{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Download Data\n", "\n", "In this notebook, we will be using the [Robust Human Detection Data](https://link.springer.com/chapter/10.1007/978-981-15-7031-5_103).\n", "Karthika, N. J., & Chandran, S. (2020). Addressing the False Positives in Pedestrian Detection. In Electronic Systems and Intelligent Computing (pp. 1083-1092). Springer, Singapore." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "download: s3://deeplens-test-public/humandetection_data.zip to ./humandetection_data.zip\n", "download: s3://deeplens-test-public/pedestrian_safety_detection.zip to ./pedestrian_safety_detection.zip\n", "Archive: humandetection_data.zip\n", " creating: humandetection/.ipynb_checkpoints/\n", " creating: humandetection/annotations/\n", " creating: humandetection/images/\n", " creating: humandetection/test_images/\n", " inflating: humandetection/val_mask.lst \n", " inflating: humandetection/train_mask.idx \n", " inflating: humandetection/train_mask.lst \n", " inflating: humandetection/val_mask.rec \n", " inflating: humandetection/train_mask.rec \n", " inflating: humandetection/val_mask.idx \n", " inflating: humandetection/annotations/Val_image (118).xml \n", " inflating: humandetection/annotations/Train_image 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humandetection/images/Test_image (66).jpg \n", " inflating: humandetection/images/Train_image (117).jpg \n", " inflating: humandetection/images/Test_image (201).jpg \n", " inflating: humandetection/images/Test_image (177).jpg \n", " inflating: humandetection/images/Val_image (129).jpg \n", " inflating: humandetection/images/Val_image (105).jpg \n", " inflating: humandetection/images/Val_image (80).jpg \n", " inflating: humandetection/images/Train_image (608).jpg \n", " inflating: humandetection/images/Train_image (615).jpg \n", " inflating: humandetection/images/Train_image (784).jpg \n", " inflating: humandetection/images/Train_image (154).jpg \n", " inflating: humandetection/images/Train_image (428).jpg \n", " inflating: humandetection/images/Train_image (299).jpg \n", " inflating: humandetection/images/Train_image (439).jpg \n", " inflating: humandetection/images/Train_image (109).jpg \n", " inflating: humandetection/images/Train_image (203).jpg \n", " inflating: humandetection/images/Train_image (328).jpg \n", " inflating: humandetection/images/Train_image (777).jpg \n", " inflating: humandetection/images/Train_image (161).jpg \n", " inflating: humandetection/images/Train_image (165).jpg \n", " inflating: humandetection/images/Train_image (622).jpg \n", " inflating: humandetection/images/Val_image (114).jpg \n", " inflating: humandetection/images/Val_image (27).jpg \n", " inflating: humandetection/images/Test_image (186).jpg \n", " inflating: humandetection/images/Train_image (315).jpg \n", " inflating: humandetection/images/Train_image (722).jpg \n", " inflating: humandetection/images/Train_image (141).jpg \n", " creating: humandetection/test_images/.ipynb_checkpoints/\n", " inflating: humandetection/test_images/test_14_image.jpg \n", " inflating: humandetection/test_images/test_13_image.jpg \n", "Archive: pedestrian_safety_detection.zip\n", " creating: humandetection_iot/greengrasssdk/\n", " inflating: humandetection_iot/lambda_function.py \n", " inflating: humandetection_iot/utils.py \n", " inflating: humandetection_iot/labels.txt \n", " creating: humandetection_iot/greengrasssdk/stream_manager/\n", " creating: humandetection_iot/greengrasssdk/utils/\n", " inflating: humandetection_iot/greengrasssdk/SecretsManager.py \n", " inflating: humandetection_iot/greengrasssdk/client.py \n", " inflating: humandetection_iot/greengrasssdk/__init__.py \n", " inflating: humandetection_iot/greengrasssdk/Lambda.py \n", " inflating: humandetection_iot/greengrasssdk/IoTDataPlane.py \n", " creating: humandetection_iot/greengrasssdk/stream_manager/data/\n", " inflating: humandetection_iot/greengrasssdk/stream_manager/streammanagerclient.py \n", " inflating: humandetection_iot/greengrasssdk/stream_manager/exceptions.py \n", " inflating: humandetection_iot/greengrasssdk/stream_manager/__init__.py \n", " inflating: humandetection_iot/greengrasssdk/stream_manager/util.py \n", " inflating: humandetection_iot/greengrasssdk/stream_manager/data/__init__.py \n", " inflating: humandetection_iot/greengrasssdk/utils/testing.py \n", " inflating: humandetection_iot/greengrasssdk/utils/__init__.py \n" ] } ], "source": [ "!aws s3 cp s3://deeplens-test-public/humandetection_data.zip .\n", "!aws s3 cp s3://deeplens-test-public/pedestrian_safety_detection.zip .\n", " \n", "!rm -rf humandetection/\n", "!rm -rf humandetection_iot/\n", "\n", "!unzip humandetection_data.zip -d humandetection\n", "!unzip pedestrian_safety_detection.zip -d humandetection_iot" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from bs4 import BeautifulSoup\n", "import lxml\n", "import matplotlib.pyplot as plt\n", "import os\n", "import random\n", "import cv2\n", "random.seed(21)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "DATA_IMG_PATH = './humandetection/images/'\n", "DATA_ANN_PATH = './humandetection/annotations/'\n", "DATA_PATH = './humandetection/'\n", "FINAL_IMG_PATH = 'images/'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def generate_box(obj):\n", " \n", " xmin = int(obj.find('xmin').text)\n", " ymin = int(obj.find('ymin').text)\n", " xmax = int(obj.find('xmax').text)\n", " ymax = int(obj.find('ymax').text)\n", " \n", " return [xmin, ymin, xmax, ymax]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def generate_label(obj):\n", "# return obj.find('name').text\n", " if obj.find('name').text == \"person\":\n", " return 1\n", " elif obj.find('name').text == \"person-like\":\n", " return 0\n", " return 2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def generate_target(image_id, file): \n", " with open(file) as f:\n", " data = f.read()\n", " soup = BeautifulSoup(data, 'lxml')\n", " objects = soup.find_all('object')\n", "\n", " num_objs = len(objects)\n", "\n", " boxes = []\n", " labels = []\n", " for i in objects:\n", " boxes.append(generate_box(i))\n", " labels.append(generate_label(i))\n", " \n", " \n", " return boxes, labels" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "imgs = list(sorted(os.listdir(DATA_IMG_PATH)))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(columns=['set','hsize','lsize','w','h','class','x1','y1','x2','y2','src'])\n", "for img_name in imgs:\n", " img_id = (img_name.split('/')[-1]).split('.')[0]\n", " img_path = FINAL_IMG_PATH + img_name\n", " img = cv2.imread(DATA_IMG_PATH + img_name)\n", " h,w,_ = img.shape\n", " boxes, labels = generate_target(img_id,DATA_ANN_PATH+'{}.xml'.format(img_id))\n", " splitset = 'TRAIN'\n", " rn = random.random()\n", " if rn > 0.7 and rn < 0.9:\n", " splitset = 'VALIDATE'\n", " elif rn >= 0.9:\n", " splitset = 'TEST'\n", " \n", " for i,box in enumerate(boxes):\n", " df = df.append({\n", " 'set':splitset,\n", " 'hsize': 4,\n", " 'lsize': 5,\n", " 'w': w,\n", " 'h': h,\n", " 'class': labels[i],\n", " 'x1': box[0]/w,\n", " 'y1': box[1]/h,\n", " 'x2': min(box[2],w)/w,\n", " 'y2': min(box[3],h)/h,\n", " 'src': img_path\n", " }, ignore_index=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 1626\n", "0 1368\n", "Name: class, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['class'].value_counts()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024 611\n" ] } ], "source": [ "train_df = df[df['set'] == 'TRAIN']\n", "val_df = df[df['set'] == 'VALIDATE']\n", "n_train_samples = len(train_df)\n", "n_val_samples = len(val_df)\n", "print(n_train_samples, n_val_samples)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "del train_df['set']\n", "train_df.to_csv(DATA_PATH + 'train_mask.lst', sep='\\t', \n", " header=False, index=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "del val_df['set']\n", "val_df.to_csv(DATA_PATH + 'val_mask.lst', sep='\\t', \n", " header=False, index=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/ec2-user/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet\n" ] } ], "source": [ "import mxnet as mx\n", "mxnet_path = mx.__file__[ : mx.__file__.rfind('/')]\n", "print(mxnet_path)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating .rec file from /home/ec2-user/SageMaker/humandetection/train_mask.lst in /home/ec2-user/SageMaker/humandetection\n", "multiprocessing not available, fall back to single threaded encoding\n", "time: 0.0009083747863769531 count: 0\n", "time: 0.23420023918151855 count: 1000\n", "time: 0.36174440383911133 count: 2000\n" ] } ], "source": [ "!python $mxnet_path/tools/im2rec.py --pass-through --pack-label $DATA_PATH/train_mask.lst $DATA_PATH/" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating .rec file from /home/ec2-user/SageMaker/humandetection/val_mask.lst in /home/ec2-user/SageMaker/humandetection\r\n", "multiprocessing not available, fall back to single threaded encoding\r\n", "time: 0.0006139278411865234 count: 0\r\n" ] } ], "source": [ "!python $mxnet_path/tools/im2rec.py --pass-through --pack-label $DATA_PATH/val_mask.lst $DATA_PATH/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "BUCKET = 'deeplens-test-public'\n", "PREFIX = 'deeplens-humandetection2class'" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "s3_output_location = 's3://{}/{}/output'.format(BUCKET, PREFIX)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upload: humandetection/train_mask.rec to s3://deeplens-test-public/deeplens-humandetection2class/data/train.rec\n", "upload: humandetection/val_mask.rec to s3://deeplens-test-public/deeplens-humandetection2class/data/val.rec\n" ] } ], "source": [ "s3train_rec = 's3://{}/{}/data/train.rec'.format(BUCKET, PREFIX)\n", "s3val_rec = 's3://{}/{}/data/val.rec'.format(BUCKET, PREFIX)\n", "\n", "!aws s3 cp humandetection/train_mask.rec $s3train_rec\n", "!aws s3 cp humandetection/val_mask.rec $s3val_rec" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "arn:aws:iam::418262057251:role/service-role/AmazonSageMaker-ExecutionRole-20200925T102744\n", "CPU times: user 313 ms, sys: 32.4 ms, total: 345 ms\n", "Wall time: 1 s\n" ] } ], "source": [ "%%time\n", "import sagemaker\n", "from sagemaker import get_execution_role\n", "\n", "role = get_execution_role()\n", "print(role)\n", "sess = sagemaker.Session()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The method get_image_uri has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n", "Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: latest.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "811284229777.dkr.ecr.us-east-1.amazonaws.com/object-detection:1\n" ] } ], "source": [ "from sagemaker.amazon.amazon_estimator import get_image_uri\n", "\n", "training_image = get_image_uri(sess.boto_region_name, 'object-detection', repo_version=\"latest\")\n", "print (training_image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "Now that we are done with all the setup that is needed, we are ready to train our object detector. To begin, let us create a ``sageMaker.estimator.Estimator`` object. This estimator will launch the training job." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The object detection algorithm at its core is the [Single-Shot Multi-Box detection algorithm (SSD)](https://arxiv.org/abs/1512.02325). This algorithm uses a `base_network`, which is typically a [VGG](https://arxiv.org/abs/1409.1556) or a [ResNet](https://arxiv.org/abs/1512.03385). The Amazon SageMaker object detection algorithm supports VGG-16 and ResNet-50 now. It also has a lot of options for hyperparameters that help configure the training job. The next step in our training, is to setup these hyperparameters and data channels for training the model. Consider the following example definition of hyperparameters. See the SageMaker Object Detection [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection.html) for more details on the hyperparameters.\n", "\n", "One of the hyperparameters here for instance is the `epochs`. This defines how many passes of the dataset we iterate over and determines that training time of the algorithm. For the sake of demonstration let us run only `10` epochs. Based on our tests, train the model for `30` epochs with similar settings should give us reasonable detection results on the Pascal VOC data." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "train_instance_count has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n", "train_instance_type has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n", "train_max_run has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n", "train_volume_size has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n" ] } ], "source": [ "od_model = sagemaker.estimator.Estimator(training_image,\n", " role, \n", " train_instance_count=1, \n", " train_instance_type='ml.p2.xlarge',\n", " train_volume_size = 50,\n", " train_max_run = 360000,\n", " input_mode= 'File',\n", " output_path=s3_output_location,\n", " sagemaker_session=sess)\n", "\n", "od_model.set_hyperparameters(base_network='resnet-50',\n", " use_pretrained_model=1,\n", " num_classes=2,\n", " mini_batch_size=32,\n", " epochs=100,\n", " learning_rate=0.003,\n", " lr_scheduler_step='3,6',\n", " lr_scheduler_factor=0.1,\n", " optimizer='sgd',\n", " momentum=0.9,\n", " weight_decay=0.0005,\n", " overlap_threshold=0.5,\n", " nms_threshold=0.45,\n", " image_shape=300,\n", " num_training_samples=n_train_samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the hyperparameters are setup, let us prepare the handshake between our data channels and the algorithm. To do this, we need to create the `sagemaker.session.s3_input` objects from our data channels. These objects are then put in a simple dictionary, which the algorithm consumes." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The class sagemaker.session.s3_input has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n", "The class sagemaker.session.s3_input has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n" ] } ], "source": [ "train_data = sagemaker.session.s3_input(s3train_rec, distribution='FullyReplicated', \n", " content_type='application/x-recordio', s3_data_type='S3Prefix')\n", "validation_data = sagemaker.session.s3_input(s3val_rec, distribution='FullyReplicated', \n", " content_type='application/x-recordio', s3_data_type='S3Prefix')\n", "data_channels = {'train': train_data, 'validation': validation_data}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have our `Estimator` object, we have set the hyperparameters for this object and we have our data channels linked with the algorithm. The only remaining thing to do is to train the algorithm. The following command will train the algorithm. Training the algorithm involves a few steps. Firstly, the instances that we requested while creating the `Estimator` classes are provisioned and are setup with the appropriate libraries. Then, the data from our channels are downloaded into the instance. Once this is done, the training job begins. The provisioning and data downloading will take time, depending on the size of the data. Therefore it might be a few minutes before we start getting data logs for our training jobs. The data logs will also print out Mean Average Precision (mAP) on the validation data, among other losses, for every run of the dataset once or one epoch. This metric is a proxy for the quality of the algorithm. \n", "\n", "Once the job has finished a \"Job complete\" message will be printed. The trained model can be found in the S3 bucket that was setup as `output_path` in the estimator." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2021-01-14 19:16:35 Starting - Starting the training job...\n", "2021-01-14 19:16:59 Starting - Launching requested ML instancesProfilerReport-1610651795: InProgress\n", "......\n", "2021-01-14 19:18:00 Starting - Preparing the instances for training............\n", "2021-01-14 19:20:05 Downloading - Downloading input data\n", "2021-01-14 19:20:05 Training - Downloading the training image...............\n", "2021-01-14 19:22:25 Training - Training image download completed. Training in progress..\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'label_width': u'350', u'early_stopping_min_epochs': u'10', u'epochs': u'30', u'overlap_threshold': u'0.5', u'lr_scheduler_factor': u'0.1', u'_num_kv_servers': u'auto', u'weight_decay': u'0.0005', u'mini_batch_size': u'32', u'use_pretrained_model': u'0', u'freeze_layer_pattern': u'', u'lr_scheduler_step': u'', u'early_stopping': u'False', u'early_stopping_patience': u'5', u'momentum': u'0.9', u'num_training_samples': u'', u'optimizer': u'sgd', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.0', u'learning_rate': u'0.001', u'kv_store': u'device', u'nms_threshold': u'0.45', u'num_classes': u'', u'base_network': u'vgg-16', u'nms_topk': u'400', u'_kvstore': u'device', u'image_shape': u'300'}\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {u'learning_rate': u'0.003', u'epochs': u'100', u'nms_threshold': u'0.45', u'optimizer': u'sgd', u'base_network': u'resnet-50', u'image_shape': u'300', u'lr_scheduler_step': u'3,6', u'momentum': u'0.9', u'overlap_threshold': u'0.5', u'num_training_samples': u'2024', u'mini_batch_size': u'32', u'weight_decay': u'0.0005', u'use_pretrained_model': u'1', u'num_classes': u'2', u'lr_scheduler_factor': u'0.1'}\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Final configuration: {u'label_width': u'350', u'early_stopping_min_epochs': u'10', u'epochs': u'100', u'overlap_threshold': u'0.5', u'lr_scheduler_factor': u'0.1', u'_num_kv_servers': u'auto', u'weight_decay': u'0.0005', u'mini_batch_size': u'32', u'use_pretrained_model': u'1', u'freeze_layer_pattern': u'', u'lr_scheduler_step': u'3,6', u'early_stopping': u'False', u'early_stopping_patience': u'5', u'momentum': u'0.9', u'num_training_samples': u'2024', u'optimizer': u'sgd', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.0', u'learning_rate': u'0.003', u'kv_store': u'device', u'nms_threshold': u'0.45', u'num_classes': u'2', u'base_network': u'resnet-50', u'nms_topk': u'400', u'_kvstore': u'device', u'image_shape': u'300'}\u001b[0m\n", "\u001b[34mProcess 1 is a worker.\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Using default worker.\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Loaded iterator creator application/x-image for content type ('application/x-image', '1.0')\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Loaded iterator creator application/x-recordio for content type ('application/x-recordio', '1.0')\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Loaded iterator creator image/png for content type ('image/png', '1.0')\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Loaded iterator creator image/jpeg for content type ('image/jpeg', '1.0')\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Checkpoint loading and saving are disabled.\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] nvidia-smi took: 0.0251889228821 secs to identify 1 gpus\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 INFO 140028921575232] Number of GPUs being used: 1\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:30 WARNING 140028921575232] Training images are resized to image shape (3, 300, 300)\u001b[0m\n", "\u001b[34m[19:22:30] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/io/iter_image_det_recordio.cc:283: ImageDetRecordIOParser: /opt/ml/input/data/train/train.rec, use 3 threads for decoding..\u001b[0m\n", "\u001b[34m[19:22:31] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/io/iter_image_det_recordio.cc:340: ImageDetRecordIOParser: /opt/ml/input/data/train/train.rec, label padding width: 350\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:34 WARNING 140028921575232] Validation images are resized to image shape (3, 300, 300)\u001b[0m\n", "\u001b[34m[19:22:34] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/io/iter_image_det_recordio.cc:283: ImageDetRecordIOParser: /opt/ml/input/data/validation/val.rec, use 3 threads for decoding..\u001b[0m\n", "\u001b[34m[19:22:34] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/io/iter_image_det_recordio.cc:340: ImageDetRecordIOParser: /opt/ml/input/data/validation/val.rec, label padding width: 350\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:37 INFO 140028921575232] Number of GPUs being used: 1\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:37 INFO 140028921575232] Using [gpu(0)] as training context.\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:37 INFO 140028921575232] Number of GPUs being used: 1\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:37 INFO 140028921575232] Create Store: device\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:37 INFO 140028921575232] Using (gpu(0)) as training context.\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:37 INFO 140028921575232] Start training from pretrained model 1.\u001b[0m\n", "\u001b[34m[19:22:37] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...\u001b[0m\n", "\u001b[34m[19:22:37] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:38 INFO 140028921575232] Loaded pretrained model parameters.\u001b[0m\n", "\u001b[34m[01/14/2021 19:22:48 INFO 140028921575232] Creating a new state instance.\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}}, \"EndTime\": 1610652168.545611, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\"}, \"StartTime\": 1610652168.545516}\n", "\u001b[0m\n", "\u001b[34m[19:22:48] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_9.x.42.0/AL2012/generic-flavor/src/src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=0, batch=64 train cross_entropy =(1.1626787707784663)\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=0, batch=64 train smooth_l1 =(0.7271474510607825)\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:39 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:39 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=0, validation mAP =(0.0007791793236688483)\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:55 INFO 140028921575232] Updating the best model with validation-mAP=0.0007791793236688483\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:55 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:24:55 INFO 140028921575232] #progress_metric: host=algo-1, completed 1 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}}, \"EndTime\": 1610652295.751416, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 0}, \"StartTime\": 1610652168.548924}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=1, batch=63 train cross_entropy =(1.0283035360577935)\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=1, batch=63 train smooth_l1 =(0.6873220232940427)\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:25 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:25 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=1, validation mAP =(0.002452813293271103)\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:39 INFO 140028921575232] Updating the best model with validation-mAP=0.002452813293271103\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:39 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:26:39 INFO 140028921575232] #progress_metric: host=algo-1, completed 2 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1610652399.567367, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 1}, \"StartTime\": 1610652295.751721}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=2, batch=63 train cross_entropy =(0.9230886804417635)\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=2, batch=63 train smooth_l1 =(0.6623542567780021)\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:09 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:09 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=2, validation mAP =(0.0090138131938528)\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:23 INFO 140028921575232] Updating the best model with validation-mAP=0.0090138131938528\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:23 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:28:23 INFO 140028921575232] #progress_metric: host=algo-1, completed 3 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1610652503.939208, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 2}, \"StartTime\": 1610652399.567567}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:29:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=3, batch=63 train cross_entropy =(0.8291172083465914)\u001b[0m\n", "\u001b[34m[01/14/2021 19:29:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=3, batch=63 train smooth_l1 =(0.6400071117187152)\u001b[0m\n", "\u001b[34m[01/14/2021 19:29:53 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:29:53 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:30:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=3, validation mAP =(0.01387906555127559)\u001b[0m\n", "\u001b[34m[01/14/2021 19:30:07 INFO 140028921575232] Updating the best model with validation-mAP=0.01387906555127559\u001b[0m\n", "\u001b[34m[01/14/2021 19:30:07 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:30:07 INFO 140028921575232] #progress_metric: host=algo-1, completed 4 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1610652607.871257, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 3}, \"StartTime\": 1610652503.939413}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:31:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=4, batch=64 train cross_entropy =(0.7935027760018161)\u001b[0m\n", "\u001b[34m[01/14/2021 19:31:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=4, batch=64 train smooth_l1 =(0.6073920852480857)\u001b[0m\n", "\u001b[34m[01/14/2021 19:31:40 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:31:41 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:31:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=4, validation mAP =(0.0113728081810649)\u001b[0m\n", "\u001b[34m[01/14/2021 19:31:54 INFO 140028921575232] #progress_metric: host=algo-1, completed 5 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1610652714.590979, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 4}, \"StartTime\": 1610652607.871595}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:33:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=5, batch=63 train cross_entropy =(0.7793052068854938)\u001b[0m\n", "\u001b[34m[01/14/2021 19:33:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=5, batch=63 train smooth_l1 =(0.5790464248410498)\u001b[0m\n", "\u001b[34m[01/14/2021 19:33:24 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:33:24 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:33:38 INFO 140028921575232] #quality_metric: host=algo-1, epoch=5, validation mAP =(0.011753619011979933)\u001b[0m\n", "\u001b[34m[01/14/2021 19:33:38 INFO 140028921575232] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1610652818.309891, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 5}, \"StartTime\": 1610652714.591244}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:35:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=6, batch=63 train cross_entropy =(0.7750923076675882)\u001b[0m\n", "\u001b[34m[01/14/2021 19:35:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=6, batch=63 train smooth_l1 =(0.5780578690582877)\u001b[0m\n", "\u001b[34m[01/14/2021 19:35:08 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:35:08 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:35:22 INFO 140028921575232] #quality_metric: host=algo-1, epoch=6, validation mAP =(0.010853725234569355)\u001b[0m\n", "\u001b[34m[01/14/2021 19:35:22 INFO 140028921575232] #progress_metric: host=algo-1, completed 7 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1610652922.534243, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 6}, \"StartTime\": 1610652818.310076}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:36:52 INFO 140028921575232] #quality_metric: host=algo-1, epoch=7, batch=63 train cross_entropy =(0.7670526716827807)\u001b[0m\n", "\u001b[34m[01/14/2021 19:36:52 INFO 140028921575232] #quality_metric: host=algo-1, epoch=7, batch=63 train smooth_l1 =(0.5548604400772854)\u001b[0m\n", "\u001b[34m[01/14/2021 19:36:52 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:36:52 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:37:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=7, validation mAP =(0.013305676959468191)\u001b[0m\n", "\u001b[34m[01/14/2021 19:37:07 INFO 140028921575232] #progress_metric: host=algo-1, completed 8 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1610653027.092688, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 7}, \"StartTime\": 1610652922.534442}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:38:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=8, batch=64 train cross_entropy =(0.7667130533393037)\u001b[0m\n", "\u001b[34m[01/14/2021 19:38:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=8, batch=64 train smooth_l1 =(0.5512262283837049)\u001b[0m\n", "\u001b[34m[01/14/2021 19:38:40 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:38:40 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:38:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=8, validation mAP =(0.01205007872598302)\u001b[0m\n", "\u001b[34m[01/14/2021 19:38:54 INFO 140028921575232] #progress_metric: host=algo-1, completed 9 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1610653134.373288, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 8}, \"StartTime\": 1610653027.092895}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=9, batch=63 train cross_entropy =(0.7627963493258849)\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=9, batch=63 train smooth_l1 =(0.5364717294993617)\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:24 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:24 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:38 INFO 140028921575232] #quality_metric: host=algo-1, epoch=9, validation mAP =(0.014216072548070693)\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:38 INFO 140028921575232] Updating the best model with validation-mAP=0.014216072548070693\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:38 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:40:38 INFO 140028921575232] #progress_metric: host=algo-1, completed 10 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1610653238.455773, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 9}, \"StartTime\": 1610653134.373514}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:42:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=10, batch=63 train cross_entropy =(0.7591742487237005)\u001b[0m\n", "\u001b[34m[01/14/2021 19:42:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=10, batch=63 train smooth_l1 =(0.5248669546214876)\u001b[0m\n", "\u001b[34m[01/14/2021 19:42:08 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:42:09 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:42:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=10, validation mAP =(0.014025219843051946)\u001b[0m\n", "\u001b[34m[01/14/2021 19:42:23 INFO 140028921575232] #progress_metric: host=algo-1, completed 11 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}}, \"EndTime\": 1610653343.741983, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 10}, \"StartTime\": 1610653238.455983}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:43:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=11, batch=63 train cross_entropy =(0.7607084529476392)\u001b[0m\n", "\u001b[34m[01/14/2021 19:43:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=11, batch=63 train smooth_l1 =(0.5252125564998897)\u001b[0m\n", "\u001b[34m[01/14/2021 19:43:53 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:43:53 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:44:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=11, validation mAP =(0.016193658006342124)\u001b[0m\n", "\u001b[34m[01/14/2021 19:44:07 INFO 140028921575232] Updating the best model with validation-mAP=0.016193658006342124\u001b[0m\n", "\u001b[34m[01/14/2021 19:44:08 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:44:08 INFO 140028921575232] #progress_metric: host=algo-1, completed 12 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1610653448.131567, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 11}, \"StartTime\": 1610653343.742235}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=12, batch=64 train cross_entropy =(0.7574746852638448)\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=12, batch=64 train smooth_l1 =(0.4958933197291551)\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:40 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:40 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=12, validation mAP =(0.025519190958869536)\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:55 INFO 140028921575232] Updating the best model with validation-mAP=0.025519190958869536\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:55 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:45:55 INFO 140028921575232] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}}, \"EndTime\": 1610653555.183331, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 12}, \"StartTime\": 1610653448.13195}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=13, batch=63 train cross_entropy =(0.7549675382464243)\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=13, batch=63 train smooth_l1 =(0.4872043027925938)\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:25 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:25 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=13, validation mAP =(0.03062938952041645)\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:39 INFO 140028921575232] Updating the best model with validation-mAP=0.03062938952041645\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:39 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:47:39 INFO 140028921575232] #progress_metric: host=algo-1, completed 14 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}}, \"EndTime\": 1610653659.507123, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 13}, \"StartTime\": 1610653555.183541}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=14, batch=63 train cross_entropy =(0.7543425005773331)\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=14, batch=63 train smooth_l1 =(0.4889014778212125)\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:09 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:09 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=14, validation mAP =(0.03960564866811882)\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:23 INFO 140028921575232] Updating the best model with validation-mAP=0.03960564866811882\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:23 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:49:23 INFO 140028921575232] #progress_metric: host=algo-1, completed 15 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1610653763.369103, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 14}, \"StartTime\": 1610653659.50734}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:50:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=15, batch=63 train cross_entropy =(0.752859555546285)\u001b[0m\n", "\u001b[34m[01/14/2021 19:50:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=15, batch=63 train smooth_l1 =(0.4774806690818023)\u001b[0m\n", "\u001b[34m[01/14/2021 19:50:53 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:50:53 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:51:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=15, validation mAP =(0.02951790405717304)\u001b[0m\n", "\u001b[34m[01/14/2021 19:51:07 INFO 140028921575232] #progress_metric: host=algo-1, completed 16 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}}, \"EndTime\": 1610653867.817538, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 15}, \"StartTime\": 1610653763.369909}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:52:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=16, batch=64 train cross_entropy =(0.7531667882622853)\u001b[0m\n", "\u001b[34m[01/14/2021 19:52:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=16, batch=64 train smooth_l1 =(0.468259805309764)\u001b[0m\n", "\u001b[34m[01/14/2021 19:52:40 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:52:40 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:52:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=16, validation mAP =(0.033364353000870914)\u001b[0m\n", "\u001b[34m[01/14/2021 19:52:54 INFO 140028921575232] #progress_metric: host=algo-1, completed 17 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}}, \"EndTime\": 1610653974.673142, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 16}, \"StartTime\": 1610653867.817845}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:54:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=17, batch=63 train cross_entropy =(0.7465571675860736)\u001b[0m\n", "\u001b[34m[01/14/2021 19:54:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=17, batch=63 train smooth_l1 =(0.4541617343157546)\u001b[0m\n", "\u001b[34m[01/14/2021 19:54:24 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:54:24 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:54:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=17, validation mAP =(0.03540934665557506)\u001b[0m\n", "\u001b[34m[01/14/2021 19:54:39 INFO 140028921575232] #progress_metric: host=algo-1, completed 18 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 18, \"sum\": 18.0, \"min\": 18}}, \"EndTime\": 1610654079.007723, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 17}, \"StartTime\": 1610653974.673359}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=18, batch=63 train cross_entropy =(0.7468851374059557)\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=18, batch=63 train smooth_l1 =(0.44919686622353155)\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:09 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:09 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=18, validation mAP =(0.040414367964153854)\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:23 INFO 140028921575232] Updating the best model with validation-mAP=0.040414367964153854\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:23 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:56:23 INFO 140028921575232] #progress_metric: host=algo-1, completed 19 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 19, \"sum\": 19.0, \"min\": 19}}, \"EndTime\": 1610654183.450938, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 18}, \"StartTime\": 1610654079.008019}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:57:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=19, batch=63 train cross_entropy =(0.7471834382661602)\u001b[0m\n", "\u001b[34m[01/14/2021 19:57:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=19, batch=63 train smooth_l1 =(0.44339747689550113)\u001b[0m\n", "\u001b[34m[01/14/2021 19:57:53 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:57:53 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:58:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=19, validation mAP =(0.049167985312268764)\u001b[0m\n", "\u001b[34m[01/14/2021 19:58:07 INFO 140028921575232] Updating the best model with validation-mAP=0.049167985312268764\u001b[0m\n", "\u001b[34m[01/14/2021 19:58:07 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:58:07 INFO 140028921575232] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 20, \"sum\": 20.0, \"min\": 20}}, \"EndTime\": 1610654287.738194, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 19}, \"StartTime\": 1610654183.452094}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=20, batch=64 train cross_entropy =(0.74495713819157)\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=20, batch=64 train smooth_l1 =(0.4267975034361536)\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:40 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:40 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=20, validation mAP =(0.057140125626260044)\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:55 INFO 140028921575232] Updating the best model with validation-mAP=0.057140125626260044\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:55 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 19:59:55 INFO 140028921575232] #progress_metric: host=algo-1, completed 21 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 21, \"sum\": 21.0, \"min\": 21}}, \"EndTime\": 1610654395.256599, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 20}, \"StartTime\": 1610654287.73851}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:01:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=21, batch=63 train cross_entropy =(0.7428122926214377)\u001b[0m\n", "\u001b[34m[01/14/2021 20:01:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=21, batch=63 train smooth_l1 =(0.4268689756142194)\u001b[0m\n", "\u001b[34m[01/14/2021 20:01:25 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:01:25 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:01:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=21, validation mAP =(0.04641424441852649)\u001b[0m\n", "\u001b[34m[01/14/2021 20:01:40 INFO 140028921575232] #progress_metric: host=algo-1, completed 22 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 22, \"sum\": 22.0, \"min\": 22}}, \"EndTime\": 1610654500.488519, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 21}, \"StartTime\": 1610654395.256845}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=22, batch=63 train cross_entropy =(0.7408979025480444)\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=22, batch=63 train smooth_l1 =(0.41547477213083034)\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=22, validation mAP =(0.06207618859418191)\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:24 INFO 140028921575232] Updating the best model with validation-mAP=0.06207618859418191\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:24 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:03:24 INFO 140028921575232] #progress_metric: host=algo-1, completed 23 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 23, \"sum\": 23.0, \"min\": 23}}, \"EndTime\": 1610654604.627712, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 22}, \"StartTime\": 1610654500.489126}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:04:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=23, batch=63 train cross_entropy =(0.7370817319701618)\u001b[0m\n", "\u001b[34m[01/14/2021 20:04:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=23, batch=63 train smooth_l1 =(0.4075671142848632)\u001b[0m\n", "\u001b[34m[01/14/2021 20:04:54 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:04:54 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:05:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=23, validation mAP =(0.0719226334219173)\u001b[0m\n", "\u001b[34m[01/14/2021 20:05:08 INFO 140028921575232] Updating the best model with validation-mAP=0.0719226334219173\u001b[0m\n", "\u001b[34m[01/14/2021 20:05:08 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:05:08 INFO 140028921575232] #progress_metric: host=algo-1, completed 24 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 24, \"sum\": 24.0, \"min\": 24}}, \"EndTime\": 1610654708.854774, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 23}, \"StartTime\": 1610654604.627915}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:06:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=24, batch=64 train cross_entropy =(0.7369874350964487)\u001b[0m\n", "\u001b[34m[01/14/2021 20:06:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=24, batch=64 train smooth_l1 =(0.41304469449239595)\u001b[0m\n", "\u001b[34m[01/14/2021 20:06:41 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:06:41 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:06:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=24, validation mAP =(0.0661314012692259)\u001b[0m\n", "\u001b[34m[01/14/2021 20:06:56 INFO 140028921575232] #progress_metric: host=algo-1, completed 25 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 25, \"sum\": 25.0, \"min\": 25}}, \"EndTime\": 1610654816.230836, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 24}, \"StartTime\": 1610654708.855026}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:08:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=25, batch=63 train cross_entropy =(0.733165732517855)\u001b[0m\n", "\u001b[34m[01/14/2021 20:08:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=25, batch=63 train smooth_l1 =(0.40629847721227996)\u001b[0m\n", "\u001b[34m[01/14/2021 20:08:26 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:08:26 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:08:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=25, validation mAP =(0.05655998301356134)\u001b[0m\n", "\u001b[34m[01/14/2021 20:08:41 INFO 140028921575232] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 26, \"sum\": 26.0, \"min\": 26}}, \"EndTime\": 1610654921.016419, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 25}, \"StartTime\": 1610654816.231053}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=26, batch=63 train cross_entropy =(0.7265569966708177)\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=26, batch=63 train smooth_l1 =(0.4009181707609251)\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=26, validation mAP =(0.0751119282955006)\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:24 INFO 140028921575232] Updating the best model with validation-mAP=0.0751119282955006\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:25 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:10:25 INFO 140028921575232] #progress_metric: host=algo-1, completed 27 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 27, \"sum\": 27.0, \"min\": 27}}, \"EndTime\": 1610655025.091137, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 26}, \"StartTime\": 1610654921.016652}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:11:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=27, batch=63 train cross_entropy =(0.7225229951224583)\u001b[0m\n", "\u001b[34m[01/14/2021 20:11:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=27, batch=63 train smooth_l1 =(0.38742446587981955)\u001b[0m\n", "\u001b[34m[01/14/2021 20:11:54 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:11:55 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:12:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=27, validation mAP =(0.09428761084595615)\u001b[0m\n", "\u001b[34m[01/14/2021 20:12:09 INFO 140028921575232] Updating the best model with validation-mAP=0.09428761084595615\u001b[0m\n", "\u001b[34m[01/14/2021 20:12:09 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:12:09 INFO 140028921575232] #progress_metric: host=algo-1, completed 28 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 28, \"sum\": 28.0, \"min\": 28}}, \"EndTime\": 1610655129.904659, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 27}, \"StartTime\": 1610655025.091356}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:13:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=28, batch=64 train cross_entropy =(0.724879147126999)\u001b[0m\n", "\u001b[34m[01/14/2021 20:13:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=28, batch=64 train smooth_l1 =(0.3839775121563113)\u001b[0m\n", "\u001b[34m[01/14/2021 20:13:42 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:13:42 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:13:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=28, validation mAP =(0.09339684766403974)\u001b[0m\n", "\u001b[34m[01/14/2021 20:13:56 INFO 140028921575232] #progress_metric: host=algo-1, completed 29 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 29, \"sum\": 29.0, \"min\": 29}}, \"EndTime\": 1610655236.986494, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 28}, \"StartTime\": 1610655129.90487}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:15:27 INFO 140028921575232] #quality_metric: host=algo-1, epoch=29, batch=63 train cross_entropy =(0.7227017072119952)\u001b[0m\n", "\u001b[34m[01/14/2021 20:15:27 INFO 140028921575232] #quality_metric: host=algo-1, epoch=29, batch=63 train smooth_l1 =(0.38044110557685745)\u001b[0m\n", "\u001b[34m[01/14/2021 20:15:27 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:15:27 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:15:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=29, validation mAP =(0.08481851984861509)\u001b[0m\n", "\u001b[34m[01/14/2021 20:15:41 INFO 140028921575232] #progress_metric: host=algo-1, completed 30 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 30, \"sum\": 30.0, \"min\": 30}}, \"EndTime\": 1610655341.18944, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 29}, \"StartTime\": 1610655236.986752}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=30, batch=63 train cross_entropy =(0.7193530045350678)\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=30, batch=63 train smooth_l1 =(0.3748174524118778)\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=30, validation mAP =(0.10053686938971812)\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:25 INFO 140028921575232] Updating the best model with validation-mAP=0.10053686938971812\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:25 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:17:25 INFO 140028921575232] #progress_metric: host=algo-1, completed 31 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 31, \"sum\": 31.0, \"min\": 31}}, \"EndTime\": 1610655445.278621, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 30}, \"StartTime\": 1610655341.189725}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:18:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=31, batch=63 train cross_entropy =(0.7156553164621915)\u001b[0m\n", "\u001b[34m[01/14/2021 20:18:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=31, batch=63 train smooth_l1 =(0.3704632999935067)\u001b[0m\n", "\u001b[34m[01/14/2021 20:18:54 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:18:54 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:19:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=31, validation mAP =(0.10767112140578147)\u001b[0m\n", "\u001b[34m[01/14/2021 20:19:08 INFO 140028921575232] Updating the best model with validation-mAP=0.10767112140578147\u001b[0m\n", "\u001b[34m[01/14/2021 20:19:08 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:19:08 INFO 140028921575232] #progress_metric: host=algo-1, completed 32 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 32, \"sum\": 32.0, \"min\": 32}}, \"EndTime\": 1610655549.002099, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 31}, \"StartTime\": 1610655445.27885}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=32, batch=64 train cross_entropy =(0.7123008899848554)\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=32, batch=64 train smooth_l1 =(0.3666173692522102)\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:42 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:42 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=32, validation mAP =(0.11332254307072033)\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:57 INFO 140028921575232] Updating the best model with validation-mAP=0.11332254307072033\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:58 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:20:58 INFO 140028921575232] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 33, \"sum\": 33.0, \"min\": 33}}, \"EndTime\": 1610655658.056226, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 32}, \"StartTime\": 1610655549.003758}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:28 INFO 140028921575232] #quality_metric: host=algo-1, epoch=33, batch=63 train cross_entropy =(0.7075266119267263)\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:28 INFO 140028921575232] #quality_metric: host=algo-1, epoch=33, batch=63 train smooth_l1 =(0.35642830848347434)\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:28 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:28 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=33, validation mAP =(0.13108974358534992)\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:42 INFO 140028921575232] Updating the best model with validation-mAP=0.13108974358534992\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:42 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:22:42 INFO 140028921575232] #progress_metric: host=algo-1, completed 34 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 34, \"sum\": 34.0, \"min\": 34}}, \"EndTime\": 1610655762.690649, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 33}, \"StartTime\": 1610655658.056781}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:24:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=34, batch=63 train cross_entropy =(0.7007501857165984)\u001b[0m\n", "\u001b[34m[01/14/2021 20:24:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=34, batch=63 train smooth_l1 =(0.35842444883948343)\u001b[0m\n", "\u001b[34m[01/14/2021 20:24:12 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:24:12 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:24:27 INFO 140028921575232] #quality_metric: host=algo-1, epoch=34, validation mAP =(0.12801966666630973)\u001b[0m\n", "\u001b[34m[01/14/2021 20:24:27 INFO 140028921575232] #progress_metric: host=algo-1, completed 35 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 35, \"sum\": 35.0, \"min\": 35}}, \"EndTime\": 1610655867.171009, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 34}, \"StartTime\": 1610655762.691435}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:25:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=35, batch=63 train cross_entropy =(0.696770951665681)\u001b[0m\n", "\u001b[34m[01/14/2021 20:25:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=35, batch=63 train smooth_l1 =(0.35351762860670854)\u001b[0m\n", "\u001b[34m[01/14/2021 20:25:57 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:25:57 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:26:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=35, validation mAP =(0.17391209224967652)\u001b[0m\n", "\u001b[34m[01/14/2021 20:26:12 INFO 140028921575232] Updating the best model with validation-mAP=0.17391209224967652\u001b[0m\n", "\u001b[34m[01/14/2021 20:26:12 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:26:12 INFO 140028921575232] #progress_metric: host=algo-1, completed 36 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 36, \"sum\": 36.0, \"min\": 36}}, \"EndTime\": 1610655972.236948, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 35}, \"StartTime\": 1610655867.171445}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:27:45 INFO 140028921575232] #quality_metric: host=algo-1, epoch=36, batch=64 train cross_entropy =(0.6906131487717702)\u001b[0m\n", "\u001b[34m[01/14/2021 20:27:45 INFO 140028921575232] #quality_metric: host=algo-1, epoch=36, batch=64 train smooth_l1 =(0.3557513957772233)\u001b[0m\n", "\u001b[34m[01/14/2021 20:27:45 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:27:46 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:28:00 INFO 140028921575232] #quality_metric: host=algo-1, epoch=36, validation mAP =(0.17948229538583452)\u001b[0m\n", "\u001b[34m[01/14/2021 20:28:00 INFO 140028921575232] Updating the best model with validation-mAP=0.17948229538583452\u001b[0m\n", "\u001b[34m[01/14/2021 20:28:00 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:28:00 INFO 140028921575232] #progress_metric: host=algo-1, completed 37 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 37, \"sum\": 37.0, \"min\": 37}}, \"EndTime\": 1610656080.473753, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 36}, \"StartTime\": 1610655972.237209}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:30 INFO 140028921575232] #quality_metric: host=algo-1, epoch=37, batch=63 train cross_entropy =(0.6893642435163237)\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:30 INFO 140028921575232] #quality_metric: host=algo-1, epoch=37, batch=63 train smooth_l1 =(0.34210885301629845)\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:30 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:30 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:44 INFO 140028921575232] #quality_metric: host=algo-1, epoch=37, validation mAP =(0.18205867081424354)\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:44 INFO 140028921575232] Updating the best model with validation-mAP=0.18205867081424354\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:44 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:29:44 INFO 140028921575232] #progress_metric: host=algo-1, completed 38 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 38, \"sum\": 38.0, \"min\": 38}}, \"EndTime\": 1610656184.888027, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 37}, \"StartTime\": 1610656080.473968}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:15 INFO 140028921575232] #quality_metric: host=algo-1, epoch=38, batch=63 train cross_entropy =(0.6798917518780956)\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:15 INFO 140028921575232] #quality_metric: host=algo-1, epoch=38, batch=63 train smooth_l1 =(0.33929544274402)\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:15 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:15 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:29 INFO 140028921575232] #quality_metric: host=algo-1, epoch=38, validation mAP =(0.191057582767149)\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:29 INFO 140028921575232] Updating the best model with validation-mAP=0.191057582767149\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:29 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:31:29 INFO 140028921575232] #progress_metric: host=algo-1, completed 39 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 39, \"sum\": 39.0, \"min\": 39}}, \"EndTime\": 1610656289.420694, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 38}, \"StartTime\": 1610656184.888242}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:32:59 INFO 140028921575232] #quality_metric: host=algo-1, epoch=39, batch=63 train cross_entropy =(0.6761811739128518)\u001b[0m\n", "\u001b[34m[01/14/2021 20:32:59 INFO 140028921575232] #quality_metric: host=algo-1, epoch=39, batch=63 train smooth_l1 =(0.3364569696228257)\u001b[0m\n", "\u001b[34m[01/14/2021 20:32:59 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:32:59 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:33:13 INFO 140028921575232] #quality_metric: host=algo-1, epoch=39, validation mAP =(0.19546254116504425)\u001b[0m\n", "\u001b[34m[01/14/2021 20:33:13 INFO 140028921575232] Updating the best model with validation-mAP=0.19546254116504425\u001b[0m\n", "\u001b[34m[01/14/2021 20:33:13 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:33:13 INFO 140028921575232] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 40, \"sum\": 40.0, \"min\": 40}}, \"EndTime\": 1610656393.974633, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 39}, \"StartTime\": 1610656289.420904}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:34:46 INFO 140028921575232] #quality_metric: host=algo-1, epoch=40, batch=64 train cross_entropy =(0.6660510200289852)\u001b[0m\n", "\u001b[34m[01/14/2021 20:34:46 INFO 140028921575232] #quality_metric: host=algo-1, epoch=40, batch=64 train smooth_l1 =(0.3380641044789038)\u001b[0m\n", "\u001b[34m[01/14/2021 20:34:46 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:34:46 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:35:01 INFO 140028921575232] #quality_metric: host=algo-1, epoch=40, validation mAP =(0.20601181953356812)\u001b[0m\n", "\u001b[34m[01/14/2021 20:35:01 INFO 140028921575232] Updating the best model with validation-mAP=0.20601181953356812\u001b[0m\n", "\u001b[34m[01/14/2021 20:35:01 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:35:01 INFO 140028921575232] #progress_metric: host=algo-1, completed 41 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 41, \"sum\": 41.0, \"min\": 41}}, \"EndTime\": 1610656501.485261, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 40}, \"StartTime\": 1610656393.974873}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:32 INFO 140028921575232] #quality_metric: host=algo-1, epoch=41, batch=63 train cross_entropy =(0.6712239372602167)\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:32 INFO 140028921575232] #quality_metric: host=algo-1, epoch=41, batch=63 train smooth_l1 =(0.3331427872018114)\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:32 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:32 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:46 INFO 140028921575232] #quality_metric: host=algo-1, epoch=41, validation mAP =(0.20739687653968417)\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:46 INFO 140028921575232] Updating the best model with validation-mAP=0.20739687653968417\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:46 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:36:46 INFO 140028921575232] #progress_metric: host=algo-1, completed 42 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 42, \"sum\": 42.0, \"min\": 42}}, \"EndTime\": 1610656606.35577, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 41}, \"StartTime\": 1610656501.485647}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:16 INFO 140028921575232] #quality_metric: host=algo-1, epoch=42, batch=63 train cross_entropy =(0.6610725946322006)\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:16 INFO 140028921575232] #quality_metric: host=algo-1, epoch=42, batch=63 train smooth_l1 =(0.3309110360493806)\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:16 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:16 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:31 INFO 140028921575232] #quality_metric: host=algo-1, epoch=42, validation mAP =(0.21898220920848016)\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:31 INFO 140028921575232] Updating the best model with validation-mAP=0.21898220920848016\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:31 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:38:31 INFO 140028921575232] #progress_metric: host=algo-1, completed 43 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 43, \"sum\": 43.0, \"min\": 43}}, \"EndTime\": 1610656711.499255, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 42}, \"StartTime\": 1610656606.355966}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:01 INFO 140028921575232] #quality_metric: host=algo-1, epoch=43, batch=63 train cross_entropy =(0.6567580198031958)\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:01 INFO 140028921575232] #quality_metric: host=algo-1, epoch=43, batch=63 train smooth_l1 =(0.32823889252949895)\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:01 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:01 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:16 INFO 140028921575232] #quality_metric: host=algo-1, epoch=43, validation mAP =(0.22392649633736722)\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:16 INFO 140028921575232] Updating the best model with validation-mAP=0.22392649633736722\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:17 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:40:17 INFO 140028921575232] #progress_metric: host=algo-1, completed 44 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 44, \"sum\": 44.0, \"min\": 44}}, \"EndTime\": 1610656817.123226, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 43}, \"StartTime\": 1610656711.499487}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:41:49 INFO 140028921575232] #quality_metric: host=algo-1, epoch=44, batch=64 train cross_entropy =(0.6502174281641545)\u001b[0m\n", "\u001b[34m[01/14/2021 20:41:49 INFO 140028921575232] #quality_metric: host=algo-1, epoch=44, batch=64 train smooth_l1 =(0.3245313100212744)\u001b[0m\n", "\u001b[34m[01/14/2021 20:41:49 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:41:49 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:42:03 INFO 140028921575232] #quality_metric: host=algo-1, epoch=44, validation mAP =(0.2171633609060347)\u001b[0m\n", "\u001b[34m[01/14/2021 20:42:03 INFO 140028921575232] #progress_metric: host=algo-1, completed 45 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 45, \"sum\": 45.0, \"min\": 45}}, \"EndTime\": 1610656923.740184, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 44}, \"StartTime\": 1610656817.123528}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:34 INFO 140028921575232] #quality_metric: host=algo-1, epoch=45, batch=63 train cross_entropy =(0.6467657556652412)\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:34 INFO 140028921575232] #quality_metric: host=algo-1, epoch=45, batch=63 train smooth_l1 =(0.31682004489075466)\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:34 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:34 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:48 INFO 140028921575232] #quality_metric: host=algo-1, epoch=45, validation mAP =(0.23024741226917944)\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:48 INFO 140028921575232] Updating the best model with validation-mAP=0.23024741226917944\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:48 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:43:48 INFO 140028921575232] #progress_metric: host=algo-1, completed 46 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 46, \"sum\": 46.0, \"min\": 46}}, \"EndTime\": 1610657028.409323, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 45}, \"StartTime\": 1610656923.740365}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:18 INFO 140028921575232] #quality_metric: host=algo-1, epoch=46, batch=63 train cross_entropy =(0.6437871887319435)\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:18 INFO 140028921575232] #quality_metric: host=algo-1, epoch=46, batch=63 train smooth_l1 =(0.3223095217681941)\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:18 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:19 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:33 INFO 140028921575232] #quality_metric: host=algo-1, epoch=46, validation mAP =(0.24109892834469668)\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:33 INFO 140028921575232] Updating the best model with validation-mAP=0.24109892834469668\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:33 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:45:33 INFO 140028921575232] #progress_metric: host=algo-1, completed 47 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 47, \"sum\": 47.0, \"min\": 47}}, \"EndTime\": 1610657133.406557, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 46}, \"StartTime\": 1610657028.40959}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:03 INFO 140028921575232] #quality_metric: host=algo-1, epoch=47, batch=63 train cross_entropy =(0.6392606820998917)\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:03 INFO 140028921575232] #quality_metric: host=algo-1, epoch=47, batch=63 train smooth_l1 =(0.31303637849697463)\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:03 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:03 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:18 INFO 140028921575232] #quality_metric: host=algo-1, epoch=47, validation mAP =(0.24675138788476858)\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:18 INFO 140028921575232] Updating the best model with validation-mAP=0.24675138788476858\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:18 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:47:18 INFO 140028921575232] #progress_metric: host=algo-1, completed 48 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 48, \"sum\": 48.0, \"min\": 48}}, \"EndTime\": 1610657238.482521, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 47}, \"StartTime\": 1610657133.406935}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:48:51 INFO 140028921575232] #quality_metric: host=algo-1, epoch=48, batch=64 train cross_entropy =(0.6389357308804364)\u001b[0m\n", "\u001b[34m[01/14/2021 20:48:51 INFO 140028921575232] #quality_metric: host=algo-1, epoch=48, batch=64 train smooth_l1 =(0.3152178572727406)\u001b[0m\n", "\u001b[34m[01/14/2021 20:48:51 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:48:51 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:49:05 INFO 140028921575232] #quality_metric: host=algo-1, epoch=48, validation mAP =(0.23303445845396248)\u001b[0m\n", "\u001b[34m[01/14/2021 20:49:05 INFO 140028921575232] #progress_metric: host=algo-1, completed 49 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 49, \"sum\": 49.0, \"min\": 49}}, \"EndTime\": 1610657345.782077, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 48}, \"StartTime\": 1610657238.482839}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:50:36 INFO 140028921575232] #quality_metric: host=algo-1, epoch=49, batch=63 train cross_entropy =(0.6340896892513389)\u001b[0m\n", "\u001b[34m[01/14/2021 20:50:36 INFO 140028921575232] #quality_metric: host=algo-1, epoch=49, batch=63 train smooth_l1 =(0.2997782155951591)\u001b[0m\n", "\u001b[34m[01/14/2021 20:50:36 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:50:36 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:50:50 INFO 140028921575232] #quality_metric: host=algo-1, epoch=49, validation mAP =(0.24213574186382636)\u001b[0m\n", "\u001b[34m[01/14/2021 20:50:50 INFO 140028921575232] #progress_metric: host=algo-1, completed 50 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 50, \"sum\": 50.0, \"min\": 50}}, \"EndTime\": 1610657450.968482, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 49}, \"StartTime\": 1610657345.782293}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:20 INFO 140028921575232] #quality_metric: host=algo-1, epoch=50, batch=63 train cross_entropy =(0.6215332316513286)\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:20 INFO 140028921575232] #quality_metric: host=algo-1, epoch=50, batch=63 train smooth_l1 =(0.3041137701605072)\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:20 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:21 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:35 INFO 140028921575232] #quality_metric: host=algo-1, epoch=50, validation mAP =(0.24910865058733328)\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:35 INFO 140028921575232] Updating the best model with validation-mAP=0.24910865058733328\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:35 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:52:35 INFO 140028921575232] #progress_metric: host=algo-1, completed 51 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 51, \"sum\": 51.0, \"min\": 51}}, \"EndTime\": 1610657555.176351, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 50}, \"StartTime\": 1610657450.968684}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:05 INFO 140028921575232] #quality_metric: host=algo-1, epoch=51, batch=63 train cross_entropy =(0.6191610462700342)\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:05 INFO 140028921575232] #quality_metric: host=algo-1, epoch=51, batch=63 train smooth_l1 =(0.29591365320692165)\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:05 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:05 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:20 INFO 140028921575232] #quality_metric: host=algo-1, epoch=51, validation mAP =(0.256884915390128)\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:20 INFO 140028921575232] Updating the best model with validation-mAP=0.256884915390128\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:20 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:54:20 INFO 140028921575232] #progress_metric: host=algo-1, completed 52 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 52, \"sum\": 52.0, \"min\": 52}}, \"EndTime\": 1610657660.265647, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 51}, \"StartTime\": 1610657555.176563}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:55:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=52, batch=64 train cross_entropy =(0.6179962528366056)\u001b[0m\n", "\u001b[34m[01/14/2021 20:55:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=52, batch=64 train smooth_l1 =(0.2964959561866863)\u001b[0m\n", "\u001b[34m[01/14/2021 20:55:53 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:55:53 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:56:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=52, validation mAP =(0.26177572463274973)\u001b[0m\n", "\u001b[34m[01/14/2021 20:56:07 INFO 140028921575232] Updating the best model with validation-mAP=0.26177572463274973\u001b[0m\n", "\u001b[34m[01/14/2021 20:56:07 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 20:56:07 INFO 140028921575232] #progress_metric: host=algo-1, completed 53 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 53, \"sum\": 53.0, \"min\": 53}}, \"EndTime\": 1610657767.85397, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 52}, \"StartTime\": 1610657660.265872}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:57:37 INFO 140028921575232] #quality_metric: host=algo-1, epoch=53, batch=63 train cross_entropy =(0.6162566656583396)\u001b[0m\n", "\u001b[34m[01/14/2021 20:57:37 INFO 140028921575232] #quality_metric: host=algo-1, epoch=53, batch=63 train smooth_l1 =(0.2952914929256518)\u001b[0m\n", "\u001b[34m[01/14/2021 20:57:37 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:57:37 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:57:52 INFO 140028921575232] #quality_metric: host=algo-1, epoch=53, validation mAP =(0.2527410616675342)\u001b[0m\n", "\u001b[34m[01/14/2021 20:57:52 INFO 140028921575232] #progress_metric: host=algo-1, completed 54 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 54, \"sum\": 54.0, \"min\": 54}}, \"EndTime\": 1610657872.479512, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 53}, \"StartTime\": 1610657767.85417}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 20:59:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=54, batch=63 train cross_entropy =(0.606498830377277)\u001b[0m\n", "\u001b[34m[01/14/2021 20:59:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=54, batch=63 train smooth_l1 =(0.2903641187227689)\u001b[0m\n", "\u001b[34m[01/14/2021 20:59:23 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 20:59:23 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 20:59:38 INFO 140028921575232] #quality_metric: host=algo-1, epoch=54, validation mAP =(0.26173533030454244)\u001b[0m\n", "\u001b[34m[01/14/2021 20:59:38 INFO 140028921575232] #progress_metric: host=algo-1, completed 55 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 55, \"sum\": 55.0, \"min\": 55}}, \"EndTime\": 1610657978.05203, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 54}, \"StartTime\": 1610657872.47986}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=55, batch=63 train cross_entropy =(0.6062471499709176)\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=55, batch=63 train smooth_l1 =(0.29308524589852686)\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:08 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:08 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:22 INFO 140028921575232] #quality_metric: host=algo-1, epoch=55, validation mAP =(0.26880905293955315)\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:22 INFO 140028921575232] Updating the best model with validation-mAP=0.26880905293955315\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:22 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:01:22 INFO 140028921575232] #progress_metric: host=algo-1, completed 56 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 56, \"sum\": 56.0, \"min\": 56}}, \"EndTime\": 1610658082.782546, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 55}, \"StartTime\": 1610657978.052229}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:02:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=56, batch=64 train cross_entropy =(0.6032464094870548)\u001b[0m\n", "\u001b[34m[01/14/2021 21:02:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=56, batch=64 train smooth_l1 =(0.2888952588170523)\u001b[0m\n", "\u001b[34m[01/14/2021 21:02:55 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:02:55 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:03:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=56, validation mAP =(0.28329902653285866)\u001b[0m\n", "\u001b[34m[01/14/2021 21:03:09 INFO 140028921575232] Updating the best model with validation-mAP=0.28329902653285866\u001b[0m\n", "\u001b[34m[01/14/2021 21:03:10 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:03:10 INFO 140028921575232] #progress_metric: host=algo-1, completed 57 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 57, \"sum\": 57.0, \"min\": 57}}, \"EndTime\": 1610658190.151119, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 56}, \"StartTime\": 1610658082.78364}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:04:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=57, batch=63 train cross_entropy =(0.5986068148457159)\u001b[0m\n", "\u001b[34m[01/14/2021 21:04:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=57, batch=63 train smooth_l1 =(0.2806324229642445)\u001b[0m\n", "\u001b[34m[01/14/2021 21:04:40 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:04:40 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:04:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=57, validation mAP =(0.27702000488008566)\u001b[0m\n", "\u001b[34m[01/14/2021 21:04:54 INFO 140028921575232] #progress_metric: host=algo-1, completed 58 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 58, \"sum\": 58.0, \"min\": 58}}, \"EndTime\": 1610658294.811468, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 57}, \"StartTime\": 1610658190.151325}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=58, batch=63 train cross_entropy =(0.5964586231453047)\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:25 INFO 140028921575232] #quality_metric: host=algo-1, epoch=58, batch=63 train smooth_l1 =(0.27958898537521387)\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:25 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:25 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=58, validation mAP =(0.2836873115094977)\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:39 INFO 140028921575232] Updating the best model with validation-mAP=0.2836873115094977\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:39 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:06:39 INFO 140028921575232] #progress_metric: host=algo-1, completed 59 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 59, \"sum\": 59.0, \"min\": 59}}, \"EndTime\": 1610658399.663809, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 58}, \"StartTime\": 1610658294.81171}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:08:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=59, batch=63 train cross_entropy =(0.5883770313580601)\u001b[0m\n", "\u001b[34m[01/14/2021 21:08:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=59, batch=63 train smooth_l1 =(0.27312176578032804)\u001b[0m\n", "\u001b[34m[01/14/2021 21:08:09 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:08:09 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:08:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=59, validation mAP =(0.25146816559315316)\u001b[0m\n", "\u001b[34m[01/14/2021 21:08:23 INFO 140028921575232] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 60, \"sum\": 60.0, \"min\": 60}}, \"EndTime\": 1610658503.882323, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 59}, \"StartTime\": 1610658399.664024}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:09:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=60, batch=64 train cross_entropy =(0.5909001702557344)\u001b[0m\n", "\u001b[34m[01/14/2021 21:09:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=60, batch=64 train smooth_l1 =(0.2822687179140748)\u001b[0m\n", "\u001b[34m[01/14/2021 21:09:56 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:09:57 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:10:11 INFO 140028921575232] #quality_metric: host=algo-1, epoch=60, validation mAP =(0.2946435994581378)\u001b[0m\n", "\u001b[34m[01/14/2021 21:10:11 INFO 140028921575232] Updating the best model with validation-mAP=0.2946435994581378\u001b[0m\n", "\u001b[34m[01/14/2021 21:10:11 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:10:11 INFO 140028921575232] #progress_metric: host=algo-1, completed 61 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 61, \"sum\": 61.0, \"min\": 61}}, \"EndTime\": 1610658611.609399, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 60}, \"StartTime\": 1610658503.882622}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:11:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=61, batch=63 train cross_entropy =(0.5843866329828216)\u001b[0m\n", "\u001b[34m[01/14/2021 21:11:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=61, batch=63 train smooth_l1 =(0.2721264035395299)\u001b[0m\n", "\u001b[34m[01/14/2021 21:11:42 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:11:42 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:11:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=61, validation mAP =(0.2822543177462261)\u001b[0m\n", "\u001b[34m[01/14/2021 21:11:56 INFO 140028921575232] #progress_metric: host=algo-1, completed 62 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 62, \"sum\": 62.0, \"min\": 62}}, \"EndTime\": 1610658716.1988, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 61}, \"StartTime\": 1610658611.609615}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=62, batch=63 train cross_entropy =(0.5771894605420537)\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=62, batch=63 train smooth_l1 =(0.26814900719301643)\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:26 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:26 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=62, validation mAP =(0.29545248024670767)\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:40 INFO 140028921575232] Updating the best model with validation-mAP=0.29545248024670767\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:40 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:13:40 INFO 140028921575232] #progress_metric: host=algo-1, completed 63 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 63, \"sum\": 63.0, \"min\": 63}}, \"EndTime\": 1610658820.488083, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 62}, \"StartTime\": 1610658716.198991}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=63, batch=63 train cross_entropy =(0.5782406871346222)\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=63, batch=63 train smooth_l1 =(0.2669993306713543)\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:09 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=63, validation mAP =(0.29890321309743195)\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:23 INFO 140028921575232] Updating the best model with validation-mAP=0.29890321309743195\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:24 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:15:24 INFO 140028921575232] #progress_metric: host=algo-1, completed 64 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 64, \"sum\": 64.0, \"min\": 64}}, \"EndTime\": 1610658924.130155, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 63}, \"StartTime\": 1610658820.488294}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:16:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=64, batch=64 train cross_entropy =(0.5682730624225799)\u001b[0m\n", "\u001b[34m[01/14/2021 21:16:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=64, batch=64 train smooth_l1 =(0.27311742641827774)\u001b[0m\n", "\u001b[34m[01/14/2021 21:16:56 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:16:56 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:17:11 INFO 140028921575232] #quality_metric: host=algo-1, epoch=64, validation mAP =(0.29909160382324884)\u001b[0m\n", "\u001b[34m[01/14/2021 21:17:11 INFO 140028921575232] Updating the best model with validation-mAP=0.29909160382324884\u001b[0m\n", "\u001b[34m[01/14/2021 21:17:12 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:17:12 INFO 140028921575232] #progress_metric: host=algo-1, completed 65 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 65, \"sum\": 65.0, \"min\": 65}}, \"EndTime\": 1610659032.103321, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 64}, \"StartTime\": 1610658924.130745}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=65, batch=63 train cross_entropy =(0.5647101600462235)\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=65, batch=63 train smooth_l1 =(0.2609552492680083)\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:41 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:42 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=65, validation mAP =(0.30198463118248187)\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:56 INFO 140028921575232] Updating the best model with validation-mAP=0.30198463118248187\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:56 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:18:56 INFO 140028921575232] #progress_metric: host=algo-1, completed 66 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 66, \"sum\": 66.0, \"min\": 66}}, \"EndTime\": 1610659136.27806, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 65}, \"StartTime\": 1610659032.103575}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:20:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=66, batch=63 train cross_entropy =(0.5678177710052392)\u001b[0m\n", "\u001b[34m[01/14/2021 21:20:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=66, batch=63 train smooth_l1 =(0.2624885237221202)\u001b[0m\n", "\u001b[34m[01/14/2021 21:20:26 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:20:26 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:20:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=66, validation mAP =(0.29019133119482177)\u001b[0m\n", "\u001b[34m[01/14/2021 21:20:40 INFO 140028921575232] #progress_metric: host=algo-1, completed 67 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 67, \"sum\": 67.0, \"min\": 67}}, \"EndTime\": 1610659240.125003, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 66}, \"StartTime\": 1610659136.278273}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:22:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=67, batch=63 train cross_entropy =(0.566135025024414)\u001b[0m\n", "\u001b[34m[01/14/2021 21:22:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=67, batch=63 train smooth_l1 =(0.25140966128953646)\u001b[0m\n", "\u001b[34m[01/14/2021 21:22:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:22:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:22:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=67, validation mAP =(0.29469666577870857)\u001b[0m\n", "\u001b[34m[01/14/2021 21:22:24 INFO 140028921575232] #progress_metric: host=algo-1, completed 68 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 68, \"sum\": 68.0, \"min\": 68}}, \"EndTime\": 1610659344.759067, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 67}, \"StartTime\": 1610659240.125248}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:23:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=68, batch=64 train cross_entropy =(0.5589555419317567)\u001b[0m\n", "\u001b[34m[01/14/2021 21:23:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=68, batch=64 train smooth_l1 =(0.2607310184372319)\u001b[0m\n", "\u001b[34m[01/14/2021 21:23:57 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:23:58 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:24:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=68, validation mAP =(0.2955621886460067)\u001b[0m\n", "\u001b[34m[01/14/2021 21:24:12 INFO 140028921575232] #progress_metric: host=algo-1, completed 69 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 69, \"sum\": 69.0, \"min\": 69}}, \"EndTime\": 1610659452.271167, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 68}, \"StartTime\": 1610659344.759374}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=69, batch=63 train cross_entropy =(0.5644979881454681)\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=69, batch=63 train smooth_l1 =(0.2577962785085737)\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:41 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:41 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=69, validation mAP =(0.3082132938046272)\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:55 INFO 140028921575232] Updating the best model with validation-mAP=0.3082132938046272\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:55 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:25:55 INFO 140028921575232] #progress_metric: host=algo-1, completed 70 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 70, \"sum\": 70.0, \"min\": 70}}, \"EndTime\": 1610659555.922163, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 69}, \"StartTime\": 1610659452.271365}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=70, batch=63 train cross_entropy =(0.5604906725394062)\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=70, batch=63 train smooth_l1 =(0.25609764028504445)\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:26 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:26 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=70, validation mAP =(0.31325376926202686)\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:40 INFO 140028921575232] Updating the best model with validation-mAP=0.31325376926202686\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:40 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:27:40 INFO 140028921575232] #progress_metric: host=algo-1, completed 71 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 71, \"sum\": 71.0, \"min\": 71}}, \"EndTime\": 1610659660.815466, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 70}, \"StartTime\": 1610659555.922367}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:29:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=71, batch=63 train cross_entropy =(0.5544088644069242)\u001b[0m\n", "\u001b[34m[01/14/2021 21:29:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=71, batch=63 train smooth_l1 =(0.24949053712343383)\u001b[0m\n", "\u001b[34m[01/14/2021 21:29:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:29:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:29:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=71, validation mAP =(0.31150304093614734)\u001b[0m\n", "\u001b[34m[01/14/2021 21:29:24 INFO 140028921575232] #progress_metric: host=algo-1, completed 72 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 72, \"sum\": 72.0, \"min\": 72}}, \"EndTime\": 1610659764.373721, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 71}, \"StartTime\": 1610659660.815965}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:30:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=72, batch=64 train cross_entropy =(0.5567296035457805)\u001b[0m\n", "\u001b[34m[01/14/2021 21:30:57 INFO 140028921575232] #quality_metric: host=algo-1, epoch=72, batch=64 train smooth_l1 =(0.2562208305433424)\u001b[0m\n", "\u001b[34m[01/14/2021 21:30:57 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:30:57 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:31:11 INFO 140028921575232] #quality_metric: host=algo-1, epoch=72, validation mAP =(0.315349131778183)\u001b[0m\n", "\u001b[34m[01/14/2021 21:31:11 INFO 140028921575232] Updating the best model with validation-mAP=0.315349131778183\u001b[0m\n", "\u001b[34m[01/14/2021 21:31:12 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:31:12 INFO 140028921575232] #progress_metric: host=algo-1, completed 73 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 73, \"sum\": 73.0, \"min\": 73}}, \"EndTime\": 1610659872.07886, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 72}, \"StartTime\": 1610659764.374009}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:32:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=73, batch=63 train cross_entropy =(0.5544836341161847)\u001b[0m\n", "\u001b[34m[01/14/2021 21:32:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=73, batch=63 train smooth_l1 =(0.24873614029722266)\u001b[0m\n", "\u001b[34m[01/14/2021 21:32:41 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:32:42 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:32:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=73, validation mAP =(0.315342245595607)\u001b[0m\n", "\u001b[34m[01/14/2021 21:32:55 INFO 140028921575232] #progress_metric: host=algo-1, completed 74 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 74, \"sum\": 74.0, \"min\": 74}}, \"EndTime\": 1610659975.874905, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 73}, \"StartTime\": 1610659872.079533}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:34:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=74, batch=63 train cross_entropy =(0.5385600318650988)\u001b[0m\n", "\u001b[34m[01/14/2021 21:34:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=74, batch=63 train smooth_l1 =(0.24772541240464385)\u001b[0m\n", "\u001b[34m[01/14/2021 21:34:26 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:34:26 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:34:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=74, validation mAP =(0.30792447956064206)\u001b[0m\n", "\u001b[34m[01/14/2021 21:34:40 INFO 140028921575232] #progress_metric: host=algo-1, completed 75 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 75, \"sum\": 75.0, \"min\": 75}}, \"EndTime\": 1610660080.238879, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 74}, \"StartTime\": 1610659975.875106}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=75, batch=63 train cross_entropy =(0.533759373799434)\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=75, batch=63 train smooth_l1 =(0.24033040010268616)\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=75, validation mAP =(0.3399263123373797)\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:24 INFO 140028921575232] Updating the best model with validation-mAP=0.3399263123373797\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:25 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:36:25 INFO 140028921575232] #progress_metric: host=algo-1, completed 76 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 76, \"sum\": 76.0, \"min\": 76}}, \"EndTime\": 1610660185.104215, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 75}, \"StartTime\": 1610660080.239082}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:37:58 INFO 140028921575232] #quality_metric: host=algo-1, epoch=76, batch=64 train cross_entropy =(0.5416462246403777)\u001b[0m\n", "\u001b[34m[01/14/2021 21:37:58 INFO 140028921575232] #quality_metric: host=algo-1, epoch=76, batch=64 train smooth_l1 =(0.24340669451749503)\u001b[0m\n", "\u001b[34m[01/14/2021 21:37:58 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:37:58 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:38:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=76, validation mAP =(0.34839695469689647)\u001b[0m\n", "\u001b[34m[01/14/2021 21:38:12 INFO 140028921575232] Updating the best model with validation-mAP=0.34839695469689647\u001b[0m\n", "\u001b[34m[01/14/2021 21:38:12 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 21:38:12 INFO 140028921575232] #progress_metric: host=algo-1, completed 77 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 77, \"sum\": 77.0, \"min\": 77}}, \"EndTime\": 1610660292.872274, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 76}, \"StartTime\": 1610660185.104437}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:39:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=77, batch=63 train cross_entropy =(0.5321700481599599)\u001b[0m\n", "\u001b[34m[01/14/2021 21:39:42 INFO 140028921575232] #quality_metric: host=algo-1, epoch=77, batch=63 train smooth_l1 =(0.24221807601912163)\u001b[0m\n", "\u001b[34m[01/14/2021 21:39:42 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:39:42 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:39:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=77, validation mAP =(0.32503508909231027)\u001b[0m\n", "\u001b[34m[01/14/2021 21:39:56 INFO 140028921575232] #progress_metric: host=algo-1, completed 78 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 78, \"sum\": 78.0, \"min\": 78}}, \"EndTime\": 1610660396.363154, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 77}, \"StartTime\": 1610660292.872493}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:41:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=78, batch=63 train cross_entropy =(0.5364092777556877)\u001b[0m\n", "\u001b[34m[01/14/2021 21:41:26 INFO 140028921575232] #quality_metric: host=algo-1, epoch=78, batch=63 train smooth_l1 =(0.2390727552214727)\u001b[0m\n", "\u001b[34m[01/14/2021 21:41:26 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:41:26 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:41:40 INFO 140028921575232] #quality_metric: host=algo-1, epoch=78, validation mAP =(0.33689667549436375)\u001b[0m\n", "\u001b[34m[01/14/2021 21:41:40 INFO 140028921575232] #progress_metric: host=algo-1, completed 79 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 79, \"sum\": 79.0, \"min\": 79}}, \"EndTime\": 1610660500.458826, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 78}, \"StartTime\": 1610660396.363383}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:43:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=79, batch=63 train cross_entropy =(0.5302004443763845)\u001b[0m\n", "\u001b[34m[01/14/2021 21:43:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=79, batch=63 train smooth_l1 =(0.24208797148857)\u001b[0m\n", "\u001b[34m[01/14/2021 21:43:10 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:43:10 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:43:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=79, validation mAP =(0.3172300662390329)\u001b[0m\n", "\u001b[34m[01/14/2021 21:43:24 INFO 140028921575232] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 80, \"sum\": 80.0, \"min\": 80}}, \"EndTime\": 1610660604.042106, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 79}, \"StartTime\": 1610660500.459018}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:44:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=80, batch=64 train cross_entropy =(0.5371596546008669)\u001b[0m\n", "\u001b[34m[01/14/2021 21:44:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=80, batch=64 train smooth_l1 =(0.23879009639394694)\u001b[0m\n", "\u001b[34m[01/14/2021 21:44:56 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:44:56 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:45:11 INFO 140028921575232] #quality_metric: host=algo-1, epoch=80, validation mAP =(0.31438577963845116)\u001b[0m\n", "\u001b[34m[01/14/2021 21:45:11 INFO 140028921575232] #progress_metric: host=algo-1, completed 81 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 81, \"sum\": 81.0, \"min\": 81}}, \"EndTime\": 1610660711.267552, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 80}, \"StartTime\": 1610660604.042318}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:46:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=81, batch=63 train cross_entropy =(0.5206561776158781)\u001b[0m\n", "\u001b[34m[01/14/2021 21:46:41 INFO 140028921575232] #quality_metric: host=algo-1, epoch=81, batch=63 train smooth_l1 =(0.23357126055360056)\u001b[0m\n", "\u001b[34m[01/14/2021 21:46:41 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:46:41 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:46:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=81, validation mAP =(0.3281333700015323)\u001b[0m\n", "\u001b[34m[01/14/2021 21:46:54 INFO 140028921575232] #progress_metric: host=algo-1, completed 82 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 82, \"sum\": 82.0, \"min\": 82}}, \"EndTime\": 1610660814.970366, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 81}, \"StartTime\": 1610660711.267911}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:48:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=82, batch=63 train cross_entropy =(0.5306176332860228)\u001b[0m\n", "\u001b[34m[01/14/2021 21:48:24 INFO 140028921575232] #quality_metric: host=algo-1, epoch=82, batch=63 train smooth_l1 =(0.23490187724924452)\u001b[0m\n", "\u001b[34m[01/14/2021 21:48:24 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:48:25 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:48:38 INFO 140028921575232] #quality_metric: host=algo-1, epoch=82, validation mAP =(0.3359713285361275)\u001b[0m\n", "\u001b[34m[01/14/2021 21:48:38 INFO 140028921575232] #progress_metric: host=algo-1, completed 83 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 83, \"sum\": 83.0, \"min\": 83}}, \"EndTime\": 1610660918.818013, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 82}, \"StartTime\": 1610660814.970627}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:50:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=83, batch=63 train cross_entropy =(0.5207694639395368)\u001b[0m\n", "\u001b[34m[01/14/2021 21:50:08 INFO 140028921575232] #quality_metric: host=algo-1, epoch=83, batch=63 train smooth_l1 =(0.2358320938211566)\u001b[0m\n", "\u001b[34m[01/14/2021 21:50:08 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:50:08 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:50:22 INFO 140028921575232] #quality_metric: host=algo-1, epoch=83, validation mAP =(0.3199912800822074)\u001b[0m\n", "\u001b[34m[01/14/2021 21:50:22 INFO 140028921575232] #progress_metric: host=algo-1, completed 84 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 84, \"sum\": 84.0, \"min\": 84}}, \"EndTime\": 1610661022.978965, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 83}, \"StartTime\": 1610660918.818228}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:51:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=84, batch=64 train cross_entropy =(0.5209703474205579)\u001b[0m\n", "\u001b[34m[01/14/2021 21:51:55 INFO 140028921575232] #quality_metric: host=algo-1, epoch=84, batch=64 train smooth_l1 =(0.23851929472523023)\u001b[0m\n", "\u001b[34m[01/14/2021 21:51:55 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:51:55 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:52:09 INFO 140028921575232] #quality_metric: host=algo-1, epoch=84, validation mAP =(0.3235731761219738)\u001b[0m\n", "\u001b[34m[01/14/2021 21:52:09 INFO 140028921575232] #progress_metric: host=algo-1, completed 85 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 85, \"sum\": 85.0, \"min\": 85}}, \"EndTime\": 1610661129.942963, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 84}, \"StartTime\": 1610661022.979146}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:53:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=85, batch=63 train cross_entropy =(0.5119327732045194)\u001b[0m\n", "\u001b[34m[01/14/2021 21:53:39 INFO 140028921575232] #quality_metric: host=algo-1, epoch=85, batch=63 train smooth_l1 =(0.2363024615911464)\u001b[0m\n", "\u001b[34m[01/14/2021 21:53:39 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:53:39 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:53:54 INFO 140028921575232] #quality_metric: host=algo-1, epoch=85, validation mAP =(0.32836190041081814)\u001b[0m\n", "\u001b[34m[01/14/2021 21:53:54 INFO 140028921575232] #progress_metric: host=algo-1, completed 86 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 86, \"sum\": 86.0, \"min\": 86}}, \"EndTime\": 1610661234.02571, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 85}, \"StartTime\": 1610661129.943177}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:55:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=86, batch=63 train cross_entropy =(0.5248233354607109)\u001b[0m\n", "\u001b[34m[01/14/2021 21:55:23 INFO 140028921575232] #quality_metric: host=algo-1, epoch=86, batch=63 train smooth_l1 =(0.23041142027127093)\u001b[0m\n", "\u001b[34m[01/14/2021 21:55:23 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:55:23 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:55:37 INFO 140028921575232] #quality_metric: host=algo-1, epoch=86, validation mAP =(0.3331037313250344)\u001b[0m\n", "\u001b[34m[01/14/2021 21:55:37 INFO 140028921575232] #progress_metric: host=algo-1, completed 87 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 87, \"sum\": 87.0, \"min\": 87}}, \"EndTime\": 1610661337.080043, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 86}, \"StartTime\": 1610661234.025913}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:57:06 INFO 140028921575232] #quality_metric: host=algo-1, epoch=87, batch=63 train cross_entropy =(0.515406856653042)\u001b[0m\n", "\u001b[34m[01/14/2021 21:57:06 INFO 140028921575232] #quality_metric: host=algo-1, epoch=87, batch=63 train smooth_l1 =(0.22607297036264243)\u001b[0m\n", "\u001b[34m[01/14/2021 21:57:06 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:57:06 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:57:20 INFO 140028921575232] #quality_metric: host=algo-1, epoch=87, validation mAP =(0.3372417903111604)\u001b[0m\n", "\u001b[34m[01/14/2021 21:57:20 INFO 140028921575232] #progress_metric: host=algo-1, completed 88 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 88, \"sum\": 88.0, \"min\": 88}}, \"EndTime\": 1610661440.212755, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 87}, \"StartTime\": 1610661337.08032}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 21:58:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=88, batch=64 train cross_entropy =(0.5161682798056135)\u001b[0m\n", "\u001b[34m[01/14/2021 21:58:53 INFO 140028921575232] #quality_metric: host=algo-1, epoch=88, batch=64 train smooth_l1 =(0.23395992890517284)\u001b[0m\n", "\u001b[34m[01/14/2021 21:58:53 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 21:58:53 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 21:59:07 INFO 140028921575232] #quality_metric: host=algo-1, epoch=88, validation mAP =(0.34651943183060696)\u001b[0m\n", "\u001b[34m[01/14/2021 21:59:07 INFO 140028921575232] #progress_metric: host=algo-1, completed 89 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 89, \"sum\": 89.0, \"min\": 89}}, \"EndTime\": 1610661547.316654, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 88}, \"StartTime\": 1610661440.212951}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:00:36 INFO 140028921575232] #quality_metric: host=algo-1, epoch=89, batch=63 train cross_entropy =(0.5102023058286778)\u001b[0m\n", "\u001b[34m[01/14/2021 22:00:36 INFO 140028921575232] #quality_metric: host=algo-1, epoch=89, batch=63 train smooth_l1 =(0.22529372001669856)\u001b[0m\n", "\u001b[34m[01/14/2021 22:00:36 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:00:36 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:00:50 INFO 140028921575232] #quality_metric: host=algo-1, epoch=89, validation mAP =(0.3362373890207417)\u001b[0m\n", "\u001b[34m[01/14/2021 22:00:50 INFO 140028921575232] #progress_metric: host=algo-1, completed 90 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 90, \"sum\": 90.0, \"min\": 90}}, \"EndTime\": 1610661650.332738, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 89}, \"StartTime\": 1610661547.316868}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:02:19 INFO 140028921575232] #quality_metric: host=algo-1, epoch=90, batch=63 train cross_entropy =(0.4996144131873841)\u001b[0m\n", "\u001b[34m[01/14/2021 22:02:19 INFO 140028921575232] #quality_metric: host=algo-1, epoch=90, batch=63 train smooth_l1 =(0.2239779858175478)\u001b[0m\n", "\u001b[34m[01/14/2021 22:02:19 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:02:20 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:02:33 INFO 140028921575232] #quality_metric: host=algo-1, epoch=90, validation mAP =(0.34732034195916867)\u001b[0m\n", "\u001b[34m[01/14/2021 22:02:33 INFO 140028921575232] #progress_metric: host=algo-1, completed 91 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 91, \"sum\": 91.0, \"min\": 91}}, \"EndTime\": 1610661753.734399, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 90}, \"StartTime\": 1610661650.332961}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:02 INFO 140028921575232] #quality_metric: host=algo-1, epoch=91, batch=63 train cross_entropy =(0.49821356714178167)\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:02 INFO 140028921575232] #quality_metric: host=algo-1, epoch=91, batch=63 train smooth_l1 =(0.2203895146589276)\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:02 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:03 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:16 INFO 140028921575232] #quality_metric: host=algo-1, epoch=91, validation mAP =(0.35039484659391107)\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:16 INFO 140028921575232] Updating the best model with validation-mAP=0.35039484659391107\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:16 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 22:04:17 INFO 140028921575232] #progress_metric: host=algo-1, completed 92 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 92, \"sum\": 92.0, \"min\": 92}}, \"EndTime\": 1610661857.0057, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 91}, \"StartTime\": 1610661753.734611}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:05:48 INFO 140028921575232] #quality_metric: host=algo-1, epoch=92, batch=64 train cross_entropy =(0.513648436935058)\u001b[0m\n", "\u001b[34m[01/14/2021 22:05:48 INFO 140028921575232] #quality_metric: host=algo-1, epoch=92, batch=64 train smooth_l1 =(0.2253886661439177)\u001b[0m\n", "\u001b[34m[01/14/2021 22:05:48 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:05:48 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:06:02 INFO 140028921575232] #quality_metric: host=algo-1, epoch=92, validation mAP =(0.35029013637503803)\u001b[0m\n", "\u001b[34m[01/14/2021 22:06:02 INFO 140028921575232] #progress_metric: host=algo-1, completed 93 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 93, \"sum\": 93.0, \"min\": 93}}, \"EndTime\": 1610661962.442423, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 92}, \"StartTime\": 1610661857.005937}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:07:31 INFO 140028921575232] #quality_metric: host=algo-1, epoch=93, batch=63 train cross_entropy =(0.5073452314540632)\u001b[0m\n", "\u001b[34m[01/14/2021 22:07:31 INFO 140028921575232] #quality_metric: host=algo-1, epoch=93, batch=63 train smooth_l1 =(0.20698051833009873)\u001b[0m\n", "\u001b[34m[01/14/2021 22:07:31 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:07:31 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:07:45 INFO 140028921575232] #quality_metric: host=algo-1, epoch=93, validation mAP =(0.339868416894077)\u001b[0m\n", "\u001b[34m[01/14/2021 22:07:45 INFO 140028921575232] #progress_metric: host=algo-1, completed 94 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 94, \"sum\": 94.0, \"min\": 94}}, \"EndTime\": 1610662065.473679, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 93}, \"StartTime\": 1610661962.442768}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:09:14 INFO 140028921575232] #quality_metric: host=algo-1, epoch=94, batch=63 train cross_entropy =(0.5064948528016248)\u001b[0m\n", "\u001b[34m[01/14/2021 22:09:14 INFO 140028921575232] #quality_metric: host=algo-1, epoch=94, batch=63 train smooth_l1 =(0.22083009977745577)\u001b[0m\n", "\u001b[34m[01/14/2021 22:09:14 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:09:15 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:09:28 INFO 140028921575232] #quality_metric: host=algo-1, epoch=94, validation mAP =(0.3327090620202885)\u001b[0m\n", "\u001b[34m[01/14/2021 22:09:28 INFO 140028921575232] #progress_metric: host=algo-1, completed 95 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 95, \"sum\": 95.0, \"min\": 95}}, \"EndTime\": 1610662168.886465, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 94}, \"StartTime\": 1610662065.473893}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:10:58 INFO 140028921575232] #quality_metric: host=algo-1, epoch=95, batch=63 train cross_entropy =(0.4976329479954037)\u001b[0m\n", "\u001b[34m[01/14/2021 22:10:58 INFO 140028921575232] #quality_metric: host=algo-1, epoch=95, batch=63 train smooth_l1 =(0.22096471552468516)\u001b[0m\n", "\u001b[34m[01/14/2021 22:10:58 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:10:58 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:11:11 INFO 140028921575232] #quality_metric: host=algo-1, epoch=95, validation mAP =(0.35064761549423085)\u001b[0m\n", "\u001b[34m[01/14/2021 22:11:11 INFO 140028921575232] Updating the best model with validation-mAP=0.35064761549423085\u001b[0m\n", "\u001b[34m[01/14/2021 22:11:11 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 22:11:11 INFO 140028921575232] #progress_metric: host=algo-1, completed 96 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 96, \"sum\": 96.0, \"min\": 96}}, \"EndTime\": 1610662271.995589, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 95}, \"StartTime\": 1610662168.886664}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:12:45 INFO 140028921575232] #quality_metric: host=algo-1, epoch=96, batch=64 train cross_entropy =(0.5016114220428737)\u001b[0m\n", "\u001b[34m[01/14/2021 22:12:45 INFO 140028921575232] #quality_metric: host=algo-1, epoch=96, batch=64 train smooth_l1 =(0.21475217973608648)\u001b[0m\n", "\u001b[34m[01/14/2021 22:12:45 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:12:45 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:12:59 INFO 140028921575232] #quality_metric: host=algo-1, epoch=96, validation mAP =(0.33564926802170214)\u001b[0m\n", "\u001b[34m[01/14/2021 22:12:59 INFO 140028921575232] #progress_metric: host=algo-1, completed 97 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 97, \"sum\": 97.0, \"min\": 97}}, \"EndTime\": 1610662379.938451, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 96}, \"StartTime\": 1610662271.995808}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:29 INFO 140028921575232] #quality_metric: host=algo-1, epoch=97, batch=63 train cross_entropy =(0.501544652085156)\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:29 INFO 140028921575232] #quality_metric: host=algo-1, epoch=97, batch=63 train smooth_l1 =(0.21163986320523187)\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:29 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:29 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:43 INFO 140028921575232] #quality_metric: host=algo-1, epoch=97, validation mAP =(0.3621312815449464)\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:43 INFO 140028921575232] Updating the best model with validation-mAP=0.3621312815449464\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:43 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 22:14:43 INFO 140028921575232] #progress_metric: host=algo-1, completed 98 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 98, \"sum\": 98.0, \"min\": 98}}, \"EndTime\": 1610662483.478988, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 97}, \"StartTime\": 1610662379.938729}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:16:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=98, batch=63 train cross_entropy =(0.49945524637774497)\u001b[0m\n", "\u001b[34m[01/14/2021 22:16:12 INFO 140028921575232] #quality_metric: host=algo-1, epoch=98, batch=63 train smooth_l1 =(0.20919122438725457)\u001b[0m\n", "\u001b[34m[01/14/2021 22:16:12 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:16:12 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:16:27 INFO 140028921575232] #quality_metric: host=algo-1, epoch=98, validation mAP =(0.3415566148172554)\u001b[0m\n", "\u001b[34m[01/14/2021 22:16:27 INFO 140028921575232] #progress_metric: host=algo-1, completed 99 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 99, \"sum\": 99.0, \"min\": 99}}, \"EndTime\": 1610662587.130463, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 98}, \"StartTime\": 1610662483.479204}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:17:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=99, batch=63 train cross_entropy =(0.4970630669700725)\u001b[0m\n", "\u001b[34m[01/14/2021 22:17:56 INFO 140028921575232] #quality_metric: host=algo-1, epoch=99, batch=63 train smooth_l1 =(0.2091536835811598)\u001b[0m\n", "\u001b[34m[01/14/2021 22:17:56 INFO 140028921575232] Round of batches complete\u001b[0m\n", "\u001b[34m[01/14/2021 22:17:56 INFO 140028921575232] Updated the metrics\u001b[0m\n", "\u001b[34m[01/14/2021 22:18:10 INFO 140028921575232] #quality_metric: host=algo-1, epoch=99, validation mAP =(0.34204347280753467)\u001b[0m\n", "\u001b[34m[01/14/2021 22:18:10 INFO 140028921575232] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Records Seen\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Reset Count\": {\"count\": 1, \"max\": 100, \"sum\": 100.0, \"min\": 100}}, \"EndTime\": 1610662690.258012, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\", \"epoch\": 99}, \"StartTime\": 1610662587.130726}\n", "\u001b[0m\n", "\u001b[34m[01/14/2021 22:18:10 WARNING 140028921575232] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n", "\u001b[34m[01/14/2021 22:18:10 INFO 140028921575232] Saved checkpoint to \"/opt/ml/model/model_algo_1-0000.params\"\u001b[0m\n", "\u001b[34m[01/14/2021 22:18:10 INFO 140028921575232] Test data is not provided.\u001b[0m\n", "\u001b[34m#metrics {\"Metrics\": {\"epochs\": {\"count\": 1, \"max\": 100, \"sum\": 100.0, \"min\": 100}, \"totaltime\": {\"count\": 1, \"max\": 10542958.321094513, \"sum\": 10542958.321094513, \"min\": 10542958.321094513}, \"setuptime\": {\"count\": 1, \"max\": 11.50202751159668, \"sum\": 11.50202751159668, \"min\": 11.50202751159668}}, \"EndTime\": 1610662693.608321, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"AWS/Object Detection\"}, \"StartTime\": 1610652150.776858}\n", "\u001b[0m\n", "\n", "2021-01-14 22:18:15 Uploading - Uploading generated training model\n", "2021-01-14 22:18:53 Completed - Training job completed\n", "ProfilerReport-1610651795: IssuesFound\n", "Training seconds: 10730\n", "Billable seconds: 10730\n" ] } ], "source": [ "od_model.fit(inputs=data_channels, logs=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing model in the cloud\n", "Once the training is done, we can deploy the trained model as an Amazon SageMaker real-time hosted endpoint. This will allow us to make predictions (or inference) from the model. Note that we don't have to host on the same insantance (or type of instance) that we used to train. Training is a prolonged and compute heavy job that require a different of compute and memory requirements that hosting typically do not. We can choose any type of instance we want to host the model. In our case we chose the `ml.p3.2xlarge` instance to train, but we choose to host the model on the less expensive cpu instance, `ml.m4.xlarge`. The endpoint deployment can be accomplished as follows:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-------------------!" ] } ], "source": [ "object_detector = od_model.deploy(initial_instance_count = 1,\n", " instance_type = 'ml.m4.xlarge')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the trained model is deployed at an endpoint that is up-and-running, we can use this endpoint for inference. To do this, let us download an image from [PEXELS](https://www.pexels.com/) which the algorithm has so-far not seen. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us use our endpoint to try to detect objects within this image. Since the image is `jpeg`, we use the appropriate `content_type` to run the prediction job. The endpoint returns a JSON file that we can simply load and peek into." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results are in a format that is similar to the .lst format with an addition of a confidence score for each detected object. The format of the output can be represented as `[class_index, confidence_score, xmin, ymin, xmax, ymax]`. Typically, we don't consider low-confidence predictions.\n", "\n", "We have provided additional script to easily visualize the detection outputs. You can visulize the high-confidence preditions with bounding box by filtering out low-confidence detections using the script below:" ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [], "source": [ "def inRestrictedSection(ImShape = None, R1 = None, restricted_region = None, kclass = None, score = None, threshold = None):\n", " statement = 'Person Not Detected in Restricted Zone'\n", " if (kclass == 1) and (score > threshold):\n", " Im1 = np.zeros((ImShape[0],ImShape[1],3), np.int32)\n", " cv2.fillPoly(Im1, [R1], 255)\n", " Im2 = np.zeros((ImShape[0],ImShape[1],3), np.int32)\n", " if restricted_region is None:\n", " restricted_region = np.array([[0,ImShape[0]],[ImShape[1],ImShape[0]],[ImShape[1],0], [0,0]], np.int32)\n", " cv2.fillPoly(Im2, [restricted_region], 255)\n", " Im = Im1 * Im2\n", " if np.sum(np.greater(Im, 0))>0:\n", " statement = 'Person Detected in Restricted Zone'\n", " else:\n", " statement = statement\n", " \n", " return statement" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the sake of this notebook, we trained the model with only a few (10) epochs. This implies that the results might not be optimal. To achieve better detection results, you can try to tune the hyperparameters and train the model for more epochs. In our tests, the mAP can reach 0.79 on the Pascal VOC dataset after training the algorithm with `learning_rate=0.0005`, `image_shape=512` and `mini_batch_size=16` for 240 epochs." ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person Not Detected in Restricted Zone\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "file_name = 'humandetection/test_images/test_2_image.png'\n", "img = cv2.imread(file_name)\n", "img =cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n", "thresh = 0.1\n", "height = img.shape[0]\n", "width = img.shape[1]\n", "colors = dict()\n", "\n", "restricted_region = np.array([[200,280],[300,175],[430,190], [430,280]], np.int32)\n", "\n", "\n", "with open(file_name, 'rb') as image:\n", " f = image.read()\n", " b = bytearray(f)\n", " ne = open('n.txt','wb')\n", " ne.write(b)\n", " \n", "\n", "results = object_detector.predict(b, initial_args={'ContentType': 'image/jpeg'})\n", "detections = json.loads(results)\n", "\n", "object_categories = ['no-person', 'person']\n", "\n", "for det in detections['prediction']:\n", " (klass, score, x0, y0, x1, y1) = det\n", " if score < thresh:\n", " continue\n", " cls_id = int(klass)\n", " prob = score\n", " if cls_id not in colors:\n", " colors[cls_id] = (random.random(), random.random(), random.random())\n", " xmin = int(x0 * width)\n", " ymin = int(y0 * height)\n", " xmax = int(x1 * width)\n", " ymax = int(y1 * height)\n", " \n", " if cls_id==1: \n", " R1 = np.array([[xmin,ymin],[xmax,ymin],[xmax,ymax], [xmin,ymax]], np.int32)\n", " cv2.polylines(img,[R1],True, (255,255,0), thickness = 5)\n", " cv2.polylines(img,[restricted_region],True, (255,0,0), thickness = 5)\n", " plt.imshow(img)\n", " print(inRestrictedSection(img.shape,R1 = R1, restricted_region= restricted_region, kclass = cls_id, score = prob, threshold=thresh))\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Person Detected in Restricted Zone\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "file_name = 'humandetection/test_images/test_3_image.png'\n", "img = cv2.imread(file_name)\n", "img =cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n", "thresh = 0.06\n", "height = img.shape[0]\n", "width = img.shape[1]\n", "colors = dict()\n", "\n", "restricted_region = np.array([[0,260],[0,50],[250,70], [200,150], [325,175], [250,260]], np.int32)\n", "\n", "\n", "with open(file_name, 'rb') as image:\n", " f = image.read()\n", " b = bytearray(f)\n", " ne = open('n.txt','wb')\n", " ne.write(b)\n", " \n", "\n", "results = object_detector.predict(b, initial_args={'ContentType': 'image/jpeg'})\n", "detections = json.loads(results)\n", "\n", "object_categories = ['no-person', 'person']\n", "\n", "for det in detections['prediction']:\n", " (klass, score, x0, y0, x1, y1) = det\n", " if score < thresh:\n", " continue\n", " cls_id = int(klass)\n", " prob = score\n", " if cls_id not in colors:\n", " colors[cls_id] = (random.random(), random.random(), random.random())\n", " xmin = int(x0 * width)\n", " ymin = int(y0 * height)\n", " xmax = int(x1 * width)\n", " ymax = int(y1 * height)\n", " \n", " if cls_id==1: \n", " R1 = np.array([[xmin,ymin],[xmax,ymin],[xmax,ymax], [xmin,ymax]], np.int32)\n", " cv2.polylines(img,[R1],True, (255,255,0), thickness = 5)\n", " cv2.polylines(img,[restricted_region],True, (255,0,0), thickness = 5)\n", " plt.imshow(img)\n", " print(inRestrictedSection(img.shape,R1 = R1, restricted_region= restricted_region, kclass = cls_id, score = prob, threshold=thresh))\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pedestrian Detected in Restricted Zone\n" ] }, { "data": { "image/png": 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yj0JjSt77I+/izOnT7GzeQovKXDqJQhuisXQJ/vLP/iUeeOydaC/8b//zP+bapafpZnvc2PesjyJONB/4sffxwZ/593j+c5/j2c8/CTERu0CKQtCJpA3vefxhvvK5L5DKVVaHJzh+8QIn3/AQdnyMa1ev8aHPPMXeq19j89YEImztGmKCxnaMih3eeLbkvo2CX/7y17miCppmxDA0qEJxdiVhKsvLdshg9R7ued/7KcZrrA8dk+0XmN66iQs1enKV4XCF2u/jU4vpFKUdUVhYGY8IIQcZQsTpnO21MXL51h6Xrm9nJ9gHUiBIiAzL7tvi8OsVuSMivwn85v/Dg/soUaFU6LlbQS1dUya+vwkklVKEHgDuqjX1/O8dXhqWkQCEKMQQ0QZSugO6qi+AxLgsgC2zhJTTvv7fQsiAG1NCa0VKCmNAtEYk5SKVCDEm+KY7u3MvcflI/Q8xgbmrcpZiPOSzEQgpkFJiMKiyt1a6d3b5nCkmmrYjJcFZQ+c9Pgoh5OzEh57OSvTOJBBCxPaOD5aFyozunQ+5UAk9SGRwTz31hIDui8BJcnGsH0UweXwSQhfa7MBipGtaYgy0Xcfuzh6+yWmz0xofIqIio5UxxlnqpmY2i2B0rq1Yk1NQo9HGELUhSSKGZXFaQBSLuuNgVlOKxjhBFwNwBbYYYK2jGI7ZujJnMm8gJGLqaZQlfSfCYjE/zFSqskRbR2gbmvmcFBMx9K80ZaoupETru8P5oIAuwjJZUyoAgSQZLLJTB3EWIfW0oEb6yTsYDHjkex7m3IWLzGYzCIFvfPkpbl6/iUqCD4EQQYhEDaF/N1prWp8j7+WQKK3RrsC5gqoqMUSUMohWLJqOpAt0YRmtr2J0piqdVqQYsErhjOLifQ9weqOkLAt2JwesrwxRMdG2HkHx8MMPcfJez63bt+maDkUJKUHhIHb86A/9AE888S4+/mu/zH5dUxnF1m7NdO75m7/wn7G3u83HP/QRdl4D4xxtiojSLOoWVzhM9LTTA5hscuHsOa69/CyzNjCwJUEEi2Jva5NLn/0ULz3/Eot5TWEsWhtEKbx2rPmGZhEYXXgIPdzgzMOPEhiytX2dnT/4DTCOn/yL/xFP7t3mhUubtJ2ijQ2JxCCuohKsrpR0ac6BKNRCk0JibxZIuubMUPN9D72F5FZ57pXnuPbFG6SyJFSrnLl4hsGxexiWJQPzENVsDx2mTLemtGFGOTpOi2H7YIEth3S+RqtchI7aUgxGvPd938fMJ77+2Sd77FuKLBRN++3h+3UD9/83JuSFAhkYKmP66r0cRshKZU4uW47MkbiUZBxW/vVhse9ORJqUwvTA1XUepQKpL1jmqD8vstgD1VKpgAhKcnR9SL9o3Re3MpeeuT3wEnrHsqy/9quMO4qR/pTkguSdz3wIfZSn7xRFeycRQ84YnHOZIvLh8AUvqaxlNtF0nhBi74TyeWLvLHR/X0uaaKlYuft6IeZzp94ZLc+d+jGwfb3CKn1I2SwLxCFGkFycReVIFQR8y3w6J0RhXnfM6wweEhJiwEQohyURQzksUdayN6t7R9I71uCRMo9XJxzWM4zSqN5ptj6wM5kxSmCdQ48sD7/5YVqf2NrexhaWadPShohJic7HTKWYb9INYYzFWMe9F+8BbfBtTfItMUHbdbnWgsJgwNCTZBBjfich9epitaT6+iOMyYVOFUg5dukL38srJ9ZWx1gNoVtQOsvbHn+cs+fP8bWnv4pKglIJ1QMXRhNSSxcXuLJgtL6OEoU1jkE1xBUWVww4uQripxgJJBJNF2m6CEXk8bc/zvs+eD8HzzxJs5/Q/TtFa1zp+Ft/+29z87VnufTkJ0iiOZhMKathzlys4T3f933sp8CHP/xRpGlwWkNM1F6xZoULp9Z44A0nsRJxpgCJpC4inedrv/8kjW9pZ1OatqPQA7Q1NCGibc4GX7n0HNVgha0XP0/bjFlZ22Dn9k0sGk8kREGHFisNpYoociEbUZhqxGA8Rq2dpnrDGzh7MVEvWl740h/QbG0SFhMKP2Pl9AXOnzpOkyLGFYS6gSQYpUjB00ji+atXGMuCWW3RnabTHbFQqFhhUuAHf/BHGK2eYvufv0R36yoxaVbCgM2nL0GxwmDlFKoqqUar2Oo4p+55E0O7QGzBje09grYU1YgUWkJs8npWUJQl5y+e56mvfJ0E0Ad1h2ufO+KOP86+I8AdyQvHakPqOkyhKJ1CkeVWCZCk0NoRQgYcryEpIQWBpPAxHUbFh6VFlSkNQWGMJaVEF7osBUOjiCTpDhfYsHSHapTWd0uiP5/qriKmJkf1WmtUSGi9jNS/9bHu8J8x5cKoUpmuMMocfmcZIVubC3r5shrvQ6ZDugDzBm1MVs8sz987DW1ywavxOcpfXRsfFphbn+sHppfrxZS/s2gWcChJvHPv3seeKsgqpCSJGLPzXR6llAJt+uwlonymwpq2yaCfIl3T0HWetovMZnVfdE0gmsrmYmDq6aXFdI6fZs647Tyh66+lFZKEJAWm9aycdBw7fYIrV29kWigs37nJNZU6slsf4Izle9/5EH/pL/4MQSKtD3zus7/P5Utfo+4aAvm5gwiOnLWE4DFOgzasbqzxM3/hz4LOorirV17jtZf+kMlkQmahFGIgGYXR4LTNvLk2DCqLUhpjHGsrq1nuWpWsb6zgrGU4GHLi5CmK8XFGoxErG2sYaxmPV9i8eYM2JGwxIviAM5a/8bd+if/yv/mlrAZCYVTK4xI6/smHfp9kDGu65Dc/9n/i6wUxJK7tzpAYGJaOK889xcc//BGaWSSGmuNDRzHQbDeRQiVG3QE3ZlNiioQQWbOWQsNiDovnP8Xs5hZt3SJi0a7oi8Qa7Vu2nvsiN2Y1ukukaPGxycCD4H3i+rNP8/ntZxGtMEFIWtEVA4qh5ZMf/nXcaJXBoELpATF1NHVk1nicG5LE8+KlP+T4iXM88/XnSd7xpvvuZ+fGJjtpG82YuXimN+Y8/TtP0+qAfvxdjM9cwA5XeOXyNba3duD6Dd5zcsitL36WzddeZdousUYhCtyiZj6vmU4O2Nub0LQJYgsCjbTc/8Yf4oN//sdZLVt+8+f/O6YGQopMlaFixrBwDNdG+PY2e3v77M7nlL5AOU+JYNrIE295kG88+2VWZoq29uw6yyRqBicu8on/o+TZF55nMpuwurbByvgeBusDzhw/zko1YFGewCfDbuOp6wWknGEpgVvT+tvC6ncEuIukLHmMHZaI74TYRZQEUi9LA7CmJCQQ0Whj8RKJPkJKRBF8LwFTSoNokhiMcbmgmHxWEvheQ0085JbvSB1tLwtU6KgPxZvLopjq+e5l5B5jjs6NAWXuKFmSZM4dkV7+1nP7SVCWXuvda6yXHD2Zr/c9BYLSdD5mVYQtSErTtF2uAWh9WBdoO9/LP4XGe2JMVLhewni3MH2poc6F1RATKWQQPRwHIPVZita6B/ZEJGcQ0hPzxhiatkP39QHodTzie7llZDFvmC8aFouOGNKhvl0rRV1nnbgtClxUFE5jjaHphKaJKGNzJuE9IpHSFqytjHj/938fbaP41X/xL0kxoBAsENXduvB8f+OVVaIIxhbYBI8+8ghDN8Bhe7WVIUkg2sxjOirWxyOcs5w8c/LwXSJw4cIF3veDP8RbH38bo9GI8XDI+voaeuAoi4KN9XWqwuFcgVvdoKoGVGXZy/IcxgnJDhABLYEizQnzzJN1IdB0HVordre3ML3DNS47iYPNTXauvIwKHSp5tHhCLaQUqHfmuDeeZtFO+IOPfoyYNMaNuLlVkxJYGlK7Bb6B0GWZZDIMnGWUhHayz7WDKfW8odSWSMDZAqtgY83yjW98g5Q0pVb4IJTDEcFHRAk+wc2dfbabNtfJtMb7lqoq+5k559auYmN4Dmtz4T2K4Jyh7eYs2gaTElav0fmW0XAEESaLlkFlWBs4HnrwAe6/717SlQd48tIzTMIECoXfr3EjTaME/+aH2XjkCdzaKjv6gFdfu8relS8xPtjnnDUUJzd45xNv59VnvpgLlkFldZJVSLcgxkDdRUIEbSwhzA8l01E7ytBwWiuOlUO8FpQVlIfYeEQFTFmRktB1numsZt56ah/pnAUtWC10ITCfLghVIrSRxYFnrwk8eu4e/tyf+Ql+7X+6xvX5Nvs3LjNJiU4XvDLeoBhUfPG5Fzl78QwPve8DzA72ufLiJSb7e0wP9pjt7n1bXP2OAPeUhKZtIAWsjn3aGoA86D4KGLCmIyVIaApXEnsqYemFs+xOZzYzARSAIuNPjuaXaY02mZIw2hyCnqAIaamNt/29RZbKndzIk8+TOfgeCFOW1y014T1TxLJhSWmTJXiHNLcmSuaKU4KoFGAQlZt6cpE04jMK9zxwoosJFe+0KCkFtQ+H2v7O5yKnEA4j9yVDFHveepmBpKQOgVnuop1MTxflDKGvhSgh9llCkqzusdaSUsC3GZiSUjR1jTVZB79YBBZ1IEaN0NckYr6Oc0V+X9ogaNqQ0NaChsFKiRiNcw7rHKUruXjuLIWzbO/tc/mlq1hbUA2GSIwU1mBLcyiPda7AOcfa+noeR++JKTEcVDzxnvdS13UG46piUFUM1sdYV1AWFRvrKxRWM1tkTX/ouXTQ/Ac/+x8SujbXJ5Y1IpV1XSlEUq/zD6EmhAVxMmFnMc8OthP2gqELiWa6ywlVo1RH0zTMFguSCLdu32ZanOGtb3ucmCLaWlIU6s3L7F29ROFsn2sqNEXm10NAhcSwXGPNGGI5pE3AYgejHasDw85iTmhz38KgKujaSNtFfOs52NujaQKkSCISJWbQFSFoy0//xJ9i8/ptPv2pT6NUQd36XAfygQS85S1vpdza5fLWczSNh6QgZWmuqwLvfvc7+OHv/3H+0T/8RURpmrql7XIz2caxY5TWEXoBQxsCKiXKagBa0abEqTMXiUpz79u+lxtffYoL1YC1c/fSLAakU8c4eXqDC4+9m90O9m5ewX/ti+gYOalgzSaiX9BNQRYz2rqhCxFRjsZHNFD2a9kWjnkT6EJ2WtEHtDJ4FQmzfWR/D1ussejmJCf4LpE6T9AtSa0xi4pFm7i5c8C0VxBNTQQVKW3i+s4Btyc1fi9h0LRdJGDY2qz55Mc+xAvf+DoHt7ZywxUJh0OVB/iqYnLtNW69OiKWK6yurnL27P2MzinOomi3XuC3n7v8J+Lqdwi4J+r6AJKgkzAjUViFM5pbWy37kw4RYWOtxJUaZw3GtWgMMcQcXWZ2Bm11r203WBtRQSHKIspiTFY0tJ2nKEtEGbKYJwNzoNe/L/n/JDnCPSxiZKol9anEEmRzR54+pG/0IYAuj5BDfjpJjux9T6GICNrZO5LEvgAqAloZlI5Z8qk1qY/uY4i5WKkUIgodl4XcLIVsuvqb7kPgMBpXSh0qdbirCYr+mKZvCMODD1kZY5Xqufd8v0kE6vYwAhbJskcvjpgM1jpWT6yx0evsR+MRZVlRViXj0YjBYEBZVqyurWILx9rqKg888ACD4RCNOlQKJRHMYIgET2ENP/9z/wl/6v0/xl/7r34Rb8f5uXwHUvdUQKCuc6q6ezDvi1Qt2uZGsZ/84SfwXUMI8VDa2jQNi65jf3+Xy5cO6Jqa4doG73jP+7DWolV+by/8zq8T2nnm0o2mLAp8mznP0HVZIRMis+CQFEnBc+/5U6yujPDlmGe++BWM0Zw+eYI3vPvtqKJCKcXm9Rv81id/l81btzh+7yqxy3UhnRKFK7CDNR597w+ilCGZit250Oy8wu71KyTxOFtRVqt86UZkYjy7e1u8/8EThC4xqIasnjrGU89fZ3N/lxhqpBNWg0WM4kd/4r2UxYiP/F//moNFAymytz3FCLjhKhe/53vZXzzNpA6k0OX6Qt8XEa3m9P1v4vLBV3MtIraIn7PoEqLBVuvc/+AjmGPHuXlzymYK+NAiqmAwWuX0hQdJSfHci5fYmtYcLFpKY/ExElRiPBzz6Ps+gIxHlJ3hsavb+NGA8fHIzfQcaX/C9rOXeWDlGD/1k3+aL3/m9/i9vSsMigERy/UQCAhWNPuLxCu3Dnh1e0Y0FSl6drXBkDhWztm+epm9zassJpuZCZBAjFBqxUvXL5DlsfIAACAASURBVPGPf+NfU2hDt9hCxFAkxZoRhMClFxt+4a/9dbquyRSlGKxRKDRBAk2MfPoLX2Z9VGJch8KgVKRZLHj2+ad57eolnNGQAoOyoFIKiyfKDqnJQajfh7WQSDc0r1xyRAxRGbRb+ba4+h0B7jEl2q5DC2jJkXeIuVC5P2mZzhMpCDE0WKtwpWZQOWIUfIg0dW4hVlozXjeHkiHrUpYXmqyHz5Zbr6Pk7sAu+MNtAbS+o5a5o7RR+HRHzw4cctSqj9KTkHlYJEeodxVPY8r/tvy+77tCpadHRBI6ZQdxh9vPjiJIQkIGZfHhUN8eY4TYUxA6S/qy9LK/jyVNsWyHZxnBHxYlDjOPZWEYlg7A9tSPohpUuMJSWkfRR8RlWTAejdBFSVUNWFtdoywrqnLAcHWFqqow1jJaXc3FyaJkMBpiTZEja+v6pq1cK0giRBI6dYSugyR0TZbcOQnMD7YZDgfooFitCo7768iNL3HzIGRpa9cgTXaUTduys7ubi+XHzqO//wdAa7Qo2uC5ff15JHQ4V2R1i1IU3iM49ts53Xyf6WSfpBQhBKrC9e3uhgcffzfWGZTRaGvQzhJ9ltFO9w84mBwwPZhSX71BvZhRjUrufeB+urahnc04bzxl6Wh3brL5ymXGZ88yGo64/+JFfvqDH+CTv/spJtqSpO8e7ufq3q2bfOl3vpy7pq3DR4cCHrrvPBfOrjJdHDBeO8mmj1zdPWBlvMZ7vv/9GDck+o4bt6+ySB9nf9Ei0aOSwU8bbOm4vrVLoRZMFoFQJ5CExIRGcH7Ov/oX/ws7t7fY29mini+I2pFixBpH0Iqvf+M5Ll95ibadErqGwgYgIcYQOuHDH/ttBl/4MvNZh7VwYmMNW445mAvPPv8yTYDJdIZSBqMtIjmbtANHORpza3/O3s4Br+xPOZgnDl54gY1S88pTn8FrTxUjs+m9sFowpWNze0rpspLnIHV0klhrPF9/9kWu3t5l1iXasMAQadEUzjG/cYtf+2e/SrN/i/USvJFc73AOoxQ7s11+72v7uKRZGzsIudFxIR7tDN2sRaYdRkFpDePKZmWaVrRJ0/mAiKa0FaVxueY0UFTOsGg8NnhCSLiBAwI6QrKOtg25j6WJOCMcmOw0mm6O9ykHHubg2+LqdwS4dwHaNhcWfZCeYsn64O15yA0L2tDVAkrQ80SSBqP6omVMh6qFydQyHCcGI6EcCEpZRDWIDr1CJLd3m0JlLx3vFBul7xhdFjq1zoAbU38NSShlWLI8d9U2aaPPn2vdUzigpW/NJ9MmPgS0Kg6jaCEeatql1+YnrXrJ41JCmSV4Co0YT5RAEtVr/embZdShNke0plI5UlXakLTBWYu1eTIba6mqiqIoGKysUTjHysqY4XDAaDxkdf0UVTlAKcWJE+s4p1GuYGXlGAqFURpbJJJ12Um2oNoaK5FJqHNmEhMlJQUa8ZEb1y8zr/eQ1OKCMDSJrsn1gS5FktG4ZoF0NRqYp7zfiNMwF8OJM6cxRN7/1vO08xk7169iVUEXEkY0K5WiNQOqtTWuXbtN4xeMxoEUO1QMaG2pCst73/FevDXE9Q0KZdCTGaE5QJThy1/9Oq9evsp8tiC5GmUcXUo4wPuGb3z588T5lPFozHA0IipYbE1YdDUPPPYo5++9F/uWN/NYdU+mVdKMT/+zv8/mjZsYDY89+hhrx04wWF1nfOoMKxvHUEVFjJpz6+c58dXnaRctXRQCUKqEksSZe97IAw+9kRBzB+agWqGyCTdY4ZXf+yKlrVDiuXT1Bmvrp3n4nvtoqxWOn1yju7WDUaucMwopNcKYucmZ2fraKp/59GdZX9tgUAiddqRkWDRdDghC5DOf/GhWgnUti3pOYS3j8YB5PUNry6c//mFEOjZMYjiIaGUgaYyKaL3L1z//aYbDAd633FuNmO7XXD7YxTlLQSIqg4SOswPBh5akLA+/412cePgtbNz/MJ946ina7ZscG4155rc/ymR/jyfe9Q52J3OUEuYKXnn1Bp/5yG/x9Fe+wcu7+9xpVMzBze7BhOf+0X9PSpEmeJTSdDExcJZSJYrCsnvzCkikNAknEVdonAOtEoUSBJ+llWiS0aQYcUmRmoDVqVdsKULv7JUCnRRJabQFUcKkmVAZh7WaUhsKDNpCmzxWKWxfB0MgRM+kC4SUJa140CmQkqdtU69461ikuwDoj7HvCHAXpfjGK1NKZ3DOEJOiaef9w5o+Er7DfWfOe9kQtOSg+9Z/lWA3YvY9a6sFgypinUb1hUjVd4aWbZk7W42hI4MyqU87ZbkdQT637vf2yIFvQFjSLp4sOcwNSLov6sUQ0EYTlUJbc1jsW1tdRYxDG8NoPD4E2+F4lcLl4tz66iqlc7hqQDUaUzlHVZaMhiNcVaGtpawcxurc2WcGWSt9V1Qe2y43T6WIxECKAd03ECFC9B0+ZI/adR3zxZwYA+I99fUrzGJExcitdoa1AqHLTK9zGGMZjisWrsQp4cx4BWUGiB3hQ4epG0of2Jts40OHLg0ShTNrG6ydOoU6fQZPgVMKJI8P1qCrIbqbUToFbpD/j8a4MV3M2d3vfvkS1/f2+Lfe9QGOr6xhVXZc3egEbjhg8/pNvvK1yyxmNYOU37d1OVpKCNde+zq7N27RTYX1kyewqxXPv3yFxaLBGMeKhZVjq7A6PiymKqMoypJ3//BPsFoNc7FXqaxVN6BFaBcNdeOZbs6Y1E8xmcxRseXH/vRPoYqS4EpKV/VBiCL2m1glZbCVwc928GEGFH0w0Dt2hHve/GZ01ChRuELRLTaReYcUAw5mDVamrJ44zVvvewMX7r3AAxdPsF4ZXLTIwFKMIqMxjMuA955RtLlYM9lGQmC+2CKI6aXBipG1SBRSakixO2y0WikNQSWmswnGZcVXXR8wtrlT2plMB66MSqrCsagDpSnw3qPRvJYKCjznqkRSCX3sHsZnzzM+dZqLjzxGalvmu7tc++rn2fzS77D39Cf5r//BrxKrE6yalo9/9EO0Xcvvfeb3eeN993D9ymWUgucv/SGXnn2GJMLASlYzociq5oTvGlZcgfe50N753GRX6ky7iO/oQkvTRUQUIWYpsHOW0pK3fZAsnqi79jD5jf0uadZqOskiAu8jTRd64YGAylH8MoAMcpDHXivou5p139Us9HtCLSllpTHQNzYmtOsDTfp9qJRiaP5/IIVMKdF416soPNpavF9SJT2N0f+X2YvM+7qsbD0Ed4Ck+q0DoqJpPSkGilJjrc1acmsw2uSmj74+mlICY3D9otM9pWHpQbtv+FFK5/1EjMP0u+VZl2mGYryaaYuioKoqyrKkKkqGg6yYcM4xHI5Q5QClNUVR9KmVoXB5Yy8luf26344sR/UxISkrW2w9A4nE/Y42BmIMhLYDybWCtm2JKWG7OmcRkqAHd4F+s7DYO8E73HbpMg1TOMvlaaRtWiRG3vbow0js8F3byxo7Oh/Z2q/x0y2kqwnjASfP38u9j1yA82cpg8fs7XPpC3tEUXSdRmHZnwVqN4XScW71ONvbu9z31nfhVjYQW8BiF7/b0k0PmLcHdM2CLnTsHyww1ZDhYIiOgXtOrDLbvMozT36KUVGiEKIbMFpfp6k76p2bFEYonM51mCwUwhUl04PA1Dva1RF7c4+ddZx/4GHW1zcYDVf4+Md+C600jR3n+dTTVUYbhmvHUcsdspUiKYMmp9/iFIPhCisnTnKmmRKTYn6wBxaCMqR+jmrdL2wNqOzkk4a9vT26tgXKXM8QgKzXjqHF6qy8QXwu6roRV2/u8sqV67zpsRNIilw8eZy3v+URHnr4XtRsilKGwo7xc6GbB5SHobI0RnLzVadpo0IbRy0dJIhdoioS1mgqkzuuOx/y9hIpsQh5v6JE3qCPlIt/odfHF7ogdCA215vqriUlMIWjms9QRYm68D0cP/8Gzt//JiwJ3waufuFzNHubtAfbLLY2cU7TlCXbV67QmW3YKME3DGzOaN/52CPs375BiJFF0+aaW0rYHmwzdZlBsxWPlkzfRoEmepI46BKdyVF1ktSDct7mI8SAcREtuSs4CxryJnyJkDsRlaCNytJlpXNHeBJin1UrgcqZvFGYihTOEKRClCKqvCWHc4aVXsUmSmc6MCW8djilsAqcVohEXN5hhS72UmljGDsNm38yrn5HgLtWitFwQAr9rjI6N6IAuZ0dnTXtEg+LmITceJRjnDs6bRGNcX2Tkxaigi6qTHs4l/ezGFSIKyjLgtFojLEWWxQU45MUZUnVF/uqsqSsKsajUVZvWEdVVpSjMdZaxuMxReHQ2lBpOWwK8t7nBimB2La9vjzi25bUzhEJNLMZKeUJcVBPMujGhPTFvhA6vM96W51JeiSF/nn71JOEJh0WSsuioNCaLoEzlrKoGA0qtAIfE66saLqAto5yMGRtYw0QBsO8Na5zFnvpGtev3UAETpw6zeatTaaTbW5s3gSlOHHiBKdOnKIpxnTzKSG1TGYzbt+8wVrt2V/MMcB9j7899yikxM3LV9m6dYPZ5mtMntrjlYEDVxJJHL/4RlQxJOzd4sVnvsr25nXmXWJUGEpnOHH2HFoCG8dWuf+eczzz4mUms5Y3PfIow2qAVQrfzfHa0naJ0fpxxBrsYKVv2spO0zrHw299L9Pg2R8MWSkHlPOW42c3UH30FCgw2mJseViHgAzgy60ltDH9HjQJ3SawDleUWVGUOkqdKcSyqFA6krTpm2ukb73IBW1tCtCGkDpu3t4iBAG7LIvo3jkDPlCslCjVkboGqypeuLrLJ578HLuzmoRCk3j8LW/ikYcepHCGVmoSGu8bWl/jQwsq5VxXPErIKqZkMnWQNNZYmrbLwoLO48sCU1qSUfgUSUnnoCtBoRyLLs/xNnmamBCdM7BF1zGfC4s2sb66RuMDyhrK1dOcue9BLr77Pfjo2Xn1EnF3l+1bW1x9+SUINVZFFnVNMg5XRV780ucYWM3NteO0dU1oW5yznDpxjOFwxK2tHXyEJHmrZy8JKwmlhM5HYhTaXhUWQkRQ+DaSTG6osyavptiryRBFCrlzWMeQgy2TyB0GWXoh2qB13kE2F07Jm/PR79Ta+2anNKNCoySgyFTPorUk1evrtVBoUJK3UomSUH2PS5cSQw0GwShBGUtpAKUxJjMORVnQddNvi6vfEeB+5tQpfvG/+FmUMlTVCOPyfi3WWgqXo2NX5EJdlsjlKFzKvK+HsRalTQbZYoSkkDMfUyKiSd4TpM0bCoVA03gUGUx93RJ8IHiPqrcIMe/L0XU+K2diQ5jVtJ2njgmvNaLmKIRpr/tWSh9qs3OhLkdqSikan7toxXtOrg/Q5QraOlxR4KyjHBRINep3EwRrcmMPzqJs0dNQAok8cVPeskD1fZKiclQuKfVpHcwX88y9Dwasrh/Dx0hRDNk4dZpoSsQUTOY14dqLzGczdg8m1M2CxXzOaG2dRx5/jJWVNUbrG5x66BGMbyB6dFFQVENUMUbUAMhSsWZ/i26+z2CgsevnSYMRyq5R78955elnmI1O8Laf+iFOnDwJixl+scCurJCqgnp3h9IY0j3nOH/fOdrJhE45tAiIYThey/rosuCHz97Lp/7qL/Cxj/02f+fv/jL0WxF479FGE5qGL3zmC9jK4qoCpXTepVKTF8jDD7KqDG/oFDF69HlN8AGjNHSBoXVIiHTQF7d7SSuCIzdrJQ/KOIphRbQ2C/naltAsUMEzHa9TaEe1agizXYzR2HJMlLw/EViqlbW8BYLAYlrz2rVt1o+fp2kyV6wF6HdBpDqGNw1NUzOZtPzy3/uXvHhrm2o4YuP4CZwtGBWWjWqVtgmEaAk46npBFxSbreWlSWRrr8NpQxfzPjrRGD74wQ/wxHvew9/7u3+HS5evY7UlzTpGA0eVNH/j536Op770NT731FeIpqepgKZOJF3wxBPfy69/9LP4pLLW3ZW4YsBgPOSJH3gPjz76GMPhgFdffZGtG5d5des1nvknn2fvYEGnxrRdTTM5IDSzDNLGoEgMBwozn/Kf//I/ZbSyzs//x3+ZV28dgGQBwId/5/M8+tZ38vyHPkLtO7r/m7o3i9E0u8/7fmd7t2+rtfdlpqdnbXI4HI64SqIiUpalyFHsKJKlBEEMZxFsBAkS5DZwFtgXuUmACAFkw0GcIAGlyKId2ZRkSiCH6wxn45Azw56t967q6tq+7d3Olovz9RABTF2P6mIuGo3GVNX7/d9znv/z/J7OonO9crWlX57ROUppMuXQkiSREDFZhXUKKRJKWUqRQoDCg0+yr0mhDSSGvMrToUfC/PAQiyGKiA6RqhBsjAeceuQKO7dvsbNzl7ZLe6NgNN56yqyiqAYcN47JwNP3gbLIWKsMW1XGsMhW+A9QIuVs5q1lnGc0dUPXdQzX1wlKIVef+gfp/eNmgwTg/dd/fSiG+2h9nZ/5N//dVTJSoMSD8oTELvEh+bv7vsMHj/eW0Dtk02L7nmXXYW2SYAgO1zWI6LBtm8IzEXo3p+1qFnVLrtZQ0ienCpEYEhAksoKQyeRCyZRKulcx5KDvqZdLymHFyTPnyLIM9SCkkmXILEk1Uinyskhx9Ki4PXWgc1xX8+SmQmUGhESXAyIqkRzbBa6pObq/xysvfI/prE7AMBlWlsh02otSfyBBaW0wOkeYDCUVOstY394iL0uCyOh6x7JvaMI8newJ7O7s0kfYPnmaO1ffILgFZ86c5ZHLDzFfzJkfH3H+/MNkmQadSIUIhQ09qpwgixEiK4gKYoioGNA6MFifUI0GBKnReQHSIIQmaMnO4oi7t+9y+dIFsmFF09a45YIggL5EY0CBsJ6Iphhuor0jL0qUMTjbk6/cNaOy4OTJE+wcHOAkBG/JlCaXyTff9knSc2iMzjBhJW8JiRFQqRLf9wQtQJnEp9EKHjhCF0vsYkm/rhCG5CNXGQFPaC1Kp9N21IYYDSFacD1du0RnBXcOZpRqzFqZQm2tqciExqkca9vUD0Ck7x0IjQ8Wr+Hpz3yKTOd857WrEDSZSHJBJFCLnra2HB/1vPTS27x9/z5hNGFQDij7FtFajBHcvrVHvWyxrqVf9rg+cGfnNj/40ZvsLBoWXkKfdkQxBowOvPn6D2iOZ+zfn9L3kTZYlIzgA33u+NKX/yX7B8fsH8/pvaD1NilKzoLWfOWbr9D1LRFFXo14/NmPc+7SRU6dOUMmJdevXWX/zm2uv/EDZssjuq7H9h4tDYiaKMB6SzQaU6xyCjKglMAoQ5krlK+R1pKZpH1nWcZiueTE9jqPXTrNzXt7xJiRCUcUaiWdJb5Mur0XaBXBe4xW2N5jTeIzSa0YDsdsbmyzs3ODtunxHrxPO4XgM77w+Z/j6Y89iYyBf/AP/hdEzFiGHu0l50+VfPqZJ/hb/9V/x5f/7/+T/+ef/gEHMWKEJhAIZcVwbY31kydZ3ruHaGtkBFXlZGsVJ06cwPRzlo3FeZHsrjFSFTm9kDRaIzODUwHhwAtJFyJCKyKC+oFt+Sd8fSiGu+sa7l59LbHLvaN0fQI1BY9z6RsItkXIZJkUMZIJQcCmYE1IHHGpJEFmiOBWMf6ANgV5loPYJioJKsfkAzId6buOssjQRiOCR8h0LZdKonS2uoKnoNOtO/d47fU3GG5tc/GZz6RQidFpU+5DkoJUWtay0u5jEDx2foINAhUs+2+/Qmh3ybOMPqGcEAK8i3T1ksVsis2GbD98hhOnTzMaj7E2kd/8apPug6frW7TS5FmZbgHGkOU5o/UNdJYsXLbt8DGg85QYdK3l+PAesigZTiZceuwysl0wHE8oqgGjtTXi2dNIVRFch7UdQkDfNSA1uhqAzpOfn4AMlugttksZBKUNymREkdACINDGsDYa0q+Pca6jcx29c6iiQOYGaQxoic4VwnukNinp27SJCikcSj0Ikzmk0Zy/cI73bt3G9Ra9kqMQgSj4MSKZBOPyqxYcL0AqA32DCml5nuQvj0LgO4dwjmoyIC8MZjReJYxJp1Up6csRUukEbUPA6sAhhcYXI2Zd4MV3bvPJn3sEFT25j1id4zzQOlyXgHguCpb7BygsMTi6eo53nrld4JaHuMkIJxRWagQaf/MdXv3hMdfvH/CDd97EDCZUuSa2S+7OZjwqJPMgeP07z3NiMqKr59y4dZe6d8zqwL3jY5rGEYKkXdoEDZeC1gXevnmfW/eXTBc9UUi8t2ihsNZiRcYrV2/hXEfT1kQCmY2gFEGnBX5Xdzz06U9x7sx5zpw8TWlymnbJ4XtXuf7uVWbTY7quYbGYYbyjlJHhUCOIKB0RQSKHZTIgSIHW6oPdkJIavcIdaCOYDEv0Sg5RocW2C05trdP0lo2NCf1ySj1LbHwpfpxMH2+s8cTli7z4ve/R1DWZgja4lWZuOLV9kl/7rb/F7/7O77BYHmI99DEVAeVS8MzHP8PP/vzP0C0WSP2PUdIwkCUDU3L5sYd46qPPIrTmUz/9M5TjIe/fvM3urV1y6eknZyk3T/KRp6+w3HmLP/3jL9MsO0ZDzdZmQZYH5p2nF2nR5rDEFVYjmpylS3bh6bTGu+SWsg9wIyLx+v+irw/FcM+ynMcff3JVMxbJdHrAEAKjDdb2eJFYJEKlBFmlJK1NgLByOEofbFSquupr7PyI3/s//neapkEqePaZZ3no0mMMT5zicHqfwkfu1XvMpzXj8QglDYt7N1d6t6NpuxSjbyy7u/t0Xc/B8Yzj8YQ7b71K13fI4HHO4qxlsr3FcDhiNEqyy3y2oOlb5m2XmnxGQyZG8PDHPsH6iVOIag2ZF+Ac2NVw8o4/+/o3WfjIOBvy5OUn0gBSqWFHxdWVTK5c0CIhZyHNIR9Wi+ZqQrWhEMFj6wXEgB6PGQ0kIqvwusCMSlgsWS7nSX4QEpkPEbJYwbNyZtMjqrV1dDVEyGy1fXb4piW0i3Sj0hlSpJNE03YE4amUITgLRJpmxltvv8FgXHLm/BmKyQQlEm9FCoguEJRIaVJn0SiyIsevuDZaJLeRNgbb9Tz7U5/gW997ib5pKMYTZvMZw7IiAE3XoLUhkLgxTqwgaUpivaWR4ySjuISH8MGT13Ok0DSN5Wd/8z+gbVreubFD31mKLCME6LqWW2+9Sl/P6boOZy1dn8JPi6NjuiBZv/goi6gp+/tc/dY3uPOjHwCJpWPx5Cr5uBsbmXpBnkUmgwq7rDk6PGbZdrD9MLkpaaanGI0yoOf2bMA/+pf/K+QjJsOTbLPkrz1zjq985avcn7V0zTFZcYk/+OZruOMjlO2SA0xI8uho2hojEke8GBUokZDXTmpi7GjaQFUp1nSOsx1llg4srdDYkD5nlSo4Oa7Ix6c4/8RlHvn858Arur0FN779NeY33+XtN7/HcX1E27XkSjJAsGUypFEcVTm9h7IscH3guWee4YlHL3P9+nv88Oo7tNaxtjZBKUOzmNPZnscff5z/9u//fUSeEdqer/yzL7G/u4smkKF57bVX+dTHn+YXf/nf4lNf/CJ/9vtf4pWv/+kqjZ7yCEJJth/7GL/yy3+FvaMFN27cQPpIoSLCe4rRJpc/8imOGPHv/PbfoW1bjAg88/BZ1qocYyrWz51CFQZTjvnS7/8uCgdmQN8uyKKgbWpeff37YB0bpy9w67Cm8XscTmtUc4f333mLO2+/jg2B5bxi6RR3bxzzo2sLBhun8KEjOoeKkbzIVrTLtL8RJkcKjXOKxmhQAlUWK4qoYIoEXv+Jc/VDMdy969m9fQ0lJVmeYSdb6DxDKZU42FIigkO6FnzCke68dwN3vMvh0ZTOBarBkPFkgh5uIYLlzo3rbKxtsH5pjUWz5NVXXuHqD3/I9vYmnYDc2dTOIyXVYMhoNGLtzJl0zc8yRqPxKhUquf7++8xmc3rrGU3WKUclzjnKPKcoCgZFQTleQxmDMAa0wS5rnPc0XUtwPRubm9THx/RdzWJZU2bjxLaLMVWghdRaFG3Pzq07iL7hk89cQcoMpCTTAhFIi9WYItxC6HRFfpA6zdLfjcFCvyJfKpn+jSAwkw1AIVFI1+FNRjVeS44ik6dGG5Ic0Lctw7UNRFESvCBGi4iB4CzdbA4iJEZHXiY3j7cURZEwBd5x6/p71PMZa5Mxv/Wbv0k1GCKVQmcKEXR6oVhLtJ6gNEoZvHUEz+pKLZIbyCW8gljxdM6dO0dVDeibFl8NGI3GBNcTSIts8cBiKTQKjfc9LlgODndo9r63Gsyp7tD7gG3mdL2l7y0Hh8d0XU+Xr/P0J5/BeUdhBsynHbs33kuMlpXuHGM6RY4yw0AoTqwN2b83BTMmyyesj7Y5d3qb3JQwyDFCIqTmzWs32L1xi/Fwwqc/+1n2b9/lpVdeo6NBe8/V732Hduc9fuU3f4P3bt/ny89/HVWuUZpNNvSAx88eszjc4fTmkOr0GWSMLKdzzo9zdo/bVJQiHHYl5+WDbJUFkWRAmaVT98I6IklGbFuQ0lEUCqlSt+2WzpicOs8yCqzUPPNTH2d8/qOURnLnrZfxizmfefZjtFvQ3VlyYq1iPBiB0LjQsJVLHrtwgYjkTt3Ty4C1jsvnL/FLX/giN967TkTTekMrJJ/76Z/l0Sce563XX8FkGZubm+SDEd4HZCYYjSbYtkMQyQZDpM5wQiNMTqENZx++xO67D5OZjCIvKaoSiFz+9Oc5/fBl/s5/9p/T1jW37+zwwgvfxbYdMh9yYmub3Zvv8/FnnoEYsO2So7v3mPUtSydZvvR9mr6n7zrm+3scH+6CmdD3R3RLj/WRzs+Tg0ZKWutZHB6joiAfFkigaWpGmydxJ3um9ZLtR88Se4s3JctsgBHpd+XzjD5EZgEWNmBW1lujBdOQMAkWwdb2NuPJGut4fvAv/ugnztUPxXCv5zO+/cf/jBASR6UYbVIWJWVZ4L2jrpvUyylJgywGDu7dR2hYW5swXt9g2bfsYhlKjAAAIABJREFU3Thmb+9FQLKxdYJlucZg8yLPfuzjXLjxNvOjPXIBT1z5GCKmwop8METkBUFrlEgShiANCO8DwkiunD0PMSC0wUeNDHal5+nVCTAQbPdBIlUKjR5MyKRENQts1+JRDE+fp925hu9qjAhJNsoMqsjS4OxbsuhQfY1dzHBtg14NtQd87sTKURDTQpUHLh0pESKxWoTrViUkEoyB1JCJaGo8IRVFWI/TikxopFeIEBOvHIfvbTp06iy5a0i42eA6ouvJJHhp0HmRfiZtA8F/0GoljeLMiW3c+hilNdlojIhxVeeXNpXBR+g7CAKpc4RYJYNXwLVEVlSEkBbcYtV1OBiP2NxYZ9E0jEMqcxEx2WTdylmV/FMSmaq0CN4yPbjHvbdeRnjHAyuMlJJgSqRQlEZydHA//f7WhkCyQAIMhkMe+8gVMilXqV9NRBGEYrq3S1t3qLWTyGnARIWbt8zv7bPftpisojcS4TzSGPbv3ac5OqYLgfvvvMfs3gHz2ztEpWmmR9A0zLtDXv7Oy/yrV97j7bu3GFcn2KwyHjkh+exjT+Gi4dTp89wTA27PLDor+aXP/zRf+7qltzUbZQVBIJQhL3M6mxKUmuREy/Mckxe4EGnqnuPDu+kmGiObp04yWltjsr7J5/7qv83VW3scLjrarkM096m84XMPP8zaZMhHP/EMzzzzDLPFjKoswEmMzrn1zrv86IVvJzdXVfIf/qe/wbLzDMqCSydPkhnND994k0yWXLr4GF1Wsn7yAhefeIYLjz5FbjRZkSG0RHiLW0z5lV/9VY7u3SMrc1Q1oMxKxlXJ2rmL1J3l4hNPMN4+AStfS93WtG3Nsqn52p9/FR8Cy2XN2+9e48bb71O3HdZHXn7tB/Su5dv/4vewwROloBSRgdGJPbSxicpytJJksafUEmkEZ09tYsQEU1SUmaCsBhSDAabI2L12g6O7d7n4yZ/ltfeu8/hTT/G5jz7B//iHX+aortl+6gny4FnYjrEcIqInBo8vh/RSUcSUPXEx0nuLdx2bszpZZ0Mkhh67f5f7dfsXztUPxXCfbJ3i1/7L/x4RPa6tcYtlspppzWA4xPmI7VuESC3mYQXB6mzSZKVJnBgpVVpCLmqmR4d89Vvf5HDnLsenz3Dx8uNIHiMIiTUlmesxYtU43zukdXQSREwwYLlqt3fWk3UpRRmyFIrpHGgtCH2fpAOtKB784GUq+Y6rCGsQAaUFw2GBlwazts5sOk1bemNAGrz3K6hSz7kz24yM58JDD6Fzgwysvt/EbVFJ8cUTUcJCPkSohIC1bZ3klaxCZUn7/gB7YIbEUCFsR7Rt4q2Xhpdvey6clKwte4pCE0OLNnla+EqDjB7l2iSZrAiVQvTE0SZSCdqjfVgBnwI6WTqdQxUVZBXS9QgXwWSIXGHrGabrE3e/t0QlkKYFDMb2uOWSbjhhHiWHRzMePnsiPfwiQFVCzHjkyhWCbRAyJgSuiAivwSps9HRRoRD0waYXo8648NAjPP7YU2glsHbJ7OiI3bu71G3qb82Mwb1xHe88Cp0kMJlsp91ywTf+/Hlcs0StMg5CSpTJ6WZz5rbmsc9+HisyeuDysx/j0SuXkIUieolzLYfXbrC+sc4zZ86yLyBcfZfjw31u+sjgyecoioqD155HjM/iT5/nd/7oeUw25sTJC/zy5VN89hNX2HzsDGG/xUuQoefdezPuvf4+uml57nOf52/+7f8kFXcXGbnQ9LNjoo6YEFDWEfoW0SqC7wiV4p033uS1777E8slnyU+dRK5N2Dtq2dk/5Oa9axR/8v9y6fQmz53aYuvsGbwomB7sc/W1VzjaE/z+N/6Epl2ATHiPLCvJlOFg0bA3TwE5lOAXqyEyNxglOZgtUQTOX36Y115+nd39Yw7jATcPWt55/ybT6QFNvaR3AWs7+sWUQlkO9/awfU+wjqrIEUXFZDLm8uVHOTg6ZjgcUZUVWZ6TlyVbp7YpyhGj9YJz586gdIL3/ewXf4E3XvwuN6/dZTk75PyTH+Farfn3f+vX0SIync45s1ZhuiOyKgeRoZQBEXG2RvQW29TkGydxQqNDoK0P6G2fXgCy4PbJs7z4zW9yYpjz8OnT3Ls34+hyx19/7ml+8Cd/xDf+6A8xrseNCvKTGxRZDh6W+0e4zlLJATJEFKClJFMCJxUmM4gs7aWqzKAr8RfO1Q/FcEcIjEh7bpMbOp9sTFIn7VcJyKqCaPt0QpWS6BxSdERA69XQkZJetDRuyfpGhT+4x0OXHqXyHbkClRfYrkeoVEotWDUJhfRDyqUAoX5MUVzJFFql4m6vFdE5om2ZTmuWdU2e52xvbRFlhlCrXtXVMjLKHOtJvzyhEcqAUCitkwRBj84ksV3gux5F5LErV+hnZ8mHw7T0Q3zwxrYqtSxlCIxK/6bzdnXqtsmpojN8SLRJaxtc2yKlYOEWKK0wqxLe4XDIsRrg5BHD8TqV1vjFAlWUSKXoug4ZHUYqguvIhCS6Du9a7tx8n/LCR9han5CFhuAt9d07uHrBYjnHBcfRwTF127OsW5782HMM1jYYb25hdE4vLGLVoqWkAh+wh4eE6AjRUxmNtKmFSRMRwRNN6oHNBiXnz59n/3jGtvVI6yhMSo2KlVtJrh5rIQRZltM7z8svvcrNH77KeFASCNTLGik1dW/prYUQCW2DiKksPBI/KFgvi5Kf+au/TJFpjM5XJdqS4Dw/fOl77B3u8eQTV3j7u2/T25bN8YRBtk40GiENxMjW5mlUliG0TmXNgy3OC0/9ylvsHddE65HnPsZdAS/duk7MDesbZ9jK5/z8Z57DrOd0xzOKtsZpkJlia1LwxCMXqXQgL3J8VyNdzeLOMXMnaGOHm88oljX+aErnWsLuHrVU7E02ud865NnLFOUJ7k53uH/9Fbofvc5YCU7lGt0Ibk9vc/1HnqIcM+8atjc3ee/mbZrO0blAU6fykRAjvrPYvmfWB2ZWJQyyVvw3/8P/hJCp4St0CVC2nM+YN0t8F5FFSZ4fsHf3PSaDgrwoGU9GnDx1kio/S5VJ1ieJ5Lk2mXDq9GkckrKs0OMx/XxJVlUfwPeElKg8wfhESHiM4B3WplLq0WgC4h6g8S5idEYfp3S9p5gMqPuOTZ2DD8ToeFCVFZ3nQQl3sA50KmBRUaBCOgjIXDNYm1BWGQf7uxT5iP3dA1Q1Zrjl+PhPfZY73/8hWgrsuOLmQnCiHLBR5ZzZspSZQpRDvDHY4Kn7joPlgpPLo1T0bTIQyeCVT4//wrH6oRjuiWAYiM4SbI/UBrlKb0bxQe01vXNopdEyhRaUSENYRI8UidOeCU0XBc10xmMfeYorTz+T2oIkBGc5OjzgxOkc+QBtKx78h4Q+Xf3gkDLpe1EhuhZ0hiDimh5vO/qmJtg+WelIldaQpJpQDUBIYvAouaDtOlAtelCCMmR5GqAxhiRnaM1oowIh6WYzzHgTVVUfxOKNTidi6Vf1eCHQec9SVYz9gqZtiN6h8gqvekRYNVkFT4YHHxhIhZYZ4Ig+EgNkwXBxPaO5ewMVWu7euMHa6TOsbW2iBLTzGX1voWtp64a2bVAisHPzfap5jd9YY3Fwj6PDA6bTGf0y3UiatmE6XaCUQZcVsxdbTp17iE986rN4aZBG07mWIssSb8d5MiEJxhCkgeAwLrCWK/zREbIqUFmR9rl90r3rpiMbDBEuVfYhEoFSSokkPTtSsHJbCS4+9DBrJnB8sI+UklMfO8t4bZPWtdSLmp3bd3n36jtEH/A6X4XE0nNhipwLly+nVCapKCG4QNP3OJ0xXtsiy8oUgJIyQYdUcqW4vkP0AT0Y4UlFJqWFWiukd2RKoKNjsZyzpxTXdm/QScW5k2e4qJY8+9AYv9ij6wUiePZvXsN2Dtct2cvWqc5fIcSOl175FvHwPqKZUbct3qUikf5ohm4cygXsqMRlBeXGFtnJC9S55mDacOv7f4xdzii6muHyGDLYaSsOjyNeJ9CedB2dNIgbOzgX6W2qFOz7jhDBmIxu2UFIOGCZlx+UsbjOYkrIs4xzF84xGY1Q0XPr2vuJ2jqoyMqTfPrTH+fc6RPoomRc5YzKgBBgtEK4FXa7KBE+gkq/48ViwWR9LVE+uy4VrEiJyZKEiU+4DUWSqbTUDIfj9LlShtlsSTZZZz6PeB8ZaIPxPa1vqKoiHT5WvBglV5UpSiWjgwQRIhKZgnDOEUtPOSgoygGLRQ3DAbt39vjSH3yFJy+c5KefepY333gH0Syx0wWXli16tkcmBcZ2lJmhGEiG62OkNgQhsNEz9D0qCDLhkSvcSFZpfrLi/iEZ7oSAX6YWeKkNUiQrnVgVCqd2JI8QOiV/Q+pcRCYvsgsp2SOQzPePCR0MTj3Esw8/jpKaQirausFkhrzq8MsZweTEmPpLH+Rcg0y/NGLAdA00HXHZ4NoetT5GDCtynZGrilE5AKNAJl67iJ5ITP+/eYWLEtEvGQwH9HOHbRpMNcahkLogOJc8/G1Ls5ylfUxeUGhFs+w43NvF9clm2HctXdvgl3NsTClD8oo72WkuNndoFjNicBiTJUuiMqsawsh8NuN4ekwxHFHkWRqEDxajS0eMc+7fnyFEg2sassEmRZEnfG3TpVuSD7QhlRlsbW2Qa8Gd117mleWC3geckGTlkL3dPc6eOYnJxnz/+jVilBwvF+wfH/NXvvBv8LFPfSZxc6TBVENc32EEiCgIi2MwOn0e+y69fDONGk5WGFaJdD2ha7n00Ble+M73WE6PyZUgG66n9KUWaUEtDXmW45xLS1xtePiRy+iPPEEMDlc3KGOIWQ5di3eex54O7B79Hov5ErIShFjF0PUqAUxiDz2ghgqBNAXj9W1uXX8HVHoWCCnLEEPD61//58h6ivI5jQvM5gti8EjfI4uSymS8cadmIYfcmy24tpizdu5hnqsqqne+TpA9r1+reeX5r9D3qfAhF4k3rwj4k+f53G+cZWuz5PvvLLj+1k2UiCybKYWSrG1v8dCV5xiWFZnSvHdwn8W9Xabv3eP4xbfxXYsIDuMaukzhtGQQMmQvCMqh8ozQpGYuIwDlMVJQyI5BmXZTEgVC03aONqae4aNZQ9N3VJMxl86d5bf/7n9EfvEcAjg9GjHIDHfffZcXvvo89WzJohB0eo3JiZMUp05zXHuUaxjt3qXcPJEKQpRGFQUxgigLQhAfMGC6rkmF7DoFGpUxiSsP+C4VlUgpMUrRup7hZERZlfhmweHhPrM5/L2/97tJ7piM+NxTp/nFTz7O+XMnQEfESgb2JDCf0hmxtwgTiCKkvIv3uC7SL2vyquTE6Qu88t3vkI0Fs937XLu2y/VLJ/kbpx7m13/qI4yO9xCupZYPOioEnUuk1hADSiSaqQiCGAT9pFrhtn9MmDUi+wvH6odiuMcYUlBHpQflAcQoRe4jQksIidXufaKiaWUg+lT4EEitOxGyYYpN6+hxqxg+MiMfl4hgKcdryO74gy7UhCFLHhHp0qCP1hH3j6BuiSGgNtaJRhEUBByx9+gsR8TEU3fOgU/oBGKHnx8lS6WPyGDJtWS+mOGlxjVNGureErxPtrPpHkfTGZ11PHT+LF295N69Xdp6SfSe4C1GqcS1MBmFbTGmJB9r9u/fIZMRKQIOCErSrPo87Yp3IWyPbQSuWUkcwdHVS5wvgSnvXLvH0s1Sd62eEREoYxivb5KXZUIumJLGOu7MOu7f32fvaEHX1kyPD0iMBEU9n/HWtRsQBffvH6cCcSkZDirWhhPydKYGpQnOIrPEulEBXJEn9o9QiJyUH5CSxnqk1KsUsETjKZXEdS3L2YxqbcwHHBixugWuNHFERMkVDkKb1EPqPEYkyYXo6XyHyXKWdU3wNpFGVzhg50O6HcaAQ66kvJQ+DiHiuppmsWBtbbzyaKfmJ+8ckZ53Xn+RcHSP0foJDo9q6rojxMBgXNC3qYGr2XiU2/WS27MFZ88+xSRfMr/+EtOD+3ShJXOKqXGopsch8FayiBYdPANref/mTcbVwyx0waGqCELyyLNXuHhyk82NTe4e3mV/dwc/PeJw5xZt1yJQDKRAjxKPX/gRvbcIINcQVESgwXmEibjo6EUgi6ljQa1ut51LcDrrPc5Lli4Zv1rnsMTUMasibV8jo4RVbiUq0JmiKwyuT2Gw4DzL4wV37v2QN9++yUdOr3Pu0gS5sKi1ISI3hJhK2r33KGXSMl0AwadOXilXJNdkrU0nhQQKW63QwTuqQUmW6Q+W9bd3b9D0EuvhYHbMy+0+T5/b5tTprRRsXQUekli3osi6RGxFpI5mqSQKgbU90RkmGxPaZk4+nJBJj+uW1Pt7tPt7NHlNX08R+JWzi9WuJyS5UkqcSM8/mSRmMPap3yFKlQ4yQqwKjX7y14diuIMAmeFXLhWlVwHbKFZN5skVomKX/qqUWB/QIiNiUUqk1BsCGVbEvS6gJgVOpDRdKzWGnL5rES3okHzYMUSiVClm7jv84SF6WSf7nyyohxpxeJ88BGKWs6cysr6mbWo6u0xMCRFpu4YQI9pk6eRAWoI01qEBZy1KKoq2pmsaslyTa5UcHq6nW6Ql0q5dYEPALmfkeGxMp5Fp0+BqR9fNaCQ09pib99/g1rXrDAcVJjM0fbIC5kKRS0Xfd+gip3WOEeCtoLMdUXqIihNnTjKvG1RZUQ7Pk5Ul2do6tu9pm5a784b5zh7ddMp0OiXGiAs+ZRBIICyTaaSWGCU5c/Ysa8OKSTVgtj1lOBhiEdy6e4vtExs435GRGDlSr2oMlSJEjwiJna/LnNa6ZKtsajKdMKtd33N/bx+6JQLPC9/5Dk888hAjcxk9XkOoLA0kBCYz6CxLvbkxpUWjDCtgV4R8kGxr9RJjTGKt2ARhk+qBm8djlEHEgCKVyEgRgFSX6F2Pt0saZzl59hJRZpRKYMOCNgbKXLE3X3L/6IC1puXa3UOiLJDkDBdL6i4y3DrD4Rz263sU5Um245zbV99gdnCTerlAiAwjU62biDpx1nPJUEjaXhBdze07t7j89NNcOPsQNSWT7W0ubQj2brzHD69+n/nBbYxWOBeolCMbpbo/G1aOJKlol8uVuyoiTcI9B2sBT1jxi/KVg6m36SXgnUtDyJP2M0rRLOZordLzn+UEYVBqgOs7vvbtt9nZuc1/8e/9El1bo/Ihozxj7o8RPtL5mj/8yre4P10Qoubo9i4/f+WL6CqlRW1nKcoKHwVKZHiXgmmZ6OhCArkZpekjSCRZANqWqCTClHglcTIie0deDRFG06NS+bwOxK5JxdgIutayc3TIR3tPppLtOFHgJKZIKItmMcdkGTHLCEKA0jgJXd1jXaReLNlfWly2INceo3ra5TEH0wP02YL1YshMzKlc6n9IcrBIagCCHPHBDiHGNGOkWrnCbE+e5wT7lyDEhAAnSG+xGHGkZacQaSkYOp9cIFLhvcUtFlR5TvdgyJgc5yw2BOb9nPvv/pB+ep9cVuQodFWQb24hlWaxWODrKZlKXuuuSeiCpmmI79/FtW26Om+tM0XQz+bEuqVFkq2dYN+sI8cZj5xe5/AHL2DbNIxlyGi7Hh8SgW7RtFgRqZ1N1XsIur7jcNbgQ6BuV/hQIen6doUYSFV5yaqtqRuL0ZphWYEPVCJxqPGB4ByDsmBtsJ4Wk05xqpqkwuFBhreW82fO8PC5M4BnDEiv6LuGpplz584h1VChc01Zlmm5bD1OWUylqU5tsD4oUVISZM580ZArzXK5YHo0JfYe2/VM9w5QMSbS8vQIN5/jidy6v8+OC2Q+curiwzx25hJZNqYXoIkEH1bwFtLLxHqEiCha/tE/+b9omo7FcsnNa+/x67/2N/jBG2+ytIH/+u/+NtFbAprbd+5z5ZlPppOZ9wgERVbSy5RwhJXrcbVS6b1BmRIVBSHYD5xX0Vpyk6EyQ9dZtHnQs7pqriKxjkLiyJKMUIrjpmW/bXnuypP0pqARChFKsiwiTWTj3LM8/8pd8B3PPH2O3Xsz7u0HuvXLDC8+wu3lHGnWePTCo9Dc4ocv/zHe2RSVl4JIj8gSldC7iHeO6aJHx0hUGV2zYHjeonvBiTM5e7fvcvMbX+X29Ijt9TWc0WRuRnSRum4ReY7t06nP5CYdbL1HhZ5xmZj/zs7wPt2ChUiauY8RF1M1pPdJMvBBoKKg7Xqs9USbbtG98/Q2sn3yBKXOWRtt8qO33uVPvnebtuv4VycrfvWXfw4fF/RNhwiBZrbg7v273JlafDGhWdSopaLtA2XvYFikm6tK2N7ou1Sf2HvwDht6irwCG8GkYdj7liCSTq2jToU9EaJ1bOQVF86e59qbbxMXS4rOE7sZShgigulhw7e+/TJjbXnu4x9FG8Ph4THlaEwHxL7jvTfe4OBoipcKRIkRUC+mvHP1bbz31F0PIXD77h4HsxlBaubW8eqrL/Ls2c8hioqwaGnDjysuw4Ou5ZjK6R/8uXMev6qojKQCojhv8X8ZEqogEFoho1xhkLPEIg8uaWky1eQBGG3wKr3NtczTNVxqIF0XF/tz7l59k8XhHcajDQqZg3cU4zF5WWK9Z3//Plme7G9xhcvte0vjW7xt8W1P1Tva3tP1lnnsUWsjLl3YpCjX2Vo/Te5rvvujG0CkGI1Z0nFwcMR8WWOtx4VIFAoXBEpptNH4ELB9n9qabIrsZ3mBWL1ocqOZVFX6PhGMpWZtMKDKDN18QalzEILW9Zhcs74+YTCuUnm1j3jrMEqh24bjpmdyfMgwWHCW43oKXUjDvV0S6sDRKsYcCRiRNPo2pIWUEjAq0kuzFpGmt4zLkq7tsX2PbzsAOpc8/73zKO8T295oxnmFjI5WReRkjKwqms5itKUJXbrl6LRP6fsUf48keuNX//xrlMMRyuRY66iGY06fOcfzL7xCvZziguP8hdOce+gszrdIpxMawLlUXiLT0msVWvhgwAutiayGdAgoKbHBoWSy1QpWPv0H7VUxee4RfOCeSbiDJCUaNDpAt6yRlUZ6MJmitzOCEjz93E/x4vd/xK1r7zA98Kn8OxOsn3qSnfkxrbNcPrFGWLzDrTdfZbk8RkkDXiQCanR457A2IZ+dC/SdhWAJ0qKUYcsHuuA4vLPD1ZdexM4OGayN+dTPfoqsWkfO7jI9PuLPvvkiLQHbWoxRjCcDpJL0rkeJEmM0UkLfu8RjEomo6WK6UaHSzVCKVMYSV5Jp3Vq0WWm/0qQ9hY74AJnJqcoBp7ZPgn0fvGMxr3FNy6DM2dzeZG//gK5paesFMir6bokRKdS3s3fIZFySAtkpDY11qVEqgrOJOhqs53h5wN47d9g4fZrNM6fxwkFMSOCyHOGaJTZ6jm7eZudwn/dv7HC8mONsQpgYFYH0YlJZzu2dXb75zY7Yw3y+4PadO2xubHHctoTeMjs+oO66VdZFQkxNVn7FuIoR+i6FrpSSKXEtPAcH+/Q+Mu8dB/tTbN/jbE8MHkEq2m5X6fjEAYo0bUMIEZMZ2qYmWwU8f1zj+a//+lAM92RYSUUaIkRYLTVhdSXxkSj8Su9SaaERPFHnQFxZ2dKDt7+/4DuvXmX39jWG6+tURYmyjuVsSRQyMdi1Ylo3ZJkhUVWT9nbswEiFBkZ5RbWyFkYjGYcUUMrqOQ2OzlvuzZfkRckaGUJ5JuubbJ88TVFVdF2PUpLRaIQ2GV7AvO7o5zNcZ8mDYDAcMJxMWM5nZEYzLku2R1Wiyq2i9+mk7mhmc9rZnK7p8G1EC4GaLymPDyGkYmzbdRCTw2E5WzDb0+wIGBYZtY8IB1oEAp7O59AmKylK4mUiTsbYp/IMrZjOujSAgyNzlqZtAIGOgkLkBCk4deYcXkmq8ZDda3chk5iqxKmSyglapTh36TzVxjpd12CKCmNM0qezVBknlUQXgw800Dt373LizAUGkxylDbt79ykHFdP5ghdffom2nlNUBXVbY71Fe08Qgb7r8YEEkJLq//+MCYHSitCnE1XwbrUEl6AUy7pJZG2h+OConx7ARGgM/NhVFVfgtJDogPt37nDyYokOYF3DqMyQyjMeDnj8yuPcfv99bt7bR2YV5y9dYfdol15Yzm5c4ujmK1x761XsYkHd98l+G9NwFQSC1Km8QRlMbsgrTZYL0AWT8YRsOKB3HZNixGeefpq9Ozc4kLBx7hST8Vk27YTjw33ev7nL2zd2kVrhpcJG0Eii0PTB4JxAEPDhQddwSChsqZL2qwxWtGRZhgAyrVeBM7nChoCQyUK7WHYrMmrinZdZxiCXtJ1lejzD9o5RJdk8sYl7M+JcT/CWEBwqBjIl8K7n+W+/yPz+OR79+LNsb23h2oTPrusFy6bn6PiYw+NjZAzgPa/+6fOcPH2G0xcusvnQGeaLGdOjYzJdcrxc0OM5vH2b48WMo2XPsmtTtqZPL4IQ0wu/cz1OwsHBnG9840W6tseHwO1b+wiTp2FMMlAIkcCDD6BwrmtTIJH05y54hFhZKkUqBPIR9ucL7u3ssWxb2qYhekuRGWzfM50tAIExyRjRtimsVFZVcqzVLcPB4MfP40/4+lAMd9f3zI+O0EqjQiTkColEknydyQTlE45XKYgCMxzjRYUkEOMUbIcUkdbusrU14OLkCvfv7FLfv4/0kXN5kXgn057gAkV80GUaQApCCJwTyZOcK8ma6yhqQa+gQePveHbeeZcmRISM1F3Po8qgZnOK2ZQuK0Cq1DFaNMgiQ+YlLG3yv0tFVg5xeYHtOkzXU9cN7d07jNsuSRMxcihCqqnTAqNgFiNRSqzzEDQIhTOGLjNkWUE52k4PlXOsrY+IAl6+e8zwydMsIzx65aOcPrGN33uP/uiIxfKY1vU89ehHsV1icOhqhPcxFZGUA4TSeCEpJ+vJWwuE4DGDAUhFORoxCTVIxfTwgOFwQtN2rJ04iQsOqQQvv3Gbq9d2ET7njL9NrtJg8lWZHCreJcdQXCVEqJpGAAAgAElEQVRpwxIRHCIGzp09A9rQ9o5CRO7d2+ELX/gC4ctf4R/+b1+iyBR7Ozv86Z+9wObmFv/4H/7PSbKYztLJUuo0gGGlW6YC8zjdY7lYMhpNENYhtSDLSmzbcTQ9TgUPMblAkhwjVovZsGrjSs9MKixPJRS60NzeucHGQxewwiNkTnQCKRSDIudX/9oXUWHG+/vrLILn+uwe588/ynqWsbj/A06P1rj4uc9zZBuUzfDCIo0gukiwEEKD8z0xRELwLOsG5QKtg36xxC0XiK6lo+Pt6++jfODChYuMl0uoOhyWU6fW+Y//9t/kn/zzrzFZX09tWoPhByG3zveE4AnR413EuUBYtV/ZvsfaHmcd+cAnW68P1G2LdZ7pok43IG9Z1h1FWTIYjbEIQnBMZ0c8/61vQJgjRcvVq1f58j/1/OqvfIF50zKra2aLBVmmoG+olEUGR+Mlb16/ydHuTV548WWeevIpdu7tcfP2HXxMgD6pDEYbFospW2sTsq7l1de+z1vv3aAzhj569PEifXaKDJEb3HJO1nXMbMAMC7pumTzwskCqVQmJEXTWcu9oHxHdqtRHEnqPdh1RxUT5FIkeG5xDGZPKYbwlWzV0SS3wfaTMC2a+oywrbOtoux6TJ9RvsGkZ7GwAHRkUJcEF+jYF/URkVQ4i6JuWM6dPcnR4xHg0/Msx3LVWDLwlzuao3tI9dBHd+xV/ISOISPRJj/athRgpQkSJnt4vV1anDK8062vb/MJf/xU2Fw1vfP0Flr2jQNA2LZ31dMFTdw4vUxghOgsxIEVkEMTqgyuI3mODRzvLiee+gCxzxnbG7ltvE7VkXUryskhsea1S2YFJ0fSsjixHBUpLdt97n4xIdzyj7C297+i9J6tK8ioN0rI0uEGq0avyHFUM0WVOPigYjieUwwkL6/AmlWysbZ8kywfkZYXa3EgUSmUQUpBlhvDiC4kdHuHZTzyHlhrpniPIVOUllUYJlc4eIXVZRSRCKxypHETEtECMAhAKE3o6LQGTrGERIiWmOUCaxIaZTQ+xXqTl5Oweg6zDGMPEK3rbIHtJ5TpUdMwO9ymNoRyPkXmWbK4WYqbJTc686zk8nLG9MeLo4ICRgrZ3/ML/R92bxVp2pfd9vzXtvc90zx3r3hpYVWSxWCSbbLLZE7vZaqk1dKvVkjoSZCeWA8eGAT/lIYCRWDCQFwMBlIfAkREEjiAbsWIbGiDLaiua4pbUknpUk83mWEUWizXcurfufO8Z9rSmPKxdRT04VGxFgHIBom4Vqk4Vz9lrrW993///+3/mU/zI53+Q//l/+ie8+OLLZEqAyRG+pbKRzNXMgk/mISmTttw7MKdo+osUI0e5f5dcAkEhrE0msc7wIrUBpbqoxfR8Rh9S9SU0WifaoJ0fI5VGI1GNA1ciRE5oHPQFIXMpFcwL1k5d4cWt6xyVFaPhImsiMLnxVfCWHRlwRFrXInxqCxVFgVaKuq1pJyl6UApQMbIoBS6DPIvo3OBcQy0Na+NlxuMRxijMcJFoFsijxZBh0GT5AGEKJkcTlJRMjk6w1iYXaWfmcy5B82JIKF6TZTTOPfB9BBeTgYt0gw4iUBQZUgSM1xR5hjTJRBeDTO1O2zCvAnU5xWA4OTjkxtvX+fe/A6+/cZ151eBsS9tasihoXVLOCJXTNI6ZzqiIHFoP/QGtUink2zuibWhtTetaTuYzNlYXsWWNjJ62tAQitpnRZgVGSXxdoW1NJSJROHxdEVpL8JaoDb3FZTwCHSVKerQNmLqi6eddymebZmQhMEAjXcRmsVO9pHJUqJQxIL2njSktzftIGx0DrXFK8s5Lb2DLQ/rRg2+Tco7E9xc6mRzn9TFKG4qiQJiMKFOgB0bSBEfjPPmw//776l/elv0f8RUi4qQiKIjLC+i2RdmkXffRI6UhKoHp97uhQprcBxcSwbRDhIoOwNMLEXlygjo5QbWBqFMwRjSa4Bz9fp5gVECmDVortNZJA06nj72vabYVB8UirTL0xkNWV+5gRK+LBrT4sqZ2jhxHrURKWA2K3nCDIIYsXXwcnWmaquL0qTVqD3mvx+LKMkUvT9VfPwNdIHRGkSmEGUDHiTcmRypD7SJapWxW0SmDEArhU3ivcwHpHNNyylOPPYIPXfTe9JjSeqL3jNdW08DXO3Kd0YaU5C6ISfftE4MkWpeqBpHe/5aIc5EylGgkf7Q5JzY1j+SSk5NNpvsvczKd0diWk9kc71p2do7YeOQpVk9dYhZSWIgxGl+f4FpwTcOkrJB5jyLrd9JQixCR6XTK4ayi9hHnegwGQwajEVpLmqZlZWmJn/qJH2Nna5ul8TBFL3bGtqZpkhFOdP3IGLvhfCSTqRoPSgEhKaXwRJk+f6UULka60KRkXgFEcrqldDCpkinG5MSyTK2JIIkxuVml6cSSjcc5yZ29fW4dTpiVJYuDU5xdO82tt3+XvJsn93t9+kanQVkH8tJKYW2aBdw7PKSsakJIAdSt7eITRUwB5GqJpnUs9fsYbWjqkkH630400bYiCgNRoJWkKmucSO0SIVIcYRQC0BR0ge6RB4lRPl1/gNS+1FLi2/uzAEG0A6JPbdQ2SZNoEfjW4Qh434Xee4cNESMV86rhxZe+S2s91geiS1mtPkTQGqlAKEXrLPPKo7WmqsuE9YghacG7z9T5dANrfcPaw+e4tbdH6x3VdEKmFC7cN/4lLEaMgTbGxElvLd57jJQPWiBBSDQpW7lRgtClLYkQENYR7ZzesIcjIoMnuhbtUr5yECnb2XuH8kkRlun0PiufMlolkquvvcFCX3P+1BoqG+LriCNSWUFWZGleYIZIkyU/SGPxMXGWdLFGfySRZshgYel9t9W/Gpu7kshzZ1JLZlpiygZqC1qiYg+CI0qN6IIEQ0gLU+mIiCmMQylDpGW1EOijY9TmbTZE4F7d4kKJyiVaCXpKo3s5Vmep19sboLIMpTLExgJCaqTJGC2tYLKcJhuy9eYtev0++UPnuPSjP8koMylNU6SNkRCI0uBJwyYjJV4KXFV2gLG0+DOZU8qUch5dwyDXZDiqqmbmwJPcaFUQxCjRPiDC7MFgT7uM4CLe1qlvHAI1Pr1eDOzcucXs+IAf/cIP0dYtzWzGrKpxzlM1jnxQ8OIrL7O1vYVRAh9TFOCPfN+nEwVyYUA5P2HYH+BdoG5bTmZTdg7v8ti5x5F2QuYsR/IKm7Wk3vwuf/ziN+lZT99ANlqibEtiaNGjJciXKK1gaW2d4WgBYWd86/d+hwUqVlfXKTYuY/IcsXQKjrZQuSJ6z/nzD3Hn2y9jsoy3r13jg49epDcYoKRke2uf25vbXH70As998HGmJ8f4KDicVOxMWnTRB6NRKhJDp3mP4KspyoqOMmk6wmVagDEGMpNaciHGhCKG+4mpqQdt0u/zTUsqANSDqkwKQfSpD++FJ5JhveKf//Lvcu3uDt4UPPX4B5jsXSdv9nn+ygexRUr7mp1MUgRcCFRVRWjbxCoqCopeThgvMD12ZHmfIJIM0LaW1reJxlkUGNNjOJbkWcbJ4SFSJORxtEn5hVY4IXno/Hn2drfT5i3EA7y19eGBUiNGwf1weGsdMXZB8QHK2kKEajajrmucdYnZbh3eeVrnGfR7LC2u0rYO71uatqVpIc8kShWU85q9kylDCUoHECpRThPvjYXF5TQsDQJfVzSuxUfLZHLMmY0NlEqTXm8bpFYYoyCm+cr6eJn1U2ts3ttBLw3J84zStUSh0m1AK6zNEDGmGZWLyMLgbJPWgw/k/REiCiQOxkvMdjYRIlI4QaYlXkuE6jE6ex6sJZwcMp9ukw+G5DrDCUFUoNBEnXrsqvXkPhBJOc2PnrnIwnjI7GjOaGkdHY8Sq1XB0ukNxgT03gHWRgaDIca3BBQuWJbOXmC4fpGjg0NGa2ffd1v9C23uQoibwJR0aXExxo8IIZaBXwYuAjeBvx5jPHrfF5KSpjDkNiIGC8RyQpAgs4yg0nVHKvmAwR38e7mhEgEh2YITEya5umTRp/99n+Ty6kWcGuF1hs4MWa+gvzAi9FIIslemMwYkrXaS2ne9VgQywA+Ylxn0++jeCDFap/WTjpTYyasknPgGI01XXTp2t7fpjftY3QUsF30m02MKnbTFmVLE+Yzp9JCmVzBvAyFCbSsqD8FLaHwXYh3Sgjbp36ZNqu6UlAyyXkplMobhwiJZljGdlOwfHGLbRNQsy4qiP8RLye27m1x76yp5pvE+sNDvY+PHkdJCqGnrOWV0SSJKpHUWrTXbm+/iYkN9PKGRJT0U1XTCk5cv0HMCESxWQOt7SAFtmzEaKDJVgwh4HCLLOHPxUQZuivWB21tbDIIiKxtEeYRrmhQVGJNhI9MagueVV1/lf//Ff0mmNdt7h/zhH/0JtjxE5xmnz50BU9CqGooh6AK6bN0Qk25ZivTvU14SQ9rco/e40Ca2vBQUvSJ95iFhLO47UYld4So1vqlSpZflNNZDZ2pJO1NScMmomTvHjevXuXZnG6/7bCw/Ql/t0R+DmwUmzSEiZOgs4/TZs+/J3ZynaZvOAJc++yzvcWtzm15uyDPD0mgRY3J0pjFKUmUZ0XmK3oCFhQVODvYRdFiA2tN6i5QrmGLA8bzmaNokrHGTYieDD1TWdQhk/0ClobXEe7AefJRpUN1B7NqmpalbnLPMJjXWR5zvQiSyjCWVp3zPtkpALd0nlxClISsgCAdKdNGQHeYhkDwGHelUCkk0qQWjFNRNYiQVRU7pqu6WnZAf1jmilOxs32Pj9BnuTUsa72lcUqBg6y7MHKyOyCDQQhNMwkFEunUfIlpn3L/0BZeRG4PWgtxBVIJylNEbDFFLq/QyRZ0JXDsl7/cTMttbpDCIGGkJ6RpETGyq4Bnmhu07t7l2dUa/t8yz6xvk/XSDMVIgTY/cRHq9FiEtJiswuqC2FukBJRgWQ46PT9BK8n5f/19U7p+JMe7/mZ//DPDlGOPPCiF+pvv5P3i/F4gxomzACkkYGmSrkKoHvR6VDxhAizSVdy7pP40xOJcm0cILCJ7WNUz1gEE2Jl9TLF15Bnn2KTA56PRghhiSDM8njbySCZPbpUh3rR1AJE2zlYpTjz5OlCkez9XHKSEqRnxrk0TNBmxsmJYVSMmZixcYjoYsDRco+r2EJ5CSzf1bTCtL1Vj6vSGxmnO8s8V4YwNleggEg6xPjsZJjc5TOynLM7K8h5Jp41FKdU48ifKp+vERRuNFomuYlXNGqxvY1nJ+cRmtDXmvx97RLr3hkA8++wxSJhewBt66+noKCLANtbdEF5LLUhmCVBzOS0x1gh8UhFbQcxMGKtDUEtkeEaMhComSlkIkfkchBbrdI6I5OrS83uwThMLXDq/HzKcn3LnxNuUrr1G7gLNNIkUGuHP7TtIq1wkZsX3vHv/2S7/J+PRFXBvYOTjmaPc28+Mp3jb8nbrFo9G9BabdJiSkorsrp89URoRwyNgNPeP9tkzs1l+6EaaZi3ygMb5fw0fEAzWFFCK1YB88we8NtqpZwzde+w5f+9Y3kP0hG6ce4uxyxg999JPcu32Da9eu0kiLco7WOaqyBiVRSjPoD+h1hMr7xcv60ir7Jwe05Yy6LNk/3ENHjZc+ab+bPnVVIsUi08mUm7fvcOqRJ6nbFulT8I1SiiA1h9OSW/cOqeu0wadq3b+n3ydp21PQe8RHRZQZphjgtAEvO0JpRBsL1lIwQkdBa9PQWfYKojRY61JlGwImEylOEZFMa96SGZMYOcEn8F2ICAwoiRYSCwgruR88PZ1Ncd6S54bjoxO89+lG4SIuBCKet+5u8tFPfg+rs5rpSc18OsWWFVlI7wNSIFROFpPTXQiBdS3e+vSZxtglfwmUELgQ6I/HCBUQjcMbGA0N2jnMpKIVFqYzsuGAqDQuAg+yCJJQQGiIjrRWlWQw6GEKwdHxEaKv0FlB0Y/4ssSHJN08vTBmOLDEMO9UWT5hCBzMTuaIoaSZ19Sz6n035r+MtswXge/rvv8XwB/y52zuQkh0lqeelw/EcYFAE4WhrxUxaQPJnE0J5EikMWgl8HiEzlEuIOZTirKmt7uJruY0V34aJXvMdrd49fWXUpiDlDQNRJ/YLnWT+pkRmIfUmyZ4XHS44Ag+pgUoEvehbRqsc+8pKrr/vHRoVfD8x1/g8pNPM7iwwGx6Qus9ucqQUnDh0iNdnFng+PiIuLSAWVnDl/NkUrCWe8clzjvazljlvaO1LVVdE4JFxchkPmXWlGjnsVXV8XAEn/vs51Ba48o5dd1w89ZNrjz2GN4HfLTs3Nvi+M4dbNPgbYNtPd47Xq1nnXqlpZ65lAoVE/YhAnXd4KNEm4zRwpAPfPBJvvaNP2U6mSZ0MOmADgS8S5GHuugxGKwiVM6V1ZzxqdOMe5pY5PR6OVQNJ4fHOGB1cZmPf/aHyYIDKXjlxhZ7k5p8kPETz36Ko/kB//1/+9/wP/zTX2R7d8LZ0+v89Be+h1/5lV/j9//467z0O7/GmeURo60dpkfb+P6YLFMonTbhpHoRtK5OLPZEtsCYLBH/ROonuyBoo6QImspaonOsDEYI4YiuSrJAwLWWQggOfE1jFdoktn4zO+Dn/pd/zN68JRst8YnLZ3l0bcATzz5DzDR3r15jc9aC98meDynz1acKL4oq+QSkIHa4Yqk1VvZw0TFeHFEsFGghMXlOL8AsZLjQUrmWwWCEbRuIOcd7O5wyY/ob65RK0a8n9JYuoMwRKrQY4f+McSamPn606NigFCB7oEcEkeNlMlJJHZBCpqJKOQINMjeo6BgUKQBcmQJPJLQNrm2ZzEqWgyTk6YbZKo2rPdXkCExONj6NUj3C7IA4PQZlkHlOEZOVv7ESHSxHoSKsLvNTP/JF/tk/+TmqkylJfq9QPh0Q5cmE/Vvv8PxTj/HHf/oqMhdErwlRdfGPikylZKzoA1nQCGuQPqcNOY3IWD+1xkMPraN7BZPjKdtvvkIWfSr4lCEKjQPKUEGMyNEKea7SAenS+kyDC5dmPYAcBD54puBg1vL8c09w+Zmn+B9/9ufY3Xybr/7uHWSE4Dz1PM0VitwkjHeM+Ohp2hbpQgLjKZmukgLecIH3+/qLbu4R+D2R7kj/W4zx54H1GON2t+C3hRCn/kN/UAjx94C/B3D+3Nmu35f46oqumgYQqVIK0VFbm4iBHfHNB42XAiMUUQScFJgQ0bJHHPQoZEGQULuSd2+93cm9Ao0LNC5JtfA+XdmEoNESFdNAzMWknSYKitwghUQWGjPuk/WHxBDJ8qzb5MEYjTEFF86cIboSax2hbTDFEN+0BCkILtLaEw6P9vmDL//7dKi4gHHTtLg7140QXa0YEyBNSpUIcTKnrGrqcs7JyRG0Fu3brpJX/J+/8q9SL7GaJb17CLz4x1/GeY+zkePJ5IHkDeC4SkYkZVR6aACTF+l7Kcl66cDVgx7DwRgiZIMBx2XN0oWLjLsWBqRhnVCaGAJSRvL+GmfOPI3ziufPOk4/fIHMCITsI6hoj44p5zOOZjPyfsHjly4h6pr6+Jh+3kudNpXx7u4Rn/70R1laP8N4YUzlBPOmYryxzrmHHyJ+9etcfftdNr7n4zx85QNs3tpNbBh1Hwf3YB6YHNBKEkNIzkwhkELiRbLQp5AQQRU8v/X7r1D0FF/8oY8hG4EULVmWov6kTH38zHlk9EzblpvbO2zt7bBbWtbXH+fc2dM8e1ny6NoKqigopSKonNIrgutMe1KhdY7QXfqUzlKohoS2nCJEpHEOmQ9499o7iGBBBPp5D2UUq/0BenGNFdKBsDBexGQZIURmtcM2DSqCSn8BxXgFessI0YLsevYhEG2imiayaUuUESkNQfZJVHH5YA7hSUNWF0XXi01vcOhYPDZAWVcIAc6lAkUtLFDXDdlCH4NKiq2swGd9VG8EaHRviIkxcZKiT+9HrwAisjqkGI4xRY/xyhqPX7rCm699B6FkCpyPic0ihOD25m0uPfk4k+P9BJrTw64AS0N3lMJ1h7QAlA9oIRhGOGwizzz9BB9//km8hL2jhm+We2hXk92Xvvq2o9h2xiUhiM30QRvNtakjQF3iombmAsEYljfW8AdzTuYlvaJgPOpzo5zhY4PRCi0VSkakDFRt3YXvpINCxIDMklGPzjmNgCL/ywWHvRBj3Oo28P9LCHH1/+0f7A6Cnwf48LNPx9C1SUKMRO9JDDFJ9L5LJHdYZ8lUdy0OjtA6ovB43+CbhraaMbj3Fn56jF9YJ1ooFexMa0orqV0y7YwWx0RfI1XBMM/QQiKJaHS6lscEj1JaEwT4qkXEQKY0IaSTtHUW3fVJQ0iBtkG37N56h+pwB+ss0+lJqsK6gZRzDmNrnGuI+zexdY0UisNminWWtrHMu+uZiBEfQ1pMITmrJ2XgyqVHefXNq8yqioyESk4HjGLj1GlAogcLFEXOcDRiXBTkeUaWGe5sb1I2c0xhEEbyVDZCKElWFMgsBYLnRZFUEl3ViADftsggEVHSVA2DXkbTG6TF3j3UQoh0FY9pkNbKAtVfx9aBkTlgIU9gLR+giOAbRx5AI/DWIq0lHJ9QBEG/12M4HCKl5Prtm/yth34S0bbMjw44OjjhsfNnKbRhPBpTaENZOxqbQFVeJDmheLCjiweYA0j6c9eFY2ul0goPgaqqEyY4JsnZzm7F6toAES0ZRaKO+oCkgz96j/CC4B27VcX1r36dre17LG6c48rGAuN2iw9cfoEqqiRPdQJUQVA9gpQYkxDFWd4jEgkitTaMEknJZSxGCkQmyE/32DiZsXXnFraZ41yNC5H5yYxxzLgUAtEHDo4neCHTTU/A7jygTxr6asrSIMOIgA8KFxU2qERWDElGraRGiZiUWF3byUfVqYAgELp80oj3XXEa07NJF2yC0PhAZ5d36fdGR5ZJeipHaE0uNTHk5C4m3k8zwShJPT9ENDUXrjyK6RWYLGc2nbG1ucly/yxFkIidGbN7h5x//kO8c3czKV+CJ/iII2AKQ01k63hOb+UUmYg0UTPIdcoFILmisy7lS+cZxNQSy5Tl+t6E48Ndcv00NnoeWRtQn1+i3L1LJgKCFkWJEIL5vOycp57t6TwhG5TEtZbgIzpC3XjmZY0TieHfH51lXjUoFJcfvcQbb74BMnFlnIhgFEJrCpNhTCogRBp0JBaQTH17R8J3NO30fffYv9DmHmPc6n7cFUL8OvAxYEcIcbqr2k8Du3/e61TlnNdf/mYnhwrgLNZ7WmfxMWVPIg2awPTkOA2LAkwOjwGLFBFvAyFYmsMjRIzo4g7169eQMdA2NfOmZFbOybKMY6HxPtnQ6671QQSf67RZO899brOXElvW9DLDk08+xqtvX0uSKhKf+j6DJCuGLC2tcu5MSdHrI5XCmJw8f8+Rmfcy3FDz3VdeJvTXUKNUYfaMpOjaPOsqcUyQMmmv5X2pmkDNKm5ce4sf/5s/TauSnlwphVIZMUj+85/6Lzk+rji9koIL6rIkL1L1E9H823/36+zu30OomPT2IbUDGtviXSB6T5xXhJjyLtum6dAMDa71yVjjPEWmOSrnWJdImHmeMBAnhxNCNxh74tl1hEw962Vfo8uKYAqUsgjryaxFtRZR1SihCXs7mCzHD4aEEDk1WmC5V/DG5pQv/dpvsDbMqU5mfP2Pv87Wu7f44ud/iMXxKv28z/LigH6muX77DhFFpnRX3aQNXkj5ABlNjIhgybOUdRudxzpHWc5TwpKHto3UvqFs+rz2yjs88dAKp1YW8K5FawMkxVNJn2Ol+PrVN1HKcP7sGQrg40+f5uELH8W1FhemOJlh2yQMGA0XcSGFwAilQCUy5nw2x0VHb3EJk+UEn6ieLkLsLXDhmU/RXzvP0a23CMHSOsvk7h3MZIq0jlwo+qMlRFawu3fI1268xbQsWRn0uPTweb7wwjM0cYxtmm4Dcg8iDaVOvW0bAtalJLLQsY5AEYVMnhCRsNoyyyFEZFBkvZwQbGqnkxg4WT4kBOgV/XTjlZJMCr74xS+weO4s16++iZweg9TIvEdPOCSB0kY2zlzkyacfp7UN0QdODo+pdrapwoy7t+/x8h/8Hp9/4XnOfP4T7N67i/JNunXbgMWCbQnzO3zi0grVbMr85IDFLJLJ1DqctR5vU59+utMwmZdUdUOdGS588AVub94mekOhC5ogmLSKl165ji0nNE1F9DwghjrvCBG8T5wiISShK7SkUiwuDFgcLZEpyYVTiyxdfpg/ef0a3nk+8uEP8Vtf+xqXNtbo9XpkmSEvCozJKJQgz02Hu4AsM0iRDl1tNHlRoLUmBMuffOOl/8d99T95cxdCDAAZY5x2338W+EfAl4D/CvjZ7sff+PNe6+jwkF/6pV/GhzSxD23bDS09UWikNDQu8sLHPsLm7Ztsb23hIfXSok0Po0+UwmhrZG5YGI/xix4xd8xnJwSV2j66DUiVg0kPnVleoDA6VRW9IrVChCTv9ZEyXYWG0nB8dMDcOp554XnAJP5KZ16SUiJFwYULj/D0B55hOFpIPUqVnnhnHVpnBO+xTcn2/hZ6IHAko5QgcV3u90DTMC+1ce5fzQietpqhRKBuKxqjaKylaD3BQ2YGGCWJvgUGOOvROid2Kg4k3NvZ5vbmTSKOqi1p62R0snWN9zZhdlEduMjjW5vUSdETosAow9nTZzk62qfxEu8FWmuQBUJJoEEg0TqnN1ggyIDHY4SBYEAOiMbjfM3ebMLDly/xxMoyRd6nLed4EXGxoHGWXCgGWY956/jGS69Q47E24qWAvODlN6+SmZwf/eKPs3FqkZXlRW7c3U+q/Q7eCKkwFwAxIS7oMM9SS6KSBOuQUiCE7IZ0krLyWCm5e3DEG9cbVpdylhf76TbVJkBZ3Theev0qV2/exvQXOLe+QV8F7k3hlWu77N65zfOffIZgFwGFlhFFpKc189oTlQTv8TENdB8FUeMAACAASURBVL21SJ1EltZaPOq9w13lOG9Z2TjHwa3rifcC6TB2Dl/XKNnNrZTi6PCI1qWIG6TmeFLze3/0LZY/8XkaH2hjUsDcbwTq2HF0PMQgiFIShU/YBZHkoTbcz79NWOwQQ5JQRpFkyg8QDbGbZ+RokSYemckRzuN8w+rKmDPf8wJ333gFNztKprlmTlM3ECQn0ymBSJZrdBTI8ZDjV98gNA2jNiF5xekVsvaAk9kUP9lPBsLWMnctGQIbNAtnFziczmkbxd07m0SfCrKTxuPqOT5G2ihorE+FjAucvpKGyNpEnJ8jVMFw2GP3YI+2nLC8OGbYTx6XKCWnT6/TOsf0+ITRwgJCKOrWIaRiYBSXH7nI4tIib1+/QVYMOXNqCfNmGrYur62RDUZcefg8/cEAKQTGZMkUiUBlKiEMYkRliiyYJBLo2qfOO3x4/+37L1K5rwO/3l1/NfCvY4y/I4T4U+BXhBB/F7gN/LU/74Wk0iyf2kApjTEZZjBOCyEzSbJHREVJZSs+/pkfpF8M0G5G6BvGvkBmPaZH98gGntVXXyScehLzgReYra+wv33Aq698g9FY471Pd2opUTJPZiiS+SQKQS78A160MQVGJZ2sqWuyaDk+mbC0uIqVnSg3xgdXM0cg62cMhh3oSELT1GS9IhEoAURgXjkOJiV9XeBDCnSOzuO8JQSf9NfR4xzQWbxr58G1FNMJKkZuvn2DyjvyLGd+dIiQmuWV9ZQb2rbECG+8+SbjhTEXLl4kxIBwDYeHuxycHNK2LdI5XEyqmAeSvhjBxweJMx5JlAJtxhBlMnyZBfI84mWyP8ku9SbGyGhVE3y6PmopEG7K0B5y0xaMDu7R2NsclhWzuuLunR1u3t4iCMW8CiwVnmeefoJeP2M2nTOxgdneXZp5yTwE/uBbL/HQ2ln6gyX6gwXqpkEXhizP2d8/wKLJlaKOSSZqulg+KVWaqyiL1wOCd5iiwEWom8DAZFDNoDW40NCEjLm3WF2QG8l8PqV0I6woGMiG2gXqKPm1L/8+L75xi7o65gOPfIg2zLBe8X0vPMfejascbR1hhYd6D+QKeTZEmxSzl/XSEDSEpDBpW0twjlzlCC8QHXaYjk3fNiW2TYPY0blH2bv3LnF2hBeCJjhG4YRIQEqLRjOdHoEIiNi5NF3Lje0Zo5MZQhgEAoJ7MC9JD2cgKp82cxRCGGJIUwuBJ0sAqI57E9NMxMf7ew0hpOfGx0ATATsnZAV1a1NqUlujfJ2aI1Lw7mvfoT48JtACNXXlULLP+ImVNAfpDj2R5WxPLeX+CS4qjiuL6y0g1x9hd/Z1Nm8d4pWGYJnNGmybuDbfe+4Sx7MZ7XzGnXv3UkpSAJ3l5KYgVwpXHuK8pRU6AfecI5cG5zO0SgiIc2un+fRHnmdcGB7/5LMMi5xooZ61LK0u4TLJb/7rX2Vp1Oehi4+w37SUsxOGvRGffP7DHOzvcuPd60wO9skzQ6EMlXXkDy0zXuwlemSmyKREOk8MAmUkpVO0jSBXPZT0GDPA+kDTWMr5jNWVJRo3f9999T95c48x3gCe+Q/8+gHwA/8xr7W+scF//ff/uwfX6EwX4B0yRnxbEQncuX6dF19/GUnk8uUrLI0KjFNImVPGKc2eRB5ukq+cRV55iuzpp5nQcHTrDkJLpj5VMQAIQRbtAwHbfdlbHXUi8QUP9ZwQSlrnkZMpB1u7LC6NubN9D99R32qn8EFQVg3LoxEfeWqJTPfZuXePyeyECxcuIjoZVBQgouHOretsb95gcvUAZxuCtbjGd+YahdISrWW6/rsGj8SrDDubcn51ldZ7Dg7mZEXBfDalnFZok3P5ygbHszlSKpqm5NFHH8ZZS1VO8D4Qo0Tly5w9N8R7zzArUp4lsLCwQJEXGGMYjVI+qFaKotexvekwxCEgY2KdN3Wd3IL3o/9Iqpq6qrHWYYwnLzfpjQbY0ZA4HNKTksGs5fbb11g5d4mnn/80IVreeOMaeW/IY089xnjY49KfvsEb72xy8dJzbB5M+cynP0ZVHvPtl69x+txDjJdXOLuxwbNPXOIf/oOf4eKZDY5OJkkmS+ek1CmQ2bsWLUMKtzYwn0+wBPpLywxQhCiwoaEqj1FNhZ3NIM8YjgwXz6zyuY9eYjQaYpYWObr+Krc2d/n9b77Cd9/dZnR6lcdOn0a397jw8NPsWskzTz/OK5Ndbt18hzeu3eShU6fIibSAE4Kgk8zVzhxCKGJUOOcQMiOiE0VUCMoqadFb62mbFjpEsg85S2uP4op99nZPCF5xPJmipEFlBZicYVZwfO8Y71qqqsJIQVPX7G5vY9tuRhTpQmMiTYjc72KlAJt0QIeQ5imRNPCFAFFh8uTalDGgsjy9DinzVohkKBSACw7rFMZolDXc3driskhu1YOdu2xu7XIySRAwbQxSST58+koagEZBFAJlMvZOSl797uuECAvLq7xz/SrnHz/P+sXTfPubX6cwPZYXRjxy9jTGaPr9Hh956goffuoKQufMJyc8fPkSuj8kKEOUDmRB3TS89JU/4jt/+FX2jmdIO+fhU0vE+giRGYLOWTy/wo/9Fz9GFsCsnEa2E3xbgp0xnx8ykj3+5l/7FNVkRm+0hjz7JAHYefnL6JM3WabhP/v+K8znFfHkBj/5iVVef/kbvPtHDd97doPvfTjHBs9sMiEzBY0QxFZQ6iFLV56gNWPevnOD8ekNrAts3tthjkCUNaP+6H331b8SDlUhBD3TQ3TDR4QkBJmqi86EcnBwSD2fsWPvcvHCo4xHPXyhiUrgp3OMq8j291FZjlgYYLWgnUdu7x5yuH+EC2VygFpLACqf9OJKpAg/7zyyA1cFl6SIgZTOoquGQZ5R7x6wW82RIeB8wPSWMPmQsracHq8z6I0ILjIajhKWU6i0HmQagFjn2N4/wmFonCY4j7cBLTO0iSijk6PPBZSWqJgTUCAKej1NayWj8RILZxZorCXTirMbp5FKc+bsQ9y4foO3377Oh56+zHg8ftDmAQi+5TOfepasi9DTUqK7a1+RF0lxoyRaqAeHneoYKykfOAWYB59UCe9RO0O38COhGxqnJC1NWZYsLC+RK4kWyZi0OBC4W5u8ePUdJkiyXHH15m0GwzEbG2vkhWFxeRn57hZ7R8dkecGd27f523/rr/Pm9du0tk4V7GBAvz9gZXlMWZZMJmm4JDoLvVKqc2GmatRZB0YxXN7AllPaJiS1BRmiJQ386kDuEzPE2Za+EZxaHRNCYHo44a1bd/nKN1/inZ0jlh86y9m1FU4NPU+c+yRyPOTg+n7yD5D6Jm/fuMPy+BTGtPRGOVmmyTLNfNLgmhbb2gcSedlx5avZlBAjZV0mQ1GUCJfaZwJQUtDYSJ4PeejiYyB8Qka0Feun1lhdWWN39xhvLSm/zSeXKpHJyTG2zkiy/o7pGyLeBVAd6rjDKETfaeBD7C4RafePxC4JKfXklUwGJH/f2ds9K+mZS2ocpRLttZqXSc4XU2ydMRGtBLnKsM4igaqucN6n20v3DK6fOUPzxDxJpvOCV178NuXRXVb7I774o1+gVxScOnua0fgUSii0SWx6k+fo8YCD29eJoSE2gdamwI4oIsEfMS7mLK4UHE2PWFsQPPHkGv3JVYTz5FFQuwnz1jFzko3+gOMbr9E0Uwa95NRthKJEYasatz/nsQvP0baW16/d4lpIUDRpFHsHx9QzxUyWrK48xYc/9RmmRzf55//iF/AkGGIIgkkbsWXFh1/4GM8//EHMoGDJKDZMhSoMq2fGhPUBmVYsjsbvu6/+1djcY1KoENL3ndk0tU2EwLaW46Njytmc/lDj6hKdGZwMBOWw9YysqVCTErWwRMwzXAjUVcvW/hGb20ccHd7DByjLisZ6osnJsoxenqUH1PsUl9XhQ1tvEVpjhGQMZL0hZVkRnWSuBFGnDE7V61EMRpxeOUWOxjctdZmIifu7e5TzkrKuqduWeVlx+81btPst5UGbLO8OrE9BB0iJbQURjZeBIgNtFIacc4sDlDI8/cxzrD/6WGoRLYxYP7WU8KpRsL9/jGtKvLMEZ7v8U4cxBiUMl04/9B5JLgRid5hKKVOEGSnl6r65R8r7SDH1wGafWgmSIO7LIEmyy5gChnOVBtVlVZP1h/R6PWLwXbaphhj40HMfZlbDt197i/XTq3zw4y8QraU3GuFi5OzZ0/SvvsPDj5xn79a7vP3ODV56+RU+/9kf5Etf+m20DBQmQwhY3zhFeTzh8PCQ6HjACKGz0CPpHMgapzICijooNClo3BqP1YqlCw/xbD/jnc0TjramSRVDQgMHAb7x/OYf/Al3T6bU0nBhJePCUsbHnnmKpXGfezOJ93sYLejlObPpnDvvVFRzzyeee5YrZ86z0O+xUGTc29wmNgFbVUkZ1R1CxuS08xRbJ4RAS5kMVi7Z/mMX5CBd4vCsrJ5CC4ebTaGeoKOjqmv29g/wLkUG3u+6SCGYz2cExh2vXRI6oaMX7wHz0sJLA9T7B4rspH+RVPETUtydVmnmFDvzl5JpXnPf4UknRtVakglBJiTRp0Hy+Ycv8viVy0xOSr7z0mtY2wCOtk03lt4w0REBPvaxj/PRj30UKQWutfziL/4rNl/5Dp/9/Bf4nhdeIOJwRhGmu6nlZKGe1jRCspg9i5xPKGdbGBom5ZSh6uPmM6SvWIsZ55bG3L23RZE7lDvm5NYtqsmUqpLcmU5oW0/rFD/1xMf4xnfeYGdvB+ctddMQrWNrMkM6RxCaf/T9fwNdFLyzV/PNr3+TqnVECTYE3EzT6JbP/sAziNGI+YFnc2dG41LLMxCZtYbSS56MfbwqEgeqnnE2aHp5n3YgE5StbbCHd993X/0rsbkHEZnYdGJLKTFtTLr14Jjs7XPn5ru8c+suZVXRNJbNG+9wcrhHrCS1gIM7N7HTfbLtbcLyBuXuV7H9VxlWx8wPd1hXgdwJpFYwXMRHOHR5eljmFao7RLIwJRMwzDLy0YBoNJM2VW378310PkDFnKFKSNkwLwmqJsbISX/EK9/+Fr1ezmhhjNIFWW4o+gVaaMajAUVvzLWj30ec3YD+Ak5lKSIQS38wQEiNsi3WBSIZRZYCgdu24c7hO/TCPn/jqcfQ40XOnl4hep8Wl/cordlYWeDiD38/ZTVnMOjhnMPLNKSLmUYpiW0sSklMXtDWVSq1ZLcgBSAytEnVr3PugVtRSbpbQCTqiPYQfZdTCWgpCVmClfXyjNnkiEG/IAdcSEjYoB09DUNqfuhjT3J38yYnOzu8czJlc2cb8YkPc+q5D2BU5Mql8/z4555ntnOPr89O+Kf/7F9yeqEgRMm1yQHGZBweTMh6QzIhOTw8ZH3lFM5acq3QUmK9A2moQuDopMHuvEEInrquk0GmmqNKz0l9xHTWsLW/w16lmasNlDH0igH7B3O+/JWv8MprL1FiuHj5aQa+5bG9m5zsXOc3Xv4yt+YFl597lqiXQUTqumZtdY1Xblzj7RtbfPtr38L6lqqqKasaJQRVlWYsUosHhyox3TaUVOgiS5WqfO/QFTEmk5qrAMmhUww0XMrXWdMtL999l83bNxEiS/TKmABwMQaUFInfrnRqE6Y+IVEIMuVJtI1OJdYNRZWU7w2kRcSFtNEPej2yQmO0Q+mcqk7PUJHneO+RzqXchCgpckNmNOujHo+d2wDvEFmPH/7xn8IMlpkfH3Br6x/jG0u0nto5jg/26efr+A5HMRoO8e0Ua1uyXPEPf+bvE0JFzMb83i/9H2y/c5Wyrnl7u0mRf0Km/Fut+eLf+SLtScOXvvQH3Lo7ZbfskWtL2Wp0Dgv5lGcfWWF1ZcQv/OqfMKsE3gxx3pFFqL2kkDDKHD/xdyu++dZdvvK173JSZqSDyyOyEbKZkOmIkz2msxPe3jnm2vYUosSYnHld0pOW2mucb5BMOLx3wL3SEkKGVC1aeKaVxzqLjg4tA1Z5brz6Oi/9m1cpioIQ01wr+OTufb+vvxKb+8HuPj//c/9rGniGSJ6JVJkHz72tIybHc5CBC+fPAoq3b93D37jLyGVUCkQzx8xqrFlg8exl8lNrOKW5vXOPW4eW1inmYpHoBEhFwJMXPaRS2HzY6aIlrdxACkmpM1x0yMzgh5rh5E1sNmJn+QIDIRgNCnxIqgMpBUVm+PjnPsWTTzyO0QohAkpEYkxM+iTDU7xzZ4vF8TqTecuwn6d8WB+Qpk0ZsD6QOYNvHLVrybWgcoDMOK0bfH+JYAZkMvU5lchw+BS2gUfrFEOY94aJttdhFSKAJwV2d27DtvYosmSpR6U5Q3p7Uig5YLpeeoakwXdDPoe2Dq8zOiQHEXAxokmKmugjK8urAHiRMPTGZCAEVdMiZJ986Pm+T32Ew8mcjz7zAX713/wWm7d32HvkDO++u8X5Mxv0hhkfePwJ9nb2KU8OiLmgrwRHs5rX3rrGQHj6psedoz36lefC4imkEkkJIxLx0nvoG8WLv/0LqJBaRlHmRKHwUdI4T9nOmEwth01Dq5dx/VUyAda2/Lvf/W1ee/27TNqc5584w3qvZWmUs7LwHGMXaF+/Qb+6yUAbDuKQpvU89/zzTNeGfOX6dbzz7LYWE2pkcCwMDFprBlmabQRCF/oeic52OAyH8Gme0XqHUl3kHwILib0iFcrkIODUxilEpllaW+Hhi49x9cZbBJ/aacIq5DDHm5b1wnAEhCBoRIoYlLZBkWFwEKBRGuEthsgs6zHyFTIYYlRURuHbikLlaBTRCPoCgjHMQmBRFbRxSl9Y/MIAHSyDPGdQZAgp2bq3w7n9LXy07LqSfjakmR/xofOJMX/33j7vlBXTV7/J5O2AUg11jEnimEWs98y9YvkH/zboAoPl5t6UL33tFpXvUTaeEGoUlkEGvTwjtIKx0TgLc2toQoMIHicETQs9qQlWo0OOVAVOO2QsGUiPMILZMZRBpKQyp7EWqtYyaVucbSgMLOBxISBkztZBxbXbR2xt77M3achkQSEsgoZGKWprqZUhNprtnQPmFUCHXQ4eHysaD5XJkVoxIDKzjolQnJQW0anZQJGL/x8EZBe9go989CMIKZFCYfR9iBBs397mW1/7No88folPfuqT5L1eMtpEUHmgiTX6jRfp7dxLodef+wJidQ2vFb9lI/d0n72jOUpa6HIgtYzko36i/FmL1Gmh4dNm5pFEBUFrpPQEI9CLI8xCH29bQl8RUdi6BS1pdWBpuECudOd67IZT3bzgvjb2+OCQ1nuCTLAxFwOIAFlOzHO8tdgQqRuHFdCE5Bhty5rGOdbX1h4MOdO8oNPCd++j6r67rzR470t0s+r7W3H3q1I+cOfed6iKP/unuraLCxGd/d/UvVmMpWd63/d7t285W21d1dUrl2aTzXVEcjic8UgjKSPZ1mJLEJIIThDlxgESILnIhQMERoAAQYDcJBdOglw4QGAbMBA4giU7srVrNs4+oyGH27DZ3Jq91X62b3u3XLzfqSal0SAXCUB9QBPVxapzTp/leZ/n//yXLOkPejjno7e9uk0hehaRSCwkuK8QXd1WKmj0/HxFXVeYzPD0M0/z6iuvc3BwTNd1FEVBlmm2d9f4pV/+IlkO1TLwzruvgp1y3h8giNTDjNA0LJoF85N76NgiXYeIKc2qKAbYdklRDjGjMYPRmMF4M/nfK4NSOUfTfd587V3q4yOqNk9LQwN37n3IG2+8QWfh4s46a4XiwfO7nL+wy2K5pJ2e0LQdbVXRVnOy0XkUgvXJmPG5XfKixHhHtazJTfJbESoxUcrhMGHYveBKSIHzkcxkPYeZxHwiddBp2a7ITEaM6f1q8pIcT1FuI6TCmIz1tc1TYZ3ODZny5MJSrA1Z29nm4MhT6ALpGlTQdGgKCSp4Mq3ICagIZfScLTqcEyxiwcPrNROjGWQTNiYleZ4zXVYMy8h7e55vvN/ya4+X+FpAV1LwEBnJTvfxizkfTg33ju/y7vdfwtuGTeOROhKlYLQ5IESFOBKUTcfe0V1cAc622Cho2zkRxXJZIc2AZ355gLBLgm2praOLkdpZlm2HiBYtPMHbFCZyPEcYzbRqWbrIvEuWvG0AYqCio+osQTTEWAFJxR5lpHOKpnUYqaht8tU5PFkyrzuaLqlivXV0wZFpQSFrBsWUjfGUpl2kQBMl8ErhYptoxyFyND2h9Z6j6ZSmS6SGENNuROmUvjaUlsXhHl103N0/5GjaJl2CrVEkFfOZtb8GC9XRaMRP//zPJKVbz/EWQiQrTvctjh8+yxd/8WfZPHfu1HdbSomoPBFLqBu07+iG62SXL+LRKcqqCajsDE5CpyqMylJ2poFMFmnZo5OEVyIJpexl/wEnREpGX1jWJ9s0wx0mTqDKEco7iJGBzJABpI2cvXCOqFYy7IhzFqUNMXq0kgQReffOhzQR2pgM97VKnX2pDQNjkqhIWurYElE4JNF1lNpx4gU/99TTKfcV+rgun6TlaaeF7wt9jPdP9PtKzbT8TF+kfYaPq2XrfY+KKOJf+t0QgKZDIMiNoen8j33jJFplz5n+yCWEOL1d3R9IikiWl+ztH7NYttz68CZXH3mMixd2ONj/fUbDgvn0Kb79ne/zG7/xKzz73DX+13/0TznZLxiwzqPPPk9czqjeu8WHN/fBO9aGI/KNC8h8hFQFuUmZnFoKrj7zAlKW7B+fcOegohgqunbBrXvvsFjOmB7WzBcNIisYati7/TY3ZgdcuXyZjfE654eeB8/vMCpLhAMVc3Z2L7J77hilGj773LN85Y0Z+MSnj0WG9ZausxQmYzhZI8vSnie9YImF0tkUhiGEwpDYPg6Fx+OlJsRI61MEogqKXBhaJdE+Yo9PyOg4rEcIk4PO6RwYrRHaE5XmP/jik7x47SLON5CVDGRHWZQoOebVWzP+7WsznhgtGArH1mjAvLMMS0NRClS74N3FgD/+YMlP6ZvUaowRmurunKbPDJWjwLjbAbb54StfQbaa2npqVxN8YJjnvHrzQ0bFhNHmNv/o//gXKUWqC+RKMm0dJh9houOZRx7i4gPb/JN/8yUWbUu39IggiSogUeQ4rj1ykd90JwRyyEccLFvuzaZMK0twEik8UkSia8mzjDffeJuHL+9y6+AO+8eepQvIXGJdQISOZeuZThXvRcetOycsbMB6h4yBTElE1MyamkrCP/yv/wdeefMVunaKCBIjocgk2xfOUE1PsMsp/83f/y26tqa6e8xW6ZAsGeY5jz31BB+8f5eqrrnxna/xP37/JXzXcvXSeoLnpOTBBy6xfWaLSWg472ccfPMrxLLk808/Sfv0kxgCqp5igmOoBBOj+Z//8Ct/ZV39RBR34LTbJfbWvt7jrOPu3h7r62NGkwlKaiQKESVdazFDi+06TGwT/FBuJDFTTJ1xlDLxg50n2sQrFoA2mnm0pwqXGDx4yJtxisRTEG3ANgHhoB0YDqMjdB2jtU1A09gW7yOEgJZgjEpeHST8OglpYmLjuCQImR6foLsO3XlKl9wuVfTkArQPEDqsrzGxYV0LzmeC8TCnmZ7g1zRnNtcTLW3V8UV661M+otZJsMrp1QtUVoX6o4U3xrQYWy1JV0X/o7+bKNEeY0gxeCGievu2/zfX6v5i/ziUlMgYiTH0PiiBo5Mps/mcwiyQ6jzVMlnrHhxW1I3g5GTJhSfO8B/+vV/huy+V/F+/8weIwTmK0RbT126AUsyXDbM6cvHyZhpyVW8zEANIw59+7duExqGlxgmJUJJiMGQ4mhBci1zLUNLS5gWtsSzuHhOd4+zGmIcunuVnn3+C4WiEzEuET89J7Ttuv/0hx8cFrRdUnSPIHBdAtC2Zlj0zRKCkJMuTV0+R5Xhnk0w/pIkrIhnkuv8syNMGp7GOuq5xzuFsh20bOjlOMW4hqUen84DJC9bXz6BUOjyU6m2IY4dvK4a5wnVHHNUVxWAdKRWLpkLlmthOkRLaRceHh1OCCCyj5OTwgHLrKsX6o3zpla9DkVM1LXmRURhNmWnWDyvkaMhYDvnRjX1sC00Q1ESkMuAaHgsjtgc11y6MmHaeeRupaoeiRamcUB+wNjDUbsaimnAyq5jOl0QvUSIpUbLMsLOzxrndLV5/9YccHFk623F49zaFsDjZYWUkBkuZZUgjicFy84ObvPnadwntCRtFybqIBFcz3BixOTlDNT1gMd2jq/YodWCyNkJnIwa5YWtS0jYN+/sHKCXZ27/O7mbJ9sYQoXLyzDAaZDx89VFuvX8Tt6g5OyrJ1rZ4/OJF5sEzMAWlyRhsDnnuwcs4axloQVfXrI1HNDot/7WSvfLVMYwwChZPpO6WqEyijUBFj3MzuqbBBc+9WPzEz94norgLItH5UyhBKoXWEmLGdD7noUsXKbI8xcWJVPyNNGAFtmrRDpA5jLfwJkORuNjWwNzWBCHQKu9b0IiRfcycc4nH60PyR8nnSAF5pkEmr+myCyhfYURJFixFoxEIlA+0jSVTGiNh7/2bBN/bp4bYj9QCW9c412GtZ/+dD+BoRhYCqoewcR1mL6KNphCCEZINH4i0BD/DNxminjIelqyNJwlK6lW0MYKgj8IjyU0Q/XKuv0LPsEgUU/pUof55j5yaIIneae5jB8OKpqcER/t30FKytraBUiMCPxnv+0tXT69c/ZECtDaEEDk8OOLcuV2W8xZnLZO1CWvra1x//22q1nPn7iGPP/oghU747mRjgpCQ5QP2pzVKWHKt6X2d0TrF4iklkNoghOBX/s6vE13i5I82NpJbpNZkgwnvXX+Tb3/9FVpZs+gCewe3GQ9LHnnoaZ5+9CLPPvkwmc4IUeDqOtnC9oeq6L1/5suOhY28deN9yquX2NpY59zOFvfu3cU5R17kGK0IPnBxe5PR+hp7+wfc2ztEKU2IYLsaqZI1rfcBpTQ6LxgXZSKx9D7vVW0R3lPPa3QhUUYipMKHwLKqE8tJJgbO+BokJQAAIABJREFUn333Vb73wx+RFwURSxsEbTTIZs7W1hZ651P89tdfJ8dDEHzYBLqYaIBnCnhY7mPkOnMPrq2pO0vUgqqpkNGTXbpIXVeU8TadHBDyhvUQGescZEauFM9eOcfNew237+yzO87YGQsybU7j+kaDgrO724ToWM73uXr+DJk5x3BQoKQg0xnZeMD5rQnjouD//Of/ijt376YWo6158co5YowsnSc3ks2NNRSCpml4+/YBn3v2Ea48cJHaCtYKhY+KYlAyyDVdtWB9WJKFFi8MejSmcT4toYkoGXH24aQLiIJikMzUbJAIIiG0yLrh/IPnMUoQ7AlSeOpOYlpLLgTKdywOlriuQwnBSVcTnGN5EmlDCn1xztPZLlkga4E9mieLc6VwInn8gE8cexcgRKz4Kz9twCekuLet5cbr15P8Ozii7HC9rP7urX3G4w2+/u2X0cUYQsA1FbJd0kVB3U7Jbh7RCclmc8jJwT/Du8BivmD29g0G8wUliizLcV2Hdy15E1l2GTGAzPSpGjDvFLFrKXLDQmuMBeo5U2XIsnnyAp+plD0ZA15JgjYMxxOaee+E5y3Xb1zn2eeeRRpNbjZROkEsv/f918gvrbGsakyWoXTypB+cW0eiCC5y884BbUiikVwLLIqHNiqunJ+QDYYJsw1p9BQykgnxMT67EBBEKhZ9BntafIoeU++paloKYm8rGuR9WEaq5GW+6uIFAhE7hJSYcoAXGkKH+Ch23/9ZTQirsAsfejbN6bQg+qSjjoaMMR5tCn54/T2evHqZV998nYPZgvfv3UKM11jaY5btjC9945t8/7XX+PQjZ5ktKn7uZ36GanmC62B3e50/Dx7vK+7e3WP3sccYhYJMOKwfIBXkMbB1/jwyK7DVEl0W6bEqzbJbovOSmCte/dGM6XLKhd1d1rcu8VNXL/P0pz9HqVuMAlxKrko+ZBblOgbbJdxY4zuvXUcVa1w+u4lxNaGouXD2DDfvbXF0cI9c5YyLNaSKPP/5T2EGGQf3zvOdr/6QYmDYPrfOxQvnGE0GCKX49rff5PbtI1zwhJBM2ayzGFMwXA8c3fyQm6/9W6QoODv8AgRBrOY8udPxqxvnKAj4PGeuS+qo6UQST5VGgHNMFAyNIvolz/ztz4HOkg+7TweWlpFRkdKponP4iy8wbyMyz5DDAV1ryVApsajPIC6eO0dwlhAFU69pvSCEiPGWJ86XlFLy1OXN1FCpPFnZGjg4WqKVARwXxjluawRIggxorXBth65PqD6ccSQEQ9nxyHZG40B2AYLDC8HCBbyzdLM9ogtoJdmSM66eu8DXX/oy2lfcaeN92NKnsBLfW367GOlcoAspZjOTie/v+4k/hJCCg6TsXXeTDYMLAggIESmUIkRAJrfV3r4HHwVakQ5tkSZfpQSjMieG1FzZ2KKUIYTApQeusXvtKoxz/uif/EtuzyuCd+iYjMliFCitfmJd/UQU9/lsxtf/5EtJsKQErohkStM1iavdtB3Ldh+hpilBp61RviXWLXVboytFMBmlyiiGQ3wU3JnOWZohcwNSZ1iV4ZXDeYeaDECPsLbD9KMyQIOkazsqoxlMBmRNQzbbpy4m2HKUuN2DgpKAaDucDajoWDt3hocev4pUEmcdscg4c/5iSqYJHd578qxIxXQ4QhfJaCvEQLARMygTi6N1BJ3EJTE4pMjJ8ORaMBoOEs/ce1QPp0iRrBREz1VfFVER6Hna6U0TiQQp8L1vDT1spERyavwYVBNWjiM95h5TiHYxGGBMGgOjIAlg/sIV4qlRc+KYw6lV7GnXTupAhZan1sOz6ZTQOJ771JNcuXqFl/70X6ciahueevQhHnv0US5cPM97L3+dYZkjg+Db3/4uDzx4nslkjA+BQTlgY2OjhyMS2yeQbJOjSM+Pdy2qzPskL4WIgoDknZt7vHXzLsvqhCsPPMZotIbIJqytb/WOngFrO3KTnDBD9EQiejhCaEOmFed2z3LkFOUwQyuIbeCpR6+wfW6Lb3zjm3QNZEYmSCPPyMsBma4ZjQq2z25x8YGzjEcjpAZPwBiFVhIfXW/Nm5xCtVJoaaikJnhBDA5bd2SDAUonV9NBmSHamiYGXBS9TawizwxNZ3tbAYkzGVJlLG2LlgXoDCfS/VXOUtcuTYbO0bYt3nXEVtHOptRVQ+gCmeh6IZvAGEHXWrq2ZdY4fOyjCruGTmuUAONtssMWmtg1RBWp6iTSInTITKScXxd6Flti9zhC7x0VGUSJFZ7Gw8hIord4IahsTCEcMfRLxxT6YTLN/tEx7WJK4wWOfomdOhhWvpeQIhWJCfqsrUdLyIxBSZGsFkQKoVd9gY/AxAwxmSIKh1agVntBleyNu86ldDfnyfKczgaQgrazDLQiBImIsGymKKlBCS48/AgPP/8ZGBT82T/7l+wOFJkyZKJAiuTpNFaB79+e/ZV19RNR3Cfr63zxN/4uSgqMTlmnsii4ff1tDo5O+NSnX0jZmMETrcO1DRpPNJ5w/VX0uz8iXLiC/Pwv4fIztN7RfPNlPuQdOJoitKGLMomQiEzO75K1x/i6IisHrPIku84jdY7Tiqgiho5BqFDZJnMpiVKSBYfWCqkMGkUWO84PC7yOSRRSZDz6+LW+vKQCmwHzpkXJgO8iuczAu4RlR8HZwToxRg6qI9akovM+YekS1ocF1Cfs7u4mz5eY5F2QosiA5EHSd8uQZOGrr1NkbyQZiPXcZiIhkuxE4bTgQw/b9HdxOg1EMDrvhSw/+VrR5dPDWn2I6JeG/XMikg+/7O1tu64D55jNDxkPryCCo8w0j129wi9+4TMMBiV107BnJC99+SvcurXPb/29f5/huMA5jxSStcmEC+eS/Nw5f3qYrB6NEALvLEaSXmObiuIPXnmTL3/7a7x/cI+rDz3DxsY6B8czsiwjoNNk4wN5VqQDeCXw0opq0VFXLUZpNtbGLGcNxiiMkOR6SKYET117kNdffYVGKs7tbBJFRGtDZjKGg5L19SGTcUGZm1RIAylDNU8wDsKk971PtsC5yZLZmRLsnL3E+d0dLl66SLWYpQ5aK+4eHKG9ZSEUC1XTeEHrY3Jrr5cgRDKnkkkZuayqZDcgJM53yBio25auS2ZyIiZzLUXaKXURnPUpnyCmRC8hZfI0B0oZexO65BSJs/g8S9Ni39kHlYRxKRgmCcq0sOQ9Y44IVdfRWU8sFcRAKQUmCM4/8hR1YTDFkOtvvsHR0ZQuBCIFgoAUkGmBkpFOCe65jKaccDRboM2ATAWyTKeYOkGC6ozASEFhDGWWYb2n8Z7JsEzxmzH53fvYpUOmp0ED5DIjzzVeBDrnUhYCGqMznA/UssUHT1FKjqZzOhsYjobIPPkg4RUxeAqz4rsJyDRWZmiV8+j5TYbLfTSRXPU2zFLQdn8NeO5ZnnHx3LnknxE9Ls9ZHh3y9ttv8/N/82+RjSdJwSdFCskuR0RhsG99j2zvDmK0Qbz8NHG0hZIZh3dPqBfHaCrWh5EYLTFCHh1FmbEz9jRZyeY4FXYRYvLHyFsIAZnBpBhydHxAyHM2dtcZR8UwN4ykxKkFIFM6iszY2ch45zvfJUaPcxZvmyS5JvYBwR0q03zx8V1ks6BrarI8Q6uS+WKOWb6BF5FLE0EYuNS5i4wbdkxpT/Btx9raesIoIxilWQVzO6F6nCAVMsEKV+/l5T4k8Uu8HwwX+w5E9FLzj+1QQw+tqF6VGCMEmeAB0cM2QiLsjynz8f4UsCrqq6MFcb/Y4wSu8+S5Zm19jaOTGS889yRVaFhOpygU165d4LOffYFCBIKrKcuCzfUN/sv/9O/zne+9ylPP/BTEjh++/F3Go9Fp8ERnHYOhxmQry1RxenirCNFHOu+4t3/At77zfX7/K19BF2d4+NKTmOYOh/ciVRyjZYYZDBFRcHfvAFfN2Lt7gPOePMvY2lpDipxqvqDtuj41Kzkntu0S2+0xGI3R+YTf+s1f5/io44MP7zKvWwqdkeuMyxd26WYniZUlYW0yorU1QknGo5KyyMiCQguP68d1rQTalNjNkmef+01e+PSjVIs52CVnz+1w2Fq++tpNovesjwr2j09orCMiicHTpBcf7yNCqD431vWZwaCCI+U69zGMfTKU9x6E7Dvd0MMSUOp+PxAFpRS9XUikMCrZMMSIKTJi718T5Ipy6xnlieHlY2RoBBuDHAtJxBcFizoFmoQAZTQMAD0c8A/+8f+OajtigP/tf/rvWR7fJdATIWI6WLJeKb1oap7YyShefJz54QQtNAvvcd7T2g5kTwO2ERVcaiyGOSfzmryNbI9ymjYdMkprClEmC5MYKU2OkgqnwJHK16AY4Xykcx3WQdsFfJTYaFm2BTHXPPXMVfYPjrl56y5aOrrW4azH5FnKrnCRn3vsMo4O0cKEGmsiLiQHTo+gsh16tWz7K65PRHGPEax3vd2uQCjDyckJJycnmKKgPyaJMQVTJO/pSOMWSCxduUE23OC4aZjECjvdZyuHpy9t0nUdISab0sIYskwxyAUxpKzKrmmIPiAwyJCjokeoyP7BlGaxzyQL7Mo5+SBnkLe0PuBnc4Lv8M4ym9Xc+uA9XltYEoM7IIWnzJOB0f7xcepmBgVNV+MWyRbBk4RTVdMglMMT8ERs2xK6jvW1Mede+AV0N+fDD95FKJMUoTLxw1MsWgKA7xdUeqOv/u+SHjMnfShDgiqCWBX4/vtixVVPH+KVcVPgfmcvpSISklhDxNOR9uOvY394sFI19gNvv8heddEypAMoKWBT564ygYmGo6MFg3LM1pk1skJRSEXnofMBL9JUZ7uOyw88yIc33+HtG+9wZq2kXlZY58hOaZ33+eOCVNxxgYjgg1s3+co3vsnLr73OcG2DrZ1djO1YNyMWrSeqgCckjxKjuXD+PLk+x9mz56nmS5pqSWdrEJZRUaROTkiihGAD0VukCjQ2MDAjDA1l7sgzRZQlmdFpoRgCw2GOqBNmvdL7K5VC1HXv7aMVCPr8UhEZKEUclrjG0dUV1XyGrYfk5ToPXXmExXTK3sERzzz1CF956ZscHZ6kLM4oyaNLz4le0WZ7vnlMh2OhBIVJWK5UoHvdhhSa6JKXTKEVsu8pjE4Eg1xLRAhoKUlBXhHZv7eElBzMWurWkqn0gd+cFORGM28t00Va0goEJYGhSbDRxGT4GGl95LD1uBAprCMuFV2WoQcF5doa89ke1rWJCWUD3jqcVGn31lhsEDghaUJIoRsBNBJhsjTFkmjCMgq01OSmZG29JLQNXipkMSArBds7O9x85wNcAOssbfAQPbXVeNdgcji/YziZnnB3VjCvwLmQlKQqsGznhOD5j/6L3+Tl736fl175HTaMpaoDShnc0mF9Oij0aIJXKdoyHxSMso7cGO7tLYkqRUiKUxD0x1+fiOJOcLTtEt2PQ92tQ95/9TXsbMbezesMh4M0SkoJvckXEUI5oHrwEebO4/dew09vgAgUTcMjA0etIpV0WCeoKku3nNF2LberZbLrCIFltST0YblDY2jcknygOT48YrPMiUqyd+8uQimct8ncv/I4m1KTmibZBUwXVcLQncPbljzTGGOYL5dA6oajD4T7g1fy41ASbUyK1VoxWWKkKydcvfAQ58UWJ3u3kKZIdE8REw+370hlTLeWoJVUHOIptz3hrUQS/VCkIpvgmBS8IUgS9NOuGvVxSuWKoip6GXw/HaRCI/vdQfxY1/7R6z61UpwaUEnRoi0sMMxnDd5KvA1Eadk/2aeygcP9Y2zdUSvde9JEsC1t0/DYgw9wZjTgwGh++Vf/Fq9+61vYqkLIiEBjtE5MB6VxzlJk6Tmw3kNZ8vt/9me89e5N5o3j6Ss72PmM0owwviZTkUUMGKNBhH4nkQy8BmVOmWWwtZlgHqGo6oaoAmeGQ/brGV5q8kzhvCL6Grs8oVCJfSFkwt3HRZngHREZ5gXtfJl2JBFUMMioyLJkgOXCKkwjJAhBC4pSYQY7eN9ycDhDeEsoxoRsxOUnnqMpN7nUVJzXlmdu3eSOdOTRUkSHzwsyBJ1L8J8kcFK3CBTaFImSHD1FrjHBE5EIlYJyRA9J9kKL5HWzCmpXql/5RAwBD1TOEYmsFYoupklhdy2jMBGtTC8UEjjrcMaw7EBpySiTabLLDEpEcq9oY0fnIpU2yFGOjBa0Yek7BqUii5q5TRNHEALrEmy5VuQMi0BUisNaUIp4WnCFFgSh+fRzz/LW+7d4573beA9qmlLhPAbbRQKC8xfP8au/9h/ztf/2v+ODu3M6lwgPUmuqziNjx3YpuLTZcTK13D6ULBqFJ6aFbQy0GAqtKNbXWT+zBQi06Mh1npw0Y0dUhrUgiS4yEYbZUCCcwJgJUhvW1pL4TWrdNwR/tb/MJ6K4H+zv8U//8f+CNhk6zzg4XBCbDmc9v/17f8jJ4RHtoj0dr4NzxBiISuKcY7UMCd6z7CLeObyzNDZifcJInU+5mb53dFwtQ1Zp91KqVAdF6nvHZshzTz3O7f17dBGKckA5KNnaOsPWmibPc7a2tijykqIomExGSCETpdGY1H3phOkp0S9Jk88qgqT6jCS8z59CFoqeHs/edEo93uX9b7/KeG0jpR2t/p1xVTIjWY+ly77IJpphP/r2RXq1GBVR9LeRkomMlD28Hu4fCFqfsl2CTAU8RN8LoHqDKSHAyNPir3v4Rgb5sQIvVp37X6j5nUgmZG/cuM69/XuraR+B5O7tWxSlxrUWvMWRHAJ1PuDW/gGPX3uKF37+KjIKmsZy5YErvP2DP+ckOKxtTyP2ImBdi9GaKJKNw6vX3+Ff/8EfcrBouLi7y09tbfJ3f/7z/PC1H/GDd+5SS01XC1SW9XhqJMTkd48PGK0JKj1SpdIex3ufWEm9PS4xYtsGlWdUXWCoc4pCE0TJZNFRd47j4xMmaxOESQ1LcMnyWWuNj+m9OBgOGAxHSGPx/n50nTaCYaESa0oFLl1+gNgtaJY125OznClL/skf/BH1co4qR5h8g81PPcb61hlGW2cZdAuq6R4Hb36Pcb2gOTnhxIOPHukbzgwGrA1HaCy+XiKMpiPiQkemTCrkIhXqKCVtP9UJIZEhWT5UAWZBsugSh/ugbVhKSTEqeL/xyAa8l8yDo7EGq86y7+HyeJONyZh3j4+IWrCsLF54RhqWGBrf8cS1a1S+Y9TUxK5jemh5/XrNSSvpGkumEpSUy8CozPkH//l/RrFziT/+QcXL791hOeuwUuO9JssFWxuGf+/5X2Ax+xqvvzHlcN7RuASp5rkgBIPJDBcGO8RiSGUl01bggqH1IsE5CQulsx2DMscHSeMlQWq0gkwqlnWLVgWZ9tjpHCkCeEduxkSRMqKl11S1YGES1XGRecyyYffMGpnJkUKzMfnI5z10P7GufiKKe0RyNOuo2p7b6VpKlfH2u+9TvxWTr0MUmCxHa4XROkXceUduDEU/GmdKci4fIkghGlk57EcYTdYHTQshUqesUjdtjEnSbpm8qH305NmAb371z9ne3ub5z32arXO7KKUwxlDkOSH20EVPAhRCYNT9TletsGoJ9D8bSVig6W0VfGqxkTIVd8QqolhivUcbA7bBtQ1ro/EpI2aFkYcYEnSi7jNSVuCHET+ug14dIKsCLFNh4T4/HpHCj+l/VnLfkuCjSlcQvZNkYo0IkcJ8w0oB+9Gf7G/nY0VfaqTWFEWB95ZBmUK5o49oodjaGDMZjVK8GAGkpGkaxuMJVddx4+13WBsNcV3HqByipOzjALs+VSk9aNEvn7vO8tLXv8mXv/oSs6rh0gMPsrM+4YHzZ2H9In58TLlb4G9eR1WOYLt0iK1oRQnRTdhsTMu0KJOthHNpilrBWclWIQWwtNZy885dhtslIRoa2wHJq7/rut5qQ2N0itpbOXOGFdxG7A/y1euToK8QV4EqMdkOZAXFuAAZUwh5sBRGcmakONi/xb2DD9jTmsHmFsXmhKLc4uyT/w6iO6Fqp3zq3h2qekZ0DX6+QHhHKyUxG9IGSZQZ6ZUIGAEqRozWKR4uRiofWXSReRdxNiDR1FGxaD1SCsZ5wWC4weVLl/nzH7yCliqJoUZjlhG8KskHBRvnHmJjkLE3b/BBMo9tOlxjZOlzApp5EzGFJsoR0bUJBtIlTSWxQYFI3HStI0JnyPkxx51lnAHCEXPFwEQ6qwjOkwvJKFqGWdab5xlQvZd9FCAMQWikyfHBI0XvhClUUsL296cEFDrFdPbqE4yWCJHIESubbAF0iyWus8TgaG2WLMFFxHaSzsIwK9m3Go489w4OeKAokFEikPcn/Jj0QD/p+kQU90VV8+Y7t1HaUAxK1geCzc01nigfY/3sDuvlCFPklKMheZZRZBnDMqcwkGlNpoGYlKKFyVmlkou8IOoMkIn2F0MSGYnEARdwaoEroM9SFXin+c5XvsdomPPAA+cpxkPoDwxvuyQ66ZkTqn+CcwWeVOCUvj+irj6wQkaMVL23doJJRI/RK6VTp+ljGuN8MriqFkcE1zGe7JxSr5LXfeC+o8xKoPQX/s5HC/IK714xR/rv9li7QJxCM/i/XKB/3PVxJWs8PTw+ev/9nfRQ8kceX0h44fbmJuPhgPW19RQb5iIXds/x4K9dZDgs075ApNdFRM1bb77JM+WEYVly69YHBG9xzqKNwsdAZzuU7H3JEXjnUBLmixl/8qWvcnQyZ7I+YXdjxDNPXOOZa9c4UiNcsQm5JM9yCA1arxTT/VK6/xDfp+uLpIC2IRV32b8mos8dDQnDnh+f8M71tzn/2U+ByPoP5MqVMfYK4WS/632aGKMQ+NOGoFcTnFJe0yEaI6f/v20cWZkhi5zWW6QSlMOcunLsXtiimd5mOjvEECm7Y/y9wL6ZcLh1EZFpyu0dLjzyOUaZwCjP8XvvMDs84uS44uhkD2kUlx48T+n2uLC5gSJgqwVSKSaTCc4Fbs9a7h03fNh6miZQKPAio/FghKDQ0PocM9yglkmMKAYSIQqkljgKPBoXFCEGqqYhijRBapnCTJZVS25SpGWeKbzOEBY6VyNIjKksN+jY9fF+geAlt996HzdeZ6wMWb+3KyI9maCkcBmDqmA0GeCjx4YIfbOStEICZyOB5PdDf4B7JEIkqMxojRKOolQpJJ7U7GklCT7t0xASgUchmZ7MWExnPVQY8XhCdFhnCAJqF/k3m2POvbNk8cYROxpGPiHssQ9ViSIZzv2k6xNR3M+f2+Yf/lf/CcGnLflX/+SPeOzqIzx09QrZoERbT6EFUiSaEyL5rjuVVG7RtYhg0UrSCd1zrAVeFSBTpy116P3i0ymbxP/0qTRJxZn55El+Mp0y3txkcnYbozO0jakDj45CpwIvlURpQ4gJuiAKtFaJexxTonyMqaBrlXDK6ANCaWIMaN8ioyV6R9MaPNBaT+0EddNRucDyZIqtKh659kQ6VHqI4aMdsr8vT03iCimT/zfwkXpKjP2pvyLW0Ds2IlJ27epAiPc77RDDfSrjx1gwEW/9KXzjcH3XmvzaRby/5PUh9uq6fs8gJToISp1xdmuL5595mru37yCkQEXFE1ev4lyNkgUogxEeHQVZbri0NuQLL76IUvDyKye88doNrjxwidFoiFKps1RaIZXAesfh0RF//Ed/xM2bNxmvT3j+ypOMjeff/ZsvUk7WkesbmPeuE2+/ht53NMuKOBjSujT2KpEgK9FPXc52RKWRWfoQtm1L3TRIpU79eSKKm+99wPf//M/Y3b3C3/7Zz/Hu66/ywJXHqJsO51uOj07IjOLsxV3W85LQH0x1U1PVlqqtWTaeQEzmcqs2QNBTFgXWBXxwvP/hbbbHBQ9uPUgMHfX+ETuTNe40NUYrLl06x+7WGBU9wzwjho4TcYY795bsrElefOwsf/Kd93nt9Rs0VpDnksHQILYmPPLi86yt77Jz5iyDkzuM7HvUs2MO79zCaAPlkA/2OhpV4vMGwpScQJ5nzFrIM0lpIkPZYLKSqp6B2eBkCaXpUNHRWsEyBmJsaGwAn6FlRkQyNBJva4TQ6AK0EaAlzTJSiAUhOOpoaIIBkaNEm6wcIkRVko3WufB3fok//dKXcQPF2fPbVDf2sN7gtSIWluHZMQwsMydYOoGNklJqMiXoROCksWhZkg3WcS5ZWgiRNAMidNjgmHcgomM9E3gv8M4zNAoRGrpgU3RnlBidasT1t67z9ntvo/sVV2s7Qkx0VUtLWRre+d3v83KruSaGrIcFTo/6pqXPVQbcX4eFqhKgpSKKAXt7R+QDyXh7HVFkRN+SZSClQUZB7HNGtRLgkpmVkBJkH0CtEutEoBKvVqSiHHuVWcolDghaIh6B61klksoJnI68v3ebPAgmuYFuQdt4qmbJ+mTMveNjotV4H6hb35uEBVorkro2OAgtgoCSgeATDdORKF7Sp+mh6xpYPSbt6ZBUrcMva7wIxHKMbyzl5XOo9RSwQM9zP+2CBURne/56un8h5Gns3eqKK268iGniiGmcj7LH4gWoPvxYiPS7aYpMXuBpN5G+H+mfP3Hfk0bKNBU5505Pjvtr1Pt84BhTL6rikNZVqCxHZ4Y337/Jz9pnyPLsFCoLIbEtQjbE2ZY8eobFkJilJCEfM17+4Y948cXPUWYDVJ7jqdGxBJUOt//7936PW7duYZTkobM77GxK/sYXfpGsm6H0Bq7LweRkISPW+ygioWnxmWaERhiNIS1WnW/SMrlf1IVMElyH8DlSWNYma6jlCVFGLjxwnksP/zpf+8abDLKCFz/zLIfzgK8s1ayiLSRjkWGbhs5orE55u861LOaBw2nH/vSAphFkymC974M0FMFHyAVSKOZdS9PMCcsGea1Dbe9y+MYrSG+po6d1Fb6VSBP7qdKii5ys6xhKR2EmDIbbKPsOtR2wNwuUylHMoTye8sqH32CUC7K1Ha48eInJuSuMdws2tqfI9gRRnyBeSAf9AAAgAElEQVSPjwldS54ZrHdoneiMAoeMConCigypFAKFEBYnHZ6I1L5v6AxKK3IJKkvsnEXrKIuUdSp9WgT7kDKOQ4w0Xc3ieMFs3nA4jQSV8h+CjKAzMu/IdcfIbHL31g3OnLlEWUxY+r206BZD1n2giIFycw0zJMEoLgmpkDJx+6Mgix2F9jgVcbFLYjJRMxpA12mOKweyRJvAQGu6mGJQrLNEIYG0LM2iRGrFaGeXkx/+MGUNuEghNVXd4KRBScP2+hku5iNUKVDSs1hmFP3kHaPq0YeAjf//BWT/f3ZJIZDRkuUlH7z/NgMzIpMFXeVQGrrO4XWfedq70S2tQ+hUrITsu0SfMKw0Vil8rJP8t1ee2rbDWssqgkAqgQ8tzjsEig5NFRfc2b+F9R03bnzAjevvIoJDSsH29ha379xODpIh4kMajUOIIAbQD9JKp4JmVECr2HMSBVEolEgTRl7khJDi7srRkCbAWBcUsaQJns4o3nntFSbCUJgcFwN6JTrq16DEj7BchOgFNikv9aMY+arDXilE043E07FuBSElLN/1sM1q+Uo/AfSdOwmioOfDxxiJPkFNK9HT/btIP6M+htdD8I4oA9Ppgh+99S7TRYdSihBjSh+CUx5117YYoxAKdFlgXQfaUIyG/PRPf54QwWjFma0tiuEa1jbE5Yy33nydW7dvofOMrc1NHrp2jc9+5m8g8iEc3sBGS5DNKWIl+/tbsY76pzQpgaVACUN04fTfK9HE6PHeMyhLMmMIMWJtB3E1QSi0Tipl6x2t7VLBKIfp/dY6wiBJ3LvGsXfviLv7LYva0gSPkBnWpXzSGJL9gFaKGNL+San0PivLHJMnhSnK4Pv3Jy45liqTilVQEm8tmTGc3c7QxiOEZzAsUSrRHK0L6OjogkBkEpVLykxy9+br7N1+A50PGZ+5TDlepxxsM3logbEdcTpncy6oZksUAt0JZN+PON+iQ451LsU0hsSaUjHx45XoU520Xkkz0KaHU5WhKAbM6w4TA0WwHL33Jov5FG+hKEsyY6k8pKi61GzFrCdJ6Ir5Sc16CWcnG4xMhY+Shg4tI0WZoYymcwKbhBzEmBTJzgmk0EilUNrgPdCruumbnhBSDTI6kOWJsy+lRpK6dB9i2lWoJNDMMs2nn3+Ob33jJRprKYxK+gsEznuC82yUhjNFpGsqAkkQtYJlQ/A9WgCTWP/EuvqJKO4ni4o/+OYPmE+XVIuKCxPNKz/4AVEk1ViuFSZTWNubKWlDCIIo0yezKApWjmqqGCP6xamzqYcQykNMAdRGK2KIhGhQUSBVkuYDxGC5d7fhO9+6w/NPPMbDVy/TeYuSGufTlHC2uJxg6RjToeMdIXhcZ/FR4EKksRYfAsF6YmNPfVZijGiRFJTWdn0xcPiuwRLRxqBNxmhni099/gsUH3zIT7/4M2RREUtFdD55w/DRAn+/S6anGnbe9UvO+0h3iOGUFxv7r2NYuZelZXCMvW9Bf4OrIOXVbacfXBXAjzN0Vhv8dPv3MfiP2Q7EtPaN0iG14OY7e9x47zbKDDk6OmJ7exupFF2fYB9JjokiRPYO9lj6jjfeeJ2yLJjP55zdPYdUGZ33XHr4MSwaRM03vvwNFp3lqSefIIuWzzz9GGfHlubtPyGWG0itGIzHLFublrank0/iwUsJRqWYO/ouSegcoQXOdfgQUG2XKG4hYF1HZ1uaZsnxyQkbBqy1GGOYTmdsFJ5ytEY2GtFajQuS4CT1wrJULSdHFdPFAtUIZrWlcoooDN7WZFLw8AMX2T6zzvr6hLVxyfooLdi8TqwO3XR083sYb5B6iFMG0Xga5zm2HZeGBYV3dCJQSoOVAplbGhfYOXOOL/ziLj96/19QNQuWVcq7JSTRjBaaaw8/THfvDR7cHiUbBHkbN3+PeOS5U8NxGzlqIoO1MVs7W6yNR0zWNkFq2qbm7s33CN2cKvi0iFTJMiAG2StyE1TZeoss10BqJJKmS26qXTOn8RnOR+adYPPBxxmbAuUj8nf+AKWmDCQ0rUQJnSC0EOhcYLq0LFrBvaMDHnnoYTI0XliqYLFKY8YTQj6ka2MiM8RIKUCJiNGGyrUIKZlMckJo0884D95ilaILyWM/+DnWSSjPomWDDg0yH7JsPcE7BgOTovh8zW//9j/n1u33CaRUMJ0ZpCkQrWSQG44OboJfJnh1MGE2tkxMwBjTIxxp7zL/6+AtY6TgyfNneHn/DsLWPHz1M+yePYvSiuU8pdq7UFNVFc4lV8S2cbSNwLlAc7hIaTYhMPd7Sa3oHF3XkNiHjrqpIEYkomcqlITgsb7ti3Nyc5zVSQ36re99iy996+sIneHCaiyGqqkhKBKQk/D6tJdcFVqxKrv0QDuwan4DQnikELjToico44AOnxZpRrHz4GWeeMEzGJQM18apWNdpcafl/fij04Ip7tdeIUQa3WVPeey/v9q5xlNrgnjKnIkx9MU9PfrQ3673vTueUqewTDpWVgva/r99Ufc/pnOH+3j9KmzbI3q1pURg0QLquk6L2X5ZLPulpRCKernk6OCQ6WxG+/Z1lNIYqbl4/gKz4xMOT6ZcePQC67sX+L3f/V3u3bvH1cefQLjAL3zx57l0Zkh75xW66W1y6VB6m2BDmthWB1B/+Iqe/aSkRKoUnh58D/TlBSoavAgEn3Dyqqko8pymqaiq5enBKKVkNpty+95tPvvMI3gjsDHiZQqomC3m1E3FYjlnXi/J8pyz53Y5IzXv3Drg6HDOZJRz5fJ5Xvj0s4zKPOlBmgW4liLLUIWh7VqcS6Hh2PQ4fYyYmILdG9diZUYR0+IvOoFAo5XAREk+mDAyijLPKLSgUgotHVpFXHAoXfLIw4/wwfwmvvXpfWWSB4vKNU8NPHeWkRuu5dWbN1HScFtFitEag9Ea6xsb/NQLn2doOlzT8OFRS31c0VQdPjc46xKzJM+5+MBlrj36MG+++TrTRQ1C0flAKRw+aFAZ0ZTMPAjx/7D3prGWZed53rOGPZ7pzmPVraGrit3sbnaTbA5qkZRkmpQsK6YtObblIbZiw4bhBAZiA/avBMgvJ4jjxE5gQwgcxwYcJ4qgSIok0qZINSmSzW4OPZFdXV3zcOdz75n3tIb8WKeKjCw2GUUB9EMbKNTtU6fPuThn72+v9X3v+7webSyx1mghaJrQNpNSzNVjnqqp0T6c54PZEJFpJkUYktZCkUvJdDLk5tVvsXf3JgpHEmkEjjhKKCpHpBVpGtHuZBhTB6UcDi0skXqY5mVQQtBpLZC2FxDiiDwVeC2oDJj5tRLFATExOT2kmY0RtiZWCdJbGgtVbXBSMpiUjKuKSHii2lLlimYOJlNJPF9ggbPVO9bVPxDF3Tn41tXrPNg7Ymt1nU+/8CJlUVKVJaZumBUFpgm23IfbniRNEXJe8FSQOWqlSNpJ6CFLiHWQN0Y6pZv3Hq16VVuhpJ+XqdCf9R5q47i/P6DVyjm7vk7WXUQqPU/CCYqIVt4ikipo2uVc7SIFUgTNqRSSKAoyQ+8DzU8JQaz0vCdukUoilMb6sKVzjURnaRi2yhY3+6ekSM5sbaDzmMZrWlJj5vS6UIACCfJhRGAoUvPCrR6qLOBRQIfgkXPVEZC7QYA+V4HMV96eOabguxQv3n+nUD88Hq7aH7ZdvPePhrLh//n35ZgPfxGpIryvaaURT1w6S9xq0el08N5TN82jwG7jCJwhIVlcWOTc9hk+8PzHsMbwlS+/zOuvvMrh0TEqcqTdVbKFNabFlJ2dHdaXFvnEH/kky0tLNNS0zz3J0e2EeO0CZjxGek+nlzM8HnxHdTAf1gs/b6sJ+R14VV0jkyjs8oQCoShmM0zTEOfzOcF8AO28Q1iHVpq6aVAiRkgVKKFZik5jeu0VsiRiZXERISWdhUWiKKYUgmH1Cq28zfMfejcr3ZxEeprZAKxB46jKkuPjQ27t3mc0muDrhufedZEob4hVhAC085wpLUNCfJ8XCislURTOk7q2eKmJs4Q8boV2WBOSn/ANeEEcx8RJwtbZbe68Zoh1g5COqqznCihF4T3IjHPnzvD67W9SFhM0EaYcMT4ZcfjgPrfeep1Ob5GN1SWe++hHqYk4fnCHenCAORhxeDRCxZqL77rC4088RtZKmVUl1gantCHgLxIFubb4wT2aoqSyntmoH673OKWeTueYBHDCUjUF3/raZzDjgkYotEiIc81sFsbkKRZzesj911/i9PA+sQht0rqxKAfWKDwSayXOSgQRUoQY0FakaSUSoRSNF2Aciwvr9BbX6XV3yaqSmVcU1mMbqJtgotM0DPsH0MzoJZosjUJ+hRdopQMmQ2h00iZSgizvkutZkPXOfTLhs5coEb9jXf0DUdw9joOTAbrbI04lf+lj76MVxWgc8dxMIoVHJQlCBaBT3l6ktibkoDoQUoFUZDrF4nAiSA2FdUhTIWwdwEe2xtmamAjrDbU3eKkQMuLGvUP+zy9epXIJf/ET78XKBiFjhA1Fs5lrqZ1K8Nbim9ByaaxjVNTzQurBWKwzWCPIWws4D/2TQ7RrKEaBt00ckXcWGI5LZs0UZxqEd4hsiVrGfPXT1/jjf+7P4UmJvae0TdCdezDOPOoVh960e7iMxzlL8rAvSOjhqbmNHdzc5CTmgRnM56wPFfJzNMF8VxGm8/MJxXfr1AEhHJLQbnpY5AXqUYsmqDblQyXkox2G8RYlPdbC5voS3XaCMY5up40xNd4LVBTCoWOCIc1XY77x2/+OOIr4jV/7EnGsePDgOkoKulnC+cuPsXluB5NkfPi9HyLLct799LNk3R5GClTSQvXW6S4/BU2B9bdRsULZEmssJnFI7QIW11qUk+hY4FWKEjUKGQq7rRBagUxxepliUaAuFNSDY9rdHludiMUE6rhNli7x7A8vkaRtkIoulo31rTDXkBHpfGtdlRXONdTljFv3bzGZjjm99S1c3fDV0zeR0hKpCB0nqCgjby1AlKOVRsUrLG6sU9cNlSnRYkq6vs7TzzzH+NIltmTD5o3b3Gr6zGRNQkQiPMZD6SFNczq9VWwJC0sdZLxLXDiUDrtpHcXBYOYta8s9VDWhrgucDxK/KFIondNRGZ21RXKtGJUqYBiqiqYxaC1IEsHZ1ZztruAzv/wLFFVw2XY7CZ12l+0nnmSh0+HlG/e4ducuy2euILMBJ4e7+KJGNmEH54zBTYbcfOnzSKERUY2dVQhbIoUjiiS5EhjT0HjF2XOP84FP/Fm++o2b3L4/YTQq8D7A+EQTEml7i5usn3+a3r0J7uAmlhjna4oq7CAtmlynKK2ww5r11CE6GTEFmbaMyppOJFheNvSigqlJeDCW7J1qMAqPxbiSumkh45LLqwtsxAnDKGMowFlBItVcwhsWXssLK6h2l1hLerKhpzVpEiGVpjA2XEMKYpG/Y139A1HcszSio9uUTcWZlR5LLU3kHZgG6QJvOVYxsXEIa1CRxI9HpDI4EtM4AytwxuNdcHJKEQaQwoWVaDGdhAGYMxTTKVLl1MbQWEljBLWBg6OaPFlFWs2XvnkPr4O5QOPCENAYytmU0kXBVOTNvNh6fMWjFk2aaDweqQwra2ElOu4fE8l5gUXSTQWtpETaijWdESWLIGP2mozDieHC5S067aAUiaWet1RCRVfy4dfm5wEk39GYCyHmGaoPAWABO/qwwAblyxyHKyXOzm+Ej5rzj2xNPHSl8lBv/Z13fRQT91B7z1yP7ufb/zBoMt+RU86dRYKAc30IJcvzfP76YQdibE3d1GEnocDYgJG9dv0Wf/Kn/jhLZ66QpBH/0z/7BhIwtWVra5s4iphVNScnA973/ku0ux2cb+bJTwbqgtjMqMZDZF0ibAsnHNp7YiGD8cyFTN1aWbwVKCeIrcRNJrh2CmkLW3qMs1h1QjtRPHZ+m4R1uguLtJbDtlwJQ20NSauLd5JIAHWN8KHH2h+OqIZ9ppMp/eNjyqoIFvykxd37e8yKmk98/EdZ7OYkaU6UtJkWhq+98m3e3B/wvicSdi7skGodZHHOER0fkoqYzZVVzvz4H8G4gkTGfP4X/w+GNwegHR0zYaYypkYyrcBnkhLmvgyHcw16jl1gLpEVCIx1IUeX77TdHk5zpqbBFwYR98mVp+8MjggtIpQCrSDSjkRGaKewRlJWDu8MbV9SjEqGgwH9LKLXynHtBfIkIul12D73Q0SugcMjpvcPqGYjFs9c5OnnfwonFT6qkb9+k6nZxyuBVBUOh/MCLRMiYnRnHasyajPm5OSYRIbwb+UkkdMMjk/4+tdeZnfvAfgAtFPSIaXHOhkUcDicNygVJLlpE/AAUaJJGolSgrydcfnxx1jZ3MZ4T1M7ovksz1oPviHyjo2NdTorMebu3cCxkYo43EGR813z0sI66cICdTUm8mNUIqlcgrQhQEQIhXGCqrfzjnX1D0Rxl86hmjE94VlOPWo2DulITRMwwEpQUzG1Q4xxZFlGEqdUs3mkWBKS0a11eNkLAwfhaKYjmrJkVNQcnJziTIMHTNMwtI6qbsjyFnhFUVlG+31mLsIJxWQYk7USTDWjnWm8aZDOoOsJK3mCVoI8UvNCKWmpEOOH8Ggtsc6ClWggiRI2Fj1xpGmURicxvZUldJzQWD/fLraZWMWNa6ekvRXSPKcuSpZW18IWEYMx35mePiyWjxQqIhTrhz1zN1d8aBW+YufsXAY5r98inMAOM/+95zeJhy2K71bWSMl3qv9DhO9DhUx4Te9CqEEcx9R1SRxH1HOZ5qPgbMJNxzRzeaT3IMMwt2lCT9K5cCFFkUZKTawTbu7ucvvufZZW1ui2I6SGZ555gr0HBxwe9Gl3O9SAbRra7S5J1sZLOdcDPzSLgZz2kbMxaRKkecbNHaHW0TSGOI7QkQFbEEVgrOV0UlEcTxieDDkZOPqnQ8qq4Jn3X2Frc43WchftPY13LC8voJqKkHJbYyrDZFoymlTcvfk2J/1jptMps7KiSQNKdm15gdXVNRYXl1heXOLqjXtcu3mXJ595Bi0C+6SVZNy9e5/R8SHFqCKuuqynMa4u8dZgTE0cp7SymMnJKaf9XUwzw01L9g/uwqTAxZaqUYyjCaVPMY0k0BIMUZQSRaEPr1Qw+DkkTV1RlhFFWYeCYgOKISg3AmSrMALnLXvH+yBAqwjhNdYLpIzQypNFAUgmvCXSMUkS4v9yCfXM0JQGLVPW1xYRdcHB4T32a0tr8woLrTbtlXWefPwpmskxS0ttqjhldveAaTWgMQ0q8lTO4q1FRWFWIjAkssFW4TMSWEbDE7IsRhpHbMMueG1tlfe89ylOZxW3jm5hmoQojxA+DMt1lJBnGVmegfDMypqy8OAaCh1GHbWzjAuHTto0HqrKIUWMcyZcZ0JQ2hmJ9fQWOvS2V5jUX6Xb62LLIiADXdgtCa1IWl3SNEP7gsxAK9JMGkmEpSwbirQDS9v4d30Y+Cffs67+gSjuw5lh1m2TNGNuHva5uXuAFhLhfHAAasVCEhFLRVUbrBPESY5qAVIgpMIhEUISSRvcfFoQR2GFnUaaZ65sIIRCCIXUMcIl4Bu8L4AQaP0P/uUDLp3tcWVnhWcubaDnMi7nGmxdh0T1qkRgsU1NNZugpCTJMrJofa75DgXXGIv1YSVYWQtJi2hxmUh5hE6okhWmKoY8JknbjBpHoxVv3XiF939wkwe3b9LuRKxsrlOXDSoOQP+H9vNHbZl5L84RpG8IHjHHhZChkM778XbOOVdKBWXI/HdtTI1/uFq334nlC8lYfs6vDqam4Mx1c5VM4PU8fD/vHU0dCnpZlSilqeuaoiwxxswHtB5vCWohLEqHtttsVmDnK/rprAqfYWOY1XDvzk2+ffM2/+if/yt+6sc/zvs++H463R77+8c4IWgtLBNlOe3lVX7t9l2++s032drZ4ZOf+BF2zmyGXrYD4pwqcTRSEVtB4ywia3H52fey88TT9DoZRiWoNCUXjpmtuXrvlAd3drl17wHTqQkDbQxZT1H271MWBafHJ5imYjoZYlRGIgXtJCHPIpSWRMYiyoKWNBBJ7tzZo6U9K8uL6OaI8QEcNJa37/fJFs4TdRdxwmJdjM40V1/6NF/57G+wmWkutCMm1+7wpdtfxjo37/V73vdDP0FqUpQbcvdrvw22YVBb+pM+ShtUMeXUenrZMlpE6ESiZA3FKXpxgTgJSOvJJNy0vQyI56qxnJyOSJI29eRgfq1YlAyqEVcJ9EKPrSuPc/LyL+EKg5cJJQlSBpxFN4aNjR7v+cAzfPorb+BcwEQ0xNSuxtIwGoxQW1usdoNJyVlHPLxNMoPTWw3XSoWScLK2yK03rtLEOa28xZn3Pk1+sWQ0rZns71LPxjR1gTBjxrN9vvbZX8CND0gx3L51nbNnz3P75tt46bDCcPGJS/zwn/gTvHr1Dkl6h7ZxSO2JZEJjHI0L11GWZFRVKOKmclgUs0GDs2A8KKtpd88zKY7xTuNMQ5aZeS2APBXkmUREkqy1hEzbTBtLLh/G7HmYB6qknQQdS5Tq4GvJsNNj3N2A7grHNmW6fwvfv83GKy++Y139A1HcMQVLkyEXFzTnFhxNU5AoTaw0lXE0tkFmQKzwIqG2gQvtdRvrPMXcLVk3hmJcY4XASYnxNc7MY7BUQW0sjfU4J2lcINAFDk1EU1sameCjhHsnA/a+bYijKHBabGi/CAFxnGKYD63kCnEUkUQZupWj5wksQZkSBm+xjnDOglIUSZvGNDgfhjTeelQjw43DeOp6xsXz5zi8d5tyNODK5UtUVUUiFdY03zXjnAeIzwmU1ga9dV3XoQjX5tFAs2mCRr9pDNPZLPSwvaesKrIkYzqbMhqPHxXqqqznW/SQA2ttkG0285+NtSFmsK6x1lGW5aOiXTcVTd0glWR1eZl2u8X1W7cZjkdhkS4FSRxjqgatFXGsSdMY5y3GVCHEwXre+NZ1BOFmpvMupilZXV5gZgy/8qufIU/brK9vsfvgEHyfWVlSI2klKXfv3ifOezy4v8fnP/fbPP/8B3n3uy9T1DO80hB30FJgfUimknFCa7VFV2pSVVE6sFbgK4NoSrqq5sSOKY/u0RQzGmdQ0jG4VVDuJ2gV0U4iWqlmKe2RYnFVjatnTE9OmJQzqAzH/SGnkxmDWUV7aR3RWKqBJc/Cai1GUgxPyFpn8I2Z0xk9prZkaYvV1TVS7YjTHGeDbHb3+JR20qHTW0BZiURTO+jtnCWOFH6/j97dhzQnxTNREqFy6saik5DmZZ0ilmFnF9RKEi80UoDB0DSG+7t7dKYjWmF1QWMMUkdIpbBRjaorZvcOSbxj6KaoKMIagfMS48OOcnv7LO9+5r208l9mMisQeMpqRtNUZO2IThpz4cwZqmYEuo2joa4Nlasw3mKaoEe/8thj/NhPfZSjO/sc7h/z6198AZm16Sxvs/7ERVo6pqkt1aDP4xfP8NRHnuff/tvPhTmctWyvrHD/7RukQtJNFakSyAo6GlZaksganGvwxjET8Ty1SqB1jGkqymZ+jQmPVwLhHdIGw5OzDqksggYpPUIalJBEWlO7GkPYUVezmmJWk3ZbeOvDua4IKjwpkcaTL/SIum2ids4gVZzOYHJvD3nvNZao6Eae5c2Vdyyr37e4CyH+OfBTwKH3/qn5Y0vA/wacB24Df8Z7fypCr+C/B34SmAF/xXv/je/3Hs4LdJ5TJIrdUnD/OHDatYKD0xEITW0bKjPAuKCisFZQGIMnpNMYV+NxRCJQ/byQxDoK3WKv55x4iRGSjcvvptOax5LJuXrDeT5+5SnaeUJTF6g4xjmLnV9Itg5qGG8bYimxxmCcwSAYWct0f4DWEQhB1RisM2EQ27i5vR9mjcU0ZYjjKyvkfMjpmpqyrJgVBaPhiIWFLo9fepxXv/bNIJOLFLY2aBlQwyeDU+qmoqobBoMZliAltLOKsq4pqhJjLG7eKhEiZLtKKcnznDPbm5w9u8PG+jpFUfLZ3/w8VVWGVXxjKIqS0XhEXTSoSNPKUxCCpmkCS14phBRzgJtC4IkizdnNNVaWFlFS8tj5HXSkOL+zyrVrb1OUJUJIklaXnc0tqmKGMxXb21vkrRbF6Jg33rrB3QcHLPY6bK6tkaQtBpMZwlm2N9cZ7T7ApDlvvvY1PvaBJ3hsvcPRXcEXPv/rPP7sB1nqdXn2g+9jc22DhcVFNjc3WFpaCLspUry3iHnheuhWxnlMPaMyNYeH9xhOS4pJSTE6ZVyX3D84ZtrvU1WebqTp5W1WVnrsXNgmMhWnpyNGh8fkqwtIbUl8RCYjjDCgI2ZFzcGkopp6hBFk3nFuZZkkEUTasn5um7jVQStFtLjMLF7h9uEE5VOcdNRFTdJJ2d5coa4n5FGOTiOKWcn73vM4Mk5wSlHYkpbURHFGEgviPOXM5jpvvP46tqnnuypPFXlq64hdkALeevUlWsu7FIMxRR2BaEgFIatTKyINK92U4aihPV+wRHGKUhqpJYlVdPMul55+gtWvvkV/6vAiQkiQwqIFRPO2nh9PEVLReEeEC+loxmEr6LQSFld6HPQNBotzCi1KEhSVFMRaowgu0p3HLnDp3CXK0z6f+62v0N8dURyXyOQOkYqQmWJ54zL9Br5x4z4Ll9+H7ZzC0T69ToZyDUspJJHCywiTOLqpppsluLpiMg2yZLzEz/MErNdgToKyhbDriQRUDpSU5FFMnEI9ljhbA5q6BiEsDiibEGwSa0nRzJA6uI2t90EAoyKETlje2KZ14QrdnW2qyYTBwR6zozdZqWa0XENb10QyiBW0+F4V9Qcs7sC/AP4H4F9+12N/H/hN7/0/EEL8/fl//z3gjwGX538+BPzT+d/veBSV4UuvXAUafG2YNQbpfYi3kpIoSanKOiStAI2xaJ1QzmbEcYKWMCumRJEk6eYIFVjqzgRFS+MgS+IAl7KON25fp9tZCEwPG5jTVemAjmsAACAASURBVF0T+YiiqgDJrKrn6TSWONJ4TyA1evBKB2mbDkG3zgfWilRBjhmnSUiniSLSJKPb7VAWM5I4ptUKELJIPSTYRaBSsgyynmNxuWah2+XLL76I/tpXETK0plKtydKEWTmj0+2SpRlH/T6RiiGOSGLND73vKQ6HJxwd9Tl/7jynw1MO+ydsbKyT5x2EDLFnW+trRHMH5Wp3gb/2F/4Upmlo6obh8T5lWSKVoCoc196+xeJij+3tTRpjOHt2m83tLeqqZjgYYG1D//gIPMhI08pzrl59i2++9BJHx33aeU4aRVzZOUf/dMBwVrPYadHbWuPrX3uJL77wNgjFJz/yIZ5/9lne97Tly199leFoQhRHjAYFw+GI2XhAlmVY47h37waXz/4Nbt66zeV3XWZYeiIn0Y3lz//pn6bdSnHWBANMVTE53uVo/4DpyQmj0Zi93V3s3C2ZLyyQd7t0ux2yOKLwKWMfs3PpCT6ws0Ox+4DP/fIvMxnvk9ZgR30mB/e5fuc248JRWIdPIu7ffoCoK5pqhhnPkD6YetaXV3jumad46tIax7dPeeVb9/jk3/0rqDpBzEZhGJ23Md7wm7/xX+OXNJVL+O/+yb8mzjTvOn+Z+29/ibOZ5GOf/FE2zl2BaoYxlqKY0V5cxFjDay9/A3U8pNPp8dyPfIpmWqCSiEpIvvjZ34CqYVHFaDtkKYnpJopmeswbX/oMcmmDo/0HlGWEjASptqQ6xjtFrBV5EpNvb9PsjsL57oJiSwnF8UCQ5112rjyLd79CFgcSqwjhtcSRpDElR/0B9+89wJuaJII80tRVQ5TkCJXiRUza7qD6/cDYKXXIYygaJtMKnTnSXLPYTcnjDtevfZs3X/s6TeNxTjOdlviTCaKRRCkkowGDQ4uSH2H36jc5KWtIcr49szz1Z/8iXsH0wQPaZ95FXXc4mUwYzSzHM8us6VFWBudiKiHIWj16i0vs3bhKTNjVxsqi5+IFA+RJSt5KOD2eZySoZu5cB48ijjpEsmJ5cZHptEbLhFTlrL37Clceu8zGmTPcm5xy++2b3H3rDYr+HqmryEaHPJ4NqKTDCz/fOTus9yxGvf9vxd17/wUhxPnf8fCngB+d//y/AL81L+6fAv6lDz2BF4UQC0KITe/93ju9h3GWo8kYLedQqnnSUIXHmQacAS+DFFBAbS3eVFghwDRkicYmEY0QFGWK9Z7GNORpinQa4+G0ciQ6oawq0k6P3tIidd2g6hodzQNtbZA1SqUZlzVxkiK9Y2d7C6k1DoHUMUop4jgia7XQOtiTH7ocI61JsmSODIV4TrE0TY0zDVLouS7cBoeoc5xOC6w1SO9IcZwe99lYWeDS5QtkrTyAsaxjMh1wenLC+Z1zAaMgFb/6md9iNh6yvLRInsec3Dxka32dB/dvc+XKZZaXugxHY174whdQUrLQbjO7dIGFdovGOabTMWmimU6mNLVBK8Hq8iKx0Fy9fQcvJY89dgEhBW98/Rsc9ftsHhwyGIyYzWZEkSSOIry1HB4dkkQRVVXz2IULrK+tcXo6xDZBPWK94/yFHZYXF3jswjkubK/zyutvEEUx/eMB7bZlYXkBLcDUNeurC/yLX37ze5w1/ykf/n4n7/xYBc6/5wd88u88duDnftA3+v4vRdA3/MN/79808J/9/Pd7hf/q//H8dP5zBHzodwgnHl7Yz//p8Afgv/nrf5RYRxgUxhmEkiRJwrSscUYEL0lA2GCsxXhBYwRV06BsWIEGue087MUSkBqBXQA0JJHHSaiaoAozpkFknihrMZzMQpJUGgfYnw8ZA8ZaQFLX7pGJDCmobZgZ6bkUUcgQnHLvrZvceOsqh3u7SCUpqgIhPLPGk5CQSI0dD5k1A179/L9mmQV8I2mcJ57c59oXXqWJU7bOX+S1G9fZnVQMO+tk5yWtoyF2/4RIeWaTuaPcOaZNRSUDnsM4QSIEWoWbV+i6SnQShQg+Z0mUorE+mP+EprGeWKREcRcjDdvvfS+rW1usbC1Tjqa89vprXH/jNdpC8tx7nkFkbcxoH2cLXFMFpsy8xSWEwBvLXv97h2N/9znw//ZYf1iwvfd7Qoi1+ePbwL3vet79+WP/XnEXQvx14K8DdNs5z334h7AGkkQxaUCKIA1y3mHdHEvr3bxP6DEGXBSkYFkWB925C3e2QGQUYdtlDJUJq+pYKaqq4sqVx7l07jzWOUbTybx1IXB1GSR4OuLwdESaZdBYkiTB+TlitTbEBP5KUcyC69A5huNJWC16R1HMKMsCaxuks9RVhVKKG2/fpNfukKZJMOuIcFJcvX4HIeDM1hofef8zTCdj8kQwm0xot3I2Vlbo5CknBxn7d/aQDfTvH7Gxuc5ir0tbBLdnVRY889QTnJ6MWF5axDnLYDCg1+3xkQ89x8HBEXdu3+ULX/wKSws9psWMd10+D65hOp1y2h/yRz75R3FNzb29Xc5cuMjRyYSXX73KeDLmuN/n5v1D3KvfZmGhixSS/+jP/zmiSDObzfj8Z34Dj6Dd67G2ucWHL5ynNpZ/95ufhTji+R/5CEtrG0RC0M5SVjZXOb9zFudhPCqYjocMRwO2N1f5x//m07/HU/MPj+91/N2f/ywA//Bv/jFK5xE+oLCrwuCcwPlwbTXG44XFIDA2SPnSKME+DGWZEzMlnlYqSJIAAYu0RykLqJDzKwMZUSvYeewxzl+48AilXJYldVWAylBS4TxMi/IR2E5JSeVAS40SARHiCSa3G9eusXf/Ll958SVmbgGlI6qyoHIS5xXaKHpagHf07YDKdSiaiLJyfPT59/Nrv/AZHuwecnd6irpwlv17bzERCXl7ifNrF2guWaaTEePBiGs371I2M3zZYE6mNF7Q+Hn2gnNzx2poU+pYzzEfoAXIKMJYMNahkoTs4jZVt8PGwgqtzR0GowHXf/vLHO4dMBiOMdWMpbNnidKEUqQQZ+g8xqCwzdy+KFXIfPaOOG2/4/f9+z1Q/d26QL+rVdF7//PAzwN0Oy3/mc99Ca0yHA0xIZQX4amcA6UQzs116hYhZJBZCYW1TVgFe49wYv6pBplXnsTzYWjgKct50Me3v/EK2Vz+ZQFrLUmasH9wxNHRMe1Wm3Y7ZzqdMRqP5/b7gF3J0pg0iui0c6QOapCqqhmMx2RZykK3Q7cd4sqUlMRpxmRaMhiOyfMux0cneEL0VtM0CAndzgLCW+qqYdaU9MennNveJs9SXnvtDX7xF3+FhU7Ge575ENfuH/PW7fuc29liLdlmOh3wgQ8+x7PPvoevf/5Fts6d4Yd+6D28+sYbAPzoj3ycF7/6VW6+9TaVseStDq3uIkmrxUaW8OR7Hmc27tNKM7rtHvuH+1w8u8mFrVXevHNEmue8/9n38MIXXmA8mfDEu67wqU/9BxhX4a0NuxLj2FxfY+vn/goLS4voKObW7fs0KqKbx/zkxz9B/7RPp9NhMhzzza99ncHpCaYqSJOUTq/Dj//kn+RwMKTXzvh7/+hf/T6fln94fPfxd/7pb/CP/+ZPzJVMBiMypC7RicejA5ZizjWXMmI8LeguRtQ2BMlrKYkUSBw97WA6xkxKRicl5QyMdhgrqWcTssgibUMVSRbObqGTlNODI3rtDnkiqJxCpSGspbu8SDOdzmmjIaw7UxIdw3ER1GDD0xkvfOlfcffGbQajEY89+eEA3PKejgLhG7yrqJ1nUnh0I3jiqR4HY8eN+0Neefk+emmNcr/iYr7Iz/3pn+Hycz/M3//bf4dbb77NYDSh01akCcQy4dKZbdp5m8++9NucObvKx37qp9nbG3P3ra9Rzk6x3qOVJEtT4jxlPJ0GZlSseeb9H2Tn8rvIl5a4c3yEu3uDydEuX/n0r3H/7n1MMyNOWkwmNU5ERGlKlC6igIQInMA0NU1pqORDv0m4AVsn2VjffMfv+fda3A8etluEEJvA4fzx+8DZ73reGWD3+71YYGELjCmD3C5SSAdSKpI4pCxFWpKm8aNEpDzPSZOYSEcs9LokSUKet1heWiCOUnqdFqY65Zd+6ddwPuwAyqoCLyiEIorm/XJjiKSgf7jPxTMb1FXF8WDCrC5YXVgi0RqtA2Gu1+1QVhXTxjC1Ctc0aCkxSJ7/8HNEUcTgdISZMz6MF5ganIpZXtvAWliLY5Ik4WRwSpplRFFEU1myPKbdSjh/7hwf++HnWVle5vT0lKWFBRZ7XRbbXZQyvP+pC7z0yhu8feM2a5shtPne7X2uv3WXZ598gldef4MSxVNPv4d2K+cb33iZrc1VIml4sHuIsY7GOra21the6XB2bYGrw1P+r3/7W6yvrvPux87iRExnocvzZx6n0+sxmZWsri0zHA7Y3N5GtxbpYBkNTjg+6rO4uMjRyYh2rrh/+w5plhMDGs90NmZczhgVU77y8kv0el2mk4rJpEapmHLS4JVl//4dlLfcu3WbD/weT8o/PH7wozISU9dUKLJkiUxNWWrVjKcgfIbUFlxJGkekMQilGTcCYzTLiQFvsCqnKBXdtIVQjihSFLbGS0HjHUYKnBCkaZdOWeGog9jAO3Q1Q8eSWWOxaaAojkvLfllgVYvKWBAJVpaYpk0SNaSiopkeUY5P2B+NaGmBmsdWIgPB0TcGScqoaahFgi0Mm1PPYKKY2ozDO9+mbreRQpBqQSsONxHpHP1BxazJqeuKhcwz0ZrO7C51tosY5bx4VZF2uqysbvLDn/rzUBRM+gfcfes1Zt6QtzYYp0s88fyPsP7u95O2JMcPbnDtyy/Tf+smhcv4j3/uL/DaSy8xLWbUVqDqirLSWBGTKU8ZJ7gkpSkKlDeUVU0tDZWZ03NF6Eo4J0iy9B2/499rcf8V4C8D/2D+9y9/1+P/iRDi3xAGqcPv128H0Fpx4czmo152b7FHK2/RauWsLi3TbrXRSUScJGitiecFMsgOHXmrFfp+3iOiGOUVuIa3rx6zvrnGQneJfv+Uum6YTguWFjUqibHW0uv1aJqGu/fu8e5L5xmNSrqdHmfPrGJrgxeSVrvDZDZjcc5Vf7C3hzGWVpoAwck5Go2IdIQQDo+h3W6zsLjMeDYDPFEcMziZIGSKtZZ2b4EojkIEYGzRWnDx4gWKYkJVFtx7sMva2gaX3tVj++xZJuMxg8ExTVPz4x//Mfb29xieHiOEoH96QlXUtDotatNwcPCAyfiU6XSCbWp2zm5zZnubLGsRxSnD0TjINJOMsmo4t7PDT36ixSuvvkZ/XJKfTuisbPLqt69x5coVlILRaMi5nbOUdZgNnJ4ecP36Ncpixu5ecIdura2yvrZGVVTIKKKqa4piysnJKbhw8b311nVA0WkHWWieJehYcPXtt1hdWiD6fhKAPzx+X45BBYtZG9M0lDTUwmGEonaOxpZo4/AqZBBYA4I4QLCMYDqPtLQGDicGOTUYmcxxBdU8fSyYD+NIg/BEwynydEIuwzUuYo2QFUgdVq4WXvjtL9I0k5ANOmcnWWkpqgFRliEjyUH/hLv39oiFo5UkSG+QXqKEwDRl4D7JcD2Vs4qi8owawaAwNE6hsg5XLl3m9s2XKLzGqwSsQeJoZTHTusa4hsZKJpMRhypiUXV4cvMJuv27HL/1JuXtGxx0F0i6S4ikxYWnnyZtdXjt3gHbT32AbGdMc7LHG7/1OU4Ojzk+mXI0y+i2j6hcyfGkZFQLjI9w9QSI8aKmJXISCd74QL4VghrJaSlpbGjLNK7GOI81ko3avON3/INIIf9XwvB0RQhxH/gvCEX9fxdC/FXgLvAfzp/+6wQZ5HWCFPLnfpATbXNrk7/1t/8WAh+izqTEWRvcqZHGzXMqA+9EPWKXeC0fBURYG3TXrpggncBUMxZSySd/+DmSrEOed9jd26OcFXQ7HZz6ThC0tZZvv9mlrqY8+8QlNre2WFpq4U3gcKAVZ8+c4cHuLpubm0ipOTo64hvffJXJZELTGCbjgum4IIoisjQlSxO67YTt7VXW1zbwHibjCaenp/S6HVSk0VGA9Cud0mlnLPZypqMTysmIaVEx6B+Bdzx2cYdIzEhbGSutNV78ykssLnR46vHHyRZWuX37Lvv7BywuL/IzP/MpTgfHHB4eMezPWFleZjwY0ltaBiF55dXXiKOEtbVVdLrBrXu7YAxVWbCy2KUsa/b29jnpnzKcjJlNB9SzGUop7t69y8HBAd451tZW2d/bx5sa60FFMW+++i3anTadbgcdx7TaLY4Pj7h96w6tVo4QgoXuEhvr6xweHzMcTmhqi/dB8z8aFVjv+VPf4zz5z//Gn+H2/X0QgoWFHptrKzx2YYdXvv4qw9MRSZpzfypoohyNJopyerlmvS1Z6WX4uqHd6VBHIih7Wi2mkwJnDd12zuLKMjqJ6Z/M+MKXX0EozTOPn+en/8RP0FloI5xBWItwjroqaIqaWVFwdHxK3TRYY1EohJKoOOLC5ceQSjEeDXn9669w5+YNrlw4z/W336SJU7IooRhOKOqGk9kEU1a01jYQnR5/9a/9ZZSvUUJzerDPtz73aZ544ml2Hr+EUjE6jqmOj/BNSRwnvPDNV5ESNs6d4+KTz4UhXF2S7jzxu36W/UqSxhKJ5XhywmhSMJtGeCdQ0pAngloItHI0piCOu8SuwVnB6bgm0QIrPHsTSTZxlGXNQiZZyGBUh6jIMFPSjCrLQGmu7e0xm01w1jGaNaSJobYptdEoWSO1pB6W1GWF9Cl4jakVjoRZAb6u2Td9lPQsd3NWFzuUFion8EJS+YTKGZyp0bWjmTqWe1t8+dohjZFY5/nW7Yb3ffCDoAWv3t7jcOxZdw33+sccnu4zqUL7t3SaqrRYB46SyXid8bRibziltZDzxGPPYMspb77yMqd2wNLyEtdeegGjNHGW4bKcbOcKWxefYUfCtH+PyWDML/3qrzIaHtNJGiJhaC3nwa2tPCqKaOcpvVaXcWMpa83d/pg712+HAHbCsBolQSi+/s/+23esqz+IWuZnv8c/ffx3ea4H/tb3e83feUipgrRQhvBqKWOMaLDWMJ0VIR2+nFGXQYtd1yF0o2hqRqOg2hAihBRXkxFNaei0Ei6dW2E2KWm3e3zjldcoi5KLO2fIz+3QWlwhiiPevnGTfv+EF198iaX1VZI4Z/T2dSaTE7Y3tnj88XextrrIyeE+b77xOrv37rC+ts7mxibeNJye9Gm1O+RpRpamgRFiasbDKZNJoNStrDyglecsryySaoiEYzQ44b3vexYBvPbt6/SLMafHjk474+zWNlmr4dVXX2F5cYHJaEyeplglUVGCF4pZ2bC8scWD/T67B4d0Ol3OX7jAnXt36HaXUDJCeMWVxx5jOp0itGJlbQ3nYG9vnzt37jCejBgMhhwf94m1opXnJHHO4KSPc47RZMThbpuFbpfKOLI84/TklFh6ilaPpmwYj0YB3mbGPNjbJYoi1laXg3pAK+qqYm15iSRNKMqKB7v7jIcT6qamrGqmzpFGCeOyYjieBKTp9zgWeotsWIiiiG67Q9NU5O0WzHk2dRWKghYiGIBMzXRSMEay3Muw1rF/fIzutGh3WoyGw+BA9J7prGArTZiWM5Z6GRfPb7O6uY12M2QSYeqKKJI42zDtn/DKK29wcnLKdDplPJ4+PP/RTmLnQQ4Pbt0BIRhNJ9y4eYs0SXjjrbcpx1MqUSA9+Mahk3hOpgyhDsFQZgI2IYIoTUkWOuRba6g0JXDiPF5C1OkhdERpLMIZilmB8AYlLMTf+7PsZBLpa4raUE1rmKcOaSSpViQxGGvQOkKIsBJORTDjjWqH8zrkwXqPZI4QCSKXOXDOzUmkitLBl27vUu/eY9aEwBHtJY2tMM6jtCTLM37sxz7O7vVrvPBbL6DjDAis9+ks3IxxJVoK2nk0P1c1w7piVofh5qysEdYgpCaJYHFhkZ/9mZ/gv/wf/2diHZFqybSwlNUEpRqUMbz+6qvs7d1mNJmQZRqlHUrHaCWRPUXkaqLIsnfrdZbOXeJTP/vT9FbPYmXO4c2rnF/uUpwcM5sMMBbStEMzniK0pb5/gyhtoVuLtHeeZOf8Elmece/Wmxwf3KeZjWiNduch35B2Wrz34gpXLm7x6vU9Ih0j8GQxaBmD8HhpQ71UEqr/f9Qyv69HU1cUgxNGoxHlbMbte7sUsxlVVVAVM+q6oihmPAxMruZuyVhHFGX5yBUZ6QglPHGcsba8RP/kACkiPvoTP8Fzf+wMb735JreOjrn2zddIRMTm+hq/+cIXSeKYlc0t0BoZpRTTKT/yox/j3t19SuMYTisWV9f5sT/6Ca5evcpsOmV/7z5Li11a7YxzZ3ew1pLlGd4FrnmeZdy5d5+ds+d5sLvH9Zs3+MQnPsrLL73Cjdv3OR0O6Q/GXLlymUvndpjOxrx59S22trcYTCquXLmETnPwsL62yng4ZnWxTdbK+chHf4zBaMiXv/YGTz35OJubZ9hYW+fFl7/JytICW+ubXHnX0wz7ffb2HxClOcPJiFbe4qkn38362gonR8fs7e+SRoq//Jd+Foli/+CAL37lm5RlzeJCl9VWxuJiD1uWtDo5cZKQaMXO2jLX7jwgiiSmKWhKz9mds0zrktFozHH/BC3AGcuZ7XVa7RTnPFtb66yurRBjOTo6nrdtCs5uLVHXJUJa7O86kw/Hndv3mDmL1pq93X26nRaTacHJZIr2kCQp9WRGq9MlVhFJqwNNwfb2MlVT0GrnHOwdUFcVO2lKHmlELKnLkv5Jn4XjHgtLi9iq5OmnLnPlyafZu3sD2xQYJ3n7669xuLvH8fEJD/b7Qf2HINJRKGheYOakzPD73sY5z6gocEJTNY6yrEiEJk8zrDGUtqQ2DZ004XQ6RMGcqZNim4LBYIofl+AlSbszJ3JKcJao10OmGV5EjKYF3VZCojXlaITGMDjss7Hxu3+Wq7lAWstp03B0OqFoPE6BdyH9yM+Hp9ZYnBM0Qa2IFJrKS4wN472iaGiMR+iYYdnQLxoaBJUJ0ZbWWqaF4ZU377Gys8LRySmllcQiIood03rKxJREcQcdpWglaEzJuKyZVZZI1BQ1qCij8gUtr1jIM7qJwvuGiGAmMtQkqkFJB1KRxoqzawsc7d9l58wG0jVoW5G0WqQtxcVzK9y9eZf/m7n3erbsPM/8fl9aYeeTO6DRAWgEAiRIgBBEEaJFiRrJY1kjjcYljVRzPy5X+cIuuco1V773xfwB41KV7XKemvF4RpEiNUwCA0giNYjQOZzuc/qEHVf4ki++1YdyFYAr24V9gUI1Gt37rL3Xu97vfZ/n93zzr/6C2reoULFeCEIOBIdUkiAD2dop1nbO8Njjl3ju2We4s7/H3u33qa++jrYVW75iNhiSG43RIunfVaQv+4wzizKeafuA99/e5W4bUXmfc898nhd/4Sv0+wOyow+ZHxxyvH+XPFRs5WlsHJwnNA7tPeMikClJiL4TkLRIJ8g+OasD8fHc7f//Xv0ij89cOIO16cbdHJXEGCnLgtFgSGstxija1hJCoGlber2Stk0gsEcZnSFGdAisTcac3tnh/oM9mralHPS5/NRlzj12lqZteefKuxR5zsUL57H1gvl0ylOXL+FljtaGtm0Zra2xu3ufLC94/PzjHB9PmS8W1FXF/u4dhoMhZ86eZbVacXh4wK0b91hbG7Oxvs50dnzynhbLJb0izbmffOJSWhp3TOb9g4dMZ3M8mvliiVaKuqpYLiukhJ3tTSCBhwa9Eh8TQ72qKozJMEZzsH9A8J6mqdlan6TINZNAXc47tDI452htgnzNFwuqqqYo8rQM9paiyGgaS/CRJiratsU5x2g4QGvNeNDruOoC7yzOpgSqtm0ZjYbkmenekyAEUFIznS2JIbKsLVL/nDcfgqeQibNvXVqu+eBRWd4hDyx/8qff/8jvyR/9vVeSmUwZpssVRZEzGg956XNf4M3Xf0T0gc/+B3+P/+Qf/z7R1Yisz+0b12gPH+CmU37w7nX+5jvf4dmnn6Qoc7RWbG1udWTKyHK5IM8NG6dPMxYZs8by2IVzvPZv/jXeJbx0kqNJev0+vV4fbQxCq2SYc56VbROkzYcUz6aSVvt4PkdKiW1bSiWJnQGuyDVFlnHv7n3m9Yr+5mka0+fJ555n2C85v3WK9z58hw3f8MovvkAxWicYQ7SWhw8PaOoKV9e8+eY7nNrZZLw25PHLT6PLAUpnFJsfTQ78J7/2CnkvcjxfcO/OMfM20MT0ADJKk0uQWlCUhpde+BxPnDvDD1/7G1rrWdQkZ2WMNDZyan3CL7/yMn/59a8zX1XEmJqwdAskTXxZFMQQaNsq+TsIRNHhPwRolbC3AsA7FKmjz4scLZJibiDhqc++yK//5u+QDwre+ea/5zs/ep05FqE8vSARMuK9RSmBURqJpzQCLQWFNgiV8dzLL9Isl9x672fJRyMjWaYweR8zWCec+RzFxhmqWPLg2tu0h3dgsY+uHiLrBf3O3R5iiqpchUiX58nAZMQQERKcIOEEZEJsSw37h1PWNzeIEVaLmt1FoNED6mzM2qlLbG2uM1nbZFQO8fNDHlx7i/0r3+ZwNqeuGtYKQ6YfpZ5p/vn3774eY/ziR33Gn4rOXSnJoNA4IzHGUJRZSmjxngf7SYhjtEnZiB1ZUAqFda67kD9PAlof5qyvj9naXqfs5TRNg8lzjg4POphPQaZTAbt1+xauqairCmtbTNFPMibnuHbjNr2yRCjBj15Py9fFYkFR5Nimpa5aIj+iVxZorXDWsqymHBzt8Yj9Qky6eEEKlZ4vFiAeRWYkkiJC0LqKxXwBJC2p0YI8z5nOZsToKfKcpqpSwEBZsr9/QN00aK2S9bksyMuC+WrJ0XyG4OeP9EfpQkE8AoElr0BtLbK7QV0MHYw3vaeEFciw1tG2FoLvMMpJO9wrSrK8B6hkQGlC2nfEAFFg6Yh62jAeyhP2TAiSIP4OiF7KE15OomssLQAAIABJREFUWtoJjP74UULbWqqmYWd7G10rvPdMp1PW1sYYo/CQXKwhEAPY1nL2zFm++9ZPePO1H3F7uiDPc/qDAf2ywGSGumlw3jMejVjfWGc2m3FwcMBwfZPoLcFayn6JC4HxeMJgOGQymXD6zFlMv4/KM2SeEVpLaFtWi9VJglVmsoTTDZ7vvfYaTdNQ5GsIBEUxpCwMTz/1BAf7exwczVi2llXdgix46aXP0ysMpdT0xxnXv/dtHtzf5RSao/mUxWxOY1uGoyGjcZ/xZMxwNKQsCvrDAchkpPu41+HxQ+LMYlswmWCgFcZHeiqQa08mOVGp3b16hemdq7TzKc55pA3JcChB20BcNPzgW3/B0ESithADWoGWqQgJEZGxAhmRZQpb10piVPpOmRjRMuWClqRENaEkTkWEthTWE5XCSM3h3gMuPvsFzOEem6/8CldvvcXpAqKVDMwQrQECq8WKVdUwyHKkdEgFvVLSWEtoInmmqGSFlRKdD1i79EX05gWWasB0fxd35R3E/BZydUjfNxjfIqmplSRGj7WRKCUB8NFRFDllnhNtQMSID5HGuS5eMwXQh2AYbZ/j+u4ewUeiC8wWC+pwTBvuM713h92yh+n3GW6eTkEd5SYbr/w+p+KUxf4uR9feoWobVq3H1O0n1tVPRXHPM80T504RAJNl9Ioe1lqOp1PuP1hinWPhlngfumQljVESqSTloEQKifMOozXbmyNCcDzYe4BSsmO2rDBZxv7eA5SUiXOiNavVAi0Ftmm4decuUmoQgrLXYzo9SvFq0eNjgm8J0sxVCdXdtAkNEDo5VtO2NG2LNpK6rolRJodaTGqZxXKZYKpdVF5iZXu0Ekwmg+6BlgI1mrYhhkCeZ8kBGtKsLQRP3VQ8Qu82tgEJ/V6Puk5ERu9CR6iU6WaUgixXFH9HOmWMSTPqDplgne0KeZpzGq1xHWTM+xRK/qjz7pc9qtpiW4+ynixTnSw1PTTrJuWHDgYDwDGdzRIT3CUypdYmHXuFIiKIQiC8/Dvd3ke/lJJMxiOc96yvrbFarWhdw/b2Jr1eyXy6SHm6QJCKEBKTvF7WLOYr9h4esr65nkJDvCe0IYUoS4l1juFoyPqaYf/wABdsCnduVlx44iJbO6fYOrVD2euT5TlKaURWgkrB0zKCFo9CyuGEtR8jMVhu37vL4dERn/nMZxhv7YCLZFowmgwYDPq88fZ7TOcVNqbkqfX1Ca5dJqaKFljbcv39D7h54x6Z0Wxub/PEs8+QGYUuSvIPbiAEjMcj8IEQXJeY+9GvdjXFOoskIzMCoyQFnlxDLkMiOoaAxuGXCzL6GB2RWpL1FFpBlBHXCKT2FIXm4XzBOPMYINcCoxVaCpSS4BN1VHZBz0ZCL6a4POVCAvR1ETNJ5C6ovSe0HqkEMgRMDCyPHlJKj2gPGQxKEC05bdp1hBSsYoyiN8hRRlEHj4wKSUA0gVwVBHKM0vQ3LsL4PGL9MtNen+Xhfar99xF336Ifa4yMKBFRQiK1oY5Qq5aVjSzbJCRwPrJykiLz5Jnjwe5DnHNUjWXRWFxweB+JXqDzPhubm7z7/nW8DUigdhqj0wNWFDN0UTIWgcnaHWSeUOCj4RZh6zxrpz/H+We/xOLhPXavvc9bP/rhJ9bVT0VxV1KyMeoRhQAhaW2DkXBqa42t9RGQuNNSyZPgh+A9iEcJQx0KN4SOVNgk5K73XYq9oqqbtPyR6aHgbAq8bgUJLRASdiDGyPF0xtbmFq11GJ2Ok4/CMIwxON8SoycIQYx0f9ej8ZAgRkGRD1NEG6mQNG2DFDIpgHRysiVcrk9wosYSQ2DU71PkOcuq4vA4LUy0VoQU8YpSiq2tzU4h5FOWpE7KH02Pum7xwlKWZUepLFInJCRN02KdxVp3cs1iFGQ669DKgvW1DaqqYrlc4lwKFddGc3x0hOlMYHce7NFl+FGWhsUq8dejj91DkEQMDI7D+ZK8V2KyHB8tQUqqpiYnwd2c9bSto8wCg8GQssw/9ntS9vpEmXS+d+/dRyowWrH/8D6PnzvNPfEAYxKtMFH6WmK0jNeG2CyNT8pen3PnzjMaDHh4sMd0UVFmaeTV+nTauHThSVxY4mzDfL7i4rPPsr25Sa/XJwgBUaCMQXggCmSIdLUpwXtC+k4KqUAIfDRINKPehJ3zz5BtnCLzK+zymOhSYpFHkJclWdFH9vopQSwGfHApFD7LmayN+fyrX8O1VaIy5jnWB9A5tW2YV5E1O0ZEj1amy/r96NeTo4wYTLr/ul8LQVCoFBxRk8YzyS1qiCLFUcoYEznRpSxYbzTWW+bHKzYLjc47NHWICDx4jwwpZSBlDafdhHWRh9GjRIptVzJdS2JIMYfWs9mTjEpJrgxX9xowkscuPcHrV97FHN7DmpI3rh0jg6OxEaRPpNHcoEggqriyTJVB9AdMTp3iF7/8KgeDbeply527gvtXPuTg4Jvcfvs21arB2kBLAnqlkJuEwBZKo4wh+C6Ws/tHCr/x6RSjsxR+Ezy0ETJxYutUQmBkw53DKSG4FNvpAyoL4CPRQq4iwnuOjOJ4t8YAOIcMEZlFJr2cW8M+3vRw+ZALL/8af3v9f/rYz/hTUdwhRSogZDdT7SSKJ2EPoHT2/2CJK6XxIfWvoStSUsoT9oIQYEx2kkz0qNOOMYVZ5FrTtC3KmFTAtAaRslILbbAugb8gkBWKEFIxlTqSRoAK+XdaI6FiylU1jyKxZDfmACxIVXQuWkevzJgvFgTnECEgu3gHKRXz+ZLlssL6lNrjnKLfKzsQlqR1luADunPHjoY92rqlqhsKk4NPELNWWkQusU3qvpumSZ14CDgfMEZTVU1SKRUyXR/vWC5XCeMbUvevtSaGiNQpXSqEyGjUo25qiFAUOZGMXCm0TDPphCNO3fvacECvXyKFYJnyn7AyJzOG1kWiD3gpWJ8MEELQ1NXHf0dixDqPt50SwwcCsFgu0kI90yiZwry11JS9DB0aXnr5RW7t7nPh2efo9XoIqWidYzyecP3WPZbLFZPJmF6vhxUOJQ1Zz+BEi/OB92/cYHo85dlnnyPv9U7SiRRATORQJQRSqFTk4WR8KCJIIylzRVuvsO2SGG2ilEqFCB1D30d8TLAzKVPwuxaG4AJSa6QxlKMxwTkyDRFLtKSOWEmKsiDPM7RJs2Bscmp+3JBLCU0UEUhpZ92UjCakPASpNISUhx28J2qBVOmzlSHgSdhki0wxAloQQ0jxjTLFKcbEAUFJiRPdIjjSsZ9S5kHK7U1juhBjij0MAu/hwdJiFCBaHlYC4T3z/Z/yr35wBVyklYGHu8fUlaPVoWvkHo2BFHmeM9w+zSu/+lXOnHuMnZ1tDg732H/ruxzt7fL2T3/CdFYznze40OBRRKXQSqDxBKFS4pro9gLC4VSqLbqr21KCEQWpqYsIkx6IRT9R+gKBSCCTMgWhhICSSdGVCUFlEzpESgG+RckWFwIBRUDgdSToZDo7XDWsmoY8W1D25vT6n+wJ+dQU9xBJ0W0d91vKNNt69O9SpgVU7HI6Q0hpMVKKk9Tz9EphFCexdJGT+bzoQq6VlPgIRVl2N5SgLAcp7szZTvkQuhFBpGnTElFKSduuAElZ5GlM4dNpwcdIEIk1EaMnywzBB9omMdCNNiADobWIsmQ1X6J0kpL5jsMeSWlNUgaCS+/LuRSWEHxAZ2k5qrRCa89oOEIZxeZkDQkcHx7jYsBGi3MO51wKJ3n0FOrSmaRMXByEIIRI09bokMwmdj4jBI8xqauL3ibC5aMUJZ+48lo/ksHFbrkT0CpLpxLvUWVa+CIKZstlskw7x6AssTLD+ciyWhKcRytJUZQ0bcti9fHFHSlpVi3BO6RIRS0A12/dpacUNkTy0qTPUCu0SCHljZfs7GwThU7BIdayWNTJUVk3DIYjtM5S4xA8i8WcU4MtopszXR3TuhblA2/89A1OnznLzvY2JteAIHrQRUb0AUdAdzugbj2RulUXWR+t0SxqRPDki2NEtMiQBAFlkdPLNY1VFOTpNNBBstJYwSNySdEvaGUqXo92FfVyiaprbNtipcBIQ5BpdxIfPWk+4iVUB+l79Hch8T7ipcS6xI0xHRvZhZjGCE7hA0hhcAHq2jO3TXdCFeB9goHJBOBzNsUsJpKBSe5okcY9bR2JURJkklxKbWicRaJPQmW0Tvegc+DRSSlkQGWOYB1InxanhcaLSCYUQUny/oAXv/AKFy8+Qe/0WYb5inb+kNuvv8WNdz7A1ZZLTz1JU9dk0jHIA1FppE6wrxRraRAdQjxNiSIDEWliWhEro/HOJRWLSM2pJElwgwAdU1C3lhK8IhqQUaBNTlU1aAFKCYZaYL1HCMVkbZNeUTLIC3KtmS5mrJoVKkvGphigX/bQJscDf/Cf/zP+u3/3Wx/7GX8qirsQSYEQwqNZseiySNOB0XmP70YuJ2OZGFNQtIeqrnkU6Ox96Cy6P4+JezTHHo2GKQauaZEyo6kbmnaRukHHCd7AOtuNMtL4R0iBbR3Jz2RorWW1lAx6OZHIqD9ktlwhRJLVyZjjYsAoCK7GSI1vLYNRj6w/oq4qtNEpMKPIqW1LluX4EKhtQCtDcGm8UfZ6JzF49XJOnhl0FDSLlqOqpRgOOTxa0FQ1SgmapkFIaBubNMqEE2qli6nIS2UoBz1ETNFyUqjulJC6jLa1VFUK8i7yjBgjvSJPKGCpWC5WNG03T+4nJ2KeZZieQEnIdUaIln5PUdctRHAe0Dl7R3OMKajblsZFlFQI4OHhQTo5mY9fqDZVxcHhEbkxaJke2su64fbVWzx78RLDcoTHkfdKdm/d4ac/fp26rhKNk0hZlhA9Z05vI6Tkyrvv8tnnP8vuvXvcuH6d7MlLFIUhCrh98zbDYZ/5YsHh4UP6RcaHH1yn/NkHGKP4R3/we+RapxNVK1L0XAh4nXcPO0Ho4v1cdJQbE8LhA0JsWcz2GBV9VvMZ3//+D5BSoYuCftHiw5LIgH/xP/8l09mCYCNDWXNuDM89dZm+9AQypBT8j3/yJ/yLP/nvqVYr/uCf/CEvv/giHsXf/43fTWHuMeO1dz76Wv7pm/fSyTLCeHMT7wWrqqJulhA10YYuZs4gVNoxWXtMJxhCStP9jIlUGGM63QkhCIk2lkYsIjLIC2xsEkE1KCQq6bWz1IRtDMfMF3OGpt8F2bikmZdp3m2VRERHqSXRWXLh6UWJlRkPQ87a1jaXLl/ml/7+75AbuHPjA+6+/TqHH36L6689YDWbQ4T5qmEw6ENWMNkYsDEasDkeYF1g7+CQKARKpqagbSwOSRZgfTLg7LmzxN4O2xuKoleytrnFqCypplPuvP8+a6MRo/6Ag9k0nUyqmo1zW1gLwgk2c9g68zgUOQcPj9BR0BsVHBwfEGJkc32T06fPpRoiSw727vD2j3/AB1feYmeYszuv8K2jWlrK0YBaFVx4/qVPrKufiuKeVA+zkzxQKbvOEGiaBiklxuTEjsT2qJBXXYBGQoYmOZUWCq0Tpc11Ol1BMlpUiwWUJVmWUbctZVkglSMslwhNUlz41Em23iFkYtAYozFGp1l1hF4vI88Mk3Efbz15bqiqtLwzUlBkmjwzZJnBdRp8VeasT8ape3EekzuiEEitUEiqOo1InPXE2KYOwAfqWKGMZm1tDesdeV5AlAxRCfDUKXOUNGnpGUTCucouL7Uz1LQhnnTwIURE9EiZjt3GiG7zn7Ic0+9L169uLHmmaa1FSEmWZVR13bE8BKt6mWameojWA7xPcsBebtBGs9nrM18dMFuuaH3qVJfzBd4HsiJHklROm5sbrOrUUX/cS4lIkWXUdc1gPMQFT2YM3rY4axEKcpOxd+8uP3v3CrPZND0IlCZ4S9s2NE3Kus207hj2NZsbawTXcv/BfbY2N8jzDEKkWlVYa9nbf8hjj52hbdskQdWK7337O3z2uWdTsYgglOm61xQnGHzABo8Xgugc7njKms6JsyW3b97FZCCV5IXPXGY42eB73/4uN/N12q6wFj3HpfM7PHZ6k8O9O6xlkUoIbh0tEDGnzAzv3bzHvGkxOqPIC4qyR+sizjqaxZKmm5N/1Cvq/OSEMV/M0DLJBge5QiCJeeyiKyPKCKQWGL2GEhHfpPsuRMiN7mIYE7013YgZIYaTccYgS7hsYwxt69PJWpd4JYitJUbL9uaEQiqaxtPapGASEbTRCGXYWF+jyPMkkljOWesPWArNS1/8KtvnLmLygpvvfsj9e1ep5vssb11n0it49pnP8JMf/wzvI8OxJC8UpZCsmx5f+fwX+Z3f+HWu3rzN//Cv/w29Xom1lnGRQ4i0NtJXGaN+zsbmDqutz/JHf/hV+sMRSmmMlKwOHvLGd/6Gdr5AKcnjz3+OumowSM4/e4EmSkIQ9Pbvs3n5SfR4QltZfFUhDCyXC+7vPqCuapzMWF9bpxiOOXXuNLa1zJYNKnoe2zS0dcV01dLbPMM8lp+4MIdPSXFPM3TVbdJFSjlyj4q4TmMUH4hdJx1CpN/PcYETJEGi2AmyLI0TQkgyK2l0WkgG3yk/HMROfSIg0xk+TzF9zvlOIRPJurEERIaDfuLS+CoZVEJEEBmUJbIv8DZ0qpmkbtFKncxbTZYMLnmRc+v2PYRKix7rXOrWnUMImXJgfVKmZCZLR9AsceaLouTUmXMEpcmLEq1yMpNojK5ZpRg829LWNdPpjLpeYoylaRqquupGVIIiL4iRLi7Pprg5G4hlUgJJqWh9GgcJlQSVUklCjIwHaSbetg1lUWBJOZoySjKlyEvFfLHs9iKRPNNY6+kXhsWyoaotUaT8Ua0VmUmGs6a1ICRVY3E+zWI/7jXsl8yqhqpKJ4yEqFBkeUbjLJnJ2N3dZf/BPtWySvuMjubZWMty2WBM2m/UTUOeFzTV6uQzfv/DDzBaJTOaT4qRYS/B3ZxL35HpdMrh4SH39+6xWsx55plnePz8E7Q+dtmxS6r5guV8zvH0OGVohogihbg3D/YR5YD+zjabOzuUvYK2auifeYLhbISdTzmYHvF7/+jLrI9KjInYeBms4/r71/juT39EJipe/aVf5N7hjBdeeplTWxuM+kOEMkiTk2mFF4HIxxf3ycAkF7FSBCVQIo09ZGxxLqJ8it1zRJRJy8RW5Bgl0njQWiA1NI+WoZlOyqvgBE2MKaxaCkqlUEIitGQ40Qitee7zn2WYZ+jG08jI9ulT5FEyvbfP3d1dFlW6106fPkU7XOc/+u3fQWbJnf31b3+HJz//IsvlkuN71zi+9RPE8pDl7UNMC8tlS+17bF18mscfO02pJsyOpty7f5cXnr3AmMgzO5scKcW1118nCsWlzU0unn+MS+cfQ9QNZZ5Tu4b3PrhFYTxerFgJx9Hc0lvr03R8+8nOWb78q7+KP55SljnysbOEOiCsQ+VAf0DMC45++AOqGJn0SgZr29RHR9TNnDNnn2Vt5yxEuHv3PvcPjrB3byexlch56nOvMF+11NWCZjHDrWr05Ay+TQ/QT3p9Koq7gBPGy4ku26c3rrWCLvw6dfYSKWGxrLqsRpeQBUak1XVMnYGSgqTgCwRvESQpHQisbcgzxfHxPsT0UAkhoE2yjgsST55OGbGc192JAVzbzeJNZDlfkmc5eZYhlKG1Ne2qYTAaMx4NaRqHUIkZ3xwt8V7iXRohCSFomkhdp/mbcx5jNGWmk/N1NGBj5wxCaJTOGK9tkel4Ije0XZajGw0xRtPr9dBKslwsONpNsrumqdi9fx/nXRpB1XXHmxYURUEI4IOnaRJ6WAhBnhXpxOM9JjMIITASDo+Oul0EFGVO20SUhtJkSG14+HBBmaekKmMM1gqEzHEi54sv/xKLVU3drNi99wGD4Sb1csHWZERAsHd4zO37DxJjJnz8kijYFkEynBACSimci5QbQ0S/YOPUGVbLinq+wCiZOmeXAHNV3SI7GNXBwQHL5ZK2baB7mJbDAa/8wsscTaccT6eM+n12tneYHh0xGg5ZVStu3LzFalURiVx+8gLvvP0z7tx9wBe/HHn6uecQ2uCkQekew7Ut+j7FHGYxpgxa55hNZ3zz699hsfwWOztbBALHswUhwPHRAbKcMBmO+dvvX0FJwcsvXuLU5phvff3/4iff+RtGwwHBwV+//x22wwFPP/U4eZ5kn1JnLNua7Y0+ug/WSD4iSgGA33zlBQhJkz3sGwa9EVmWs3+4x+HhHLFxjue/9GVeePELDMdjdJFTBMf0wV3y6pBgWxCScuM0ShmEc/DggNA0UM2xmaDc2UGOJ2RRU+cKrQXv/e13yaVk/+CIg/2H6BC4K+CNN9/j3NoOzz31BF/+ylfYOzrmnQ+u0h+OKJ//Mjfmlof3rrI82ufMKEfsvsudt97ka19+lbOvfBHdm3D/wzf54Op1fvLmFf7rf/pf8aOfvsGZzTP81m+f58p772Jj5NWv/YcUombVhC5wA6KQvLR7yLe//S1e/d3fpljOoWlY7e6SbT2D9jPc6iG0Jfdv3kzhPZlCRk8TakaDPiwrfNsijUEFAeGY6oOblOfOEycT9LBPOzsmLCdo1ScXqXmKOieUfaL1PPnc8zifGDJCGm6/d40rb12hrY9Q5RDlHTZYKt9gdEE1W31iXf1UFHcQSKUxWZ40x3XTzdh9Umwo3TnixIn0Mc9zXHQoLVPGaEgGp8amLomYtvPWpQ7daIWPSb5XNxZTGqTpn4DIZIwp3IMmBUqHNJ6x1hOihK7ziC6CEjihOVg0ZCYSwor5qkbJRKlsmpRmrrTG+cBiUSMlPw+wJiko6qqmX5QY27C+MQKlkUWfMxeeoD+aUJaDtLBpK4Q7IDqFUwJjwGSRpmlpllNMnrNYHdGbbFEOhuTnLiJMzu0b1zi7s8XxdEabZdzbvY/WGUKJNOLRGY6IkBqtFE3raO2KEGJnMlH4DhFcFOXJAy5EkCLgrU+uxtYmHfuiJQTLaNSndZ7WSkpfsKoOCBYG2YBnnrrEcuYYndpkdrSPkBoDrI/GHM9nrNqPH8tkvZLseMms9lSiIZOic/o59vd2WbY55UjTL0q8TyekphsVZFmO0mnsNpvOaJoGLRWjyYTWWg6nh1y9eh2IDIZD1sdDjo4PsW1NXmQcHB4wX61OlEZt4+j3ChrreP+dNxHVIVlsOTg8QnjParkghnRCWs7meOtZ1ZZZ65liMCHw4MZDzo4Ma9bSuhREHYoxk50L/PIv/z6v/eD7/NVff4/LTz/JlSvXWDycszlax9sluVL0TIHykji3RFHh6yWZHPLKc8+xpQU1FvjZR17LX3n5RZ57/jkWUrFz9hxGpvHH9Rsf8M1/9xdUXqHzLVTRp1eOECpA7HHqsZy+uQBocJbol1gUUecUO2dhPscvl9hRCUqR6ZwYIpWtQJd84T/+fWhbYnTYw3vY1vPDH7/L449f4s7dO7z6D/8BrmmYXr/JePI4Qg84uvUa06MpZ8+e5bkvvcTTn/0cs4crvvjSl/jrP/u3fP/KBwip6Jd91ta2+M/++L+gXq1o65bnX/5FvJ3jqpYnn36GUmcEZdB2jm8biAZdlOgyZ+XaBC0cDrF5jlkfcNrcwB1r3KJkbc+yf7zCiQTuchFUkcGDI5yR6P4G1A2hahDLQ6RvITiiiAyNYXn/iCwfIPKCID02gvaeXpZztDgmlp2ZUCpi25LlmqzMcFLRsiQagw4FsXLEQnL17vQTq+qnorg/0pD7EE6MM0IItNZJieLbDo2blm2xU30okaWHQDerV0qipE6deJcYI0RyXtL9HUiBEmkrFGJI3UtHh7Q2bb2dD51aR5wsZZVSgCIvHmnq0/tuWofzPplapEALTVEUZFmG847Do2OUVjjrumVTGksMB31ObW5imxYjB4zGE/KyRJqCyWREr9fDR0d0gRhqlHIJBgf4Nil3jJL0co3WSVt/fLBHf7SW2CRFwelz59l/cJdyMEI3FWuTNSKR1apCScmqShRLY0wn6xKURdLFPyrkSqm05dddobeOumpR+tHiO8njvA/kxiBlnrJhVfoZB6MBt3cfQlBoEYhqzrA3YT5fdJ9JYGt7DQ6nKB3Jlx+vzbbOYXSShjZtjVTQ+Mj+0Zy6jlRXb/DU5TPoCE6oNBftPAzD4QDnHCubluVaKaRUXL95k9VqhXWOra3NhGXo3NAP9nYZ9sq009CCvf19pNI8eekSg0GfzBhq66jrmqtXrxGqKW4+7fJ/k0JLGQXCUK9qlqsVUWhm8ynRCXbWR6xtnmI0HLB/eMy7Nw7JTY9e2aMsBL/86hd4562r3Lp9k6pZJce1r1ECvBBMtrdZugg+0hcBEQLTh4c8+czTjLREFBnwv3zktSw3dhg9domtyYQ0EVcIKdneOcP21jb3DucoleTBQqZ5ufcKiUaoQIwqyX/3l1D0EP0cmRnol0ij0GvDhP4NKclpEhYI0SC8x6IQRR95fpuBXfKqkBz3JxQ/focf3J6yOFjQLgTT629zcHCXcnyWr331d7l44SK6KFg0DVevXecz57f5x3/0R1S1w3rHsNdHD3rY2RE/+OBDNtfXyQvN7oOHCKOYrI/wLuGFEUlqqE2eJgK1Z7McI71CZTmqKHBVy7f+/LuIuqaazdh84nM0bkE9mzIYj2nbFlDYuoYsIwiRCnsUIEukKfFB4KuW2FjIMtq6xa2WoAzWOlRdEyNY11J1zZ/HE72nDTVeWJbtAl87nPUs2gVVACvgz77xV59YVz8VxT2pIENXtOVJsQXIs6Rg8ZFOz55+XWuD7QwetiPNKaVO0KvehzSG6aRLrtPbqs7yHkLo/lvAqG7+KCWlSi7O2WKVLMIBqrbuinsq6CFE8jw/wQcIIfGdWMs6x97BIa1zLBYplUUJidaGyWTCcDxBKU3WGyCIrA0HZKUm+EhucrQCCOi4xODJconIAkSJ922X1UoyP0WFMkmRQ4RSgFvsk493mGwIN9YSAAAgAElEQVRssLa9w9bps9imYXZ4n8naHrPZnOOjY2azGUoKQvC0bd2ROSH69BmkEVZSUyiTIbVCahAqpeOIGLHW0rS2s7kLer2sm8EqlssFN27fYLOpmc1rZrNlijPUAskR66MBO+s92qbG+oZJP6deLYj24+fEvm3p9zKaEGitpPWByfoWtSw4nB0QpxWnd9ZQndt1sjahqmvKomA2myGFZLFccffuHcqyZGtzi62NLeyopa4blFbM53Nm8wOOjo4hRqrVkl6v5Pz5s7TWcWZzK30ffcTKdDpr25bDyqb5/AAuPH6eouixceYMR9MpV9/9kOP5HUxuEM5SuGkCg60qDmctTkYo1jBFjyAUeTFg994x9+7d5f7+nN37M2IsKAZr/NP/8o+58957XL12jV/7jd9EliUIwXuv/4jBaEReDnj6H/xDZAbSRuCPP/Janr50GaFyZDnEVXO00qA1IsvRRQli0ZkCPYvFkuGoROssmYM6XowQApFnXVA8sFjAcp6ardyANoBCFSWYXvJO6IKI5eHefY694Ph4zu3r96gXV7DNgvG9q+w9OKDI+wy8wIgehyLjZ7fvcuqpy8TQcOVnb1OtGnr9NYLUjLZHiODAO2y1IqCplxVPf/YFXFvzxptvc/7SRUx/lPhUwoH3VMuK1jmsdfzbP/1TXv6Fl7l97y6BgPOOYOHa8THNYopvV9y8/TZNZXHM6A3X0l4wNJTz+ymesHUQElp41bbY5RFeatooCPUcsazwUmHzLHGamgbf7QitTeo2KZOrPd2bgbb1HM8rXBvwShJig4jgRUlz/f1PrKufiuKeSHddQRecmI1iTJmJwrmkgxc/n8fGGHFdcY0x0IYIrUV0R3UhRPrCAi4kWVf6H8H7ZPNPGACB1qnTLEuNkqlbWaxqXHBd95q61BgiTd2gtEYpTetbRDemCAFsTDNMpTSiU+1obaiqmvF4xPbWFuPNbfKyRxSGIs/SKCgswCcaYPQObSRaRggepSLRJ5MWncY/xg7RJ2LnBSDtHpQGKXHVDFH0yc2I2C0ubVnifKDf6zHslbx9fIRLAVhpTCQESpsk/UScLLedc90OogsuRhAiaJH2IMltm8Ydq6pKSqSmZW00YrI2ot8z7O8lRIPSgtZGXHQMAhjTgygpNNTLOmnh/cdrs0NI7kXnXNLF1zVl2acsx9y/f8hisWK5qsAnW/tglMBnRVGwXK3Y29tnb2+PUzs7PPbYWWKM3Lh5q3P6Ck7tbHP+8fOYLN18Win29x7gvWc47PGX3/gOWmc4G5gfH5PlBds7W0yGKSwmoGiLMZdeeDm9r/GYHQGnLjzN3/7Zn/Pw4QHOtpzRmn6v5PKTl3ji8lOMTp2i9ZE33noDUQ6ISvH1b36f5aqldYHVYsnQG4piQj6YcP6JJzg4nuKFopAaL2UyvCBpQqddD+Bb+7E3uJJJZx2bJUIlWaN3DttYrEuntqZJDzzbtkCJcw7bhWBLmR7wYdDDS03UGuMchEh0nthYZFQIpQlBIKRDhMjevOZ4WXPl2gMOjo5Y7t1jM865fOExts9dZrR5mh+/9lP+/E//kqcvXiSagJ1WZFEjm4DWipvvvMcLn32OdjXDLgNifoyvV6yWCxbLOVLA7p2bSAJ3s4y3f/IT5keH3PrwKlVVIWJ6mF+8dIm33n6Hpq64+d777L7/Fi6kRantEqNu3dkFW1Mt5qjBkEJqdq/9hLzsE0OSaGZaUHSqrMwURCnJlEFLi5YKY0p64yGiLNFlwXBzA6M0KgYG62sphEjpdGIre5i8jzKa2LTM5yt+8OO3aKOj8ZG6WtHWjlWreG/5/00S0/+rr6S37TACHY8DOCkuMUaE0j8343Qv0RVcIbplalf8E6Pk579X0rFEgicKn45R8ufmHkGSXmqd8ACRyHBQMl+suj9SdYuO9JuVNifv7ZG/WGvd/QwiGa5Eh6BtLVubG0iRJJX16pjgG9bWdjDCYeslhCUCjVA5vV5JFI62qZDeIUhKIS1zhEhALh/8iTvWx5hiY5XA+5hchstDvLUUZZ9ekbE8XpGbnMl4TNPUTI+OTtx1Vd1SSEmWFYTOqBRiCuBV3c/Rti0xhM48pdHaUHdZl1qrJIVUChlalNTYNrBYVqyvjzEy4q1lPptS9A3WatZ3NjEmY/9gCtGyNh5TNY6qtqzqj0+XWVUVpTGE4PFA4z2tj2wOC2xbo5Rk1bRMZ0uUCFy6+DjD0ZCqqnj77Xc4ns1Ym0w4ffo0h4eHRGBjfQOlkyFlvlixe3+Ppm04//hjrK+v89jZ0901Cbz4+Rf49t9+n4ODI566dJ7j2ZKDoyM+//yzKV1IKHZv3uPuMw+48Ng5fvjGd9FZxkBLGheRWcFkPOazj7/A5acuM9reTHLWEJDWkushDYZV43mwnBPQZNp0qAOJ1BLV75G5EecvPsHuzVtcfOpp0Aqtclynlz/efwjVKu1fPuZats7RNo58rSQER/Sk4YzWKVFJSJqmTruYusJZR1n2UNERVUzKJ+eIQYMyIBRNjEStiDISuoBqqhVVCATrOZ4veff6dQ6PDrn63tuEo10e39rg8nPPs1pZfvLmh0xnb0NcEDjiG3/9HiJmqKLg7oc/5rW//N8J3nH7+g2+9+c9CpGgenXd4Nsak2U4ZwnBISI0jWVtMsb6wN7da0leicAAUUo+8+QF7lz7gFW14NTpHvPFAzYmE8q8QMoMmUkGPcHy8Ajl+lD0aMSYx86f5cKlp1Eip9/L2Nwq6ecl496QbDRC5QU4RxtnYCNG91EbQ+ytXchysq0NcJawnKPyATLPAIFf1ZiyxMVUmH3dMj+e85N3byRH7rIiDynCUxoF/uNPufBpKe4h0DTtz12mSp1081kvWXvl3+nE27ZNrrGokJmkdT65FTvgoFYyaUBDUiisTSbUbSIF1k1DMGmOKGRyhmkl8c5i24KoJUoJlILRsGAxX1HXDUWeJ35Kr8T6gLct1jZkeYbWBnwgChBSYl2LKdYxKln6CenBMl8dUZYjJqMxfn6PZbUiLwp6owEJQ2JZLusukDtZ0FvbIoVEGJk4FxpE6B420UMQtFEgyFBK4L2lV0iUXBKOPoTJRYrROjIrWG9r9u/vogWcO3+G46Np0qo3TaeOkeRZMk1Zawk2GaAQgvSVgmVVQZVkhqKTUj6SsmbaEKNnY31AZgzHxwuUUBzNDqiammXTIEQy9WytbTLY2GTYN7TNgtEw44lzpzizMQZ++pHfE2cdK9Hp61tBkD3e/uAGZxYto/GIu3fvsZyvGPd7XH7iPNY2/Oxn9zg8PsbkJZeefJIYI3/2jW/gnCPPMna2Njm9s81oNOT89mkODg6Yz5ZcufIue/t7/NIv/gKbmxuc23yMr/zyi6yaineuXGNja8xkOCEzBqMlMThsu+LBw4f8N//tP+crv/QlfuvXvkqMgrZZ8ZVf/wqD0QBjDFpleFNitKFZHKOloswEpj/Erloe3nuAOv0UbrGgdp7gWuoYmLaWf/Un/xtVXdO0DVVVE7/xWkf69Hzxiy8xco5vfPt72Krh6c89w9de+Oh77q++9R1CY1G5YbWoqJsarQX3bj9gNp1TtQ32x2/w7//8f02kUDwh61FKyyCTLF3A+0iwlkwbpADdKdnUeIPt579INhijsgy5PGKDFiM8B7fe5+jggNPDEWuP71Aow+uv/QCUxiGoFiu2tzf40lPP8L7oI2VGkZV88/33ELNj+kWP9fOf59nnH+fVl19ice8mTbvi1V/5Mtn4FLobn/7L/+Nfcu3aNS5efJzf+53fBanxCIzJ0CadAu/duMmXvtry5JNP8vxzzye3uHNIo5P9TET+4v/8M66+9z6+cUQER/T58Qf3+Zsrb5BlBUoK/tl/+ntsn11jPCiIcpB0/iqSzTRRtcQARejRnj6NXTXkUSKygmZZYaTCizwhMeyUrN8ny4eIagEShoNeGjd7S5YbjhYRGyOqXVHNP8HNzaeluHfLz6Q2iQn+JEQChZFMN0ol9rM2GmJSx2QKpILE2O7YNCEVPaM06Jyy7JHnBWtrI4jpwXA8O0yjmpj44Vm3dIt/Zy4vhcLHNLrp9cqk8bb2ZF7vfaAsCqSUKbQ7y1E6PTS8T/gAT+xGCcmePR6uo5Qmes+qqlM6TF7gnEKFBDTT3c8sYsRkkhhV4q/4JFfEkzACj66deAQrIyUAJbUoqrOOe58i8lrXgjSUgxE+eqqDhn6/pG5bqsYmGWobkLSIjimToF6JTz4Y9PEdm8Y6h+jGZnmWJYdiiLTOksnkZlQq8WpWVZNGZ4iOUAmrqqbut2RFnsxTIn2eSqkT0NJHvwRaKrJMoD2MJhOE0tg2JXIltggnS/YHew+ZzmakeL8hzrU8fHjAxmRMlmVMJmPOPXaWXpnyAx4eHnL/wR4PD49pWgcm57s//Clbm5u8+gXJ6fObXLp4gQ+v3uboeI4UhkGvR5aZdB26BXtVN9y8c5dzz1xGCYPAE9oFsfs+CaE7prmGGAgdcM3oNAo6tbPFg+BSUpMPPNrqexe4c/cudGMzuhOrFDI5ZTu0xp37u+ze3eX23et87Q8/+kreuHGT0LT46BiNNxJzH8PmzlYy0i+nNMrQH62BUAzLnJ3Tp7h4eo3NnibKnKLo4ewCgyRGw9wVTBcVSylpgiWsjhlYeOEzFxjvPMFk1OOd73+Xax98iBAZ69s7ONVjMITQVsR6Tn/ccvbcac6fP8f96TfwXiCl5f+m7s1iLcvO+77ft9ba0xnuVHNXV/XcZHMSKYaSGQuyaMWUpTgWJGSQASUPCuIASew86MkJggQx9JbEeQgQxI6DIAHsxIGNIHCsyNZgkZIoieIkDmo2m13V3dU13vFMe1hTHr59bjWjLoqAoEDeQKPPPXXuOefuvfa3vuE/TPZv0A2es77nqD3k0W+fst4Emrji5Ref4dVb71DWJ0pM63vKwrK3u8PefEYMAzn3hFEq5OF6yfHRMZf39/nkn/0kB7t7JNTKU5yMrV7Vubl48QJvvu7waaCoarqlSpRYOyGTGfqBV7/1Bjdv/gBYi4oJjbHEFQpOyEqyNOWEwhsSDmIgIQrTTioRbXLG5ETKnpQ9OSdc4Wj7jrZV5njfe2JSOXRJ/wJI/iIwmzajGQLnPVDFqgjOGv3jrQbvsnTEGJiUBc7prD9n1VBOPpMymlHXE+q6YTbbpW4moxSukK2D5Bm8Z71aqUnGMOCNOjzNZ1Occ7Rtp1k5mbbt6LqO6XSqEEGbMEbVHa21zOdzZrMJIQW6rh/JPJG6rohDpJlMqKsJ682K466F0LK3t4txltLVgFK1QdsuKQWcUwu4lCMmq+jWVlPEiLZktAc/yi0k7cFLigREF1p7issekZreR1xRY1xFUzVkItNpg/eJTdsRhkSyUfVijB2Hqo7BJzZte956qusaYhozSBUIs9bgY6CUgnXbkSvtvQefeHh0AkaHkDqnDZwul5ycLagqh7WZ2PU0tRqGP+mIMRJ6T+EqCmf46Ic/RCTzxpt3eHj4CIDTs1MmzVW6YeD49Izd3R1W6w1lWfLgwV1Ka9k/2Odgf191+EOgHe0bd/cPqCZTnn4m03YDpyen/No//yzv3H3ItLRcvvEpnnvmBrs7U+6885DVcsPFCweUZYm1BVVVsFx3xITCGlPGTGsIHXmQ8ZyWeB/BZq3qRtEsjCEknR1dns85WUXalBRO6dUNScSSQlIiHCoNHJNyEqL3kDJOhP0L+xxcOODZZ68B//A9z+XP/szP0J6e0PkNB5efYudgn6Iq2XSBz//6b/DGG3d51FV4qQjZMWC5ePMlfvQv/jDTOpNxCMLZUcvZesnJesWbb9/mAT3u6Ij37U65+tQ+k8mEm8+9zO5Tz9Gt1yx7w6J3SHacvX1GcWGPsHODclgSh7fo0z2O1gu+/+mreKvrTkRoB4ur5lRTQ9ut6fqWr379Nf7VP/dxep/41jff4NrVi1y6eJH53h4vvvJ+JEZ+97c/xy//6q+wv7vDarng+P4DYjXh6evX+cCLL1JVo1xEHiVOEriRiGit4crlK1R1zWqxohJBciTHQIyDJlk5cXi8YBhUkM9WotW1z0pMRGdEzveIafAhUtUlxjrEGZIfKCrIxtKtN9S1Ko+arG1gsUrQHAaFWA+DBv4UPTb/MQ2y//84SudwKgmmOtIoDHLbE9/K9K6W6zFzz9qiycLQe4qypChK1SaZ7cCov7x7cJHJdEbVzCjrkr5TDPXzexdoVwsWi1N8DPi+I+ZIYQVrNENXdcNMzoprns2mTKcTvA/Md3aYz+cYVzKbztjb3SOEwHK14OT0iNPTEy5euEhVl0TnaHYr9g8OOFktsGRijuzM54gxrLseK63OFQxUVXEuOuX7MVjnjLEFedTIgcdzCiOMqB6FFTIuinaUXZC+J6xPEdswbyZsNpmdnT1q5+j8DotmQemOGPqG09OlGpHESCJjXKE9daObZzdq+KzWa4pRAdJaS9u2io13mdPFBiOWC/tzrl+7zP6FHd5+cMoQI87C8fEhVV2Rhp5vnktLFBQm4CxM6idry7Q+kLJw/drTWG95eHLMC889zfXhIm++tcvbd++RRDg5W3B4fMRyteDjH/0oV65cpigcL7/vfTjr6PuelDObtkeIiA2IEX7r974KwNXLF3juxnWai/v8/H/wc9y+/Sb/6Bd/kZiFj3/8Q/zsX/lJ/rP/8r/FOsPObMa9h4cYhKapeXR0qhIWuxNInjwyga0tAEOIwlogZUdqI12nkNwYBl76s5/m27cf0PrAtFjwoOuUxesjTVmpWFVSzob3ES3j1AgmoZXupHG8/6XnePmDH+GZp28AP/+e5/KlZ5/n61/6Eo2puX5wgdnly9i6Yn26YHdnRt+d8Og0Eetd+mSpjOGzv7Hh4TsP+PgnP0AXIw/eeYc333iV1HYUMfLidJcf2NnluY99mPn+HilGTFninOPLn/kVXn7lA9x+9dtslhva/lSr7eUaZneh21CGHpMdby0e8L/+3b/Ppekebz1aEenIviVLRpKhIFEWlomFiRGO7t6n7TxvvHZbFUyNtl92duZ8+sf+FS48d4M4tGTfs350yOnJhrbrOXxwgrWGxXLJZqlwWOcszzz3LLODfZwTdi9fYj6f8/abb7Nar4kBbNhQmgy+xwB/8I1vcvyjP8hsVmBCj3Ha87dGocEiAb9Z4HIg92s6o/yH3Hu+9qWvcbTqiQmO7tzBVg3Xn3mat99+m8PDY/zgWS3XmKKm77QVDJEUA03+FyBz15ZIUt0UBJcfY8+3zkGCYq2tfSy/O2mmhBCpJxPKsgYylDXGWmazGWXVUNUVtUnQL6lR6YKcLXkyRaxhtT5jI5EhdGMmF5FoCEHnAPP5VI2eRwxqVZVM53NmO7u4oqasGrAVKSb1GK0qrLEqK5BVdKsbOtbrBfcf3KWwjnnT0FzYVcIE2o4CnTFEP6KGMoikc6ZsCok4amJvpXdzzlhJY5Uwysyi2Uckqy5IVCmBwnTKhqscIQmkioQwn8FqeYrkzHRS0ZlAWG9UGjh4fPAjo7WiKEoGPwDCMKpkbqGhMSVCrzDI3keayYy6mSFiKZuG2Lb0fU9ZFJTWUheO1XKhlnXNhN4kDCoV/KQjj36kL0ym3D9cIO2aTd+yv7vD7u6ct+/eJadITJG26/nYRz/G/v4+9+7f44OvvA9EIbVFqQqUi9WKRw8P6fqBk7Mz7tx/QNNMuHvvIbfefJuqdHzklZe58fTTfOpTn+LXf+03uXhxzg/+4EfPB+YxZdq2p64rNm3PerWmrgquHuzjvKccB/t5NCZp+57OVcRksbag3JmosJsRzl4/oivmkDpEFqMUbibkjKsnNBMh+fU5D2TLR0gpYesKjEJkrQi1taT45Jv/zTt3+NpX/4C+25BT4P27u0wnEwrn2J3NsRTEYSBZw3xnjw+/70Wqa09R5A2//fkvQB84vXufq3bUdjKZ6x9/mWpS89bDR3z7N7/I6dmSmAI/9W/8OHUz4cFb77BcbVisW8QV2GSRzZK0OSEmYTAlRiw5D4QIs7pi1a1puw1iUXw9YFwJ2bDu1vzuF79CDhlrVaeIEehQloH1suczv/Tr7F67RMyRoe85vPMOp4sFMUZ+7C/+GK50fPkrv8/mpGO5WZKJvPjSc1x76ioXr13h2rXro+2kJ4YEKWLygGRLTjr/adcrvvBbn+P+5R2qYk4znzHb22V3poAGWxZ0yzPeuvOAo/tHPFquWW7W+OWaW2/eok2WLBbTdZii5tXXb4/KqwXe93SbFlt7hn4gj0iwGD0S/wWQH0AE6+x5hZqDCmIJQlk0lIUu3G0boioL6npCVSkWwFUVZaX973K6C0BdFxgS1mRy7PDtqQplGUumwMSSQhJVXdD3Bc4UdJ0nBk8vPdPJVMlHMVKUDmPVLm46bWhGHY/ZpNEevQz4uMGkgWlTISKsVqtRb32m31OEuiqoXUlZqJmEdTpj8FFlYrfBcttiMeiQFlVBIBP08XgeVLUynAd6UR0HYlDEi8i4cdrIKCyJlZKQLIvlBmMNZT3BGEfInklTUxj1R237gSFvzYQiw+BVwjePsxEZoaHDMBK+dODpdfXR+8zJqofUkZIGwpxR2v+6VQuyEMkYOh+4vL+j7bHvZh0m4IqSoqzYnUxpJjXLxYrdnb1xCK0b/3qzph88k9py+41vsTPdpe82GClZdxvuHh/z8MEh9+89ous2pGywruTgwlWtUkR4sIKUeo5+8/M8e+0WH3n/c4h1fO0b3+TapUuUjeP0+IRm2nDz+jXKomC1WjMkz37ZcOPqZawIqduoxrnoyaxzprp4WdfgSLDbCnjt7O4hxwEnhgdvbc71kHIM+H5Nh/o/+LiF/OaxNaCwx+A9rrTszKYURnkRTzq+8Pkvc+feQ0orfOMbt3l43DLd3aEsLW+/9TZ9UfPiR15itn+ByWyODGsOv/1l2tND/NEDqghVH1jWMxjF677wu79P7zti9LR9UE8EY/idz36ef+mTn+Trr3+L9UY1Y0LXQTLobR9JqAVjNomcAzFZbt15myF4hmRIoT/H1KsfiuAls1xvmBQ1IXkcyjb3IRJihwi8dusW+a3b4ywnk4cAwQOOxdkZ/dBy+/U3iUnPp7GG1755i1u33mE6n/DUU09x+8491kPApEjKASHhrGgikiEEwxe/8Pu8tTvF4LBFSTOd8/zz1/nBH/gYSOSXfulXeefhCb4P9EHoB08OEe892eo9PUSBEdGXYsT4geAHQhgogrpRDd6PlV4gy5NtFOFPS3Afg1nX9iM+XdsOriiY7O1QT2akiLLuxiy+LCZ4UZLTbFpjrdE/2HeQAnHl6Ye1miKIwZlEzIEhJvqo1l9ZSi7tX8IlR+w8m06p5iJgnXDhYIf1pjtnuA5Dz3IF9XzDtHIUYYGRSBw82auJhG/VYMR7r3Z4ItQ+0jvH4mxJbGpIFZuNw1koiwJjHTEkQk4YK+fDY9ni/nMm8LjCSVGlFraDtpzTObZfH1cK0xQdyg5+ILeRxBqMRVzNtKlZt57lcsNLL3+A45MTFoslYbNGrOOitaxWa8QYjs8WtH1Pu9mosNkIOcvoQM9aQ1PXlMUUHyK2KFgNkYe33mZSlzx8oJaHIQRCyoSYGDqtCGIK+Cy8sH8R5xxnpydPXibWsOmFJAVXLhzQdS2TouSf/epnePvOXW23xAgGLu7OuffgAc+++DIhOt4+OuHrf/AGhw/vUaREOdsnTS+x+8wrzK88S7F3k2HveaJ1TBpDahfETnvin3/rFr/8W1/lP/7LP86XfveL/PPP/g4/8UOf4B/837/O0Pd8+/XXKJwivK43DZ+4cY2//P0fwwwBSS25LBgKg3QtadVSFarZDgoWMCOEtzIWH+Ho4UNOWk8UiDnSxZ6jkw25iFzav6h2izETRY0kQgikbsNms6LrOh4cnvKZ3/jf8Tnzn/6t9z6Xv/+NV4ndQAHcjw+YblouXb3Osx/6fuTlq9yYvsZXvvglvv361wBD8J6cHTYFdiQSnSXHiDvakI3BVTUPjj0hB1LUxEzIGHH8wauv87VvvKatRHQDTuiGNryLsBhTxIxG6e2ox6N8lpbKQU4dxhVYSdjoQBzDesA5DxY6txxndYIo3IXeC2LVPjONFa+LiUTPg8Ujdnf2WPukhjqiaJ/YByQHjh7c5/a3v61rLwWS78DNEVPR9j1+GLCSGUymO+lYDx1OhOl0ytMzx2c/9+v801/5RaLPkEs8GWMdBqcm6jnicyDTAaKAiJxJ2ZBjImUdnMbgOQ69wpdzICUVKezDk6U64E9LcBc5V4V0I9utqEqqaoKrGsQWFKOlmnNmxLBbJpVDTCb1K1IKeN+TfU9Oo+xqUJ0YI5ZBdLdT5cYJMXu15Sr2qKqKsixYrJcUSdEvOY+Gz6Oo1zAMDINnNpuS/ECOPe2mo3AQk4dcQMz4odfdeJTa3fqF+hDOFS9TKhFRM+mcAylqUE5JtSWU1ZrZih8Iys6V7dRdFEGRBaIqKMAWo55Go4SYyFldbcRkoh/5AiZiY6asLJXJUDlOT8+Y7+xgyprDdzp29/eZ1DXrzW1KJzRNpTyBqJtI1/WjW1Bg6wcbY8anDKjs8nK5ohtvgNWmHVtq6uuqeOTEZDIbyTIddx4cMWlq9Wt9wlE0OwybNXcfHHHpYJecEouzM85GKYMtJBPg5PiU5198gWHwnJwe8tqrX+f4bEGSEnvtEyxm19nMr/L0i5+kb2ZQTtic3MeZBEy5fnDAbFJz0m8wU2GYTnng3+Dmczf52pe+wpWbF6hKBzliclBnnbLkz3z4Ff61v/DnuXnjihpiNA1iM0VWIl0aPOOl/0OHtQVkod10o066kJPo6DJGhIAbPJSFtmuCV4RQVpVMK1BYS9t6jk9XIxjgvY/WOGzt2J3tcP1Dr3D54gF1Yfna5/4Zjx48ZDrdloIAACAASURBVHV6zHITSKZEXI0bh4E5ZCJqaBNTJLQttqwwdSYQiVnNaWqrLckQglouops6OY4igIxcDZ05bKU5sMXIHtf1HGMkhoQ1jphHkcEUiQSy1cDns9F7IyiCKIsgo82ekcDIajvfcGwGcqBMiZ2yQvyAt/Hc9CfnkYGL6sdow9MSImRZ0IeesqoRE3BO2JlVrBdLug1Mix36DN947U2QQOEKkklsNq3CNFNGbEJIGBOYReXV5BQJYRiz8p6cFZEGOosrjFqGOtne/4m6WfFL3yWs/qkI7tY6ptNdlQAoClLKNM2UqpkgzuKKAieZ6FssGtxD8EifSDGQk9cp9uDxeXwuZ5wzkJLuxjkTokoJW3pMoy2N6DcYAjKWlpuNUvEnk4aUIm3bY4xKHASv3pRNWWByxA8bDPoZYp0aDJAZ/Aj/G6FXYQhsNirIBUBOWFFjC/WC1YsIkNTjgJgjpBE1NGY3o98N8dxuMBF5DOME3QRWm37sy0ecE4pCJVczGUIi+I4wREQqSiOsfE/XWuqyZjafaUApKmazOZt2DZhR2E3ZvJuuo/cq6haz0LcDg08U9YSiKEhYukGzkbPlimwM7RCAEfI60vcnUmCMkIzn6GzJct1i7JOxkK6YU5WZ09MF06Zk6HuuXbnEarXRWQl5rGoEWzVYU3B2dMg7d97i0cMzmr2LyOX3c+fGv0UnNdN5wbQS4rDmdHlKWRr2Ll3iYFJSJ09cnCEBdosdrl1yPLDCU1Zo5m9i6gtM64Ju45nVDVVZcfXqFf7Nn/lpbjyjpgu5qWHSwGoJwWOGAEPQ4P4eEjpZHAnVNEpBB4M2gg0J03ly7hjSKbIrFFWJS2q+bbNKM/frNcNmSY5evQH6J2d2L370Y9Q7M8qmJty7xbe/+AecHZ+wWSwIgyd2ARNLjCkRqUhRZ2EmQVyt8V1PUVh8ShROAQ8pBp3zkM+9XwVoB7V4tCNsWCFfOiuSEemWBXUxG2cuWyJdTBFnSs3EUyL2PTlHskv43FPbSLI9SaAypd4jAjaraX2KCZLCgo1oa7LNhXo/mB2Cr2h7j9s6a40tToUb51GdVlExNoMJGbGWm888O7ZxLFUSbi8X5P6MeTWQQmbHNsTQIiEDkf0yUXCm0Gkzgh8kM8mdQp+t+qQCFOIx44ZkRMZkdvx5TOREMlmerMMEf0qCe1lWXH36OVVnNIKPK5WONRlJK2LX44eAH1qCFTRLFdooDMEr6cmoZ6nNY8aThfXIdiztoKbYWUgJNm1Pv0wUpcVWLdmUxOQRLCkKRhyLsw3WGdqNTr9Bmak7czVxbrsVeWhpN1mNcfMJGUdd1uzv7bLpOh36rFYURUlOGWeL0XczMww9xujGc34Rxp1ae+yGEB/31nPOpFHrPKX8eJiWHxuYbP8LWU1K+j6oHohRkaccFcdPTkjK9KPedwww2l1STHeZzw/o+kwoZ3RDxNQF82qqptMpY8qOzis5I8RI06jpcVE3apoxbmreB1K2VOUcY7zKFJPpfNBhYr0DKDvzdHE26mo8eZ1EEk1RUhjDarOhaRpu33vISAzWys9YUjbs7O/ye7/ze5ytV8TpLuX3/zXkmY8QEZ6WxLRuqF2ppXhhKF3F3txRG49ft9zb9DjrePb6JSoH83jMq/cS3zbPM//gz2KOfotXnrnK8uyUn/yJH+eHPv1prty8SSWO6DtCSriiISeHlI4sEWlK/EaINqFWC1vQgG7t7xye0Ab1Jy3FkGMkOad6685AH7FZ+70SPf7shNR35BAxF3cI6xVu6NidllgTWH4XHPTtV7+CXyxw3iPlDiOeltxbSnHkKnK27kh+g6QB4yxRIiZ27BmhHAJ+vcFdPYCqYi2eWemYFjWRRDt4XFmoHISMTOatfSWanOQIiKhia4rUxuFzRKxFtpWszYSh5/KFPa5cvULhhOw7yrrh8PAh63t3mFghmcxMIhY1dXGimS4m4FA7SCFjcgDxOAw3DgPhgefj029ind5bMs6qBEBUimAce2FzIo2ValreIiSh94lZA5cOEoUBgyGlTLDqzeCsIcVMYc0IctBbfEuoV2s+xtmJbkxpG7hlXCMUDLMPIkVDchOSQJKIkxnwPzzxGv+pCO4peML6mIT2mZwkJBsGLGIGEK+GGDHSh+2ZgRjGSBAjeYSFJcNjT0cUAx60SIPRybzzHlcoZj76BEWmqTQLEREMkarWjLgsYFLXDKNtWIEndQskerp+bFXgR4y6oSgjhUk4Mj52QKnMUiIm9kSjFnxdtx4XjX2XbgujpMEW+TKen5xo204DswiMEgF6P8r5a1LSG3TriJOTKikGiZjIuSGxMTK64TjNnhza38mQuzWnfUs0jsn8MsnOWS9OaOoKH4KiZ8qWg6JksVji/XDuuXp8fMZ0PqdqZly9fpOmrlRQKSQ2mw2L01OWp4ds1mtyCBw/fEsxvU6FkkIIhC2Q/z2O5ekZ1la0Q4/tDc5V3L93dD7cFQxl1eCHyOm65+ToIdP9ayyv/hDl1RdwwxnOWqRU7Q4vhmnhxqF0ZLVq2VjV0TmY1tSF4fbtb3F89IjDkw3N7gHzg0vYoeHu0vL09CKzp2/yr//cX2cqPbYS0hDVoBqQrI5V4mpMVjnpobTsM2CAhOOsh3tHK+49OObhSWDiSh5lB7Yn2USVCnoEiSA+kff2KKyiL5ohYaRikEhhDdXuHvbSDaoO3PxNLly5/sRz6YZIStreLE0cyXmZ7CDnQPaQsmf/YI/JZEYMp0wu3WBzesLJt95gpyroBk+de1wIVHVJInK4PCX0QowyBlfBFRbBISkjJmlrxAZq2yG5xNgW4zySDTVqr4dJ4CKQKcuebO+zu3lAzoFV9uwNwh4t+aI6gRHVbW1rw2DGFo7fVsXjspKcqRCiGC4wYeHfoEmR3oYxYoyzK4FCP36MJUoYnOSMzyo4aC2crCOXayGWgk+ZMmeEhEWlyrNAthmPUCJIGmOTKMw6GYOThEHoMUpkEsE7g8lgYmKThHzxY/inPokzQog9ptphCAv+9Ad3YN13FDaT4qAlijhEDN73xDiQH1+fx8NEk7/j5wz4aBhb0+clmo/AaMm31U7pY9TBRs5EMWx6Q+ehKlUIqx80YBkrtB580J738eIUl8O7NczYmlvHmAjrNTFbDAlnHasuELMOHNXz1TMMUVs5yDl79bHssX55lSp+rLFTluVjApMx5z1JrPmOrP07vtO2EkCUwNSohgVopVpmzULECtkoPslHjyFB6LD9CQdFxe7FGf3QU0xrrJuyXhXsHFwm+KDQSFG54MoKPkI2BcOgPebZpObB4SNVf/Qts0nJpKnwfQcjCSPlBKVQlW6UY37vY+Jg0a5YLT3kPRZnZxwfHeK7JXF0DkrJ40rH5uSQ5pU/z8Pp+8k+MH/rNzEXblBOZjx45zX87gV29i5x3O9grVCXllmthhJiDIPvCD5x4fJlrjz9NJGKw8MjzlpDV+3wxs5P85f+woRPf98Vmp0dRAxD2lCEsbWUIxIFCYkoGQkdqe9ZnHS8+uVjbh+dsWlbFqt2rDwdXY70oaWuDY3MGVKiclOK0rLuzhisoR063KNDHJkOT3BCuTfldut5mYp2s6AsKq4++zTp7OETz+Wl/AaxGMgm4sZUVUzWpi5weRq4fuUCUrUMsefNdw5x3duYOsLLCwqD+qRypP6xogxkqUR1ZxTexVZG5LEHi7ZgSBkXgCyUhYzaTcLYED/PZAGsCIPJSL/BIJwsPbMdNdpO26hgNHt/HIr1s917JAvBgsTExEwZigo3SXzndGKMJ3abwufz+65HW6/loPo5zdQRDDgyhQDiiAYqCzFErB2NgIDebN/r8Te0I3AiG6G2QhH15+jUY8LYTGWFtX+LZ27+KJevXQfnefveMVauPPH6wvcQ3EXkfwL+EvAw5/yh8bn/Avj3gEfjy/6TnPM/Gf/tbwD/Lsqw+Os55+/W89/+jWwVIIPvWcceka370igpK+/aQcfrlbYrZrvlonK12yB33q8eM/jtedWXjz3aDEksIoWaZw8DPg/s7c4xxoz6LorfrmvVTsHIdwRSMSoZkDNIyhTOYUxi8Fk9WJ0SOZACy5YsZR5P9mW7m4Mkc37p3x3cjZjH7YdxI8jj3652W+/6TtssZXwfNRPPYOwoU6wL1YdRttWAMPqxZiVRIREfOnLqlflLYvAqkLY4PkOSEn92d3ZGhyx9r6KuKesJR8cLur7HVAUmBQqT2Z3WDJsBjFC7GkHZlUYgESmcOx+IvtdhrZpxR585OjwEIPoeJxEkjgmAyrUuB5DiIvHsARcPLrD3yo8ymU71XB7cJGeDLWvy0JOip207+sUayQmxRjV/JjWTmAj9QHYV09mMurG0m0gqLvH5tyZ0/Qn/9o/vIt5TJEjGaOmeQUgQA8EVY8UYuPPwkG96Rx8K7SenSA7q6zodPG5zhsnHXNhpSc5RhEM2/ojNzhmuXtHYSL3bY7KS4ULOOLtgvQ406WNMmobdk0PK+1+ie/jtJ57Lp9yRsmRTwo29MDm/x4SdiSVtTlgcPmTAsDwLPD3TQR4pjWJxhoBWijnn0clMs2NjtOXA2HbYjpuSmG2Pg2QyzoKPAawOQov8uG8hGSQL2UJhdIblrEMMGOO3vROt3DI8NsbUcSiMGfy74mlm9H41UFcFqW8orRCtOb/ftrdRkcCimlUyxpA83n9OlH8zLQzWZAoLLmknxWdAEvVUASCCo3bbeGR0BmYMYiDWFiuAMZRGsF1P0VTY3V2d4Qmkasaj5YQr1/a59vxNKDq8V6nu73Z8L5n7/wz8d8D/8v95/m/lnP+rdz8hIh8Afgb4IPAU8Msi8nLO21z6vY+UEqvNiuQ7YvAgW1qtnGfIW8VFEc6DWM7uPDPYvt6Yxx91/vxYto8P9flRUTKP1G9nS0o3QNS+YBgGlQZGmE4mWCs4m7FGoY6MJJLtofIH2tpwY1tICoO4YmydBJwVnGiQEtluCuPtNN5YMgb9P3SOcj5XunycmyguV5Kck1oQOTfONSPBKIRAYbUSSuNJMMYQy23FMN4BCWxSfQxjDEIaZ19ZtSwE7ZsPLcsjtZu7OH2W2WzG6dmCpp5T1iVFU+C7ktqCc5ELOzUu9wximZoJm3ZDSImd+YSha5lNaqpKN57ZbPLEdeIHT2GFXFjajXqfCurhKUk008qoKYYtebhIPHXtKlefeQk/2SUNZxpXvEespTKWobKkECGO1y9FrUKmc2xV0kWYVhV+CPRDR5WFg6agA1bDCb9xV/ixe0ueapYM4ZS2FULo1asXQAxxWLE+PeXk0SPuvnkXs/Mse6tHQCbHfpydBKLpCENH3CyZdgWdVNjcshMGzF6LzT0ybAhuIMWES4ocySlRVDXT3Ck7dnnE/PQWqT164rl0Lp2bkxtGTRS2iapwvBrIIVBXBc5BN0Bh48gAtTCu0zokrfxE8AmcKCJFZPtu2jc2Y087MqrBZyFGcAQVoRs9G3DpnMTHGOjFOAUEZL33YrbKKn5Xwgaoxo2eVeI2XvDujUsfSMy4LHTVmnrvGuHoVertnSWc8w6y6AamPs2GEBMThHLiMBhcJTTJUiPMZwptLsTRtj3lxHBw5QDj1OXMOYHSqn5UpXpMYgzWjedKDCZG5OwUM51gLlwdW0yJVMyZvir42Op97BxVURLDH1M4LOf8GRF59o963Xj8JPC/5Zx74JaIvA78APC57/ZLKXh8uxon5FGNI2C8Gmas0vIoRyDnZB3GQCujI5AIqrG87WFn3YtTSip/jow7d4Yk5CiIU7ZXDEsqG6karRhyClgC4hxFEbBiKEzGxkA2IxbeFOcSCUP0+Dgac1hD6RwhRcptoBfBYSBrFheSGm0nRiNvdCcHXVxm9I09rzZElPSTx2Hq9v/DiDoQLVNFYGj7EVKo0/yUwYpm3YwT/rYfcEZZvzJuODlnGsk440arQfWeDSJEo73kaVFQX7vIOuhwtfOe9uSMwQ+sF0uSCH2InC1XBO9RvxQhxUy77rh59TJ7E0dMkXXX0RRQpJ6i07aMaZdPXCdSlNikAefKhQN8HJQw06os8jBooBs8hP1X+MSHP8gJDat+4CaHpLrGGEtTFvR9T+wWY7Wkiy0bS0YH74/O1mBaJqVjdz6wkwMbW7MZBiampdjZoypnVCbxC/+0pwiRy4++zPMn/whrA0YSpTUoY8GwEYeViI2e+fEXKcNo5L4d4IkjWq/XMAfWQ8PDk8hTjWevcvTZk0zCidUKQQyDZLyAOENXGu7ff8jFh++Aawg7OzRx/cRzOa+mgEpsFE57wHrfqDDfqnCUqSamjnJq2fUJu6NBLaN8k5QjuLlyJ8RSuRIxDqyac4grEVdhywldvYt1jvlsV5njuce/9ks8vPNVqrgiRSgslDmetxPd6KMgGQrnKJwhJcMG4cZTqsZZ15odWwdlUylPpLCUdYk1gqtUw2UUsH/8WApWleEpfpiXbIHkzePgv02uEoCqoooxtMcnrN++z8H7byLTA3JckqNwds8zvzanJTLLgfbRIcvDM6584HlSthjjRjapG/tUBkZUlFht7IcsON+BDwx4nJXzz85FwWT/lK/c/gIX3/cRjASmu3MOz46/W1j9Y/Xc/yMR+XeA3wN+Pud8AlwHfvtdr7kzPvdHHto60T402ZwH7JRHDfMYz0W8ct7OFcfCL8l5QE/jRiDyWIAsjhluzgptclKQjdG7OoybQOyQrXCQK0kCUaBAqF2BjISRbBqcdfjBs1gumc+mFIXDjIE8pYjvB2WEOkNMAWNUDjdkgyUqCmAcosQUiD5ohm8s59m4GTXpsyKCtsE656Rtkozqr/uIsU775d5jBLpNh7Mj5DCJQuxigKy46BQ8VjIRtf+zRtgWPL0BmxRKStoiBxTnmzDE7NRhJ/UMfuB4sVHY3ThvaIeBkNVftXCOISS6th0DGJys13h0I1qu1srUtZaZZHYOLnHjhReAL73nGol9ZN0PZGBaV4g4msJhJOKco5TMOrTI9BLzpub+nTco51PcbMZb33wTPwj1ZIZrlGHs6ppsawYf8DGRjSAkEjU5Zlz0hOw5CwMLKzSVomyapqFQ1gvJGUqBYGpe3/mXOb71m3zgoGOWjulQrX2boTS9btrOYSgYHEgUoFdYpHO4aCikIKSMxTFjSm16Ap3aR5LJhQaCHA3WpHE2ZWkqi0lrVodLdqaOqpnRDwdPvuGuvALGUliLcXPEWHAlpp4i1rFvamw5xxQVpqj4qHXQ7OogXoTpZKqZZzXHGIuxlnpkiRtb4IoSYw3WqGGNM0q8UxHAiE0ty8ueG6+uiPmQQgH7lFWpVphOKGtlhhuxuMJinVaXB9865pmXJ5RNRUpgC4Uxbs1kVHubx4/PK93xoTEQIzMj5O4MV1xG0ruNxLcVfxwriAwxU09mmmBtEqbMiNRa/fsBGzPzypK9suQnD06REUJMTmN/aIu9GRtHIpCsxnAiWEhNDes1OWmMMuKRLmBnwsFiYFNs2E0Fl6sZbwx/Mnru/z3wN8dv+TeB/xr4ue88i+fHe8IfROSvAn8VYD6pWHdK/DHGYmWL79Zf14GlqOa7PBarKo3ojl44JVigJtp2VHTzwxYKJmNPT3GvQw6krMOOoihVwteHMRuGjBl75EJpoRQlPyl5IhFH+KFJnqFdEQYFWsUwatcQ8T6MJtNCQgjj959P1eVHDKMkcARr8GnQfrvRqfv5JidCTGqGrQVLIqQtWkYRIqHtQEb8OzDEjI1pDOYKqYoZ9Q3V8kYlfrMyMEVE1SdTIsG5ZaGB8+CesiEiGgRQFAAxMXjVuU45k0PSqickRCwpiWq5VPU5pr8fetLYOCqNBaPuSpeuX2LvwmWFtj7huHrlCn27UskDsXoNjKGIBaVLuLgkhshOWbLyG2J3zOnqkOMMqesRiYh1FM0MjMUVNdXVl3FVQzYWMZYYByScMHRrUsr00zlFWWOrCrdXskGlYyd1QRF6rDVU1QRjYb67w//z8FluJc8nr13nJV4nWkfewvusw7iCJIJLgZxLPImm0faiKxpMKkEy00lNdbmmqQuKMo+bvQaqtS84WXRkES7sNVzcmdL1JW27IjU9xYVned8P/izeJuAz73kuX/mpv8HYvKauSxD9+8VViFEuiTWFytgaSzZCI4GcBe8HlfsQ0Wsp8rjSzpxX4CqjkYi5pW/1nq7KkrK0GFczvf4sO8UHkPRA1Q9DwCiQW/vfZptcuLF6VVRTtdMDPE6Cxor+vFlOPm/pjJrg3xmQxt63SCbbgJjHjOHzoZx+wuNNAs43nTD0WMkgBrGZOEStjrMi0RhFD/N5EH9X70jkOyPi9mtmRvZ4MX5kQExx/rmuqqkLFA5dGP0e6U8guOecH5x/N5G/A/zj8cc7wI13vfRp4O4T3uNvA38b4PLeLMeuUyx6HNikdH5+t9fNIRRj1iAxYkLAA303nPfFrDE6sRcZA3cxLjbBR11waqZtMJIZ+sQ6bTQDsRqMBu/pckvKQtd7jBV2d0b9jBAZhp7ClWP2DSm1YymtmYcRQ1G4sWqwI2lDB7ggdMMaRGGJ2zlCyjr+UWJeGhcyY1tmW6kYiKPJNhDiaGJtHG5k9g4JLl19istXrrA8OaI9O8P0HUaEsnSEGBDUTq9sCsQaXbC2QKwaSqeo1c1jyWXt6xZlOZI23NgOGpR5mCEnFScjZUzhmO/u0kxVimB1dIzvu5GBG2nqimraYIzlU5/6Ya7evIlNmdn+FVKOfP0b3wD+j/dcd2VRUBYH7ACrzjMk4cKlp6jqXU4ffpP++CvU1lCRaQOsoydWM+x8h7QviJvoTV01iHVEDO2wQVIgGYe4krosydML5OklSmtoCqWky/qE1eF9yIJxBWVdcrBzgaIqVFF09Au9t97wzjpz/9Kn+Ik/89f44ffv88pNw4VppRsfot6rZL71zhmv3X/ID3zsQ+zYRGktkkfzdpNIkrHZIARGZThCgNt3ej7/+6/T5cgz3/cC77u+x/HZXV773Ge5UDfs33iW4trTSHqSDxPk6QEhBmKKHC8yKQ/qQTAckVIihIQfvBKGUsT7nhB0XQxj0qSifuEcqx9DOifb5bGC3sJ2t5Xz+9/3Mh/72Ee0Sp3tYTc7hP6EAkt2BSa3j7Nl2SID7EjD1kA53y3p2o5qWowsVyUSPu5rbrP198o1dePQJmkiFguQyyB3xn99d5ZvvvNna7AXdvEnK4rrGeN102tD1Ko862ZpqopqVDfNOY4tYr3X/tD32468sNo2mjiK5QmMkgRkbR3XUrI/25C9g6qkrARJfwLaMiJyLee8rWN+Cvja+Pj/Av6eiPw36ED1JeB3v5f37Ac1uc45MwS9kGRl6SFKYDHluwBLOdOHMXsdKespexjNZZ0RDJaQNIsWyYSc8DFhraNwjpgjOXlM3iob5pE4lMhiSVmIQYW9nFXfVOfGFkTMo7CTsuhsZqw2NJsVxqRHCkWjZEPKAVPqDWdlOyA2NKUjJ/0OwzCAqO55VZWjJENBVZXEvhuZqWOv1hg2vfbnaufokgaPxaqjbyPTZjpa3Vmi7xmGHhGrEslVScr6+UkcYq3aq+U86tFHNYoAkEThCt0cjGZpVV0roQmlzReuoO9bTOFomor5fKYbXNuySYm6qdnd3+P5F59ntrfDbDrl2ZdepHSOfqSwBwlc2ps+cY2U1rEaYaTGWuq6odnZJcYCMQ4jWTHrdiBIwd7191Ndego3m5PKRLU4JgwDjx49pG032i6yc6rpHtP5ASZbUu8htRi/IcWe1bBCUsRdvcnla9coqpqiLJjvzJEslGWBdVrRDF2PbxrqLlBl4Y1FZPWlE0K6wo98dBdCxIrqFiWgtIHG7GIwhMFgTDeuCUeIHRDo/GTUc1fTdx8Stw5vM7glSTJ3jm4Rwozl4X3OTu/zvuefY7m4z4OjJZs+86k/997n8qtf/RopBSXaEM8hwil4yJlgK+VtZ+0Jp+Apsq7HLWkuxvi4dQFoH3msfrcBejx8GLDWqqhbP9AYdaQSsZSVhT48Bgpor/Vd7ztmqCkhEimtoe/5zpbLeXL8rgz8CUceYS+ShGxacq6RbP7wr8j2M2Bb4drZHBYnxJy1rSaQxajOfmXHPv2oXUUmy+ie80cd24BvHRS1blY2AxbJAZ8zpmgJIROTpfweIvf3AoX8+8CPABdF5A7wnwM/IiIfRU/pbeDfB8g5f11E/gHwDSAA/+EfhZTRDzFIOSONI+08LLeEubHkE+3TDnoOdHKtSodmHBJaq7t7HyKSNMVpvTrybI23Q8xgHTeevol1JZv1ikf33oEUKK0SOpIIYhyuqBRvnyLzsqYsS4qxXSHGshW92vbPBp/GfiT0/dhjHkkJMvYTNSvXIUlZ1rjRmNjZLeNUsFZQN0t5rFE/QjL7GDACZd0AgrG60fi+VWNtU3LaDjw4uoeEnsmlfUrnMAI5F1SFWgI6V2i7JKmhQDQWLUGVGSjGUBhLUTudC2RVw4spYABXOEzW6sMWjsKWIEKTC1z12O4vpYQtDTdfuMmzL77AC+9/hYOr1yBHUj9QWM10xBXcffubHN27w61vfZPnfui9l8msbtiEpc5mQiCmzHqhRiCpX1OhJJzCBqr9yzz14U+QXU3ftXTrhwzNFWIZubj/7KhZEinqiiFm2iHqfCJq66acTMc2SNbNVTKdrWiHhIkZ7waK5Cl8QVVXygjuPHHd0ubMYr1hSkGb1/zaV24x3/FcnHkaE2jXkT55NptMnTa89dqrJD/QiiNlNZFJQybHQVt3efT/zQoYKIyw7ywhBtqHD3nr8IjVyTGu65nvXuAoDQwbz2Z4MkPVRUfOFpszfVZxvAQk68hAGQUXEmDJJGIuiSMy51zuIucRZ77N1B+3VBLjYGx7i4/JT4xqmGOMVZ0f25CGhInjOxmj91S2bKN7NhmMGwOCwZYQN+txzY4xwddeHwAAIABJREFUY0x2vjO4y3dsMJy/o+j7SyLLQM4jgCO/61fhXZvHNsvOmMmMJEtiyrgkCi22luwDUOmbGB2YkhM52zG2v6vFc/45Mp510biw/VhbvUuRxOr72IgtWjrvCSFSFQrd/m7H94KW+Svv8fTf/S6v/wXgF/6o9333MYSgC3FsNVRVg5IYMm7c9CxQjxCilFW6dwg9wzDo8+O+v1MJfdJe8twVRJMpxBClwllLTIFFFzClI0bh6sE+RQ4UVpCiGBdgPIc5OuewrlCXqBFXX5Yl1hi8HxegCNNGT6VxBQfPfYLcTHHBc/r61/V61xNSr4JjOUd679nue87W5MJgixJSIkevVYJ1pBBIMVAJ1HsXtB9qzDnULCShqko1hXBThv4urzy1y8HeLs7KiNYB8ogH3m4KIRIHzdCdaKGagieJZVZWSNXgrr8IUuP6BadvfYNpDdOmZLq3R2MN165f5+JTN+i7jqPDQ973fR8l955/8o//T5Znd7n21E0+/WM/zfVnXyKL9px9u4LFQ0xVMbgdckiYqubhO4+4des+b999MlqmqAt245y271h0C5wN2M0Rq7NjYlyp/oepSLsvsffsx6nqCfP5nLK+iq1eYVI4mkq5BiEk7t57wC//w/+R5cPbNBKZX3wBqZ1KOkz2NIOaXqSVgk0zQ+od7dm7kiF5Ep4itOxXJVLCyeaY2mSidcx3G7AQzJTjtufv/MoJMSfmdeRnPtoQfacevjlzctKSs8enTDIRn3uctxAM2UTyyKzWFl1AQZZe8dFjtjyZTKjdDTopaZp9zOpE5y3/L3Vv9iNZep75/b7tbLHlUlmZtffKbrFbJNUUSZHDTbPII4wsaICxDfhibuZu7ubCf4UBG7CBAaw7A2PLMxgPNNZAY0oakRK1GKJINkV2s/eu7tpzjYztLN/mi+9EZjZZ1XNjA60DVEVUVOTJcyLOeb/3fd7nfZ4nbN7Evo8Uevpvut9EH7S7GImSJBOwHpLrI04U8QzSjqHXq0/YAkBPauhfXdOWRWK+rOqGrnPkmUHFIUGPUMsS5LLHprmQhfeYtRcgfbqGUZgywx8kZUyhSb0qJdN4+no741B/9LwFCRtH18ioyFlhQ4PBI0RJCrYdkHNOyb4Q9bMhcWNM1iwRxQThLbLSzKeWcmKQJoIrYWMH0c6R1Q5YTTDujPeP0Amm6T+XdKoRlEkvj8YwmxEvZcSY4aVHONClwB7NORQ5h6tHzGZ/C5yYBtWAa5vbhLZBEalFWuWlFChBLwOQp4nRPlh1XYvWgtGgSnBOSNzYMstwInGWlYv4NWcVjSZA1EyXM5pVzfL0lFVsKDPFxnhEpbIzVUp/Aff3ISScXyTmjA0pM0mDCL0OuzI0bYsREofE2oD2kawaUtd1YtjEOoktuUBeVRiT44JHFkO8KjCDDTIpWB4/wHcrokuYv+kFmCBVDkanBpOUkhADmTZomeQYnB3z6V96hUvb24yGQzKTmqhRKDofWdU1TWc5OT5BtSusd2dsmxACs5NDmsYRi4IwHGNDxijz5Ff22JpU3LpxjavPPofUGmNydFaC0dxazAndiunpKfViwedevMmtZ57n8t5lnLXILDEqbNeidIZROSLP8KsVr//gR3zvez+gbZPp85O2LDMYC63tzoafEKLvgSTec2fGmMENsuGYfDQkKEPdOQYKorA4FVND02Ts7F3h6W/+t9x+9dss3/1r1KO/wZcjopTEaYaTBdnwOjob0JQKMdnFmwFisEGnJ4yMIquGWK3x3jJfBhqVo/IKMdxm2Qm60JGpNCuxqDs+uH/IdwrNZ3YFg8qgZUeQLVK2KDfAe4ewDqccMZcILzmL7R9zD6VEISRdc5ny8CcDE5wH9o/b5wVBuvQ74kceQ4+vQ7pfY/z5IzzzJ4hJsdR7n9QhIWHa4uLo0eNwES5g1OcPMSa5BC1Sj+KxMpuP3QQplfYkcEH38h8p005QTF/JrgHxNazSN0NFboitI2ZJzKssc5aHc4KQveAXiDyDZo6ogF6Z9nFfoFhXH7FPKr1IvrtRE70gSkFUGbqLGOV55423aB6uGA0aiuGTeyrwCQnuShuGk01Cs0TY1InWRicTAu/J84y8SL6mwTuc7QjOkhU5Wmts2511zbXUyaoOCBZWMaDynBAE0lpkaFE4iuhRhSaXJd47WmuZmKy390tlplb9lxp8mgj0aYI2ywqMVmcceiUFQUQ2BiUuQrOYoQYjfPCI6FAEtISszLBdi84LfBRY63toBTavXEMNL+G7FQPf0p44tITcJAU723VIU6K0Rvd/iiLn5q0bGCPIlGR6dMql6T5f/3v/AF0USKMRbYfQKqlP5kW60ZQmdh3YNimG6axv2AW65THHB6fMushRsU2Imo24ZG/wLGWZU5YlMisgXcaocpCmXUXN4nifN3/6Bpd3LvH5L34FU47QKsMpnSof78iUQpohMQTqwyNe/eGr/OD7P6RZNqmp/XEhSSSFS62TAqETEWstQug0JShgke+gx09Rjia4ANYn15xRpRB46Fk/bWdpO0u2fZXNX/wmtXW41/49mYqgCoYiEmNDfXKMkyrxv482QVWEwRizd4NOjJgrTXb9FqO8ws/nSdUwB0xO0zmarmElI7nOqRc1tZO8vu+596hjOJCMhxllVjIcSfaqfXRQSDFABovDnjcX4bHB83yLOG9ZLOdMBsO+V/Ux7//YffFzQf1nf/fPBvp0BOf/vhjshThTekoywc6m5rLQSNlDGfQ88LPAuw6sj2mOijT851xITlcJzzk/p4tDgI+rXs7glqRhJYRP2fTZWayf9xBR2tHZ37IoiYsTGHmCUFRlwXF7QoxJsFAgIC/wU4sKPTviMfsCiD70swEJfpUCMILYHiLaNsk71zXdccujw3tIXuGpq1/lyhXFT9957efP7cL2iQju3lmEhmI4QFMx7geOZE/D8j4N+jiX3F1Asrt7tce0Iq1qzspAITWup/jlwzHVxhYMtsjzCnfyiMXDD7i5N0LiKTJNYRRZXlB3lhB69Uep+kZZypSLomQwrKiqisnmBuOywtrk3aqNSU1g2zuSu44//94PmD06YmNri1/52hfoWsto9yqZUng085Mp9+7epW1qbNtRtxbZnELo2CgMNlcMnrrB9vYGeVYyHo24dvU6YjBAaJ3MPPqsywoNwnN6csJznxmzf/t1lo/uoPMMaXKCMEiToXC4k8Nef11SDUdJbCsvicWQ4DpU9ORmh+vb10BKvGtQwRNjkmKIXRIJs8spgojOC7qjGdJIlvsPqDLN1772DeRoQisiVuZEAVoZhFREn5yuaufIioLf/bf/junJDCENQji8B/sx7C7v0pSsFEkDPLjAql6RiYxMGwZOMtv+MuXeczRRoclTlugdna0xMuPkcIrNS6TJcV6CX+IHEwZf+E1W8wPqB39DlbUsmo5cCTZ0kpu2colbLkHkdKdQT2/jJs+gJpdxBwXLruPeWz9GNkvkcEK7qmmrISrXDIoNVvNTpJBsbk9A1pyGnNPW8+EysaGkdBAmZEpQmcAvXlmyVQnyXrr2YjB9XL4dY2rE5lmBUtnZuP+TNiETw+SJC4YU5+vK+k/o4Zs+8Kah5jXOTv9/Hw3yZ7vrBxNtl6QeUJIgC5SapJ8QHmLqQZ0Hwp/Bz3tySapeM7ouUMUkyyG0SInK2Qk+HpY52x0p641eEvUhMAKx7I9Fcb7AyAs/lAw1/GCAunsPdgOd0FSDCoHEHi/RO0UiWeQF7dxSXY44DdrLC4vthcVHQsRClMQWYudpbMv0nbucOIvAUGyO2dwcs3PrJsvVjJ3JgoGouLq18fjvrt8+EcG9yhSbKjVCiyzH9VxbhMS6ZEQgoyWG1EjQJsOjkt5ECChliD3nWZoMo9KwRG0lUmpMllEvFvjlnEIJQpL6Twa44zF7e3vMFjWLeobWmvFoyLVrV8nzHJNlDIYjyuGAPK8wVUVyk0mNR9df0L7rOHz0iKMH97h65Qqfv36dS9eusT3ICFEyuHwtddUFRGe5+vyzrE5PcE2NUALXWbqm4dKVPXzdkE/GDAcjtE5iXxGR+MYxYr0nxJAGKEJLnmkKLTn44D3uvvcOG6WhqCqkyTHVGJ0VeJGMQ7KioO1q5oerxNjJSvQoGTk72ySzcBdpXECXGYZkvp1lGe1yga1XNPMTgkxZ2OlsgW0btAh8+rNfRqgi8cbtKcJ3SD1AZBkxeIK1SKUwAlaLGfPZHCEVq8aipT6Tb3jS5nxAG9NTX5M0gvOeTIOIKeuJgyu4XCGjoKs7QoxkWuFcA0UO0vBw/5i66zB5zoHPWM0OsPNjRD4CXSJYMhlKCtlxedxwZWeD4Ctu352z6CxaZixdTVU/ANkwXx0RmgZx+C4Uu2hd0FjP4WxBVijqZh8dBE4kF55B6AghQxmBR6b5BBGQQTFzklBDrgW7Vc6t8ZxSf7wRctokQqgzBgqh1zJ/0vafrQbER+Njf53TZ+Rrzaa1JEZcTxZeeH6259jLfNDDMtamqWqp+oy5rzI+MnB0sYK78Fr/Fq00a5mC9bzH43EP8ZiXU6N4vYhEVkBBpOYMzhLhDBdf/9J1feGVQrgk190zq1FK0TSWIiYv5yBkT/hJvS68gLMi4kLmLtaDm4Hpg0NmB6ecHi3JZ0sG17YYXdll86ldhDK4UFLXLZ1Y4kLBZHvvCd9d2j4RwV0RePr6LleuPsXk0mUency4c+8e09MZIiRBJucjk9H47MNqg0W6jsKY5FFqNEYoYktSkJMgcbjmEGVPGGmDGgqKrctMNjfQWYXZGpDNljz9xc+jshJ7ukBphclz8ixH6hTElUpZRww+FY1eYEyOtW0/Jhzo2oY80+xeu86zL71MUQ3SdJp3CJWaPyhQzoEQTEZbTEbbfWXSJgEsKVC+pTVzTk9mFFlFa+s0AVt3qKzAe0vTrfDOEjvL6vgIpTXGZNx59w3uvP8OzbXrDF2gyDrEYoHWqZEstSYzBtc5prMTwnJBNCUuGxJsi22WOB/xEZrGsbE1JtiW1jqiaxjkmq7t6ALIpqbrIgvbIHPFB3fu8/IvfwNZ5AkP9yqJREmNUBkxNAjX4kIgE4J7Dw9BZrguGaWQZdi2wX1MdHdRpGZYP74dvMPLgNLJFNroAXG0Bc6i802qQmG0YljmbGSCVYzoQvH0tUspKFgoXcZCWh7KSL6xQ5xOuFZGbpbvkQXJc9cN3/x6yebuFidH2/zgJ3N+8OacNw8srn1AvZrSMGJmockvIbKUNBw9ep9ZZsiqTVQ+YWQ0k9EQ7RuWziIyRWgdZVbiogWpcI1PDetBwcFixvFKMV1afumZgLYOFUs6VWO6PquWvexETE5FrraE2KJDks/1vn3iZxlCqoJCDCRB7J7N1afrQibzjDWWHSFJTsN5UCUipWatyroWkEvv6WN9+q9EuZQCQhpkS1oxAZdVmACoIVHaXgqinx5/TKJL7NeBTBN8mtyWGYkt+RGjlz4wP27xEoZklhEQUiXbvVIhrERE0wf1NZZ/McBHhIzImNGFgJrVlKMc8GRVyfFxw9b1MVFGtI84BDhHUBUQsFIlQbUYz4x4wmqFO1kyu3fMo4cnlIOcnWs7bD07QWUKtbeTegAxR8eOoZgSj+f4vV3KQfnE7xc+IcF9sLHNZ77+D1BSMJmM2b0OX/ryLyej5uWC48MDqtEW1WiDvChBRFw7g8t7FFIjHx6lC2nVEbUgTCqClujaQwCnRXKHF4nqFqQiNB0xl5x8eAfQFMMxm1n6sKIAa236AiK99jZ4oYgywwhLtGmYydoOk1cMRoqyGhKCxXUtoVkQY2Q176l70VPXqzNlxdFokqhMStK5JASllEAJSVkN0HHKu3/9p5SDkq7tEF3AKwVK4kNH26zoVjWhW1A7T+cFUufs1w0P3ngb6xOn38Y+69ckiqeU/TCQpPEZMpwSbZ3YM0hiiNQ20AY4bT8AIgMCN4aGenaEbdPQVzcYUJYVzq+Yzo5ZzFdIY8DoZJwSIkIlNUvXWVRwfZNc0TjP3/z4dXwv1mW0xsaUlefFk5tEzgcE9lwVM0a8c3S55JIIHGd7OFOS+RGrVjCfTSkzQxgOMFsbbJQGpzxL36DKAhsFu/MTZKFQTUehHrKz8wZiOmdTQlE4fu3v32D2sOWdd95lPm15epTzpV8dsX2r5KQd8ec/WvK73ztg2Wm8K2hWjuXd+yxuG7Jbf4duuCIUK9TkEvP5DGV0WmRFhw2WwaijzHNYLREmB+tpZ0uyPPngLurLzH5ywq3NJdc2awY+MEcRnGOIQDqHdJ45ju2YBt2W0ynTowNOuifzq5s20YSlECC6n8PQRZQp21zHRi9wvWF38P4CDn/+3J8xa/xHWTYx0ToRyV90enJKjGs5j9S/SWnw+uh+BmM/e+38qc4zfGsRSiJUSBZ7rFnXFwLyz7Vw1vs+rzRE9ARGSBYQXdKSj+tGqrjwkwIZUzTospzl/iHD0QSCZzwa8Ma9BzwTdbJXFJ4mQNZ1MEomQbprcdZzcvsRs4Mpy/mK7e1Ntq7tcOkXX2D7SzleCjQKebRPXM4Q1hFNxVrscJw5jh68wfbVTyfbvo/ZPhHBPYTIZGOErZd0i2OywWXqziWNcaPZ3NtF6ZKmtXQuWeo1p0e4D+9SeCitI9MKuo64tUHnClYqopaB2Dlkoanb5mxQo+uSWYIUsL29CfMj2maOi5xxdb1L/oghRJA6MSiEQpocHX1SdpQZLgRUCDTLWd/sbVnOp0TvCD7Q1CtESDxl27U0bVIdnBqN0unjF73tXBSCYjBiMByiBdRNTdPWNE3LyfGMk9mMICQnLnAyX7JYtqwWU3xIWa0TOdP5iloNQCTIqPExjWCHgESjpEQrRV0vQGpUsCi7TGxbU4LOaGzAInDSEENkw3hu/sOv4Q/uEdol9WrFbNViT2tEXKaLTFYkkVfZP/byy/Rj8335LKXi+MF9Tk/nrG8cpVSyQXQfT+2K/XsFaUjE2p7O1weOU72FDD41bivD7s4uVaHJjUaXOaf1KvVtuhV5TJ6ejz68x8p6FrOGZxffRTZzvJHcvGp4+aVLvPCZHeSnC9xqwb3bNbODmvmsY/bjI8bVKb9yOePyNyb86MPIax84bjtP19bosqKuT3BqyUhL2hNHGwS6GmGlpJQZqMg4n5AXea+0mJKPGAQ6S3MOsbMc6DHTRcF9a/ns4D7CTxFScOoDoWc5RdsxjhYp0rSyaxsW8yfDMvPZPCmD+rVkRs9fTJhEglLWWk+Cno3mzuCYi8qk6/eonr0khE5oyIWsOVM5TWcBQdNLaUBECANSJrrnGWL/s9tHg70QyeDFNSSWy2Mbr4+Dd852cM6uWQd4X56d9zlE9LgfTbMruipp5nPwSWOo6LNo0V/rQQQiCt8GtPUsD6fUBycsFysWy5bBaMCNGzsMr+9BkRO1RnQS4yUCTzyD19bVVIAoqDJ4dLRP9P/58aFPRHDv2pr9D9/D2RZbzxgXD/EiubtDRCKSLKtUiBgxROzBPvHDE2bTJcpIVJWnEeedy7SlpibA0mJbixhmOJUcYJzzED2dj5RCMPnsS9j5krpuWHQBREz+pr0hd+hBtdhLdKI0ISSuu85yyrKi04bT/Qd0XUPTrHBdk3wSYxLISqyaVIZZH8DDyiZlx845mnlDax2tdTQuEIVEZzmz0ykxeBbLFbfv7bP0GrKKUzNgbsGKikJP0nR2jLQWuszTqAIhkgHC0tfEAJlMDUgVJXhYLDyZ1ggXqbJh6m80oENNkJK2dSBlwrc7x3e+99eUJuK7FW3XsbMx5pUvv4KvH3H/7gdMj2ZIbZA6mZqFkKoVYkBqBT4tnCHA22+9S7NqekXEpOoZ7FpQ7clsGXmB+aB7R/sYQQbHSo45zq4wyodMdvZQZWRvUlEZidaS+9NjHi47Du7fxTQ1w2rIaLhB6CJN55m0DxnVD1kKj5Ser39tl1/47HUMWapINic8e+kyoQ20tePu2w85Pl7iTubcko7x9YLdMvC7r2oarwkmp6kPyL1GdUtWaoIaXaZdgZxsJ9133zIoCpRQZGWBb2tcjDjr2D99wMZkQtRDvKppmozFacaqzHi2kmQaYmyS3opvMa0mmhRgg7dEZ3EfIyxl29WZKb0ShiQ3LZJeixBImWzp1lRfAKPlR0JlBJTQZ5h8CGvrjID3rqc9epx11Ms5LkA+KmitJXiP1hCFIYrENpMfG9x/5lpQOjmOrXsAH3nfxzOuPtoopYeAyr7h2S8yZ+JjP/vjCR83gwH1dIpoGnxZIIxGadWTcDxCBoww1AczuqMlB+8/wEsYbo549pc+RTEsITcImeF7KW4pVaoMogdTJA0c16W44z1RSMpMIP0p7WqFnhRPPk8+IcF9Nj3hf/yXv81i1SBDZDAaYfIMLwREQ2Yy0JJF16Vyse7wh1OWYoVwgcYHOmPoQiS0HbIXvhcSrBEIYRAB8l5VUhA5bhu2dQH/8c+4MhixWVaoIkNpidGKXCuMlmRKkhvZm/tKtIJMpKbe9qUNTudz/sO3/ggfFGVZEIVktloREMk8OgiUSQtBNDm6mqRMuqjIqgE6z3G1pionFGVFExw+BJqF4/DRnG6+IMTANFzC1zXGCXycY7sOESPFzoSdjRHbk5Jnrl3i0f4+P379PrauGQ9yvvDKcygRuLt/wsHhor/2ApMbE6QXtL7kg/0TurZle2OALjcZjYaJSRRWaDxWaKxt+dSnnubpZ5/m67/+64hVkl64c/tVvv2tb7E73EZoDSrZqSUoKhkaRJ9UIgmR09M59x8eJG5916JkonkKmeQd/Mc0AYsiJ7rk/DQajWi7jqZtWIqSg8nXeGf7ZS6dSk6GS5hZNvItTo5OcMHhN7bYKjq2n5qAazhdzjhcrbjfKL68+jNeyn6Pb/7WVf7dd+4wn3ue3p1QZBki0+Ag8xlRF+g8onN4/vNDpA8EZbDKoKznG8dLfunZQwaf/qccV8/xP/zP/z2Lg5r56UN0dZU2NOjxLq7L6LKC1f5d/uLHf8nTL71CHE3Y2NxmWa+YTqdc3t5ERs9AnHB8OKBtTtgaeI4fHnJ//w1W9Yp7d+/jnOD5lz7H155TLLJADI52vmJ6dMC9+4dP/Czv337zrNdog8U5R9d1LBbLNL3bi8L5vnnvrEX0eulJejr04nXJASvh6v4Mj18nwSk7F2gfeeZTL/JLX/m7TOfLfl4lAnnfWOUx2fK6o/vz8FKagk5Mo+DjhUC2/pn+8YmmvOK8KSACMWSg0/M0ZKQ5h3nOtxgEIjrkxibm4T2ao2O4dhVpNLsbI07v3uPowUNmD6bEBnavbrLzwhV2Pv15XFBorRGhx/Wj7r1Qe0gqunSq3iEGA5hL6JaE0M90KI0qApVZsv/wHrd2/xawZVY2clftYbYNKtM4U+FCkpkVuiQKicpUYmEkS1nayzOC7XA+abpXJmesDE3X9nifR0tPJQSoZFFXFQVGKbSIbASLyQwuKqgqjhDcPXiEbcDkJW7ekCtJaVfI9ghZljihyLolLYOkbZ5Nia6jHT7LIJMcy4yQD7CX8uR1qHL0YAOdZb3htaf1NURJFlPmxqJmhWK6dIT5jODBB0fnIpld8F98/Xm+8vkXef+DB5zsH3Htyh7T0xneeYbDAZtXrrGzc4nxeEQ1mTA7POTf/pvfp2stMQhaq3HWMa6usfn0U0BqhCmp0HkSC3vl80OKLE/2dKuGrk2Ux6PjY6y1VEYTfeTDuzXvf/BXbO9MyKRntXRcu7rH5Z1dXn/0xoVCnQRjSUWICtoaISIWxWzR4oQkCJXs6BBIURBFGnMXH+OQXWpFHXOUbInSYMoC7yMfFs/Tsc0kDsk3M1TdUGYabTx+UKB9gOUCWWygWLAvDTIvKRczbrRv8kL5V/zX/3iX0w8OqVA0Jrnj+MygQwHKJ1Gn4DgznxAStECK5JuJkqidCU9/foDbGPPciy/y3+l/wX/8/f/Aj35yl9Nlw3LxPvXiHu76l5i7ioF0hFyxEIHT1Sl3WslI1DSLY1xX89TVK7z23tuUt77IXtmxffxjNtxdMEtWWmG2Ig8Ol7z34X12q10u3zxJ+ag7YdEe0yyeHNxf/dGPsN6RFRkClWSk+wlxeleuSA+BiNTE7hP4VIkJTS+0eyZyt9YdWhvOpNfTdyxEQOYpkFvbN5CjR8uSgEXZDES7pub0mXOvfx4UiIuBVqKNQ+oKV9eYYa9HFP055CISGyaKeM70gRTso03fY+wbtyIgcgFdBlrj2hpUwHpFabo+xqcGa5QBoTXCO7KtS5wenDDZksza99l/MGXx1iF6YLj+zGUGozF2tcAMBhAUWpeE0IKMxCiQKiBt6BUGkowBCKJS0EnQI+LqPghLDANi1gGGzZHgdPFTIi9/bFz9RAR3LxRq5ylMOaQNEesanEu8CakNUUChDEHlNK1LXs6qQGTlmQVfh0wOYFmGc67nrCeDDykjRkucMWnK1BjwijomZsjxbEHbtqhsgo6p419MtolSMa+XMJgQqzGNKlHeYa1FKcUsuuTG7h1BWIIuaIKii0mxLVqLWC2Jy+WZoUbhTiAG5ssWvCXXsFFk5HlJ21iauqHQkiGa7cmYz/3C57h+61lMeZU3u9cJIWc4rNLIeVmyOPUcHXxIWZbEEFitaiaTywTfj4GHPj0Top++vXAD6vS6FBIvkiRvNRoyGCeVzMn2FlJKimLAYDTGd5b9/bu89sZdfLuiLEpMplm1LbbzZ/glZzd12n8MvvfbhLIqCc5hbUtuTC+ClhpOST73Y/QypEbrGucrymxOq3PmObD5LFRXoZikcljnFEZSL+Zok6q1fDDE2Pu0413iwQG+a2B+yCuDH/LFZz1b1WWm7SOikv2A4npq8SNLFueNuL58Zn3aPe4sJcIYtFZ85rOfZmdnk+/88V/yn779XY6pmTc1zcmHbGzdwNu6LrGXAAAgAElEQVQWTu8x++mcsHkV0wamxYRGGsJmMuCwk2sMP/hLXH2fOhwjhxLftswXM0ayJbsygRsvsHXzGttXHuKIVEbx/DO32Lr+PD9voJa2wXiM844sNwTkBVJJkvvtIzNrnwQhxVkj+3E0ljVmnxqlPRP+QpM2+oS3x5AmVaGXrSZpC60590KGc6ZNj+ETA31qC1H0OjVJ315mBfiSGOsef5cXoPt4hoGv/50IQWsv4YRpJ3mHBCGG6NMuJBRCEoNKiUpMUK0MiugivrN0XeR02rD8/k9xfoFuMzY/f5PJ1pgsM9j5EjoPtJAV0NlEvozuTM0yhjUlU6amcIxJftooyDNCkxFV6vPFziLMFoNRxt0PH3Hl/w/J3/+vt42NCb98zTEppwzKnFxHqnLM6XTF97/3OhLF9taYdiU4nq9obWBVN0RrkUqmhpRQxADOpwatj9C5pKCoSF6ISqlep1oRokMDZZafaaNnWY6OFhMtjTLU0SCcxbiG2kVyFLn0tE6gtEISk/iViGiSEcMw9kYvMWIJSKNQJAkAIzS5EgQioirQZpTkesuk/Kg2NKPRsMf9JTZKvv/afd6+M+eFp67y4me/ynA0Yr5YYrQmy7K+KRZQSjLamKRzk75f1Ho8tR9IQZ7frH2XDEg3Zuil/KxzZ8JQTZNUKG0MLFcrmlVDtbfBYnZCbHO6ZsH/+Xu/x83dDbZ29hBKJ8KDTNOHxGRu7GIaddfGsLu3x6/93b/D/Xv3cJ2jbi2zVcud+3eJMeG6T9qKskLUii4sIcsJ4wntpc+iL38WGxVCRra1pCoTT7wymqgVi3rJW++9hcg3uVm+g29bVrNHlPE+n9Pf42hf8PA4kAeJ7aWahTE9RfBcBIpeAA7oA8i6kXfO+wuRXlYaBlXBs88+xdNPXeGf/De/yfH+Ad/+T3/Cv/q/vsv0qOW4DRTNPnm7DxEeSEl0DYVdot6/z7vv3MBXA/ZO/4KWwCNTsrjzY65sbaAmlyk2txiJDk5epZUP2Z/NuOsPuHXtGi+++DLZ7q0nfpaXr1/t+3SBoBKjKvVT08IV+wB8kfEi+hH6i43SEM9ZMSH2TdmwNoBP7xUxJmG+XoV0LXe9lkBGVggZEaEhkmiKaXgqTaw6LGs539TQlUQZMapAVhHrAzoEXC8AJmTspSniObJytg5FfOgQKOSZOlcAr4hRp4lVGcEFYtEQO0XEE2XA3j1l/vCIw6MpuYKN7TGT3DD65RfRxafwdok9eki+/RX87FWkWxJPlnRqSqiBMCV6A07igkegEbT9gqUgRLwPCDVkRUQ1K9zpKaV/n6gMhCFUFZpIffge9977WzChKrMC1zlWzYzVYccLL75AsIF21SIxCDKaJtIRUDKjLCRGZr1Dkug9SSVEhcoMSmqEVEhlIAqkSuYc3ieTirbtcDHJCTuXJE0BQhiQKUnwHVVeEmwgCkelSkof6SJkYYVXI+jF/ukNp/NqiFIKrRVFljRqtMnTxOAFG7KgLKbIyEdjtDYUecHmYMT2pUsIIRkMSoajCoTg3dsfEmJgb2eTG7vbBGkwJifGiHO2Z5lwdvxRJJZPlCnAxBCouxZnHRFF03Y422GtY7Va0TQrQgg0TYO1Fu88dVOfCad571MFJROLYnoy5ehgn5PjKa985utcvrTDYgmzpWN7NGTt0SqF7DnUPZdCJJsyZQwxSq5cv8LGxpC8GhKEom0cf/AH32J6OmO1XD3xOokxJg2eOKczW0z1DnX+Ap2DvFRkuUDFDi0ExhimRwfI3LCxtcW1vUu89e4Rr9dzbmxGcnfI9sM/5MezY557eUhhxtzpDvA+IgnnqeO6sXbB8vDcUef8s19vQqYKBMA6h1aJBVSOx1xWht/6x7/J737rj6nbQwZBMqf/nFbHjOQAo1bo9pjWLVnVms2mxXULagQ6asrQsFfl3J7OWYScW1sD5PKA5fw+p1uGkx3JzetPcf/+QyayYO/q4z9LoRPrKJLopOuzWDdH19XIWQUWE6aeFrLzAG/65CL0xtkpcyclFH3ADzHQWYuQXbpGfJ9x9h9xECbZ9wkBrqdi9npIoJN4oOgVWIUgREPrBSo6nHUINST4OUpn5yfRO5dhzBr8J8EqyYaPKMAlj4Fk3FMT2xlt0yBxhADtwkJtCN2Kh/ce8s6PDsA7NsYFzzy9S1dLDg8tD189ZPflL9N2p5RLixQ5qu1oTlfMVw43bxjmLSKvULoCE8llks1Y1XtYG4lBIdM0Hp2XnDpHJlryapvh05foThfce6vj/aOfMjIR0Xne/sN/88R7BT4hwT3Pc05PAqLYIET4yRvJT9V62Lz+XMqmfMB4EGVfwlcBF3uNZS1Tp1lovAhJMF9JMpVcj4L05GWiOvnQT7PJBFEYkzTapVQMjeinHiOZybBNi5UR6TuENARlUKHB9VBHXuQYo1KZqJNJR54ZirJC6wyjMwbFME1Vmoy8LNGFRueaYlQmFyFpUDHifaL6JkaJQ0jJC8/cQhAYjsc4VBIHC2vqWjIW+f5PXiMS6do2Zd3eM53Pe22PgPMuZd99wF6rSq6Fo2Kfna2HUiKB6PubVKU+gW8Ci9ksQV1SJw0NLWlcJC+2aZsOMUyOTzGmzCo9h3VETHxkSVe32HaJNBLv04yAHA/41W9+nVdf/RvefOvtj71WrAzoYJjGLfbV06yKAhMVja1RRlFMNggq4JTi0t4uTb0gtktuXt5iczLk/XtHuDvf43L9Kpfn77K1k/GZF3bIFcw6gSMxsmBd2KyD+FqydU0ZjGfZ5Fn215tGrNklSiXdkkFWsPIx6d8ox2/+w6/y/e+/xu0PH3F7WdF6jxE1hVsl0VgdmUbNZphj5veY2zkyyzmdHpHVlh/fecBSDbm2tYMTgXI0wtSnnNaB/ZVn3rQsplMenRzzC599/Oeo8iwZ20TQ6pxKmLLv1AS/yFVPWHwKmN6vZQsizvq+AevxvXmE7xuxyUEtJlNtRLJojBHvUjN2fW1IbYhtXx1pgNBXBH1X1rep2bgespKafPACcfQ20RdI8zyuuYtrp33lAPReD53tkkNaTHBQjAET+kZs4Mw5LbvxHG52wrJeoYVPa0wD7/3oHqt6STY0XH/+U4wmE5SUxGJINBXVKOK6yPTOIePNGeOJIfi3sBQs5U3Y3GChKxYnjmks2BheJq9O2NgOZJnk7T9Z0TkBUqGK1MfR0XLcecJpS9k0bD1/wnwpePf9GQtd0nooW8/B6f7H3iufiOAuY+Sf/rN/njLZQZ6aLyL5DxqToaTqNaDTVFuCIVKJKGQKqonWpdBGnGF/6QJJBtK2t9GLIWCtpet5721r8S5JGOxdudxn3xpjDF3XJaXE2YL5fM5qtWK1WiFl0o+vquqMTibP5Er78pR1wS7O9FCkSuYGwVrs3PWCWyB87K0BFUIrYkg2ddPTGVmWMd40SOcIMfm7Ku+IRN57/0O+8xd/DEKkaoUUeKVe09nkmeflxVJ6jaHGvl9xDlKKxAboVfKCF33jB7Ym27TRQ1nz4qWrPPPUM5xMO5wNBNGwNb6ajm8NR2cFsmuI0SWYSRmwHX45TVZiQqBVfmZavKqXnEynPW76+E0QqLyiE4r75fO05iZZe8Rw+1oq0YOnaVui7bBlTugsWZXhVWQcWkaV4vlLmtUP/jWT6Ye0QXPnfsP/9jvvoeXbeCFovMY4+PDRnCt6xMa2RciMs+AdZXLdQgAtSEOMHRGJjAXCW7wwKARtSA5inZB0q8R6UHnJ0cl9ti9Hbjz9DC8fex7cn3JwuODweEoHCFMyaFqUWxBjTdOtiO2Srq1pgiMXkS1jqU/vc6ereOmZW2xdGvP005t87sufZbRxnXD6Ogd3bj/xs5x+eJ8QFa3tiCQozkcQ9Fo2ZDjXQUzGMzImE2obIpCMv0WIuD7oCpGSZe/T2L5QHSIMQUZkUBAtXbtAqhzQONclr2LlCW4b4w5gMaNzkShzomoR0YKJSO+IOu8rQo0IGaE9QBQZ3p+ishNkcQPUMTobg7YETBLsi6E3lElzBL6zSJXjsCgfObrb8eiRYdhdwfuMqvKczBaUo00Gu7t8ae99ys0DopT89v/0/5BvqWSr544Sldknj+D56q959qbh7391h+XDI/7kD+5ibRLC8wim8xVbA5mYaoOOf/JfvYDZqPiXv/Ntmqb3bxAtSiTBYdDY4PjCUyO+8Ktf583XD/hf//0PmS6WlL216BsPZh8bVz8RwV1pxadfeoFgPai+xBOC4CNdlxTkvHdY21yACzzOp8nKuq7Tgi5E8ujsM4mus6nsch7btvjeaMB2Fi970bEmjTDfvHGTycaI4XDYLyKxlxhOkEpRFLRtS9u2VEWGEOeBEzgLqELIs8ztnC2QtkSq6AMvfWkbI0EnBpAUa9/FtHXWkWXZ2T7WgW8N9Z7O50zGkx4ikj0mKhOSINP+krmIOAv260fdL4ZSCpRaGxFLlNbnTbR+UZBKUyrD0fyUtz+4jY4qMSxcwDpPg2W0uYUgpGAQUvMtCEkIiZkDpKnVs2Ptf0dPr7u8d5lffPklXn3143FErxxNGPLADqhpGOoJm8KTZwmCi1mGyErKMufKICeElqAj81nNzvYm3f6bmPYhTjoylXxsAaJM5urWO1TUfOv//pBh+ZDnn9/g2rUtLl0bM96eIPV6PZREUyBcGt0XIlnVOUfy840Q8XTWUrvIdHqKUYL56RGbkzE7Y40Qkr3NkqeuXebkeM4b7z7g7Q/uczpbURUl7XKOigEhznVdus6yqjsGRUFsGyTQLeds7F5le3sX63OycoLIiscQ+c43oSBah5Ah6aSESK413vZexNKRTJ1Tz8F7AaxSszQaiIoYJag8GbNLgfcOqRLEgvDJiF2SqkGVvHaNtojME5oVkhXBNvjFAl1qrC5Rc02gxMcGFTO6rgERmZ04pDBkpWa0EYAZuIzDhzP8vXcYyIqsuswg28FJi9Ka4I9RwxNidDiXhM6kFGBbZGxxq8DRnSWv/qRlT9zgR9/7Ia5tadpkkiJlwStPZfzK13K2dkv+5r19lj+9z6r1uJicvwot0VHQdJYq38K7q8xPa958/yGHJ22S4BCSznk2M4GWcP2axiEIOE6Wjrr2SCEZ5hIlBPMg8E5QFBmjyYTAkvmyo+4iK6sIQSG847T+eMnmT0RwT42cQJYp3rn9LrP5AkiGz7btkj9jcMmhpocX2q7trRUDtlcrTNmpOg+aawxQyt5Bph+T9o7GdsReX1oA25e22N7e7o8nngU2IaAsc4oiI8YhABfcRc/PYY1R9ucTIiAiFwt3zkrRXp6orzAa77Fdh7OOtm3ITMb2xuaZrg2AVOoC/Jv0dVb1iqvX1pa1abSfKCkHVR/Q5UcmOlVv3LuuTpKSIBeCLX2zubca7I+vcx5coDk+xvpIVlUEL/Ae6q5FuY7heAPfJS40PQMmCkOIChkhOIu3HUL1i09PaU0LnaRrO5bzJU3zZD33CPjYMXVD7jWSwAnl9i2qUmFyhXXQ9XB4KZL3qBKevKiYHS8Jh/fwd/+YytV0MkFSqv9eOhdBJOvFJnqOZ4L53HHwaJ9hNaXcinzlK9e5dn2bwUaFyCR2HtFCY8rknyqCAZMhZZ6CgxAs6pa3bt/l+U89z+zogMVyznhYQBewztF1S4pMsXu5oBzeJEjHO+8cJGs12RJDR5llWO8QLQSpaG2yXRsZjYqBSkuUlkQ0w9Euupqg8pyieLL2iNMSR+J0R5v1FW2HyqqkJRQaTKYSTBlAmJxoC4wRdO2cqNNQTdFZqlynZEtYXNMSpMZFT3ANtgsIafjc1S02JiP27G3yHB794HcoQwNOUtkD8muabFxyfD8iigmtNcSloPGB6ckRQhoubW4QBo7xhoC4YnqygrDN7DQwunQFlY1pBXRhhjsBas+GqNHGJ110Ii4K5MpDt+SdH97mJ+8HvvP9mq9e+yJ/+Odvs1o48sIQo6XNPDfby1RfuIrolsyOGubeMu/AKYPwlqGOVLnChQYUNELQ2sjKa45WyV859nrurlEIWnZvbiKrq9S+5mThcC4x2WywEALL1lHmgzScWRYEaTmettRdpLaKpougPPXfBraMUhIfAj95/Q3+6E//iMnWhLIs8c4znc4IIdBZT9OmgkVIkWR5VaI0hQvYYOy7+TGs8eZkrHGmpNiPuYsgUpkcIzvb21y/9txZNrmelBQ9A+IirPHd736XOrT44GnbNjWTQkgDFWENAdkzSOhikAwh0PHRf8cYic6n43SOR/fu89Uvf4V/9I9+gxgjg8HgDL8UfaMuCkHrOxpn+dSnfiFR1kgBExEJvbejOBvUuEhaW8MyknBhcUrMCCAmlnPCzNMxFoUmdI7BYMB4PGFUTnCkEeu2WXBjr2KgM1RW9qbbGlkM+/mCiD19hF8tUARsCCitCCKV96AQQvLuW+/w2k9eI/on89y9jyziFn8132AiOi6NRkzyFJitb8izjEwFsiyHsKIxI6SqmHU1e5sK+5f/C+LeHydnHBXoXET05t5BBPL8ErE7wUnBKkyo2wNMDBzWDWLf8Mabb6EUaC0oK8POpSHDQvDLn91gvDFAFxNqnkJhEjQnBBvjLb74uU2sdYz3LnNpUnHnvTdYOIfvLQ4FkSAchW7ZmlieftqQiQxX73D3g479g6NkRu4tlzbG/L1f/QbP/cJTLB/u8/2/+iFoGE82mTc1y9AQg0OGgBBPvr3/xX/5RYIXzE9PmC+nbG5sYLKc/+P3v8v7R0f81pc+xbO7OwyHE6rhmLprWZzcwXctndviT3/0Lu8eLHnlxgY6yxmOxzx77TJ0DTMM949aTpaH/PAn7/JgPic2jjYs+Ytv3UvXXxxSx5pc59zcWPFrv/Y8N1/a4F//wWt0Zs607Tg+bAmioeo6rl6OaH/CrVuC33jqBk4UvPruO/z2v7rHyapBobBth3UZQUpG2vIrL+X8s3/+PNW0xp6saI5m2MYRBxWDieGpL3yGN2b3eHf6Jl8JkXnnmTtg1mE0mNrzQdnxqMoZZkPeaSzKQWsDLqyIwTEnIrMRWmQMr/06l175Ld759p9y++RN7hzV6xsuIQBSobVnzz3HMvsNBIEH0/8d0CA7YrQQFDUgF6dMVpKjox3uvuX47g8e8WC6pMMjncbGFRXmid8vfFKCu0hYr48CZarEm+6zbOuTZveqtixXCYYJISCVpFRZmnTrA1iMKbCuce81vW8tMBXPMneP7bv7udbsbG8yHJTJLKIP7GvdjRhj3xgDiPzZn/0pvkjDEetjSSbd6VzWDaT1cyHiGX0sYf7n3N/0GOichRDJtKHIDVtbG0ixVtk733pP7f48XWI8CHkGtyS4xmOk+cjv+Nnn55DImu6Wjhag69zZ+XddR9d1ZKVG+Mjm5oTbd+8wKCs8HhcsRgmu7lxiYDQyK5I2f4gIneMxqSSOSU8nMznRujSb0JNPJGmxqqqKqqyomydnIz5GPjgKfBjHbJQZW6PLFFnSzzESBiZnqAPjUZlUETuPMxIlFHK2T/3wLbqFxcokAeG8J2kaClyMlCrZKUoi0frEo44ShSJmHtPPCaRmtuLDhwuyqOgWkUt7LVeekmxd0xxPT9m0HdqYNbEQqRVt0/WNx4iODtHLMQgSXh3EgDS56JBR8OKzz6KE4dHhcf+9B371m1/j5ZdexGpLUmWUDMZjFsuWUQFKe4JboUmuW0/atDFYktzGRllSGYNUhrwYs+oO+X+Ze9NYS870vu/3vEtVne2u3be72d0ku7kMmzPkcEbSSCNZ1hItluSJJDhxgCBKFBgyECBBDDhAjARIgAQI/CWLjQBJJNmBkyixBTiB5EgyJAtRJFnLzEhDzsqd3ex9udtZq941H946t5sjNq0AQTAFEPfy9L3nnlOn3qee9//8l7Z1RN+xmB6zmM1LLrBz+G7JPGZEw2h7kz959Wuc2tsjpMyd69cZ6EzQhqraJcYjuvkU7+FrNzzLxX2UzkQCXTuk0oX6enxuwfeHZ5Cc+PxX7nC3XTL1kdXKkE3LTkwcLT3KT/FiyfI0Vi8JLnCwOGaZV+CGxKzwuSNnweTMKhu6t+7z4PYdrNUMN8ec/eQFzOYIqcG1mtat8POjcjMksGqXGFWRfCQYxZ3Y8Uum466uaNvEwiWIqU+7Kp5OdEuGdcVo9xJ+eJ4bx5GD+Ypl11sHABmPkQqlW9rO8+pXXsd3S1wsTL4MKFXIDAMjqNqwTPD5q8dc+5Ul710/QFc1VpYYoxiaMR9/8gzv/d+vPf4zfuy//P94ZEp02tKvCMDhgykpxILhpbK9zTmgsic5h3NlCLpce7irfouvhJj8CfXpAyKKVCCW3FO6lKY30TLs7e2VYJBv+p3CqRW8D1hbou7mqzkq9li6Un1El2Dq5gTysH0WK6LQulgVKFUGxGshyKNYfMy57CKM4d7N22zvnkEpQ9MMTn6u3KgK5pDyGtctrJ/8sExDVsR8cqc50RWdvK/142ktOnr4flNKONcWLL73TBdF6TxMEQV1rmU0aJgvIKpMkwXjhUkzIvtFUdwlQdjHCCSl0L5Fgkckl+AOFF0OZfqqFYLm3NMXGY63ePetq4+9TqIYri6GqNyycXrM9qTGuTmpWyDDMRhDlyKtLwVrPr1HDkOijaTbX8QwZTwwvZVtJquMpngYqaRI7gGaQqPQHDHQJYxeyCVP+dFkoxzQIqAytw4dd2aRt244RmcnzHdPc23Z8BPf+zKS58CgNAlmHUReEUSVOEjV7zZVCTsPyeBShTUaqWo+/vEXef3NN+m6jsvPXObKlSu4mMjWYpshzz9zmeODKeeeeo5hrdEuoVRN02zzxFMvPPZc/jf/wz9l1foi3lOOLoIyFYgnLVquXTtg9mBBDKmEpCQHoom+w05GLLzwQz/yo/zCF75A5+8SMNx/cMTe0DHtaprmGjunNwubTaRQm1dLYqa4S6rCdrNBMVsEotSAcGfRcueoYZEDygTEL2mN5p0DhYpHbMwbhAUxKxY+s3CaLg7JUYgS6HIH0TJRkbZTHBwf88xLT2InI8xoQB7UdNmjUbg8YuYz87AEWZJNi2NBWM5R4jFNxe9Mhfm1BfXv3Sz5uN6XXVlKiCTGVYWdFNrr73/ht3jv7uu88dUv49ySygpQPKVO72xx5tQO79+6wztX3+IXfvEuxMD25oRKCYbMcDhgsrXF5b0JKcGd/WMm4xFV3fDpj+8gKTOoItY0ZK05vbPDP/hWL+5KhJs3b/D7v/vb7B8tiKIIIRBc6QyN0pAdjVWMNof9INAwrBu0VlRVXTBlbcjoE9jB9sPB3HPa17xdEaHru/i6snzXd3wGbc1JAZRHCnBRuBWB1Hy54C997l/m7NkLKK1PYI2UM6m3QSgybPpdwiMU2/75PiyyDClcjOP5nNX8D7hw8SmUNlRVzUPXO3ppfvl+OpsVAQgPlYQfUO5/E7pxIlz6pmMdelH4yYmqDxunHwZrox8ae2UIIbG7s0O3WOK7SDYZbRWbqoLpomRHDoa4owco5zCjEXI0I82muM5hLjyBGtQMu0DcPyA0A9TGFgxqXDri3fev8Rcec50sQ8XR0X2qScVSVbx1bx9lNPVkD5JnsJqyNSqdd4yWeVWT3vtTBmqH9PX/DRXmKN3fI/viLrm3H1GF/aIogzJr08PzdvJ5PSJjz7FAe1KgNrym85H3Z7ewacH9L7zJRl3zmU8/x9AqMnE9YS87hWQJSRF76C7GRERI0pBwRMBnw9b2Dv/6v/kzD1lY2vQh1orR9hk2d85y4/ZNjDEcTudMfaDTsHtuh+e+/bsecybhi19/v/Cs244YbT+UX7A5aRk3FXengcNlJPkAKVBJYul7RfbCcXe64L1f/scsjGK2WOGCJjjFLeuwynFxr+bW7SNSqhAvHOeWw3aBc4WvbnSNpmJUCfvzJZ//6lWOui2mhzPcfFF47UphtWKWIg0Vz18+S201/+V/+yrd3PPW+yW3d6AVxsJQGzZGmifP7PLZjz/P1ga0Hbz2Dc20DXiBP3j1Vd5+54iQM7qpCSlx8czzqOmCH7hyiWuDd/nU80+zsbvFW7fuEZLF/pM5th7Q/Pj3EueeqqnY2RgyqBS1FkiGpBvEDjBG8dJ3fgzz2WdJMRXOvyRqK9SmwUUQM8AoyL5D3DHZtZgQSJJJWtM2A5b98EgEYnTkPCqpaKspIQY6H1k9OHrs5wvfIsU958z+/ftITowbw9aZC+Wk5Exli02tNRqj1zCELsNBpKch9mNLAW2rsoYoMX2PskMQThguB7MVi+Wc2mjGmxNiLuSpD3ttWmtCjDgf2dzZKS52uXR8ubdL1cqe4Nzrxz4o2X70ST/4vyG6wvRIYG3FoGkgczJMhYc3mnWHvVouS4iHekgdPOEOf9Pzlz+Z+eZ7Sl4PTlPh6BR3vYevd32uQs9fXq1WOOfISfAukHxkuDlmMBmU2L8kZKWhGE/iZgsIgXGOSGUKLa2yJN8hyxZiRpoSdiCzFfffeo/Vncf7odw7OGRqzjHXW8znLVsqsV3X1G7O9kbD6e0Jd4+mHB3M2Rk3XNoNXNc1g/0bqDwtysNcYCCyPAILrU2KwUZBElhdLqj1xCLxQZvbh/bGQqKnR4qQcyw8fzvgzXfe55lnztLUgw80DmXg3vu3SCYlVZStscxLUhKKrtoQUgZV4MKUE5HijpliJipL1VTs7p1hevMm44ktWp8Y2b9zi/fevsoP/qsffi5DaEkIznWkBNZUKJ3xbUcymqPZrFhYBw8xUkkiiWLQGHz0eJ9YPriPyUIUqDVYG6kazfZ4yHOXz/PVt68hIgyAJIGtht4JUWjqiu2J5cLeDoPc8faX73LrrWM+e+U0Pmt8imQSkjSDofDSM+fY2dpk/2DO9bsLRjZx5fJZLl3OTMZDDIaBTWwPMyMj1MrRtY6UNKYZlh2ZwKXz5xgMTnEwX/H0x54l+VcnIMsAACAASURBVBUjnXj6yVM8eW7M9b0h26Map2u+78XLzPUIrSpC9GykFatQgkyiK3x4iDgLWXe42JFEEOVxzrNatGSKpUEILcPU4FOm88XqQHJk5n3Jloye0Af7HLnAfLkqs5gYUZJxKUCKVKlQU11IhEfW/ocd3xLFHYTDgwcc3L/HYtny8qc/Q4oRrRTDUVM44mJpmmGh7SmFcx1137kXumJ5niQfFE+si2GMhW0TYuTo8Jj7h1PmiznPPPUkw2ZQFvc3de5raAYghEDnHMZWGKtLcHsvtEg5o/Kf8aXjm6vsyWD2m+q9yqrnlyu2Nrextgzk9NpIaM34yZzIwtuuKzz5Dwkd/Wbb3Effx6OH1pqcBfTD4XG5P+RH3oj0ftsKa4v6V4mi9Z4QHM2gwgyq8rNal1CTGPjSF7+APzxmdPoUn37hOZJL6GFd1IqLJU0CU9ckU4LQj998n5tffgM7fTxb5sadWyS/TbAtKWbUaEI2msHQMJxMSAjD4YiD+QE37854aWdEQKNmr5MFilZHTq6Vcu4LzL0u7kYJSlHk8j33v3yS6iFLKq9/X1EYykJe+5okT4ieHDLLNrBYdr2XSr8T6m+YgkLy+hrt4TYUSVTh8ChdUqxEo7U6me2klEgikASMZuE8ta343d/6p7zybS9j8/dAF3jv66/za7/2a48t7uJnKBS1StgmsDXSSIzU1qBItIvjHt6TAl9WltF4yO7WBkZB5yOdDwyfOI1YjVEanQPjrQE725voGFCVYRVhKANScoQYEWVQyrC5PWF7otkYWOoQyUFo7ICmySgttF2iSx3OaSoL46oiRdje2+TiWU2WJT4K2WRqUxE6SEEY2EjqispZq4phXoBfsCFCzI7L2xVKV6ACO4OGelxjkmOxus/502d4L7Ts789YpYaVcdAZXBZUSjhV3Ea1EqLviltmCCy6wCpERBtydBgSkgLdyvXwbAbKnC/0c7S12ndJRMVMYxVNZehCxPmi8lYCOUU0AsqQYqLrXVO9D6zyR/gw8S1T3DPvvfM2t96/yk/8xF/mp378xxEFs/mM3/pnv8lgMMDlBYerA9ZxLV3XkUVQqu+EUuxVrT1rJSacd6RUFkTXtuX/Y+L69eslzDrDt790hY3xEJUfFvQPvLJcxCtaG6bzBaYZkMO6O9cnyz+KP1n4mYd4PY8U1XUK/MPiLj1tMBKicP/ePhcvPokoCM4/zInsn0+kMGZ8Vwad29s72MqeNOvfnKaz/l5ETtJzPvDeUup9UNbFX8o8Yv2a+68xJZKPHDw4YLVcslyuOGJJDC0ja9HDIbK9AZNh4fGTefOdt2mXcwbXb/Pyi1fIW1uFktkqGjtE2hV5o0bGY5h5/uD//E3SMmLc49nZnZvRSEI3mnOnDJurW6T3r7LdzFAHe7jJWdLoAk+e3qHSmTfuRp4/+jxb8depKgNrMzmBPs8NlSCpEoQgAl3WaDLDQf9h9tBX7j+rh6dQCL1/SupvvDElqtiibSJpSGTa1rFqO7RkjNEnsJzSUpxPJZElgVrvNMtwPPV02uI/spbsF5aYU55aNC4rlNb4o2Nm0wd0qaUzghpPMKMhB4vHD1T/g3/jLxUdRWOpk8YEz1BHRpuapQetDcskBBRRipXHQDLBd1Q5MLSWlCM+LfFiiK0nLzJsbNMRsNlw5tQTTOcP2Og8XmpChkVQuCTMXMvNOyveb1v2BjUH+/sYDSksCcFT6y0O/ZRVyFjtWHRCiBkjmZpY/LjQdKmjrkrYjUqx+OSTGBtBxczSJYyRHh4pczdPpjGGL1/9owLfNgbyBp/73E/x+rvvMd2/QYiKzgvZKjrvUVKKpcOUhiBEnE8kNC26+CeJoBEqUXiViVkTohAi1PUAQ8T0MyalCh05qkIOCSIEhGqgObWpyKll0NQIiuAh5w7RhoUrytqRsTxdZ3719f3HfsbfEsU9Z4h+hdWaT7zyKUCIPmBF8+71a9hRQwwR7/xJB9OFYtupetjlhPL3iABHG31StHLKbIzGtF3HufNP4DtHpS1n9s6R0ShbeKbKaLIULnbOoHp3xRAD0/mCzWZA1qn3+u8d8JRGfUCq3nPRUzoREK3T6JVaS7mLmCclqHODUoWZsbu1g4oJHzKide9/A1pnyIIPBZ9tVwFVBwJdweUfwvGYfpKasxBVsQUQFEr1SUcZRCwqdljRSFXR+g6rDbUtfHpy4eKrPjJvRsdoPGFYDegEJA3pnEdiwAZNq4ZYijmUiolRVSOrJVIl1PY22SdWrWO0NSbfv4vTMDAWDo5Ii5bRaJvb83ssPyI5LMcd6gtXGKYlZ2/+NjtNZLq8Q+o0Oh0yTDeZ3f4GPPEK1e55htd/n2ftl2kMzK3HhOIBXsKZ17BMItuKGCNGCqykJaJVsUwglvuArPM5c89KQrD9Y7EolkgCy2RZVSM2rbAKwuJoxVvcpak046Zi2FiMKe5/64Sq4kqbyNGXZKwc0FQ9hFQu7arSZU4QSmFXGiya0HlE4JlPfjebe1dALCoZnnz+Cj/37//HwL/1oedSAxvG0CRYre7QZuiU5uheIHlH6zJdCMScCCETo6ELJWBl5TqEgEqBVesJwWNUUWKGXjvStl1PdCjDRtszpkSXax6ExpZm5/rKUVnT73BSOfcypbJFBdtJphFFbS1diAQxZAm4tmViDRICbfQslUJyIjphIobLT07Y2NQMBxXaakxlMLWmtj25wVt+5w9u88a9KSEppjJmKmNuLjaIGGyMOInYPCY7wwPjGPgh1iSaSiOmSPDEVDRRYUUwFYSkGGvF2Y0Vy1XkcJE5tzeh9b4oULMli2ZghBw7Qijrd2gjtSqv0fuI6ddCThUhWbrQMl8sIBdzvNZ8dPn+lijuCCXd59Qpzp45VwQ7OXH3/v3CUfcerQyj8aiXIAvKGqpqgNG6UM76Ir9WdCqlijG+9AIepTHacH9/n6+/9Sa2Lk6OTdP0iyyhU6G/iTppm/ohppBiIEdHZcv2O510vT080/NVypH7oV1ZvMVYqXTHwZcCq5RGKFS4QzwL57i3POaFzY+RjMKtPJa6DNwkE0m96jMUbE8F0IFG24LzPgIpiTYntxlDmerH9bBXStRdToKXGlR5TXUzwHtHovh/SErEHvJRKSIx0E4PicsZdXYM8py8OqDSEaVasjmGLiLG4L3HZIdfzVCxYnFwB101iBIWx3O0bwnR0x4GUtsRly2dnlNtlu7sccfTZyumqzdRacX+7a8yTStWs2Pu3HvAxtYm584/wemnXmZ562tMNrd46YU3+IHLI0azjzFzB3SLhHeB+WKFcxHnA4sjx9JlQgDlwfSZ0kOji6GXFpRVvW6hv4f2u7wSl77eNgkqCzqqE3qusYamaTh7egfIWF3YXO/fuF1cAUWwpgIpwrEyEC/fr80otSoFr8xMEroqxnHktaW1RhvLK9/5WSaTCZqErQYMtvb4+KWPP/Zcfv7zf4jzHZNRRepcKZqxhG8oMquUCptIl7lCTgqTyuQhS/HJUTmx6BIpRiotxWdJqzJ/6ROERASVIsclF77voAWrDd5Jv6v2WB8hg1PSCxLpYy4Tq6xRPkJaFANHIPQRji46UOV5dZdRxiJiqC96XvnRS7z89AWqumQV945mSISsYXp3wVfeXLE10xyjiDbxzDPPkOYt6IxOFa3SSBSCS6y6ltEAqqqE+MS+x8tJoSJYIPYNVBRDG4UgpoibfMRgUTkhoeh1cjaE9ewnl5tgVmBypNblZ1PyCILzCR89G8MGqyuMVSd0zMcd3xrFnYx3gR/54R9la/c0QsYFz1vXrvKTP/1XyJKRXFzjyuIqnbtkOfF2WRc3rdTDEtvTVZRS5FiGENO5xzRbJB+wStjY3ERJ6s3yqzJskzKcfDhMgy4ljhZLdnIqVsI99r22DFg77AElET4nEIUSgzJgcialgDY1AmXAmyGFyIAh8+WCeLRgbzBC+WLir5pAXOVeiZvxlO3h8ugQ2hbjxkz97ARPXztBSnTk3p9aUiDHiI6Zzq36obMhU/x4dI4sj/fJqYQwSIglMDg4VrNjUvConDk4OiJ5z4W24+1f//ssVWYvJ7SJXH/r6/zq3au0x0tiyqxaV4amfczaz//d/5o2+gJF+I7gy+fVOtcnj0cWriM6cCHzc4+5So7vvUY9e517B/vsbu+w6lqm0ynz/bscH+xz5+Z1nnz/HT75ynewe2+fv/aTM/RQ0WXL+XAGpXuIS9GHOCSihyQVIQm/8Ut/yn4QhkZxercMNQ+nrv90e7vbHp4plNQC24S+xc65bMlj01BVDXWtGI0q9raGiNbM5nOuXruDaYYs5nMW8wX3718j54QSw3gwovUJVEXIiUgvzJOMrRu2dnc5mh4T5h2Si80zVuOdwuma+zdv8dIrTxHRRDvhj//wVS5f+vBz+cbNW4RUusycXV94y2C3FGRF8A+Ds42SMgtIGa0EY8pNLGVBS8IKWBVKWhkJLQl0KusxRkaxZCpsNBYFRZGtEkZg63SFATQlUUn1o+soQkJoUsbHosJVWrFoA0ogCLQusttodiuNi4m6URytEsOdmrPRMhxZTKUfQqY5IBLIqkZrg45LXjynuT2bc/T2TT7zwid5//XX8L6mrkCnji61RBs5L9tsjoCsEZWASOcymzajc2JgFQuXidmySiuS8zSmotpQNKlFpAKJnN/25Bg57mDWVgy0UOuEF8/SQUdRoH/Hpy/w2e//OL/wi7/Bwb2OWmdGRuPjnI5I7l1oHnd8axT3DHXdcOnpSyUnM8NiuaL1noRglO3x4Vg6Fa0xOWNkzSenB0PXw7JHjh5rw/bskOjJviWHiGkGJUpN6G1uHw7bCs8bIBFyJqaIURTlmqlOuPPrQO2UHhqGadVDTcET/bKM4XImuQ4XIylmiKFw8nImsiIeHjKMd9m//hrHSljMF+h+YKq0JoZAyGWC7tqOdjbj0N3C+jUeX3D1EGNxrgyeFAJtu8I7R4PQrhaEHEEUWRlcNYHgqUjsHxyzP5sTV4siromBdrkgxcRs6Zi3vqhaE0QXWHYdlTHotTe31lTal12D0lS2BDBIjpi6RhmLrS0oGI8mGK0ZWUtlLMYYWhe5c2sf7R7vlyE5kWPL6dOnOH32aeq65s7t91lMD3EoVj5B6Fjs3+DsFTBuhFI1g/ESCf2OjFLUhQRJoRtQWTBK0dSagdVMmsj3/eQnSSHxz37tNZLLhJAppKH1BSZILsNujfQSBIXWckLLVVJcNgFy9Azqms3NCdtbG9RNTdzeYjIacXx8zGw64/6D+8zbQIgQs6Z1nlUXGY2GNKMJ2zu7tN7hFhGkGHZppVHNgJgrbDUkZAVKkULL/OD6Y8/lSEFTlWLXGINeD5F7j52hZIw0ZaYFWKuIOZBjKplEUpS9VgtWNEbkhHnUoKhETpqsEBRJF1mD1SVswztD7KmlAyt95oJwX8r3kjMPFp4ulB3NkU/4TnAZRuMxdYworejiArrYC/ky82VkscrESSSygJgh9rOulJGUAY+YirRKBBdISqiVcLj/gE99+tsIknEJVBf51Ce3OO4WXLt9jFo6MnN8JxgVUCqiXMbWGsmR7BPt0qHUsOzSnRB1SyKiU2QeNVEcmyRSF7h7HIlqQjKZpCOrnDheCU6E3aHCVh0XzxmQRHBL6kqTUuniRYMxHx0o/y1R3HPObG5usHV6D2Im68T+/oNilatt4asjmEd2IY225LgurI+ktzy6U+nXYUiR7B3BecJqSqUjkmBsNeRAEbVGJLtiWpYzEUoSfUyE5FkeHWH8lP1rbxJcKIMZH4gxnAzZTmwCRJXcQyKxXZUOKHmi6woDAmE5m+LaFeRMlZZ454irJa8evofzHowmG3XCtCi2BqU796sOHyKBjFsW58oUE8t2VcRNsSuxaRR6nY+BsIqE7HsEQUBbojJsjYfsbW9x7eY9VD0oISSqFKeqPoXShs1tGCXhcDojJuH8qXNEsdy7c40zW8KnX/44Tz35JFv1BGUs9WSD4WCIUqC6FjEWOxhRDQbkpu69tnvTsP58f/Gf/xFX37nF0bQF/smHXieXnzjF9lOnaZPhzJMfQ4jUdeDe21/h/rRFJSH5lun9G5xmSJxYFEPEK3JVDLJgzVYqLCHQhb520iMkGgujQYcaj/juv/AsX/3CVY6m4aRDX19ckqRg4jkTFZBVny3QO4Gulc393KeqNKe2N9EkCC2SMlvjARvDmnzmNLceHHHzzgHzlSf4lnv3DmhXjp3tLVoXyme8mhOzUPVDPNGCtpahatjZfAo72kIRUd2cU5PHsyn+tR/+i7jjeywOHlD5BTEEVE6gy3tSWZXwCDIuZUwFirLm6l5dHCmQo5YTCj8xZzzgcyamoi/RVsr1mzJR+hB0w4luY9VDUjlDXJaZlFFCaqHtEpUxNLaYmokLTEaKppYCnUbFrIssVx2nNgzETOcyk6RQrkQ9YlQPs66ZKw0hJXzsOGozb99pSXnIKE35hIPjoyEhdCQV+amfvsRX37vBu796HZOX3J11uJXQ6EhTJSQqfLKk4BECs1VmMGyKMC6WAXrCM6grul5k0WiHl6KxyFqhDGQiEY2qGiQplHacfWoHvaGYLyKjSjGqNVUP9XpVblofdfwLi7uIXKRkdZ2lbKJ/Puf8d0RkB/hHwNPAVeCv5pwPpWAEfwf4cWAJ/GzO+U8/6m/4bskzT53j+MFdjg7vkhPcuXWDrcpw//U/pO1WSAz4dkVhdoD3DumK+b+LGWNrsijm0yNsVbFqS2cZvSPFTFUJs/kxSoTdpHhi7zzHh3f4+//538AFX3JaTUmJKV18cZ4kg0uJ6aKkxfvWlaKZM0jxi0g5Y3rviOJbbgGFllwEC7YmuiWfenKLd44TWVdMxmNElQQmO9jg/mzKdKr46e/6QbKqSClxancHbQp8UlW2RNAp4d0bN1mljG0GDMcTRPoA774wp0ecFwv1DnJWPGqnm3Iimsj9L79WCvjl58quicKioWcZSS7v9f7+Xd54/U28y5w9/Ryrw33a+Ic8++wOr3z623n+mWfw3RIxBl3XiLZE54mrOd47bDNA2aYMintcPUQP693JxhkuffIUazbUhx1PvvACz23sMTua8sLLLzDe2WUx/QQ3vvYas/m7GCKT4RmeurDJ5RdGVLrwG8rwOpZFXt7hw6/ZoElkFTEIUQlGCboaImK4+MITXHjhDG6+4Hd//S2OHnRI1rgcQa0FSSCxFDKTDNoMyDIgKw8UozqtNDn2AE/OJb83JA4XC46PZzw4OGRxMMNFIVJocWfOnmJ7Y4P9gyMeHBzy1dffwoeI1YrxaMSZ09s0dTG1cymgg2Y+nYJK7G40TL77Bx57Lj//1jvkekS9cYkndk+zORwwVIEwP+Rw5Znu3+Pw9vtUPd9/uRLGxjNWipEufk2SytwhUfD5uSsYfUJx0PlCGRYY64wEhfeBYd0b00lhVbVKUQfL3ovPsWGX/N4f3yRUFUSh0R00mSOfUDKgrhUvXRnwXd97mZu3Dvn85++jhhaC4EzN292MygxRg8CLLzzB6SuXej5b7kVqPe+1vxRa72mTwZsaFyzhuEV7obKRYSUMlGY0hheuXObbvxJ49WvvcHo0wTaZJi+ISrNKFX4doVkN2BhpWq+Yu0iqhRxAR0VtI5uNZqdu+Nl/73sZJfgff/kLfPktTyMdAwIxGqSCKkR2h5Yze5tEpTFtYnPUUFNmd42x2BiZfzQq8+fq3APwN3POfyoiE+BPROS3gJ8Ffjvn/LdF5G8Bfwv4D4EfA57r//tO4L/rvz72WEwPuffen3Jw4xsoq5gvVrRdKeRHx0dlGp8guHCSzO5DQEImpEJ/FG1IGU5PKja2t7hxdx8X+0KlGmxV4XyHD754T9S32BgPObd3ikZrlK1o6hG293IfDgZlOKsVLZmrN2+wtbNbkpWMBRFMVffF11BJb6GrDcaW+LusIknb4kmymvH+q7/DD3z/D1CNN7HWFrOwnuP82ldepzk44NPf/X34kFmuOnZ3t0+oc4VaGckimKMFE6UZTCaI6bH7nImpx2lDGaICRXSUC4PmBLLKfXFXmdV8zu6p0+gQKEu1nxv0lBkBApmtpuKpc6cKd7huSMMdKl7k6fMThpMx8+US13VgPNK5wtQJAen5wEsfe1YvSCza3pDK1hqEM+d20eqhGvfDjs/95X+Fzm6R4hzVWJQeU515gksXn+Ldq9dIKTOphOcvBC7sPXpp5xOcnEe+FBhvTXMs3TW90ndtxdYr07DjAS9/21O89+Y9bl0/RnshxH6IbxRRlfMvmZNksLVZ1JpaGlMqnucY5osls9mc96/fJIRIXQ84++wpbt3d5/7hlJQKZW44GFCfrXrzPM982dLNSoj5tfduYqyhbhrO7u2gJ6WzTt4hObHOeP2wY8Mfo9OKNJ1y/Wifa6ZBTM3e0x9D751j9/nM8O41uqN7rPbv0ty/Q1aZkCMdikXKtElTx8DSJzyatnfqcSmQTE1OBheKGvVMEwk4gjbElIlRI1lBveDsucv80E//LFs7V/nNL/0f7AfFvOvtuLNmmAWrhmw3iXpvxJWXzzHY2OC111tmuWa5DGQRfBiiZIjRAakqdGX7G3qBiE6EZoReM7AmPoC2GdsI2QSUAS2KWhuMHnDv1iFvvXUdSbZ4zsQIRnAh0sVATpY49xwdzwm2Ymtvswx4lQFTBuedjyxNYruqGZ3dRtsB1hg0/oToZpTCi7DRJHYmQqMdKnmiz6QqkygsugjF0TY9fq3An6O455xvA7f772ci8g3gPPCTwPf3P/YPgN+hFPefBP6nXCYxfyQiWyJyrn+eDz3mixVf/MLraF1RDWoOjo/KCcxr//MyODJNgzEVxlYMNoZoa6myMBxPsHWDsRX1/DbVaJNXnvs27GQbLZqmqhEyD/b3OTg8JMZEPdzmY88/wysvXUHrnlVjmhMTLg1oY0CEo3ZJ/dWvcP6pp7BVU1AFpAwwKTCMUb6nD64LdiaicU6wJKrxgKgMo+09qvEWsLYgThwdTln5TDUYUw9HpFWHCgld2TKk7T9DwRQfmj5sOsdI8q4fFj3EpR7djOd+krQu7o9u5GKC82fPMp5s0jpXbmY5FkhByUkgQxJhMB5ybvs5ck64LqPziOcvncIaj1u1PJgeF5aEVmQlRF84pLanjOIjoZ+LxBCKpUFPIUQrtNWIGITHbzVFDckIw+GYmFtCNyWsHPPl/gll8NSm5rOvnGN7tLa77VfvN2kOHnnWR+ChfpioC6ui+MCsBV6Kvcs7NBsN2lzn2rszrC3CEhf6WD6tUKlcs8VLyLK2jyj0V0Fry/FswbXrt1gtFmxNJmxtbTMaj8niOZjNyfsRsikwjwjaCCKGQW3Y3tyE08JyuWL/+IjZcsnh0TFH00OeeeICL790Ad+u0Lm37XjMsd9aUoTQOeKiw2dDEsVsviAOJ1R1w1PPXWH3qRc5Ywyz2+9T778NiwXz1ZJb+1P2W8Up09KpmjZWuBAYDy2rlScmResScx+oRqfZHSzxuiVng08RnxWVWCqTsJu77Dz5IuPRATFrXFC4qGlTTcwaI+DDmG0xzB4sufHFt7mzgoXL3G8VwamSYkbFqk0MGqgGFcaWOZhSfWHPZUGIKalPRRFcoLYkEWUEVOrtuhVKp7LmQiSlQHDFjM7qjLWGVZdxPjKqa2bB4XwgIHjfltrRI4DWClVle/viyGv//dd57uIFuvseRXmNMWeyFPh1pRMbowE7wwESYW6g6aHimDKxkPs+cq3A/0vMXUSeBj4F/DFwZl2wc863RWSv/7HzwKOTnBv9Yx8o7iLy14G/DrC7u8N/8Yv/kKoeEyXxpa99lclkQjOo+0FV4ZOuAy5yzihj6EJXBp+93D9n+JN/9PNc/NjHuPzyZ2jVEC2w7Dq6LBy+/gZRBvjk2dt6gief/yS7Z88CQgieypp+E7dmwYCkyOGdGwzqmsloSIoQlSB5bcGbS6IRw/7aiWVYlzNCROnSSc+Ppjxx9im2TULHBSF00IsqRoPI4IWLaK2Y3nmXVRdISnF/db/Qwig7kNi/93MbdQ+1LNGqNxUTMMb0UMrDFv0kkUr0I8LTMjAmQdiZkGNm02gyRT6d1zbGKfXxaKEM2mLBpCstqFyGvCtXYCzvHCoFfAx0wbOaTYmd7733XY/JlgJrTV1opEoQranrCluVG6zRlssvfvj1d+POVYzKHLlE2wVct8/NN/6Ye7evcvlMze7mJv/pf/IJLp4bUqELvnoSglzUvuvzUlbeuvNRfQCF9MZvpZiTVMHlJSNiQCLj0w2f/qEXOP32PvduT1kerTi4tUBpg0uZBkO0pugTUuDB4THNsCmZA95x7+5d3GrOlY9dpLIVyhStxmK5ZLnMBO8w1pTrqN9Q2D6PNKeIimXWUg0yl0+dYTIeMZ3NWDkYxkxsFyhVMfeZz3/pj/irz374ubztLpagEtNiQ2SZIEhGzR1usc/37Vku3Os4vNpxZ9GxiDBrdtgcbrP33BWufGpMiEK+cYODNrE/bVnu32FIYE6NVp7dcWSc4W6aEqMieQGt0UqRbCDFOXVXMxpFpAHNGAZjcgpIytShBPWsUGjlWdFx6qktLn3HaQ6/cEh0UyRaJFeEkBjUK6IyhLhkoxa08j1BY51/KyXLsh/fLhceoxTDxnC4CKhG6BYrTFZY0Si1JCrh2efO8rnPvcL/8r9+kYrMZKBIIRGjpQsOiOxXmnuN4fxkB2sTRz6go2ZUeUa1xybFwNbIKvJ7//xV/uf5l9CTCrvRUPK4M14yPgXEC+2tCv8VTXd9TrcsJnvarJsOQHJhVn3E8ecu7iIyBv4x8DdyztMPk7Ovf/RDHvsz+4ec888DPw/w9NNPZd07ESpRPUZe9910yQIVMQ8LVSo5owqLyqpQDJXGB8e8bQko2piJRuG6Fq2gPZrTzpfEzpFVXq8CTAAAIABJREFURGthMChMGVJAS354Z6ecQ6H0fYtVS9U0xJBQqXCVcyp0L8mZGDzZHZY3nxNGFTM0BEIsjIbKCk9cuoxKEVyHVfHkbzVWGJ7ZJYuibVeF8YDgUxnWnqhwRaMrA6Gk86SYMNqQpAi7tDKQi0pvfdJz/28ql8K8VrzmUDrrZHLxtIiQdSAqe+LISSxCm9RvY402hOj7822KHUIIBF8ey25F6zpa37E4PKRbtsXyIa0NlIquIIRMzImqqlC9TqEyBQIz9vGX5GKxj1ENQmR/eoAVaAan+Zv/zvdQ2WPOjiY8e2GM6EFJmMj+g5feB67Z9Wa4h15O2Fa9AZs8AsuUD5KsFGIMOUaefHGPi5fPMH2w4Iu/+Q1mc4+gMVoeRibqAsXcuPOAS0+epzZCt1pw/uxpuhCKZ1ES7HCACZBmRyXLdzzBtQuUzhhrehl6IOV0InxDKayxTIZDsvdkCle8bZeELKx8ZjpdPPZcKtVilUYnoYstgsYqQ43CJqGSIbui2awU0dSslKHzh8xmD3jr1eu01YDJ5g4Xd89z9sIptusxd27eJs2mDO/v06zuUquWRZcZRl2gSZ3QohAiQSJWC8NKFY0BEwgVdfTUOeFEFS670tiYGYowVBqyho1TmK2EqiqEliSKNmU2RYhK4T0s9pfcf+M2k82G8d42qq6LpUNK0JsLOl+cSrVKWK0KDr+aMqiFqueQSzZI8gTfoTIYtQ5Q1wSf8D4zbAI/8y89T2MTv/27N5h3AZ8UOWSwCSsZkzOjKuONMGqhOw7kiWWz77hS37lLhlQlumPH13/vLaoq4bLBhEQQyCRir1dJ/1907iJiKYX9l3LO/3v/8N013CIi54B1WusN4OIjv34BuPVRz6+VRpuKFGJZ4KJRSdBJ93xtIelUpBy9cKhfi/hYorwgEr1j69RZxhvbZd26FpMDVkEVl5zbbNistlFace7sNpVyrBazPktUTrDrDwYDByaNZUyF6u1wG8DnSGpntIspla3LRXsinupzS0UhpmCuWltEEnKSzVQ+yRNLgFQKqE+F1yy9Z/2avZO1QiMF+ugxYa0USfWqwBSJroQUZ/pOj34LlzO6F6isCb/lBhmJlE4/iy94YpoX9k1IBbun3CCCLxxmhF55q4ghklPAdY4QHMl1LJdLnHMcHxyyWi5BCnwlFL3BoGlYuRZrLNpYtLUoVcQ8SqkiOHnM4dtANC2V1UTvGI1HhMkOL33iAqe27jCUKRIVgutbjHKTPDnfJ3X+EexdEjkJWWcEjcoZqzLZ9DtFEuRSqIVC40sIEiLKKjb3Rnz8s5e4c+OQu7eOcFONz55VBUMxbG8MOHP6NOPRAEVmb+80b1amsKfIZKVRKFQu6uWqthg03hdDL51Bq6KWSbqX2ReuIKDwEYaTTVp/gI6wXK5K9mhaMagebyylq4yNgYCmrmp80LiYixW01iRdzPZcK/ikaKxis56wXUU2YuJLd2e8e7TE798kmBEynLBz7gLNxV12Ll+CxRF+vk++f4fx/X1yv8PcGEMXE5GGJgcqE0nVgKBrOtnE6ISKmQqNl363IhTPG4mIckhMaL9CSyakskaMSqx8aVCsHiExsX/3iOP3A83kAcPNIaONEYMzO1BVaF0V+rLqIGu0aMRWjLdPIRIKnTeDqstAM7R9U6npdQlCImGUZtAYPvltT3L+/C6vvXHA8bszKgWTkWNsAjonTGWYR0VyLcsFmFiEWNI3Wz4mPLkgFFozbQ/50lsH3Ft61O6QlDIBTY6pRCNKQps/0zN/4PjzsGUE+HvAN3LO/9Uj//SrFG3z3+6//sojj/+7IvIPKYPU44/C2wHGoyHTgzskMjFEnjhlQWbkkIpalYz2mhNPgB5u8MGDwOzwEO8dw2bI9//IjxFE4VKgyhFNhKQ4f3qH871SMOeMqTQSlsyOOqwtnNuu7U6Gl8aak+I10LkMxkzpopNzQAYjjCYDsrJoVZUhZZ8ML6rImnQO/bC+hzpC4ScXrxdObiDrnVAZ+GaEAn/oHivuUsnqRB5CU+SH4SO5745zCkQfyo1Fa7LSxJxLAU+FFyv931Q8Gh5eOuvQwzExxofzOHl4zsmFwpZzbwcRA53r8N6TfSTF8t4ODw9PqKHr3xURYohYmwjOkaIlR1vsHqqyBecjosOKHULGRc+obqiVJtUNtd2nkYaqrvHxGE1A0Xunn7Bv0gcGqevv80kiVGFSKHKRfesSwFIQrhOf4P6z7GGbXLyNzr1wjrNXzpMDvP0nHVt/8bMsty7yx3/4FapqwPbmmOQj2mia2mArjXK5v7nHwleXhLWG4ajBqUg7k4dqbFVeszYWHTXKGLwPKFFs727RDCxdN8O5JW0XSyhKSHzsuecev+j60OqsFZUy6AQ6RWIO2I0t7LDB+7I2RDTWBKw2VAONXy554uxZfviv/By/+Xf/Mx482GfqDrn55ruMhiWYGqNJuqLZ2uXjP/gjTDa2qKIQ3n+Tdv826vYdtHjIiuRrqpypBpt0VLjoQQxaQwqZQWPQKtFUmckYcvJ0XaIWeO7UkMWyo5PMqB4wdR14xaWXL/CJT+4U7UoIpGXL7MExb//2l6ELkDP358Wgb9FnMitjqYfjspasZcsakvYEElEbxqMR89kRnRZ8zsxTxClhWA+ox0OqRpNywioh+MTEQiUBDUQiA5d5Ryt+RQlbWw1PU7ElJbTFhYhYqDSMPBypGSMPV1eeZi7oKlCZmiSZyihECaOPbtz/XJ379wA/A3xFRF7tH/uPKEX9l0XkrwHvA2v/uV+n0CDfplAh/+1/0R9QSsh+VQQ2GaziRJAj686VUiQfNceqbKHRbW6N++5YFz6wJBJCZTQkT8oFU+9/EyXl5IOibsa94CgQfEtOucBDpN7ytJiNKa3R1oJocB1BBEJLzh4XuxN/kLjm0/a4fBHMFPglxR7C6KXs6/eRgjsJqw6hdPAkT5BY8HFliGpAj9ac8PoLDluEJaQIKZVi0UMIWhuysUVGjxCj7+8PvZQ9lySp4PskGNGEPoGqDHt7x8OcUFp6dkHCqFJcfAjkFPuZQ8Y5VzJuY+oTh2IZ/vYXoTGaurFolUoAijH90FoxGpRgEmsfHx02mowIRIyUCcxytuCdd6/xYz+4g3aenEPZNcWK8kYDJ4Vb9EP2yAcWhZzs2oCTYroO7n7Y5X9YF6x77C5Bbwo8OTViWCXGQ8X5vQ1U//6yRHzwHB0dkYLDqGKlLAi2UkRvTgRx9EP5/Mgg9tEbZErFdsB5x2x2TFXvMBoPGUhFCseYasBguIFbPt5hM8ZMJWXQ512L1oZawc7eFk9/+jNcNgvSta9DysUQSymMUbSronJuO8eb71wlhFRUrqqmlkwTI8dOUQ8qbE64/Tt8/f/6DYajIRsbp7nw7BWG5y4gTy5J/w91bxJjyZWl6X3n3mtmb3D3cI+ZwZnJZJKVVZlVpa5ZpWrVAAndgCAI0EorbQQBLW210U7ojZYCWkBrIQgStJEEoYVGAY2GurvUXWOWujKzlGQmmZzJIBlz+PAGM7vD0eJce+5kZrB6yTSADA+P5+bvmV0795z//Of/H73PsN1w5g84VcUxw3nPbCZkrLoxNYwEueDFs+gCKspqGBAyS5/xs8y62IBiI0AA3xrU7roZkiNu1nJ4sM/ezZuM9085PTnhx391j+22mKRBsab4ufeD6ahTCjFFttuRs/VITp4xODYJNqkQi7dnvbLZUsxAYdFWiQYE5wMpN/RJ8AmOfGa/dTjXIDLixRKGVApFMgPwzV96jqcXHXy45rtv3aPNSotjmxJn48CYIvt/Q/T+N2HL/AlfeBwuHL/3U16vwN/7m8578Sglk8tAHCPeOzTar5Oi5GTBvTjdeaCe+49mxjjSVhckzUIeJ7eivGNQWDNRrMSm4MQm1Zz3JBdsklQLw7BhkteNo+nHlBqgxDtcDCAelyPqA3F7io4bxnHSjgEwcaTJ5CHnaDIhIqQ01gBcX49pZ6Q0Gk7r3I5BYgriVbXMBWgMlpIafOu1RuNgWHophv2XbM1e50nO45q5BXaUlEb7NwHU9MEn03ARDy6jNsrL1HRUhFysTLW/l3M2jRPEmY5NCAWNmTQC3rO/vyTGxOQjC8Y+2t/fR0uk6wptNyc0LTjHcrEAEcKXiCEdXDokasIhjK5nGJVXXv0meZiDPMKFh6j2+GiV0251C1QqzOdPWHF1ETcNC1cKYzFFXRUbcNIvS5FkCr24ILSdJziHI/Lic9fM7KK+YhwGHj96SIoDTfCUaE3lJgRy13Cxj+WMj2kVYJ0S1Vpdee9rhQh9PzAMPVeuXmWtpxwfPyZ0M2haYHjiu54mRVWEPmeyOLzANia2RfBtd4FgpFCUcYxVMK+wXq/4kz/8Q75WvVodQuM9XhKNb8mjMuscqolHJw95Vlfk1SNev/MuLBdcf/HbdDd/jsXMc9S1nG4e4XVDcjCkgayRxSxQxsLgPLNZQ3A1mXC2Jn2wauNS19CiDINSxkJJ0LSCa1pyzAbbeme0yKalOzrgqXyV7u0tMZ6hsVZklcHlRKo5veALNOoofWLVj3hatslxOma2yZKAbu5xbQtNII/2tHWNVZneecQ1nK0cbj4St4lZKHgfya63HpmW3XvMObN28O//e7/MjZevcOUvPuOPfvxPmCVPGpRNn+nH0QYr3c+A5G8umcfHx7hgphiS487L1Dszz25cU8e9ywVc3LrqWoQhm/65EYawwBo68AGvoMn0VpRsgkglE7MnZ8gqlNrMkMoWidXCz7BiG+0f4gji6MQTUyKvHzOcPEBLYSilGirYphG8ZWw5pZrIW/UwZfGTW5S914xmR5aLmvItbehQMcZKUdNh8Xg0pV3DdshV3AsFTGQseDHBMcRovs7TitG0LMaZVo9zxtf3IVS7wECu3ytiY+hZa/ZRsxnFdOCdNKSUTNs61UndPJglG2KWajnXzLMQQkPXtszmM2IRutkc3zQ0TUsR6MK54NuTjhtPPVerDtPeMGUtz8fzBZ2zIZ4jvYd3f4zPtwmfg2I4R2guBvka4AvnDVW7L6Yp9LkeyRePKhVscE7A9mJlO3oWfs7Vyw0Mxoqy6yaUarqAKm0I9EWJaSCXxGzWomoDdCF4tHLbXfB2b9W406Gxc6XoCX7OZhW5d+8hsjXjlfv37yA5ktKTx9PbWSFvI3ns2AzFeN5BuHL1Kb7xrV8lf/AXNCKIrzMPJZO9aYx3QUn9mv64Z/+6J3shbHpC4xn9jCFDI4ltzhSUNZ4YWi63nlt5TY5bVm9+Dy3f4ZMycH/vMq//4C/ZrIVLr/48L115lvlySf/4EZ99+Al3P7vDyYM77DkI7RUKgeFh4u69kVVMLEOk6xzH44iWhmUTmIWAJMFVfwepSqBNAm2N6z4kKBpQ9UCibUyJc4wJL4VRMj/6J/8v69PIZ288xC87zk5gkzOnY6QozINydLWhmc/JSXjwYINzmZI7NimRGyUzmgxzDsyl8PXrRu/cbiLOCeM2U5IykvEidFkYV8ecvPWQD978lD1pWeM5Od4yc0ZxbbxyuPhZUIVUM8kmFxpMQ9q58ylLm9NRChmVC/Q+55DgLLvygoqvDIkajDRCitZx7+agZiRRGEGaatBgzRGHyb1eHBqqb81EmrQYJ9VDyqapMZZCCYGx3xIr7a+UwnK5MNndkmFiOTjBqzWramvOBPmdR6ouuIhltxNlULwSXAPO4xR8a4bLjT+/bd7pDg8u2YKSw/RFcAHXdogPZKArE7qg1QzAGqTeN0YpdR4vM9s8LRe1klyMSuarbrlllM6ooPW1ghjvvth/Q78x60HqiD5ilnizDu86EMGFgBNvuvyTzeGXZMnNfJ+SsSqt4t8iwlwDHujLmpIKnZgRBdrsGshIAmnrtTof7hEJJFEYI72zgD7zkEuDbz25OJxEhACUej4x1oYKEKlKZKAOTbFCMQVXAtnXXoJ62nbG4aXLVi2JknJGpSVFh287iq5xLpCGLeI9qRono4GKSZrSKVbQzeczupnBFzpEtv2WvT3B5YH1eMaYnqznToRtnuFKYr9pyZIZy5yzuOGHH3zMNzYjCxHyGBHnCZ2zSUw8sR+sSehmnI4DqbgKE0GnhUXj2eRCJ2aQLrKHlIKmLfdWW7Z09GVgrZ7O7/ONbz3Pf/Jf/DesHr/H3/+v/h53mEFY8trLL/O15w559hd+k9NHil/dxR0mUi6cDEpUoXEB8cLAgOLpRDgKLX6W2czXNGmG1GqeWqXq+NjMNAbT8FFfkBIoGsGNTOqpncs89cIRn312gusahtM1l7yjcRlJJhUd00ibHHLnLdZuwegzs7FBWk9PISRlpplts0W08Pv/7g1+81de4h/9X9/ne289QqU1VctW2GaHiGeQlpvfvIHkwOxHWxp3m5wyQxpYzAONZuadRxZfHla/GsFd5HOj8X6SrJVzmzrLaCf1R9OosKBhBgeW8DqKNhY6P5cFWnZm5hkeIVSzYjCDaftZLaWWv7obnioopLzTkXfB07RzchlpZwtKGkkxIc5MLpqmJYSmZqwKzhNcMGH+Cu9MQzEW3B3BdfX9nr9nEWMQiQ92bcR8Y8VNjkAmXDZJv06SsKB4qRru4qHtEPEkPff/nGb2dh6xznjZ6gTvuornG05vSpwGMU3qm1Iz+102X4Mspexgp9msq/o6dm8mSCmEUO+BgPdVFtbV6/7lvF2bdzDRL2uVWoCPsTAMA0hEJVp/5nNm1hPMVKr10nS+OqRUP2/9pDip6INo9QrwRq08/8F6IR0mkuKtKhATtnMyYeTWxwjorp80n89BHLFEUoJmPgcH2763SqmY2mO/Tee/TaRSYl01eXC7Jrzx6S0xKGqmNOKqeFh58vVUpupRaZwjpcgwjsy7lqdv3SC8/6GxULwlT6nqAWkpRIUcZrC4wjB+wJgLMRnLLWXFuQDqKdpCM6c9OmLWfIRTJZZCFON1OzIBk6V2Ak899zQuH5Kjo8+F119/C5ywvHSJ5155jctXbnD/eODdewu0+5D9645+vaGMkb7PpDyQA5wFSPe2kB0n7sQ2dms8oeoZncEgZ6s1KSYKnpyFpulIUc2az0O7v2B58yoH0jL4h2zUMP75zPN4DKy3UNSzWo9s2wWzPGfYmib7MEbUlx3FvnFALty4fsjRrRs439JIsGrcValrNTMWLYWuaznbKGerrYHJORqq5E03XlDmPwtOTCKCa+f2oGEGChf/DWrm5y64WGqFoy+4QosIWc/NOiZvVamQg31dm7OlluHeGbNEBFfOR/TdLrhbRaHZMGppG4K0CMq2FeL+IWkY2Jw9IpfCbDarnpliWbIYnh1CoGnMi9GqA5NYFecI7ewCHDO9f2fZJ2Ye4nyw4an6M0DVgIEp6IirGxOuJsBuBzuY2l/ZQQKUbMJoO1iCuvmx2+ysFVxR5QsNR7v+ccez1R2TZqIeCrgGwe8GzKZ7FrMyOeKY5eDEQjEf2Z/AxS8cw2ZVmT6ZuNPFF2IaSWNGQmJv8YBF2yNkC7pWZtSG6hQu6/SpWuXhcJRcXZAoNE0dgKKYiqBFVj6XuYP1cLSx+k9GE6kqvqpPmqVcycZSaoJDERbLfUK7ZL16gPOmkhlLJqtjM8JmSBQxyiPFsF/qhjp5AadsTfcxjrapUXC+pfUjZyd3yDHStB05P9lAuSgmPFYSjSu0rjAWuHp0jW/94i/Rbz5k/LBUarh97lKZXoN0xIMr5Eu/wPzhZ+Re0WgbftRCSKOpgkpHXh6ymb3MvtyhTYkmLlmN1jxeiHLQQKuFEDxRGlbDyHrbMKSG40fCNiYkPObTj/6CZw8d13/rkB/8kfL/fZRYN0dc+fbXaZs9/HzOw4efoWcbePiAvZdv4K8VjgAZF2jxIAUJPcKckiJjvE0IDb4oA5EbN66wWp1SVBnGDF1De3SV4UFiXTwFR1stANe54zjZou6WgdXJyD/7l2+yaO2eOe8gJ/osRPE8Pe/wc8/1mwdshp47D9d0YUYpPU4VjVaRO8RMSpqO1cNHfPjRA8aUWEjhcNmR4kgWJYjw4jMHfBnL/CsS3B3dbHmeSe5yFpgeJO8MZ9w9WFrZHDaFBNiDntXtMvfzjcFwecNUQRmndPP8wb+Artp7Og96OEV9NZMOxomlJELTWnOrmdHHwTiviwVtM6dkNVw0j8zmM/b29iw7GkzFkSoRKyK4MGMyqpY6uj5dF7NeC7jQ7DaDi5CRr41igz3qaH/RmnmzC2hTlmsMjLz72iAuO1+pTWDbFEqFZc4zWmsC13tTJ2cB07nItfmrCiUR42AcZbVNaBrSsCqtskzE13sgmAXe5yu4Lx7r9XpnJBzH0e6ZdyiJPDqQkdGvIUxBeJrmu8B4Ua2Bu2L2xXxCS5bzikjExt4rg0ImNs2EnTmpkEyxvcnFeso5Yl1KFONnK44IpJJwtV9gbJdAyWpcdhwxKnfv3We7HchSzeA5X+8Xj6kfNdFgm+DMm1OgpAhiPY2f2ieoRy6V5JMLznTuCEG5c/8Rf/X9t7n24JTLXoytJjBGk74OmJqhuIbi57XfIPXZdGQ1KbGUBnANqSQ0tPR9pqRCzMI4ZjNQHyOdNqYZlRTvZzhvZuIpZ1IZUVfYSyOz/hZN2fDi1xpuPXPE6T/8K959cJePTz9ilJbu6Ihbz7/K5WdeY6Etj9YfEEKkbTJdEISClIxER8Gg26wOpKFUo+vlcs4wbhEpKIllt0CzourJoxKqgTkukNRRNNM1jmefPeLqszdous922kJjhkYhFaGvDmptIzQijOueOEZGrUyuaBpZxVn/UVQx+ehA4z2Nh3kTCN6xGguaFd8Fjq5cA9584j3+SgR35xyz2axa0tXxYP38a0TK55K6CaaxryOltr8azoP69DqPUCpXeypfJ2qbnWdinzQ7xsuOqihSXZMM5y8Kw7jl8eMHLGe+Znme+eFlQmhtoMm3lIK5rlTtl/V2MKs85xHxO9TXiSMX06epY5wWI8UkY3FW9tt4gz1AvmqG5FJIEzwjatOzWigx7nRrNE+0y0Ih2ROtdTiklDo6MDUosV9cN9FSr1EsWIM6FyY5Wy35c8ylGCOaLaCXlBnjhpIyrv6cmU6Y9eE5O6fCMs60XWQK/E841menlDySc9q9F4C2U8aNI3QTGjOdowbwurEI1cibqdFo9EgRv9vgRNQy6mqizI5Fs1tQlXoKBPNAhYhKAA4o+RFZCwETptpstqxVuP3hR7ROuHJ4wGI2ox+3QCGOmdVmzcOHj/noo0+RtqGZWaNZVXbQ3cXFP220E+yi03fVqg2zfhR23o4/5cjZVfKm4daZkcXc8/BkzV/86x/zreEO1xyUZJRhgFIJBp5MAzS1MinF9O6pvSvxnjJGaArETHANY1KzLPQdRUdWZytcM8OFFlc8TbdERRliRlxD13qGFIkps+2WzMIpIWQWwDwKfduwTYnu5JSbyznpzgMe3P4xH7cHhMUzpMevcev6N7h+/QFf/9qG/W5Fx0BHi3cDRYUxKmf9wGnOuOxow5zVqkcwM/jZvMXQEiEmu7ZZE20zxxFwKrgiXLl6gETlqZvPkuOPKU7ps8WaoQhDcaRR2cbM8SensLZMfTPaxmpFryfVhCs4INlw4Ga9YW/u8FrvQdcSU6RrG5Z7yyfeX/iKBHcwTIkqBdtlqc4sYnoLAtnZKD81k1QtlLglxpGum5knojiiKoKrmY1lXKPTKvlpD4iTYF+qAr4Oi9jP1g5qfahqoHXWFEwZG9kncHjpCsxaosJsYSyrCbudoGMtBQlzIzXmyLDdUHyw99qYDkymQBqtWay5Vh01eG+iYd7Ooc4ZbFTPq6UyUUqPIMYpr+5Qvu4cuQp5IVUgKZcKJRjgommoSHRliDhBk7OKIgQipmqoRSkpV5qmVlTHGsU5mYfmGCMlDTVxT/TbzQVLQcc4DqgTLh1cYoRzjnvF8l2wIJbTk5UMt+sTxjjYAFjJZn2ohdDtc+nSZTpN9IOyKaf4MuJLwHBipWjEVWaRlmq0ApQRVg2sHm34dOvpU8+nw4KDtx/Sjo9pkjKKh67gQ2eU1eBwvja8ZYELBfWFMBsZwhGtNDSpI7nM4+2GP/2rt/nmS89w4/KSWQMPTk/48JP7nJ6tWY8mGHe22iKaoXhWZ2dIgeViiWtaVM7dxsY6NZyykjMkvBmYSwIvxNKS8zEud2xWT4ZloihjVJZ+DmVLP8BJ7Ll21dOXzMPYogWi8zS5MseqiXgpEU0FbaBpAyEZqyo78w7V4tiWlpKV3I+kLAyjyVcsvOOo8+Q0o2ihjwP7+0tSOUP9VcKswadMysnUVpOj8ULrWrqm0ITAqJHHJ4mSPY9H5SRa4ide0e2a/eFt/qNf6zhev87Ht7f8j//0mFXaI7lDnvn6t7ny9JznjjrS5WPCyR386UOGszVXrxzyg9d/RB89y0YIy4BzG7arLWerLUGEnBzr9YBznT13Gf70z26z6Qd+4TduMfxvHq+OXguxL5wVzyCOmAr7N5Y88+pTvPmDjzk90WrIgsFvIqgkZtIQJCCt52y15dGjESgMWUxFk0LTGmR69cbPQHDXiqNahmkwgUEENuhRnNgOqlXgHir1LlXd9WSBzznjpE80OaZdUXbZjdRMPJdcN4oJaxViLW2d1GlA5xhj3DX6pqYq6hAdDRurAlVSfB3pL1YaU4wpM0ESOVLGtTF8qv2e1kzLqJ8W3EMIeNeSS2YcBss4nUf8eUabY6IUM2+Q0lsmN703bJOZGmBSDT8EpWQbBDMka8LcsSrGVWOJbJmX5IahVEqgGv9Xd5sflGwpbcqRcewZhoESTZLZATFGcjJKX84jMUXEO3I2CpmJJFQWg1qpu+vkPuFYr8z2r5RCv92Qc6JrWg6v3qSbz3BxyzCOxNiDjjZSjlT5VWVsDAbyLuw29FyEvVCYpczRfI2XOVeO4PKtjtYicASiAAAgAElEQVRdI+SZZeU+Iq6qPIpYr8AJoh3oaLMUocP5gve1CVpAi+BCw6f3HrI6O+bKpQXvvvchn927zxgTl67crN6pidlij+I9ZUyUYTCGScmohs89J05sw4wx4sQRGl8zRcv0gw/kpOQvoUJqMSVFJFe5aKvgLNkJTJXA1IquXRNgwv+Nbw9dpRqasF0qBc2JpI5Gpc5S2N2OeepfBELj8K7gG8eQDBoLIsaeKrmuL0vQNJvhR2hsQ02jbXIpuyqFUfCiJpmRI8F1XLu2z61nL/P1V3teee0yj04Kn90b+dE7f87rH1/hLTrmV57nlX/7VTbbDesPPqQ7uMa9x98Bceb61jmQTGQkusi2FGbeho0GCqULoIVf/qXn+NVff44sazNZV2PhpOIZsyWomyKENbz3SSLvXeEkZ3o1dyopYoSH1iDpUgr9+w85u7dmFYVOO7a1t2b5pZj2u/9ZoEJOOHA9xpxRLFMdSiZ74RwQt+w7jiNoJMVIVWutWWasWfl5c08r3CGVJ+KcY6zYpEEcE94qu8rAOXOVTzkaKbDetFKyGYOkntQrqT54Tm2wwjLZLWje4dImdpZg3FpDV8W8VZ0NW7Vax9BLJjuzwMtFSWOs8IwHF0wSVNUaWyVZ0E5bJqjcVfw6FbVxau/R5HZYuRbDxQWtjUmYKIlaLeScAynBhp2K0SOLyvk1FgtcpRhLB52CjWVvvmrg9KXQta0RSbLSdaFuUErrvTncq0FHUjFvaz4/GSf2TunaYJVL9GQKXWPj+EVsHmE5X3IwOzTcvWL7gp27rRO/3jUgwdZDSgbrnZzQlJ4UYd70zJfg2hn0nZFsgkOkOYfzKMYMQeqgiqdopBKVKnvIrmlBuPf4lJPTwjvvvcfd+48YhxGVKglRBmZB6JaXOd32lD4RKgRSSkJpjLlENvhIba2jig+BECwIenHG1moCcUyk+GQ3B5NTMvzZ1r39F5OZtk+J0HRPLuzr9hTWXsQ2Kv2YSan2pahZvLIbkAPzW8gpkrPaJunVVBtdIFoGUzeyur+rTWZ7b5XmGAfbHFBKEmIBRXBiMIYXC3qhaehah3MmzNbky7zybEd5zlg+v/zahuM7Z7z30X3+z+++DXvXmC8XfO3bv8Ejv8cL3/4VPsqZzfFdQjdDZE5JHjc6lnpAHrdkCmMCFTPJfuqZJfO58uDeGYJnjEpSx5iEmAtZIqtBeP7SgiEKP/zgHpv6PI3Z4txYChIEpwUnhY++/y5vv3vMg83A5ewZxCNSsfkipOJ4/fU7XxpVvxLBPefM6epk9/dQCr4oToXolMiEd+iuWZpzouRELhkfAoplZEqu0MHUQL1odWZtUyeOXBHHIv5C8/ScHnje2LWgWCFFG8xJPf3xfVwZiOoICJp7qPyR4LRWA3YjTO2t4Evc6ZI4CbU3Z00xcRNLyIh+It6apSqIeoozvrRTJY0VmtCCI1qmUKuGyebP+VCF2NoJMbWg7rCg4IWU2DUMJ867NXMdFGdiZz4g+dys2a4hzForq4s3c5NuNmPrNpZJKRwcSHWnMljEjDlMLdG1gR37yZnkbdt01Zz8ycH98rWnjVKmcFULKUZzOZrNabsli8UejU94P0Nli7rBgruINf5q49zgKxsTd5IhezQKXXBIbvAIYexAMoiiTUay2N91knMuNAr4KsZWHI4VWmbUbrk1ur31Bly35PjkIR+/9zGPH58SfCY0DSVvmDeBRpTt5jEtDYFC1oIEdlZ9IudrU0u2bLa6dDkHTZhTho1JTqjSdU2tjH760YVimuAKZwOMyZElEHOszB5IhuTZn6qESuOVmiB570mtR5tEW6BrHIsA6z4wrhONGPPUIQzq8cXTqNK1ntk4mpx0geznqJvVQTqrfJwHTQlEmHViZjwoSsPQb9luM0MObFNhNA4pbeOR4BjSiPhCyQMEq+69BLrQ8cyNK9y8cY1Xfynyj//Fv+L++yMP3H2Wd26jNy/zgzffYr53hctP3eCNR8+x/ePL5HTI4c8vef/1t5j1puGTTzc0fmTRJLo5lNBx53Hhs21DnwrbOBKTPa+zINwfhGtHmV/+1oz3PvWcniqpER6phwypeFxyOBJff3rOjX/nl+g/+0vur5WTrBQZahM/MJaM95nf+I0b8D89Oa5+JYK7TpBBPQrGHDGHL09RK/WpjVBBCN6ZD2ixhlwuteGqJjRmMcICt7iJtFeplu7isrcMzwmGKbpzCmUtAUil1A624PC4nHE50jAgWuuBqkdSgZp6IzKJ+vCrIpqM+SJTnWB0yayVpcGF4SLvEDX+dHETzGSRzYvH+dp3qDiLq+/RRMfMESqnzHy+JKWMiBLHwdzpQyDHgWZmZiTOGTTlnMO7BnEBfGAollG1rasG4OfyD8Y+qpRGTEtm3nZ21YrS9xuGbU8IDYvFkslE24lA1zBpeEy88Na3u+rrScelo2sEL/U+n4uwNftHNO2CuYDfnkJpcCXWTugE/dQhpB3IUJvoUig6Y1RPFkfjO9MzqVo0ouaC5Ch2P2p1Z01ZtX2whJqNyq5BLM5BnjaQQtt0bH3DGKOpnjqrWhovtI1jf++Aa0eOh6cjq22PSiB7qSSDuhoFs7HTSmzPaj4EXWdVnDjms4UxbXwdfHrC0bTKOCppUHxxpAzqhaZtaGYBWQvshNPq/lUKxqKqm7wIuXhKmSQxCmPWKumsRuGtg2+bIdGVQufENG3E/FmzAq6xyYUCzk/MGyMJeO8ppef4WNkezhHXMGzOiMnosKt+wJtiHgtfKzGhykcbDci5mVVJueCWmZALeRDSEFnHluM047XLL7O8foXH//pHrE/ucHb/Lrr+mIcfHuKXN+gu3eLf+v3XyOs1Jw/v8uh7P2B9ckwcVsQIqx4+ubvlwTqzLZnNGEk5EFRZNI7+JLEZGkLXcvZ4w2ZoeDz0VjUla+rnIRIk8WxacG/Tc/ukZ7NWHpFRTSY/LNU7t8m89daX6jF+NYK7cK7fUdVL6tcWcD21W1kHeKgTk0DNzB3e1SnJui6UqRzHSnA3EfqoU5f+ws9LDbSVfnaBjilgcrdtg3OOIWbII9PQjsMYMEGN4mKa3kaTUlUC9WEEREKV/nUogSLURm+ydyamkCguUPDMF/sV0441szcN9RBCzdwVlVx/thLnKk2vYMMts71DK4/jltA0lSvtKLMZ+DkhBDRr7TU4XDPHOY/3gTmeXJTQmKiZiYWZLdg528eGgMzcw/6t5Ex8pDy6/5BGHEdXrjFf7hmW7zz4iTFTt1wRmoklc2Fu4YvH3vLQQEqosJetFD87IDjDI/d8riW+w9ry00L5AsVyNwkbEE0oBa+QnBIqjKfq7YFyajiGr6JmlR6YKyWx2gBBgoAnOMvwHYKThibMSDnhBGbdwuQWvKNpPM8/c4vLR0eUkjk9OUXLiOaRJszRbBWlqwFeKi9gkhLzzlNixqnp2Yw1FZdi96ORJ2fuFotL1ZsrDEnJBBrX0ISWwdl1KdExKCQioU4++xTJYl6pBWNo2bStRzQTi8PlgZRbpDKKjFXjSGLYuwJOMwWhV8WTSKXBSWFMhtlrzpQ0cDpuQFqcF5wmzrauOiuZJEcuCeeEs23BzWfEEgEPOYEEuyaW1qGxrgiFdd/z+CSzdZmUPZs+c/v+Cu9a9jrlP/4PXqDrZvzl99/nrbfeYHnzFv7wOebzlt/+vb/NuB1Znzzmj9/oOf2TN3jjh+9xFgujBEbnjbk2zQiM8N331vg/vM333h04wzEWTxGP8fmMyKDS8tFK+R/+lx/y5hsrss6JKEECWTziO7wTjo4O+aj8OvDHT7zHX4ngjghhdqHzm88lcJ1zVfb2XJhrejCTFoLYYMdUfu/00S8cTnzVWz/niE/p0IRTT02iLx45ZxZ7+4AQQst26OnY42GKzDAmDL5FxxXjMCLOMwLeNXTz6jhUwUrnwrlcrA/WI3BCcDMz2kAYcqZtWtO3VxiGLQ1KN2soWFB0Fft3ArO5XTdFKztF8BJoWjOp9s3CJA00MQ79rrfRNC2bYUUTAmm0LNc7T5gvcOLrZuitYV2y9RNKoVRa4zQ/6RSoej8GYUEpiYMrN3jh5dfMRYiJQAeKM0eZLxw/7b598egWe9U1vlKS1HRrNGfS2NM7OIx3wK8RKbXvcvGQ8z93G4sHHSglGX1RCo2Yvo5l+w4nBSODmxlKvZn4JDAWdNGBgzyYefrJWHAp89EHd4CI856DhXBpdkQTn+LK/Oc42O8Y48jBwT4HB0vOzs4IriA6kOKWgNBIMxlB2boJDh8CYxwJPiBqxuhd2xAlQyOMY2G9XRNmS9ovEWFbbSLrEXIR2jJSNNAPI9t+4OpeRzkO5LUi0tjUq2RKPiU5z8Nt4XGTGSgMFXPPImz6iNeRIpEUE2Pp8M2ccPkS6yIM6jhZjcTU0w+RroWsketnAwWljJG7DzIPT0b6BCIZpyOrE0XaLY9PhZN15q07x7zx2QmpQkbeO4ITGp+YNTeYzSAVR2gDztzLqY0Kg02LMqRMM+8owVFc4Hh1zEuvPE1qZqyio4+Jv/8/f4J3CS2OYSz0H76FDG+yv+y4dlCY+8TMF/7swQsc3XqJF3/7d3jm9/e4c/cOd378Fu+88Tpx6PFi+Pyffrrgz//3DwxObJfM2OMkOVQSwXsW8xlaCv3K8y/+7C7SKO6S53LylP3rvPj1r/O7f/cPWOzNODt7zPb173zp8/KVCO7OeWaLvfPgnNMu6zaIRBDR82ezHrlONHpfM/odFeQL5y9yIbjb93SCMz434fqTAzRtK6RkdL1SikEPY2YxP6QN9t5d27I+vcc2r0gp0XUt6j3t8sggjnpY87YaeYRQcXmHeJtedaFhnjOlQjMCzHJGnE11ZvGUoniqFIKA8yZdYPCvBW4pBR8apmH9lCy7l2ZGhdwtG+j2AFPPk4q3m35IQYpptqualmaMk7SWs4ykfibdyQ9T+fpC4zvGtMWJPSTOByu1FVJO5C/eSLv49seXrJOkagqVpUod1wAvIric65xAjxCh6q1//nc8+dwlX5yKNnNqFa0bxJQyX9iA1DYC8Z60XqNAaBrGzcDp3fuc3hGCjlw9XDBrNly/csCl+YLnrx/x1hsbTs6OSZnqYmUUv+kyOFfQPCIarROk5rNJUWLKOGd2kCmNpigYHCena7abFXutcHj5KevbfIlOT3YNI5moSte0kJUxZcZkcONZLLxzEkkukLa9Nb7bOVmV4yicuQav8NlGWK0SJSvbMdN6xZVM6gd885DlLHIj/An3j89I4xaXDTZcDz3eZ8R5PvrkE/7sj/4fInCyeUAi4YLgXcJJ4fqNSNaG9Ub5P/7xj7j/OHHjcM4wJhbzFtXE/qLlP/yDX+Pm4U3efve7ZpWYrYrbVVZTSY9V2csuMO9sdiWuz+hcYBYaY9ypsWJEvC2BoEgKtIslmzxy9ySyPw/MQuH6o8+4+/Az7vzgz3jqW7/OrZvP8bXf/V1efvllHt/9jOP7d9iePELygE8FaNhmY+Q9K9uatydgZVCSCxx0C3tmwg1e+ubfxj//Igf7M4YH7/HwzU84uf8J/ce3v+Rp+coEd8d8vmB6+lxJ5xj5TwnWWvG4smuaVrhF9Vxp8eKRzW3IXThXnhptO2VG/amKhCb65cmpZqxpCzkz37tEaKyEbWYzmm7O2fAxKW9omyWL/QMOLt9E8RWjnH5/OIeP6qfMkiy79Z521hkjJlcWCsbJnoJv0UKujcRSJ0LPh7/qhhiiaZmQwdm0H1BfJ+ebmgYz+a4QU1ZzWLL1X4wuWp+H3TlErKlcw7tUjXet9zGlhIo1saQKfOWciHEka+VLl5+8zl+qqluP7XZVezNlx3VHCz4EGgIJQXWsfQdbKX/TYUJ0UJLuhr3sGgVrSO6avBfeYE1CYiN4LTRNQxki63uPeXRbKYdrLt1oeeHpp0nbNYeLFc89dY1l05L2F7zzQwxvD4FcMpvNZrdBevE03oy3KdG475i9HuLICmMuBIKxNnLm+GzF3Xv36TdbmquHtO0e6jyL5ZN50I/OzjjrTcK3Hzd48azVk8ZL+M0D1utTfnDnlOh6mrilr815zSNZHOPyIxYskP4uGk/QooRSaMVmUtxeoPXCvAzMPnubRTeQJFGirfW9Rox9pPDxe2/yD/+7/5axZBrp6WZT0pWMjZUyTWhwKfPeO8c439FJom0KQXsOlh0vPnsVd/wp33/9DdbDI0gv2rPt6lTy1NdSS5RQYdkoR3NlhmfpElf3l9w43Ge7GshkVIxiHbzHL+astiASEdeCtvZ9LyTX03gHceTB9/8VJ0HYLG/x3Ku/yKuvvoD7+VfYxszJ3c/YPL7Lw9vv040Di5AAT+fE5JZTISP4ZuC5l77N0VMvcvjUcwz7c+KDD3n05h0+efN1xtUZ/XpD6J48zQ1fkeCOKhoNr9Vacpe6w6pOobFSESs+qjXonp+Dms1e+EgXmkHeBzPhcB4fvDXHRM51ylXPm7JwYUL1PEuFQj+ccfnwGs43hG4PxVGcwy2WPP/KTSAbLc01VcXyAtfY+Z3RhlSRLxGQdO7clNLINAWaagUjYsJCUvntWnKdQs02wFVVNK2ppfSVE1tiJvhsQTenyqywQJW0EMtAypnGOTSbVZ/UJVFyIqaxTuy7OmVaOftq065TYJeiRt3MY2WS2PcnG8EYzz+TXdTmJ8KuXrhfL37tpy+TR/c+QWu2anTQqvfTzFj4AzaiXL+U6GZtbaZ+cSDqSTuI1rmAmjl7q7CKwyYDne4G7OoYGXk7Mn74iJNHJ6yOT9k7XLJ/9YBnX/l5Fj/3LMz3cQHwC37l51/GEUkxk7Piw5ym7Ugxsb9/CIj1VbRBaGpzOdNim3vjPRKC4dNj4sH9U7bbLSknXnvtG7x49Sa/9VuZfjMQz045fvQxvm3J6ewJnxdeuKSMi0xwmZiVkEeitCyO3+HH/+i/R8XTxTN6HIehEDMs1dGmhHaBtn/M8MkHeEb8nlV42QmNGDR3pgVpPV4Cy+i4nkeUQpjZRh7aQMqVqKAK4wPEC2HfRLYqBd+eaw9zd0YskIvDlzNyqNRlFaRfMbz9iL9+Vxmiw809sTSoD4SdG5hBIypCdkrfj+y5DV87bBhzBs6Y7RWuHAZWwwOicxRxlEFp20xoBoittXykNmwDiAz4UhjHjKrjbGNVONtj3vnOv2TeOpxm2kZI7SHM9rj5a3+H/YM90v2P6LqWTz/5mCElvvUbv0t3cAU8PHz/O3x4+/v88Af/jE/fu8dGWhoKV2ZmKSilMKyfTD6Ar0hwVxSXksn+AuMFCMZivKLeqIVSYNxuEAphdsleU8qObXLeDK2UxnqWLCaKT05oVLTyfE0OwGiUxRtn2YmQxp6UEtkJOm5JCBnP0fISbrZAJJDU1w3HNFSKmi0feSSptYaLGN3R1UXqOG9gIuYvGreniHd43xJme1hINxhIc53ETBGnxQaC4oph6I1KNj+40BC2nXwYVztecmhaXPAmh1BsqES0Nj2HE0AYKlauqmby4KTqnlhGntWR86Q6CTkXnNPqzGTUU5M7Hmzys1YeuRhd0dVqQJxp5ZR0dgEiqwboLtik6pdoy+Q0MMbRNpca3M3z1JP8SA7ZZJVzhVEwHXS7ErUpX9eTQXi+egQ4pLcA34ngmwW4DpUGlRGJjuQKkgtltSXefciHr79L74VLV/f52m//Am6xRJznweMGP7Y08zmFYpBUVlsv9OSywQePD7kmHa4yQkZoFNdmUtlStKE0cwKZtBlJecum7zk+iYyS2dvruHr1aZ55+haNT8x9MnkJPOO20DYj/kssC19qHtM7ZZ9iMgsN1qCsh1PFz611Lija1DZ6Y1rodufM5FpN3LBOgoNrGvaGVNlfVaSvUXTe8GCV8ShXzLHdaJtVJ15Vq2YLVoGqJVslF0YxS7yEUryJ/BlcZe83ibL0M+b7jjWZVJS4XRNC4NG9Fes+M0Q4XSdycdy/s+L2cU+QgHcLNAfuvn+HZvRcXszYxkxKoKEy9ZKwcAOFWHt8hTLaG83NghhHUsmcrHtwBkV2YeBhHm2wizmtO6V1mU8/fpPUdMznC178td/ihV/5bfaW1wmLyN0H7zD8+E3++J/+c85WIyl7tnFjscsJxXV23cqIyz8LQ0xKHYqxr5Oc/4Nt7LZTGYXQMkjnYBzNaWYalrCS6zxTnvjtahM+u8yxZN3hp1rZN04cYza5VQfEsTclSO+RPKLOoa5FfUPaBpxrUDeaaBYZieYMVappBWoTe0qpm1Y5V6G90DxUVUrsoWLvvt+CWKc9lwgl1XH5jOZCiiOaTXbBIfTDekJjdtlwGUem+c9zqS/D6I3WZw+Mq2wKqfCWKkg3Q4UazGuTWsJugASkBv1SZRDs4TVHqX6XJZVi0ElO2XTgKwzmm1BhJqk9rnp/ClXS9svwGVPY0XpvC4bBEyMuGITyZZvDTz3KhNufW9glIuoikm2tlCGTY8/DD29z/94j4jjy8qvPsHftKm7eIpcWliioQwLg1KSpxFnjW01fx/kG3y7Ibsagl4zD7vfNWm7ecfNyYG92icPFEULDcrHH5VuHrHubEzgIga/vX+dW0+C6wLufHrPddtzNyqtP/yLNLbjzyZuM+oBWr1CGJ9vs9f3AkJW91qQtdk3/evlTOW+a21yA6elMdEyoTLK6qLMIQ6n6TjFVOg42Ha6Qo6Ip4lMmOGEYcjWbtupkGpTKpHPqJ1ax+wJlzCZ2VplKIORSodk6NHxMj8czFs8/+Ac/Yr48RHTGW+/d5vHphiFaPZTTkjGuGUePuGNSuUe3vMyVT97nrdvvMq6PUTyxeEMKKuslxUzJrRGvpgEtcQx+sGvkwGYjhc4ppVdrxAchyTHLpoXiWFw6wuuaIT/ibv/PWV36Hu3+ES9/+3e4unyG4aU5V1++jb/7kPXxCh8zKQpOI27YmndFI/h55s7myUv7KxHcc048fPzAAlSxRtUXD50aq5ptcMVDrjZiO8NoMDyN3Vq1B7YGKcvm7fV+NzB0jqlKgQigBU3JaIU+IJgPa2FLvz4zU49KZ5wqBl/i7n3kOr2plarnaplpmi5UuqLWh8aYELZQbXpPpWLrVdp1YvOANdHQZBBO1UeZ/jdBWTL5uMoUDu26FECzbTqoEqvCpHfhnJZYlnWCBEqd3s0ajfVjr6ifMe8amhSjwqUYd1c+l2zDWbk2c10Vci7Jzi+y86sVQGqZXcqTg3uhELzJxU6NXgeIDzjnaRrHuTyAVGhmOt/F7e/CuvKm2ZLGhIon0nJyosQ/f4fhdGAsA/O9JcujS9x44UVu/txruOCIDpoaWIrYdKSIY7UW9kRRGWkiNBRWEe492LDaDJyeDYTF17i59yzzLvD09X325g2LLtBi+t84agWYEd3ixmQ8ZxJpVFzaMJQt/eNTzk7uUOKGP33rNt//zvdoQuE//y//U2SENfMnXsvTaOvl4ahVNMl6j6H2Y8ZcJQmYehFKn2syUj1zAbamElArRQEcQ7ZGbWEK2OBdh6uS0yqOTcqE+pCmqsNWAB2rA1g2rjziKI3BflM1OolI2/syppyqmmggSreY89f33yeXaico5rmLeFS2RH2Mlszq9CFeCiVlXphd4aVnX+CPyv9NP1avCN8jUmicIwTBzzyqY+3P1T6XKs51NhQpwrxxZobuHI6GMFuwf/UWL/3q76CXb3B55ol3P+DT997k9o//Gtk+4Pj0NrkUTt7+U2ZNx8Heglf29vDfuIXfu87i6efwjdD3I5+9+xb9/Y85YOS5pz3/9f/6+hPv8VciuGsdL56CO+5CcL9IWYMdzmqCXunCOaaS++KPnsv4nv/7pGpo2YjUAR5LW9hp10idDLQHTXaBKW1PTYwLRyqu4m9AShegJMvYNZskrE44XQ3qWuGiCcKQxlslUco0F24wy+QYSKkMmmzc42z9CYoF693nrW/Ae+N828yX1mzW2++Uyv4ohVxpfcrUgHSoZpsrmDDHi0jGdIEnnFTPQ6YwYdVTYjWVSfYzUn+viDO+u9j1dt6uvQ/Njpb6pKNpul3GVHA2qt2A+obQtkhTh4fqtO1PLIifdtT74cREoDZ94rP3HhKyZ7nX8dI3n+Lg1nX8wT5ah9Rw3kwVhq016p0FIRBOPvqIo4PvMzu6wrDNpNRz1t/n7MExpSgLPAfNjFm/pg3C3qlSUuSkHxjHSCyJlCPbzZqSEtsY2a4HAomSe9argSFm+pR4fDygY4JhzQd0vPXdT9m/dEDkBN9cZp2eXMV8fJbIau39dZyCteH7ThypTAlH2RnP9MXYWiaJYEy2Uc6rPktmtGa7k9Kq/b6UIs7XNSKFrIKvhiMTnFpUCdWZSyuFVsSZcJlIlXVwFb4bmCaxpzWsYh7MXSgsmoiXgOSepjF20TQ/gzcV0ytHjsYHNBdeuLnPC1f3eP5qx8Znii94WjPmxhh5jRditrXivWPq82U8znmGmIiqqG/pvPLMK3+L6y9/i0u3nqdvlf7BR5SHnzC8/X3mp2d8bS9RtgUNLUUdo08slpk/+M2bJEZ+/P4H9NtPefzjH9J3zxD2rvH1v/X7zMvArbnym9969NUP7vb8VRErz655BefPuoUrw4x1J1bKhdfJT3zt3SQtUGq2UYdB5HyI6VwMxO0yFNRBhXcm2GaHN8cep1OwrA+7gsZYM2yH97X5y3nmk7UQnDV0qFnntA2MKe0C/UWv0rSLoUKRQqgVjRbrqtsgx4Wm4VRSqzfGjdqyxxUER5AJ+7YgGurG5n2om6cgTahNZpMfcAgUR3DnwmSqagNYE+iTs2nVp8hFB6ycM64zsTcffNWeMW7/lPlMr3W+rWJeTw7uy8U+k4G4qsFBWhTfeJp2nzQXMzHfral/g0PZVR+II2O9nWdfvs7l565x8PxlXFRS6WnmS7upOeFjgkVnP5dM50id5+GPvsv733uDk9MzYhGfjFwAACAASURBVF/I3jLd9XowqqhCHAuD95VGmquaqCJOd2vGq1pVEhrOTnsuLRtmjdI5j7rEaiwcb016N5RCKNd5cQ/aPcXrNWbNNS7vzZ74sT/YmD9AyhmqWqZpkZcLa9DkL4q63bNml9/v+jwEX+ETy8km2Q4t1mdyAsELrRMygRyMdhs0o6UBzLtVavNdRavbGEw0165ACBVm1IIXR/AdYBvypCukOqeU0fjusgKqSTVuV+U2QSixCp85g3kjmWWYoetCm+13zS+1HO11NM7MyIsqy73A1WszZl3D4eGStnoA7+9DW+DDDx/yl+8kdHaFp5//RQ6+9WucbE44ufMer/QfsLe6za3rgaf/s1cZitDN9+mSEPtMjJBnAReEw2uX8NLze7GguSGnyIfvnvDGe7f5kz9/k9gc8s7hU3wcX/vSpf2VCO4iQgg2/TfRHC86E9kXAM4CaxUISmILy+/s2rAaeWJmVDhD9ILJRA3wUyP14pQkmiyTs1cZg8UFtmlgLi0+Cidn79FmR3DgJo35AiltrTEmAurP/UU/9zlq8PDWWDSsuUJA3jRWivg6li8Ezs0sioJM0MhsytbLLsuHczqkBuPPGxuord+f2Dnnn9dkGARV04x33iHOxLEEEyszd6tyHivV7tGkjz/JDNtwlWV1ToSPP/qAveUe+/uHtO3swlYsFMfnKKsigle/05p50nF07WmDzC5cV/5/9t405rY0u+/6PdPe+wzvdOd7a65qd1d1uYeye7DdtNvGdpCxHceJgkIIIIIISAkBhPhAAAkp8IUhKOJDHEfwAQVkEUAihFhODO0Mdrvb7ba73dXdVV3jHepO733HM+39THxYz97n3Fv3XneIgQ7ylqruO5z3nD2uZ63/+q//Pwt9cGVhbEbkufjmEmuKkwZD0T8s6BtvqiGHxMJHslVc3jZ87Ide4NzzT8jrswELLnlYna6zDSMSDkrrYmih6Q6ORPx+cYtxWlLVibqS4ZTzL2/JPWG0LKBWroUxIh+MUri+WlQMrCY/vcjJ0TnOX+lYra6zZSNRJY6vnvDNr9+hjnD+fMNsb5uXXvl+TlGcBs+SFV1+tHDYy+ciKXpyUozqRhqQMVMhE8deJYK2Ql81GZsUuIxLpbluMo1TqKiFwlckAFzWZFWTqcQuzmqef3KHW3cWzNrIvFy0Jo3IWYxGnrpY8+EPXSH5jl/6BzdYJSWQYIjUVoZ8VITv+9hFfvwnXuLv/q0vcfuOxxmRnF5FTUfNuUZjbMcP/uAT/Nyf+LjIH6gKSpwY4DqQBEvUv1BK4815zOgyP/5TfxSlalQSiCelINcra1qvsGiMzUhHwrHqDG/dO8N7yx2WF0Y8d+br2ON3GOs3+NT2kssvPcVk92VS/Sle/5u/SHV4i/NPXEKNdtDdJuOvh5MLVVO5dcmPYvfCGT76A8/yJyuxwVzNl/zWV3/jkdcXvkuCO0rhKsEH7yv/UcXurn8aS8BEgnsq+Gr/Gt075KgeGih/lR8y1d5nB5uZYsEgeuZJb3dn64acFNZmVs0WThmZECwaODlGqsLpDiFgXY3SGquL3G6pFrQxOCPBVvXZUSlzdcGhE3p4sEUaWCqLXBapXjS+z/3V8P+N4F5go6wVijJtSc9T3zgx/Q+UKz8TqqgMA2l65UQ9oJyU4M6wAKecC8SFyK5GaXbvnbmItZpqNMbZfoGRa5jUGqbpz7fesEd85G2CUFeHoZ6ch0Mx0WP8Cvxcjnl4+8dDPcMjVd5LFyGzIRAUDwHRxlnLGcTgUdaQfCbOO2bHJ+y/d4vaGvZ2RmjrOH+xYWtvzPjyOaZntqXBqlUxveij+MYx9wubKtcZhcka/4Zn54mGs/oyOljIHWp5h7o+pcniy+rOTHGNZWosB3Mxa9kdP5rnrjBYK/7BMULOMsW9vZf53pfOkTv45rcOpWqM0hS2rsalTLaBp547w7NPn+HgeMYXvnKbo3nFzCfBpmOpPLTn0pUp/8qf+wSf/9/e5ItfuUFeNqQUmNQi7VvXmhc+sMcf/xd+gNX8hG++OWcZIQivjMZZklZUJvPKx57kY69cZqf5KAf7HQapiiKaoB2jcSNce9dhrWT2gx5JudiKNUSrbWmCK0VUNTrXkBxKC9kgKoNOCnwUgbgx6GCJQXM0u8jd5RY3Z5Y3b4Cf3UYtbvPy+X0ufMCwe/5ZLj0xkYG0cALNFnZrwvIggyt+ymw+if2TrHqEbP1bBbocR+5E0nxc13z6lQ8+8vrCd0lwlwnVLfmmRxpUeegLPiuDKWpAh611g1NQX8ZppYozzgPvn+W/+6CbArPDxoKS1lmtKv+iFURNM2qIyyXj8R5aKayR0fycIjEEZsdHKC2mBGe2JqBEuEiEy9Rw6Uwf6PoPVaqYHctxpvLAS5eqt9krOHLBu9P66qN63Yw+5cuKZGRsHiiNJMmqNoPhcB4KHVT0aMpH90JlqphZb8AxfXQfzLJzkkEghPIZYocxlguXrwh3W8nJLjJpUGCPzR3JIKYl5MdOVQbfDat07+6kFGJmHjtcmGPCAnQNpr0fmtl4xu8/AXndGEYyZnHA6odfYln31GCSoooBSl4FFsdHXPvWVU7vtaxmiapR7G7tsHduxKUPnaOa1FCPUcaVq6bIMSLcQ+6/HsNwVxqutdIrkjpBqYmI2tltSLNyjBUma7KybO9OsVoRtRLT55yYjLYeeS5jsGhTseo8W6OITlt4b7l4UfHTf+wV9Enm4Oqvo4u3gDNgnMOmQHYtP/T9T/Lsc2e5edjx2luntN7SAq5WVHj2xhVn9yqee3bKC89eYfwzjkjHzVsWbTJndgyV01SV4+WPnKdpNJPJGf70v/yKBFVrMUpjnCHqQG0nnNmrGGnDRz/+ccjzwX4XDdmK21pmxLXX36KXnhi8HTYYamp4VvoObQbbSZ/fWLkOKqGzQ+kgiYQyuNPEfh5x63jCG4cXODxecHTvkK3Zl3lmL/HcizVPv3wRVdeMskZ1S5SKaKvJK8doZ8pCAURsZRDd4J6EwBB/UIbBs5dcKtAyl5ODvE5rjJ4+8vrCd0lwN8awNd1e/0D3kAYMuVWK68ZnFjw95PsDufiQlpWBjYRtoPqtYYk4jJKvn65EKWOzUPkGHDxX3L13g5RmnNl7iqhkmjWnMBg2q8lZmrqh7Tq2dnYIId6nUS/7I/v/oPuZHiYhCyOoOEn1GH6CXquqVBt99g59WKRf+PI6fIISqV4o+OI6a5HzxSD6NaxvMSJyaArp6OohqA/uRTmhijRBf4766T+A1VImEVGbE8OqPEeqXN4+1EkjTec1pn/5aR66zY7uEbJMAcRYeNTakHKSCUEq9LgF7x66kD1syzmRg1A2RQFTzMwpOj2oMjSXEn6e8CcrTvYPeeNb76JXY7LKbJ11XLq8xYUruzTPPCG69SHhsyYagzGBHIqNYErkZKWtcx/kSGnU5xJ3hHGl0jYuzrB5hEF0cDAaVVnRVkGs6V784JNgNWne4bScbTc988jj/m//2s+gqyzH145QTiigrrJsT7dJecl/9J//NKENohLZVwHLBSotMXt7XP3WLT75oy/yyR/+KNm3qCpAUOg0RjGDvCiJzJTLFzT/5r/1s+RwKoNxeguVgsBedDJAZwwf+8SzDPSbUjWJMb0mlSE+H4+o6qpUOLJqq5xIoUbpBmPGcvlNkkCdH3jgijeutNd66HYbkkYlz8o6YT/pzLU753jvaMTdec21kw5z8g7p4Cqfffa3+fEPbVF/akzFCzLyjujz51lLNpqUNEovQN2gjZZq7yxBV8Q2k9o5ChF7KxiFVBEoshW56Fyq05wT2YkxyXqUO5HU4WPv7e+K4E6WCdWh5FXrh3+dVcc1hzlD0roMB+QBBwY5Sb0uzRpf1u97kGIPA7B+XU52gHT6ybmUi6E1EWsRFTcjkEcs9whaUzWGrDXVpGbpxQBZaTtcINkfKYP7wE1ZvARnk52TaqywD/qgiXC6TVoPZQ1ZtO5xknXgjr3DEpBU8UmVbtR6Ueix87yGJLLKqLTG+RJryuKAc2dhUPQZx/p30IUAxcJPI+JmPdV0SPpTRpuegSHnIpUZBvL9i8+D23whTkxKqUHAzFpL0LBUhkjNxUnCqkBvYC0f8uj3lAUnDQFAlX6HsPCyuH11KxbLyNu/fY3FcUs3a2msZXzGMDk75qmPPAFbE7KxaI8EDxKVjkS/IMbefauwiZyRRuUD+2KSLld64540gaCjGDUDGA/ZFSjNyN2eSxXVehnbiiLBHH37yOM+e74WWmpuYMuCEbMMpasiSFZj9wzKryAHAh3KanQV6GYLVBjT1BaTDKnN6DwmL0+JMWHsnIRH21rMvNOMalIcm9QYciK1EaWjtEJseWoTDJqwqejCKCUVpO/Ec7UnO/gMOjLQ0XJdBowi42mRbwgZY3LpiWxCkeVP5AYUZlhYkvOKrOB0sctsbrlz3PDVW5rTRUea3+JyepNnLyWe+cgWl66cEx+AYFj6YxnOQ7HyxfshZWJy4vmbYMWco9mSG6crmnduo7CoKOYrOUMMGe8TKWVaL05TKYrXQwiJNvY06/KMZGgefVsD3yXBPedM9MXfEohGDcHLFr10lGJ/fx+jDZPplKqqSrZYGCQDylEYIZkBwhkC/xA0KUGujP2Xz0pFP304Z4W+08WAqysqrcjGyc2ZhI3Sw0VKiZelUVrIF7LLg4GGDOjkQYelp1yuG64ASri9/QGVBSpTfEBTHiqFflHYbD4LHQ0oXNwiBEAGfJkkFQ/V/m/W2XRZ5ZBMvewbhd0TNwfFyjRwCsP79P/1srZk1vr8fbVS7AaNMsXgYr1wDxioWre9H7YF3w1YZYprlyucxeGp0jXwhmQ7iBY9rCh5jcHf9wFK4LqQ8FERY4IqCQ1PK5JfcXD1FsdHM+Z3Og4OZmijGO1VPPf8BcbnKuqtKWa8NTykg+x+EkjHFFx3vZgrGdhCD4v7enfkmqgCieXyM5sNqEDKokGSSGQtNNtgFatY2CJI8p+zWNHwGMlfraIE9AAqy/nKyaIIIiOdHHhfGn7CLvMkTFaoGMHAqPSdjJUbL6NRVgOdPDvKkoqeEKTCKJOqStvSyVGZnC3GVOQuyZqVS0beR+AuQWWg7ZAMK0JvPZhUSZo0RjlCSmg3JrEAZyB4km4gCayW8KXCV+Qgz3GIic7uYgMsVnu8dn2P/eM5BycLrr7zDbrTW4yZ88HvOcPVA8XNwyX81gpiIobMzIMOEULguO3oZAoQvxA54pgzbXiV4+DJd25Tfe2QmC0ut0LKy/J8hVAmwotl4WZlnKBYcOp1YlQ9fmDvuya4+yKyT0YOcoACwGZFpzKmFgXEVfAsu7ao9jHIzUKZPqVkYCVDzimxno2RacmB/957nOZidtG7GQ2YcqbzgdoEth1E26KiXJG4qVtBrwNTjLV7s2ql0SqD0iyjh+TX3OFcgndfOfZCaEhWl4Zmsljl6ZQGfrrEijxMe2YyMUizJYVVOSZKO7RfhDbOOUiTVinQtiwgAiP0TVLB6+UBiikMlQSI1HJfBvW6PMKWScXWbx22TDFTSTFhraE3GHnwHrA9//0RW0qx6NeAFp4nUSVSVzNtMntnXxdXrFyhTC6UWtnfciDlndTwr84alTJdzOTkaFeWgzff493Pf53YiruQGxnOP7vNS599ATNu5N4IQQy+yz1mACOWW+vghJWPH4YdMpu9k/dv6/2i0A9VAhsqYXxZBckUDDihVWaVogixZYUKEVECDfi4wjTLR55LSTIUnkBl5dwq29KrlsaUUKYs7mjIEdV5CAoTKlS02FwRzJKYOzK+GLMbTNQytRx9gd00caRQwaMJ6JzJi46IJ6KIyZK6IJ6vXtN1nnblOT2d04XEPDccHM4wxtB1kcN7M/TCsvId95ZzVj6xaD2td+R0TNtFuqjxq0hQFfiIM5qcZTjrjA50WbP15Ae59IHv5eILH+b5ccd77/02+3evcufGG6yWC5bH4m+sTSKqzK+8HhkVlVdTpqm1ViQjz8WotjRNxWrZkbPC0GCUEA1qq9ljxd2p44ID2y7o6ky2zQbCgAz7xbWrWv/MaVOIC6osZr3g32O23zO4K6WeAv474BLylPxCzvkvK6X+Y+BfA+6Wl/6FnPPfLn/z7wP/KkLR+PM5519+3GeknJi3S3qRMD2018TVxWRY5UQyfQYok4p99p03Qex+QnXITksGXTJZUCjjNmRGwvBam0SvZPij8j4heAwdPmQSbcFDGfZFVB6L9GwJmLLqUkpAwb5jCBhknDn2MrxKkcw6W0YZuXhKiflBYcqsYYs0fH7OiZhkSCkj1Y8xhhS7NbxRaGC5ZI3DpG7BugeevhKJ3+KhXSocMTamX0gGnZayGPaNKSFUyj6Vcza8d1nY+rmiXLLTcp8w8KWRsfaHyS73m7GanA0qydytVEci3JaUJnIG9M0iS9jvDeUirD/3vq3AU2hFjIqj05Zvv7qkqhXTbcUTHzjH3qWz2L0Jxjko5bG2buP9f49t0JXfhAYe8rcPjfdrmKxPSeRcqsFTt5/TEAqrGjK7HB/dcGtbJUydaOhaX5IRVVhcasj6+2cmxUycJBbLBYvVnOrEcrLqmNxYCfU052JOpehCx9HJXGSigyZ2ljvzBduTMc9evEAOCe8j7+5H3r1xh3dvHtC2gbbr6NoxMch0c9t1g2Cd95GUoLKOkAJJdRiTmYzNcGYmKePajq3GcRAC57XleLTCBejNJrd3J4y7Ke7i05z7zM9irKE9ucu33v4qp4fXyd099kInvapdjbMTemmJnCFmGeAaYo6CKq8JZrnLjHEkkyDKXEPKxUDFTFBpRhdA2wldd4JRa+gslWpdZh3XwT3mTKXFEawnKEjsecw9x3eWuQfg3805f0UptQX8llLq75bf/Vc55/9i88VKqQ8DfwJ4GbgC/IpS6oM55/u7ixtbTonlajFUzb3gkZQoWQxkFcR+0CFncG4j+17DLrEvRTMPBPf++RYYZhjLUD0HvueXlh+XN08JFOIIk1IgEqDnt+cy6t8/AlrK74Epn3vUtwQEMiQpCymwAkrMoXP/oVoaRUkpKY+LHnUqbVIR5SryBlmavlFJ8EohFLXJzetR1FjKSpOHAK8LO2bNlpGJXsn0k0JEtZDJQJ37cFrO6VAJ9K+RYRFzn5erZKvGSK+hx9ZjibNSrpfPLQNNj+O5O1tjjZU2gy/KJ0YYIlEb2tUL5Ok3RCZ3gEL65tkjshyliqJlgdlIVNPA0y9eZOvMlN0nLgHiHkXKsohr6Wm8T3QSHhGgh/+V7RHH+FAYRQZ81qt/4cCronvT3z8pis1fVkXQLZP0Ix85vvB/fhOtFNOdKSFIshFThiwQoLJajK5jIkZRPdw/zBzvzzmZLVDGsmw1sy4W+z1QpSF4GiccLT0pe4zKVM6yM1J8z3PP8PpbM3zb8t61axwc3+P4dEE0MuFJBpvuIJIYmpHTKKNocsCMNJW1OBfxoeVMbbhyZZvv+74rUtFoGR5s77XcvOf58mtHTJVnlCe0KpHrMarZYevyy9iLF2G0xdvvvsrJ9dewp7f5sR/8Q+zrxGuvX8X6HRqXiXpFG/pESc6rVnqdOJVtmUKppEuyRZmwjaUvpcS8px1dJOgjDnPF7u4FDm/fo04d674hA0ST+gxL3oEYViXw9/26zKOvrmy/Z3DPOd8EbpavT5VS3wSeeMyf/CzwiznnFnhbKfUG8CngC4/+jET0M/m6f2jKQQy5YlnBbW+yTB70TnTf8VaISXY5S32jb5hIVXqtA9I3Xul5zEmU5rRcyF4cKSuwzpLmHfiWyuYhW+9/L+G/lxQQSV87YIqCDgjUo8imQpB8hv0TowkZGqJM3mnWMK0EeCVsxpTQ2g0POJQKIiXqUc/NL01Q1kJaUYWyYpX/ssGVAYrYs3OyZqzFKFz2rUBY5T2k8bomNZLLEFO56YJfDSVmSuvM3pSBrZwzxtiBziiN5f4m2KSuPXzb2T5D7D1pi+yCdjJ8ldSUHDuC1rioIXvWfYRyEz00cc/4ZAi+IqnI0x/Y5hM/9DT1ma0C+/UYuGSnquf/ZyhKRA+84UM+RG1+sUl3fHALD/mZgmL6XpoftESqsSMiPqgxJHLqCF3H0dxDtqRYgXn0ENNf/xtvYDUoldECdEuyoBLWaJEBSH2zP2GdplECf0v/CKK2bBXWkjSiZXd33AnPVEp6HiljEOz99PW7LKzcG5VSXK4tF+uKGBPOZEiJLjvysEKmIkkgQOW5s5YXnj/LJz/9JNVkREqRd64f8sa3D7h17Zj39jOTcy9xOL+BM2cYPfUZjs7t4S5cYbWc4Y9u8fZX/w/S598SMoZRoncTAvrHHV/99usc76+weVlYZqb4IQtka3XPVl3HFblCaw9kUyavUxahPqWkR6K8pkqZEFaM2pbYRVyK6BwHhE4refbsMJSzrg6yLTFEyTUDfl8y9/VtptSzwCvAF4HPAH9OKfUvAV9GsvtDJPBvjk5d5yGLgVLqzwB/BuDypfNFvKr8LkuzR5pEWhgWPeVTK5SRcXZiP+izHjgamqg9PpyRadTSGFKAU4VqBPQi/qAxJVD1I/n9CUyxBVeXmxhSFsMIpcRNSXQkAzFCM5qy7DqSkdVVo4bjMsYQghJWihIow2mLSi0+W7Kt0SrjskMbx9Kv0FrjXFX00cuqrhigG/mY+xcy3YslZRkqkglQMSOuqlp0tK0hxYRzNSGJUbUsRkaMQLLMEsTgCSSMc+QQsVrjjBF9eRBN91It+JG4x/ggxuEpg8kUmqJcP2MsoVRgVVUN/Q2SwlhFenSBhx6PmUTDspvhbaCbd5xtanISQafpyBK7jOudnftulYKcTGGxFJNrYqmQ9vBhn8pHrK1k0TcTovIQDbZqiYPhCsijKolG1VP2ehNuFenVLkttLYe9gan2U8VsaK/0+ZmcTyfNP90iswaJZZyT9XmUWpKdxSXodEUXItFntE7cee2Ad799ixuHkSs/9lMEEpZHyw98zzk7BBVhPw01GUpJLyPm9VErlbGVYhQyK9vgJpfYP7gGtqYZn2ex2mfadnSqk2qoHJwQkQIqSvO0WwpuLH21UqGbPMwt6Ex5PhQmFThDWxoVcQq+2QW+/PotmqzoUmbuM6tFi07Q2inBOurLH+Hyyz/BaKQ4nt3m8Kt/m3x8E7s8Zm9+QDtRpARGAUlhlWFUO7ZcRz0xeCI+KfFFJpeFvFRL91XGawh42JTosQosmTdetSS0LZOQWGoxManbgKoK7Ck4qPTi1TqL7/9VuuyHykMhWD1GZA/+EYK7UmoK/M/Av51zPlFK/RXgL5Z9/4vAfwn8aR6aH72/WM05/wLwCwDf+/KHcl2tb0RdZZSSEf5ceNYpxyEb1FphrcUmOUpdNFJ6GKDs8TopVJvl/vpBy3kdGIFBKVIGWeTmDxmUVcSVIbaarBJONcjqvT5Uq2XK0sdM1UzQRjKqPnvNKYM11JUjRI9SSTrpSuN0jcGSjWM8GqGSYdV57Ejwvl5wzBRhLgniejia/hh6iqXuMf8MunTUTXGDn82XYgOoNDjEb1W5IpSW8WTIMrHadZ66aaiLSJKp5FpkH6hrWYytMjLBiCoYaWDcbEEWSC34VnjtqQ+KgZEb4b3n5OCQ5bKVRaQLHJ8co1TkiWcffg8+8/wrD//Fd7A96jEwwLmX4NxPwscf85p/nO1xj6B6xO83f/aBBwYRDbAN/LEf2Pzp17g0fP3IInnYfE7EmAe2yP09KkUMZRYk99UjdCliE6RRxTiDbzXkFu06fCcN0GgLEDmwSyXAayk+CVm+VlphMiJfm6Rx3T+TfXFjkmTtodaopJiHyOregu26YqEMisBUa6bNFtpMsB/4YexzH6M+Y7mzv0979XXyvauku+/g/AIdvQxkWVVgx0w/KLRaLcgpYEh4RAfKPAC/9ESB912r9/0wy8Jx36szmYgplXxBcAsZpH+Oy/tthEs1/Kvue7veLP1x23cU3JVSDgns/33O+X8ByDnf3vj9XwP+Vvn2OvDUxp8/Cbz32J0wjr2zl4peu8JHsWgTDFtGvntcvHzixgmVLLXXf8zm/Zmf6ktrNuDPIWuR980ZoSpmEI6JlFYxJUy2BKPIowaI2FyVdxYLtM1A39tBy7i6YM1DoFUaq02Z6JRyTGlLUGNUXmF0JuSGqCJupFCYwbs15yxQTkrQq+mlVDJ0gDzcKGQGFb2hKRM1t+7cYTabcfbMGba2tjFGmAmVs/jlgpg8tpoKvGUNvs18+8032L9zkx/7pz+HMUIXNK4iaaF3WmXIxvDuW2/x6quv8ekf/BQ7OzuShWgLOZBDJ+p9RtMuFswXLfeuXuM3v/zbOOt49pnnSJXir/7CL/BDn/4kP/LTj7tb/mD7/dhC9PTZqDW53KfrB8OZ8rypfhYESEYCNEvU6l22XUvoNGZ5h0lsabaMyPU+AIEpxZr6W76XFpDGqJ7ul8qEOX32UqSAJJuvlUhPJKtoxzuYnadwZ57CPfNJtE6sZvvcu/Yl4t/7bXZW+xzPF5ybyHCcRpqPUUM0GpUCLpeJagNaV9y9cx3fLdE5URsGyvJmeFfkhwTy73xTgHOK5VFA7VSYSg0WvfctI+swV859jy7cj/A9mugq23fCllHAfwN8M+f8lzZ+frng8QA/B/Tak38T+B+UUn8Jaah+D/Cl3+MzsK4qMTdTmb77L+i03Cv3C18BG5lGP9nJWogny3vloZR58DI9uK1/knMikklRSqzYiRyxcRUojStWfilFcV/K0vR1zuKj0KB8EC7tAJ6X91/5spgoSxdEv/1ksaSxKxqTWC06dAOTyZgc4uBLGkLAFjMQlKJyDrQEf1XOQQGkUErhnENpNVQ7WWteffU1Pv/5v8+f/FP/HBfPX8A6w8habDMmdYZuNaepa7AibuZPWvavvcfdW9eY1jXOOSgPOKofIgAAIABJREFUoyoj+ilGkjLQeUbOMakcsVtRVxXBt+QYCMsFKFm0337zLa69fY07d++yNd3imaee4qknLvP6zXd49qkrNNU/bp78B9t3so1Mz6NmSEDWkSMLe6pPiIa2hXjsNmRS7Ag24ZPCqQ5rRErLGC0QzANZrzYFXu0npZVw3UOIGFf6QjlhoXDXAS36/mNj8CmRnMXuXmb7pZ9Fnb1Cqhz3rv4O6vY3qfbfZbs9ECzbwCJZfFIYJRi2yuJmbHIs7miClKMU2TpuXH0HQ8BZRRvzWgFk4xhKy+l928MMZvJDXifRDJmELclX33e6H9pRQ2CXb9cAzz/K4vKdZO6fAf5F4HeVUr9TfvYXgH9eKfVx5PjfAf51Oaj8qlLqfwS+gXSI/uzjmDKy9xReucAihlSohZqYxSy7HzTaFAIaQnSPg2W1zl43tn4Kc3NhiL0nZs5D5iva7fJ9yjIhlnJmtVwW7Fxh6ppUuOoxbEx4tktu3HyPg5MjPvrKpwi5AhRVZYt3af+gaBaLJaczz9Xrt1ksArk95sw08JHnn+RX/86v8+Hvf5GPfuRFqlGF1pq2E6eXNLtH23lMM2G6tY1xNcqKimN/HjZL64FTjTi5HxyfsGxXjKdbWGNoZ8ekkLApQfSk1ZzjwwOqyqKNYrk/w/kZZ8c11179+kDVan1Hm6Uxtpgt0MZweHefe/sH/MPTI0AGV+aLJTllVq00WhWKk9kpta3w3nMcOr52tM+r3/gaq27Jk2d3mB/c4z/9N/4U/8HP//XH35V/sP3f3v6znzk/aJRhIIQ8ZNb9NkhYbP7QKtAGm4RB45xklJZEZRRtTFhUaQg+EIV0eXRTIicY1YbKwfnLE+4ddyy7SEqGkNLa6UgbbFXjA+y89MM0l74Xe+YyB6ubzN7+NcKda+wcvo3WHYvcEU1E17BKMru7kzJdH+GUpH8pZ+EuZFXktxXGNZzc28epNFAadYmuGykfOavHah9tbo9KH1WWxWuzR0ZeC5gP1UJmI5Y9fKX4vRqq6nHj3v9vbR/7yMv55/+Tf4/F6SH1yKG3L+GcpqoqGS8PIu7fjMZkZFDGWkvIktVWVU0IoXBCIz4EVquVsFOSZBPeB7QWv8oQAyhDjJHxuGG5WqC1YmvSsFx6vM8EpfHJ09RjclIslwtCEN1ylTPGCtsmpojWhu7knvDnq4bRmfO88MEPkxHV95REI7vrPJrEwb17OOvY39+XNWW+z2Q8pq4rDu4dsL27zagZ0bZSMYRyPGJaHdBaEWNkMp1y89YtvA8iYrZxs3hfhn2MwXtPTpo2dRid0cHj9++xmh+htaKZjACBoOJySUiZduVZnM6Yn85ou4hPYIwjZ8Wi66QfEWKpKqR68XGFgjJArkSnPqYCH6mhcUfpn0gPQe6/gOhrgyKXYaxM5jfeuvP/0V35/7/tL//h8+T7IM2+7wQ9NjColSKJj1br72PBxA2ZiCJEuLe0nJ9EjE9ES4E/c2F3CaHAkchGMOznn9/h8nNbPPl8zRtf3Ocbr59y3In1ow6KbGrGzQ5q9zLh+/8ocfsS1fIe9776d2jvvEU+3WesVmgtrKnNIBqAyioWIXP71HK+ydR2nfBQjlWX404GXLJsPfMSs1vvMl/O0CRUlHOxJv5unq+HBO2HRdmHrAEdhkolru4r6klir1ZUG5h7D4FldV8OC6z7PpvXzijFf/gr+7+Vc/7E+z/tu2RCdbVa8av/8NdpKkMzciTzBtYqbIFncla00eCa8UDLM9YQk4x8W2epnOPk9FSs0nygbdsSBNN9GTuA976APTDZGtN1LV3XorImRmHZ2bqR0lFrurZjuVzivSeEgHMVMQjPOgahr/nFvDQwK37ws59jee9Imkg5DpRN7z2kRAienDJt2+KsI4cVi+VCTAGiDHAArBZLtNF47/GdJ5VgKhh6xBjDvaPDjUnZAmzFVJQBBcsMPpBjJmipK1WMxNmcWITNrDPDoulUEG3vEDExE7oOH+JguTbggxsVTt/UVcXcOOVM0zRiBKEiqgiIGaUKjU4UEuu6otf3KW5p9OGBkmX91A+cHWipa0pmRCvwITKqHRfqOWnl+d4PXOLP/jsvgq5KvaLve8iCEoVHozYmYeeZ9373Hd786iF3ui2+55XzvPSJKdZW5Q4pAnJOyeQlQJF3VjmjYkIMX3rF0kA/+CNkAAU6Uoy/5HfKkOOK1QpuvdMxv7PkYH/JrVOF0Q4fFDNviFoTqxHLsIO2lqoxjO2U0PO66xFaW7LW1CNXzN1hZzvz/AtXaMY7vP7qHU7f+jVufPFvgFkNRMzhkZAovoYR1BpFVHktR7yONf2MiDQlI4GkwBrQtjT+E8JMKloUO9Oai7uOC5fHPP+9Z/HK8YX//dvcvNPRriBZg9eWZnuP8bPfh9l7grhzmfb4Pfybv8a9G2+hT27h/BKTPMpAeEiQjYBKUpWHmOm6hMmD/1LJiAVyokxgK6UYNRMOw6rMiPSiHQ9rlIrxyPu2h+XHD/ljaxQuK8Z1Bp0Zj8wQ3Ptz3P/tgwVC/7H9IgAiK/W47bsiuJ/OZnzxK19j1DjBuItmkO7Loqzpsuhp9xiV1uIiIxrfmslkzPHxCUpXMtgToxhipCRN0TLKn3MZkY8d1hjqpibFyHwxJ4QMSgJ81TQSCJWszN7LtJwxGmMr2lWL1hofBDYiFL6qgu2xTDOGdklOnhAjKYq1V4gR5yyL+UIWiJzFniuEwYRXleNbrVYD5p6i0Ps2x5JTSiXwlqy9YIhCQcuFFlkYLimKa04xQ5agJAyFulYDrjeqHV0SC7ZpU1HVDdl31Eoc6o2RUXudKVVVEL2LnKmsmK5k5HcohasqkYq1BueEDZSMRWvNeDweroupZdLQGkPj6gEP1lpjjZVxeKBrPeJ5EZjPF7x79Tqrg6uE3HG6WCBUw1AWIbFgA0CDTlbul17XJUO2C7zOtFaxjCIoibUMDlfWCNffmzKcIBISykEqQm7KWHyXCBlSqAkRfMzF2BpC2CYERQoiixKSJvinmC0879zp6HKF2Z4yvdCUgKi5dXPO4XHHhy5d4pMvPYetEtoGnGkYOZkdqGonRjVK0xg5LldXfOGLvwqqIinLJz79CtfdbW5+2bFsl9iBSlcy8txr4/fNziKtnfuXbASfdQI8wAoxiaZSiGLqYpUwW0KSebzx1PADP3KFc8+M2d4b886rd/nWl6/y1hsrTizU2bB79lnU5WexT3yYsLvL/NZVlq99gdVbr6IWh2iWWIqmkIIuadzDkmWl0DFjsxxHSqKGOgzd0i9NGZ0VUSmc1kynE6yOuKbQd5OWhfjB9o96eMB8GFCTHobDK7BJM2kiqwiTRmPj5oHk9Y4+Yhvo4vw+89z/n9qOT075+A99lunWmNo65l1L161IKdB1gcViRewy89NF4WBnUoz4+RxrLW3bcvv0mBgiL3zwWayVq1JVFaYMPQ1fa01dN6X5IwMTRgvne7I3KTammenWhJwDzjVMJtvih6qVvI/NdG2HMZq27aR5iaauK+anJ/zVn/8rzOczRLpXsmKtNNZaXF2hjWHkBHKyxjCpGqxzOGdxTh5Yay2TybiYfSjqusaNx4NGfA8xWWvxviOljHOOqpLfp1TYLFYMpauqxjhTPCxFv8IVAbKmrgaGT4yyYADYFEh+Rdt1+K4TwbKcWC2XtIsFVeU4OTkplVJH9mKcnRKcLpb4EPFR7PeO5kva7njgtceUaNuOECNd27F/cIJSGlcWgRgDldWMRw3OCjXzdLYk50D0LY6WUVMTleFjz13AqcgieIJpiNljcsBkkIhsIVmRUFeKrB0Zmfr18UnUznl2XjjBqXOcf2oXby7Qml26CH/v17/Oqs0czT1ntnc5f/4yly48zZNPv0gXbqKNJUSBvDCWatQN713phhqF1qk0ooW1kUKiSSJ8933aYcsitMqeEDustfxP/+vnmZ1cZ7pT8eGXzlFXNSrXdCljOCi86Eju3cOWJyjjmJ0EjmbHBAxbWfHN3/486fZbWB+oQy6ywpubwj4AkUu7Zt1g3czye1WHiDQqTRdoasPIKqKCvanj4lM1n/7JD2ObWqiFhwccfXWfV2+t+Pa7gc6OsD/6s7x8+UVoDG+/9VucvPNN0uu/xUU1Z+wyYwWnKmCm0FiNzmrwZcgZvOV9QTBiMCGBU8ycwvnMzjSLttP6EIgoWRyMRmM5e2HKHSfPaG9a3xMxHtweN2R337YxI7D+W7G63DujuHEzYpSi3qgEBp0qtXnS378NDe5/EoJ75RwXrjzBk089yWqxhOyJSUygjXXEmHFVLfACks2lGLFAU9es2pVAHM7RBcmy27YlxUQosEmM/RSUoq6q0nxMhNIUjSlwcOfewM89PTzEGMGNezqiOD5pVIy0XUuOic77ou0OrjJMJ2P++B/5I9KQBaq6gdKMrZyjGtVCNdSy0CjE6WZT/AvkQocC0YQQcFYCnlQRRdkuZ5anM7quw4civKQU3nu6zg9/632HqWsWsxm+6/DeSxaHFvjG+0Fpsqd/ZjK1BlIgBtGY8TGyWrXMlytMFmjMWYvWCu8DdVWjtSwgN+/cJYRI3VTUVUXbCcd4Mh6Tg2jgTKdTpqMR3npWrRy3LvBN1loeLmVZdrLIHxyeMK4tKgcund+i84HFssMCq5A4Pg5E/RRZZ6JyRLbJVGQqUGNSvkRWlqwqkW5WitXelPHE8MGPRtp8lqrpWKQFAc3JouOLb7+JD46Ji3zuD/0Y29sVVa049rcZVzuIqFvEjJtSTfYaQ6AKKyJ0gd632yiwNmOCwHWtP2GWRPvfdIroPW1ITP0J59Qpbn6Nm984ggQpKE7aO+iVwGbL1Uq46jmjvafNimgckwvPsjs9y7gec+/4iFG7YjoZ0Rp5/ePi05AZ9lPfG7/LKsvAXsoywWmgWWkapxgZjXGac7sNe6Mxr/6DN4QnnxTvvT1nHid4dqm+/3Oo7acYqcQ7736bxe03sDe/xlnrGU0hJFi1Eadh2zl0zPgYZZxfsfazeIiCg1UFvlCUBEdhcsLqjWpDbjJUKD0GpbC2xmkZbLJlQlu4CA/Pvt93zh6Saj+s8Ro0mKSojSZrESPTpq+gKHIfsrBuLg2KNQRzHynkMVLW8F0S3Jum4Zd/6ZfxnadrW1In4lrGaZyrpTHjdMmcHXXdCGZrjWS71mGMuCUdHJ7QdR2r5YqcM6vVihgDy1UrAV4AY5xGxrbLKtl1nq7t6CddSQnrytAQEaP1MFpcVzUA7aojBHFRclahbYWraj72kRdJXrwXfYr4zq/hm4IH2AIxpSiTgCkKvFJVDacnM1Ztx3wxJ6PoulYye6OotaFtW5a+xRnDuK4LRp/pfMRYK1l0YR4F7+m8B20InR+chnzwmDJiDrEMPonS3GQ8YXd3B1KkXclgkjGW2lW4qbB/Ki0VBkYCmO86Vn5Fu1zKEEzyYntmKpxrSFlDCHQhMrZSRYU2sFq0+BBF4iFmrDWlyjGE4PE+cjqfC2XOaKqmYTlriUF40UZDZEXoOhYttP6fIjUNyTkSDTInYYGarLvCgCgqmGi0S5AUSSdCPqZCo6LHKkudFKNsqXVNzsecPXsGFTrUKjFlTMdKHuyUMCqRQ0b5IMqdMRD8UtQRg8f7rhxPoGtXtH4l57tdkEMHMdDGgErglx2H777J+dGImpqbt1akzjFSiagzGIEMx6MRuiwksRh9uNxxYBxxuSBay/kxrG4ekrVQAI0toWiIERtZbYFiBPlUPSNv2BxKuOJWsGOl4MKW5fyuY2w0xkWCD7zxziFdCylrgm6oLn4/ce9F6q1zzLrM8tZVrl//HerTW9Rpzu7IyxUJUCXJvKOCmCLGyITzGg2XfbIPY3lrVXo3hpHVLJXC2lBUlEqDVJWmsSmGO2i0Hgk0WKqVnimzufXnLG78oH9Jymyc0x4Pzw/8vQwXWq2Z6MTYGVYd7IwpjWwGD5GwQZ3uq3CjVWlwM0h+2IdAP5vbd0VwPz0+4s7br0ljEABVJHspxtlyUnLp9mhj6HwkxMy4bmhXSzrfoYwusqBeAoQTuEUacoaYJes1DtoIvUCRaM/AuLIDL1xZg7FWLnQSLQ3vPeOqkkw2Z0ZVhVfgQ+DO7bvkbIkpc7h/g1HVELNisjXFaVMwQDH0bbuOxXIlDbANU+imqdHKcXR8ymy5FAzTWHKKWJ2xTcO0bgi+wzQ1e5MJvp1LhqQURwdHxJxpXMNo1JBSpCkCa8o5zl65TOUM7WrFYiZWxSknQkgFw8uMJxOaqmI0HjGfzanqKSlBU1fUdUXwHbVtsNrSek/UlAVKNHu0smilOL97RrTXVWZSW6aNYPE+tPgYsU5DjFij6GJk2oylWgmBvb1dckocnR4TCJw9u8V4POalD32QL3/1NULwrHzGWU277Dg5DWQf8D5ytNxivHWRqFusFliMrFBJEe2YnKQJnotFYlh48mqJ9wtateBo1ZJXC9qTeywXM5qT38S6irNs8ZVf/K/L/GIkZE20Y8hQG1l4tVIwvYjRGms048pijMI5aQLnojpqY8DN94kxMJ/PS6Peo+b3mC9bNJkz/gQdNCzf4upsSfCWJyZS/ciznyGGoaeUc6TN0KBIP/bnOY2amsRocZe3v/4bBL/EGoXtG6QDni5RapNCu4nr9lvPOzcetlFcfGHKR37iw9zan3Fu4vB3j/nCF+9wmh1m60l2XvlJTp1juTzk5re/wer6L1GrjvNqzl4OXCATXa+/spG9FtjIwDCpuk5WN7LZAZtY72XS0vjWCbYdzLAYGwWeU4qcRfoj5ky2IuxnnMbVI9AyxS2KmuUcbVQHfazW/T5sxG53XzQbXvjAJrl40JmRhq1dzXGXubyrhfSB9MwUUGeFLcOJIUq/sFVCxTRaJMRVvyI8ZvuuCO5aa3anU9F0zzLcIFoTUWh4IRK0YntrijXCHw/tApVlbHjSVIwah6kctask2KRIKJQ6rWCyvV24uVYm35SmXa1YLVaC37ciNKWdwzlHSolV25KArelUWAC6w5iK5XKJcxWL5ZLFcsFy2dKMx6QgwWl7OiFlTVPV1HVNCB6rFdY4trYmdN5TzZc0ZTBovphT1/La2emC3Z0tLl46j61ksGu1mGMIJF0RYsQwpqorDLBfgrQ1DqWgqaShvLU1pXJV4eNHjhYLJpMxH3n5Jaajmps3rnNn/5CDQ5EAqKuGqqrY3dthPB6jteLmzdsSjFzF9taE2XzGql2xs7PLyWxGMxmRC2Q1ahpO58dEF/He44xlZ2eLmDNKSXVVVRU+tpzOZmitxNxDW7Q2uLpiPJ7QdR2XLl1isVwSSHRdy2QyJoTE7776GqPKUZ07x92bVxk5S8qZLiacAmMi3ewae+ct885y3HWEuCSGBT4cYfePSTGynM9LJh04PNlHdx6/ammTI4dTsh1zugrcuX2AyiNwFvPBC0yefkrOuzZMx3tUtHStJ3aiKKpyJKzewntPu2w5vH0sC8lin65dEIIn+I6u66iDkXtK9aaGUrU4pUgpUrVLnNbkPJLq01tCncgxsMpGKoYySJaVZppWpKiLqYvAZ8ulYTweD6QApdYj63oYrgPYmLBWQ09+43sJ+CNlOVGelSlwRvDkbx3x9VunHC8Sx+d/iHzh+wjnL3P13V9h9s7bpHuHjNpbPLOlqMm4HNFkbGVYxvQ+WuZ3CmnfNzhU9j2bXOALMRUxChEFLXJCKovaYq//I0J2aiMOqwGOAsmQ1fCb8h5DI2K9r6aXFuJ+2GSoLcq59LnUixnGjWLeKlIhUCitsUaQyAC0ZKwR966UoEm5GJnIB1n1fievB7fviuBujMWYBmcVdVNxdHQoJa61jMeSbU0mI3Z3diBTYBcPyjMdj/jcZz4JSjHd3uXFl17kxo0bnJyccv78OUajEfP5nGefeQ7QxJyYbDUsVx1t27GaL1gtl/jOM58vWLUtp6czrl+/zt179zidzZiOpwTvmU6njOqGK1XDbD7ndH7Czs6WwDhaOO0H+3d5+unL7J29xIWLV7h86WLReIfXX3uduq7ofMdisYKsaLuWs2d2uHD+AovFislkzHw+Z2t3h+dfeIH5bMZydsrYKTosrQ+8+rWvcfPaDWbzU86eO1caQYnLVy4ybhqOjuZyA1ipcNqu5fzeDrvTBmLHeLTNP/MTP8Z7t29z8+Ztqqohxsx4NOaJJ69QVxU+BF57/dtYZ1ksFqA8y+VCMs1l4DOf/UGUMRweH3Hj+nXaxYqzaYfZ6YzgxZHJOQeuFvN4LQ3iGDvGzRRFYn56Qt00aGNppls4Z4kxUTc19Vjq1RQSxhoWiyW704b58T650nQxkXKHyrBaGaxx5NDx65//FbZe3UGHFaoNIqQYMykquqpBG82oqXDOYK2hvvA8o6ypqMmj80x3TtkbbWPCCTeufwvUPq4xPPOcY+puo5KH4Lnz9g2uvXmb5aLj6HhBDDJfMQoDI5nh8VM1VikqSoDVjtNG1ENVKiqjGWZBF0grY+oJnozvEhlLVBZUxlrNVmxlnD4KIySROcWQtaEiM6ob9qayQGM0zlq8FZ65GoIQG6l5mdaEgY22+b0qf3CkImqkmaKZ3V3ym7/yJt84uIS69BK8/EGYPEM8nXH46q+y/N2/z446YXukcGctbhnQZeYkAqc+UZfos8HCfER0fwDiUBv/Do1P2U9dxNacEaZMyjIdO0ga9Il5lnmMvplsrUgGpw0xLvdA8zkXFk7/9fv254E9Hhwwy4tHZa6jirBVKw5iYssakoaQZJDMKDGQEolyuUec1VRZxNiCVjTWFFLVPwGZ+9b2Fp/50c/iKsNiMWdqNU89/SRV0zDdPUfTTFi1S7anI8nQNdy7eweTHNdvvMMHXngCZytan5gtPE9evsC12JL8ikVsaaqKGzfexTmZjBxPas7snGGnrji7fZ7We4wTn8YQI77r2Nr6rNzo2hGioWtbDu7t8/obX6eLsLuzw3zeMp1uoZXmZ37uD5MWc179yhfZOb/Lhac/SMbi25bT0xPGTcOnPvUJxtMpJycn7O2dHRaek3u3MaZCKUvOgVe/8bu8d+sm166+y8c/9gp7Z89w5+577IxHHLcLXvnYh8jHMwLg67FIExjFb37pSzzz5BOczmZsbW2xs7NNUzfsbG/z1qu/w+3bdzhz4QKjyRZf/NKX+JEf/hxPXLqEtZqT0xNu377NqFJUjcYly/e8+AyXn7jCdLzDzRvvog2kEHnyygscHtwipCAaNTnTLlbcPZ2LIYnROC0spZVfcfvmXQ4Pj2iamvnJEcZodqYT3nn3LWEqKUU3X+Ccw1nLqm3x3qPGmroZ03WesW1QWfHkOUc9nfLe7evMlyty7PBqRTPS5KQx4Rr/7I88iTYVRgUY/uvQQZyJMsf0doHp9IRvffEq195acsuP+cxP7nHh/CWUj5y5NKObdaQE8Y3rfOOtI04OWtpFJnlDcGKi7ZLGkqmMoQcWyZlYFEcT7ToGZVARcuvJWdFlkc5IKCJLjFIEL0N7BS8hhETXwaKN2LCi1abQXYvdGpk6RjpEsiOlhLIVMXusFZhM4GRxBgM1CFeVnaWE/IcGKvleMcWS3C715U+zvXuOlQqk1VVW1+4w+8pXuTjeZ+QyZ62C8xCKjID+v9p70xjJsuy+73fu8t6LNZfK2nrvnukZ9ow45MyYEsURSEo2bJIWRBvmBwI2rA8GDHgBbBiGTUKAIX/wBxvwCggmvMiSvEm2JMOCAAGmRVq2KJMjDjkcNmfvnp5eqmvLLba33cUf7o3IqKrsntZ4urLYyANkZeSLqHgnzot37rln+f/7QChSzKtzx40oQW8U2DrbFpnL5tA5L3tYu7UYDKJ7hqIZlgYfZZOvXmezROf0vOhUoHWOgRaMFUDlVGnKweuMTqtVggE3OtX+rDljYrJGnz22JqWacmdaQrFVqTalS5T0WLG0k45/+KWez+7XTJ7aQ++MsEWFRqGMYDOyqyotb7/xLv7ukp2bB0yfv4HEBAMR0fArf+G9jPJkOPed6YQ/8zM/nZHZAohlZ3eKMmk4xBizQRwsrcU7z3Syy8nhEVVZ8dab71I3Dc8//xISe6wOvPjcDQ52duiaBofmdFHjvGNUlty7dw87O0GXBZOnnkaUoYiGUA4xAtUwZch8CJTaYG1KsezvPc+Lz13n9uE9BoMBWtmUsvGR1eE9lBbK8ZjJ7gG+bVI7Y2kwMTnxyXCEeEfftcxmp2gF7fKU0mr6GAgqsjve4TOvvMLHnn+ev/N3f52mnnN8r2NgDW3TYqPQdw47GqJCoCzHTApDYQ0/8YUvYLSwXC4ZVhWDwZDFfEbbtjz97HNUgwHTnQmD4ZDjwwPq+jjVJwYj9nam7EwmHK86mlXN/cNDpuMhh7fvsBqcMp+dcHR4hO89v/2bv8OoUlRlwXK2YLFY8sxzz/D1V19Nk7vK0nqDwyFEjo+OEISysMxOT/n4889wY/pxju+8i1aa3jkOTxZURdrViK1oXeCFT7zMyb27+HoFfcPupMDVKa8vUYg+UGhL1wXCYIBjycnJLcS9hhaPivosLxnT0ArAGmtIIJNCa8RFWoRRUHB/xutfucvJYU3fJNRCJ0Ld9OSmotQf3q77vf1mVNxvwVmsj6XXZF7g7ODDusi3lcJdF+ZiXKcEhKgCeGHlHHXbpMWBLi8WcRPUBh0xnU9NB3ZIbxzloKJnAhpUCBRF6nQRScQqKqau/8bmnHtg43CdilQCISg6CvTBK0xHu5jxdU6Oar7+jddZnt7Cz26xO4i8eNBRSIqUo6y5FUjrE3EzrLNOkRsEEzeUOWd5d9apmvUOIl2vs1719N45Vs9NDilHEg3QQVEWNFrT9Y5hqXnm6QqtBaJgjKCtQuk0g0E1YHrdsP9Tz2OMxmhFURZp12MVGL0hkRGtUcqmul/GvkoYMcmO65mbNRnOepu0QXQUAQLaUcB0AAAgAElEQVR99Kiy5IdPbmHMXaxWjK/dJIgF32EKlWgPo3B654hyEZh8+uXcaWc2U8TndelsyxPh3IWI9h02pglCVVV0bU9s2jTe7nt87xgNRpx0c5TSLFcr7p/M+M5rb3Dtyi6379zltTduc+OpA6w29G3H7x4fce3ggJNlQ9329H2PMYrlYsHXZ6eMphMmt+8xqEYYNO/cO0yta97xsZde4vXXv0PT1OxMJkynI05OT3nm+nXunR4jIlRlxWKxxCrFatUQok8dNlpTlUNAGBRlassMacS6cT2niyVKG7TAld0JXdMyqxswBbujEa5eYo1FQuQ3f+P/ZdWknHzwiSzi9PSEokh8suPpDiGkic2D3SnapMWwa1qsMcxn8wRN4D3z2Yy2a6iqCpHIO+/e2cASDwYVZTng6WeeRikY4Di+9SZvLZccnxzz4nMv8K1vfhtrCl57/buUQ82VnV1c03Lrzl1MoXnnnVuZCk/Rx4JiaLk6rij7hkFZUOQumrfeucWdu/fyjiNuCrVmUGKUZTAZo23Fn/zjP8bf/3u/zoqe0W6JVZ6lh+PTUybDkspqlosl4Gk6R98HTk9WNMsV47GANmwYmeTMKaQ7Ljn6xUnD3Xuz1KmgFf1py+9/9RbvfrdJOPE55eXXBbmNw87w8LDVGx1Zz0NuhoNIgFgx/x9yn7ZsEEjPiplrwpJt+F0vHt9repe4fBGQDf/uZrlCaY2KAYlCvZqzqlcYpSi0oSpAbIpWO1MQSK2MacgNRiSyjcT+lTiMi1oIoylx7wrjmy+in/5hVCe8/ubbfPfL/ydFCdfGAT/qmRQwGUTwgraa2AVY889ILlS6xEegdL4WWhJcgQhmHQWLYMsELqa1wtp1dKyTU9YKrRTWanSe50hTucnzV8MhsRGKgdAozbv3IrgFH3vlSgYU1Cn/brMOIX9HiueQZz4Oa16ITeomt69saAzXxQg5+xHOVqxcuN0Q4jwsSgGegEepkp3JgOHeFe59+w6j/WPM3gFEj+892inqN+9wfDTjmc+9jNYlG1rL79HfvvGrTwK2zP7OOP7UZz+JKMEYjfeavu8QgRvXr6bWO1NwdHzKyXyBjxFbFjRth1EK1/W5E8PR+8BoOKCsCnzX471jsWowRQEk8oqUS0s37XAyTKPI3lPktIA2mqZp82KQcrPBO4zWHOzvgU718a5tKKzhxvVr3Lt7SN3U+OCSXrag6xzL1SoVeHOvuraGzjmGwxG70wnRdSxWS4qioCgLSmOw2rBcLrFlwXK5pOs6rl+/RmEM1SC1SjrnGI1GzOZzvPdUZYEtbMqVZn5T7zxW69w7L5Slpc9pEx88OsBgUDIYlBuEy8XpCU3TokQYjQbpxpLUXxx8wv5eNf0Gc0QbvWmFlGKA0gpRhmZRE0NDFOH+0QnWWpTotM11DcTIUzcOKKylbRqEQHA90/EgsfdYS4fh3vEJMUSOZ3PaPvDi9T3qtmHRtsxXDW+8dQdbGp7dn+LqBftjy7/wz73CzRs6g1Sla953PrW+xojrE1Z58DC/d8r945b6yPHtfsIP7a/QPuD6lBsOcU3Qku7rdQ51K2h+QPwZy+PmwZrgIW49sU4TbMv2rbjudI4qMGsNh63m2WEDErHZmWgJqZALfP6zV6iKVJj+cvkLvPgjf4Knb15jb/5FTr75V9CuptQGXST/Y40BMSA6R6VC6+Ddd3eZnVbc6gccnZ5S6Y5SLXn5ecP164bKBEaL28TdfRpdcXJngRbH1ecrFIkUJxLOAMtzxKoTpyMonX2hQrsc2ufXJd8om93Nxlg6btlvjfSq839bTzMlajtyAdVFxWIZeeOrb/CZzz+do20LwRNQiYdZUmXB8TkkfDHZgRThi8gGjuCBC73x2dtFi63HsjXW+vAXJBEbgIK+8zR1oNABU3Qsj49p5w7fB0ptGVyZoCcjlDHoYM55s7wr+OH//MnGlgkBZo3HGs0z165D57hz9y6zxYLd3R289yyWNbooWbYt1WhIHyPGGOrlinrZ0Lk+LQ7aMNzf5+DgCm1b03UdJ/N3qE/neTAhMCgrmrbLjgiGVcVoNKAqEtZJ3/fsTsdpWAjSVs2mSVitFcPJhKZu0JL6T+/evQsBdqbj1C0zSUM9YShpEKrt8N7QNx1d0+NCoLeWul4yLAt2pqO00FSWYTWgNJZBWRAITEYDgvcMRwNWiyW+TbtFpYWuWWKtpcxtiqtVjSlSUbKpUw+2yQNfvmuZjAfUdYOxmul0TFloSiNIcLi2oW5WVNWA3gf63rFoesChcLi8O4gxDVrZmIaalNEMBwOGVpg3c0aTMdVwSCWRQimGpWJs2oTCKcLudAJ9RYyBq3tDCmvQakjrEzeszY607T1tr7BVKjDbomTWrHA+sDuZAB7vDONhxaLpaHtP1wdms5Yv/fabHAx7JLeshpCG2FR2ij6kaDtF34JojQShbRzz056hDUQFEYUPgncplXEWYcvG0TzyXT7H428A00i92usExEbW7YnbrX7rV0hAkyAftKRJV5ULoErIbXHw2Z+4RjEeENWQ178+JASFk0i1P+HZTz2Hoib2jmDLXIwTYlQ4FPPVkNWp5XhueOsY5suGw+O3eWEy4/kXxhw8v8PkuQklGvoGfXcJ127SyoQR7+LbGj3UKAoQTYz+zDbrrht9VmjOHyx9kR+2o2xTF2aH6deIqls7sBxeR8l8uQgSTPovQdAoxkWkJCYnnaAm81qSYArIhNMq+jz8dFYv2ZQhiFsp/UiuGPCocz/n9yNZk7OoP+JRpcYvBcqS8Y3rjHeAEHGVRmmb/rsLBK1Q66hhk6s67/0flCfCuffOsVws0Frxzq1bNMuGpmmIRE5miVs1RdMNRitmxycpigw9Rmm0EsrCoI2iMgVWRZazY4rSoHBc2RnRNu1mWzcYVCnXrBOGxmBgGQwrQtfR9X3CkMn75zSd6jg5WbC3t8O9+/dQxzMGVcl0OiJ4z9HxETcPrqJizDzKyRn4vmdQFkxGA+pVTa1ARDEcDWk7h9WKtlkxnoxSXhSHwmN0gdfQe89kNALg8OQQek/nWpzzXLt6hRjTpGLbdfhuhVE+EW/3HbuTiqqocF2XintOMx5VSEzTraFvQDSu74ldpHdp+GY6GlJozfFsQdOlBTMArncYBeNBydXrV7ARVl2LWJ06WmLkxpUxYi1BGerQUxlFpTz7z15FEPoQ6TpHbxOaZtvVNG1gUJQEUjpMtGZWtyybjrePG/Z2p5RGGNgyT9JGJqMRzWpGm6GUm7YjxIg2lr7rOTlsqZzHhDOk6ZQSefhuSDjfvetRKPq+SVF+9JvpP6U1RiV9N3RrrHGP1p5rK/u5JmvfuvO0ipsc8drbbQeAsvU+W4fzfxb6IAyiYlgoUB6VC5U6559FQIYFviwQb4nRobQHHC5oTCwIoUOKiKo9aIP3Jct55HQW+NbbFb4zHB3OqN1rXLkuvPzJCZ/42IuU9GgtRKeJfYuIA9eCXzIEem1AG2JUiLYppx/VZkIzrn1jWDvjzMCrIgS3Tqpvfebi7IJtfH6Zj23Zd1P51Vupt5517i31sGcHF5IdYz4um7x+4ggWMfl8+T0zGWzMXUwPOvjtFMz60HbkvvX8Q5K6bZJ+6wG8Vd8yUVXaKRiFE48LgSrGhE6pFAF/9o35oL2iPCHOfVAVfPaHX94s4OtIPcTI4dEcEUVpNV27ZDIdcv3aVUbDKSamUeGwjshtGk0PJIq7pvdYY5mOr1IvWwC0UXSuZjwcJgyTKAnYKziU0UwGFTGmPGHKfQbKQhPHBRID4/0RUaUUx3KZ8Gz2xxUhtAQfKYxGYkCFDq0Ba7BKOLi+S/RDXOcQiQzHY7QyDAdjWtcCgtFFggrQHVWpE5mHJPb1YWVRVojeUU1LvJtT2JJqqBgNCmIUjuc1SlmK0YBV07BczjO4GpRWkNhx/WBEYTTKWtp5jVKKq1f3aPuOGD2zZUvdtbgQMEVJlMjAWGQwQGIaCFvUjkIBognB0Pqeru2RJo3ZxxgYVZZF7bhXe4gtw4HFmNTDHdr0mfZ3JvlLruhC6jM5XSyZzedcubKLLkrGxRjfRHxoGF/bTTUE11IWA4bRUugTOh9ZLVbsjUpaFThZdVwdWwoV6EPG8lecbeDljF2oU4FRFLrg2ZOA1Y5hufaYiVABiRkhkU0KQQT0FkHKOtbOlMqb7gkQoj7LNae8s6JrHTSK4YHBFJayIA8YpQCktGlX6bTgoubtNxquXh8zmRjagaJSiq/+g9uELmGnRD1GF5bYC66f0/qGKJ6i2mc1H6DDitnsKd6eXeX00PHut07x3Sn3j77LZz7xLT7x8g1u/vQuxj4HGbAtFe4G6aaMnmBsHuoDOkc7GlCUPW7piG4nRaMq55s3u5EseSppHQynZ/Q5TvCcHIg0DzpxhMSPu855b+fCVN6VCGiFGU2IZQnePeijGYBaIFIRvU4pKthynjHtl84UPovsH/hk61z8Q/nwc5ywaEnbRqUwhYUuwWrvXBlBG4g69ecPVV7gskppCOu8c78/sc0T4dyJUNcdak12TWBUpdVsWA1IgFKGupljC4s2OiEoutRzEPMgQp+R47ROefMCn7bfwVNVqcdZiWBcHuVVKt+0aQXX6DyhRpoG02m8pK4btIJRVTIs7SYlWFVVhj7QdL6jKksGRcF0MkyTgzGwWCVY39WqJiLs71+hrleMxxVd53L9YEDXJ8z0ZdNTFCk37T3UTZ2RFw2JjCSlCtIIcqR1jq53JK5VTd22OCd0fUfX9axBxjoHq6YjBAOVoq5XtI1Da0199zQ5HSXUq4a6SbuXQidCjr7rchogJsRODLa01HVD3S3QxuThqnQuqxXLZQsx5DxsxPue4CN7O2N6nXKaIaQCegiRxXyOMZaqKAnjUXaswtHJCQMLXe9Z9tBFwVZD2tkS0Zr9vV3ePjwmkBYeo1KEVihhZIXAGqMGlFlv79PXThCGogh9pACqEnYOSnYLoZDk4JTJTk4rtMmQxTp9N3wu+NlCp++WUphSNlPHpkj1CKMsyuauCy0giltffYtbby75/J/8JNVoiNaBaBVIgkZQKi8m0bBc1ew83XD9mYApNbE1rA4XfFPfTd/5dStJSO89qAYo0VSd4c3lKXfendCfGuarp3Anxyzmd/HdN3juZslnPz3gk5/8WNJbUiuQWMum8rslIpIiZ5WgJKwIvTpzhpuKdZRtL5pFbV72veXRHdaZIz/vvTcKPnLIaLNZTM6yK7kabg0xaIJSefhJHozI30s2O8DtF36AiDpkHbNdtVZE5zOz2bqb6n0+3wPn+d7nezKcu0BhLGucciMJWtaHQGF12h6Jp7AJk0RI+CiE1C2grU4xQUyIe2ENnq0SyiD4DYAYkpAd1x0ORp+tfs4FQgZ+CiGkDhe9du6CNYbdnSnOeZRWGJNwup3zWGvpvaNddPSk7pSubWmaDhHNom4S6qWNeBR37p9uIIs9kqBstaVtU07caIW1KSJ3TlCFQtsCiZ4oChd62qan7T3eOZTSiffUOSSCURrM2Z0Uo2K+amg7Rx9SkdNHlaF5V5gMhaCNoLWhMpLwaWNqFUyIkIlacFAmusHBYMCq7XB9AixzXcdoWKFI4GxCGrDp+zbTuUVms5P8vCUIDIcDYohc3R3TtGkx9h5OTxcEU7Jqenon1F2LsuPcgSKMRyMm0wnWGL76ne/gQkBbjU1ZKMbTkuv7ZerE0AmyImR8Imt1mkxUivrOKYeHDQ7hmadG/MiP7TIea6yWBIxWFclxm/yT+5xTD3Mq6qn0Tw7n19junPmKtPffFEmdCyxv3eXO7RXlwRg7HhLoKTCpcJt7rYmR4BVaO9Skx0wUi3srTt9suPXGfSQqykrTu57oUw5d0BS2xJqSTglferPhnW/sUMwtVfMVdvo3eepA88oXrnL16hivFIWPQEL8FG1IK/ijTkaUygzXBroOoyJerTt84GzW84N48K1UxrnPsZXu2HJomzTOdsQMD3SnxPU+KnMGnNc0IhGiJqyZHfI1elB94dHPsrXIfBCH/sjnSncFMaOjukRUbmza8b8n0dNW+ip9sf6QOHdrNDdvTDNcbMBEizIam9EEe5cwvJerBjKMrrEaMzUYBaWWRNLhoXEtIQbUGkdGRRAFbRpz11rjQ08MKSpt2zo79EjnEjuQNYad3QnOebqu4+DaAVqnqP7doxnj4RAdU5FuTZ5RBIvLRcO33zkBQEgdGaIiSg04PemZz24jIpRFxWAwIsTAyeEMJDAqCm7cvIlRiW5sPJkyn68gRpyvOT48omubhC6oNWVZAsJkOsEYxdH9w9xmZ1AqdUQkomzFvK65sr9L9GnRevbp52iaVSLQIFDXK3zX04YCT4dWsL+3kxw3iWAk+EDoPMvViqPTlFoZlCXjYUXdtNiJRqmAVgnErCwspRIqWxB8WhQGZUHbNLnImMe/jeB9wVt37vH23WPKomA8MOhK4WKCSTWmwPmeqeqRbsYnnn+Gpmm53cyorKEHnERGOuD7np2X9/mJn31hQ+8mukS7dWJmTQgNd3/jyywbj+sVV5/e5+XP30QVGgnJeUSVb5GHZ71zCuXRYDEPFm2nJfrUSrdJ68RA6QyVt6g4wPQVXgyxMoS+zzybltgHqBu+8ztv8N1vzvi6LBk4jblZcfXGLoe3a2KAsioQ0YgpiH3a7Q0kMDQlN8whH3/lkKeqY67tn0J5A6EgNAaCp5SO3klyLtYmWMYHCodn4nweCKrGqMUx0jYUNgGWJdCrPFp51u7yqMj3cIobvJT1b1knqx99nygb/PvNSrruygHwkb3dXXB1xqxRKWARAStEb9FYVFylvs2NfutLed6isF5YYN26+ig08Ht8vnVUvqZZUoL3kb7tKQcGUOdytKa3zBgKDyx877+IPhHOXfKFMsrS+Y7GtVRmQO88xyczfIhMJyXeh8w65GjmDTvTAaNBSTms6HOLXt12BO9TFCoRrSw+BJqmzeiJQlUVdJl0OgaHzbjoqERp5zM2R4xpF7BcNIkYIZNS3z88prCW8XiIVpkAwzXUqwVd7xlPp/jMZtT3Pt2wSjEcWaILFLak7zpW+bNMx0PAIyqyWpyilMFHGI4SI9BqtaTtGrSKDApDWWU8FlNQ+5a+r4lBc3V/j7Zp6TPzEqTIWQNKAq5rMVqjJPGa9h666OnbJVoiw3GJdDrlen3P0ckpi1XDsDRYLRv2Je8jTdNTlpbFcklb1wyGJavG412LFpXsQkExUKSmQqFveypr2dvbQSvNnbt3qVcNO9Mx908XrFzEVskWg7KkJ21dUZKGyXrH/s4OiGLv+k1Ea37r1W9gtaHue1ZNy7Uy3XjLWYPyqW4iIkSXOTNzQW89Vt84jxOFF4NVJkEBK5Ny55toaZPHWX9hH/gTOLvPzhsJL/TZdjwXBRtIKQ2JkHlAEnNQGpo6fP0Wd989Zn77hLtHDYuV4rmPj/ncZ5+j3B8R0LzxjXu4Fgg+Fy0dojS2MIiCulvwxz79cZrDrzKIpzi9g503qbBqFQ6N0xWljsmRuTT1Knqt74MfIxATAY6xKa3hO7RNAcamx/uRAuQjd/tDvx+WbfttO7Ltw9u6rdM9W+mhTcQtUFpibB58m0wuI8YQTpd09W2qg3PO8376fUAu1YcU3/qdWnUlQw2f9ci/n8P+4CkZeEKce+8c79y+z2iUBn9c39K5gA+R+bJGAOcbXJ+mDNdwuX3vmDtP37QEFK0LnC7S8I4xmug9rUqEuy4kBwOkfHSmrNMiRK1wLjKajDg9Oc20eg3WpvxnU7cUrkTrBKcb+o7OJMo97x2DQcXVnRJRI9quJ0qCfK3bnlXd4foeqzVXDp5iUExYrmqsJBKNrnPYMn0RffB0waFVwpR4993bVNUQ730iBdEKbVMBLoRI60KCys3IgKfzxYYzVmXGpa7tcD5t1xerluEgDUPMTo9BF4kHtasZDytGtsDXLYOyRFTk6GRG2zq0RMrJCK0U0UdWq1UeLDGJRtA7oKQsBgQT6TuH9566bfHOIzEwHQ+pSosxhqbt0cqxN52yqlesVitCDJvZBomKxaphMp0yr1PKJ8Q0AXn/eMFwkvLu12/cQBcV5F3Uqu4ZTNNXul86fIgYbUk4sHladRNxpe1xILWd+TbiiPRNQ2GGIOqs73rt5DfBZBr2Ebd1I0qaeZW4tSisI/X1WCvJJ0QtBIkJGTMEovdE5ejuO1azJcvDGe+8dhsJEVNYfuhHnmVRG5771IDB0yNUL7imR9Y8A2vaMrJzjgkQr90boO0u42oH7WY4InGwm1SLPTbK2UxO2vLlYZ/ztv25BVGlqc0Ef9AjlJtc/MY/PeJ8zom84SzyPVcejvAfcnoP7AC2F1+B6BOgWhCwkd47CiogZJ4AlekhHX7Z0Z4eUR2orTPFrPLaDjEfXeftzzn3w/l3kYc0lg0JuSjJnz3Nc3gXCKFDGQPr7098cKsYU6fCWfdO7ul/P3kinHtybMJsvkJE0Ah1UxMElEnTpstZw3g8SciORm+mQpVSHM+bRBKhNEYlXPBVl6ZCU+uLpG0rm3sgwQlrYTya4HrHqm6Y37mPNhqtDfP5aWY7slRV6hjw3mOtposmMSKp1CYXQsRHzWBQoGzCIJ+Md+jvHbNYdRS2RGvN/XszlAnJofYJ20QJrJoeEY33aZR9OKyIUbh3/zZG603xN0aXh4bSa7XSTMYWEUVdN/QutetVwwIhY4yENPnnSQXTedsQSUQlZegoyxIdB/gAs3mN1pq27djf3yUEYb5YpfbOTDUYfMgsTxl+1BSUwyFdiAxJkXn0keFoQGE1ZWmodJGY7veHjGzF4WxGDBFTFNiqpG5adqTnRz7xPK+9dZu+D1irGVeWThSrVY0VTec6SlvRrlr+wf/z93j55Zf4yT/2GV5/4026EDhedFSf2IN+xfK0S/elqsCl0XTJNxRi8za3R/q46cH/2ndOCb96yEsv7XHwyZtINSZqgycQjcagUAHwEQmJrELCWRrjbCEgHVvnftBnOd2c/xVVgDd86+9/m66P9KtUIJ5cmbJ3bY/P/+k/ih0WKG1oQ829t2sGOwblJC9Sqc1TKwPiCFGI2iZ8/rBgtWhQtaIZ9IyCAZ/SW6h17UmzaeHcpItI+fR1JL4BPlMQNUYnGAWxBSiDb5eocoLWKuHYU7DBCdjYYL16nOfIz3H4suUsH8nubC20Z7O5W6/JE8nRg4SMhKk5Pj3m2sFTSDSI+DQkpxSBHhMUcroAmWxOJmsQoIeKq2kR27LJeocQ8mfdUjZm+57pDDFYgg5IItOljxq7Y5kd10yuTlL8oHSahIbE0Zt1kJAIxyNyxqYl3Tk2PZMnwrnHEBBiJrNOPe3BO4qiZLmqcS5FPm3TIKJwRlGZBJcronABRFRmaIobTPYyDyW13qN16mXdlLVCYDoe03cdg6piNCx55/YhfR9oW0dhy3xtM1KfX/fkplHtPnh0TMQdMbiU9gkWZVRaGESjgCJP6mmtcDGgfRqc6Xu3DpYo86KkVGSxaGjbHmvMZmLWOZ/IK7xsCqfagDUFk2EFQNf0mKpIUUBM3RYR0k4iJmc8HA5SPULSgmF0Gt8WE/Ahdd2sVguIKdoJPuHYmIHF9XmyNQZGoyqNk8cEDjU/WRIQ9p8aA8JoWGK1JcTEYrW3s4tVPUagXi0ZVanV63jRMF81rOoGS2Ay1lzfm3CyWHE6W3ASHEer1FXj8EzGadCsbmqGpcV1DVeuXaEqDF3v6F1IC26RMThMgofeONaQO2c2d1skutR/rYyhqR2L45a3/uAEUYq9m0vk6h5aKjw+89emAqoUKtGsKr2Zj4nhUTL2M5EcMSeH0NY9ROHWrSO0teyNCl75469g9yboQQGF4IJDORCfsGAejhhlK48fH+ivT40Jrg+k1kCdfksaf19Hlg+kMh7pAJGcYoob+0UEFVKffLSplS9mzHwfwlmUK3kSc7N74Zxsw/ZCeJ5TiGf+cr3wvF/WIgIScseQJqb5YkR7QgdReoJElBtAcTYEFoPDlvbszXMnTdzYcm2SrIPWm8cbDPxM65l0zseibDn9/Jz3qDJfk9ChQsFgVNHd64m9JlifFu+82918NBGCFYKKdH0g+qTXUM7mOM6TJ8O5R+h7lzk/Lb1rEYSuTTnixDQvaCLO97heiEVFjIq26whrA4aUT02kwVvtjiHQu27DhOJcatsD2fTz2sIyGg0zfVmdV9DMKdonGAOlJUfS6fvmQsDXK5QoqtJSmYLhsOT0ZIZEGFYlZVURfKDt+pzHT86gqMqNI4ghps+g0va67hKKoF6j0xmVonxjUBnEn5jqA8fHR4xGI7RW9D6lsgw63/iCKcAoRdu1dF0CnEq0gAFUmaZaVYKdtcYwGo+Yz+cslivSaLrgnKcaDwneUQ0qnr15g/v3D5kvG4IoUIZCJ9aslGtfR6gJ3Gu5XHL9YIK1ga6LTMcDFss6nafp6HrHYFixXC442N+lqWvs3pjORXaLYeqICh1H8wVN3RJcz7Www5tvvcPNp56mXjb0ncdJRGyFuIgPGnSZt7k5et44njOnFkKqD4QY2btaceN5S7FwvP3qHd762j2uf+yI6ZUh5e4uZjpFrCGqQONqbCxJ3dDpNhKlOV+2HKYkp7EzKLgbF1x76YCbLz3F3tUxejpGIokjNlYUqkiMQabAqED0IX+O9N1d9+vHTSQrG1ApH3pc8MS8SwmSu3rOyxmf59g3+d+zaHnzHlIQBxV63hBFIUYnEndJkLvpXfKK974e+QOKrPXJuqmtz/FAaO03rd+iEiVgUDWxLdKksvEobyF0m9kH1/VgKuJ6AQTWoHJnasfNuXxuvhA5I9lZk93A2j55UEvkUd3FQTCAw+gdRnYPN7yP6BkiL+B4G10VaYGJcbO++cUKsSbtvvL16rs/DM5dICpBFxZTWJarGYU2aFG0vfyVzCAAAAhKSURBVEcXFiswnQ5R1rCqO+bLFVqSlxVRmaxC6LuetfdNwFSasqwSrK8AwaFC6oKYLTuUivSxpT4+QYXUCqmCw1pL3TTJsDmfGHzqyLGl2QBDbSjmOkvTnrKqbUJlLAcQPCcnp8SYpjOJCqUCkYA2KuGXu0gfOlyTIs+kc3Lm1qYIN8RIURR5h0Pu0AlYW3Dzxn7uKIr0MZFkN8tVsmtILaQO0ki4FBAj9SrR5t1f1lgr7O+MUBH6zuG8xxiLD57hIC1My7bFrxIhdd20fO3176aFL6SFY286pFTC3TtzetdTWMvVKyOm02EaCusVd49mjMYVphihMlnHUwe71E3LclUT0aio6FcLPv7cNZrO8cbdU47unDAdDZhk+Nadq/ss53NcUHz6lU+zd+U5blzZYRUU9xdLTpeOq5MBTRtwdcBUOg+1QEpORVA+36+ORb7mZYAXPvUsP/RTU3AaiR2q6zn57jGz+yvqr73OYuVQ1jCaVFx7cQ/ZS6TlsZokyBAl5/uxzQ0um0C49h4Ky6d+/I9QlCVYn/L1BHRRpl1G1ETtk96S8ImiMhnYat1PLyRk8rVzSi2cWmlsaTg+OcT4BQPDg7uWtV7IQ10h205zvdNJA0QtoFREBUHt7cK9t/BKUDoB1W1i3XVeODv2mHfWj9rlfZzC2mYRotabiDjd70LcoH2u30ggFohJk7kxTBF7gCoMVn4D4jNY+zFC+6s4ZyFCFxXldIKaXqE+eS3f057gfTpd8OCTk03M5jHXWWJK/eRIf010clZaOKMDRNZTxELUFdgG48cE1RDLIeXVPcphyeydu+gDqA/vYGIOOiV1JymlCFqjjMOIR7ue+dGMN3/37fcxIE8GcJiI3AOWwP2L1uUhOeBSpw8qT6Jelzp9cHkS9XoSdYInS6/nY4xXz3viiXDuACLy2++FbnZRcqnTB5cnUa9LnT64PIl6PYk6wZOr18Pyfr1Il3Ipl3Ipl/KHVC6d+6VcyqVcykdQniTn/l9dtALnyKVOH1yeRL0udfrg8iTq9STqBE+uXg/IE5Nzv5RLuZRLuZQfnDxJkfulXMqlXMql/IDk0rlfyqVcyqV8BOXCnbuI/IyIfENEvi0iv3SBerwhIr8vIl8Wkd/Ox/ZF5FdF5Fv5995j0OMvishdEXl169i5ekiS/yLb7isi8rnHqNOfF5F3sr2+LCI/t/XcL2edviEi/9SHpNOzIvLrIvI1EfkDEfk38vGLttV76XVh9hKRSkS+KCK/l3X69/PxF0Xkt7Kt/pqIFPl4mf/+dn7+hceo018Ske9s2elH8/HHcv229NMi8rsi8rfz3xdmq+9bEpXcxfyQhoVfA14ikSf+HvCpC9LlDeDgoWP/EfBL+fEvAf/hY9DjJ4HPAa9+Lz2AnwP+DmkY7seB33qMOv154N8+57WfytexBF7M11d/CDrdBD6XH0+Ab+ZzX7St3kuvC7NX/szj/NgCv5Vt8L8Av5iP/wrwr+TH/yrwK/nxLwJ/7UOw03vp9JeAXzjn9Y/l+m2d798C/ifgb+e/L8xW3+/PRUfufxT4dozx9RhjB/xV4OcvWKdt+XngL+fHfxn4Zz7sE8YY/2/g6APq8fPAX4lJfhPYFZGbj0mn95KfB/5qjLGNMX4H+DbpOv+gdXo3xvg7+fEc+BrwNBdvq/fS673kQ7dX/syL/KfNPxH4U8Bfz8cfttXahn8d+MdFPhDY+Q9Cp/eSx3L9AETkGeCfBv6b/Ldwgbb6fuWinfvTwFtbf7/N+98IH6ZE4P8QkS+JyL+cj12PMb4L6aYFrl2Qbu+lx0Xb71/PW+S/uJWyeuw65a3wZ0nR3xNjq4f0ggu0V04zfBm4C/wqaYdwEmN055x3o1N+/hS48mHrFGNc2+k/yHb6T0WkfFinc/T9Qct/Bvw7nCGmX+GCbfX9yEU79/NWuIvqzfxCjPFzwM8C/5qI/OQF6fGPIhdpv/8S+Bjwo8C7wH98ETqJyBj4G8C/GWOcvd9Lzzn2OPW6UHvFGH2M8UeBZ0g7g1fe57wXopOI/BHgl4EfAn4M2Af+3cepk4j8aeBujPFL24ff59xPkg97QC7aub8NPLv19zPArYtQJMZ4K/++C/xvpBvgznrrl3/fvQjd3kePC7NfjPFOvjkD8F9zlkp4bDqJiCU50P8xxvg38+ELt9V5ej0J9sp6nAD/FylvvSsia2TY7fNudMrP7/DB03L/f3T6mZzWijHGFvjvePx2+gLwZ0TkDVKa+E+RIvknwlb/KHLRzv0fAi/nSnRBKkj8rcethIiMRGSyfgz8k8CrWZc/m1/2Z4H//XHrluW99PhbwL+YOwl+HDhdpyQ+bHko3/nPkuy11ukXcxfBi8DLwBc/hPML8N8CX4sx/idbT12ord5Lr4u0l4hcFZHd/HgA/BOkWsCvA7+QX/awrdY2/AXg12KuGH7IOn19a2EWUl57204f+vWLMf5yjPGZGOMLJH/0azHGf54LtNX3LRdd0SVVwb9JygH+uQvS4SVSx8LvAX+w1oOUO/u7wLfy7/3HoMv/TNq296So4F96Lz1IW8K/kG33+8A/9hh1+u/zOb9C+oLf3Hr9n8s6fQP42Q9Jpz9B2v5+Bfhy/vm5J8BW76XXhdkL+Azwu/ncrwL/3tb3/oukIu7/CpT5eJX//nZ+/qXHqNOvZTu9CvwPnHXUPJbr95COP81Zt8yF2er7/bmEH7iUS7mUS/kIykWnZS7lUi7lUi7lQ5BL534pl3Ipl/IRlEvnfimXcimX8hGUS+d+KZdyKZfyEZRL534pl3Ipl/IRlEvnfimXcimX8hGUS+d+KZdyKZfyEZT/D7saoahMYn0gAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "file_name = 'humandetection/test_images/test_1_image.png'\n", "img = cv2.imread(file_name)\n", "img =cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n", "thresh = 0.1\n", "height = img.shape[0]\n", "width = img.shape[1]\n", "colors = dict()\n", "\n", "restricted_region = None\n", "\n", "\n", "with open(file_name, 'rb') as image:\n", " f = image.read()\n", " b = bytearray(f)\n", " ne = open('n.txt','wb')\n", " ne.write(b)\n", " \n", "\n", "results = object_detector.predict(b, initial_args={'ContentType': 'image/jpeg'})\n", "detections = json.loads(results)\n", "\n", "object_categories = ['no-person', 'person']\n", "\n", "for det in detections['prediction']:\n", " (klass, score, x0, y0, x1, y1) = det\n", " if score < thresh:\n", " continue\n", " cls_id = int(klass)\n", " prob = score\n", " if cls_id not in colors:\n", " colors[cls_id] = (random.random(), random.random(), random.random())\n", " xmin = int(x0 * width)\n", " ymin = int(y0 * height)\n", " xmax = int(x1 * width)\n", " ymax = int(y1 * height)\n", " \n", " if cls_id==1: \n", " R1 = np.array([[xmin,ymin],[xmax,ymin],[xmax,ymax], [xmin,ymax]], np.int32)\n", " cv2.polylines(img,[R1],True, (255,255,0), thickness = 5)\n", " cv2.polylines(img,[restricted_region],True, (255,0,0), thickness = 5)\n", " plt.imshow(img)\n", " print(inRestrictedSection(img.shape,R1 = R1, restricted_region= restricted_region, kclass = cls_id, score = prob, threshold=thresh))\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Delete the Endpoint\n", "Having an endpoint running will incur some costs. Therefore as a clean-up job, we should delete the endpoint." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The endpoint attribute has been renamed in sagemaker>=2.\n", "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n" ] } ], "source": [ "sagemaker.Session().delete_endpoint(object_detector.endpoint)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convert model to deploy to DeepLens" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'incubator-mxnet'...\n", "remote: Enumerating objects: 15, done.\u001b[K\n", "remote: Counting objects: 100% (15/15), done.\u001b[K\n", "remote: Compressing objects: 100% (14/14), done.\u001b[K\n", "remote: Total 125636 (delta 2), reused 7 (delta 0), pack-reused 125621\u001b[K\n", "Receiving objects: 100% (125636/125636), 87.11 MiB | 25.87 MiB/s, done.\n", "Resolving deltas: 100% (87717/87717), done.\n" ] } ], "source": [ "!rm -rf incubator-mxnet\n", "!git clone -b v1.7.x https://github.com/apache/incubator-mxnet" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "MODEL_PATH = od_model.model_data\n", "TARGET_PATH ='s3://'+BUCKET+'/'+PREFIX+'/patched/'\n", "!rm -rf tmp && mkdir tmp" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "download: s3://deeplens-test-public/deeplens-humandetection2class/output/object-detection-2021-01-14-19-16-35-563/output/model.tar.gz to tmp/model.tar.gz\n", "model_algo_1-0000.params\n", "model_algo_1-symbol.json\n", "hyperparams.json\n", "[23:40:56] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v1.4.1. Attempting to upgrade...\n", "[23:40:56] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!\n", "Saved model: tmp/deploy_ssd_resnet50_300-0000.params\n", "Saved symbol: tmp/deploy_ssd_resnet50_300-symbol.json\n", "./deploy_ssd_resnet50_300-0000.params\n", "./deploy_ssd_resnet50_300-symbol.json\n", "./hyperparams.json\n", "upload: ./patched_model.tar.gz to s3://deeplens-test-public/deeplens-humandetection2class/patched/patched_model.tar.gz\n" ] } ], "source": [ "!rm -rf tmp && mkdir tmp\n", "!aws s3 cp $MODEL_PATH tmp\n", "!tar -xzvf tmp/model.tar.gz -C tmp\n", "!mv tmp/model_algo_1-0000.params tmp/ssd_resnet50_300-0000.params\n", "!mv tmp/model_algo_1-symbol.json tmp/ssd_resnet50_300-symbol.json\n", "!python incubator-mxnet/example/ssd/deploy.py --network resnet50 --data-shape 300 --num-class 2 --prefix tmp/ssd_\n", "!tar -cvzf ./patched_model.tar.gz -C tmp ./deploy_ssd_resnet50_300-0000.params ./deploy_ssd_resnet50_300-symbol.json ./hyperparams.json\n", "!aws s3 cp patched_model.tar.gz $TARGET_PATH" ] } ], "metadata": { "kernelspec": { "display_name": "conda_mxnet_p36", "language": "python", "name": "conda_mxnet_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }