqOGZ3A3rGU0BYufQC9KN\ntb6h1tHRiDqw/aDfO1TBzgEfgucld6XfO+FRGU8LbVkIOtj3wOYYsjdLdnN1EIXaBG4FYuawxIjP\nZNspCFaFGhZaiKTVSGenfv5Aut8gCvE8OJcr47cdXtwNsAcHdEZzgHDIxPcmyRreXNGfZivwcnir\n8GL+lejZX3PcHUA6mluQ41xHbCrL+ukb+DG/Ru9FHTOaiOV9BjRXwx3AoT5XFPEZQgVIkC7A6XNO\nm1biy3BQ6z3EfVldCXlf/bFpEfoOVvy1+mnOOhO80wnmkfyYwgV086zCy9X/PlnhH7sRt8LQk208\nYyw8yvieu/zrEL4gPARY4I35WH+3ZEc4Dlc4mvMv/lh/clDykmf0yu/weHXgs/oehPX98yefZfsE\ndmN2APY6z1Fefd4Kn73Epb6Tu8FtxBL8OLhD2P18xBXK5vbtNue5ZRa61Ahl8WtvMwtROh57MDfV\n5yR60+mzW6pO8PbwY2775fZK7QKv1Z/pnxa39akPzfpzYv/lM/enJ561efHLGvjw9eCz7fzcbtjv\nnfw/b+h/W7ltiWIGJtQe+GpXIHFNE8QqxtHCd7A+tUbvwpGzxzJoJ4zGWCL280b56UpQ4TTjHx9+\n5ulx46fXLzw97oBx0U6LjU/j4FUjv338yNN58FwOmiQWOjxH5DTaW4evJ6TAby1zEWHPStbGh7Q7\nD/3NM+huGjkCEDrFEuc5iQUZvuExLwKJ6urUXhQZgfG3KxIgFi+KwLzl3sS/m+sF/gtf+CM9c9SE\nDvhrvAFgabDpQYgOoqgYD4zz6huvYZ1LbsQY3PrfnCwO0deTtvs9f36BvAhBjX4fLF9O2gb3Lfvz\noBj5o7/Hh+puhbYkSoj0h5C1c4mCDWMNnX765p0YGUWguOWu/D44loTcBvvXStvw3GXzduWzKl2E\n8KeNgnxaEHGroKgx/u8XWk7Ip4QcHWkdrQcdobD4c9aMJsHJhjwI26A/B6d175VYG6fBEydjERT5\nsTlSoxE5hiDDm5cv8WSVRh83yqn0KtTH6Yr+/xzhl8jzvbDsD8ZvO/1b89zEc8UWYc/GCJFDE+ep\n/n1KRjnNXXaLEK+gcYJCIaJ/2zCUlFc0RGLopFp4Zid7oIrPqWuif1ixLoQnYfxa6N8q5xejpsT5\nHGgfM1aHW3mLR8i8k16Kuc1a4LZslJ8D5VOkz4ZWK4MYhTwC6XAXzTga8R+Fy9dGyEr4WViyk4Ea\ns8/OrVDfwL75Zl6vge0pkIjk/STUjlpntQdNo0c6hYQcYDJ8PqyNPrpPQd1VYWqVpd647A9++vo7\nTTb2JbJfMmdyU3JAScVYpHFtu8fTbB2Rk3F/44PulJ9XNBmfLg3cWEQdgRQiHz5VFh1IVZoGfjue\neOOCph2LHQmF8bIQnwJ8cQtvM52AsnL2wNGjlwt1wczLTmtaaWsmT1L3RXaEAfoOYIs7Y0KkpwwG\noZ60Av0VYpvipiCIubPnqd5JI2K2ODBAo4fIIyitGJe1k0djozGa0k63bffV3XFha0TrBBsk6e7a\n6Y1zW7mPlbVWVDyjM0hDzF06MrMq8ubrTAsRS97sO0x5YwHUYxnM81uHADb4NhbOQ7hn/Pi2zqOu\n3OvCa/zE0bykDiB8eWOcgyftkL2E9JGvVPdmIMCWCjKEmJ0wUpTz6cIjXT3Oa3XSKPVGOHZuWybf\nDrbzDV0SMQv5M0j28xcVHpdEN6XGlaMHlrKzPNwNFehcadg4WbXSsotzSlVfXHunnv494Eujfa0o\nTnxpav9boN9/GIDwxIEpnSAZ4oNV02nNmMDfu63ETL6rAnuczK1M1tH3voTgr82TacV8IOqHD7Nv\n+EDDVK617sDXuvqzehGwOWS/C6o2j67i48XIke8W2bN5VuD1anz4OBAT7A3ebsKW/e2XaB7oHSDi\nTZjSB+dENv/cxHfs4iHSCvfDbcO9CI9DsKSMKtCU2y3x1r1BtAscf0xQ6+sECn9zhrZO68vjmwON\n3/5NvQjmi3CKD2cNeHRnfM7qAN2SHMgbp3hIa3f2XCfbPJqDge+tfjZBTMUHyToZ/tYmQDdZZ5lE\n4rsViI3v2UHfMwTnUI750BiH5yq25gBvvTqb3ufL/pebecz7YOYGhR+4CeAPKFJ0xuzslHWjPxos\nwpI6mcFFy/xAAamDti2MRQjV3mUgqLnKc2VgiAI4fwAAIABJREFUJpw30DUio7Gug97hUQMLldIX\nmgZauPoQro08Kvbo3O8eDF1dvUwaDY1CzBWJgtEZCGlUukYIGctGOv1Btr2+Uq/OHGrw0FpU2a8X\nhEHUQeyNJQ66OJsT1JHVHhQx8Ym/NlrK5KBAI1RBTRiqWErYmFkpp3n7dA4ENUZOnjkzBx0TkNBd\n3WmVMQYhDGIwzBq0wKhGPYRiCn1BSmccFTsH4wq9GsOj5NieOo9/GP0B/WH0Ai16ENOIi6s0QvAS\nopbIofNoPrAdPdKHUbuDMG7NDzRTb9Lqilj0HVzoLMcD2swwE0EXYQT1+IE0vxBAUKM1X3ytCdIH\ncbidM9f6na5Jx0F4nDwuV86w8GVcocHzcfApPTgIvOTEUndiNiR7gRAVdNHZ6OUgYZKBipGkc+8L\nb2zI/aB3JX95ZeTow/CHJ4YEQnbrzJZ+KMxMBBFnmBiuSqwxQ2lECkUSJkak+7kZRtg856dN9j9K\nhzFwgYlSstuDbzoIEvgr33zRapGgg6MnZHTu65Wyrh74PsyVq2qccUWjs3BjSfTrQi3JGcaozqI/\nipcEacD2Tnh9ZZxGW1ZK3jizM7gpG6MHnuQka3PllyjjUcmvh+cdTRl3WXwQyi+J9YOxf+tTtWRo\nUhqR5XRpUT+dFelJOXL2QTpVIp5pwpgsbIJW7myPN2+BLQNpM6OvD3ie6f9tfAdsAWKvk8XthGe/\nHpoUuSTarZCDEeK7bMiICdQ64a3xpz00QV2yE88DomIhfG/RldJIwRhDPL4AdfXf6PTm54lr8Ca6\nxne1x7tEv09vaNfgjeYS6cOHFq+zGlNNpoy9Mcagrko/KqF18hX6vU1W27Cc0fYgmCBDOXtCWiOU\nk3hpJE5njftgrwtDI3EUQhq0y4WaM0f6zPpWWT6crPWNsmzEXrlOeVrunqkarTJujhj1L4Gqgbf/\nsZByJdFopRC6IG8FfoIjPUESRo6s0RuSo3ZvSHbjP3EopWU2TkoIBP2TnCxG5JoQHch+Ml4S4w+3\nwrXmZS/UQVpx20y+IjZIHMgM3Lc+BSWmaCuUlmhfG+6q9WbK8K7aFs9JQiHG7rlBKsgYzHCduQgq\nlhNWB2e8YuKFDO8Lp8UEMVLzRgsNWSOShUr0MpbHib0XDLQJyM1138Rv8a4eCYMx85wc/IoV1qkw\nw4DFlf2qDgD25r+v3YnhMGcLm/NfUCcmY5ggk99qnv38rmgzvwK3qTJ8z672PLGZYRhc/ffuOFhP\nB6TeVXnvSkdbfS4Z+JwTA4RXuHzw9+kBNM3ehwBszic/4eDiJTvY9g46jgBsnbieCCuHVdTlFHyr\nRnkobSrv1Dx6JzaIxV+b5mnzcO4fwGTveDTOdX6EHcYVKI4Psc5j1PkaHDiUuSwkc87vcFEGioOb\nzM9d6zx2frxeTtBjKhVnxqHN4+vBXUHpXYDeQAbojBBs4q/R4bP6+91Xg7t3JPqxSffvwPtPqhWt\n0PbGiOrZrmtAonKkSEuJLRrDTiDwnBpJhRwGL5M0unwE++8X+GuGHMj4vF1SoGjCupKaucV0uO3T\nckePTouBkhMlZs86A2RTQjSamTeUAi1khiprPYmj+xou7+o/4XM4uKcnnl/vrL0ylki6ync112Xt\n6H7yXE/e+sKK8LisLHpAh7e0kqLxcyjsFpFuLNYYQ9hrphTf8C8Xv1ZpNG/sFWHHVen9rZP+4jd7\nOHwNEev0IYj6/fGyNL7ahoXAs1RsZvl+fLxhH5WwCZIaZ1H2Q6kxghghgSK0NSE5YNUD+8spxO6z\nVfnm89F5uBJes1B7JK4LvVR0g8fpjcchCiHARQpdA0cMvp+LgbPBvQU2abwnbYwuDDX0ayG/FuoS\nuMtG2iu6V8ajcXxT8tN8YJ1GSA54/cgqAj4uzF0KdnTsw+Ib7S0i1ZCzI/vwAruceQ/YjDo8H3Ic\n9BPGWwcxJyj3ytpPPvGGRSMzPCtuEog1eKNmEC+MydpR3N2zl0AtgpRKrx3JA66gd4/38WZaxYbQ\n3pwpsec422eFvrsSU1on1uC/b4JkB+xcyedrl0ogLBFBCDZY+sEmhUifcU1C04iJ0GLiFjfsemKP\nQimDlhP1uvizP/n6FG0g0We+kLyp1YYyTGghUWOiBgf9+2m0Y9DfQYH+3hBeaOKqElkUvaorJidA\nNgg0C7QiHn+AFxTqEtGQkOaS9kRjpVIxsnYanjVn0gmtEWfMjg3xYiMDRieXRq6FJTdCanQKzQq5\neHZ3GA56JTo5OSAmCJtV4v5GzoNv6ZkcHEB/BwgXqotJiEgQlmAENVIzz5vsBjYY3RiX6MKO4mua\nzDlt4IBg7YHRjV5dpTMGEIxQGicba3QS2Bt3jcJE7cXt3iY/COeBgCit+ncwOG9P0o4Ng2AU80gc\nMyFq56KVFOaiqEIlsNI874IORyOU5kIofI2INhjWsX5Sz0QawSMp1AF/DZ1R8cy+w+91NZ953AFt\naK3I6IwUvfwEI4+D3L1kSmwQzehN+XKsHGXw/Fop1b9XIxUonVcu9LfK+lCydJr4ItVDpEng7NEb\nuTHifO46xmQ80sphmX15QoO32JsEJBrFphDmdlCWTNpPpBr6rRJ/jqQ0eODlGqVHmvm5ixJpBNIE\nUtU8AiDS3dE2BrYsjoWJzJiEMdvDjPNUxy4k84F//89/GIAwvg+UO9OjCsxQ6ql2/Q4cmuNpbkGe\n9gUNfA9wxvx1ce7DUvgx8819redizSyX2cjORfw1S3DrcXZ1JnFOZdc5IF2z8eHiduCnDT5/hkcx\n/vpPxno