├── .gitignore ├── LICENSE ├── README.md ├── data └── dog2.jpg ├── dataset.py ├── experiment.yaml ├── layers.py ├── plotter.ipynb ├── predict.py ├── tools.py ├── train_model_parallel.py ├── train_model_simple.py └── vgg.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | 26 | # PyInstaller 27 | # Usually these files are written by a python script from a template 28 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 29 | *.manifest 30 | *.spec 31 | 32 | # Installer logs 33 | pip-log.txt 34 | pip-delete-this-directory.txt 35 | 36 | # Unit test / coverage reports 37 | htmlcov/ 38 | .tox/ 39 | .coverage 40 | .coverage.* 41 | .cache 42 | nosetests.xml 43 | coverage.xml 44 | *,cover 45 | 46 | # Translations 47 | *.mo 48 | *.pot 49 | 50 | # Django stuff: 51 | *.log 52 | 53 | # Sphinx documentation 54 | docs/_build/ 55 | 56 | # PyBuilder 57 | target/ 58 | 59 | .remote-sync.json 60 | *.npz 61 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2016 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # tensorflow-vgg 2 | Re-implementation of VGG Network in tensorflow 3 | 4 | # setup 5 | 6 | ``` 7 | pip install pyyaml skimage skdata tensorflow-gpu 8 | ``` 9 | 10 | # training 11 | 12 | ``` 13 | python train_model_simple.py experiment.yaml 14 | ``` 15 | 16 | # training on multiple gpus 17 | 18 | ``` 19 | python train_model_parallel.py experiment.yaml 20 | ``` 21 | 22 | # prediction 23 | 24 | ``` 25 | python predict.py dog.jpg 26 | ``` 27 | -------------------------------------------------------------------------------- /data/dog2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huyng/tensorflow-vgg/43890b4c5d8b2b0d9e825877a63b78d019624b5d/data/dog2.jpg -------------------------------------------------------------------------------- /dataset.py: -------------------------------------------------------------------------------- 1 | import itertools as it 2 | import numpy as np 3 | import tensorflow as tf 4 | 5 | 6 | def get_cifar10(batch_size=16): 7 | print("loading cifar10 data ... ") 8 | 9 | from skdata.cifar10.dataset import CIFAR10 10 | cifar10 = CIFAR10() 11 | cifar10.fetch(True) 12 | 13 | trn_labels = [] 14 | trn_pixels = [] 15 | for i in range(1,6): 16 | data = cifar10.unpickle("data_batch_%d" % i) 17 | trn_pixels.append(data['data']) 18 | trn_labels.extend(data['labels']) 19 | 20 | trn_pixels = np.vstack(trn_pixels) 21 | trn_pixels = trn_pixels.reshape(-1, 3, 32, 32).astype(np.float32) 22 | 23 | tst_data = cifar10.unpickle("test_batch") 24 | tst_labels = tst_data["labels"] 25 | tst_pixels = tst_data["data"] 26 | tst_pixels = tst_pixels.reshape(-1, 3, 32, 32).astype(np.float32) 27 | 28 | print("-- trn shape = %s" % list(trn_pixels.shape)) 29 | print("-- tst shape = %s" % list(tst_pixels.shape)) 30 | 31 | # transpose to tensorflow's bhwc order assuming bchw order 32 | trn_pixels = trn_pixels.transpose(0, 2, 3, 1) 33 | tst_pixels = tst_pixels.transpose(0, 2, 3, 1) 34 | 35 | trn_set = batch_iterator(it.cycle(zip(trn_pixels, trn_labels)), batch_size, cycle=True, batch_fn=lambda x: zip(*x)) 36 | tst_set = (tst_pixels, np.array(tst_labels)) 37 | 38 | return trn_set, tst_set 39 | 40 | def batch_iterator(iterable, size, cycle=False, batch_fn=lambda x: x): 41 | """ 42 | Iterate over a list or iterator in batches 43 | """ 44 | batch = [] 45 | 46 | # loop to begining upon reaching end of iterable, if cycle flag is set 47 | if cycle is True: 48 | iterable = it.cycle(iterable) 49 | 50 | for item in iterable: 51 | batch.append(item) 52 | if len(batch) >= size: 53 | yield batch_fn(batch) 54 | batch = [] 55 | 56 | if len(batch) > 0: 57 | yield batch_fn(batch) 58 | 59 | 60 | if __name__ == '__main__': 61 | trn, tst = get_cifar10() 62 | -------------------------------------------------------------------------------- /experiment.yaml: -------------------------------------------------------------------------------- 1 | # =================== 2 | # solver configuration 3 | # ==================== 4 | 5 | # number of gpus to use, this will be ignored if we're not using multi-gpu training 6 | num_gpus: 5 7 | num_epochs: 10 8 | num_samples_per_epoch: 50000 9 | 10 | # run validation every N steps 11 | vld_iter: 500 12 | 13 | # checkpoints every N steps 14 | checkpoint_iter: 500 15 | 16 | experiment_dir: "exp1" 17 | checkpoint_dir: "exp1/checkpoints" 18 | 19 | # path to pre-trained weights to initialize model with before training 20 | pretrained_weights: null 21 | 22 | 23 | # ============ 24 | # model config 25 | # ============ 26 | batch_size: 256 27 | num_classes: 10 28 | learning_rate: 0.01 29 | data_dims: [32, 32, 3] 30 | weight_decay: 0.0005 31 | -------------------------------------------------------------------------------- /layers.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime 2 | import math 3 | import time 4 | import numpy as np 5 | import dataset 6 | import tensorflow.python.platform 7 | import tensorflow as tf 8 | from tensorflow.contrib.layers import xavier_initializer 9 | 10 | def conv(input_tensor, name, kw, kh, n_out, dw=1, dh=1, activation_fn=tf.nn.relu): 11 | n_in = input_tensor.get_shape()[-1].value 12 | with tf.variable_scope(name): 13 | weights = tf.get_variable('weights', [kh, kw, n_in, n_out], tf.float32, xavier_initializer()) 14 | biases = tf.get_variable("bias", [n_out], tf.float32, tf.constant_initializer(0.0)) 15 | conv = tf.nn.conv2d(input_tensor, weights, (1, dh, dw, 1), padding='SAME') 16 | activation = activation_fn(tf.nn.bias_add(conv, biases)) 17 | return activation 18 | 19 | 20 | def fully_connected(input_tensor, name, n_out, activation_fn=tf.nn.relu): 21 | n_in = input_tensor.get_shape()[-1].value 22 | with tf.variable_scope(name): 23 | weights = tf.get_variable('weights', [n_in, n_out], tf.float32, xavier_initializer()) 24 | biases = tf.get_variable("bias", [n_out], tf.float32, tf.constant_initializer(0.0)) 25 | logits = tf.nn.bias_add(tf.matmul(input_tensor, weights), biases) 26 | return activation_fn(logits) 27 | 28 | 29 | def pool(input_tensor, name, kh, kw, dh, dw): 30 | return tf.nn.max_pool(input_tensor, 31 | ksize=[1, kh, kw, 1], 32 | strides=[1, dh, dw, 1], 33 | padding='VALID', 34 | name=name) 35 | 36 | 37 | def loss(logits, onehot_labels): 38 | xentropy = tf.nn.softmax_cross_entropy_with_logits(logits, onehot_labels, name='xentropy') 39 | loss = tf.reduce_mean(xentropy, name='loss') 40 | return loss 41 | 42 | 43 | def topK_error(predictions, labels, K=5): 44 | correct = tf.cast(tf.nn.in_top_k(predictions, labels, K), tf.float32) 45 | accuracy = tf.reduce_mean(correct) 46 | error = 1.0 - accuracy 47 | return error 48 | 49 | def average_gradients(grads): 50 | """Calculate the average gradient for each shared variable across all towers. 51 | 52 | Note that this function provides a synchronization point across all towers. 53 | 54 | Args: 55 | tower_grads: List of lists of (gradient, variable) tuples. The outer list 56 | is over individual gradients. The inner list is over the gradient 57 | calculation for each tower. 58 | Returns: 59 | List of pairs of (gradient, variable) where the gradient has been averaged 60 | across all towers. 61 | """ 62 | average_grads = [] 63 | for grad_and_vars in zip(*grads): 64 | # Note that each grad_and_vars looks like the following: 65 | # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) 66 | grads = [] 67 | for g, _ in grad_and_vars: 68 | # Add 0 dimension to the gradients to represent the tower. 69 | expanded_g = tf.expand_dims(g, 0) 70 | 71 | # Append on a 'tower' dimension which we will average over below. 72 | grads.append(expanded_g) 73 | 74 | # Average over the 'tower' dimension. 75 | grad = tf.concat(0, grads) 76 | grad = tf.reduce_mean(grad, 0) 77 | 78 | # Keep in mind that the Variables are redundant because they are shared 79 | # across towers. So .. we will just return the first tower's pointer to 80 | # the Variable. 81 | v = grad_and_vars[0][1] 82 | grad_and_var = (grad, v) 83 | average_grads.append(grad_and_var) 84 | return average_grads 85 | -------------------------------------------------------------------------------- /plotter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "name": "stdout", 12 | "output_type": "stream", 13 | "text": [ 14 | "Populating the interactive namespace from numpy and matplotlib\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "%pylab inline" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 5, 25 | "metadata": { 26 | "collapsed": false 27 | }, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/plain": [ 32 | "" 33 | ] 34 | }, 35 | "execution_count": 5, 36 | "metadata": {}, 37 | "output_type": "execute_result" 38 | }, 39 | { 40 | "data": { 41 | "image/png": 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hh/znf371iMjE+W5Dq9Fo/rDZtm3bhT6FdwSj8TqebmpjMfBvqH7RFvBdYJUQ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Qh8PqWofzGWu1HILoA6rsuhrq4Ow5hEJrMZw5BAadFYxuPbmDt3F6tWffVdp+yvv34Je/aM5Tlk\nGRzsJBYbQFWzqGoYkDQ11aGqapHd/3Ydg3NhuAq8k4cf3khPj0nhPt955wJWr15ZPO8PQ/vjh+Ec\nzwXOOxLncUaciqmcH8Q1l2nTXCd9JxxuZsuWzXzjG3kNim9+82kGB8OY5hW43YM4jg+f7yOo6pNY\n1qUYxmZsWyGbjaLrCkJchJSHMQwDKbuAJaiqwOP5JPBXKEo3EEPKOUjpIOUyFCVNMFiCrr+Gz9eE\nrlejaa+TSoGUbcByNE3BstJAA4pyBNuuQojXsCyJolRhWUeQsgrLKqWkJEVZWR25XBS3e5jKyouZ\nNevS0dkfAbLZcuLxKFKqOM58Kis78XjmoGkfZdeuL3Do0M1YVi2wCU37CxSlE02byty5i9iz51+A\nWsLhKhRFYXjYRSo1gtdbQi63iYEBHU37HFLuwHGqsaypRKNH8flaOXbsAmpqGli9+rNs2fIQ69a9\nQCYziKI04PUexOdrABaSzbZiWb+joqKeSGQfLS2zWbx4PqqqjhmIthnThGh0Lb29TTjOdL7+9TXF\nSPyWW1Zxyy2rxkWqUko2bPglmnbcwQyH5zMw8F3i8YsRogEpU/j9GXbt2kEyWUpT0/LixlsoRyST\nOfbte4WhoRECgSP4/bVMneqlv38zudxSvN6rsawoDQ2LWLvWZseOZ5gyZSHRqEUsthWXazqhkIfy\n8hBCCDRNouuDNDQEi89hQakVLiWdjhCP70DXp2BZA8ydG+UHP7gHj8fDk0+ue1cp+7GljvXrNxCL\nrScancqkSQuKx1foYJk5EyorR/jKV37+vs9q0HWdb3/7YZ5/PoHj3E0gUCCPRrn//lb27HmEr33t\nTjwez4ei/fFsnuOHydk470icxxkxEavccRzS6RTl5UeKKeixGOu563qO3t4Uut6Epk1G01QsSyOT\nUZEyhJRrkDKBaW7Atm2kbEGIK1CU11GUqzHNEqQcwjBi5HKbEGIRivI4th1GiAxgo2luhDBQ1TAN\nDQEMw6K//8d4vdeTy+kYxmFgB5YlATeK0o+UF6Eoj6Mo81HVbqATxylHiMP4/QrTpk3DceI0Noao\nqJiEpsWLm4PbLfH766moqB+Vbi6hqeka2ts3sHbtv2JZq1DVS1HVNuCjSDkd09yDqs5jZCRHMHgJ\nuVyWTGYbXq8fjwccZzcVFUGOHn0TxzFRlE/gdteRy/0YKXvIZocxzRVUV7vwelW2bXuC/v55WFYJ\nljUdVT2KYbyIYVxJKNTMlCnX4fX6mTx5L15vG9XV5axbtw/bVlAUm/r6epYtu5wtW36Nbd/ChRfe\nVHQUTozEx26Ihc22rq6Wrq5DeDx1HDnyBJr2CbzeXtLpxzDNJH19gsHBPsrLl520oTqOzuDgS+j6\nDGprv0hJyX7S6Zns3PkG2WwLDQ0NRUXO5uYLAJuhIYtjx3qZOvVOcrlfYRjlRKO1pNMDVFQo1NQM\nEwqN73IoKLV2dr5Ge3sbM2Y4tLSYLFt2MatWLSoa7rORsi+UIPJqogt5440dpNNDCBFGCIHXW8bQ\n0H6i0Z+wdOlXqKhoeVuZj3NhmJ5/fhPbt3twnNXjuDB+fwXZ7BK2b989zon6MLQ/LllSx0svdVBe\nPuOkz850jh/WgV7nHYnzOCNOxSpvahpm1qw5qKp60nfyzP4M99//K7ZsUZByNpoWAPxksza2XYZl\n/Ri4AU37CD6fQSAwQCbzU9LpBJo2C027HCE2kcu9hJQLEGIKIFGUjwDPIGUE2IeiqGiahqYpBAI2\nH/3o5zh8eCuHDu2mpqaN/fuHiUQ0IIiilGLbNVjWMRSlFiE+is+3EykP4/fXkc0+Q1XVYv74j+8q\ntlYODe0H3iQS2ckTT6TweqtJJDqJRn9DNtuHYbTR3u7H4ylBUfpIJktQlKtwuSah6xvRtNmjI8mD\n2HYpAwP9+P2SQGAGUg7Q2FiNlDPp7PwP4nGNYPAvMIz7kfJ1dH0dQiwiHP4MhvEkUt6CbVt0dv4Y\nr7cFuAjDOITj9GLbl+Ny3YxtryWX28rQkIPb3UVNjaC21sP+/QPYtk4y2Ytta3R1JXG7uwkGb+LC\nC2vGZQzGRuKrV684yYAtWVJHKjWJ/v5NHDmiYBgLcbmmkclsR4g/oqwsxKxZ9Rw8+DOi0RSW1c+0\nadWjUuP5qaamuRhFKcXtPsaSJbdz6NBmNmzYiGXdytDQRi6+uL44d+LAgVcJBv8H/f1PYxi1xcFv\n8fhmUqkofv8b/MmffIJrrsl3ORw7ZjM83E8k0otladj2ALNmRfnBD/6BUCg07lzOdjq7tTVCZeVK\nFi9uLmZ98qPcTUKhGKnUx6isnDXu9091vc+1YWptjRCNypNms0Ced3KiXsjbUaT8oEXkhWv9yiud\nvP760+j6Mpqb5zJjxjRUVT1ji+eHoaPlVDjvSJzHW8KJhC/DMPjGN37Ic889i8vVcJKK4chIB6Wl\naQYHVxCNbqK8fB66fgzT7ERVmzFNF1I2IkQlpmni9aaw7ZewrBCOk0SIKSiKC8PwIsRVaJqC44Rw\nnH6krMHv/xK53H9i20fQtDqEyFBTM5lAYAoej5/q6lruuutjrF69gt/+9gX+8R93Eo9beDxTyWQu\nIJcrxbIy2PYgihJg8uQ5XHxxPeHwQqqqNmAYeTlwIdLE40epqLibxsbptLdv4LXXHiWdvpRk8nco\nSgsu1z0YxnQsK0Em86/YtgchJuM4Ko7jwXEEjmMihI1tK+i6pLq6lpGRToQoBUAIN7W1K4hG15NM\n7sbv1xAigtt9IyUlBdKhByEUNK2MVGoAXf8YQuhkMoeATyFEI5rmwXFuQIgjtLTUk8t10Nf3HebP\n/xyp1ENkMnfgdn8Sl0vBcRwGBr5NLtfH9OmXjrvflqXT3x/hW996tthlUTBgAKZp0Nb2E+LxpQwN\nbUKIZqRcRzZbgaomcLkUDh/ei22ngClks0c5fPh1pIwipZtEYgdu9+fQtGGmTQujaR5mzlxBV9cx\nPJ7Lsay2cRNHI5EIPt8KJk/O0dDQSySymZISF6GQoLb2Imprg9xyyyoAvvjFj/H5z3+P9vaL0bSL\n0TRJQ8MiwmGd73//yZM29LOdzi44JZrmoaVlJS0tx43qiy/+HLe78aS1JrreCxZUs2fPUWKxZefE\nMOUzJxq2LU4irxauS0EvpHC8ZxKyA3jyyXUfuIh8rBNQUrKIqVNfYe/ejaxbt46NG3Uuv7yau+9e\nzI03nvqa/yF0tLxfztvbdiSEEMuALwHzgRrgZinlk2f4zl2j35kBxIFngS/JPJ3+PD5gMAyD73zn\nMUZGriEY3E4m40VVm+jsHKGvby9z5niYMuV1YrESQqFGbHsbFRVLSKcfJhp9Ctu+EccZAG5FVSNY\nVg9C7EdVF1BePsTAQD+23YEQc3CcY8AnkPJXo6qVLhzHJpeLI+VNKMrvKCubgxB1CGGgaYlxkYMQ\ngq1b+7n55i/w7LP/xeBgEperCV1/Gq/3CqAf295FaWkzHR27mDUryje/+dds3ryX1tYe9u/v5ejR\nS5k500M4rKEoLmpq/opUqgfLWonjXIgQQYSIoesv4TiVQHq0HVUBHBxHwTRBVaeQF7TyFAdv6Xoz\nth2kr+9pcrlNRKNDKMocQqEAmUw7paUfH7MxGKNOXAzbTuE4k/H7g4CFlLMAG1038Xhc6Hqe0+Dx\nNNHebtLUFKOy8s/JZLyMjGwnnd6Lrg8hZYpUKkRr6y6WL58/mjlJ8PTT32Vo6BIc5wY0rYy6uiDp\ntMGOHQ8DglhsGcuX/08OHdrM4KAPx7EZGdmG3/+vlJbWFDMPjtNNLmei698nGr0Vj+c6hJCYZhbT\nVAmFDjJ9+keA4xojQHGiqW0btLe30tnZjqLsA4ZpaprBlVcuQ1XV4rWJxx8tbqLr1r1ORcWdxRbQ\nsdomg4PqhBv6u03ZjxW7msgpKUTuE0lej5XXhk8RCs0F4MEHH2FoqIKrr55+ymzRuzFM+XKLharK\nCQ1QQS/E47HGfXaqLpIPckRecAJKS+uK92LSpFVUVwvS6UEcZy/79/dy442n/o33q6NloqzV5MnZ\ns7/QafBOMhIBYBfwX8BvzvSPhRBLgJ8BfwP8HpgKPAD8CPjEO1j/PN5nFF66qqpmliw5nr4VwkUy\n2U95uco//MP/4KtffRxFUUb7+N3U1d2J37+BgYGH0PUoUEMg4KW83EaIOOm0H8eZiuMkcJwXMU0F\nKTVU1Y9lXYeU/wgsR4ghhJiBpgWBO7CsLXg8rzEwEKGlBa65Zlmxx74QIYbDJXz0o38xeqzr0XUP\n3d3/gW2vwutdhttdQV1diNLSJHfc8W1mzfpzKipWEo0+RCCwms7OEfr795HNduP1ruTYsU1IqQEX\nUF6e7wQYGOgkr6oZAXaOEi29wAtIWYXjBBHiN7jdl+E4NWiaH9P8JTt2fBfLmkwgcAtlZcNksz5i\nscvI5R4jnT5MMDiZQMCHpk3CcQ5iWV0I4UUI7+hGXgVEUZRyHEdimnnjAPnWQ5+vfDSivxKv1yCd\nfhUpl1JS0kws9hCOcznt7UdQ1X1ccUUzTz99P4ODN+HxXIbj9KNp1XR2RhkY6MXrVdG0aVx6ad7g\nzpp1JZFIhGg0SzI5F5erYlwHTyCwgnj8s2ja59C0mlF9DwXbPobbnWbKlIUcPnyUlpb84KnC2HpN\nc7Bto7ipa1oztt1IeXktnZ1ynOx2oYz21FPraW2N8PLL+3G7/5j6+i7q6ydx+PBWIpHIaHnBIBLp\nGMePgHc2ROpUZYexbc9jcSrJ64K8ttfbhGW1FQ1zNGpj24uLrbljcbYM05IldafVC5k0KcrSpad2\nosY6GH8IEfk7RcEJePPN9eOExAD8/kqGh8sZHKw95Tm8X90ep3Letm49bWx/1vG2HQkp5XPAcwDi\nrV2RhUCXlPIHo//fLYR4APjy2137PP4wMNbzPjF9C8fHiHs8JqZpoqouDh58DVVtRIh5TJ58OV7v\nE8A8amokudyvGByswuO5aLQD4Bbi8TRSrgX24TiNKMpWVPUiVPUmLOvfkfIyTHMqjrMdx0ljmgK/\nv4M77rhznJEYGyGOPdYDB9aiqitGN+/dXHttfrT2gQPrGBq6g+FhLxUVYFkaLlde6jmVcojHNzN5\nMti2Rn5gV/7FzUfPOlJWkZfkfhFYDSSBo+RHnB9BiAC2/QLbtv0f/P6r8ft9KMo8XK5rMM1JxGI7\nUNUeFGUVQoQwzQ5isYPE4w7BYIaamrUcO5bFcTSkPIiutwBhoHs0Ii7HslL4/aJIVpQSLCuvazA8\nvAnDWITL1Ux+LPoAudyv0fXJbN2q0tb2Ao7jp7R0PqY5gqZto6srhuO4sO04qnqY2toLxz0PdXV1\ndHa2oWkhslkTv/+4XHo6vQmXawFCzELTsgSD5QghgdkkEj309CTp60vT05Ng2rQg06cvoLv7R5SX\nX0RHR2vRwPp8+8hk2igvvwJFUYoCVC0t0xka2k88foSXX15BKLQCTVuDy3URHR19bNnyAyorP4bP\ntxKXq9B6+jT33beGL3/59uJz8k5mz5wq+i4r20BZ2VFisbcmeV2Q1x6rNzFWSr6n5wgtLePfwbNl\nmK67bvFp9UIuuyzKqlV3vqXf+qBqTIx1AsZKnReQdwAVQqGmU57D+9XRcirnLRisO6vrnAnvBUdi\nM/D/CiE+IqV8VghRTT4T8fR7sPZ5nGWczvMuvCSFDW7Bgmruv/9ZLGslHs+LmGYlitJELKaTzQqC\nwf3kct34fNfjdm8p/o7LtYBw+FcoylLc7losq5xkUsNxBJb1O1yuP8a2ezGMXyDEVdh2FYYRY+bM\npbz6ahkHDoxPo06Uto5EevF6rxzdvMNj/h6hpORWdu16lJ4ejUhkP4oyh3A4RFlZHalUCgBVtZDS\nxrISxGIpHEfHNI+SHy9+DflR478mXwGcA/wEuAKX63L8/hCWdTOquotkMoQQU1DVhZjmOkzzeqQs\nHdXL8CFEAEWpxuPZjWUdoqvrdUyzGsdpBB7HtlcDkxBiEMeJI6WBlDoej4vp04xaLNcAACAASURB\nVFuorPRQVlbPc88NIqUkkYigaSuRUmdk5GFyOQsYRogFqOpsEolDgBvTPIam/Q6//1pcrhlomkAI\nm2j0ewgxjGVZRTJqU9MiWlvXoWnTMYxOpMyLZFlWFsvqxOWagpRu6ut9VFSEcRydSGQEKbvJ5Vbi\n8dSjadUcOhTl8OGXuOaaAJdcYnHvvS8Cn8Ky2rj44lmjbaGVJ2lfxONrCIfvKN7fQnkkmz1AJvMR\nMplq/P7jz6vfX8fQUMtJ0eXbEX463bj0WEyyYkUXLteRM0pe56+ThmVFx4lwTVTmGXs8Z8swnUkv\n5MYb73xL5YgzReTAWY/Iz9ZvFZwAx3HGCYmNXUdV87olpzuH96Oj5XTO23uJc+5ISCk3CSE+CawR\nQnhH13wS+MK5Xvs8zj5O53kXMhLjN7hOFGU2dXW3jbLsN2HbaTyevUybtplUaiZe7wyCwaPEYh3A\nVDwencbGTxGLvUYsth/HeQ5FqcTrDZDJXImqzsG2j6Jpf47XO3s0OuvA6x2eMI16YtoawLLUkzZv\nKSWmCYODvyabnUNV1QLKytYzMhIgFguRTu/D7y8nl+ugpKSagYHXgX6EuBjbfpr8nI8DwOeBw8CD\nQCWwHViOolSjaS4MI4YQcXK55UAbUiooCth2BCFWYhjP43bfRklJI5nMfyFEEriBVOp1FOUehNiL\npn0cIdYgxA5McycQRIgbcLnm4HYnmDKljJ6eTcyZE+Xv/u4zdHXdz96927BtDZdLkE63ousqivJR\nXK46VHUzjvM6+QFmISyrFY9nOW73zOL9VVUVRfFjmnUnpdv9foV02o1hrGFoaAivt5Hq6iAQJpFI\noihDOI6bzs4eMpkdGMZsvN4GbHsT8BK6XovLZVJernLJJdP5+MevZcOGYUKhucVnaebM6eO6IAxj\nOyUldWzenEEIE03bzbRpQaZMmUJPzyESiQhu9wri8YGimmUh6h8rvT3Rs3w6A/VWxqVv2bKZe++9\n+y1JXsNOGhoW0dx8QdE5g/FlnhOP52waJo/HM6FeyNvBRPuCZel0dGwiEokUhcWeemryuyJenqsu\nlsWLa1m7trM4B2js+ReemTM5b++kPPZu8Fact/cK59yREELMAf4d+FfgBfIEzfvJ8yQ+e7rv/t3f\n/d1JrVp33HEHd9xxxzk51vN4axjreY/dLGzbhWX1c/31Crqus3VrP1dd9Zd0dr5GT89xln1d3Wya\nmv6UbPYXdHTEGB7eg98fJJF4HK/3KqqrL0FVVSorV+JypfjYxyppazvCli0SRTGw7V6gB48nP8vA\ntjO4XGBZbmB8GjU/OyKftn766Vf4xS9+Tk+PSW/vEOHwVOrra4vnJYQgmWwnl7sdy3qTw4d/gW1D\nKvV7VHU1jnMBweBU/P7N9PcncLlMYB2m6ZDL/RK4gjyZMk2eV3wJMA/4LXALHs8khMghRCe53CBS\nXoamdZHLQTZ7CMgiRALoxbavBNxoWj6ad5ytSBlAyjocpwtNiyDlJ4CfoqrXoqo1CLEdn28LlZUB\n3O4AZWUKc+dOJRgM8p//eQ+f//z3ePFFAyn7yOUOIIQHTWtC00xCoWuRUmdw8BtABbrejm1fjapm\n8fm8xU1R0wIEgxkOH04hZRfd3Z1EIm+QTlciZZimpr9GUQ4Qjb5AMqkjZYRAoBnT3EM8fjWa1oBp\n7kaIK8jlcsB0rr32ImbNaiyusWXLQ9xyS94wjcXY0pRhZHn11W0kkzcgxDZ8vnnI0QFePl8Ij+dV\nDCOLxyNwHDHaoXDccbRtgzfe6OQrX/kZuu5+ywZJ13Xuu2/NWxqXPpaAORYnZj6eemoyL79sj3Mi\nIC+m1d39vykvv6j4W2fbME1ECn2nOHFfKPBbvN6VRWGxl1+23zHx8myTOcc6Jek07N79ewxjGoZx\nkECg5aRn5kzO29stj71bFJy3PXseYe/eX477LJeLn9W1zoT3orTxVWCjlPL/G/3/vUKIzwOvCiH+\nSUrZf6ovfve73+XSSy891cfn8T6h4HkfO6azf/92MpnFeDwrsKwYpaURRka83HffGtJpQWWl96QW\nuAIymQCzZ7vwevOpcNu+cDTifBjTzBPjmpoO8Y//+E8891wrXV0HaGpaSDQaobs7TV4qW+J2CyZP\nLsdxskWm/4lGotBKV1f3aS68sJGDB9fT1TWF7u5yhoaOGwDHUUgk1uP1rkSIC3C5BKHQbaRSvyWV\negq/3+LSSy9G1w9SXX0n0Wgb3d1fI+84rAa+T36Ued/of58BpqEo5bhcGlIKpMzPArHtJ7HtKDAH\nRZE4jg8pA4Ab25Y4jgn0UVp6N7ncI0AARSlDiBuBx0ZbS/sR4lqEMPH753HhhRrV1UmOHj3G4KDC\nvfe+iKa5uO66xfzkJ1/hm998gGeeeQ3bNkmn3fj9Ep8vABiMjDyMqs5AyssR4hGEMMlmVQwjTTDo\nxzCOMXXqZMrKdtDRcRjH+TSplETK5fh8laRSPyWRcGhsXE5V1ZWkUgMI8QgjI4Nks2mkHEbKMOAi\n31qYxOtNAKFxz0XBCJ8uVbxnz28oK7uJ8vIZaNrm4rPl85WTzUrq64MMDj5GMrkLx4lhWX00NoZG\ns082ra1rSCQu5ZJLbjrJIN1zz614vd6T1oR8SWNoaDGBwKbi3wrrnjgu/a0a5VNFsslkD9dfH2Lu\nXIstWx46a4bpXEX1Y8+jvz9S5LeMFRbTNO0dEy/PJplzIqdk+fLb2LlzDfv2fYNw+DYCgXoaG8M0\nNs4hmex6S87bezkXBfLOWy5Xx9y543ksx47t4Ec/mn9O1x6L98KR8APmCX9zAAm8/zmZ83jbKHje\n3/zmAyQS03G5stj2HhobQ9TXz+Dw4a289lqUkZF2Jk2aS319qJj2LaCQJlyyZFrRWBS0BFpajkde\n11xTj8fj4frrl3D//c+h6yNUVk4nmQwDVdh2Drc7QVlZECkj2LYxoZF48MFHGBgIMnXqQV544WeM\njGRJpQZxua6lrGwOVVXdtLQ0oOspVHU5Hk9D8ViF8ODz3YzfP52Wlmf5znf+hL//+zWEQlcAV/D9\n779KItGA40xByquBYfKTSCvIjzevRFUFjmPh82lIOY1M5nfk+ROfBV7FcX5IvgzyOpDCcXKYZg63\nW8Xn85BMDqAoYcCDEBZCKOQFuoYQogbHySHEIJHIWnK5m/H5rsHlEmSzF/LSS75ixPYv//IXeL2P\n8fjjDv39GqqaNxzJ5FpgPuXls0gkHsMwgihKFEXpxzQdUqkEtbUO1123kE2b2shma4hEekgkNuPx\nfJLJkyWNjZ+jr+8Zurq+hOO4kdJFIHAEl0tl0qSvo+sdJJObkXIftt1MIKAxY8Zienp2I0TPSSnw\nlSvn09b2xISp4ni8jWXLbgcKJYDjXQdebzlHjvRy0UVLOXDAYvbshmJXCMCBA68Si81m9uyq4kZv\n2wb9/RFaW4fZuvX7zJ5dPaFxLdSk6+oi49YsrDt2XPqpMJEhX7Cgmjlzutiy5cRI9q7R0sNbM0xn\n+jfnskVzbET+rW89S4HfUnDgCu//OyVenk0y50ROicvlZcGCT9PQcBllZS9jmnEMw4VlvTPn7b3Q\ncziVE5pI9JzztcfinehIBIBmjjsBjUKIi4ColDIihPg2MEVK+enRz58CfiSE+EvgefL09e8CW6SU\nfe/6DM7jfYHH48E0w6xenX9zhRDj0pl+/wrS6Qex7QCdnaFxaV/ghCmTj7B9ew/Dw+U4joqi2FRU\nRLnssm6uvfaO4np/+7dX87Of7WF4uByfzyYefxW3O4FpDtPR4VBWluKVV9YTjy/kgguqx5HghoZy\n9PZGOXQojcdzNy5XHR7PelKptaRSG+nvz6LrDbjdSebOXU48niYe70dKATiUlfkIhxcwOPgrfv/7\nV9i8eQdwAZAlkXCQch5C9CDENTjOGvJdzp8A7gPCCNGOokxByl1ks+1I2YuUDcA0VNWDlKtwnE7g\np0AtsANFqSEY1LCsLFL243ItwLbbESICLENVm5DyMKo6CeintLSPbPaGIrkwX4qQlJfPYHCQYsT2\npS/dRi73AI8+2kc6vQWXazqKEiEcvg5F+b/svXd4XOd17vvbZfZ0TEEnUUgArGKRKFFsYpVEURQl\n27IT2bJjHyc3JzfJk9zkxInt5OZcJ8fHihPlOE5u4sROHDc1Sy4qlkRJpNh7EYtIggSI3jEYTN/9\nu38MMAJYrOboOPfheh4+JIaY2Xtm9v6+td71rveVCQTuR5b/HngTv38+siwIhVzuvXc5R4/+hEuX\nbGbOfJiKihhtbcNANfl8mmjUh23ncJxfYc6c4nRFPn8Kw9iLbbcRiUQJhysIBCxcV6amZilg0dW1\nC9f98DUg8Gf5/d//ELt3n5gGFd95Zz2uuxiPp+jxMumnMWlCVrwWJcrLK6ms/Dbx+G9Oaw20tZ0i\nFttAc3OxrXXldZtInMHnW3LV5jq1J93cvIrh4aenHRMgl+umsjLJ5s0PXfO+mWyNjI6unraR79nT\nTmVl0ZtG07RrbkLX25jeDcJwrQ0U+IWNaHq9XrZtW8+OHQMlfsu12ifvlnj5H6E+er2kpKJiIbp+\nnK9+9dMfCKpwZbybY16vnbJihc6TT779839R8V4QiduA1ykiCgL424nHvwv8OlADlGZPhBDflSQp\nBPwuRW7EOEU/5y+899O+Ef+741o39uQ8/GSVFg4vJRA4SD6/imx2Zgn2vbrHK4DBiQ1SQ4gcg4M9\nvPJKjtHRpwkEijD3XXet5Pz55xgZqcPr/QjPPPOX5HIfR1E24fdnKC+voLX1UYLBJLNm3TztXBOJ\nbgqFOVjWWoLBBlKpH+I4q/B678F1C0jSBWx7HFn+CZaVIh4PAydJpbopKjFaFAoedL2Xv/iLNJnM\nQjKZHKo6gusGgDVI0qSsyseQ5UMIcYiivXiGUOhpdF3FMJYDtyJJWYTIAa/huisnJiM2ATquuxPH\n+TFCrEaINMHgeXQ9gmGUI8ReXDcDbAJcFKUeIU7h8fhx3QSadl+JXDh1nHBqxeb1evnzP/8tVPUx\nXn75II4To78/NLEYJ9H1HyPLgxjGRXR9I7KcJ5tN8aMfvU4qtQghZCIRL4nELtLp41hWLY5jMDh4\nEUW5Da+3tvS5ezzg8ZRhWYtpbMwyd24jjjOXAwd+SDYbJpvtwXHuwOdruSYEvnv3iWtCxfv3f6/0\ns6p6p5mQFUdzT7Jly3088siXJhKRo5imB4/HZMYMiyVLFpWS2iuvW9Ms6mBcubmaZlGD4siRN3Bd\nBUmaj9d7HNPcjxDeUivuj//4z645Lrp9+wG+852ddHSsIBDI0dDQWULqJo+1ffsBHnhg4zu+D98t\nwjC5gRa5Tfvp6ektyXfX19exa1fH+9Z6KH4nBVpbO+jpyeA48jRC6lT303fzmv8R6qPXO9YH7fD5\nftpN12qnnDhx4gM578l4LzoSuylK9l3v/z97jcf+EfjHa/z6jfhPGte6safOYBc3EbXkodDdfYBL\nl9poaJgzDSZ87rnXSSbXceutxUXcsnQOHnyaXO7XsKwoQ0MJZLmPHTtO4/G8zooVMwmHX+Ls2UHC\n4Q14vTngGOFwFNcdJBaLUlZ2K52dAzQ315aIoP39PZhmNbLcSD6/H8dZiSzXY1mv4zg9CJHj5Mk8\nXq+XbPYY/f2dCHHnxKikwLLGGRn5U2T5kzQ1bSMQsDDNHzIyMgx4EGIMWf4YQhxEUfYiyz7AxrIK\nBAIFYrEa+vq8SFIDqlo+kXyEEWIE2IQsF1AUFZ/Pg893P657M8Hga5SVdeDxdJNKqajqYWR5FanU\nSzjOG0gSqKoGPEcotBEhNGRZxrIkCoVEiSQ2+R1NXRynj/09NrGhxLDtg0iSH037Q/z+asbG/g7D\nWIyuL8S291NffydjY49z7tx38PvX47oBXDeELG/AMLpR1cWAQU/PMBUVCi0tEYSop6MjSXd3nuZm\nk9bWXfT1nWNkZAeZTAFN+yRDQ7tZurSeOXPeGQReZNkXW2JXaoSMjV3k7rtnlDbEKxfZz3/+e9P8\nYa68bhXlrSmJyXPYvLm4YZtmE44TxO+fM0HGayYY7GHlygXkcl2lVtzUmLrZDw21UFZWlEecJGje\nfnvLhGhWN/v3t7F/f8873kTeDcIghCCfl+joaOXQoecwzXWo6t1Eoz5isTI6Ojro7n4RXdevyxF5\nJ2EYBgMDnVy4kCASua2k3zH5fhcs0Niy5d1PnPyixiv/d2k+XC9+ke2mDxo9mYwbXhs34j3H1Bt7\nUkBncgZ7shqeusCPjz/OX/3VJ6Zd7FdCjO3tB0vVoePoHDr0bWprHyQYLGo+DA6mqa52SCS+yX33\nbUHTipMak4vCq6+eRlHK6ezsZGhoL7ncKrze9WjaAIZRnCpJJA4BAYR4CknaANyFqoIQMoryGgMD\nuykvv5/x8dOk06/huuC6hzCMCmS5lmPH9hMIBPB6501Yg8/Dsl7Ade/B51uHx6PiujqG8W0UZRON\njYMMDvaiKH+MJEVQFJ1QaBb5fD+u61AkG2aIx0MAWFaeYNDllls2UVsb4Ctf+TBf/vI3efHFCvL5\n4+Ry3fj9DoGASjTaiNf76wSDB2hvP4llLQIGaWxsRIgRXn/98ZKiY3V1G4ZhlDaJqWN/zz67k299\nq5+LF+eRy92OxzOHXG4nsvxJfL5GVDWDEG3IsoKmuaTTyxGiD0n6OLJ8AseJAH5AQ1FcCgUDw7hA\nc/MdQC1DQ0+RSITYu/co7e1ZXPezeL0zsO0nCYXuJJvtZXAwzZwphouSJJHPw7PP7uTAgV5yOejv\nv4QkaVRUVLF373cxzbuJxRahaRL19WHKyw1mzDh2TVLc5HU31d3xetft1OeYpoeXX97PyMgqli6d\nlFAGTasnlztFd3crHR0vU1+fYN26OzEMY9rCP1VzwraPlI41aaX+s5/9K4HAffh8G3Ccs3i9N7Fj\nx+W3JX4W759uotENWJZOe/vBKQqeVyMMpmnyxhtnGBwM4zgfwecrvv9kUieXG6Gubjb5/FpeffXQ\n+0Iltm8/QDT6EPH4CXK5aKn14/PFGBtrI53+KZs3Xw1IX2+kfPKxd2sYBtffWH+ZXEz/MyuCTsaN\nROJGvOe48sZWlKKoi2Ekp+kzwOQY5nTN/mtBjFOrw2TyIKa5Dp+veWIhitPVdRkh0ly8KBgePkpF\nRWjaDH+ReHeZ8fGzRKN3lOBqvz/M+PgRhNgA5ClOVfwqQjQDWRxnD4nEZSBCJnMRy9qHpm1CiFux\nrJewrLlAFiEacZwYpinjODpwEp9vC17vjxFiD1CL42jY9gk8ngXEYnD//Q/wzW/+38iyDYzhOBbh\n8DzC4X4GBs4jxBi2rZaQD8N4HkUZ5NKlSk6ceJ3u7jaqq2cSCu2nru4+liypo7s7gs83p9QOWL36\no8yYUcWFCzpz5zYyOnpkgjV/J7JsMDj4MkNDGhs3foU1a2axbt3saRXvxo238cUvfo6+vrk4joXr\nHsV1jyFJ89C0UUKhKsbHXcbHC0AYj8dHoXAMn+9uvN4WLOsAjnMCWIjr6lRUzMDv99HWtpeenm5M\nE0ZH/53h4WW47t24bo6iammCZLIV0MlmXYaH93PzzQ00N9cjhM0bb7yBLK8mFFrNuXNPk8t9GscJ\ncvLkD4jFPodldTA09Bo1NTW0tSVQ1QSPPPK5a1ZwU90djx79Gaa5ljlzFiNJxs+9bjXN4sCBHiKR\nDciyzOrVv8rFi7s5dOgbmOYmVHUFPt84a9fewZ49XZw/P72K3LOng6GhRo4dO01PTz+yPEBZmQS8\nyfDw6xQKS4nHc0QinYTDFq5rXUX8XL26ji1b1uD1ekvvY9eudn7ykzMYRjmm+QYezybi8W2Ulxdt\nyzs62ksIgyRJ/OVf/jP9/XEGB88jy3fi9xdHez0eP6YpGBrqZtmyJezbt/d9KVD+PPfT+fPrqaqq\nK3021yOeAhw5MnQVzP92hmE/+tF2HntsH93dFpPCWg8/fDvbtm1435Lo/1Hxn1URdGrcSCRuxHuO\nK4k+5eWX6et7gblzl0xjacO1s/wrIcYrq8NUqhtV3TwxKmgwOrqLnp4f0dHxAPl8JbZdS1VVI5cv\npxgaepNVqxbS0rKaoaGn6Oxsp7q6yPe1rBRjY9sRYh2wB4gBSaAZMICfULQpv3PCXOssur4U05yJ\nopxEiHVI0lMIUYXrliNJNrKs4Tg+ZHk2jpNB07ZSXX2AeHwI21bo7R0kELiLZctC+HxB/H4fqlpO\nodCLro8wMnICVR1HksZw3adw3WoMI4TjHCUev5eqqo/R1fU9AoH/i56eWSSTvdx++4fYs+cf6O+/\njONIwHrmzFnIihVF3Y3y8ioqK/+NbHYpmcztFAoKfX1HGR/fgSTdTn3972DbLr292Wnz/ABf//pP\nkeX5CDGI64Yp6lW8iaLEkWVBJjOCx1ONZXWiqj4ikZswjN3AMEJIKMocystX4fGEkeU6IhEfra1f\np7t7I7J8C0LkkeXVOE4Yw4BQqB5FCVEo/AzHcYDbKBSyZDIG7e0aQ0Nv4vO9QSx2P7FYCxcuvF5C\nqkZGdpLP30sgUM/MmYspFBLMnp1m/vzZJJOXSryKqWEYBo888jjHj88ikbgVx7mZXO4Ux449hqJ0\nEAzGmDdv6bTr1rYNTpx4nLGxg3R0eFCUeiIRhcWLZyKETG3t75SSXF0/c02ug67r7N/fietG8flm\nE4+PMTY2Rm/vYWAZ0IIsb0GWQ4yO9gMJ9u59Al2/A59vJZcuPU4iIdi+/QKPPvoyv/u7a7lwYZSR\nkds5d64HXW/Ctv3o+kex7XrGx2Xy+WHq66vw+ZrJZtfy4ot7OHduiJdfFtTU/D4jI3+FECaFgq80\n2gsFDOMiLS13ksu9N37AJArw89xPi/f1IEKIkvnfVEjfsnQeffSfgSY2bfoEfr/nKpj/eoZhjzzy\nGNu3p3HdTxMMFguInp4xHn10P2fOPM4Xv/iWSucvUvPh/XApfhn5Gu8lbiQSN+J9xVSij67rPPro\n04yMWKUe9Ntl+VMhxklZ4MkFybIcKir82Haaixe/RiajYdu/hiTNQZIEhcIo3d0KwWAb3d2X6Os7\nSEVFgLIym4YGHcc5g67bdHT8O657G6p6B7b9JLAOGKBI9TlIUUiqFlk+RCp1EdvuRYileDwhYIBA\n4F6y2QJCrAc6cN06JElCltWJ1/0Zul7G0qUbmT9/No7j8PzzKeJxmDOnEUmSCIcl+vtfQojlWNYF\nhFiHLM/H40kjxL/i9Upo2h4sayX5vI/z57+LJM1n1qylqKpKJmPw6quP4fd/ipkzozQ2jqIoA7S1\nHWb37hdZvXoWW7Y08cgjX+Izn/kag4PVmGYd+fwr2PZNeDyVdHd3EAgUiW/z599Wgk2FEIyMrMJx\nvoeizMDjKU4z6LqM63pwXYAyFOUmvN7nJjQ+FDweD9FoVWkEd+bM++ntfRpdb+DixdcoFD5KMHgH\ntl3A40lRKFSRzfYhSXOwrBCmeRBJemiiNRLAcbpIJNoRIo5l9eDznebhh78NQFdXB9lsI/39pxgf\nPwF8EttOEo+X4fPF6enpZf7861dwL7ywi+3by3Hdpfh8MTRNwu9fhq4nEWIHLS3HqaxcWDIcsyyd\n1177B/r7k9TU/BGadghJ2kA6rXPgQA+yfIKGhg2lBFhRiiZjRU7OW1wHVU1iGDFCoSJKEI+vZnj4\nf+K69wMLcN3TqKqEbRfw+xV0PUd//0I8Hpve3n/AtpeTycwnGvVRKFj87d8+garOpq5ugHx+NeXl\nPXR1nUZRNuE4NpblAGWMjaUJBh2amubx13/9N5jmA/T3J/F40ng8NUhSCsvqxbZd8nmTmppqAoEa\nZFmexg94J+OkVyIKPT1dBINWaaoGmPZ6k69/LUi/vf0grrsNiHH5ch/z5s26Cubftm09Vwp9bd9+\ngOPHvbjutisMt8opFNZw/Pip9yWJ/k7e93vR4rgWX+PKf3+QfI33GjcSiRvxCwufz/eus/wrIcZJ\nWWCIo2ljRCIaFy/+Ffn8/TjOGTyelUiSiuPcgW3/G6OjLtnsVoLBjeTzw1iWyvj4ATQNNmxYSHv7\nXrq7W/B6q4Bl5PPHcJwZFCeRuygqUd6GJP0Y216BEEuQ5bMIIWPbGq4r4fEUHTYlaSFCvIIQt+C6\niyY2HQ2vdyGh0CPU1Y2RSh3C4zFpbk4wf/7CUnVbUVFBX9+bOE4aIVYCtdj2Lmz7Io6jY9tPMTTU\nSCCwlFDIN7Gg305f3yj19VXkcmcYHGwkGMzhugX6+gZYt24OW7asJZ2+zPr1/Wzbth4A2/ZTU7OU\nkZFXyefDaNqHSrbe+fwwbW1HsG2bUKief//379HXpwO/xvBwASFm4rptKMocVLUe2+7AthvxeBwM\n4yAVFSqOc57R0X/D59OxrEOUly8kFqtEURQqK+8il/sWHR0WiqIxNnYQTdPw+9UJc7UUqhpD102g\nB0XZgCzXUyh8DSGWYNtLkeUIFRUrGRmxOHKkleXLm+np6UCID6Mos5Ckc0hSDbo+Rnf3MA0NVdP8\nKK5VwT3++BEc53cIBOKlxyaFpDKZVbS3b8cwvs/+/UWvCY9nGMdZQG3tZwgE5hGJ9JNMXkZV60mn\n2ykURsnl3iAQKFaTS5Z4pik5Os5ZFKWFp576EuPjVSjKYTRtNpGIF01rRIgKDOM8htGN3z9CLBYj\nHq/i8uUx0ukKVPUiQnwEVa1FUYIkkzoej4FlGcycuZqzZx+nqupONK2O7u7Xcd2i+6uuG/j9QRKJ\nbioqMgwPv0lHx0zmzbsfTfsBUIUQTVhWmlhsKQCuO0R5eTW2fZpUqp3162t47rnX33aTvB5J0DQf\nZ+fOl7jzzq1XqXVORSavBelPbW12d/eWzMps22BoqIcvf/klduwYuOqc9u/vYWxM4PM1X2NdipNI\nxNm37/R1WwTvNol4r+TIayUsa9bU88orrSQSXrq709OmXOJx/T0RUz/osDIzHQAAIABJREFUuJFI\n3IhfaLzbLP9KiLGyEvr6nica3UZDw1JOnfoJhUKcok7DOTwedaIaCaFps5GkMKaZwu9/E9cdorm5\nmebmrZw6lebMmX2MjvZM6CzYSJKKooSRpFuRpASOcxnXzQOvTSAQs5FlC133Ar1IkoIQLrZtI8sV\nuG4/krQeSToDnJxwxBykqirK+vUbWbduNvv3d2MYGkKYnD69j6VL104oZsYoL1/E0NDzuG4z8BNs\ney2y/FFU9TCStBVFeQPXtZk1q5KOjjJUNYhpSoyOjtDXtwPb/j3C4ToURcKyqrh8WWVg4CSVleN8\n+cuvlBbY1tZ2FOVNTHMVknRsmq23olSh63VcvNjOyMhhksnb8XrT+HyL8fmK7RzTfBmPx0WWl+G6\nX8O252NZbSjKrcjy/dTU6Gja4/j9tzI6+hSjo5sYG5sN5Ckvz6OqXjyeNOFwM6paPqVt1YXrtuE4\nbahqPZJU/C5t+ziS9HG83hn4/aM0Nc1CCJOBgSQnTvRz5Mh2DENDVSVCIQMwEUIgy14sy8/YWIpI\nxC0d58oKTghBV5dJMPhWEuG6BmNjBxgfbyedvoBt38qsWfewZUsDsizz4ov/wPCwSWNjsbqNx1eT\nzT7G2FgKIe7DdfswzXn4fAr5fA8XLuzF51uNriv095/Ctgf5/vdfJJOpxe//NI7zDK5bTiIxk1xO\nEI02omlJPB4/zc0mgUAUIYoom+tuxrbbEaIRv7/4vjweP4ZRIJMZQ5J6SCYLZLNDRCJewuFGTDON\nafbiujqu66WsbJzKSpuurtXIchpZlikrqyeZvEw4vJFk8gdkMgrh8CKEKE75VFcniMePc/q0RDK5\n9m03yeuRBJcu/Rg7dvwTp07tYdmyjdfkH1wL0r+ytTmZHE61k4dfIxJZDDBNiVTXVWybqwy3Js/J\ndRUMQ/2FtAjeLTnyWujFVM7Lhg238vd//yVGRz9OWdlteDwyruty/vwxKiqe5JFHvvSuz3ES1f2g\n4kYicSP+w+LdiKpMTT5M8yFeeeUgu3Z1sH//HhxnOaGQi64X/991bWQ5jWm+iW1vwesVSJJNJCKX\n5tSXLPkYu3d/kXx+DrIcxOutR9fbkOVa4CJe7yZM82lM0wGS+Hz3I8sSmqZPVAR+HCeN4zjY9mEU\nJYKiqICOED48Hhdd7yQW86Moo5w50wHcRXl5cfENBnV27vxnduxIs3HjvQjhp77+FlKpQ5hmB5J0\nD4rSgs+nousJhCgmKLYdIZlMI8uT1ud+hoZexHFmoSgxgIlNVOD1hmhrO05//3JiseICWyRsnmFg\n4Aix2Cbg4LTF03HGgQu89toBNO0eFEWjrCyBprlIkoXXW4lpNmEYP8V1W3HdO4DnkeWNgEJPz1HK\nyy0eeODX2L37WwixAlnuRZIuEQyGUNU8w8NvEAisKCURk9dCWdkWstnXcJxXJ3gnRXKuZbXhOJVI\n0mlMU6G9fRTD2IOu55Hlciwrhqo24TjDpNP1qGoFcIFgsAlV9ZJIdLN4cXHSYmrFO33TeEtcd7JV\nVijcgm0rOM6DyHKMjo4wIyNvsnLlAhSlEtPUSSYzVFQU3WEtawRdL8NxzuO6SRxnH7HYIurq6jh3\n7jKGUY2izAPK8fsvIkkfwXH2ks97CIc3E412kk4fwnEukMsdoba2mmBwDcHgEXI5Ga+3GdMcAcK4\nroSqmhPy5SCEQaHwAqYZQJaXIsv7KBTyJJO9OI6FogwQCCxA0wo0NdVi26fp7z8F3Ew0WvwsisnQ\nU1jWKiKRhykUXsJxjmOaPfh8GT772bsQYiZ79jRfc5McHnanbZLXIwkqisamTb/NhQtfQdd7r4tM\nXgnpX9nanBzDndT58Pmase3Tpd+PxVoYGDD48pe/yaFDPXR3V6MoA0SjPuLxSCmBLt4vzlVk7/ca\n74YcORW9CIVW0dV1kJ6e7hLn5Q/+oOgXNG/eb1BZOUJ39w8wzSIxdcGCBuLx37gm5+daMTVh6enp\nfd/v893EjUTiRvxSxaTGwf33b2DbtvUYhsaOHUkymXEcJ4xpnsbnm41pvoDjVCBJS5FlC8exSKcv\n8L3v/R01NbV4PA7RaJCKihF03YtpqkjSq/h8K3DdoziOQIgIQjwBLELXO5Flm2g0REXFRgYGtiPL\ndxOLPYRpPkEweAfZ7H4cpwGPZy+6vhxZno/rQjJ5GMf5L5w7Z7B6ddF8yePxceedv82pU8/Q2vpV\nbDuHLC+gthYyGRNZXlJqN+i6BzCBJJa1h87OOnw+GyFOEArdgmkOYNsWkpRkbCwHWMTjMDq6DyHu\npFAIU1k5VOrR67pFoTCCbXfh8VThum3IcguOM45tP4Gqrsaykvh8G4lEMoyPn6Cn5wkc5wSOUxTG\nkmVQlG0Yxm4cpxq4BVWVkWWJVMrm2Wd/SE3NQyxcOBfbPsXGjQtKAkfJpIYs12BZbxIK3TRlo/AS\njW4jleohGt1HPn+RQkHFdc+hafchy40oygESib0YRgBJehCv9yRC6Hg8axDiaVzXxTRvQdOeQVXv\nwbLKUdVumpo2kkxeIh7fi2nO5POf/940WL6uTmZgoA2vt4GLFx8ln78fRbkN1/0+QtwGDNDZuQdF\nGaGv7yDZbDuWVUcymSce99Pd/RgjI70I8f9M6IBkkKTHSSRCpFLPYhgqrrscVS1HUQwKhTSmWYvP\nN5tCoYdCIURz83oqKiRCoR0kk4JgsIzGRhMhqhgY+D7j4waG0YmmHcXrLRAMBkqfXT5/ANu+BU3T\nsO3z2HY3QiRQlNuA+QjxQ7JZP6FQlHx+lKamMK2tWeLxPurrWzhzZheFQhzXvQnD2AdkUBSorna4\n994If/7nf4LX6+Xzn/8e0ejm0v14pYPn4cMnEUKwefOqaYjCleZ9imJRXi7zF39RTByutYFfawRz\nUu4cYqUx3Ml2x5WjubZtcPbsYfr6oqhqkELBxHGGKBQayWaHqK+vQlEUdH2Mqqox7rjj/bcI3i05\nchK9CIfrp7W+olGJfH6U7373DMnkS6xb9z+prLzpKmKqEIJ9+77/tlMbV7ZbwuGTwJ+97/f7TuNG\nInEjfmlDkiSCQcHSpYvo6MihaVvp7X2aVOosrrsaSTqA61qoapZ8/scEAndh2w+Qz1vE42UMDr7A\n/PnH2LhxORcuvEE2u558fphUqsDo6FexrAiq+n8iSTuR5TJUNT5hMJXGdS9i21kMQ0NRLMrKnsTv\nzzIy8gy2/Xv4/bcQCHiIRHwkEqfIZBrw+dRp9tqq6uWWWz6BYfyAT3+6jp07QwwOLmL37gv4fG+1\nG1w3jRDPAhvweE4Azfj9nyCV+h4jIyPo+hlgEdCBYcxGURwyGR/JZCvR6EZM8xIzZ3o5cOCHZDLL\nJiSvhzHNUWzbgxBP4fNtnPDOWI0kDVAodADHSCZfx3VXY9vbEeJBFKUN245j2z14PC6OkwOagHlI\n0giGkaKo5T+CEH5mzxZYVtEEq2jetgG/fwBdX45l7SGZtIhGlyLL8oS2RieK8jzl5b9JXd3tDAw8\nyehoNRDGcZ5CltfhOH1IEsjyJlx3HCH+Btc9BRRwnH+agMolZPlrBIO1BIMmtj3I+vU1nD4tsWdP\nM9Ho5mmwfFGK4Xn6++NkMh4cpw4hhnAcHUlKoGmvkc+vwu+/d0Lu+xx9fZdIpc4zOgqplAYsQFEq\ncF0HVfUQCn2CQuFb6HojjnMOUJHlMRzHxTQBBEIsRlFexDBmMin4W16+hmTyfwC3MDSUQdfXUll5\nN2VlSVT1X3DdS+i6wLbb0LS5E8lmK4oyn1BoJbr+T2javUjSeRwniiw34zj3EAweRoiLZLMD1Ncv\nxbYTtLQ0c/jwcfL5NEJsRVXnoKrLsKwEQvyUBx5Q+MIXHr5KAhymS4f7fBuu8G55GlW1rmo9TP6e\n67r09b3Ao48+fV3OwLVGMJubV9HV9Q2giaamrQghsG0V2x67ajS3tXUX7e0V2PZ85sxpQNcfJ5F4\nlWy2kkxGJ53W8HpNysqSbN5czubNn7nqHN7LmvRuxKwm0YvW1l3T1FMB/P5yRkdj5POzSsTSyWNM\nPd47mdq4njDZBxU3Eokb8Usda9bUk81WMTx8kFxuJXV1v0Im8zfATQiRQ1X3EwoZ2PZdpUU3lRoi\nGHSYO3cJ0WgzqdSPWLjwoyQSwxOV1QDpdAvB4FoWLFjL2JiXoaHzWFYIXd+Jaa6gquq/EQ53k0xe\nxOdLUVVlcccdDWzf3kg4/ADAlN5vBMuKUCgYdHenaG6+0lr9HMuXVxGL7cW2b0fTXsI0R/F4yrHt\nArI8im3fiaa1EA7Po1B4CUk6TSikMjT0vxBiJrL8APA6slwFtJDNZnFdBVUdIBzuRAiFTGYZo6Mn\nsO278XieRAiLQGATlrUYRXkdy9qLbdciSevw++twnEs4zkeB+cBlJKkOCOA4B4AL2PZpVPW/4rpH\ncN1WbHs2kjSL4np2ltFRDUUZxuc7iSRtmKLZUYlpDhIKbUHXT5PLfQPX7SeXa0eWtxKN/lcikTyW\ndY583kGWHVz326jqeqB2ooWkIkkKrhvA612B4+zF4/kQXu9vIkSOUCjDzJlZZPkFPve5hXz0o/dM\nqKSunbaYCiEmbK0/ies+xsWLp7Cs5chyGFkWCCEjxElsexU+3wIMI4emmbiuieOcRdcv0t4eRZZn\nTHw2NrJs4vU6BAJlGEYlHs89SNJRhDiH6y5Hln1IkoYQIXRdxetdQzj8Irbdh+NoKIrJihWCBQu6\neOWVOdNM7xoabqezs5Zk8gKp1Ndw3a0oSh2aBl5vmGBQR1HihEKNpNNhCoV9mOazaJpLS0sVjY0r\nmDGjnUcf/XWee+51vvWtZykU1jF7dh3J5CHS6UO4rgchUtTVGSxevKC0yV+5SRZbCitL36vrutO8\nW8Lhlxgfby85fU7dJA0jydy5SxgZsa4rqHS9Ecw//uNisnD48BPk8x7gJLNnryxJp0/G2bNHcN3f\nxuOxURQfdXUfI5v9KoYRwrJqSKVkIhGJcDiGEOn3uxSV4p2KWU1NzKaSSCdjkrsRDAbo6hovEUun\nxjud2vh57ZYPIm4kEjfilzomqxZYRiLRS3f3AVTVRtMMGhubiMf7uXQphcezrfQcy0oRDOo0N9+E\noij4fIfYsGGEXbsuk8/3cPlyN6ragt8/j/HxDPH4OgqFZ0ilJLzejwDlFAq7sO0RVNWkurqKaHQm\n58+34fHMvapikGUL8JFKpQkE7FJ17vNtQFVBlhewd2+IWKyfO+/so69P5fTplzGMKkKhIEKo2LaE\nx6PjugFqajZRXh6hv/9ZNK0FIZbh8VjI8sNY1m5M8zEcxwRGSKdrWLiwkt7ei+TzMqa5Ck1rIRb7\nPYaHv0g+fwbXLVAojKKqSTyeXyUUqsPjOcbw8CiStAgAIULIcgOBwBxseyGy/F0MoxO/fym6vhvX\ntZGkWCl5kiSBbWvoeh7bHqW6+i22fCQyC8eBeNxgfDxOKnUIVZ1JOPwJvN7ZxOMmhiEIBLppbJQZ\nH7+Jvr5vIcsfQZZBkizAxXUtZFnH7w+QTs9GVd8iSyqKRNFhtemq6q9QSHHgwHe4fLkXxwmiKDma\nmmYSCulEIvMnznkMkJHlALZ9BNOch2VdRgiDfP5ldP1ugsEvEYnsZmTkJSxLRYh6PJ5ThMMLCQRC\nE2eiTaAtNopyBqgFWiYmXtoRIowkjROJLObuuzeWSId33dXIvn3d00zvAGy7lkTih0jSCioq6pg1\nK09Pz2na2i6gqge4+eYmOjvrCQSaqapqnmjLgGWdYvPmor/MpE7D+vXL+MIXnmBsbBmKkkKW5xOJ\nLMXnsygrG2DVqoUcPvwEDz741v22Zk0927efI5EYZu/eF3Hdh7CsbwNZFCVIPJ7jwoUxmptXUSgE\nqaw8yP79CQKB9RPXkSiJpE3efz9PUOmtNubVSpT33Wfw8sv76ery0dp6jp6ezDS/jvFxE1kOEImY\nAIyNHcQ0V+D13oHfH8NxOlm8eBa6nmTHjr0sW7abBx/cfM3zeDfxTsWsJhMz13WnkUgnY3JsuL6+\nkdbW4sj5lQnDO1HZfLt2ywcRNxKJG/FLHdOrlh5qazUcJ8/s2dW0tNyGEDZDQ/9APn8G25aRJIdY\nLMnq1WtRFGUi6w9w990reeONbhobH2Zw8Cj9/TKKUjshD5xi5syPkU4/CszFMJ7BdVdQXb2xpBI4\nNHSJvr59NDTMKlVsk39PsuEhTCbzBpK0vlSdFQoJmpqixGKzSCYFHk8fTz31pQkHyNmUlTXx6qvd\neDy3MTR0DsMYIhCowrZBUbpQ1fDE2OOL2LaJ4wyiKJ9Bkmbjuj9FkjQMo4XBwWMI0Ylp3kw63YVh\nvAB8Gll+DUm6DUlai+N8E1VdhKJ4SaUkIIcQY0AQ6MBxzlAoqEhSAVX14RYFJBAiiCSdQ4g40IIQ\nLopShxBv4PXGME1faQF0XQPDGMO2dzA6uhXLGkZR1hMMduG6s/F6M8TjVciyTDar09V1HFXdiqIs\nIxBw0fV2XFfguhlUtRVNm4VpDqNpD+DzJSkUOvB4dEIhh6amRpqatnL48BNs3apz7twAw8MHOH/+\nuzjOp/D7f5Ng0I8QgkuXjpPL7SYSkfH712JZBrLcQC6XQohewIsQtRT9BO8lk6nG7x9gxowyxsc1\nPB4PrrsCTds+oS8yd+IKNXCcBIqSIRpdj233YRgHKfL8nkOWNxMMLpv4/ATJ5CWqqw9z992/wo4d\nA9dc/MvLqxgY+AFjY4PYdhWNjV7Wrq0nm22goqKFvr4DpWtPkqTSNQZF/YuurnN87nPf5sCBc4yM\n1OH3O7juAEIojI3lqKgosGLFajweD/n8dNh8w4Zb+bu/++9cvryJ8fEabPsoQqxCkmbi96eIxxvp\n6OhiePhpbrrJwx/90cc4cuT/JZE4g2kWxxavtA2/HjT/87QYgFLPf8GCP2N8/Gmy2bm0t5cxNFQk\nxDpOBq83RTxeVMIcHj6C6/4OqhovJbtQlCLP59fw2GPf+IUkEu9GzKqIXlwukUinfgaTnI+mpnn0\n9f0p4+Pz3pPK5tu1Wz6IuJFI3Ihf+rhyquP552vYscOZWKhUKiqqUdUlAOTzA/j9F0oeE5KUQ4iD\nfPzjZ+jqWk0gUCCTSQBlAKiqTCp1knS6G8NwcJwzyPJ6YrEmKivfqoADgblYVhWyLDEw8DqFQjlC\nyEiSSzhci6ruo1CoBnL4fM1XVWYwldG9gT/5k4dKC5Ftn0OWF3DrrVGamxeWxLxefvlNentHkWUv\nkchDJJP/ghCzkKQCslzUKfB4fowkNWOaMpnMOIrimxin3IiiDGLbn0KWZ+P16ljWTCQpSzZ7BMMY\nmVCwvIQQOxGiHJBR1dkIcZBC4SxCtJFOn0eSZFT1QVT1BLa9b+LnPIpyhurq36Onx8Z1XYQw6ej4\nDrCUuXP/llTqCF1de7DtZhxnkIYGg/LyqhKbPp+/gG3PR9NSSJKLz9eA319LLjdIKvU8ltWLEFuR\nZRtVldG0arxeg5YWhTvuWIyqqhOEu4vcffcXuXAhSCbzPJb1SRRlGZZlUyiMU14eQdNuZWyslnx+\nFj5fy4R3ymEgDCygqHB6Ekk6iSwvRpYLmOY+hofX4PVuQggdyxKY5laEeGKCuxHAstrwem1qahbh\nuicxzRWl6tw0tyFJP0JRXqVQSPHyyy9SXV2FppXzyisHUdXCtMV/KiehsvJu4vEz3HnnYlKpy/h8\ne/D5DpJMKtTX19HR0Y7P1zztGrNtg507v0Fl5Sb6+gSuux6fbz+23YTXm6aurqj1USgk6OjoZ968\nWdNgc8Mw+MpXvsX584vQ9Vno+uvAb6Cqc5BlF8eRGB/PUFnZQjbr0t//A/x+PwsWVOPzFe+/a3ll\nXAuafzsthgULqqb1/N9ydz1EMpmltfU5mpsL2HYaWa6dqMpNFKV4z7puAa/3LdEqv7+cri7zF7LZ\nFuX+39mY+yR6EY/LDA+3TTF7e+t7y2Q6+IM/uAdN63vPKps/r93yQcSNROJG/KcKSZKuK2LlukES\niR9QUfEh/P65yLJBR8d30LRP0d9/jMbGe5FlGcfpolDoRdPepFA4ieOsBO5Aln+CYdjI8gJMM43r\nuqVNz3F0VLXAG28cRZJ6ga34/XPw+32MjydxHJV5854mn5+BYZy9ZmU2lTg1dSF69tmd7NwZIhab\nNe29qqo9IYaVRpJmAlH8/rcwYscZZcaMO2hu7qe//wSmORO/30GSevB678QwjgFrsaxWbHs/jtMD\n/DOwFWiY+HMR+DCSVI6q/hjL2onrqsBvIcvnEeIoQiSxLBdJWkokshJVdVCUXlauXI+qDjI+fppM\n5kV0fZhAYDk1NbcgyzLl5esZHb1MRcVSEokLgDRtJC+T6cXvvw+PZzeqKrDt8+TzJ3CclWja/8Bx\nfoyqfpd8PoNpxtE0hQULylm16tZSErF//1N0d4eIxz+BJD2FaaYQ4g4kCRRFxrJMEok0kYgHRfFh\nGBuJRE4AyzCMp3EcmaJUehKoQ9Nq8XprsKzXse216HqEeFwilTqMrp/GcYI4zgOEw3PRNAG0IUk/\nIJnM4vN9BsPYj2k+i6J4KSvTWb58MaOjCtlsOVu2PFCqNnfsaGd0dCexWCsVFfOB6XbmRZShOMIY\ni7WQTArWr+/A4+lj166iS2c2u5Y5c5bQ0lJsIZw48STQzJIla3n99ccnpnJ6SCb7Mc0ZJBKjyPI5\nUqluurtTtLW5bNkiYxgGUEQAnnlmGEn6DJFIBboex7arkCQJTfPhuipDQ30TCXYcKCpXvhcDrLfT\nYjh58kkWLXoLPZgqt130RfkBDz98O48++hKFQnF0FopVuesWkKQE1dWRK45q8V7jvSpZTqIXP/vZ\nbr72tW8wPr6RQKCepqYoDQ3NnDr1JKnUaVx3MYGAYM2aejZvXvWu3VevXBM/6LiRSNyI/3RxPREr\nXW8gFruXfL6fgYFDZDKtOM5S6uoayOUuk0ymqaiIUVNzD7ncN0mn/wkh/gtFO/GduO4ZbNuLLI+S\nTns4d66bmpoI0aiPzs7v4rqzkaQ4ZWXrsKxWdP0JdN1EUS4jy1lCoWZMc5zGxiBz5jRcpernuu41\n5Ye3bFnDmTNX91zjcZnKSh+53DFM00uRgDgp7pTB5+vn5pubmDevmc7OdgqFkyjKOXTdV5pssO3j\nCHECSdqALKdw3SUUN85+ip4jrwH3AGEUpQbH6UaIZShKCJ/vVmz7xyiKgmG8iSQ1IESacFgwY0aB\n+fMXk8l4eOSRT3Lu3BDPPpsmELhlyljrGJo2RixWhhBzSSROI0ku6XQPjqOSSl3C51tMbe1G6up2\n09r6FUzz06hqmEDAQtdNgsE/xO+/iCw3UVlZj2UJDh++wKpVC2lvP0AyuQBNGyefH0KWfwVJ+iaS\nZCCED9cVyLKGaWax7SR+fzW6bjNjxgN0df0rhtEOLAc2UdTK8OK6dkl103FuQpKewHG2Eo1uRYh/\nnBihHCSd7qSpKcqiRVUMD8/nwoVRCoUCweBWAgGBz5dj5sw0spwglVrMggWVFMW33iLiZjIV2PZf\n09z8KZYsWUtPTw9e7/ppFvCTEY22cPjwQb761U9z//0b0HWdV189xL59e8nlihWspnWxadMXUBSl\n1JOPx1eTy/0Qw7iF3t7dhMP3oCjrEeIS4XCB8XEff/M3RQRgeHgludxFNK0cIQQezyyEGMB1wbJi\neDwquu5SKCQIhfqYMaPY5pvcxIaHXWKxOe8Imv955MBIpJn9+00WL752lS/LMqbpYevWdZw508fx\n40cYGzuAxzOIaR7C620mHFYpL4+WnlMoXKKxUXtPaMT7tfmedNm97771E8nIafJ5OHCg6CWzdu1D\neDxTPUWuP+ny844xdU3MZm/oSNyIG/G2cS0Rq4ce+gonTryEaa5DVTejqgah0EcYHr5ANttLNjtG\nKmUQiXhpaPgUZ8/+NyyrA8v6KUKsRFWrUJROJCmG60bIZLJomsbAwNP4fItR1UtEIncQi5mMj4dw\nHIds9hiadg+RSAudneMsWuTQ2ppgZCTD6tU3AU5p48jlssyaNcSf/dnXMYwwluXD57NZs6ae3//9\nD7F794lp0OZnP1vDiRN5Xn31EslkmEymD8cZRAgLn2+MhQttWloaJqowPy0tn6BQ2E57u4kkDeG6\n5yf4DZuQpEYkKU+xpeNQhPQfAyqAXqCAZZ1DliP4fDdRURGd2Pg+RlnZefr6foTjrELTItx66zwa\nGuZMq6Y8HhNZ7se2T+G6agmRaWi4me7uDqLRW+np+WNGRv4PvN5P4fHICPGvWFYZyeQ5Nm/+FRzH\nJJ2eQzo9RD5/AiHmUVFRTzTaQm/v04yOjiGES3f3RXp7D5JOv0lFxYcJBmMMD/fi9W5EUbwUXVnT\nE4mEhKoKNM2HplVgmufo6jpIPl+Nqt6KbZcBVcBDSNIeFOUShvEkxURrJ0Kswbbr8Xi8QJRY7OME\nAj50/SK33KIhRCeGsZ558+ro7Pxf+Hx96LpDoaDR2pqitfVN5s79LM3NdVeNU8ZiYBhHgTZ27/7T\nCR2IM1chWXD1GKDP55vmcbN9+wF27LhEX98FFMUllRrB43FQFC/19b9KZ+e/4DjVSFIBIU4TiyVZ\ns6aoujoyonDy5JPcdNPdE0TXyRaFQNMWYtu92HYXiuIDLtPUtISmpoXY9ilM02T79gMkkyadnU9w\n+HCW6upK5s6t4K67mq4Jzb8dObCIWlnTEMHJKCZi++noOMEXvwiKIli6NIGuh4hEZvLGGy8QCj1I\ndfXUhLYNRfkZn/zkHVedx/VGOK/08fhF2Hx7vV4eeGAjDzwAzzyznc7O+0kk4uzYcaEkid3cPOs9\nW4dPXROPHz/O44/f0JG4ETfiHUcRetVwHIna2gdLevttbX6y2fM4Tj2yfDuOM44sNzE2lqKn5zEc\nZxFlZZUUCp9Clrtx3W247kWEOIvrxnGcDJnMWSzrDK47D9Mco6KiOuEIAAAgAElEQVQCotFqcrm9\nQACv9/NI0gxUNUgyeYnBwRFs+wXOnr2J7u4EpnkMn28TZWVbiEa7SCRO8vjjAsdJUFMzA49HcO5c\nPydOdPCFLzxc8suYXMjuu89g2bLdPPbYLly3m1TqZTStDOjl0iWTS5f2EY1KGEYPt912O3Pnfp49\ne/6FS5cO4zgZXLcdWd420eN2gMXAt4HbgD8E/hVZdpEkD0V7cx+qOk4q1YPrykCGWGwuS5bcwfDw\nDykrO055+SAHDpy6qppynK8TDEqsWbOg1HpobW1lYOCfSKercJyP4vWqZLP/Qi53ZmIqRiGZnMme\nPcdwHC+VlU1UVkJr60FqapaVVCXr6j7MhQv/nbGxjcjyajKZLhRlkEQiQCbTAdTi96dQFBvbPook\n3Qw4KEoAVVWAAh6PRU3NJWT5PlKpwyjKHBxnFFluQ5JakOW1SFI/qjqCZRlAAse5j/Fxk3R6HE1z\n8fuLm6KmRejuHgKKY31CmBOcmZkIEQMUZNkim+0vfZdTWxdvhZ9lyz7B+PhyenufZNGiJdfd3K7H\nNSga5a3C621BVYtTOI7TRUfHEZqaViDLXoSIEIuto7m5ZoLkFy0lKm8hADKRiId0ug2PZ86EGmwX\nmtaColQSDutEIqPMm1d0WV2/vmZapb5smTRBKG0jGj3E5s2rrllVvxMthoYGD6nU5Wmb92QiNjY2\nn/nzf4dodPYE8tFOZeVBnnjiT/ja137E8eOtjI29iWUVR22rqlRuuy3Cffetv26LYsOGW9m16/g1\nWxdT0ZMrz/md2HxPKnVO5aJ8/es7MIzP4/fH8XiKn9vly8mSi/H7tQ7/oEmXNxKJG/H/i5AkieHh\nHF5v05QbthPb3oqqxpGkjej6P2PbZVhWD46zESFeo1C4jKIsR4ijKMo9QD1C/BBFqUSI8wixAo8n\njGU1oyjHsawYFy++iKquwLYPIctzECIHFPu4HR1n8Pm2UFFRYHDwJ2jaPdi2jc/3JrFYlqNH8whx\nH0LMIJ+3iMV8nD//JPv3v8L3v3+UWKyCxkaNhx++nW3bNpRg0Qcf3EwqleLDH/5zzp2rxLLWYFnF\nDSuVSqEoXVy+fIq5c2exbt1voShPcfZsM66bQIhxFCWKbZvAISRpC7I8G1kWKMoKhEhg231AL+Pj\nA8jyXXi9cydGZx3GxsYZGnoKj2eUsjKN/v5LRKP3s2TJWsDhwoXX6enpIZsdp6fnEo5jsHr1Ig4e\n/CEDA7NwnI9TKDwJSAwNPYcQEAr9NsFgC5nM05hmNadOOcTjfTQ02JjmOKpqUl7+Vo87mTyGx/PQ\n/8fee0fHdd533p/b5k4BBoNeSHQQJMUmipTYixopW8VxLFvOSnFJNoqdvPu+2d3YKZuj1xt7I8dx\nmnxsp21cJNmSaMtWFyWxiCRIsYi9E0QbdGAG0+f25/1jgCEhSpa8cfa82sPfOTgHBzhz7507c5/n\nV76F+fPX4LoGvb3bcd0ImrZqerRxkmTyJ7juZoT4CUIEgGVYlosQCUzzDPPnnyWXU3GcJrLZCyjK\n7SQS3yeX+wc8bwOum8Dz5iHLp4AMBcnmPGDguoenqbSnAJvKSj+WJZBlFU2TmJzswnU30dCwBriy\neVy6tJNcrpHLlwev0RKYof9JkkQk0kE0apFIFLAG79yw3g1rIISYVS03Nkbp7b1MINBBXd1Wenq+\nx+ioRl3dTdNOuv5rAMAwuwOwePF6Dhx4Cdu+G7//JnK5v8M0b8TzqoAkra1lTEyco6HhCELUvGul\nPqMz8Yuq6vfDVTz00AbOnj0wa9zX3d1FPL6AigofHR2NxfPNdAb27DnGn/zJg9Pt/QFMU6DrEuvX\nz2HLlsLn8m4jiu3bz/LYY19hwYKHixL3M2OGEyeeJpXyGBjou8ZQq729EVVV35WVYpomL764myef\n3MfAgA1oxee6gA9qIRKpnHXfAoEKsllBT88QdXW/vHX4ddbG9bge/8YQQlBTU834eIJAoIDcliQL\nSYoDlQihUFJyN5HIINHoK3jeJ5HlLKrqEQ6HSCR80xtHCM/7GEJ8D1lupyBdPYzPl6aubj7J5Aj5\nfAJdn8sM2AzAcfIoyklc93ZMs5S5c+tIJI7S0fFRZFkmmx1mz54/xTQfRpbDQBrTTJBO72NqKoPr\n/imJRAlz57YSjcb5q7/ax6lTP+JP/qSgOmiaJt/4xvfp6YkwNVWO65ZRsPYGvz8E3MSZM88Si5nU\n19/A3Lm3UFHxNLmcjuf1oKourpvBcY4jy5tRFBUhMth2/3TC9DkgDpQihIVpgqZliEQCpNNvYFnL\niUQ05s0rYWDgTWx7Kfv2HUOIMxjGOvz+zdTWWhjGU5w/P8Xk5BMkErWYZguGoSBEJ4piY9vNyPIK\nFKUDRQkQDn+KfP4tcrljmGY/o6NPc/PN61CU8KxFMR6/SEXFA9O/78d111NenmJs7E1su3J6RPBx\nJGkdkrQJIX6AJD2H5+k4zijz5hk8++x3+bM/e57JySzDw8M4zhhg4fffjuMMYppdwBI87yYkaRWq\n+k943ihC7AfW4Hk6tq0QCLRh2yZjY28zZ06BCTBzfTNmYDM4EMPoJ5E4SW9vGQVX0dn0v9bWcHED\nqKtrYXLyCfbtW46qthY3rMpKk4aGI2zZ8qlrKuoDB47S2vpFSksdOjrWMj7+DNmswO/voLX1c0xM\n/Ihcbh+KcobS0htpaamYNTYRQhQ7AInEZebP38zExBCDg12Mj58ANiJJY/j9ZwmHA+TzgmRyikcf\n/UP+x//4+Sw57avj/Sr199JimKHH3n33p7j7bmZRLHt7j7Jgwe/R0dF4Df7oakbUe7Epnn9+17sm\nPrHYOJOTv0Es5qeq6orvR3l5B6OjJnv2PEYodC+BQMu7dg/e2SkyTZNHH32S7dtTeN5nCIUK54tG\n43zzm13I8h4CgUXvOrrx+yvo74/S1PTBrMPfq8NSUxN439f+KuN6InE9/o8ISZKYP7+KTGaAXE6g\n6+X4fPOw7QM4joWiNBEKlVBRsZFY7Bx+v05V1R2Mjz8zLU5kFavIQqUYpLz8PmCCcHgF8fgoVVXr\nyWafQghz2uyr4PGh6zY+n4ltx1DVDlx3fJoO6QNsxsd3E42+QjbrQ1WXoGkqqqqTzR4kl5NQ1btR\n1Q5M8zwTEzvJZIawLIcnnhjCcf6RL3/5czz22HO88orD+PgwQjyMqs5BCA9JymIY25CkFcjyRmz7\n50jSCS5dyqIol1m48CZkuZxgcD6joyl6enYixASOcxYhdgBlCPEpIDX9swQhXqEAQGwnlXoTx1mB\nqpagqmdpa7ud3l4ffn8ZFy+exjBaCQR0TPPZaWDeCLncy8TjBp73J+h6kJKSMKYZwDAm8DwJ1+0g\nmbw83S2QKbBRNtHUtAq//yfU1NyCEE1FimM+H5vVoYjHLxKJ/Bq53MvYdgr4KJLUiRCLmTE99Pk+\nQzgcQFEcFGWQ2tpnKCsrIxgUdHY2I8QKjh7diyR9FFVtJJF4GtddhevaCNGOEM9h25XAM9P3opAI\neN4rBAL3AI34fM0kkweYmNhGPp9CUZJMTGxDVTejaZuBPLW1t5FOP0UqFaa1VZrGs5iMjLxIMvkm\nAwNB3nzTRzgsI8s9bNjw/xAMdtHTsw/XDdLTM8X8+Tm+8pVHgdkVdQHYL+jtjTA+foa1axddRZM8\ngOtqlJRc5JFHtmJZC9m7N0x5eQuOY3LmzA5On95HIpEnlxsnGHQIh/+SUOge2tvXYNvbyWY3I0Q9\nul7C6tW309HRhKZpTE1dYteuI+8rgvSLpJ2vBgfu3r2HS5eijI1li/TY7dv3s3XrWrZsWUPBb6If\nywoRjSaRJIrdgKuf/3ee753nfS+AZzQaJRx+iIGBU9eoS8ZiYxhGKyUlU0hSZfG4M92Dkyf38vDD\nsztF27fv5+23dTzvnlljrGCwkkxmBdHoXjQtg+McQtNaKCvTiyZjkiSRy0VZt67xPe/rTPwiEKht\n//h9X/+rjOuJxPX4PyY2bmzFMPzEYmkGBgYRYpxQ6H5k+RiZzA/IZm26u0uw7XHKy+9E01xWrbqD\nEydOYVkuhnEQWW5B13P4/WV4nkl5uR9db0XXv82lS+NI0iJc9ykc5210PY8k7SYUqseyHJLJFLI8\nis9nkM/HCIcdBgaeJpVSEOL3gO8jSSXYtoPr5vC8cUChoIa4Hct6joGBu5Hlpeh6Gfm8xiuvHKW3\n95tEIr9BOv1jXLcCWZ5DQYxIwbYPIcvrKfhhZPC8Rdxxx60ATE5uIh5/mr6+l8nlJKqrb2Vo6FVy\nuccRYi0wh8ISEEaS6pGkYRRlI4rSgmXtxbKex/MGKS39LHV1fkKhelS1IFg1MHCadNpEiMV43ivY\n9mJM8wRC3Iwk/Rqe9y8IMQ9F8ZHL7cJ1L2LbAaAaOItt1xMINCDLEq5rIcQko6Onuffexdx++yBv\nvtlLX9+LDA0tQ1WbcRyLnp4xAoE8imIiSWewrFVo2mVSqa8jhAUEgHEKHi0h/H6LsjI/5eWdRKMO\nQohiS72jYy379/8ZQtxFLteF667Acf6VgqbEOUAAnwF+ACxBkqqQpMJYCo6jKD/HMCZIp+dTVzfC\n5KSGaV4mn9+AqjYQDObQ9TTV1TVUVX2B4eG/A3pJJCLE4+fI52V8vv+Eps0DYHR0G5K0jNdf30F1\n9a/R0TEjeAXJ5BH+83/+Lh//+KprKmpVdVDVcrJZUfR5mU2TtLnvvlsxTZPz559hZMTk9OkDXL6c\nwXUfIJM5CDxEQXa9F1U9yOXLvYyNnaWlZSMtLeW0t99UxLwUxlgDdHV1oygGra2r6OhoKv7/aoMv\nOMYLL9S9J0XS5/OxZcsaTpyI0tLyIMuXX+lM7NhxmaNHnwQkJiZuIRYTjI+fQQiZ3t4xTp/u4557\nrhz3/aSk3wvgecW6XMay5GsSn2h0kIqKOwgG95PLFTo9M9coRIxk8kW2bHl01uu6uqLE46KI1ZoJ\nzzOZnNxBJlNOJHIvgcBeLKuCeHwu2ew4c+dWY5qXCYd3sXXro8Xre6/39ItAoBcuLHnX1/x7xfVE\n4np8qOPqB22mXaooq+nsXMqFCzEuX84yMjJGKPRxWlpunkap72RsbAxNc2lvX08s9ixlZauYmDiC\nYQSprJxLPJ5G0+IYxjCDgy/i892L68aQpLcJBEIYxgANDfXk8y+Tz9+P55lYVhTPG8WyUgwMTNLa\n6tDXtxDHOYcsdyDLGp4XR1Eqp4GPKiBjWdsoUDt/H1VdPI00n8I0z1FfX8dbb5lo2hjDw5M4TghZ\ndouqnTCE625G0xQcxwMKapSSJFFVdQN+fzOf/GQjTz75OP39NsFgH563CV2vJpuNYVkGknQ3khSe\n3owldH0Bur4Ay5rA5/sBixevAihaOKtqmnzeQpLKcJxDwE1Y1tMIcS9CLEeWswiRBxyy2Z8iSetQ\nlHXI8tfwPAP4OAU1TaYtsC1CoTosK8nY2EHuu++32Lp1LX/xF09w9KhGPN6P6yaZnHyNXK4B255i\ncvIgtu0HHDzv/wL+mQITBYTIYxhZbrhhDqqqTneaCmyEzZtX8PTTf8vevcswzTm4bpRs9iCu207B\nAjyF6/YC/wEYAXzAGYSwkKQgkuShKEFWr17JwEAzkmRw++3z2Lv3n3j77d3I8u/jugk0DRobG5Bl\nmVwuw803P0Bt7XaOH3+Zvr75uO6dWFYDfr+BorjAJJLUwtTUHYRCtQSDVzaPsrKVnDs3TD5/aJa2\nAlxxy/T72xkYGCxW1AXPmcuzcBULF9bw0kt/y9mztTjO7cjyaXy+WygpWYwkSdh2O7ZdwpIl4Hk6\nLS0VzJ9foHi+k3Hiuqdpbh7n/PkY4+Npbrmlg0OHni3+33HitLauYccOdxZF8p2t+IGBs9j2bSxb\n1jqrk1Be3sHbb7+F51WTzx8lm11DJPIREokgqrqEiYlhXnrpLe67bx2qqn4gKel3A3gWxpkFfMgM\nXuXqtcVxVHw+jXXrHpjV6VEUm5aWOrLZEI888hSm6UPXbdaunUs2C67ru0YSOx7fj22vxe8/ieuO\nMnfuJ4v+J/m8w+Rkho6OSh566LYPpFnxiyi0paXv39H4Vcb1ROJ6fOjiF4nDvFNf4uTJ/4mm/TZz\n596CoigIIQgGFxAOPwFsoq9vnLVrP0V3935cd4Jc7jECgRJCoSFGR22EKEGSfh2fbz6hkI7rmghx\nkYaGXWSzeRTl4wixA8O4EUmaj6KkKS1dAuTo7j5NIKBM+1S4BIMrMc0uPG81slyGEA6u24cQDyJJ\nB9C0QhVa8O8ox3Fq6Ot7ElVtRlXn4/e3YBiTeN5loANZlgAZ6MdxUkAO08xx8WIf7e2NgMulS1FU\nVaW+fh5CXCSZrEFRVNLpl5HlEiQpQ6EzAUI04brdCFFA/quqH9fNTldzU0ULZ8cpIRA4Ty4Xw3WT\nuG4Sx/ED84E4jvMc4ACvA5uBDjzPRZI2A88DQ0Aztm1TUuInEAjhOHnC4YJFOhSqrURiE8uWNXLh\nwm4GBo5g2xcRomlaF8KmoAGxDHgb0IE0MA9JcjHNBBcvDrJgQROG0U1zsw/LsnjsseeIRO6ns3Oc\nsbEhFEUmn9eRpFEqKv4j8fij5PNTwBFgDQUp7I2AhyxP4fNFcV2T4eEhdP02XPcUmuZnw4aH6e39\nCzxPQ1Vrcd0BYrHdxOMXUVULSQpw/PhBAoFFqKqG65Zh269hGMMoioKiDGNZDqq6jmQyT1XVle97\nYbNrob//yDXaClfjIjzP5vz5ndNU4yzhcB8bN95OKpXisceeY2JiDbK8gdJSgSxvZmrqezhOS/FY\nqhognw8QjY4RCPg5duwyAwNJXFcmmTyM6y6jtrYVzzNJJg8zMCCYnDzH8PAtjI7up6TkNgKBK2qb\nMyZbM3TGO+9cXWSYzLTiDx/+Aba9lP37C6OZq8cV8bhLLDZIWVlBcl7XG8nlnsGyBD5fOxMTabq7\no9TWOrP0KmaShWuTlkvY9m6WLdsw6zyNjY2cP/82CxdWz7q3kiThuuO0tq6ZJYg143ja1fU0qdTN\nrFx5b3GssHPnZU6ceBVJWnhN0pJKRVGUTQQCGrL8NJZVSWVlwVredV1yuRdZuTLGuXMTTE0t+IWa\nFYVu0+wOyy8a6/x7x/VE4np8qOKDiMNcDbb60pccBgdbiUZPY9sFxHVHRxm33/779PUdorf3O9TX\n30Rzs82DD65k48bf4Vvfep6urmUYRoJkMokkLSWfdzHNLCUlJrKcY968zRw+/K9MTMSmxwFN1NSA\nJO3FdeunaaZV1NQ0ks3uxHH6KSlZChQAgJJUg227eF4WzwuhaYXKZyZcN4vrPksmMw9NG0GIHLpe\ngs9XOj1yuA8hGvG8kxQEpWpQ1Qnq6hbS0wMjI8cQ4jSZzE0sWbKFs2e3kcl8hlRqH55nomkbse3L\nCBFFli8iy50oyiZc90ksS0GWG5Gk7cAwZ8/+jFAoTHNzK7ZtI0SQpqZfxzC+QT5/CdddCDQiSSUI\nUVDThEvAW8BWhABJkikkFbuAvdObZwFfYds5ZPkEDQ0XaWgoMBa6uqKUlKxh//5nGByU8bz/m4qK\nRqamvo3ndQI7gQyFrsEaoA54kYJqZycQIpsVjI4epbJyLw8+uL7YCq6u7qC6usBa6O0NoiiVxONg\nWTKVlX/I4ODngeVAM9ALXEKS2lGUCJIkYxhRHEfDca4kV5rmZ+7cFlKpFMlkD4nEy7junVRUPEBl\nZRmTk7uIxRrR9QvoehmuuxdJWo3P93E8z8G2f4DrynheEs/zzdoUCuJQYJrXaiuoqs7atZ/i/Pld\nHDjwfWKxezCMOkpLSwgEVvP97yfZtu0vKS9/gHh8gMuXL5HJ1OC6fdi2jSQJLCtBaamfQMCPEBK2\n7ZDL9RKLzaW2dimKYjE+/nMMYwkjI/uBPZSWbqSqai1tbQ6Tk130979Gefl8mppytLWVFwGdjmMy\nOjrA1772Kv/0T7sZHl5OZ6dCaak7LZzlIxisJJulOJqZwSq5ro9UaoKamjYAJMlHY+OniMcPkEod\nQIgEvb1RHnzwbjZt+tispEFRcoyODlJe/kCRiREKGezc+Q/s2JHitts+WqQtV1bWUF39P6mo+J3i\nfZ8R1FqwIE4kYs5ag2acUaemFhaFxmb+Xl7eQSSylNHRGJ53uYiRKGBjNIQwCIU8brzxPhRlaFaH\nY86cXhYtWs3eve3vOq4YGTH56lf/EduOFIG2TU0rkSSJoaHsLEZJMOj88ovrvyGuJxLX40MVH0Qc\n5p57NhUfbscJsGBBKwsWXDtvXLDgVurrR/nrv36guDjPILtTqf20tHyBs2f/HsPYjeN4CAFCQEND\nPYcPvzDtEyFRXl44n+MYaNqtBIPniMV+iGX10d/fRHm5TiAwRX39CuB3iccPkEyexLIyZDJpFOUi\nnpe/agHNYttPIISMz3cvfv8RDCONEG0oSgRd78I0d0x3JiqR5QtoWjvhsERlZQRZlhke3ottN7N6\ndRuXLxcs2IPBeej6W8TjSQKBRdTWLmF4eD9C7Jze6DtQlE/iuq/hun+Nz7eJ0tK7qa7uQ9cX0ddX\nzuTkWSTJwDDSVFXVkMkYmGZ42uBLQogohQp+I4WkYZiC4JWEooAQFajqTfh8+/G857DtMOEwLF26\nmHnzPo3jPAOAaWr09x8gnb6JePxZLGsRQpwhl7uMEH8KjE1/igPT56qb/v0QBYqrQJLyuG6Ku+6q\n4e67N/HII09TVrap+PnPVPOBgIIkTZDP2wQCYWS5HM+rBrIUOh4/RYhePK8O05RwHIfJyW7mzl1A\ne/vS4vGam1vp7XWBNK57J/Pnry3+b2rqEjU1DxCP95FOn0WSfgdFKeAjZFlFkhqQpIt4XhjTnJr1\nPZ1hd1hWQVshEmnnnSqZQ0PHyOc3Egqto729qdh9Gx+fYmBgO5WVOwgEtmDbKrlcHCH06c/mAp6n\nkUh4ZDJ+ysvDpNMnKCn5NIHAS+RycxgfP4JpVqKqKzHNl5Cku5CkhUSjgwSD3aTTgwhRgesGUFWL\n9va5gMuZMzs4eHA7prkBy6qnu7sPn6+FkZErGIcZMyufL8Tx488xMKAWN9ZEog/XDRGP95NMppjx\ntikra6a5eT2ue5pVq869a6fj/PmdnD+/lIoKk7VrC0mLpvm5/fYvcuLETzh//us0NXXg89ncdVcT\njz76lWlBuMOzvC42bfrD6W6OMotd0t19gvLyzdPvdXYsXXo/8fiX8bwXyOXuJhCYN70WxbGsIyiK\nQV9fLZpWTlNTM21tc1FVFcN4nMOHx96VCeM4JmfPvk0q1cI99xQ6IE1NFezbdxxFWUpr62I0TSky\nSvL5n11zjH/PuJ5IXI8PVbzXXNBxTMbGonzta6+wY8dIcdxxtTHSO9t9M5LVV1d4XV1Ryso24bqH\n8flkXDeOLC8rUkqFyDA+vgvbvplweAAh3OLrNS2AZYXJ5QamWQMn0bRqWlvvpLf3B/T02LS2rqKq\najPB4CQ1NSdIJEaprW3i4MEzTE3tQpbbkKSDhEJrse1TSJKMpi1Hkrahquuw7bfQ9c2EQkkSiXPo\n+jokaQ8+n828eRuKan7pdA+qOh/XdejqehtYiCyPAhGEGMQ0XUKhAJHIR0mlTBSlC8f5ObKsoqoX\nqK9/kEhkMR0dOdrbt07Phw9Ne0qcoK6uhYkJnaamzfT2Hpve6M9SsOYGRbkD295BwYHRxedTUFVQ\n1TIiEYmWls8Qj/+YigoV1/UxNDREPv8TPv/5OizLIhrt5sCBPtLpMKYZRlUXIcQeYDGSVIsQBV2D\nQgLhQ5Z9SNJDCLEHVR1EVQVlZTkWLTL5gz/4BK++2sWOHZdQ1TPFqq25uYbKyhqGht7EdU9i2y2k\n0wtRFB8QR4gWCiMTHZjAtvuQ5SiyHCeddrh48QS1tWE6O1tQVbWYmPT2DlFd/fD090UUWScVFWGg\nk3j8PJp2xRCugDtZBezF804Dc4uvnRkTRCIQiZTT1fUd0ulb8ftryeUOoOtbEaKTePxNXPdWhod9\njI31MXduOeXlATKZ48Tjo8RiG/H738SybArg3m0UOjdhPK8NVVWx7VHS6dOUlqaRpFpWr/4Yo6Pb\nsawWJGkCzxtFlodQlI2oqkI8/gbp9CpCoVtRlH9A01Zx6dIlZLlACx4Z0XCc38Y038ZxbkWIU7hu\nO+FwkImJEV566S3mz2+gr+8sk5NHyedvoLr6lmnVU4FhfJtU6giK8hk0reUqiugUicRh1q6txuez\nee21A9cUF/39fZjmGo4f383x4y+h6yVEIrB48Q0sWfLruO42vv71T89aE96LNvpOp09Ns2hosFm6\ndPE1NNTCOuDnpptWsnlzLT/60RP091t4noSmncPvX0hj4+3FRK9AIz3LwoU+tm5tZOfO0eK44urr\n6O7eTy63Fk3LzzqXogzhOPOJx1NUV5dP/zVGYXz4vy+uJxLX40MT74W8vhoIBr9JWVkBsVwwRto1\nyxjp6ioum83Q3h7j+ed3sXXrWnw+3/TxZRTFJhbrQlGW4XlXqF8F/4opPG8NQozj80VnPfC2fQzD\nuAVNm0NtbSmZzA+xrEpaWz/L2NhrTE5+G79fpbS0j9/8zY0cO1bP669foKFhA7K8A7+/jFzOQJY7\nGR9/FjhNJiNRWnojinKKcNgjn38STZMJBk1uuKGUxsb7gUGGhn6MYWjIskVpqUkup9DXFwEa0LQ6\nhBDY9kYc5ysIEUeIEMHgZkzzXwkG16Hr1TQ2VtPb+7eUly+lpGSo2KKemQ+bZo6zZ79KLPY83d1V\n6PqtaNpruK6C4+wGstOYEIEsr0NR+oEBVNVECA1dj1NW9jLd3bspK7sLVb0JVS3YrU9O7ufo0T5O\nnhzCNFvJ5U4A9yNJ+7FtCSH6ESIIyEhSG0IMIUljCJEGFIRw0fVVRCIbCIXGWLFCYs6cnXzrW89f\no/zY3T3KwYPfpqrqY9TVbaGqKsWFC49g26cQohZFSQMjuBtHedcAACAASURBVO4xYAWy3Ijn/QSf\n79eprGygra2Snp7vceTIABMTGdatW4yi+FiwYDnDw4coK+vHMIaQJAO/vxvXjdLTcwYoQZJcZPki\nrjsPWZ75XnmUl38Cx/khQiwnn1+BqgpaW8NEInD58g+YP/9hNm5s4dKlPezf/w/EYutQlPPo+glM\nswxdnz9tSmczMhJjaOgpgsE7EKIa13UQ4k5cdxdCXAZWAOspeK14OE4rqqrhOBEMw6ChYYjOzkUM\nDUXo7LyXycldTE1lyGTCgEwu14UQG7HtelzXoLy8Ece5iGXlOXHiMLAYxzmNJA3guisJhdowjFM4\njg/DsAgEGpiYSDN/PhjGj8jl7sHna5rl01JSopFItJLLDVBWdhVoBD9QTjbbx/r1TezbNzCruLBt\ng2i0m1TKRog7gDpKSjTGxnYxOHiI/ft3U1trsnbtLu66a901jJJ3Fhzv5vT5R3/0Q2Zcet9tnQoG\n4ROf2MonPrEVIQTPPbeT119/iHPnjpLN9hTZH35/OfF4N6nUz9m69Y/Zs+dJzp/vIRpN4bpKMent\n7+9H1zfhuqeK1zc8PEZr6xeIxw+QSLxGaekcFMWmra2JYPB+Tp36m3e9vn+PuJ5IXI8PTbyXtO6M\n9LDf315kFgCUl3fgOA+QSDyDovwGJSWNHDiwjWx2NUIsoaJiiAULbmDHjv6rgEyF4zc2NtLTc5KS\nkt8lldqG6wokqX161gl+v42qllBfnyQWO4Kur5wGeEXxvCX4fClCIYXFiz9WnIVWVKgIEeW//beP\nsnnzQzz22HOk03cRDr9NLreQpqb/xNjY6yQSxxBiBEmKEAiEKC0tbH6O04HPF6Wh4WbWrAkyNPQ0\nixbdOL34dnLDDVLx3jz99FdxnGb8/lJM8zLp9HFAQZI8VHUZknQQz5tCCJmSkkY6Os5h20fJZLIE\nAkO0t2dmzbm7u/fT399DNHoOy1rMxo2rmJzcRjo9iuME8bzn0LRNeN4wrvtzoBWQ0bSfEwp9Ab//\nZlzXIBIxyGZ/jOt6VFRQdEptby+jvf2jnDjxFJ5XTyg0h1zulekRQx3QjefpQA2SdAlF2YTj/AuS\nFEWWz6Mot+B5efx+E1mOMmdOnspKP4FAlomJTdcoP+bz58nlPkIuV4vfbzExsZvKymYmJs4jSR1o\nWopAwEc2exEh2rHtZ5CkpThOJYriARqtrZ9jcvLHpNMnOXXqNRYsqOMjH2kiGFyOri/m4sXLHDxY\n8H6xbR8gEwwuQQgd244hxNi0DLlHaelFwmGdUGg56fQu5swZZM6cFoJBgaYlUNWHqapagGGkOHfu\nAFNTc1CUu3HdPZjmvQjxY0zTxu/3Icsq+fzbKMpmbLsRRanCcXqBu5HlI0hSJ3AjcIGCffyrSFKK\nkpIafL4MPt8l1qz5L9eYf+Vy20ilxnGcXnK5LjzvN4ARHMdjzpzFpFKPYdsfR1GCeN6NyPIghjGI\nJN2C3+9DiILkdj5fgRAHMIzz7NtnIUlZZDlDKHQew5go+rT09enccMN/5dKlv8ayTGS5DVmGSEQn\nGIR0+mXuvPMv2LFjZFZxURjl5RHitmkZ9Bip1M9w3bUIcTuGMUkud56dO5s4der9TbfeuQbBB3M8\nvRrsuWPHWXy+TzN37s1UVQ0wNHQFG7FgQSM1NXMxTZPDh9/i3Lk5aNqNyLKgrEynu9thdDRFXV2M\njo4CJucKbdVPdfWtlJZWsWXLomJ3dGTk6Ad6P7+quJ5IXI8PVbzbAzwjPVzwEJhtHVygQL7F5s1D\nfO97P2RqahXBYI6mJq24UV6Nr5g5fnv7Grq6duG6WcLhT5LPv4Vh7EFRbCTpFLW16ykpaeLWWzfy\nyit/z8TEMJLUguclCAQyVFWVUFo6TGfnIlR1fhHtnUpJbNmyhq9+9R959dVWNM1Dkhai629j211U\nVupMTIwRDN5PW9tKhoZ+gmX5UdUONK0C0/SIx58jHu/Hsgwef/xHWFYNoVCIqqogzc0R2tsbse0Y\nJSVTRKOH8bw2hAihKPMo8N9L8bx/pL5+CVBFe3spnZ3NJBLdVFcfIJFYSijUXJzDz3R7MhmBEBvR\n9VKiUR+aNpdA4CLwEEJUoapdeF4rqdQRHEdF18epqvoCul5DPH4IRclTVhbB81yqqj5Na2uOzs7m\nWUnh5KTF4KBNfX01JSWLyeencN0FuO6LCJGlICD1AkIIVPVewuEI2ey3cZxTQCXV1RGWLWukutpP\nff1hEokSIpECn/9qhkMyOYDPt4lEIkoi8RSwjJaWu8lmt1FS8nGmpr6Dz7ccyzIQohkhTuF5tyDL\nGRxHJxot8P7LylayZcsiDONxvv7136QgjGTyzW++QiwGrvtx/P55+HwGU1M/IJ1+Hp9vEUK0EQq1\noih5stnv4/evp7p6FZaV4KabNlNb61JVtZ8vf/kBHnnkaSor5+M4Ji+99E3Gx+9BUbqR5TpsO4/j\nzEcIFUnqxjB0IIttn0FVb8LzEkhSEFnOAyZCmIB/GjzajiQ56PpGwuEcbW3VuO4pKioypNN9hMNN\nJJPdDA4WklAh2nCcF7GscqAWSWpElj0URWFw8GVk+depqJgikbgMnMPzBlGUalS1FMOwCAbXYllP\nks9PAr8GrESIEZLJ7+I4GdJplVTKpL29jObm+fT26uh6Ca2t99DWliIa3Y7r+lAUm6amJiorl6Hr\n+jXFRTQaRZYrEKKCAoXzILAWRekodjUlSfmlTbeujoL+xTNMTHhEIh3FTsqMMuemTR8rgsLLyjah\nqk+jacvo758iFDK59dZNV1G4IRaL8sUv/hVTU58lGLyMbaeQpHampkwymQSGkcMwTtPevgG4Qlud\nwVUpymwQ7szf/3fF9UTienyo4p3SugCOo+I413oIQOGB87wg99yziX37Bli8+KPXyNLCFYndP//z\nB6aPv7qIwi/QtkJUVDSxalUrQnQwMOChqhAIlHHffX80jSE4Tnd3L+Xl/XR0tFzj4Aggyzn+6q+e\nYft2CIXuuaqV204oFGXNmhtIJC6RSFSjqoFZKHXX1fC8SSzrTUpLHyEaPYxtTyLEzWQyjVhWEttW\n6e19kYoKP9nsi0xOfoySkqWkUtuw7TSW9QaeNwwonDnzZ1RUqCxffieGoXHHHU1s2fLAtJNkIVm7\n2mhqZGQ/QqyitDRDNnuCycksyeQhZHklwSDI8gra2+vIZocxzdeIxXrIZOZTU+NnyZI62tvnoigK\n27efR9erGRg4wfz5LcV7I4QgFpvEttcTCFQQDNbiukk8rxFJ+m0M47t43huo6nyE2IXfP0l7ezuw\nlljsLSRJxu+v4fjxASorIyxbtpwjR3rp6OibllXWi1TfgYFuFOUMudwbVFVtpK6u4BYpyzaSpBMO\n348QP8KyXFTVQggbn89Flj0s6xCpVA+plIyuj7Fr106y2UH+4A9+hN/v4rojxON5hoZ8SNKtyHIG\nXVfQtApsuwpdr8a2XyKXW05FRZ6SknuAuYyNDTB3boqOjkUoisLkpODVV7uK47wLF3YzNKRh242Y\n5tsIkUIICVnWkOVV2PZjuO7nUZSbgeN4Xn66I5CdpqCmUZRSHKcXIUw0TUOWJfx+FSHyjI6+gKL0\nk89bxOPfYnKyAtdtR4gQqtpBOv0qQmxAVScBDSEUfD4FWVZw3RFgGeHwImAKIZqIRG6mv/8kkqRi\nGDmCwRJ8vjmYZj2eN4kQLzMxsRu4EVnejOvK2LbMpUtTDA29SXV1Hs/z0DSVBQtuZcECsKw8PT1v\nMTDQz8WLl/jjP34cVZ0iFiuML2e0H/z+Flx3EMdxgBFk+SPT3cQ8spyitDSCEOIDmW7NxIx/xg9/\nuJvTpweZnEzhOD9BVUsJBFxKS8N0drbi89Xyl3/5PZLJrVRVFdYoRSnQmmcUMWcYKjPf+6GhSwwM\nVFFevpZIZGXxmZckDdNMUl1toqrJd9BW59Lbexkov6aASqej7/+GfoVxPZG4Hh+quFpadwb8BMdo\nbV1T5K1fHTOKd0AR//BuMSOx6/P5isePRnsxjBqamhppaqqivX35dKu/kcHB71BRsWy6Mtbp7NxE\nTU03LS3dlJfXUVXVMuv4jmNy4sQ2JicPkc/fw9jYFJWVyaI0rq6HGBzs46c/3c3UlE0ud5bhYR+1\ntc1UVW2msrIA2svnn0eWP8Pk5AgjI20I4SOV+iGWlUCSQqRSBq2tpShKiqmppmmU/lFkuRzD+Aqe\n91vA/chyQdsiEknR3f0UP/vZfyccDgOFauvkyW1MTAii0QF0fROe52FZDn7/JLncbmx7HZq2EUXx\nEQj4yecvkM9PYttzmDevnLa2h9ix4ydADXfcsfgqFo1JMtlNOn0Cx0kAx2lqKivqXsRilzFNicuX\nxzGMMhQFfL4UljWIz7cBy3qGsjI/nncjZWUCUEkkXqGy8l42b76dQ4eeJZf7LLYdoqvrVWKxPNHo\nBPv3d7N6dSudnS0sWHDrdNV6Az0926mvv6l4feFwI1NT3chyKVBPKDQPvz9DOu3DdR0873kMYyOS\ntAnDGEGWn+PUqUZctxFVbUeSDC5d2ofP93vo+nZctwfPk8nl0jjOW2jag5imTSSyCSEO4jgDSFIz\nsnwRSRqjslJhx44fEI9HyWSyvPSSQUWFTnPzjRw48DqmeQOqWoemLcBxxrBtC9c9D+wHFgPncZz9\nwDE87xYkqZNAoA1FeQzb7sHn24QkHcO2jyNJS/C8vWSz/bjuWVKp9dTWbmDp0jCe183wsI3jSEjS\nz7Cs9UjSALpegut+Ftf9LkI8N21a56EoEppWSSqVIxCIAMNUVKxlfHwXudzbRV0FwxjE79+KbT+J\naY4DX0KSsggxguu2kUxOUlZ2gbGxbhKJS6jqT+nsrOPMmTcYHOxjYOA0uVwdoFJdPYd9+wRlZWUY\nxj+wcOHvUlW1AFV1kCSPQKAVx+kmn88gxDggCAR0fL4AqpoufuZXS2u/l5LkjH/GK6/EGRysIJ2u\noaByWk8+/0MMYy7pdAn5vEQ8HmFi4iUaGnJs3OigqmpROCwQ6MDvr5glHpZIdAMailI9DQrXqara\nTFXVlc6CZR1Bkp5mYqKdWGyCaHQQyxIMD2+jtHQNzc1XNDQSiW4qKk69z0r6q43ricT1+NDFO8FP\nL7xQx44d7rsiqGfmlR/EunhGYnfm+Fu2rOEb33iaycn5RCJXUOPp9AB33VXGkiUOBw8+/g662Jeu\noYvZtsHOnd/F85pIJBrIZhvJZC6QTmcYH0/Q0VHDyMizWNZqJOkWfL7XUNVbgCn6+3dSV1eDpkF7\nexl9fRJC1HHw4E+w7T8gn38DSfo8fn8bnpfHtvsYHd1OLufh85USDt+E5xmMjn4BIf4Qn28DQnio\nqgekSKfLkaRP8+ij/8KaNcuLPHwhsgwNfYfubgNJakJRbFR1nEDgIqnUOjStUGkVFuxmgsEWLOsC\nzc3+YqWlKBZCuLOSiP37n8F12/C8IJpWh6rW0tMzxcjIMTzvNLYdQNdNVLWWYPBuEokf4TjLiUSW\n4TijmKaOrg/juueJRBpIpc5hWasJBObQ0/MWudxadL2RaPQZ8vmb8fmqkaQKXHcxJ06MMT5+jOrq\nJPH4JcbHf4bnCWKxRDGhq6hYSyLxPfz+FeRyGn7/zWjabmTZxbafQVU3IsvteF4ez3sdw5iD5y1D\nkk7Q03MQ1x0knZ6LppUTDJYRiSxFCJNk8hksqxXPW4KqmihKCNMMo2mv0dKyCEly6Ol5nb6+jzIx\nMYxtP4SitJHJdOP37+XQod0kEvVFgJ+qrsVxnkSIY8A4BSbGwxQosLuAW4AMJSUNlJWV4rr/Edt+\nimx2GFVdh6L8AMcpA+4FwoRCD6PrbRjGIGNjKUxzhKamz2IYcZqb25HlEXbvjmKaNShKFr//PkpK\nDuI4fmS5kcnJF8jnz+M4BpWVCmVlezGMEPPm/VcuXfo7crlu8vk2YBQhXkCILcATKMoawMJ1n8Zx\nDOAImcxaNG0zpnmY0tLtnD59lBMn7kBVS8hmG5Ckj6JptViWgyzXEIv1UvDEeQXDOExlZQ+jo34U\n5RJ1dcvp6zsNVCNJNun0ThznErGYj9dfP0lj41xqa5O88MLuX6gkOeOfMTVVTz4vEGI1itKB627H\nceYiy2uQ5VJyuSSDg4eIx3OMjqYZH9/H8uXNtLTcwvj4s0VDtYJmiEcyWbBA97xWxsaca9anQkfV\nJBY7TWmpyY4df0MisZFweB5VVSHWrl1FLneYrq5HuPHGJQSDgjvuaKK6+k6+851H/i3L7C8V1xOJ\n6/GhDkmS3tNJMJHonqV490EAUleHrut8+csPzOp+zCQMW7Y8OG3x/f50sf7+s1RUrCedHiaZrEbX\nl+H3T2EYBrlcLWfOvEAotAqfbx6OMzZd0Y3R0LCCfL6BtrYU8+e3IoTg3Lk8paWXsawWXPcCrluY\n/RbuRRDTvIRhbMHnE3jeP2NZk9j2SRwnjBDrcF0HVRXTYjw6th3Atpt4/PHHyOXuIxLZjKYVVPum\npv4Dur6bpqYNKIrCyIjB4OBhIpHbgML71vUaXLcHaKCyspaBgYFipVVRoSBJU8X7MjMmqatrpKfn\newQCK4FaAoEKBgf34LrNlJWNIctvY9s6qtpBefmDpNM7yWReQ5YvoqrrWL36tuKY5I03HkdV7yaf\nj3P69F5qam4nFtuNZa1B19tx3TA+3x4saxWZjI/u7rcZHr6ZcPgLlJU9ST6vEY/7il4HlpWho2MF\nNTUpurqO4Xm11NcvZO7cC5w48TquuwbHOYYk2dj2cSzriyjKq+j6ZkxzM5L0s+nxRwjLGsI0n8d1\n43jeAoQ4ieO4qGrBLbaqqo5YzEc8ngKO4brryefHse21aFphnq9pYQKBjzA29nWE6ARq8LzLyHI7\nklSHJDUjRCMFwzUPn68J0HGcB5DlJ1EUHUlagiSV0dn5JcbGniWff46lS+dy8eJCNM0jkRhBUW4j\nErGpqJhHLhcnmTxIfb2E31/B0NAgd955K/39vVy8mCCXGyaXi5HJJPG8v0GIBELMA1IEg220tDRj\nGOPk868iSQ7BYAJV/SnBYA2WFSOfv5lQaB6OUzI92tNR1U9jWd8FmnGcJJL0fRznEul0H/n8fUhS\nANPcAzyMLLegqgLLkpmaSlNV1UEu9zEuXnyCn/70v5BMJvnd3/0me/b8hIsXTWw7hBDHse1jCLGY\nysr7qa2tQ5Ikzp7dz7lzB3HdTddYiF+tJLlnTy/d3WlGRjRMUwMa0TQbyzoEfBEhynHdHNnsi9j2\nShSlwOyanBympyfM2Fg3N9/8cQYGjjAwsB/H6cY05xXHiY888nRxVHG10ZfnmQwMPE02W4mqVpJM\n3omitJJOZ9H1BB0dN6DrC5iausTttw9y330Fn52jR/9/DraUJGkD8CUK/KF64NeEEM+/z2t8wP8L\nPEgBgj0M/LkQ4vu/7Pmvx/V4Z7zbuOPKhn8Fkf1BE453Hvu9+OUz8V50sXvuKbQl//iPH6e/X2Ny\ncgM+334AgsG12PYzuO4qcrkEPl8jmiZw3SkaGhoQ4hT5fBhdL3godHYWrjMc7sO2A2haiFxuAEla\ng23vwnGiCKHiOG8jSfOorCwnFFqEEKcYGjoClCDLLpLkout+PM8hECi4kE5MvIIk3VUUOprhrEci\nHeRyw4yPn6CurkBvNM0+JiYu4POVo2kyNTVrSaefA5ZSXr4K275Saa1caSFEH1NTl4hEOohGo+j6\nJkxzio6OFVRWTnD27CMkkzbJ5CS6fi8LFzZimksxjCFSqQN4nkYwmAVS2HaAsrJOBgaSgKCtbW6R\nVaDr5SQSUFsrkUpFUdXN05+Lv+hpMDz8U3y+LWSzMW68sYSmpsUcOvQsU1PbgAZs22Xduk7mzSuM\nsPL5EUBmxYqbEWIlhuEwMDBOLleP54UAH4pSEMSy7RZU1USSVFS1Etd9HM+7F887im334nk3IoSC\n6x5A1zvQNJWKiloKGhDngYINeSr1Aqq6GSgkGwXTOH3aY8Uhl3OQ5ZeQ5XVI0giqWgPcj23/PYoy\nOq1lMsPe+BSyvAfPO4brxojFplBVH5WVzcRiBitXrqO9vZE33hgiEKgHCt/xQKCSoaFM8ftewC4Y\npFLdZLMJXHcVQgwCH8HzbgDqkaQqVPUphFCAZkKheoTYQiLxz9TX38ptt30ETdN46qk/ZWDAh+ua\neN4ojjNCQQxNIEl+YDOS9FMUZRMF2/Y6dP0BPC9PLvcMJSVLixRXx3FIJg2qqiAQmEd/v4VhGHzr\nW89TWflp1q0b5PTpV0kkssTj/wr8Fm1t7VRXV1Bw2RwhmXyJXO4jvP56nKqqkzQ1hWlvb5wFxLzz\nztXs29dLPN5CgXrqIcQw+fwUQiQo6IwYuO5+FGUtktSErscxjH4MwyWT6WFg4CJDQweoqgpSUaHw\nuc+t5xOf2FpcM9ataySTqWF8/ECxa1GwOO8inZ6DqnYxNVWGpl3BVV3tNxKJdNDVdYD77vtlVs5f\nXfyvdCRCwHHgX4GffsDXbKNg+/d54DKFBOTdh9XX43r8L8QH2fA/aMLxXvFB9Ovfqe/v81mcOzfG\n5KSH338r4XCUqanLaFoHZWWfIpvdj2n2YRgn0DSYN09i/foVwNKiSVAudxnDOM4ddzSzYcNtfO1r\nPYTDC4jHT+G62xBiDbCZgjiWh+NEsO0opaU3UVJyllSqBNueYOZx9zwHRTEJBEJIkoRhjBEOt89C\nvfv9mwGord1KX9/f0NOzB8/bgqI0YFlRXDdKPu8iSTYbNqxHVUcZHn7iHZXWgwC89toB9u7dTy7X\nSzB4ira2Mpqbb+DQoWcJhz9LVVUrvb3PIMRqLMslHn+Cqqr7qKzchOeZRKPPkE5vIBC4QF3dWiRJ\nKgr5SJKJEGLagtnCdV1cV0XTZubdAkXxU1m5iampI7S3341hHGNi4iCZzApKS+8nnX4Nx6kgHg9x\n9uwAra1zSKd7r0mEstkoqnoPZWVBVHWcsbEMlnUJWD19Heb0+AygHajCtgVQiSTVAXf8f+y9d5ic\nZ33v/bmfNmVnd7bvqqy02lWvtly1kizJRTau4BgTQ4AAuThpJCQhgbyH5OQACaHkdSDFSeCE5mBs\nY4Irlm1ZclGzJctqq7orbdeWmdmZnfLU+37/eGbHklscEuDkjb7XpUvSzs4zz8w85Xf/ft+CUj9G\nyhhKhd2v+vouUqnPIUSMurpqslkTXQfPK2JZOWprk2XlToJodDOO8yNMcx2+34vnHUPX24CzmGYT\nDQ0ultVOJmMhRBQpA6LRtbS2Btj2g8TjHyISCSXSQdBdjh/vBoqMj/eSy00x7R4JFqXSCWKxhei6\npKdnF46zENOsLkfV3wHMB3Yi5QZ0PUt19fUkk8eZmPg7kskmCoUjBMEirrnmxkp4Wk3NYgzjVUql\n+1GqCSEGCdUbAaHb5otAF0q1YJpphGgqf7dxIIbvB1iWhqYZ2LaDZYlzFArmG2zQly0LfRyeeupb\nTEzMB47guvUIYeM4u3DdJmKxOygWxypjttHRMPdjmogZEjgbMU0dKBEEx1HqRkIL9fsAD6VKKHUS\npa7A9z1qa6cVKgNkMu/HNDdSKIySTJpMTOzk0KFhbrrJJhpmwVcWObCaiYkBBgd3EQQGqdQuEolO\nGhvfy+joqxWieDiCDb04pombbxfb/rPGv7uQUEo9CTwJIN7BHgshbiA03+9QYfkG4SDvAi7gZ4K3\nOyzfScHxdni757xVDsjw8NcYHc3S0aGor++iUAiDhwxjPlVVG3Gco8RitSxe7JwTXBSaQC1cqCiV\nvsuXvvThymt87WvPUirdhpTfJgg+haZ1lglvAVKG8j5dn0+hcIgbbvhlRke/TD5v4Dg7EGIZsVi8\nUkRIKZEyy/z5tZX3N73KVyq8CUejTSg1h7GxR/D9CFIa6HotSuWYnOzhySf30tY2g0TC5l3vauRP\n//R95xVl05/3pz/9HaLRlQghOHZsW0UNEn6mLiCpqpoJ/ArR6Av4/ktMTPTiOBfT3ByhtjbJtEPp\nNPs9EjmKbfdgWbPRtDSnTj1OPj8MjGBZGq2tFhAaXiUSCZRyGR5+CN+/hGLxUVx3CfBuhOghCA7T\n16d45JHv84UvvJebbvoASimefno3L764q5xNcoLa2k6KxecIk0FB06pRKiAIBEHQRBAcRdM+DtyP\nEMsQIjSi0vVqqqp+FXgW132V0VGorzeYO3eETEajp2cbU1OniETO0NqapKGhmXT6OWx7OZ73DJ7X\nj5R3IeUBNC2DEAFSZqmuNmht3YxtP4Pj+ECaUukRYDbp9DiOswVd34hh2Ej5EvPnC5Rysaw6pqZs\nxse7se1VFS8UpRSuK5iYeJhEYgNLlzYzMHAA24ZE4jp8/yRKmQTBTpRKYxij6LqPlEdwnDyFAtTV\nuUCEurrlFV6HEALThGRyFq67BCk3oNQ9SJkntCPfAczG8xYhZT/V1VUEwWsSR02z8LwJLGtG+diF\n0ApdUCxOMHeuxc6dgySTG9/kvI0xc+ZKfP8g1167nOPHt9Pb+y50fW95W+E5/XpVRbEI3/72i+Tz\nM8nnSzhOP0p1IkQaSKDUKKEF/DKgFiHiKBWQy/loWg2muQTXHWRqqh8pHaqqLBobczzySIZXXvkb\nlixpYe3aNjZuvIQlS5rZv/9++vpcwKStzaCxcRapVC2WtZjx8ZfPu/5Mh/v194+wcOHbx6j/rPHz\n4EjcQhil92khxAcJj5hHgD9RStk/h9e/gAt4U7zVSff6YuHt0kbPvWGemwMyHb0cjglSpFIBpVIf\nM2bUMmvWHUxO7iGX24Xr+iSTp4hGq+nquusNhNGQuzG38v9IJMInP3kN3/nOCXp7GykUTJQaLGcJ\naEhZRyQyDHQAGoYRYdWqdcTjtZw8+c9o2p1Eo2srRUSxuIOqqv10df0eAEHgnucdIERAoTBCS8ts\nYBmWtQIp70apO5GyH6U2Y9utjI2l8LwTpNNtfOUr0Sg13wAAIABJREFUb27y09XVxrPPhhyVvr7T\n5PNzGR4+gFIaxWKRaPQUUrYQj8/A9xdz3XWrePrp75BMrqWzM49SnDdDjkbrcd1OYrHnOX58lHj8\neqamtuF5EXy/F8eZiaYpYjGfmpoRhNA5ffrbFIu1aFoVjrMUpdYCNWjaaiIRRVWVTSbzEC+9dIg9\ne85Wvu+urtnkcldx/HgP/f0nmJqagxA2Su0nCIYRog5dN4F1SPk8mjaOZUkM42KiUZ9SaQDDmENV\nVZIgWEtnZzOO8zKwg5aWDzFvnuL06Tnk8zqpVJ5iMUZdncPw8FYymbPALwN9aNpM4GY0LY+m/T2+\n71IqHSGfX0ZV1VVMTX0T123H9/eV5Y4v4DgxNO1iIhFBPJ6gWKzCcY4Qi52gWBzCtm8jFjuG48Tx\nvFFKpV4iEZ2pKZdY7Mu0tFzPsWNDBEED9fUBmtaGaa4m9E04BTTiOPfiOJdRVbUCTRskkzlBf/9h\nNG0I06TibxIavY0TiSTLXYjVSPlXhKv7xYCOps0iGjUpFF7EMGaj66eAduLxS7HtnQTBxrJPhE0y\nGaFUSiHEdhYsiPHMM+fboHd2tmEYRkV+GQThir6v7zSFwlyy2SGUGkWIAqmURX19sqKq6Oiw2b9/\nP/n8ApqaPlB24Myh1CKUeoYwtn4d8CNCjkoWKQuAoFTaShA8hWm+G9ueRTRag5TPMTKyh4GBjRhG\nO8Wiw4oVV7Flywm+/vU/Y/Hij7N8+WZWrBDl8WAv+/b9I74/m0hElBVFPRWic9iJUwSBTiZzkuuu\ne/sY9Z8lfh6FRAdhR8ImdCFpBO4B6oBf+zm8/gVcwL+JtyoWNm68pBLB/HaxvgDPP3+a0dG57N17\nAM/zOXt2K9HoNTQ1/TETE1+kUBggk2mhUMgye/Z6qqomiccHWLToEvL5h5maOv2OuBs337yR7u77\n6evrIJt18LxmDKMez5vEdV0c559wnKuQMsmxY6eZO/cy+vq+waJFXVjWPs6ceQTXtbDtHmpqYMmS\nTp5++ik6OxcyOrq7rKow8bwxSqUePK/EyZNbMIw/RIgjmObHUOo5pGwvdxJOEgQ+jY2rmZwsYllX\nVsLTXNetfK6FAhw48BiJxHX09/cC78Yw5uH7JaqrawiCn9Dbq9PefjlBoBEEAYVCgfr6obI/yIyK\nodT0DFnKCPG4oLq6BdvejRBXE4+PYNv3o2nXUijUMT4+ybXXXsfWrQ/h+13EYinS6ZeR8jfR9QaU\ncvC8HUjZg+cN47on+Ju/2cTKlctpb6+jo2M2zz7bx+HDT3L55Z/m4MHPUChcRBCsB44Ae4A2gkAR\njZroehXV1eOUSpNYVoZIpBMhtmAYG1BqAWH+CIyObqO1dQkrV64HAsbGHkDK1RQK+ygWl3L06INk\ns2eAVQixGcgh5Xew7f8DTAJr0LQ+DGMXQdDG6OhxisWLMIy1xGIFfP/zBMH7UGoIKavRNA3TNEml\nctTXb8a2f0Q6LdG0X6a1dQnd3f+LYvFSlGolCI5jGBpjY6t45ZVTtLfHicWqsaxapqaCSrEdicwh\nn/8hSnWhaXMAl6mpx4DNCLEI0+wilxP09ipGR49w6aWXoutbCePnd6NUEngPYYLrTOBzKJVB12eh\n6534fgNKbcM0L6Om5hrgmxjGAWzbJB7PU13dQl3dGLb9Ivn8rxOJ7KnYoJ87pgjll6cwDInvOwwO\nnkHKWqLR1ZRKaWKxeWQyikJhjLa2ZoJA48CBH1JffythQm6EWbN+iYmJbqRMEwQJ4DmghdCy/Rgw\niFJby+OuNQgxA8O4klCW/T0AIpHfwLIWEgQuo6Mn2Lmzm8bGcSYm7iKVitLYGC5gNE2jrm4+dXWr\nOHr0NPH4G7uZvl8imTTxvF5aWnJvyvH6eeHnUUiEHrDwfqVUHkAI8fvAg0KI31Kh1doFXMAvDG8X\nTX7//V+lru79FWMZeGPa6C23bMS2bXbsmL44zSOb3U4QvIdCYTael2XBgj/gxIk/Q8oMxeIMJiaO\ns2rVHBoaIsycuZdPfOKT5fTBf5u7Ma0mefXVLzM0FJDJHGJqKo/rvoppXk1T00aEOEI2+wrHj59i\naOgMn/zkVZimxZ49HqtX17N//37q6/8HK1asAwJ27nyAl19+Ds9bwZw5Sxkb+xyu+15M806E+BGF\nwik8rx4hThOPX4vjHEDXNyPlTuAMjuNy9uxxcjmblSvb+cIXnmHLln4OHHiV2tpbWLnyLhoaDK66\n6k6eeuqLZLM2NTUOUo5SVxeltraZdHoeIyM/prv7QUyzwDPPCIpFhWl2sW3bEebMqeHyy2+nr+9l\n+vt34fs68CqxWA2rVl1Jf/9VRKOd5QLj3UxM7CCX66FY7OfkyVeZPVsnHm/m+PHTBIGDptUjpU0Q\n3I9SFxMEQ0i5GPgArlvHyAgEgcHoaHd5Zr6S7du/hW3PxTQXlUmrn8e2/5ogUCg1A8sKSCRqaGwM\nmJxUzJ07C13XkfLDpNO7SKW2UVMTptKa5imuvvr3K6Osrq47OXHiOXK5biYmHqJUmo+UMaAFTXNQ\n6lGEuBkpzwBtZbXOYeCfsO0/J5fLIeX/RNczNDScIJNZRTR6A7b9XaSsxvfzmGYc1w2P+UTiSqqr\nt5PP7+XEiS34/vtJJlfgeQ8TxtTPwnFGKJWqGRq6j1xulGTyxHkr43i8i2z2/0GITUQiOvn8Nkzz\nKmAm8XgdhjHB5GQE6KW//zhDQ7soFodx3V4MYzNS7ic01VqErmv4/nUodQjPG0XX6wmCJ4hGL2XG\njDSZzHdpbhbAo0Qiea66qouamnFMc5LJyV+nsXExbW0jnD7dQzTaed6YYv78Lvr6Qu+Xnp6d+H4D\nplmLYSxFqbspFrsQYh5Kufj+GI2NPeRyB1m//n309LxIT0834+P7EKKNePw9KOVQKOwErkeIVizr\nEGDjuj9EiPejaSl8/zS27SHlY2jaZQjxCkEwD10HTTPx/RjFYi2HDz9HU9Nv099/qKJ4msbKlXfQ\n0/Mxstm9JJOX0tZ2J+Pj2zl79hv4vo3rWixY4LJ48U2V5/y8XS3h51NIjABD00VEGUcBQRhz1/NW\nT/y93/s9ksnzHbvuuusu7rrrrp/Ffl7Af1O8XTT5jh31zJ8fobHxjc871xXvqad24XkNVFXVIcT5\nygHXVeRyDgsWfID29gL9/d04Tg9z5y5g3brXioVzuRvT+/BWiEQifPjD69i6NUlt7WqOH9/O6dPv\nq0QWl0omq1Z1Vqyv4/Ehbr55A7ffLnj44WeR8hJSqTG2b7+PIDDLcsYj+P5y+vr+hVjs9rL/QC/F\noiRcAUs0LY7vBygFUj6IlFegaVcBU0CUwcH/l0JhBjNmfICJiTS2vZrjx3s5cOBrtLbOwDQD8nmD\n1tYuNG2YmTMvqRAqXXcN1dXX4bqHaWzcRqnUhWUdRIgYhrGw7DfRTVOTD4DjZOnoqMZ1I0xMDBGN\nXotSYW7G2Nh2CoURgsBACJMDB2wWLVpIV9cypDzO7t1HALc8hrgSpYZQqgspd6NpCxBiCs+LUCy6\nCDGbnp5BVq68g507P4iUV6FUCSmzKCWAD2Ga+zGMwwRBhlmzRli0KEs6PZdUqpdYbAFCWFRVraC5\nuY41a5YyNXWaM2fGME0TCD02jh/fzp49W3DdqxGiHsOwEMLBdYsEwU40bQ1CzCfkEmwquzTuQakP\n0do6H9f9azTtEqRMMzn5NL4fxfNGCYJqlDqM684mNFCLkc3mSCRiVFebtLRMcepUJ4nEOorF7Ui5\ntmInHQRJbFu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RyiAWCwlrocFRmBVy7NhLLFzYwNSUZHCwgOP0EQQKTbsSIeYi5XakPInv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YLgoqamA4djF/39j5FK7UTT3sJma8JuT+FwOEgkXkfX12K1VgBPYRiNeL0uYrF+FMWPxVJE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\nou/FNhtUV1+YURKwbPp2p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42 | "text/plain": [ 43 | "" 44 | ] 45 | }, 46 | "metadata": {}, 47 | "output_type": "display_data" 48 | } 49 | ], 50 | "source": [ 51 | "import json\n", 52 | "import pylab as P\n", 53 | "\n", 54 | "trn_logs = []\n", 55 | "vld_logs = []\n", 56 | "\n", 57 | "with open(\"exp0/train.log\") as fh:\n", 58 | " for l in fh:\n", 59 | " data = json.loads(l)\n", 60 | " if data['split'] == \"TRN\":\n", 61 | " trn_logs.append(data)\n", 62 | " else:\n", 63 | " vld_logs.append(data)\n", 64 | "\n", 65 | "trn_step_loss = [(d[\"step\"], d[\"loss\"]) for d in trn_logs]\n", 66 | "# vld_step_loss = [(d[\"step\"], d[\"loss\"]) for d in vld_logs]\n", 67 | "trn_step, trn_loss = zip(*trn_step_loss)\n", 68 | "# vld_step, vld_loss = zip(*vld_step_loss)\n", 69 | "\n", 70 | "P.figure()\n", 71 | "P.plot(trn_step, trn_loss, \"bo\", alpha=.5, label=\"trn\")\n", 72 | "# P.plot(vld_step, vld_loss, \"ro\", alpha=.5, label=\"vld\")\n", 73 | "# P.xlim(1000, 1600)\n", 74 | "P.legend()" 75 | ] 76 | } 77 | ], 78 | "metadata": { 79 | "kernelspec": { 80 | "display_name": "Python 2", 81 | "language": "python", 82 | "name": "python2" 83 | }, 84 | "language_info": { 85 | "codemirror_mode": { 86 | "name": "ipython", 87 | "version": 2 88 | }, 89 | "file_extension": ".py", 90 | "mimetype": "text/x-python", 91 | "name": "python", 92 | "nbconvert_exporter": "python", 93 | "pygments_lexer": "ipython2", 94 | "version": "2.7.6" 95 | } 96 | }, 97 | "nbformat": 4, 98 | "nbformat_minor": 2 99 | } 100 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import tensorflow as tf 3 | import tools 4 | import numpy as np 5 | import vgg 6 | import argparse 7 | from skimage.transform import resize 8 | from skimage.io import imread 9 | 10 | G = tf.Graph() 11 | with G.as_default(): 12 | images = tf.placeholder("float", [1, 224, 224, 3]) 13 | logits = vgg.build(images, n_classes=10, training=False) 14 | probs = tf.nn.softmax(logits) 15 | 16 | def predict(im): 17 | labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 18 | 'dog', 'frog', 'horse', 'ship', 'truck'] 19 | if im.shape != (224, 224, 3): 20 | im = resize(im, (224, 224)) 21 | im = np.expand_dims(im, 0) 22 | sess = tf.get_default_session() 23 | results = sess.run(probs, {images: im}) 24 | return labels[np.argmax(results)] 25 | 26 | if __name__ == '__main__': 27 | parser = argparse.ArgumentParser() 28 | parser.add_argument("-w", "--weights", required=True, help="path to weights.npz file") 29 | parser.add_argument("image", help="path to jpg image") 30 | args = parser.parse_args() 31 | im = imread(args.image) 32 | sess = tf.Session(graph=G) 33 | with sess.as_default(): 34 | tools.load_weights(G, args.weights) 35 | print predict(im) 36 | -------------------------------------------------------------------------------- /tools.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import tensorflow as tf 3 | 4 | def save_weights(graph, fpath): 5 | sess = tf.get_default_session() 6 | variables = graph.get_collection("variables") 7 | variable_names = [v.name for v in variables] 8 | kwargs = dict(zip(variable_names, sess.run(variables))) 9 | np.savez(fpath, **kwargs) 10 | 11 | def load_weights(graph, fpath): 12 | sess = tf.get_default_session() 13 | variables = graph.get_collection("variables") 14 | data = np.load(fpath) 15 | for v in variables: 16 | if v.name not in data: 17 | print("could not load data for variable='%s'" % v.name) 18 | continue 19 | print("assigning %s" % v.name) 20 | sess.run(v.assign(data[v.name])) 21 | 22 | def iterative_reduce(ops, inputs, args, batch_size, fn): 23 | """ 24 | calls session.run for mini batches of batch_size in length 25 | 26 | Arguments: 27 | ops: tensor operations you want to call in sess.run 28 | inputs: a list of tensors you want to feed into feed_dict 29 | args: a list of arrays you want to split into minibatches and feed into feed_dict. This 30 | must be the same order as your inputs 31 | batch_size: size of your mini batch 32 | fn: aggregate each output from ops using this function (ex: lambda x: np.mean(x, axis=0)) 33 | """ 34 | sess = tf.get_default_session() 35 | N = len(args[0]) 36 | results = [] 37 | for i in range(0, N, batch_size): 38 | batch_start = i 39 | batch_end = i + batch_size 40 | minibatch_args = [a[batch_start:batch_end] for a in args] 41 | result = sess.run(ops, dict(zip(inputs, minibatch_args))) 42 | results.append(result) 43 | results = [fn(r) for r in zip(*results)] 44 | return results 45 | 46 | class StatLogger: 47 | """ 48 | file writer to record various statistics 49 | """ 50 | 51 | def __init__(self, fpath): 52 | import os 53 | import os.path as pth 54 | 55 | self.fpath = fpath 56 | fdir = pth.split(fpath)[0] 57 | if len(fdir) > 0 and not pth.exists(fdir): 58 | os.makedirs(fdir) 59 | 60 | 61 | def report(self, step, **kwargs): 62 | import json 63 | with open(self.fpath, "a") as fh: 64 | data = { 65 | "step": step 66 | } 67 | data.update(kwargs) 68 | fh.write(json.dumps(data) + "\n") 69 | -------------------------------------------------------------------------------- /train_model_parallel.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import os.path as pth 4 | import time 5 | import numpy as np 6 | import tensorflow as tf 7 | import json 8 | import vgg 9 | import layers as L 10 | import dataset 11 | import yaml 12 | import tools 13 | import argparse 14 | 15 | 16 | # ===================================== 17 | # Training configuration default params 18 | # ===================================== 19 | config = {} 20 | 21 | # ========================= 22 | # customize your model here 23 | # ========================= 24 | def build_model(input_data_tensor, input_label_tensor): 25 | num_classes = config["num_classes"] 26 | images = tf.image.resize_images(input_data_tensor, [224, 224]) 27 | logits = vgg.build(images, n_classes=num_classes, training=True) 28 | probs = tf.nn.softmax(logits) 29 | loss = L.loss(logits, tf.one_hot(input_label_tensor, num_classes)) 30 | error_top5 = L.topK_error(probs, input_label_tensor, K=5) 31 | error_top1 = L.topK_error(probs, input_label_tensor, K=1) 32 | 33 | # you must return a dictionary with at least the "loss" as a key 34 | return dict(loss=loss, 35 | logits=logits, 36 | error_top5=error_top5, 37 | error_top1=error_top1) 38 | 39 | 40 | # ================================= 41 | # generice multi-gpu training code 42 | # ================================= 43 | def train(train_data_generator): 44 | checkpoint_dir = config["checkpoint_dir"] 45 | learning_rate = config['learning_rate'] 46 | data_dims = config['data_dims'] 47 | batch_size = config['batch_size'] 48 | num_gpus = config['num_gpus'] 49 | num_epochs = config['num_epochs'] 50 | num_samples_per_epoch = config["num_samples_per_epoch"] 51 | pretrained_weights = config["pretrained_weights"] 52 | steps_per_epoch = num_samples_per_epoch // (batch_size * num_gpus) 53 | num_steps = steps_per_epoch * num_epochs 54 | checkpoint_iter = config["checkpoint_iter"] 55 | experiment_dir = config['experiment_dir'] 56 | train_log_fpath = pth.join(experiment_dir, 'train.log') 57 | log = tools.MetricsLogger(train_log_fpath) 58 | 59 | 60 | # ===================== 61 | # define training graph 62 | # ===================== 63 | G = tf.Graph() 64 | with G.as_default(), tf.device('/cpu:0'): 65 | full_data_dims = [batch_size * num_gpus] + data_dims 66 | data = tf.placeholder(dtype=tf.float32, 67 | shape=full_data_dims, 68 | name='data') 69 | labels = tf.placeholder(dtype=tf.int32, 70 | shape=[batch_size * num_gpus], 71 | name='labels') 72 | 73 | # we split the large batch into sub-batches to be distributed onto each gpu 74 | split_data = tf.split(0, num_gpus, data) 75 | split_labels = tf.split(0, num_gpus, labels) 76 | 77 | # setup optimizer 78 | optimizer = tf.train.AdamOptimizer(learning_rate) 79 | 80 | # setup one model replica per gpu to compute loss and gradient 81 | replica_grads = [] 82 | for i in range(num_gpus): 83 | with tf.name_scope('tower_%d' % i), tf.device('/gpu:%d' % i): 84 | model = build_model(split_data[i], split_labels[i]) 85 | loss = model["loss"] 86 | grads = optimizer.compute_gradients(loss) 87 | replica_grads.append(grads) 88 | tf.get_variable_scope().reuse_variables() 89 | 90 | # We must calculate the mean of each gradient. Note this is a 91 | # synchronization point across all towers. 92 | average_grad = L.average_gradients(replica_grads) 93 | grad_step = optimizer.apply_gradients(average_grad) 94 | train_step = tf.group(grad_step) 95 | init = tf.initialize_all_variables() 96 | 97 | # ================== 98 | # run training graph 99 | # ================== 100 | config_proto = tf.ConfigProto(allow_soft_placement=True) 101 | sess = tf.Session(graph=G, config=config_proto) 102 | sess.run(init) 103 | tf.train.start_queue_runners(sess=sess) 104 | with sess.as_default(): 105 | if pretrained_weights: 106 | print("-- loading weights from %s" % pretrained_weights) 107 | tools.load_weights(G, pretrained_weights) 108 | 109 | for step in range(num_steps): 110 | data_batch, label_batch = train_data_generator.next() 111 | inputs = {data: data_batch, labels: label_batch} 112 | results = sess.run([train_step, loss], inputs) 113 | print("step:%s loss:%s" % (step, results[1])) 114 | log.report(step=step, split="TRN", loss=float(results[1])) 115 | 116 | 117 | if (step % checkpoint_iter == 0) or (step + 1 == num_steps): 118 | print("-- saving check point") 119 | tools.save_weights(G, pth.join(checkpoint_dir, "weights.%s" % step)) 120 | 121 | 122 | 123 | def main(argv=None): 124 | num_gpus = config['num_gpus'] 125 | batch_size = config['batch_size'] 126 | checkpoint_dir = config["checkpoint_dir"] 127 | experiment_dir = config["experiment_dir"] 128 | 129 | # setup experiment and checkpoint directories 130 | if not pth.exists(experiment_dir): 131 | os.makedirs(experiment_dir) 132 | if not pth.exists(checkpoint_dir): 133 | os.makedirs(checkpoint_dir) 134 | 135 | train_data_generator, valset = dataset.get_cifar10(batch_size * num_gpus) 136 | train(train_data_generator) 137 | 138 | 139 | if __name__ == '__main__': 140 | parser = argparse.ArgumentParser() 141 | parser.add_argument('config_file', help='YAML formatted config file') 142 | args = parser.parse_args() 143 | with open(args.config_file) as fp: 144 | config.update(yaml.load(fp)) 145 | 146 | print "Experiment config" 147 | print "------------------" 148 | print json.dumps(config, indent=4) 149 | print "------------------" 150 | main() 151 | -------------------------------------------------------------------------------- /train_model_simple.py: -------------------------------------------------------------------------------- 1 | import json 2 | import sys 3 | import os 4 | import os.path as pth 5 | import vgg 6 | import tensorflow as tf 7 | import numpy as np 8 | import time 9 | import layers as L 10 | import tools 11 | import dataset 12 | import yaml 13 | import argparse 14 | 15 | 16 | # ===================================== 17 | # Training configuration default params 18 | # ===================================== 19 | config = {} 20 | 21 | ################################################################# 22 | 23 | # customize your model here 24 | # ========================= 25 | def build_model(input_data_tensor, input_label_tensor): 26 | num_classes = config["num_classes"] 27 | weight_decay = config["weight_decay"] 28 | images = tf.image.resize_images(input_data_tensor, [224, 224]) 29 | logits = vgg.build(images, n_classes=num_classes, training=True) 30 | probs = tf.nn.softmax(logits) 31 | loss_classify = L.loss(logits, tf.one_hot(input_label_tensor, num_classes)) 32 | loss_weight_decay = tf.reduce_sum(tf.pack([tf.nn.l2_loss(i) for i in tf.get_collection('variables')])) 33 | loss = loss_classify + weight_decay*loss_weight_decay 34 | error_top5 = L.topK_error(probs, input_label_tensor, K=5) 35 | error_top1 = L.topK_error(probs, input_label_tensor, K=1) 36 | 37 | # you must return a dictionary with loss as a key, other variables 38 | return dict(loss=loss, 39 | probs=probs, 40 | logits=logits, 41 | error_top5=error_top5, 42 | error_top1=error_top1) 43 | 44 | 45 | def train(trn_data_generator, vld_data=None): 46 | learning_rate = config['learning_rate'] 47 | experiment_dir = config['experiment_dir'] 48 | data_dims = config['data_dims'] 49 | batch_size = config['batch_size'] 50 | num_epochs = config['num_epochs'] 51 | num_samples_per_epoch = config["num_samples_per_epoch"] 52 | steps_per_epoch = num_samples_per_epoch // batch_size 53 | num_steps = steps_per_epoch * num_epochs 54 | checkpoint_dir = pth.join(experiment_dir, 'checkpoints') 55 | train_log_fpath = pth.join(experiment_dir, 'train.log') 56 | vld_iter = config["vld_iter"] 57 | checkpoint_iter = config["checkpoint_iter"] 58 | pretrained_weights = config.get("pretrained_weights", None) 59 | 60 | # ======================== 61 | # construct training graph 62 | # ======================== 63 | G = tf.Graph() 64 | with G.as_default(): 65 | input_data_tensor = tf.placeholder(tf.float32, [None] + data_dims) 66 | input_label_tensor = tf.placeholder(tf.int32, [None]) 67 | model = build_model(input_data_tensor, input_label_tensor) 68 | optimizer = tf.train.AdamOptimizer(learning_rate) 69 | grads = optimizer.compute_gradients(model["loss"]) 70 | grad_step = optimizer.apply_gradients(grads) 71 | init = tf.initialize_all_variables() 72 | 73 | 74 | # =================================== 75 | # initialize and run training session 76 | # =================================== 77 | log = tools.MetricsLogger(train_log_fpath) 78 | config_proto = tf.ConfigProto(allow_soft_placement=True) 79 | sess = tf.Session(graph=G, config=config_proto) 80 | sess.run(init) 81 | tf.train.start_queue_runners(sess=sess) 82 | with sess.as_default(): 83 | if pretrained_weights: 84 | print("-- loading weights from %s" % pretrained_weights) 85 | tools.load_weights(G, pretrained_weights) 86 | 87 | 88 | # Start training loop 89 | for step in range(num_steps): 90 | batch_train = trn_data_generator.next() 91 | X_trn = np.array(batch_train[0]) 92 | Y_trn = np.array(batch_train[1]) 93 | 94 | ops = [grad_step] + [model[k] for k in sorted(model.keys())] 95 | inputs = {input_data_tensor: X_trn, input_label_tensor: Y_trn} 96 | results = sess.run(ops, feed_dict=inputs) 97 | results = dict(zip(sorted(model.keys()), results[1:])) 98 | print("TRN step:%-5d error_top1: %.4f, error_top5: %.4f, loss:%s" % (step, 99 | results["error_top1"], 100 | results["error_top5"], 101 | results["loss"])) 102 | log.report(step=step, 103 | split="TRN", 104 | error_top5=float(results["error_top5"]), 105 | error_top1=float(results["error_top5"]), 106 | loss=float(results["loss"])) 107 | 108 | # report evaluation metrics every 10 training steps 109 | if (step % vld_iter == 0): 110 | print("-- running evaluation on vld split") 111 | X_vld = vld_data[0] 112 | Y_vld = vld_data[1] 113 | inputs = [input_data_tensor, input_label_tensor] 114 | args = [X_vld, Y_vld] 115 | ops = [model[k] for k in sorted(model.keys())] 116 | results = tools.iterative_reduce(ops, inputs, args, batch_size=1, fn=lambda x: np.mean(x, axis=0)) 117 | results = dict(zip(sorted(model.keys()), results)) 118 | print("VLD step:%-5d error_top1: %.4f, error_top5: %.4f, loss:%s" % (step, 119 | results["error_top1"], 120 | results["error_top5"], 121 | results["loss"])) 122 | log.report(step=step, 123 | split="VLD", 124 | error_top5=float(results["error_top5"]), 125 | error_top1=float(results["error_top1"]), 126 | loss=float(results["loss"])) 127 | 128 | if (step % checkpoint_iter == 0) or (step + 1 == num_steps): 129 | print("-- saving check point") 130 | tools.save_weights(G, pth.join(checkpoint_dir, "weights.%s" % step)) 131 | 132 | def main(): 133 | batch_size = config['batch_size'] 134 | experiment_dir = config['experiment_dir'] 135 | 136 | # setup experiment and checkpoint directories 137 | checkpoint_dir = pth.join(experiment_dir, 'checkpoints') 138 | if not pth.exists(experiment_dir): 139 | os.makedirs(experiment_dir) 140 | if not pth.exists(checkpoint_dir): 141 | os.makedirs(checkpoint_dir) 142 | 143 | trn_data_generator, vld_data = dataset.get_cifar10(batch_size) 144 | train(trn_data_generator, vld_data) 145 | 146 | 147 | if __name__ == '__main__': 148 | parser = argparse.ArgumentParser() 149 | parser.add_argument('config_file', help='YAML formatted config file') 150 | args = parser.parse_args() 151 | with open(args.config_file) as fp: 152 | config.update(yaml.load(fp)) 153 | 154 | print "Experiment config" 155 | print "------------------" 156 | print json.dumps(config, indent=4) 157 | print "------------------" 158 | main() 159 | -------------------------------------------------------------------------------- /vgg.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime 2 | import math 3 | import time 4 | import numpy as np 5 | import dataset 6 | import tensorflow.python.platform 7 | import tensorflow as tf 8 | import layers as L 9 | 10 | 11 | def build(input_tensor, n_classes=1000, rgb_mean=None, training=True): 12 | # assuming 224x224x3 input_tensor 13 | 14 | # define image mean 15 | if rgb_mean is None: 16 | rgb_mean = np.array([116.779, 123.68, 103.939], dtype=np.float32) 17 | mu = tf.constant(rgb_mean, name="rgb_mean") 18 | keep_prob = 0.5 19 | 20 | # subtract image mean 21 | net = tf.sub(input_tensor, mu, name="input_mean_centered") 22 | 23 | # block 1 -- outputs 112x112x64 24 | net = L.conv(net, name="conv1_1", kh=3, kw=3, n_out=64) 25 | net = L.conv(net, name="conv1_2", kh=3, kw=3, n_out=64) 26 | net = L.pool(net, name="pool1", kh=2, kw=2, dw=2, dh=2) 27 | 28 | # block 2 -- outputs 56x56x128 29 | net = L.conv(net, name="conv2_1", kh=3, kw=3, n_out=128) 30 | net = L.conv(net, name="conv2_2", kh=3, kw=3, n_out=128) 31 | net = L.pool(net, name="pool2", kh=2, kw=2, dh=2, dw=2) 32 | 33 | # # block 3 -- outputs 28x28x256 34 | net = L.conv(net, name="conv3_1", kh=3, kw=3, n_out=256) 35 | net = L.conv(net, name="conv3_2", kh=3, kw=3, n_out=256) 36 | net = L.pool(net, name="pool3", kh=2, kw=2, dh=2, dw=2) 37 | 38 | # block 4 -- outputs 14x14x512 39 | net = L.conv(net, name="conv4_1", kh=3, kw=3, n_out=512) 40 | net = L.conv(net, name="conv4_2", kh=3, kw=3, n_out=512) 41 | net = L.conv(net, name="conv4_3", kh=3, kw=3, n_out=512) 42 | net = L.pool(net, name="pool4", kh=2, kw=2, dh=2, dw=2) 43 | 44 | # block 5 -- outputs 7x7x512 45 | net = L.conv(net, name="conv5_1", kh=3, kw=3, n_out=512) 46 | net = L.conv(net, name="conv5_2", kh=3, kw=3, n_out=512) 47 | net = L.conv(net, name="conv5_3", kh=3, kw=3, n_out=512) 48 | net = L.pool(net, name="pool5", kh=2, kw=2, dw=2, dh=2) 49 | 50 | # flatten 51 | flattened_shape = np.prod([s.value for s in net.get_shape()[1:]]) 52 | net = tf.reshape(net, [-1, flattened_shape], name="flatten") 53 | 54 | # fully connected 55 | net = L.fully_connected(net, name="fc6", n_out=4096) 56 | net = tf.nn.dropout(net, keep_prob) 57 | net = L.fully_connected(net, name="fc7", n_out=4096) 58 | net = tf.nn.dropout(net, keep_prob) 59 | net = L.fully_connected(net, name="fc8", n_out=n_classes) 60 | return net 61 | 62 | if __name__ == '__main__': 63 | x = tf.placeholder(tf.float32, [10, 224, 224, 3]) 64 | net = build(x) 65 | --------------------------------------------------------------------------------