1Pn82RoPbIeRpVyYbNvMvMl5aEgYkfIjvGGLCueOBrQdIFEqC1PFMEXeLcrurV79X4+0t\n+gD3u3F24f6HA0Tnq3+RvzzmYDaH8q+/KY9Xo9ynsrDDGWBX8cKl7tdhFWeEA35udptutDGvz1RZ\ndu+y+LHXHT44EuawKD+sx++b2PCngS+oM9Mh/mmvPJgPVbchbcPvi5LcYsNsFWwT1HwHCNu8P1Ln\nR6mM+IXXCS6///TL1W1+QNVMVXE73DDE3Aq6yYl0Q4fAoRxRYFloaSHjGQNeRjLI8+DNxAPoD/XF\nPQysC2bGTZ4RFW7higYl9y+05ytSDNuNt18fLCvsNxjDh6y0Fm/Ylkh735CXRuiBJQvVMpdjdxDk\nLL6bSZEaMqesDuRsCawRJiuSpIFEurj1mWn5IysmiZEjpStBIksfhNNoLws1L1jxkxjuB2B088Wj\nRw/obcsF7dXtXsEX9qCeb/FeGrBEb5xd1IsDyiGuEBSw2rA6qH8/KSNSr4otyuMP0N6xh99PvQmE\nTlsTvYGl9x2iUfoEfk3AvHnuUZyRGc3LJNK7/XNurqsmJHSyecixNzrLJBvcwjfEw3MBB2pU6Kfv\n0Gr19xMSpkaWgz5zmEIAtsiRNl44WEdlt8wf/Yra4J/y766qJXo+YYPtBcrhxxY2JZ+FNCoLXtU5\nUF5kx4DDntmK37P6+50jrxSrKAfLX9yfpQKqvqh4Y67N3EHx4cKMR9yIFsjcKZKmaiJQgt9WFhK3\nvno+o0JvnolXYuCiFdaMaCVk4VWekG68jDt7y7yNlSOslJTh0agx0hSOIM4a0rmXzFkDt4dv+OIY\nbhEfhpRpIToqyoDTFXBYoDz9yYMmQjaXM4saiUaQQT0N/X0nfvXcv776Bk72Ci9XKon0EVI62Rb3\nLC77DkGIEcLt9HKU7MG/VROKUcLCiOZWd3PrUConvQxG9bKJXoxE59SEZnWVQYhOFo2TEpRQC1Ud\nJH5crkQaOQ0vMKmdZVNClxnXIN+tApp8Fx6OH+1kdUS3AtK/FwJobz+iEURpIZCHB301zZ47U52h\ntSUwsoJ11MCq0dvM9BMfrsdEPMZsFMhqiLrxWvvADmWIZ1Gd3fPbyt28qOdrZVwjXB0Q6tvq9okh\nyNdKWS78Rb7yIVU4BzmeHCRy2UnjQdJGyg39OXI5d+SXZ45fK8vHnfAw6IOWMorbB6/9jU/9N0Lq\n6FtniBLeXMENsP+/2VVWh3AciqwFnjr92TdHFp0U2OyYSoL3hSqgMlhD+bGoXRZYA3ET8kWgun1Y\nMrBXt13GSNXVQ8s1YicM8cZQHQNriQ1Besdqp+3VWXJTuFfsFCxURlJYM4kCo9I/PhNOc6AuBD8H\n4lum1pWGUiSBGD0lv3YdSlwJKsjchEuMbO2gp4W+NOyyQFLk9cH59EK9CaX6c7A45oKJx7DU7gRe\nMl/w34lAP1u+Bkv0xTqJOzjC/F0Vzy0Oc2aS4fPB7HFwQPCH2NYjSeb//44wDVe7vc737M1JwmzT\nHSKTMGw+yhzms8sifxoPgs8MMjdDqr4JuwR4Wp00HniMzDmzC6V5iccZXf2oYZZwzGNLF08gCIsX\nVgTNFDoqK7TBfLwxgG9veKvkPC6xeQz+eKOdrviLmysInX32Yx7MOJ4N6hd3yWma2HDyWW9V//Ng\nDorOfaGT3fM8LHNn0Lpfo/cB2oKf89mZ9d0eZx30bS7BcTpExH9/jq/u7piKQY3T+j1nxh5g13ns\nAusB53UCwQv/y4+cHVkUKXMNB6oEByQkkLWT2QkDlnPAAS/3O4Hhs/wQ5BdvYk8bPPaAiKPJY0YI\nnE1pCGVJHK+N0IYTIjnQU4Ss3kK8BjigxEQ4KnY02pKxDPk4QYwjZ7bHTjwbIyhrrDxIPO8PnsvO\niEJ7WmhPCwpszaiPwYd+QoCf253H5TO6evYuReA4iWFwEFzhGPy4j6b03cvQvBwMxm24he/T3FoN\no3zryC9T+joMS+540DqIdXCuK5p8Pn6+dKydlO6z7stSWZMr31tx67xh3IryGBFlkAzy5velRSfa\n/M0bUscEuQdld5EENmiTSehbYN9m8V6CgOfExcsUDagrd2r2vC8E7n1GHgik7iRO+HpiOZD/8YA1\n85I6Iyjn3ThQFu20u7oz5Sn6nWQzn/f9Xvu4INIQPGZCrwmxyMiBpm7b7TYY6nMzzTMcgzQ0DHI/\n6Een9xNUkKN6dEg9+czN14b57Cpxo61uix8Et5pq8/xk8eIxdrdJa/DnmspAZhGBYFgSbAuMAvXu\nFuK2JHqKlJkJkM+T1BvWvBVaUMT1BAQxz+1PF0YIbn0cBqUTbu6WKUS6ChYDLXhUSQ2Jt/QBtgO5\n7rTRaUtmXCIxDFIaiAx0BspK8Py7IL42vc+lgU6WggahR6NrJzA8m7OB2mDtp6uZF+CqcI1IGMgQ\nRu0eiWKBXhTdHfAiOtGqOSO3gOxCojmwKOLXoBv9m4OCXTyiqwxXitWu9A6hDvKjkLWybIamQZdG\nH4V4HqiEmcfevDgnGUS/XvlRubzttGVBzgHJ6PkHtVuKz3JPi3FKppuwt8TNVoIY0gejDzpGS4IO\n9UzBP5VP1KG0rtTqe5JWhDxZIJ3xQdxPNHX4MMEUQIZxWqQHYYi4HVdwRa2I07/zzx2P6Vz0RHDy\nP0v3fNY+6EG5hkoLQg0rpvNZOow8ugsthgsXMs0Li+iMAkkbUuHkdLXrh5W34nvDFzldNV075atx\neRrkWLGgqE67YDWkQUjOOlp2EpbuILuOTi+BfhrbJiQZfHm9MOrA6sBiwaqgqlgZHCVgYbDlQPWq\nO84aObuXVgIsGrBp07ZhTirrnI9wIc4lGZGBdQeeyrJ4Ge9eGaU7uBIGhEH8y3Qj4pZ/60bvhlux\nXbyAQbJGGA00oMObxJlgex8gpaPfdigeh1VRtPH/T4BQL0AH/eZMqC3+b063qMiAc/HhIkZoC3xt\nM9Mkgiz40Ln7PZ/yZKYb3mY8B8xw+u/14qHZQ+Dzsx/DckLYXFZv+HCaXmATuDbjKjDubsOw4c6y\npyfjrz+DbsbzJyNm43r1ltH8V2VblNuvvrG7BgcHYwdwxmMVBwQqMA7Yv3jG3x93VxBKhPsdtuhN\nQxqgiHK9+sJSO/z9nx3c++Nf4eEuLf44BSL8OtzKs3T4KRl/uUFQVxGexQe4h/n5qcDWvSQlidtQ\nylRvjmIEFc8N7bDf50CPA3V79PcJ3a0pxGlbUeirn89yuJpTo8+VfTgY2cDrAIFSfeitbyAHyBc/\ndlmAKpSLOeiYXUHaxjtg5K/vM5C8mw+jNuaGo/O90QmgbBe0u60i7BUdnbeQsd45bivcKlsA9s7z\ny1fCf9qI306KvbjtMQtinc3K3LAMBgF9WgmlsKbKUoqrydjpCKkPKpnf0i+EAOOyYjHS1o1Rb8iy\n8PiXu29w+2B5ETYVuvoidbLCo6KPRtRElEK8JNaLwrqRtoitkbMGgnVCMpbzBOus4yDmzpo6qoNH\ndCvBOWHbbg6CVZRHSaR15RpOjiVz1ur5F0BfFb0fno0oSg0X8gJ93WjJbZeqkdA70QaMTqTSxyAs\nSpz2BG2DVo1vJEzV28pE2bbM0m8c6Ql5PSl7YIhwGxuvZ+ZMbgXuuPW0x1mbGGEMhaR05nBs/rm6\nBeiVwMAe3ghe1nXapIXcTm8TXgMSIu2Xj+golGPQJZKjN+p1DX7dome+BB3o55WdlZ1ObcryuNEQ\nkh4kdQXTuCT6kvkQKmvd+dZXVnH08P8Mv6IG6zKQ5wWxwLoE6mOQL0bQgA5jdQqQOHxgFISnciKI\nLxS9c38Ie19oR0CkwprQo3B5coapVaVKxlKgiBCsQymIKoWVEDuhVKoF4nkyLn7Na8x0G5Rj4bf4\njHXhuTxopytXRIz8IoQx0OeVb6xsGKMG/jiefdMvg7EucAz04sCT4otmeRi3x0IPkS9/RBRjP6Nn\neuJsM6MjzdAt0qpBMQ4clK4SGaosoxCag4NRXCnVYvY82+IZLdneCN7tQokL948/UZ8uhNH4+OUf\n9AybVEIfhK8nL+WNS6iUFFijG4nKyFyaA7K9Zqy75R0VtuN39DjI547RoVUupVJyJr5c4eIlD1t/\nY6uNkTNPv/1OD5F1f+P29BPP5StRGlUWDk2wOHtLCL7xEIhnI8bOIo0elBri9zrVndWtDb1jXYlp\n0EciWeUMF0yceR0pIjatFI+Drgm9qA+v3WMQGOZAcQ5YjBACvTZQBxs1imco1QfBKlJxy90OpTtL\nV1pnpIHeldQLsTXa42BvF4Z6ODsEz6L6dvD56Yb+XjyzMEX6qNj5wI7KT5+F7YMxYuRv//TGt68b\n15fI8Z/+xnj8nWMT1tffufLGS/vGE29Yg+u3r/AWSPcDNqg1Ux9CeGss94PP6zesvfBg4XF55m37\nzFg2LC88ld9Z5SBW/84Gc+B6WETFWEJ1JYckyupqMOkNrXNTF0GGoH/ZiCvU18aT7e78a3DvzxRd\n6COT+8mTdF7PhdUOFqBVxb4Kra7ezrotnHljXDKrnaTPCXkSMKX/lCkhw5LociI6qGfzUXYY7+U9\nZ3zmTFf6CKgOkt0JCXJy21Z/BIo17stKugbaxw+E9Rt2Nqg7YWYz1fOHCkyHA0+lOrBkk3A1cPBl\n8jHveNa7GlCiv66vHjAfJuun3cndLtOR8A4ONr9d3AY/BS/vAKH4zNImyXido0VonikYBch+bGf3\n973i9uJt+LwXFigTJCS7a6IU+Ns/wV9/Av67A6P5BcLqxZ1SXRh8j34eJMN5gcvmQEm+QFqEvIKs\nnjGUkrEgtGnvTNU3bNf5GZbuTpDHw/cP3wnX6XbR7KL/hoN4sk7SNLgC8cB/58kFCbxOgJAO13Mq\nCF1wilU/r0ldhZizz1Rllo+Mxf9u8Ucxcx9EzDBe5rFFd+CYztkv+XGGqTLtixfaZPPjPvGlu7T5\nd7v4mrHCY3NwMOL3zftPjZH+HKkFJAz2phRZqD2QyyDEzhb9Dbfz5Kke5NlC3EJAxQGW87LSs/Ll\n8kwLUFZlnIMjuD22tUn6DKVsEYt5bgqEdRSUziKdlOCRM30E9pB5lYUVI5fiUUFHJ//bG3E/sGNw\npkTLwtFdtXc7la88kU2IIuQnZXyDngKaIbZOlsYv68lrUsbLQm5w/2PhNRl7X7wgZbjS5EHiUQP1\nYaS9Mf65k1ujXUAufj71krDnTDNl3CBelSV4ct/ynssTjXQZdBrtMfhgxQsP8iBmhZ5Zn+cmejf2\n5oh9F6UirMsgXgIyQ9xtdSL65fc3nimcTcmLsOTG2Ixvb4GHJUYS+suKtk6/d0QgXBW9CqIDQ0i9\nEYtSJNAuC8uXB8E6LQrBlHsN5NboXwYvbzcsGsv9IAWlpUCThSSD/m45y4q8ZMaHBE3oy49tsTxn\nNEbCZRCvBlXQtFBG5P4IlDEoHVJKLC8J9sLYC/l0cjC0Cu1AHk6Qh72gZyPuBRlG/uNAf07Ic+CR\nAqeKl0t0ONcEOdKfIiE40dPP02ehTdHhAEuoJ6E3TNVnzQR1h/uvSuuBMS4MC5SqTlqu3ip9hEST\nRJTAIu/qM6WnxO2XX9Ck5FXQUtBvD/TXRjPhq1wIQVmXyBlWynLlSBv94jYwWxPjrXm2uzqYFPWc\nGXvF50QGJoExlPuc0eOoLHaQ1BWjGgeWOto6YYjHEQwIY1C3jZEz/SWjW3SlX+vU1mldCGXQ/vMH\n4n+6ki4ZbRBbnbmAB6EexOjAOBqQJNjDOP/HjtTB/eMgZOGRN3d9TUBLzRyUycLx8QpZWc4dSid+\nfaVboIhvQK12XvOV2DtPPIhBuIZGyoX2W+DBwu3nlSnUdqdHM17bRsdVkbULy/3OKMZLeONiJxIH\nY4G9uExbxAi8g8RMRV3grgvfwsJf6jeyNda2e0mFnd40HlYsKFG8zMSqEeqJvLNntcFZaedgnMYR\nLhyaWK1x5WQLJ4TABzkYKG9joyalP7tkXkJE10hTgd7oPRLaidGc9BcIwbMdxRxc64exl4SVjkXj\n/ip8W194zpVvx8rl3EnSWNsDvhrLslO3hdUGR1oozUmPoA7Ih9NYyh1UacPJXRkdGYLdqwuxuu9F\nVIzjj0o7jRF97xeyX5cHC51AtUAZkb0FOD33867Qu7dNn3nhSBfCoixL4NPcA+Y02Gwq0s5K/3oQ\nekFGx0r38tHfm7fL309GCLQPnUMSv4cPFPF1SYKw4q/rFtFRyKO5kMUU26EziE1cTVucodxt4Tf5\nwIvt/I1//89/GIAwJVeIZYN4wshTedfc2jCm9aAPH2rESW0fBG025ILf2BOUV3WbCnmyo8mH0fo6\n7RIzYDoOv27RgAeEZx/81F1+LMGHPynOkGZ1q/Go/t+hQsheTZ+CEdVowRV675bodkLbXY3WGqxX\n4yyu9huzZKW9N7+dcN59eCXA4yGUBVe3xR/Or3dmfr/5ADt7BrgVPxm/75OVNT8v/9KEVI3N95pu\njUz+jwCxe4i8h5j6uX2rzjTvTdzuqz7s2Rz4Z34wO05srjoH8nmM77mQMtXGQSfpPNfmSQC6DVpc\n0SjTYkNwm0vacSYygW3y3ZbcxYfVNvhuz24T6IzGVPRMVlv+pDAFn8Ctk3rFFdSVpkKXwTGcDbj/\na+N6aexB2eqAOgj5pEevqg9dWIa/eUh+sSPFP+sQGC77R2Z5xgycv+rhSocuzmwFIfz1CX1U+GPH\njs7yLKxP4qUUQVFR4jJz+4BxOACoAkUdic2LQhJSG9ix0y2w3m8wIGdHYjV1ghhZKl0GRnD7p8Bx\nKGeLILAfCtcVRLjm/bsiT0t1xVn0EOMyItaFQaKRUHE5vFifF8VVRkGGZztKJIziYJ4Z++EPvRH9\nhog5wuVKCQFa4HYu1FN4yMrjunBe8ntaAPHde7REJHnBx5jeMGckcVt0SH5jtEE8TlrIxG8POu5f\nVmmEVghDwDph6eyW2WWCp62zpI6ZA4oiSouRe7hw0w/sY6VfDvYSOE/h0m4cYeV8ZweD20W2cbCU\nnf8qv3prmzzoTfi0PijbE2yJMQK07k1cw79DXYW2LeR60tLiYM2A0f24+gPq8FByFeibA3vWXEWW\nWnPmSiIl+/Dh6Hr35uJmbmdPYGsifr3BGkmLUSV6gHGH27JyjkQahbOLs5pJZ0AujG2hhUBaBl/G\nR77Jlavd6HcHoZZmhGtE6yBLY1s9E1CDcH9EasqcD5ezqA1OMhLEMzFXdXtW8/fpdHiFM63zO95Z\nevHWWFGSDgylSWJPG3teCf1Gagc8LdSeuV2eeXz8RBidsLsUarvfCYvS905/nNRVYPOik6FxZsAG\nFjtpshDK4TlPy+YK0PKgi5Cr/32hNiwpKRkpGYzqypIciONk9I60Sr67muByvGItsC9XlILp6lEI\nEuh7Z9uY9oyTJL4AmQRa/GEJaTH7871NG5ZCj4t/5zC6JgxjMAjDaMODlIkBYWBNMFwRaGaEed/X\nbfNrM42EOoxg3Z93uEo494Ka2/uVQT2M0Z3tDtU33L1naszUpmg0Sg8MAR2RJB5SfXmqjEMpRWjF\nIxbkgDROlgB6hQ+fDp5eCm9/ZLZcaVz8Wtx8ndKpbnwur5459LX4onJ63k85Eq0HmkReHxf+uD/z\ndn2hPF9JdWfHFzFTz7YRcxb/tMQpC20kNPj5GB3UOuPs5GWwJpvZrJ5Ry4gYim6NfDs8KLsHj2zA\nCOyUkOkDjhAJzZudx1EZhzFapJti7/mPTyt2XehNyJ8DWk9kJJeztUCP0WMppE/pnD+OS75w5guD\nzEiL38M0am9ImKqGgdujk1IAzclV0NfT84/eXhmvPqyIAd1no+iPYkw9EkSN79ZgUV+v8WXR56r5\nHJc5K7wDQoor22Jz0Ei9w8cVLoPvasH35TxOMM/mLCI2c6yHzwHvMTDSJkAY4BHm+wZXrQWB/uSq\nQQCSx8+k6rnH8id13cf/4u9T9jk/GpR1AnPDgbUZr4td8WwXfiiFcoarChfxOSurr5NHj4Qxi+re\n3TEH7F/4bp0k+jG2Cd6JTBCN6aYQ/3OCKzqj+Gw8pkKzq1ugX6YCeWKt3xWaUXzeew80j/O9FnWw\nr87P0FzQ4MUkC9ifzt37tZr42rs78LvD3bqDiTZ+ELvh/RrMeyX9aWfy57mtrZHWhEcFNri/Cm1X\n0gX6WyOkzhgTxDw7pwaSeYhqV1ca2ryRtdF/AAAgAElEQVQQe14Y3RXTB5FdAjuJ5azoolRztXS5\nLD77zUKnZoGrFZJ6lNAamwfyx8iaHeTW04itsfzxQB+N9ubZ0PshcHS6DkrzspRefd76MF03Wfr3\n0sU8GiHDz3FnCfDtshAKpF8y+93QvtN1WkhD+L4J0E2R3zxn7cK0Mb4J/SVRvzPogi0RS1DXCYCm\njvYBV/9zm182ORpRpq1vcbVxR9Ck1FfjOPAoGzoFJSbjPOHpk88q/TTsmgjXCPfCKo3rNrjGzt//\nNToBUN2WK+rNxyM5aDGafScA9qZEBk08ozn846CrsoyGJOWhmXsMqHU+UHn9cOVzeSBik1j1SKA2\nPONu+0mxYpQXoWyBEINvSt7vuewKO41ebGFF0NM8OmQ+0HoKXnQynz90o81CPZKrslPvhNJI5+HK\nnt0LatKXHX0ZqCSqdgbd7azDZ/8ehE4i9j5jPPwhKFG98KgDxyybiErPSjehLUK5Bs9KSxsjKITm\nRPgQRMVBCPEn7vuzeJi6cKAFf5CPRjg64dYJj44dg/cSlWZKkcxteUai8BIf9G0wxKjDGEeF4rOs\nqp+/bJ3Qu0eaMOgjIurnKgX/f0tzu7u2DqW7+2iKWpq4gm2smcb2vXzAmi82vQtS8e/1x40hA0mR\n2CF2V3XGXgmjTheNq9tseIYlzfM3H2/KsgzCtWLJZ4ARlbEt1FXofXCIIgjP+84iB/mtYHWwj8XP\nuwZqTnN2wBXMq8+30oeXVJYfdrraBFkCb3smr8b5pvSjobWhpSOpYzZcDFF8TbegiBoirsI8R6CM\nwJfh2QI1JFpR4nAALASQVt3FUMAWpVrwhvcTj/lphsZZP999L9VNGd0f3veLRzodkv05pcqpkTpV\nvBZdvddFaV3c7m/KNGxgHY9LsupRWmM4zjMJ03dwQxiev26Vr3UlWufCTtoLL+F0JV10oYN1QyXS\nNGMIy+mZoZpx8qAJrfpeeWelx0CcyttY2neVZ+tCbTpz4AJ9PggMVwvX4RbjHhX2QelKrG7rJvp7\nRelEHfQ8WOk8cefaH4RSGYe/ZwnD52b1op+ZfgEqrF/uSBJu6wVCZzvj9yKvtgaqeIa/iWJ2embs\ngEz9roBuDx9WSgvsi0vwj5B/uHP/nT//YQDC/A6jRxgNR5EasPp/13mCWHDf/fBnQ3JSnDCtwqRp\nDxZ/jtXVh6OlukJPD6NGcYZ4dUvIiHD16AykwRrmYCQ//o3MYOzi6kMapM0LR5bkuVexziDuu/tZ\n71+VdrodLCiM01Us90M8ILb5gxODnI1ePRD6y1fh7RD+1QtnPad4bnhycGB/Se5mUoHf3/z83Av0\nIdyHN5+J+jCagdfhx34UV0kGcwYcdbUm3SgP/27+I/jn3at4ucvpIGrqPgj2xa/J6D4A9wDLE64S\nqHDvfj22OSiuwd8jzSE1JVf9GnxvLtbmTPwdXCWwOFAcA9iLX/fyyTcIsU3QcQKf5Zzrucwhs/4o\nnFn6/8fc2/PYlm1pWs+YX2utvSPinJN5896qW110OQiEAAepjTaxkTD6R5TTJh4mf6FNXFxaYPMT\nkEAtgQQCIarpupebeT4j9l5rzY8xMMaMOFlYFFaFlEplKiL2jr3WmvOd73g/XEHKFfqvRtHhOIna\nSKOSRiP3xhiFroH+3GnPyvjc+PYJlh+M8jRITx6gioDdlNgqaMVSQo4dKYVEh+K5HC2voEpWz01x\nQkvQvtPSgowTa4loxmoDaTstnfDgeZVrUHIub1lpIkJ8SPS6YRfldgYOUQbOGm/HgLuw6kH+urPx\nTC+FNBpCIBaIMWAhTmuDM6y9ZQheMc/Z+VY3esz8HB6RKPwmPxPEuNidHCN5iwQ62nzhv4/CEd4x\npPCer9Q5ZSIG7FTqyMQlQwqcKTHa4bYFYGglj4HeYU3Ns7oKyD54WR5p8yBwaZWhkYRx++17Ym/I\nCnEMt31mvyFiMkaMmGVGiNT1AV0KulTC528QEunTN+TjTvrXn4iLkH+7EKO+3XwvSyEWo1ukjkiz\nQRUjBCHFBDFx5isv4YEeF+5cuBWlnydRMk0hjjrz4uDh0UHuu8Xgntm+fEVDZAuNvhSO63vGUnwt\nUF9s2q6Y2VtbVl0fCVeXOj+Ou9tc1zilGYZUp/ZbjqTsG5zVwfKvP7ky+K8u9G0hlsAZLozdP891\nFn5kUwbKQ1HiTwupV8rtJNwrpSmjRIQL78+P9JDcjugeGEKOnr9zXd2G2nb2o6IFntvKtr0QW8Po\nxG+D9/mOBuHjz4nlasje2Vn5/G1QVuOwzJFWjmVjicMPtNuBqdFGpp6RuxXOS2KXwl0WNmtoyQQG\nlhKVTE+Fj3lzC1VTwuMDxD8j7icfn72BN/3pG++Ob2y/fPQCIALjZe5JDwENiTN7S9hb83qAtAYn\nwQUHldZJw4FBaQPJgaUfhKjES6KLsFhFhpDGQflyEJOQj4qGgOaENXzhFJDa2WVhnJW6n3z7CkUa\nKZ1c2TFRn3J2bx8fbxso6OOG9IYdFRDGGWjrwr4+UoI3I5e+z1KpgqiRh3KIoJZ9nWqDElzlEBHP\nvVQlmE47cENDIlp3ZSdK1MrSfGIaRvAsLJysPc7IWg1rgoU8QehJTE5O53HQT+MxVhY96V+NGiMj\necbn+HyQgN/+B53HvxDWdye//8sXag0kPnD/mlhvX1n1K++3z1zlmQf7xpVnrvkZboI+RvRPwnGs\nfP7yjo/hB/6Yfscv+iP/e/k9x59v5PtOqTuWhOV8QVtk2T+SjmdiaQRzMkBjosWVPE5SMI4T+ukH\ndjuNI/jhOb8raAzItmG3EwuFaEYLibjP/NjbR2qPXNogJIMc6RVeemGpDSSTVt+7xuWCPl1pf/Vn\nxNYY5R1t7LBdiVoxMmd/9E20TaB0dFeGbI+wLth2oYcrGjeWtpPHSYwRYWAheXv5GtAohDUzfvNA\n5CTKDr2TkpJ+cOgYhO/ZwXje3wJYmUNd4Y3seOXeLDixFNTdGdJdeTYzukkK8QVChX0FJq7Kc/jr\nhRT+ksEdYa41TvN1u+/7rwcSUScso7k6kHNiOqYzN8Lzo7tRNENevVBDL44lXiNnfvgAy8WjVyR4\nMVWf2LScMysp+u+pxdV7LUCfQ9gU4VICPwZhi1BaowE2OpFIxXhBSBG2q2M4y7D+CN/+zSwlmREr\ne5uRL8HfX4iOeWiu0hv3GRUzCbeWXBX52rI8NlxiaI7FvnaP7LlG2Lo7ZWwSisskDRnwMnwQrBFs\nhTQc/6XLvBfEsSrBX2/gRKXMOOc+ydJosD67m0TmvRC6/4xu854Zfm1/HQ3TcmIY7EuiEtGlY/uA\n09DoQ+5QT5AwC9oNOfGogCCUbKhFYlnofxz0vhPeZ8o7ga7El9MHoLH43wwEGbQQiWrEPvweZGb4\nmhFNeYonSxE0NGoopL5z3e9cxfHl7QxOXCT49u7Bs7urcUokfxDWFajeQi5d2Z5PnqQiH/w5u2yD\n+KTcL4H29IDsN8oyyH8wclC+zZiGIwTqh0LvQnrf0G/GKYnLZhwPmWMrtOaZruN9RN9tYIMWhPqu\nELbxpkIyUcLpRIY8d+ziHpwzJm6WWcXIwbiPTn6Cb38ySuukECgvJ/ES52BD6OY3x0IjL/A0KlYV\nq57NdjZDF8FeK8TLzPpOPkJow/H70TOtGu//7xvLVrn8q0+gMP7dR+5/4fjoviwea/RhIFHYmhG7\nEhLUx0I/hbgl4jDiGugpomUlKLBFYjf46tc+vdxR9SIRyz6oNw3Y0YmHYVpopbiS3twZY1VnzE30\nQso0WKVRzk4xDxeVMZCXBreZ2RvDfIAg9kGoRr8WGiujL6RRyaOy0knJ4AlkMy9p+zQIMRBDom+B\nkYVeI0fzJtfxsBCTsMVOHEZaA9YzujgrEqOyjZ2qiW9j4xyZX0Ymoox6UPaD+O0g/rFie3NS6/Sc\nbAsDupGl80Cj5UiLgbMp1ht6dswioXnxYXiphDEIw+8zyYnLVUhLxz5sxGqUeyXeGsvz7uVoBiEJ\ncXE3TpfAZT2oaeFzvFC0cbWGqXnMkUQ0Cn17QtfENQy2o/LUd0ScTNIQqae6ijYERDupJNb3gfZ5\nYM1cvBJdMu2ZiU4IylOCHHg2JwK5G73u1LoSq9uWY/JCiTDMB9BHncpDXK1XDWmd5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fRC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PMGcKdFcGVhS3C8+vPx+Tu3FY/HEJNJLnZIX8yLSfwjUQ7HXuDE38C5Apwv8c8p/ud14pHa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\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K4o29FdLZWWkRS+MZ6NhaaDusw0K5ziRec2LtjeKhSLwr7mrZ2KWw5pDnb6XjA46x4N1p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\ns+/cZ9be91nPepa1xnFqaiJxNbKMw7mjv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get a batch of training data\n", "inputs, classes = next(iter(dataloaders['train']))\n", "\n", "# Make a grid from batch\n", "out = torchvision.utils.make_grid(inputs)\n", "\n", "imshow(out, title=[class_names[x] for x in classes])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "model_ft1 = models.densenet121(pretrained=True)\n", "num_ftrs = model_ft1.classifier.in_features\n", "model_ft1.classifier = nn.Linear(num_ftrs, 2)\n", "\n", "if use_gpu:\n", " model_ft1 = model_ft1.cuda()\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "optimizer_ft = optim.SGD(model_ft1.parameters(), lr=0.001, momentum=0.9)\n", "\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=6, gamma=0.1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/23\n", "----------\n", "train Loss: 0.0722 Acc: 0.6929 LR: 1.00E-3\n", "val Loss: 0.0499 Acc: 0.8162 LR: 1.00E-3\n", "Epoch 1/23\n", "----------\n", "train Loss: 0.0561 Acc: 0.8067 LR: 1.00E-3\n", "val Loss: 0.0411 Acc: 0.8442 LR: 1.00E-3\n", "Epoch 2/23\n", "----------\n", "train Loss: 0.0537 Acc: 0.7973 LR: 1.00E-3\n", "val Loss: 0.0404 Acc: 0.8536 LR: 1.00E-3\n", "Epoch 3/23\n", "----------\n", "train Loss: 0.0563 Acc: 0.7935 LR: 1.00E-3\n", "val Loss: 0.0483 Acc: 0.8442 LR: 1.00E-3\n", "Epoch 4/23\n", "----------\n", "train Loss: 0.0455 Acc: 0.8418 LR: 1.00E-3\n", "val Loss: 0.0375 Acc: 0.8816 LR: 1.00E-3\n", "Epoch 5/23\n", "----------\n", "train Loss: 0.0489 Acc: 0.8316 LR: 1.00E-3\n", "val Loss: 0.0572 Acc: 0.7882 LR: 1.00E-3\n", "Epoch 6/23\n", "----------\n", "train Loss: 0.0371 Acc: 0.8745 LR: 1.00E-4\n", "val Loss: 0.0372 Acc: 0.8692 LR: 1.00E-4\n", "Epoch 7/23\n", "----------\n", "train Loss: 0.0348 Acc: 0.8722 LR: 1.00E-4\n", "val Loss: 0.0316 Acc: 0.8910 LR: 1.00E-4\n", "Epoch 8/23\n", "----------\n", "train Loss: 0.0353 Acc: 0.8769 LR: 1.00E-4\n", "val Loss: 0.0309 Acc: 0.8972 LR: 1.00E-4\n", "Epoch 9/23\n", "----------\n", "train Loss: 0.0355 Acc: 0.8846 LR: 1.00E-4\n", "val Loss: 0.0316 Acc: 0.8785 LR: 1.00E-4\n", "Epoch 10/23\n", "----------\n", "train Loss: 0.0310 Acc: 0.9002 LR: 1.00E-4\n", "val Loss: 0.0291 Acc: 0.8941 LR: 1.00E-4\n", "Epoch 11/23\n", "----------\n", "train Loss: 0.0350 Acc: 0.8901 LR: 1.00E-4\n", "val Loss: 0.0313 Acc: 0.8879 LR: 1.00E-4\n", "Epoch 12/23\n", "----------\n", "train Loss: 0.0324 Acc: 0.8979 LR: 1.00E-5\n", "val Loss: 0.0288 Acc: 0.9003 LR: 1.00E-5\n", "Epoch 13/23\n", "----------\n", "train Loss: 0.0333 Acc: 0.8909 LR: 1.00E-5\n", "val Loss: 0.0290 Acc: 0.9003 LR: 1.00E-5\n", "Epoch 14/23\n", "----------\n", "train Loss: 0.0339 Acc: 0.8878 LR: 1.00E-5\n", "val Loss: 0.0316 Acc: 0.8941 LR: 1.00E-5\n", "Epoch 15/23\n", "----------\n", "train Loss: 0.0324 Acc: 0.8979 LR: 1.00E-5\n", "val Loss: 0.0307 Acc: 0.8910 LR: 1.00E-5\n", "Epoch 16/23\n", "----------\n", "train Loss: 0.0333 Acc: 0.8909 LR: 1.00E-5\n", "val Loss: 0.0345 Acc: 0.8816 LR: 1.00E-5\n", "Epoch 17/23\n", "----------\n", "train Loss: 0.0314 Acc: 0.9057 LR: 1.00E-5\n", "val Loss: 0.0290 Acc: 0.9034 LR: 1.00E-5\n", "Epoch 18/23\n", "----------\n", "train Loss: 0.0344 Acc: 0.8940 LR: 1.00E-6\n", "val Loss: 0.0293 Acc: 0.9128 LR: 1.00E-6\n", "Epoch 19/23\n", "----------\n", "train Loss: 0.0333 Acc: 0.8909 LR: 1.00E-6\n", "val Loss: 0.0279 Acc: 0.9003 LR: 1.00E-6\n", "Epoch 20/23\n", "----------\n", "train Loss: 0.0309 Acc: 0.8979 LR: 1.00E-6\n", "val Loss: 0.0318 Acc: 0.8785 LR: 1.00E-6\n", "Epoch 21/23\n", "----------\n", "train Loss: 0.0324 Acc: 0.8901 LR: 1.00E-6\n", "val Loss: 0.0308 Acc: 0.9003 LR: 1.00E-6\n", "Epoch 22/23\n", "----------\n", "train Loss: 0.0334 Acc: 0.8924 LR: 1.00E-6\n", "val Loss: 0.0309 Acc: 0.8910 LR: 1.00E-6\n", "Epoch 23/23\n", "----------\n", "train Loss: 0.0307 Acc: 0.9002 LR: 1.00E-6\n", "val Loss: 0.0273 Acc: 0.9034 LR: 1.00E-6\n", "Training complete in 13m 13s\n", "Best val Acc: 0.912773\n" ] } ], "source": [ "model_ft1, train_loss, val_loss, lr_dict = train_model(model_ft1, criterion, optimizer_ft, \n", " exp_lr_scheduler, path_to_save='./densnet.pth' ,num_epochs=24)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dYW5r9W72G3Om91qdMkREfCkSDgJQrzpmEd/LiMAMMDwS4se3nENDUyt//9iS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\nPsMYcwnYKyK7HTGscvYTUyo7qgQFEPVaRxKSUlwdisulpED0ifNsjol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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tl = pd.DataFrame.from_dict(train_loss, orient='index').reset_index()\n", "tl.columns = ['epoch', 'train_loss']\n", "vl = pd.DataFrame.from_dict(val_loss, orient='index').reset_index()\n", "vl.columns = ['epoch', 'val_loss']\n", "l = pd.DataFrame.from_dict(lr_dict, orient='index').reset_index()\n", "l.columns = ['epoch', 'learning_rate']\n", "\n", "tl = tl.merge(vl, on='epoch')\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2)\n", "tl.plot(x='epoch', y=['train_loss', 'val_loss'], ax=ax1)\n", "l.plot(x='epoch', y='learning_rate', figsize=(12,6), ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Fine-tuning a pre-trained DensNet with Data Augmentation and Learning Rate Restart\n", "\n", "The Learning Rate is probably the most important hyper-parameter within a DeepNet optimization. Its magnitude determines the way SGD explores the parameters space. Ideally we would start with a high LR to quickly move close to a local minimum in the loss function and, as the training progresses, we would slowly lower it down to smoothly approach the actual minimum.\n", "\n", "The issue with this approch is that it assumes the inherent convexity of the parameters space, whereas it is more realistic to picture the loss surface as having multiple \"holes\". Ideally we would like to explore as much as possible within this space. Periodically increasing the learning rate lets us achieve just that. Phrased it differently, this trick pushes SGD out of a local minimum to start the exploration again out of it, with the hope to find a better place, i.e. with a lower loss value.\n", "\n", "Take a look at the learning rate plot versus epochs. It is pretty clear what the trend is and how it impacts the loss." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "model_ft2 = models.densenet121(pretrained=True)\n", "num_ftrs = model_ft2.classifier.in_features\n", "model_ft2.classifier = nn.Linear(num_ftrs, 2)\n", "\n", "if use_gpu:\n", " model_ft2 = model_ft2.cuda()\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "optimizer_ft = optim.SGD(model_ft2.parameters(), lr=0.001, momentum=0.9)\n", "\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=1, gamma=0.25)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/34\n", "----------\n", "train Loss: 0.0764 Acc: 0.6742 LR: 1.00E-3\n", "val Loss: 0.0550 Acc: 0.8006 LR: 1.00E-3\n", "Epoch 1/34\n", "----------\n", "train Loss: 0.0535 Acc: 0.7989 LR: 2.50E-4\n", "val Loss: 0.0445 Acc: 0.8349 LR: 2.50E-4\n", "Epoch 2/34\n", "----------\n", "train Loss: 0.0496 Acc: 0.8106 LR: 6.25E-5\n", "val Loss: 0.0374 Acc: 0.8723 LR: 6.25E-5\n", "Epoch 3/34\n", "----------\n", "train Loss: 0.0489 Acc: 0.8192 LR: 1.56E-5\n", "val Loss: 0.0428 Acc: 0.8505 LR: 1.56E-5\n", "Epoch 4/34\n", "----------\n", "train Loss: 0.0491 Acc: 0.8129 LR: 3.91E-6\n", "val Loss: 0.0434 Acc: 0.8349 LR: 3.91E-6\n", "Epoch 5/34\n", "----------\n", "train Loss: 0.0480 Acc: 0.8262 LR: 9.77E-7\n", "val Loss: 0.0416 Acc: 0.8411 LR: 9.77E-7\n", "Epoch 6/34\n", "----------\n", "Resetting LR\n", "train Loss: 0.1193 Acc: 0.6812 LR: 4.00E-3\n", "val Loss: 0.2616 Acc: 0.6168 LR: 4.00E-3\n", "Epoch 7/34\n", "----------\n", "train Loss: 0.0669 Acc: 0.7467 LR: 1.00E-3\n", "val Loss: 0.0503 Acc: 0.8037 LR: 1.00E-3\n", "Epoch 8/34\n", "----------\n", "train Loss: 0.0532 Acc: 0.8036 LR: 2.50E-4\n", "val Loss: 0.0442 Acc: 0.8380 LR: 2.50E-4\n", "Epoch 9/34\n", "----------\n", "train Loss: 0.0507 Acc: 0.8067 LR: 6.25E-5\n", "val Loss: 0.0439 Acc: 0.8193 LR: 6.25E-5\n", "Epoch 10/34\n", "----------\n", "train Loss: 0.0498 Acc: 0.8122 LR: 1.56E-5\n", "val Loss: 0.0422 Acc: 0.8318 LR: 1.56E-5\n", "Epoch 11/34\n", "----------\n", "train Loss: 0.0493 Acc: 0.8129 LR: 3.91E-6\n", "val Loss: 0.0441 Acc: 0.8287 LR: 3.91E-6\n", "Epoch 12/34\n", "----------\n", "Resetting LR\n", "train Loss: 0.0698 Acc: 0.7436 LR: 4.00E-3\n", "val Loss: 0.0744 Acc: 0.7414 LR: 4.00E-3\n", "Epoch 13/34\n", "----------\n", "train Loss: 0.0539 Acc: 0.7981 LR: 1.00E-3\n", "val Loss: 0.0619 Acc: 0.7601 LR: 1.00E-3\n", "Epoch 14/34\n", "----------\n", "train Loss: 0.0481 Acc: 0.8262 LR: 2.50E-4\n", "val Loss: 0.0453 Acc: 0.8287 LR: 2.50E-4\n", "Epoch 15/34\n", "----------\n", "train Loss: 0.0483 Acc: 0.8270 LR: 6.25E-5\n", "val Loss: 0.0552 Acc: 0.7695 LR: 6.25E-5\n", "Epoch 16/34\n", "----------\n", "train Loss: 0.0472 Acc: 0.8254 LR: 1.56E-5\n", "val Loss: 0.0436 Acc: 0.8629 LR: 1.56E-5\n", "Epoch 17/34\n", "----------\n", "train Loss: 0.0468 Acc: 0.8309 LR: 3.91E-6\n", "val Loss: 0.0424 Acc: 0.8349 LR: 3.91E-6\n", "Epoch 18/34\n", "----------\n", "Resetting LR\n", "train Loss: 0.0818 Acc: 0.7327 LR: 4.00E-3\n", "val Loss: 0.4376 Acc: 0.6449 LR: 4.00E-3\n", "Epoch 19/34\n", "----------\n", "train Loss: 0.0572 Acc: 0.7747 LR: 1.00E-3\n", "val Loss: 0.0674 Acc: 0.7321 LR: 1.00E-3\n", "Epoch 20/34\n", "----------\n", "train Loss: 0.0534 Acc: 0.8028 LR: 2.50E-4\n", "val Loss: 0.0592 Acc: 0.7726 LR: 2.50E-4\n", "Epoch 21/34\n", "----------\n", "train Loss: 0.0518 Acc: 0.8067 LR: 6.25E-5\n", "val Loss: 0.0563 Acc: 0.7975 LR: 6.25E-5\n", "Epoch 22/34\n", "----------\n", "train Loss: 0.0507 Acc: 0.8051 LR: 1.56E-5\n", "val Loss: 0.0635 Acc: 0.7383 LR: 1.56E-5\n", "Epoch 23/34\n", "----------\n", "train Loss: 0.0489 Acc: 0.8168 LR: 3.91E-6\n", "val Loss: 0.0797 Acc: 0.7227 LR: 3.91E-6\n", "Epoch 24/34\n", "----------\n", "Resetting LR\n", "train Loss: 0.0960 Acc: 0.7311 LR: 4.00E-3\n", "val Loss: 0.0558 Acc: 0.7819 LR: 4.00E-3\n", "Epoch 25/34\n", "----------\n", "train Loss: 0.0611 Acc: 0.7857 LR: 1.00E-3\n", "val Loss: 0.0438 Acc: 0.8442 LR: 1.00E-3\n", "Epoch 26/34\n", "----------\n", "train Loss: 0.0504 Acc: 0.8059 LR: 2.50E-4\n", "val Loss: 0.0450 Acc: 0.8287 LR: 2.50E-4\n", "Epoch 27/34\n", "----------\n", "train Loss: 0.0524 Acc: 0.8005 LR: 6.25E-5\n", "val Loss: 0.0436 Acc: 0.8442 LR: 6.25E-5\n", "Epoch 28/34\n", "----------\n", "train Loss: 0.0510 Acc: 0.8067 LR: 1.56E-5\n", "val Loss: 0.0452 Acc: 0.8380 LR: 1.56E-5\n", "Epoch 29/34\n", "----------\n", "train Loss: 0.0484 Acc: 0.8200 LR: 3.91E-6\n", "val Loss: 0.0431 Acc: 0.8474 LR: 3.91E-6\n", "Epoch 30/34\n", "----------\n", "Resetting LR\n", "train Loss: 0.0820 Acc: 0.7233 LR: 4.00E-3\n", "val Loss: 0.1107 Acc: 0.7259 LR: 4.00E-3\n", "Epoch 31/34\n", "----------\n", "train Loss: 0.0596 Acc: 0.7833 LR: 1.00E-3\n", "val Loss: 0.4334 Acc: 0.7072 LR: 1.00E-3\n", "Epoch 32/34\n", "----------\n", "train Loss: 0.0552 Acc: 0.7872 LR: 2.50E-4\n", "val Loss: 0.2516 Acc: 0.7445 LR: 2.50E-4\n", "Epoch 33/34\n", "----------\n", "train Loss: 0.0489 Acc: 0.8122 LR: 6.25E-5\n", "val Loss: 0.1814 Acc: 0.7477 LR: 6.25E-5\n", "Epoch 34/34\n", "----------\n", "train Loss: 0.0522 Acc: 0.8090 LR: 1.56E-5\n", "val Loss: 0.2027 Acc: 0.7477 LR: 1.56E-5\n", "Training complete in 19m 17s\n", "Best val Acc: 0.872274\n" ] } ], "source": [ "model_ft2, train_loss, val_loss, lr_dict = train_model(model_ft2, criterion, optimizer_ft, exp_lr_scheduler, \n", " path_to_save='./densnet_restartlr.pth', restartlr=True, num_epochs=35)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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aY3CS3cmeMVjodoCI5IHLgbcCG4G7RWSNMebR0GE/AtYYY4yIvAG4Htg/9Pyx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\nB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tl = pd.DataFrame.from_dict(train_loss, orient='index').reset_index()\n", "tl.columns = ['epoch', 'train_loss']\n", "vl = pd.DataFrame.from_dict(val_loss, orient='index').reset_index()\n", "vl.columns = ['epoch', 'val_loss']\n", "l = pd.DataFrame.from_dict(lr_dict, orient='index').reset_index()\n", "l.columns = ['epoch', 'learning_rate']\n", "\n", "tl = tl.merge(vl, on='epoch')\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2)\n", "tl.plot(x='epoch', y=['train_loss', 'val_loss'], ax=ax1)\n", "l.plot(x='epoch', y='learning_rate', figsize=(12,6), ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Using the DensNet as an (almost) fixed feature extractor\n", "\n", "This is the first transfer-learning scenario mentioned in the Stanford notes.\n", "The principle is that we don't want to disrupt the pre-trained model re-training it from scratch on our dataset. We need to keep in mind that the weights have already been carefully tuned and so it is maybe advisable not to touch them at all, as we have instead done so far. An alternative approach consists in freezing at least the first layers and re-train only the last ones. In all honesty I don't think this is a good idea for this specific problem, reason being that the ImageNet dataset looks very different than the one we are currently working with. Features learned on cats, dogs, cars etc have likely almost nothing in common with satellite images of ships and icebergs, hence just initializing the net with pre-trained weights and then tweaking them sounds a much more reasonable approach.\n", "\n", "As you'll see this is actually the case, so the fixed feature extractor path does not really work great.\n", "\n", "Important note: please notice that we are not completely freezing the first layers. We are actually applying to them a very low learning rate, basically allwoing the weights to slowly change epoch after epoch. The end result is, in any case, very similar to not re-train the at all." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model_ft3 = models.densenet121(pretrained=True)\n", "for param in [par for par in model_ft3.features.conv0.parameters()] +\\\n", " [par for par in model_ft3.features.denseblock1.parameters()] + \\\n", " [par for par in model_ft3.features.denseblock2.parameters()]:\n", " param.requires_grad = False\n", " \n", "num_ftrs = model_ft3.classifier.in_features\n", "model_ft3.classifier = nn.Linear(num_ftrs, 2)\n", "\n", "if use_gpu:\n", " model_ft3 = model_ft3.cuda()\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "optimizer_ft = optim.SGD([{'params': model_ft3.features.denseblock3.parameters(), 'lr': 1e-5},\n", " {'params': model_ft3.features.denseblock4.parameters(), 'lr': 1e-4},\n", " {'params': model_ft3.classifier.parameters(), 'lr': 1e-2}], lr=1e-3, momentum=0.9)\n", "\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=2, gamma=0.8)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/29\n", "----------\n", "train Loss: 0.2283 Acc: 0.6306 LR: 1.00E-5\n", "val Loss: 0.1223 Acc: 0.7009 LR: 1.00E-5\n", "Epoch 1/29\n", "----------\n", "train Loss: 0.2457 Acc: 0.7101 LR: 1.00E-5\n", "val Loss: 0.1334 Acc: 0.7850 LR: 1.00E-5\n", "Epoch 2/29\n", "----------\n", "train Loss: 0.2144 Acc: 0.7132 LR: 8.00E-6\n", "val Loss: 0.1824 Acc: 0.7227 LR: 8.00E-6\n", "Epoch 3/29\n", "----------\n", "train Loss: 0.1551 Acc: 0.7412 LR: 8.00E-6\n", "val Loss: 0.1035 Acc: 0.7882 LR: 8.00E-6\n", "Epoch 4/29\n", "----------\n", "train Loss: 0.1819 Acc: 0.7217 LR: 6.40E-6\n", "val Loss: 0.1758 Acc: 0.7508 LR: 6.40E-6\n", "Epoch 5/29\n", "----------\n", "train Loss: 0.1480 Acc: 0.7592 LR: 6.40E-6\n", "val Loss: 0.0693 Acc: 0.8287 LR: 6.40E-6\n", "Epoch 6/29\n", "----------\n", "train Loss: 0.1117 Acc: 0.7810 LR: 5.12E-6\n", "val Loss: 0.0967 Acc: 0.7975 LR: 5.12E-6\n", "Epoch 7/29\n", "----------\n", "train Loss: 0.1018 Acc: 0.7677 LR: 5.12E-6\n", "val Loss: 0.1464 Acc: 0.6947 LR: 5.12E-6\n", "Epoch 8/29\n", "----------\n", "train Loss: 0.1350 Acc: 0.7241 LR: 4.10E-6\n", "val Loss: 0.1359 Acc: 0.7352 LR: 4.10E-6\n", "Epoch 9/29\n", "----------\n", "train Loss: 0.1070 Acc: 0.7677 LR: 4.10E-6\n", "val Loss: 0.0707 Acc: 0.8037 LR: 4.10E-6\n", "Epoch 10/29\n", "----------\n", "train Loss: 0.1130 Acc: 0.7638 LR: 3.28E-6\n", "val Loss: 0.0622 Acc: 0.8131 LR: 3.28E-6\n", "Epoch 11/29\n", "----------\n", "train Loss: 0.1037 Acc: 0.7662 LR: 3.28E-6\n", "val Loss: 0.0584 Acc: 0.8037 LR: 3.28E-6\n", "Epoch 12/29\n", "----------\n", "train Loss: 0.0820 Acc: 0.7857 LR: 2.62E-6\n", "val Loss: 0.0683 Acc: 0.7913 LR: 2.62E-6\n", "Epoch 13/29\n", "----------\n", "train Loss: 0.0641 Acc: 0.8028 LR: 2.62E-6\n", "val Loss: 0.0676 Acc: 0.7757 LR: 2.62E-6\n", "Epoch 14/29\n", "----------\n", "train Loss: 0.0706 Acc: 0.7825 LR: 2.10E-6\n", "val Loss: 0.0543 Acc: 0.8287 LR: 2.10E-6\n", "Epoch 15/29\n", "----------\n", "train Loss: 0.0782 Acc: 0.7771 LR: 2.10E-6\n", "val Loss: 0.0556 Acc: 0.8100 LR: 2.10E-6\n", "Epoch 16/29\n", "----------\n", "train Loss: 0.0638 Acc: 0.7989 LR: 1.68E-6\n", "val Loss: 0.0594 Acc: 0.8131 LR: 1.68E-6\n", "Epoch 17/29\n", "----------\n", "train Loss: 0.0609 Acc: 0.7896 LR: 1.68E-6\n", "val Loss: 0.0633 Acc: 0.8069 LR: 1.68E-6\n", "Epoch 18/29\n", "----------\n", "train Loss: 0.0659 Acc: 0.7857 LR: 1.34E-6\n", "val Loss: 0.0506 Acc: 0.8411 LR: 1.34E-6\n", "Epoch 19/29\n", "----------\n", "train Loss: 0.0662 Acc: 0.7981 LR: 1.34E-6\n", "val Loss: 0.0745 Acc: 0.7601 LR: 1.34E-6\n", "Epoch 20/29\n", "----------\n", "train Loss: 0.0679 Acc: 0.7880 LR: 1.07E-6\n", "val Loss: 0.0578 Acc: 0.8100 LR: 1.07E-6\n", "Epoch 21/29\n", "----------\n", "train Loss: 0.0626 Acc: 0.7818 LR: 1.07E-6\n", "val Loss: 0.0514 Acc: 0.8287 LR: 1.07E-6\n", "Epoch 22/29\n", "----------\n", "train Loss: 0.0616 Acc: 0.7981 LR: 8.59E-7\n", "val Loss: 0.0457 Acc: 0.8505 LR: 8.59E-7\n", "Epoch 23/29\n", "----------\n", "train Loss: 0.0561 Acc: 0.7942 LR: 8.59E-7\n", "val Loss: 0.0409 Acc: 0.8536 LR: 8.59E-7\n", "Epoch 24/29\n", "----------\n", "train Loss: 0.0522 Acc: 0.8192 LR: 6.87E-7\n", "val Loss: 0.0458 Acc: 0.8037 LR: 6.87E-7\n", "Epoch 25/29\n", "----------\n", "train Loss: 0.0523 Acc: 0.8215 LR: 6.87E-7\n", "val Loss: 0.0534 Acc: 0.8069 LR: 6.87E-7\n", "Epoch 26/29\n", "----------\n", "train Loss: 0.0511 Acc: 0.8122 LR: 5.50E-7\n", "val Loss: 0.0465 Acc: 0.8567 LR: 5.50E-7\n", "Epoch 27/29\n", "----------\n", "train Loss: 0.0536 Acc: 0.7911 LR: 5.50E-7\n", "val Loss: 0.0539 Acc: 0.8162 LR: 5.50E-7\n", "Epoch 28/29\n", "----------\n", "train Loss: 0.0509 Acc: 0.8254 LR: 4.40E-7\n", "val Loss: 0.0398 Acc: 0.8442 LR: 4.40E-7\n", "Epoch 29/29\n", "----------\n", "train Loss: 0.0555 Acc: 0.8184 LR: 4.40E-7\n", "val Loss: 0.0491 Acc: 0.8037 LR: 4.40E-7\n", "Training complete in 14m 12s\n", "Best val Acc: 0.856698\n" ] } ], "source": [ "model_ft3, train_loss, val_loss, lr_dict = train_model(model_ft3, criterion, optimizer_ft, exp_lr_scheduler,\n", " path_to_save='./densnet_freeze.pth' ,num_epochs=30)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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io2jctMYGmJiThstohllEJJEsLPWytbqJ7p7QnQFFRIZjXAXMyW4Xk3I87FPA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\naosz0+DRjeDlb379HbPgf6Nh9GKIbV/+OFd/CIv/CQ+tgsgW5d/eAqn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tl = pd.DataFrame.from_dict(train_loss, orient='index').reset_index()\n", "tl.columns = ['epoch', 'train_loss']\n", "vl = pd.DataFrame.from_dict(val_loss, orient='index').reset_index()\n", "vl.columns = ['epoch', 'val_loss']\n", "l = pd.DataFrame.from_dict(lr_dict, orient='index').reset_index()\n", "l.columns = ['epoch', 'learning_rate']\n", "\n", "tl = tl.merge(vl, on='epoch')\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2)\n", "tl.plot(x='epoch', y=['train_loss', 'val_loss'], ax=ax1)\n", "l.plot(x='epoch', y='learning_rate', figsize=(12,6), ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Data Augmentation on the test set, i.e. ensembling DensNet probabilities\n", "\n", "Ok, we have explored some basic possibilities to solve our iceberg challenge. To close the loop we have to generate a submission to Kaggle, i.e. we have to run our model on the test set.\n", "\n", "We could simply apply the re-trained deep net on unseen images but a better idea to make our predictions more robust is to iterate over the test set several times transforming each image each time. Phrased differently we are going to perform data augmentation on the test set. The principle behind this idea is that every time the model sees a slightly modified version of the same picture it is going to spit out a slightly different probability of it being an iceberg. Averaging out these probabilities will reduce noise and hopefully stabilize the prediction as well, furtherly reducing loss." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "test_transforms = transforms.Compose([\n", " transforms.RandomResizedCrop(224, scale=(0.7, 1.0)),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.RandomVerticalFlip(),\n", " transforms.RandomRotation(180),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n", " ])\n", "\n", "path = './data/iceberg/test_images/test/'\n", "images = {f.replace('.jpg', ''): os.path.join(path, f) for f in os.listdir(path) if 'jpg' in f}" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", " # Remove the CWD from sys.path while we load stuff.\n", "/home/paperspace/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", " if sys.path[0] == '':\n" ] } ], "source": [ "model_ft = torch.load('./densnet.pth')\n", "soft = nn.Softmax()\n", "densnet_dict = {}\n", "\n", "for i in range(5):\n", " for k, im in images.items():\n", " image = test_transforms(Image.open(im).convert('RGB')).unsqueeze(0)\n", " d = model_ft(Variable(image.cuda()))\n", " if i == 0:\n", " densnet_dict[k] = [soft(d).data.cpu().numpy().flatten()[1]]\n", " else: \n", " densnet_dict[k].append(soft(d).data.cpu().numpy().flatten()[1])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.45910439, 0.52070016, 0.61964869, 0.42500347, 0.55482155]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "densnet_dict['007f8195']" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.515856\n" ] } ], "source": [ "dd = {k: np.mean(v) for k, v in densnet_dict.items()}\n", "print(dd['007f8195'])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id is_iceberg\n", "0 4557372f 0.218215\n", "1 2c9d1b48 0.033464\n", "2 1ee45ac5 0.070717\n", "3 156bd669 0.993827\n", "4 567612b1 0.960779\n", "5 f0d55d48 0.156331\n", "6 fa530b08 0.017454\n", "7 c48b752b 0.131932\n", "8 b69eefe4 0.258191\n", "9 0b2918c3 0.893857" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "denset_kaggle = pd.DataFrame.from_dict(dd, orient='index').reset_index()\n", "denset_kaggle.columns = ['id', 'is_iceberg']\n", "denset_kaggle.to_csv('./data/iceberg/for_kaggle.csv', index=False)\n", "denset_kaggle.head(10)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }