From bd9770e29ff7c64689f35205135179cf6785b06e Mon Sep 17 00:00:00 2001 From: vfdev-5 Date: Mon, 4 Sep 2023 14:54:39 +0200 Subject: [PATCH 1/3] Updated tutorial to the latest version --- ...ion_finetuning_instance_segmentation.ipynb | 2605 ----------------- _static/tv-training-code.py | 494 +++- intermediate_source/torchvision_tutorial.rst | 669 +++-- 3 files changed, 753 insertions(+), 3015 deletions(-) delete mode 100644 _static/torchvision_finetuning_instance_segmentation.ipynb diff --git a/_static/torchvision_finetuning_instance_segmentation.ipynb b/_static/torchvision_finetuning_instance_segmentation.ipynb deleted file mode 100644 index f4b58f7ecae..00000000000 --- a/_static/torchvision_finetuning_instance_segmentation.ipynb +++ /dev/null @@ -1,2605 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "torchvision_finetuning_instance_segmentation.ipynb", - "version": "0.3.2", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "accelerator": "GPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "DfPPQ6ztJhv4", - "colab_type": "text" - }, - "source": [ - "# TorchVision 0.3 Object Detection finetuning tutorial\n", - "\n", - "For this tutorial, we will be finetuning a pre-trained [Mask R-CNN](https://arxiv.org/abs/1703.06870) model in the [*Penn-Fudan Database for Pedestrian Detection and Segmentation*](https://www.cis.upenn.edu/~jshi/ped_html/). It contains 170 images with 345 instances of pedestrians, and we will use it to illustrate how to use the new features in torchvision in order to train an instance segmentation model on a custom dataset.\n", - "\n", - "First, we need to install `pycocotools`. This library will be used for computing the evaluation metrics following the COCO metric for intersection over union." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "DBIoe_tHTQgV", - "colab_type": "code", - "outputId": "de73add6-c54a-4d53-960e-ac0032ab4009", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 10356 - } - }, - "source": [ - "%%shell\n", - "\n", - "CURRENT_DIR=`pwd`\n", - "echo $CURRENT_DIR\n", - "\n", - "# Install pycocotools\n", - "git clone https://github.com/cocodataset/cocoapi.git\n", - "cd cocoapi/PythonAPI\n", - "python setup.py build_ext install\n", - "\n", - "cd $CURRENT_DIR\n", - "\n", - "######################################################\n", - "# TODO remove this once torchvision 0.3 is present by\n", - "# default in Colab\n", - "######################################################\n", - "pip uninstall -y torchvision\n", - "git clone https://github.com/pytorch/vision.git\n", - "cd vision\n", - "git checkout v0.3.0\n", - "python setup.py install\n", - "# why do we need this?\n", - "cp -r build/lib.linux-x86_64-3.6/torchvision /usr/local/lib/python3.6/dist-packages/" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "/content\n", - "Cloning into 'cocoapi'...\n", - "remote: Enumerating objects: 953, done.\u001b[K\n", - "remote: Total 953 (delta 0), reused 0 (delta 0), pack-reused 953\u001b[K\n", - "Receiving objects: 100% (953/953), 11.70 MiB | 29.29 MiB/s, done.\n", - "Resolving deltas: 100% (566/566), done.\n", - "running build_ext\n", - "cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\n", - "/usr/local/lib/python3.6/dist-packages/Cython/Compiler/Main.py:367: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/cocoapi/PythonAPI/pycocotools/_mask.pyx\n", - " tree = Parsing.p_module(s, pxd, full_module_name)\n", - "building 'pycocotools._mask' extension\n", - "creating build\n", - "creating build/common\n", - "creating build/temp.linux-x86_64-3.6\n", - "creating build/temp.linux-x86_64-3.6/pycocotools\n", - "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -I../common -I/usr/include/python3.6m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.6/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n", - "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n", - "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n", - " \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n", - " \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K../common/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n", - " if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n", - " \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n", - "\u001b[01m\u001b[K../common/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n", - " if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp build/lib.linux-x86_64-3.6/pycocotools\n", - "copying pycocotools/__init__.py -> build/lib.linux-x86_64-3.6/pycocotools\n", - "copying pycocotools/cocoeval.py -> build/lib.linux-x86_64-3.6/pycocotools\n", - "copying pycocotools/mask.py -> build/lib.linux-x86_64-3.6/pycocotools\n", - "creating build/bdist.linux-x86_64\n", - "creating build/bdist.linux-x86_64/egg\n", - "creating build/bdist.linux-x86_64/egg/pycocotools\n", - "copying build/lib.linux-x86_64-3.6/pycocotools/coco.py -> build/bdist.linux-x86_64/egg/pycocotools\n", - "copying build/lib.linux-x86_64-3.6/pycocotools/__init__.py -> build/bdist.linux-x86_64/egg/pycocotools\n", - "copying build/lib.linux-x86_64-3.6/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg/pycocotools\n", - "copying build/lib.linux-x86_64-3.6/pycocotools/cocoeval.py -> build/bdist.linux-x86_64/egg/pycocotools\n", - "copying build/lib.linux-x86_64-3.6/pycocotools/mask.py -> build/bdist.linux-x86_64/egg/pycocotools\n", - "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/coco.py to coco.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/__init__.py to __init__.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/cocoeval.py to cocoeval.cpython-36.pyc\n", - "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/mask.py to mask.cpython-36.pyc\n", - "creating stub loader for pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so\n", - "byte-compiling build/bdist.linux-x86_64/egg/pycocotools/_mask.py to _mask.cpython-36.pyc\n", - "creating build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying pycocotools.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying pycocotools.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying pycocotools.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying pycocotools.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "copying pycocotools.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n", - "writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt\n", - "zip_safe flag not set; analyzing archive contents...\n", - "pycocotools.__pycache__._mask.cpython-36: module references __file__\n", - "creating dist\n", - "creating 'dist/pycocotools-2.0-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n", - "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n", - "Processing pycocotools-2.0-py3.6-linux-x86_64.egg\n", - "creating /usr/local/lib/python3.6/dist-packages/pycocotools-2.0-py3.6-linux-x86_64.egg\n", - "Extracting pycocotools-2.0-py3.6-linux-x86_64.egg to /usr/local/lib/python3.6/dist-packages\n", - "Adding pycocotools 2.0 to easy-install.pth file\n", - "\n", - "Installed /usr/local/lib/python3.6/dist-packages/pycocotools-2.0-py3.6-linux-x86_64.egg\n", - "Processing dependencies for pycocotools==2.0\n", - "Searching for matplotlib==3.0.3\n", - "Best match: matplotlib 3.0.3\n", - "Adding matplotlib 3.0.3 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for Cython==0.29.7\n", - "Best match: Cython 0.29.7\n", - "Adding Cython 0.29.7 to easy-install.pth file\n", - "Installing cygdb script to /usr/local/bin\n", - "Installing cython script to /usr/local/bin\n", - "Installing cythonize script to /usr/local/bin\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for setuptools==41.0.1\n", - "Best match: setuptools 41.0.1\n", - "Adding setuptools 41.0.1 to easy-install.pth file\n", - "Installing easy_install script to /usr/local/bin\n", - "Installing easy_install-3.6 script to /usr/local/bin\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for python-dateutil==2.5.3\n", - "Best match: python-dateutil 2.5.3\n", - "Adding python-dateutil 2.5.3 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for cycler==0.10.0\n", - "Best match: cycler 0.10.0\n", - "Adding cycler 0.10.0 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for kiwisolver==1.1.0\n", - "Best match: kiwisolver 1.1.0\n", - "Adding kiwisolver 1.1.0 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for numpy==1.16.3\n", - "Best match: numpy 1.16.3\n", - "Adding numpy 1.16.3 to easy-install.pth file\n", - "Installing f2py script to /usr/local/bin\n", - "Installing f2py3 script to /usr/local/bin\n", - "Installing f2py3.6 script to /usr/local/bin\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for pyparsing==2.4.0\n", - "Best match: pyparsing 2.4.0\n", - "Adding pyparsing 2.4.0 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for six==1.12.0\n", - "Best match: six 1.12.0\n", - "Adding six 1.12.0 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Finished processing dependencies for pycocotools==2.0\n", - "Uninstalling torchvision-0.2.2.post3:\n", - " Successfully uninstalled torchvision-0.2.2.post3\n", - "Cloning into 'vision'...\n", - "remote: Enumerating objects: 91, done.\u001b[K\n", - "remote: Counting objects: 100% (91/91), done.\u001b[K\n", - "remote: Compressing objects: 100% (58/58), done.\u001b[K\n", - "remote: Total 3006 (delta 42), reused 68 (delta 33), pack-reused 2915\u001b[K\n", - "Receiving objects: 100% (3006/3006), 2.50 MiB | 16.98 MiB/s, done.\n", - "Resolving deltas: 100% (1927/1927), done.\n", - "Branch 'v0.3.0' set up to track remote branch 'v0.3.0' from 'origin'.\n", - "Switched to a new branch 'v0.3.0'\n", - "Building wheel torchvision-0.3.0a0+684c064\n", - "running install\n", - "running bdist_egg\n", - "running egg_info\n", - "creating torchvision.egg-info\n", - "writing torchvision.egg-info/PKG-INFO\n", - "writing dependency_links to torchvision.egg-info/dependency_links.txt\n", - "writing requirements to torchvision.egg-info/requires.txt\n", - "writing top-level names to torchvision.egg-info/top_level.txt\n", - "writing manifest file 'torchvision.egg-info/SOURCES.txt'\n", - "reading manifest template 'MANIFEST.in'\n", - "warning: no previously-included files matching '__pycache__' found under directory '*'\n", - "warning: no previously-included files matching '*.py[co]' found under directory '*'\n", - "writing manifest file 'torchvision.egg-info/SOURCES.txt'\n", - "installing library code to build/bdist.linux-x86_64/egg\n", - "running install_lib\n", - "running build_py\n", - "creating build\n", - "creating build/lib.linux-x86_64-3.6\n", - "creating build/lib.linux-x86_64-3.6/torchvision\n", - "copying torchvision/__init__.py -> build/lib.linux-x86_64-3.6/torchvision\n", - "copying torchvision/utils.py -> build/lib.linux-x86_64-3.6/torchvision\n", - "copying torchvision/version.py -> build/lib.linux-x86_64-3.6/torchvision\n", - "creating build/lib.linux-x86_64-3.6/torchvision/transforms\n", - "copying torchvision/transforms/__init__.py -> build/lib.linux-x86_64-3.6/torchvision/transforms\n", - "copying torchvision/transforms/functional.py -> build/lib.linux-x86_64-3.6/torchvision/transforms\n", - "copying torchvision/transforms/transforms.py -> build/lib.linux-x86_64-3.6/torchvision/transforms\n", - "creating build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/coco.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/__init__.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/mnist.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/phototour.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/sbu.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/stl10.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/omniglot.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/voc.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/semeion.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/vision.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/celeba.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/fakedata.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/imagenet.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/utils.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/cityscapes.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/caltech.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/svhn.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/sbd.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/cifar.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/flickr.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/lsun.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "copying torchvision/datasets/folder.py -> build/lib.linux-x86_64-3.6/torchvision/datasets\n", - "creating build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/roi_align.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/__init__.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/boxes.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/poolers.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/misc.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/roi_pool.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/_utils.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "copying torchvision/ops/feature_pyramid_network.py -> build/lib.linux-x86_64-3.6/torchvision/ops\n", - "creating build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/inception.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/alexnet.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/squeezenet.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/__init__.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/vgg.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/googlenet.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/densenet.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/shufflenetv2.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/utils.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/mobilenet.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/resnet.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "copying torchvision/models/_utils.py -> build/lib.linux-x86_64-3.6/torchvision/models\n", - "creating build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/mask_rcnn.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/image_list.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/faster_rcnn.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/__init__.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/transform.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/generalized_rcnn.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/rpn.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/keypoint_rcnn.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/_utils.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/roi_heads.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "copying torchvision/models/detection/backbone_utils.py -> build/lib.linux-x86_64-3.6/torchvision/models/detection\n", - "creating build/lib.linux-x86_64-3.6/torchvision/models/segmentation\n", - "copying torchvision/models/segmentation/deeplabv3.py -> build/lib.linux-x86_64-3.6/torchvision/models/segmentation\n", - "copying torchvision/models/segmentation/segmentation.py -> build/lib.linux-x86_64-3.6/torchvision/models/segmentation\n", - "copying torchvision/models/segmentation/__init__.py -> build/lib.linux-x86_64-3.6/torchvision/models/segmentation\n", - "copying torchvision/models/segmentation/fcn.py -> build/lib.linux-x86_64-3.6/torchvision/models/segmentation\n", - "copying torchvision/models/segmentation/_utils.py -> build/lib.linux-x86_64-3.6/torchvision/models/segmentation\n", - "running build_ext\n", - "building 'torchvision._C' extension\n", - "creating build/temp.linux-x86_64-3.6\n", - "creating build/temp.linux-x86_64-3.6/content\n", - "creating build/temp.linux-x86_64-3.6/content/vision\n", - "creating build/temp.linux-x86_64-3.6/content/vision/torchvision\n", - "creating build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc\n", - "creating build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu\n", - "creating build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda\n", - "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/vision.cpp -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/vision.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/cpu/ROIAlign_cpu.cpp -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "In file included from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/ATen.h:9:0\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/types.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/all.h:4\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/extension.h:4\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/vision.h:2\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.cpp:2\u001b[m\u001b[K:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.cpp:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:84:52:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " at::ScalarType _st = ::detail::scalar_type(TYPE\u001b[01;35m\u001b[K)\u001b[m\u001b[K; \\\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.cpp:406:3:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin expansion of macro ‘\u001b[01m\u001b[KAT_DISPATCH_FLOATING_TYPES_AND_HALF\u001b[m\u001b[K’\n", - " \u001b[01;36m\u001b[KA\u001b[m\u001b[KT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), \"ROIAlign_forward\", [&] {\n", - " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:23:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " inline at::ScalarType \u001b[01;36m\u001b[Kscalar_type\u001b[m\u001b[K(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.cpp:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:84:52:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " at::ScalarType _st = ::detail::scalar_type(TYPE\u001b[01;35m\u001b[K)\u001b[m\u001b[K; \\\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.cpp:456:3:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin expansion of macro ‘\u001b[01m\u001b[KAT_DISPATCH_FLOATING_TYPES_AND_HALF\u001b[m\u001b[K’\n", - " \u001b[01;36m\u001b[KA\u001b[m\u001b[KT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), \"ROIAlign_forward\", [&] {\n", - " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:23:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " inline at::ScalarType \u001b[01;36m\u001b[Kscalar_type\u001b[m\u001b[K(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/cpu/nms_cpu.cpp -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu/nms_cpu.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "In file included from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/ATen.h:9:0\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/types.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/data.h:3\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include/torch/all.h:4\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/torch/extension.h:4\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/vision.h:2\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/nms_cpu.cpp:1\u001b[m\u001b[K:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/nms_cpu.cpp:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:71:52:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " at::ScalarType _st = ::detail::scalar_type(TYPE\u001b[01;35m\u001b[K)\u001b[m\u001b[K; \\\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/nms_cpu.cpp:77:3:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin expansion of macro ‘\u001b[01m\u001b[KAT_DISPATCH_FLOATING_TYPES\u001b[m\u001b[K’\n", - " \u001b[01;36m\u001b[KAT_DISPATCH_FLOATING_TYPES\u001b[m\u001b[K(dets.type(), \"nms\", [&] {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:23:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " inline at::ScalarType \u001b[01;36m\u001b[Kscalar_type\u001b[m\u001b[K(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/cpu/ROIPool_cpu.cpp -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu/ROIPool_cpu.o -O0 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "In file included from \u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/ATen.h:9:0\u001b[m\u001b[K,\n", - " from \u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIPool_cpu.cpp:1\u001b[m\u001b[K:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIPool_cpu.cpp:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:84:52:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " at::ScalarType _st = ::detail::scalar_type(TYPE\u001b[01;35m\u001b[K)\u001b[m\u001b[K; \\\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIPool_cpu.cpp:152:3:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin expansion of macro ‘\u001b[01m\u001b[KAT_DISPATCH_FLOATING_TYPES_AND_HALF\u001b[m\u001b[K’\n", - " \u001b[01;36m\u001b[KA\u001b[m\u001b[KT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), \"ROIPool_forward\", [&] {\n", - " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:23:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " inline at::ScalarType \u001b[01;36m\u001b[Kscalar_type\u001b[m\u001b[K(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIPool_cpu.cpp:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:84:52:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " at::ScalarType _st = ::detail::scalar_type(TYPE\u001b[01;35m\u001b[K)\u001b[m\u001b[K; \\\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cpu/ROIPool_cpu.cpp:206:3:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin expansion of macro ‘\u001b[01m\u001b[KAT_DISPATCH_FLOATING_TYPES_AND_HALF\u001b[m\u001b[K’\n", - " \u001b[01;36m\u001b[KA\u001b[m\u001b[KT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), \"ROIPool_backward\", [&] {\n", - " \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:23:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " inline at::ScalarType \u001b[01;36m\u001b[Kscalar_type\u001b[m\u001b[K(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/cuda/ROIAlign_cuda.cu -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda/ROIAlign_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --compiler-options '-fPIC' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(83): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"lowest\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(84): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"max\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(85): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"lower_bound\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(86): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"upper_bound\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIAlign_cuda.cu:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIAlign_cuda.cu:337:120:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), \"ROIAlign_forward\", [&] {\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:1:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " \u001b[01;36m\u001b[Kinline at::\u001b[m\u001b[KScalarType scalar_type(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIAlign_cuda.cu:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIAlign_cuda.cu:396:118:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), \"ROIAlign_backward\", [&] {\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:1:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " \u001b[01;36m\u001b[Kinline at::\u001b[m\u001b[KScalarType scalar_type(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/cuda/ROIPool_cuda.cu -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda/ROIPool_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --compiler-options '-fPIC' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(83): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"lowest\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(84): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"max\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(85): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"lower_bound\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(86): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"upper_bound\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIPool_cuda.cu:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIPool_cuda.cu:157:120:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), \"ROIPool_forward\", [&] {\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:1:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " \u001b[01;36m\u001b[Kinline at::\u001b[m\u001b[KScalarType scalar_type(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIPool_cuda.cu:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/ROIPool_cuda.cu:221:118:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), \"ROIPool_backward\", [&] {\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:1:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " \u001b[01;36m\u001b[Kinline at::\u001b[m\u001b[KScalarType scalar_type(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "/usr/local/cuda/bin/nvcc -DWITH_CUDA -I/content/vision/torchvision/csrc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c /content/vision/torchvision/csrc/cuda/nms_cuda.cu -o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda/nms_cuda.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --compiler-options '-fPIC' -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(83): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"lowest\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(84): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"max\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(85): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"lower_bound\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/include/ATen/cuda/NumericLimits.cuh(86): warning: calling a constexpr __host__ function(\"from_bits\") from a __host__ __device__ function(\"upper_bound\") is not allowed. The experimental flag '--expt-relaxed-constexpr' can be used to allow this.\n", - "\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/nms_cuda.cu:\u001b[m\u001b[K In lambda function:\n", - "\u001b[01m\u001b[K/content/vision/torchvision/csrc/cuda/nms_cuda.cu:95:134:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kc10::ScalarType detail::scalar_type(const at::DeprecatedTypeProperties&)\u001b[m\u001b[K’ is deprecated [\u001b[01;35m\u001b[K-Wdeprecated-declarations\u001b[m\u001b[K]\n", - " AT_DISPATCH_FLOATING_TYPES_AND_HALF(\n", - " \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n", - "\u001b[01m\u001b[K/usr/local/lib/python3.6/dist-packages/torch/include/ATen/Dispatch.h:47:1:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kdeclared here\n", - " \u001b[01;36m\u001b[Kinline at::\u001b[m\u001b[KScalarType scalar_type(const at::DeprecatedTypeProperties &t) {\n", - " \u001b[01;36m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n", - "x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/vision.o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu/ROIAlign_cpu.o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu/nms_cpu.o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cpu/ROIPool_cpu.o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda/ROIAlign_cuda.o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda/ROIPool_cuda.o build/temp.linux-x86_64-3.6/content/vision/torchvision/csrc/cuda/nms_cuda.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/torchvision/_C.cpython-36m-x86_64-linux-gnu.so\n", - "creating build/bdist.linux-x86_64\n", - "creating build/bdist.linux-x86_64/egg\n", - "creating build/bdist.linux-x86_64/egg/torchvision\n", - "copying build/lib.linux-x86_64-3.6/torchvision/__init__.py -> build/bdist.linux-x86_64/egg/torchvision\n", - "creating build/bdist.linux-x86_64/egg/torchvision/transforms\n", - "copying build/lib.linux-x86_64-3.6/torchvision/transforms/__init__.py -> build/bdist.linux-x86_64/egg/torchvision/transforms\n", - "copying build/lib.linux-x86_64-3.6/torchvision/transforms/functional.py -> build/bdist.linux-x86_64/egg/torchvision/transforms\n", - "copying build/lib.linux-x86_64-3.6/torchvision/transforms/transforms.py -> build/bdist.linux-x86_64/egg/torchvision/transforms\n", - "creating build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/coco.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/__init__.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/mnist.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/phototour.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/sbu.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/stl10.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/omniglot.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/voc.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/semeion.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/vision.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/celeba.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/fakedata.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/imagenet.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/utils.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/cityscapes.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/caltech.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/svhn.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/sbd.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/cifar.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/flickr.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/lsun.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/datasets/folder.py -> build/bdist.linux-x86_64/egg/torchvision/datasets\n", - "copying build/lib.linux-x86_64-3.6/torchvision/_C.cpython-36m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg/torchvision\n", - "creating build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/roi_align.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/__init__.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/boxes.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/poolers.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/misc.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/roi_pool.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/_utils.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/ops/feature_pyramid_network.py -> build/bdist.linux-x86_64/egg/torchvision/ops\n", - "copying build/lib.linux-x86_64-3.6/torchvision/utils.py -> build/bdist.linux-x86_64/egg/torchvision\n", - "copying build/lib.linux-x86_64-3.6/torchvision/version.py -> build/bdist.linux-x86_64/egg/torchvision\n", - "creating build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/inception.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/alexnet.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "creating build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/mask_rcnn.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/image_list.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/faster_rcnn.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/__init__.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/transform.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/generalized_rcnn.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/rpn.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/keypoint_rcnn.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/_utils.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/roi_heads.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/detection/backbone_utils.py -> build/bdist.linux-x86_64/egg/torchvision/models/detection\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/squeezenet.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/__init__.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/vgg.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/googlenet.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/densenet.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/shufflenetv2.py -> build/bdist.linux-x86_64/egg/torchvision/models\n", - "creating build/bdist.linux-x86_64/egg/torchvision/models/segmentation\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/segmentation/deeplabv3.py -> build/bdist.linux-x86_64/egg/torchvision/models/segmentation\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/segmentation/segmentation.py -> build/bdist.linux-x86_64/egg/torchvision/models/segmentation\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/segmentation/__init__.py -> build/bdist.linux-x86_64/egg/torchvision/models/segmentation\n", - "copying build/lib.linux-x86_64-3.6/torchvision/models/segmentation/fcn.py -> build/bdist.linux-x86_64/egg/torchvision/models/segmentation\n", - 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"writing build/bdist.linux-x86_64/egg/EGG-INFO/native_libs.txt\n", - "creating dist\n", - "creating 'dist/torchvision-0.3.0a0+684c064-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n", - "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n", - "Processing torchvision-0.3.0a0+684c064-py3.6-linux-x86_64.egg\n", - "Copying torchvision-0.3.0a0+684c064-py3.6-linux-x86_64.egg to /usr/local/lib/python3.6/dist-packages\n", - "Adding torchvision 0.3.0a0+684c064 to easy-install.pth file\n", - "\n", - "Installed /usr/local/lib/python3.6/dist-packages/torchvision-0.3.0a0+684c064-py3.6-linux-x86_64.egg\n", - "Processing dependencies for torchvision==0.3.0a0+684c064\n", - "Searching for Pillow==4.3.0\n", - "Best match: Pillow 4.3.0\n", - "Adding Pillow 4.3.0 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for torch==1.1.0\n", - "Best match: torch 1.1.0\n", - "Adding torch 1.1.0 to easy-install.pth file\n", - "Installing convert-caffe2-to-onnx script to /usr/local/bin\n", - "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for six==1.12.0\n", - "Best match: six 1.12.0\n", - "Adding six 1.12.0 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for numpy==1.16.3\n", - "Best match: numpy 1.16.3\n", - "Adding numpy 1.16.3 to easy-install.pth file\n", - "Installing f2py script to /usr/local/bin\n", - "Installing f2py3 script to /usr/local/bin\n", - "Installing f2py3.6 script to /usr/local/bin\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Searching for olefile==0.46\n", - "Best match: olefile 0.46\n", - "Adding olefile 0.46 to easy-install.pth file\n", - "\n", - "Using /usr/local/lib/python3.6/dist-packages\n", - "Finished processing dependencies for torchvision==0.3.0a0+684c064\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 1 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5Sd4jlGp2eLm", - "colab_type": "text" - }, - "source": [ - "## Defining the Dataset\n", - "\n", - "The [torchvision reference scripts for training object detection, instance segmentation and person keypoint detection](https://github.com/pytorch/vision/tree/v0.3.0/references/detection) allows for easily supporting adding new custom datasets.\n", - "The dataset should inherit from the standard `torch.utils.data.Dataset` class, and implement `__len__` and `__getitem__`.\n", - "\n", - "The only specificity that we require is that the dataset `__getitem__` should return:\n", - "\n", - "* image: a PIL Image of size (H, W)\n", - "* target: a dict containing the following fields\n", - " * `boxes` (`FloatTensor[N, 4]`): the coordinates of the `N` bounding boxes in `[x0, y0, x1, y1]` format, ranging from `0` to `W` and `0` to `H`\n", - " * `labels` (`Int64Tensor[N]`): the label for each bounding box\n", - " * `image_id` (`Int64Tensor[1]`): an image identifier. It should be unique between all the images in the dataset, and is used during evaluation\n", - " * `area` (`Tensor[N]`): The area of the bounding box. This is used during evaluation with the COCO metric, to separate the metric scores between small, medium and large boxes.\n", - " * `iscrowd` (`UInt8Tensor[N]`): instances with `iscrowd=True` will be ignored during evaluation.\n", - " * (optionally) `masks` (`UInt8Tensor[N, H, W]`): The segmentation masks for each one of the objects\n", - " * (optionally) `keypoints` (`FloatTensor[N, K, 3]`): For each one of the `N` objects, it contains the `K` keypoints in `[x, y, visibility]` format, defining the object. `visibility=0` means that the keypoint is not visible. Note that for data augmentation, the notion of flipping a keypoint is dependent on the data representation, and you should probably adapt `references/detection/transforms.py` for your new keypoint representation\n", - "\n", - "If your model returns the above methods, they will make it work for both training and evaluation, and will use the evaluation scripts from pycocotools.\n", - "\n", - "Additionally, if you want to use aspect ratio grouping during training (so that each batch only contains images with similar aspect ratio), then it is recommended to also implement a `get_height_and_width` method, which returns the height and the width of the image. If this method is not provided, we query all elements of the dataset via `__getitem__` , which loads the image in memory and is slower than if a custom method is provided.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bX0rqK-A3Nbl", - "colab_type": "text" - }, - "source": [ - "### Writing a custom dataset for Penn-Fudan\n", - "\n", - "Let's write a dataset for the Penn-Fudan dataset.\n", - "\n", - "First, let's download and extract the data, present in a zip file at https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "_t4TBwhHTdkd", - "colab_type": "code", - "outputId": "6aee5a89-b16b-4651-88c0-f050fe3f14c4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 9095 - } - }, - "source": [ - "%%shell\n", - "\n", - "# download the Penn-Fudan dataset\n", - "wget https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip .\n", - "# extract it in the current folder\n", - "unzip PennFudanPed.zip" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "--2019-05-22 13:33:18-- https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip\n", - "Resolving www.cis.upenn.edu (www.cis.upenn.edu)... 158.130.69.163, 2607:f470:8:64:5ea5::d\n", - "Connecting to www.cis.upenn.edu (www.cis.upenn.edu)|158.130.69.163|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 53723336 (51M) [application/zip]\n", - "Saving to: ‘PennFudanPed.zip’\n", - "\n", - "PennFudanPed.zip 100%[===================>] 51.23M 65.0MB/s in 0.8s \n", - "\n", - "2019-05-22 13:33:19 (65.0 MB/s) - ‘PennFudanPed.zip’ saved [53723336/53723336]\n", - "\n", - "--2019-05-22 13:33:19-- http://./\n", - "Resolving . (.)... failed: No address associated with hostname.\n", - "wget: unable to resolve host address ‘.’\n", - "FINISHED --2019-05-22 13:33:19--\n", - "Total wall clock time: 1.0s\n", - "Downloaded: 1 files, 51M in 0.8s (65.0 MB/s)\n", - "Archive: PennFudanPed.zip\n", - " creating: PennFudanPed/\n", - " inflating: PennFudanPed/added-object-list.txt \n", - " creating: PennFudanPed/Annotation/\n", - " inflating: PennFudanPed/Annotation/FudanPed00001.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00002.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00003.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00004.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00005.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00006.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00007.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00008.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00009.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00010.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00011.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00012.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00013.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00014.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00015.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00016.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00017.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00018.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00019.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00020.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00021.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00022.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00023.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00024.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00025.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00026.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00027.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00028.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00029.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00030.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00031.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00032.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00033.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00034.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00035.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00036.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00037.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00038.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00039.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00040.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00041.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00042.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00043.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00044.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00045.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00046.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00047.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00048.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00049.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00050.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00051.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00052.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00053.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00054.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00055.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00056.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00057.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00058.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00059.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00060.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00061.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00062.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00063.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00064.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00065.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00066.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00067.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00068.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00069.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00070.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00071.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00072.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00073.txt \n", - " inflating: PennFudanPed/Annotation/FudanPed00074.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00001.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00002.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00003.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00004.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00005.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00006.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00007.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00008.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00009.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00010.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00011.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00012.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00013.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00014.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00015.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00016.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00017.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00018.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00019.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00020.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00021.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00022.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00023.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00024.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00025.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00026.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00027.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00028.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00029.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00030.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00031.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00032.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00033.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00034.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00035.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00036.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00037.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00038.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00039.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00040.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00041.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00042.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00043.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00044.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00045.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00046.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00047.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00048.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00049.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00050.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00051.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00052.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00053.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00054.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00055.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00056.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00057.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00058.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00059.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00060.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00061.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00062.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00063.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00064.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00065.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00066.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00067.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00068.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00069.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00070.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00071.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00072.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00073.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00074.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00075.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00076.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00077.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00078.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00079.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00080.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00081.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00082.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00083.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00084.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00085.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00086.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00087.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00088.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00089.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00090.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00091.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00092.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00093.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00094.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00095.txt \n", - " inflating: PennFudanPed/Annotation/PennPed00096.txt \n", - " creating: PennFudanPed/PedMasks/\n", - " inflating: PennFudanPed/PedMasks/FudanPed00001_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00002_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00003_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00004_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00005_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00006_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00007_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00008_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00009_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00010_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00011_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00012_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00013_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00014_mask.png \n", - " extracting: PennFudanPed/PedMasks/FudanPed00015_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00016_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00017_mask.png \n", - " extracting: PennFudanPed/PedMasks/FudanPed00018_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00019_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00020_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00021_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00022_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00023_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00024_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00025_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00026_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00027_mask.png \n", - " extracting: PennFudanPed/PedMasks/FudanPed00028_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00029_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00030_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00031_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00032_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00033_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00034_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00035_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00036_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00037_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00038_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00039_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00040_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00041_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00042_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00043_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00044_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00045_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00046_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00047_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00048_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00049_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00050_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00051_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00052_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00053_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00054_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00055_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00056_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00057_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00058_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00059_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00060_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00061_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00062_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00063_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00064_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00065_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00066_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00067_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00068_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00069_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00070_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00071_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00072_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00073_mask.png \n", - " inflating: PennFudanPed/PedMasks/FudanPed00074_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00001_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00002_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00003_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00004_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00005_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00006_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00007_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00008_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00009_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00010_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00011_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00012_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00013_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00014_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00015_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00016_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00017_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00018_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00019_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00020_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00021_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00022_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00023_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00024_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00025_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00026_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00027_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00028_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00029_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00030_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00031_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00032_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00033_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00034_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00035_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00036_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00037_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00038_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00039_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00040_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00041_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00042_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00043_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00044_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00045_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00046_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00047_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00048_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00049_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00050_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00051_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00052_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00053_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00054_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00055_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00056_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00057_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00058_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00059_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00060_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00061_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00062_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00063_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00064_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00065_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00066_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00067_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00068_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00069_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00070_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00071_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00072_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00073_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00074_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00075_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00076_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00077_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00078_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00079_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00080_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00081_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00082_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00083_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00084_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00085_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00086_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00087_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00088_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00089_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00090_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00091_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00092_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00093_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00094_mask.png \n", - " inflating: PennFudanPed/PedMasks/PennPed00095_mask.png \n", - " extracting: PennFudanPed/PedMasks/PennPed00096_mask.png \n", - " creating: PennFudanPed/PNGImages/\n", - " inflating: PennFudanPed/PNGImages/FudanPed00001.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00002.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00003.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00004.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00005.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00006.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00007.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00008.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00009.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00010.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00011.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00012.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00013.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00014.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00015.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00016.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00017.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00018.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00019.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00020.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00021.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00022.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00023.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00024.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00025.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00026.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00027.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00028.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00029.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00030.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00031.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00032.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00033.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00034.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00035.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00036.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00037.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00038.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00039.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00040.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00041.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00042.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00043.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00044.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00045.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00046.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00047.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00048.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00049.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00050.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00051.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00052.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00053.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00054.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00055.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00056.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00057.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00058.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00059.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00060.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00061.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00062.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00063.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00064.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00065.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00066.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00067.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00068.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00069.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00070.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00071.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00072.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00073.png \n", - " inflating: PennFudanPed/PNGImages/FudanPed00074.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00001.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00002.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00003.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00004.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00005.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00006.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00007.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00008.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00009.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00010.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00011.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00012.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00013.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00014.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00015.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00016.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00017.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00018.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00019.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00020.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00021.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00022.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00023.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00024.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00025.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00026.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00027.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00028.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00029.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00030.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00031.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00032.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00033.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00034.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00035.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00036.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00037.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00038.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00039.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00040.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00041.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00042.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00043.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00044.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00045.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00046.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00047.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00048.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00049.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00050.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00051.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00052.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00053.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00054.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00055.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00056.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00057.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00058.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00059.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00060.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00061.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00062.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00063.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00064.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00065.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00066.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00067.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00068.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00069.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00070.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00071.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00072.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00073.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00074.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00075.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00076.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00077.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00078.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00079.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00080.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00081.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00082.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00083.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00084.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00085.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00086.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00087.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00088.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00089.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00090.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00091.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00092.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00093.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00094.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00095.png \n", - " inflating: PennFudanPed/PNGImages/PennPed00096.png \n", - " inflating: PennFudanPed/readme.txt \n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 2 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WfwuU-jI3j93", - "colab_type": "text" - }, - "source": [ - "Let's have a look at the dataset and how it is layed down.\n", - "\n", - "The data is structured as follows\n", - "```\n", - "PennFudanPed/\n", - " PedMasks/\n", - " FudanPed00001_mask.png\n", - " FudanPed00002_mask.png\n", - " FudanPed00003_mask.png\n", - " FudanPed00004_mask.png\n", - " ...\n", - " PNGImages/\n", - " FudanPed00001.png\n", - " FudanPed00002.png\n", - " FudanPed00003.png\n", - " FudanPed00004.png\n", - "```\n", - "\n", - "Here is one example of an image in the dataset, with its corresponding instance segmentation mask" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "LDjuVFgexFfh", - "colab_type": "code", - "outputId": "ad7713d2-9c54-4e2e-fe68-034d283ab478", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 553 - } - }, - "source": [ - "from PIL import Image\n", - "Image.open('PennFudanPed/PNGImages/FudanPed00001.png')" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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cswoUSVIM43B3PlUbcSsNDCNajbYlb6SqphiqOrGtZQk059wPP/xgGVdzPK1E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\n7Xanu/t+1xaBlGcRKZKWBj+szmPOqQgXScN4nOYxhDCnRM5jQUVlYvZ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u1bk9WyvM0anubEdVNN08Sc379/++rVK9NiIPTPz88//PDB9CVz7gGgbhpRFVVFE9FBULWZs9dX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\nqPN2I+q6NtMfYxwnA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uScQbQKsa0bC2Q9OYeEBEpCqG27zSqivqgTQVF1hCG4kueUU2Zm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r+w87HvPm+tozj+c+l1LXQcSZ6DIRISEweCBPVOWSkrEcWUU1F+eoacJnd599+atflrPEw7G+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9tSijG4L2bwvOZxeNi6WZyilLLdbhdEw2JD3DIs+Ed/9EfmuOyGlwX6yU9+8tlnnxnQc7Va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\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gIYZlFXVCU+YKVmWV1OeWqqwqyjJw4+7keJhPfv2v//qr7//0leuvfPDJO2Hohwk1iR4c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bqc1chBMQdCYG9vs9lOh9PR3/t//tfPv3z18196VZryydHtr/7Jyx/cfLecAWOw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\nSQT5QoQTIVjCSwHMzXZrvbPeBqbFc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N4yMAZNRPv7+865tm0TygeXPPKUsl3NvaajP8v0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\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Mpofp+NGjR5vlZtgMhrDQSVNCSuV8iuXkhG0O8Sxj221Wq5XoGkGD0lqZ0Q0xpfxTDEPi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\nsiwBsZ7UiFiWpXeo9SKE9q/+7u/VTRnHFIIjO51MJrOz/TcD1K7s4rh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tu7c1RU1nU91e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oKFefugbD6HG6r6nG4tKqm/dXqk2hL5MUJ8kGywVffuWDeqMbjw8FA9F6vQEh2EcffVSpVDrt\ntiKJp1dXFUmanZpMRGIoBE+MT4uC8tabbw8PjXAcp2laMhlfW3sgiv1sNrO9vfncc8/95V/+5fjY\nxOnVM+fPXQIO7uYDHOsncB4BoFLq7m6n3XwIcigUZqS+0ah3Q6Fovd4cSiSPDw4nxmdazd7kxEy/\n31dVFUXRg4MD27YRBE0fn/z6N1/odDq0i4rFI99/5XuLS3NnzqxqukrT9KNHawPKot/vz2Sypmn2\n+32v180wFARblq1+/guf0XRxeNSVL6RZjo7HwwSJeH28qsn5QjaXP2m165Ik0gxeLucDQbdpabl8\nJhT2Q7BTb1RFqT81NcXzfLvVqVYajVoTx+hkNMlS/FByeGZqdnZ6jkLxdrO6+ejeqZX5z332+bn5\nKUkSNE2FYVjXdQeGGYZheZ5lWZIkB6cqsMyQPzCUSOIIXMjmKqVCJBT67AufuXb1yQvnzi3NL0yO\njY+khuLheMDr410sBiMIBOmK2m406tVyp9WQxa4qiyQOo7BjmJqqypZjG5Zp2QAjcEmSNE2zLAsG\nEAqjGAQDy7F1Q5FUQzX6/X5fFGwIKKYGE4igigAA23TOrqyODw+5KFJXxF631WyVNV30+bnkcIT3\nshzPRCMhlmYatYoDjF6/g2Lw9s4mSeFHRwccx3W6bRRFVVWWZXnACTB0i+c8sVgslz9JJuMrK0sY\nBmWzmW63DSMOBNsIagPUgDGb4TFPgHa5MZR0IMx0+1zRRHByZsQf4gCi25CqmQKEaqMTCc6DO7Ci\n6D1B69iQijEQvLu7/3hjS5E1r8e/uLg8NjY2UEEbhqHpiiQJuq5mMseLi/MTE+M0TT311BPlilSv\nlk1L/8M//APT1N9//92f/OS1fD4/NTVF07TH4zk6yj/99NNf+8bXE0Mp3sdTLINSGEwgyCA9G4NR\nAufcPEZiMAbjFO6iSRyFMQQicRSCAAxDNE37fD6vz41hmGmauq52u11RFDVNGZjFabqiarKmKziF\nO5ANEGA5Ju9lOY/LH/S5OIag8HgqwXt5iiEhFOr02m4vT1K4rMkQCglSv1QtDb6RdlGGbdAuinWz\n45NjDuxIiigqoigLlmXNzs62Wh1dN9fXNjRNBw40NTnd6/VDoUg6fSIIUjgc9fuDAyIcRTHBQDiZ\nGBIFWddNhmHcbm8yOSQIUqvVIUmyWCx6PJ50Ou12u4eHh9966625uTkURT/88MbKyqrf73/rrbcg\nG75/50EqNdTt9vb39z/44IPf+6MvvvXGT0+dOnXv3j2KZPb3D2Kx2NIp/m//9m8nJyfT6TSCIJIk\nraysnJyckCQ5NjaWSqVSqaEPP7wBo1ilVh8ZHU8kwfbO3v/0P/+LntB/8tpTf/6nLzdaTRs4giiu\nnDplO84rr/6wVCk7EChXSx/dunH3/p1sPvfs889xbn53f++Z5546tbrq8fllRa3WG1efunbz1t1A\nKFyvN3b3D9MnmTNnznU7/Y8++uiJJ57o9sVgOJrLFdbXN4ADP368tbS0cufOvfsP12RBNDQFABvH\nUYrAKQLHcRRF4UHdROIogSEICkGwAwH7/5DPDSAbgh0YsXECaTQrNIMLovjMs0+Oj4+WSoWVlRVV\n0U8yudXVlVAoVC5XHzxY+63f+u1crhAIBM6fPx+PJ2dmZliWFcSerIi/wl/4hLlnmqZpmr9EI/DJ\nvI4gCADZA+kbgiCGYUqiLEkygVORSGx//xCCkMOD44WFhcuXL2ez2fn5xcFwr9FolUoVvz/Y7fYj\n4SjPe9bW1q5du5bL5bxeb7PZbLUaJEmEwx5RFB8/fjxwWhuEzhm6lUpxjgMRBBEIBAKBwLvvvru0\ntBQIBP7iL/5icjrqQNBHN2+6vR4IRu/cvcfyHklVMyddACPlSiUaj91/+OD+wweVSiWVShUKhatX\nrz548GDA6/N6vYlEIh6Pj46Onjp16n//3/9samrK5XIVCgVd1wEAhmGsrq6m0+lAwD3wygQApFKp\nt956KxqNfulLXyIIwufzi6J048bhw4drtVptd3cXhlGv21cqFN1u92CXMDe3MDDsuXbtmfHxcVVW\nfvCDHwhiT1GEYinHcrQgdE6dWvR63Zom7R/s8TzrAItlXQNa/L1791RVf/qpaytLS41qTeoLsXCE\nwonBTWVpfqHTbBmqdv/OXch2DFVLxuK6qrjdXL/f4RhmbHj48PBgd3en3WpWquWTk5P9vb3Dw0NN\n08r58u7mLgSQz33u85cvX4mEYyiKr69v9Hui1+sdBPy4XDTHsTzPu1wuBEEGdyHDMFOpYYIgut1u\nt9sdnC3lcvn+g7vvf/DujRs37t+/n06nRVGkKCoUCiUSiWg0OjY2Njs7OzU1NTY2lkolgsGgy+Ua\nxKYoiqLruuM4hmGIoqgoimboumnopqEZuqKpoiT3er1Go9Fut9vtttgXWo2mbei2ZTEUjUOIqWrZ\n47TY63MU4xgmR9Cw7SiC2Gt1apV65vhk6/Hm1tZWpVIxDAMCSDAYnpqcAQ4cCcdq1YbX63ccB4aQ\ner2uaVoikThzZnV+fjaeiLpYWlEkj4fDCci0FJJC3B4mEHSzHElSMIY7DrB1Q0JQOxT2Tk6NnDm7\nfO3py6Njw9VqNZ/PAwcOBEI4jg/CWlmW7ffFWq0hyzKO46FQKBQKweFQdHn5FIrizU633xNFUS5W\nyu12OzU8ZBjGcSZ94dzZJy5dYF10t900NEUSus8/u7yysrS6uqKoUqVcwnBkeCi5uLiAYciTT14J\n+EMzM6PT09O6rrtcLhsCNgLBBEq4aIqjcZqCcBjAjmJqpmlCkDP414qiaBgaSZIsx7hYmuM4j5fn\neZ4gMBgGA1MWnmf9fj9FE4yLikajkUiEZCiA2AgG6ZZ6nDkuVYqqrmAEquiKIIvxZAxCgKzJLg+r\nW7pqqIzLBSBI1TScIhmXywJOvdGwgIPhOMOxhmnu7O/ZjuMN+BcWF+OpJO1iFFmDYdg0LJ/PryjK\n1PiUYVhbWzuBQCDgCzabTY/HR5EMBBCapkvF8u72bjabgyC41xXS6ZNCodBotHjeQ5IkRTIHB7lC\noRCNxlEUy+eKLppZW1srl8vPPPNMo9F49OjRH//xH//Jn/wJSZIIggQCgaGhoVqtdnBw0G63a7Wa\nZdmKok1MTG082lxcXHa7vbKkUBQ1NTX16NGjl176km3b0Wicopifv/3uuXMXLBv0BalYruqmceHy\npf0t/eD4KByNdIXu8KSX87g/unUzmog/eryxevYMxdC/9vWvqbqSLWR1SznOFj/3+c8VK8U3f/pG\nMBLm3Hy71y9Xat97+Qc7u3uT03PNVieWGCpX61/60ldQnNw/OPIF/A/XH7Xa3UtPXGFZttXunjp9\nVjeczEkuGh/q9ARF0XAcH/A7Br5VFEW5XC6WZTEM+8SSeUCTsyxrQEIBzmA65wwSWiHIAcAkCFxV\nRQQBALIABL7+ja8apioIvQcPHpw5c47huLNnzkMA0RTt6pUn08cnTz35dDZ9EgpFGo1Go9HI52sw\nioRCoU8j0K+QvAcd0idfdRzHcSwAfqG9RVEUhlHHgRwHQmBid3f/zJlzxUJp49HmzMxsvd58/Hhr\nYWHp4cN11uVmaJeL4Q/2Dx0bnD1zYWJiyjTNg4MDiqJmZ6ffeuvN1dMr4xOjOztbqVTq4cMMiuIX\nL1x66613ej1BkTW/P3jx4kXDMBiG9fv9EEDK5SpBUB6PR1aV2fmFZDK5sbVlWNbY5MT09KztgHQm\n+zv/6Gub29ubO9vBYKgvSJZjP/HkU++++64syxRFhcNhkiRJkjw6OqrX69lslud5HMfjcR9BEJub\nmziOHx8fy7Lc6/Xy+TyO4+12l+PcFMUQOLWxsWEYRrPZ9vuD62sbw8PDh4fHv/ZrTx7sH+VyhWy2\ngqFEOp1OJFL5fLHfEznObVnWtSefrlQqkiSRBP3cc8+9+OKLi/Mzc/NTV65efPa5p+bmJ9//4J3j\n9IHHyw0NJSOR0MjIUDwen5+ff/hgbXX1jKqq2ZP8wf5RLBbjOC4Wi01PT+fzOcuyAoEAwzAjo0Oh\nUOj06dM07RqcS7apilIXw6GZ2QkAmYyLsCyjUipubx8LgsBxXD6fX15evnTpkt/v/zf/5i/u3Xsw\nMjISjyfa7XY+n6/X67btjI6ODqZVpmkqitLrCu12u98XNU27devWzvZeq9VxHIemaZ5n3W6O5/lQ\nKOTzeViWgWAgSUqt1sie5I+PM7VabVBY9/v9TqfTarWq1erATUrX9cFFQZIkBEGSpLQ6XdU0RVXr\nCGK93Wm2u+1et9PptNvtVqtVr9YQAPU6XUWWUQhQBJ6IRSHTblertXx+JBEP8B6eoXmKgW2Ho5he\np99utizLNgxLklXKxY5PTdsWbNvA4/brmtXvSbpmGLqp6/rApfvo6GhtbW1jY6PTablctNfrBpAJ\nwSaGQ26Pi+UIt4cOhX1T06MvvPDsE0+cGZ8YYly4ooqlcv7oeDebywzUkBMTU5qmdzqdUCgyMjwa\nDkcPDo5UVU8kEtFITJKkcrnqOBAcjcbC4Wg6nTk6TMfjyUAw7AsETcsKBoOyLMuyuLb2AIZBv9OR\nRSHg806Mj7oY8vhofygVu/7hu+Gw3+fzYhgCwc7FixcZhrl27VosFjs+zhSLZcuxLQhojqE5hoUA\nCEMABsEoAiGwA9kWsBzHgSAHQaBBdUySOEqgNmTLmtwTe4IsmI6JUzjn4UiGpFyUi3fxXrc34PP4\nvbzXzfOs18+HYoFEKhofikbiIV/Yx3pZhqUUXUznjixg6aZG0sT45Hi335V0xRvw9SUBwRBf0G85\nVrPTUjSFctGFUsECNoqjwUgIQqCTfBZG4XAsms/ndd1AEMzt9g6K3+997+W9nf2bN25pqtHrCT/+\n0c8syxZFqVqp3blzD0XxzPGJrhoYgkXDsfHRCVXWCIz0un37e3tPXjl/787derXm9/ri0Vgiker3\nxUa1kc8Xl5dPed0+HCWCwRBBkKIgowg+NjTWqDSy2Xw9D6Lh2OzUNIYRpWLl5s2bAIBz5851Op3p\n6WlZlhEEKRaLGxsb2Ww2Go0qioJjZKvVgTHM5w9ksvluT3jhSzMP19bCsYiiqZ9/6aWfvf02QVG8\nx42RRKVWPXv+fL5QyJdyALFTI0P/8He+BmOw1+8Zm5yIxmOBUKjeaG7u7AII+/JXv37n7sN4cpQg\nXYnkyO2P7w4NjYyNjSMI6nZ7X3/zDUlRjo4z3kCwUW/X641gIOLYcDQ25GI9JO2CIEhTVEkQFUWx\nbRtDUALDCQzDUfQXlgoDy9RfELuhT7dHEOQAyIJg27IVRRPCEZ8D9KFhL4xYmi6MjA6FghEcI09O\ncpFIoliszM0uvP32O6vLp3VFAwAWBOG3f+v/FI/Hz51bNgxDkPq/AkWf6F4/sWCwP3VYlqXrumUZ\nDrBt2x4UyLYNYBhNJFKKrC8trXo8gYcPNkLB6Fe+/Gv37z20DAuFsYO9w0Qsefni1WymYBn25sbW\n/u7B7PTcy9//PgTA/NxM0O/PZjIUiQf9wQvnZsdHxx4+eDCcSsEAKRXKJ+mT6cnpmzduzs/Ov/mT\nd0ZHRqYnpzRFvX79I6/Xmy8UKIbWdD1XKJbKVd7je7S59eqPPtjdP1hYWk4kh+7cu3f63Nmh0ZE3\n3njjueee0xT9zu27lVJVU/Repy+LSiwSV2Xt5o1bYl+6eP4SiVMojOVO8sCGcBzHMGx7e9vn8yUS\nMQiCJEmKRqMEQdTraqVS+eM//uNLl57QVOPJq9dEQZ6dmlVE9fyZ1Qd377Msf3ycMU0rnT5p1uqL\ni4svv/xywBf0uL2maeI4/sMf/uDgYIdx4e1O7eOPb/r8/MVLpz/34nOMi4ARmyARHMfWHz3MZrMj\nIyOCIBAEYds2CiOtRtO2rMePNrKZky998Yvnz52tVStzs7OGpidi8d2dHQSCK6Xy8PAwRZNDqThO\nIAeHu8Ayu61mr9/heFcqFfD5PIauv/D884VC8ec/f+e9n7+7vDTscfsEQfrg/esIgg0kwJqmDfzC\nm81mu93u9XqSJCmKMuhmSJJ0u91ut9u27VqtlsvlGo2Goki6rkIQRNO01+sNBALhcHhgCBsKhVwu\n2rKNgUm2rusYhg2sogEAOI4TBIUi2KBoEyVF1o2uLNda7Uqj2ez2ZEVTdVNRNF3RRVHkOQ7YtiSI\nkANs04pFopamSr2uJisBt3c0OZTLnJiK1irXapVqtVASRZkkaBhC+n2x3mg1mu29/aNmoxcMhnGc\nrNebvZ6g62YgEDIMi6Zdfr938PB4eADseqOCYhDPUgQGIEeHgRUNB8JBD00iW5sPS8VMr9NQpB6w\nVRQ2KQLx8C6e5TqtbrVcMXVDV41CrlivNmiSjoQiFEHZpo1AKOfiXTRraDrM8zwMISRJhkLhialJ\nDMNgGJYUOVcoMAy9tLSkaVqjWTu1umzoOs+5vG6XKPb9Ph5FAEFguirLksDzfC6Xq1aroVCoVChS\nLiaVSpEk6fZ6aZ6BSER3LMVUNVu3EYCQKM3Sbp/bxTE4hVMU5fHy4UjQ7/e7XDSKwgSBkRSOYQiG\nIQSB4TiOIJDbzTnANkydJHEMw/r9brNZF2WhXCsXSrlqq0pQGEFhAAGGrZEsZTimqiuNVt0f9Nca\ntVK97Pby7W5L1iQcx2EULZfLpUrF6/XaAPT7fQDD3W633e3Ksuz2emVZNm273W6Oj09SuGt8dMIx\nHVFQ9/YOOIYbG51UFQNFMVnSTp9arFRqi/MrnVafc/FiT4nFEvl8EUGwSrn62o/f8Li90WjUNKzF\nhaW9vQNF0cLhyPFRxrZBIpaUBfHy5Sssyx4fH0ej0f29g1gk2mw2NVW1bfvx48d+fxCGYdINAoFA\nrdqIRROlUqVSqZEk3Wq2+/0+SZKqqmYyhWKxGIlEYAhxu70AwKIol0tVSdRomuv2RBhGl1dWdV1H\ncSyVSo1OjPj8np7Q4zhu/3BP0wwEw2OxWLFsP//805ajX7/+wdjEaCgUCoT8hmF0ev18Pp/O5MbH\nJyLRxP7eke3A1UpjeHQiEIquP9qUFM3t8UVjCdbF67q5sLxy9tz5YrlSb3TOXrgcCMZKlXq10iBJ\ncnCl/Te7mV+Yzn3SjnzC5/4vXQvkAMgBwAaQCSDL5SLanbrHwyaTsVIpRzPEyNjQpStPfO+VV5Op\n0V5X5Fg3gVKWAQRBKuSKC3OLtXKVoqhyuZwrZPtSX9d16L85Br/qV5IsfqGuNU3LNgdcu8E0z7aA\nbQFDN2dnFhr1FoaSPOcVBEmWVcsCs7MLC/MrN2/ecbncGEZ5PUGa4rodce3hY9uCK5UKjuMcx+E4\nrumKA4yBCgdBUFXVhL40NTWDY2Qymer1hOPjTKfTkySl3wdej18UZQTBFEXRdN3j8ViOPT07p6ha\nKBTpixKMYAQBkkMjDMvLsmJYdiQWpRmGcbkCgRCKoqFQJBwOYxjRaDRQFFdVlWHYVCq1tbUTi8U0\nzYBhOBKJDSLs4vF4vV4XBCEciqqq6jhQvd50HMi2AcfyEIT4/UFF0URRdrk4HCcj4ZihW8nkULlU\n83kDvU5f103LcmAHgmF0e3vX43YPXnzliScmpyaCIZ/f7x4ajgLIuHnr+o0bH+i6Eo2GWNZ15+7t\nSCT02c9+VlGUbqc3khqBAdLvixiGNxoNTdNcLvrwcL9YzO/s7FAUoSgSRVG6rnMcr+tmpVzGcVTT\nJYJAisU8jDiqpiSTsWg0XK81YBge+KSVy2WKohAE03VjfHRSEtXDw2NN1Q8PjyVJoWlKUSRVVQfU\nLQzDKJrAUMK2bU0zXAwHABAEQRRlGIFYlmUYZuDMZhia4zgwDKMoimEYiuIDYiEMw4ZhmJZuWeaA\npT0QJgIAYAi1bVuWZQBgHCdlWTYBJJuWoGmiqpm2YwPYNG1JkiAIonCCIAg3xwHbVBVJFPvAsWiK\naNcahUz2aG8/Egj02x0SRSRBpjCSIWmaoGEHhmGUc3t8/gCOUankqGPDJ5mCqpihYGxqaobj3Pl8\nfkAO7Ha79Xq91+vJsqwbGorCkVBwkLdLkuQgHE4QBMMwBrHlKIoOrujB8ICmadO0Q8EIhhHNZluR\n1XA4GgpFKuVavdZUFX1Arul2+41GSxRluNVodzo9Aqd8Af9bP/tZ+iQjyfJASSOpiiAIFEXt7eym\njw4/vvlRrVR65+dvFwu5Xrtz7+M7CIAoglxeXiZxjGVood/7sz/5U5qmCYIgaAoA0Gg0EBK3YFs2\nFEEVZV02HAPGUYIhBxpgiiIQHHFgAGMIhiMoBrtctNfv8Qd9Hp+bc7OchyNpwoZs3suzvIt2URRD\nDp4xAoVRCCMQnEINW4MxoNsqQaEAsQBiqYYcioSK5SJBE32pf5Q+dGBHVMSDgwPNVPtS7zB90Oo2\nWberUi8Lcr9YKVQbFUWXJVUsVYuGrRu2nkgk2q1Os9k+PDxWFf369RsM7eI5N+/i45F4KV+ampgG\nAJ6fW3zn5+9FI3GKZGZmZmcm53KZgqUDAqPbTSEeTTimA2wod5I/f/YCjhBv/uSNhfn5drPV7XZH\nR8cPDg5tw4pHYkK3v7b2iCRpWVZ5zqPKWiyWcByo0+wlop5KsfLuu3csy7569SlBkIKBkM/nOzpK\nV6vVyclJmsYgCJqdnacoCgKwzxcwDAsAqFiqdPviuYuXXvzCF/OlYk+UPD7f6Pj49u5uMBKGUOTx\n9lYoGpFVtVqvm4595nzMG/DWmrWJ6Yn7a/cb7caHN25wbvfao0c2gIdGxnhP4O799eVTZ4dGJmTV\nvHPnAYqSnXbv5CTncnEkzYyMjX/nu9/f3tm9efO2ZUKHB2kYwnpdCYEpnzeEAIQicbeH83h5miIg\nYBumphuqqsqqKmuKrCmyrqumqUOOhfzCIA4ezOgAsAEwIdgCwEIxmyBh23Eg2PJ4WVkRotGgbqgb\nm1tT07O7O3sYRrz00pe7HeF4P10t1X3ugJt1z80u7O3txePxYrozMTWOEhiK/YLG/WnPhcE9Avwy\nffzTzkC2bcO/GCU6EIAxjAAAUhTjww9uwjA+Pja9vbVfqzbzueLjja3VU2cODo6qlfrC/JKq6Ddv\n3pqYmBwfn/B6ffl8BUOJZGKoWCwWCoXl5UUcR2mahAAS8Ic8bh/L8jhGHhwc3r59xzKde3cfXL50\n5fatO7/1Wy8Vi5VKuV6vNZcWV1ACL5RLO3sHumnWGi2cIFEM1w3L7WEJkvrz//iflk+d9geDh8eZ\neCoZjkWvX7/+uc99XtOMycnpg4O0JNkAwN1u/+DgiCTpZ599/kc/em1+ftG2QbvdXV4+1ev1aJpe\nW1v3+/3Hx8fhcNjr9d64ccPvC05Px3d29jJpa3pqhiTpTOZkYWGRd3l01RwZGWddHjfrdrt9GErE\no3FRkGEYxREcgpAb129KgriztQ3DsKIIhcJJKOzv9duBoPfsuZVTq/McT+YLmU63EYmEEonYD37w\n8pUrVzAMu3H9o7Gx8aefehpHsVQiefbMGZqiOJbpddunT62QBIbAcPbkxOv2aIq6srQCAUQQeqVS\nodmojQynLlw499xzzwynUqqqRiIBFEZSqdSN6x/FownbdFZPnVFkrVyudjoCiuKqqquq6vV6B8Nk\nmqYJAsMJdJBNOnC/VlVV13UMI3je4/F4WJZlOZfbzQUCvnA46Pf73W43x3EDUf/AyKfX63W7bVkW\nByAEw5BlGYahIQgyWEY6jjMgiGMY1hOlliB0ZEXSTQfBSBfLsByCoZphAQDcbrcmK6FQiKbpfr8v\nK2KpVPB7vCgC1avlUqGAOGAoliBRzMvxFIpDDrBN2zYdBCZ43seyHhtAzWaL49yTkzMIguu68aMf\nfjgYjLfb3W632+v1LMuiaBJGIAzDBj4JiqIRBMUwbLPZ3t3dz+eLKIpHI7FUcmjQYzkOZFmOomj9\nnlQqVuqVmiIosXAMg7HN9ceVYoVjuEgw4uE8CEB8bt/q8uqZldPRYAQeGxuDIKhUKpEk+eDBg3gq\n2ey0D9PH9WbDcZzB0slxnHq1GgmHjw8OFUnutcWD/f3nnnkWxzBFUVwUnclkAADlcnlhYUEQBJfL\ntba2RhAExdCyKomKKEhdaeA1ZGqWbTjAhhDIxbv8QZ/LRUKQY9smjiMM52JcNM1QKApbtmnZJoYh\nLEfzPKsoEoYhJIk7wMIJOBoLJ5IxjmcCQd/QcNwX8ALE6vTasiYWSoX19QckQw4eMAqNT44tLy+3\nOk2WZYulUi6Xy+VyhmG4XK5Op7OxsTEQ4hEEUS6XW61WNpv1+XzFYrFUqS4vLI8kRxmSLRUqU2Mz\nMMBU1URRnCLZX/vqNyjSNTk2FQpEKIIK+kKLc0sYSvV78qVLT8TjCTfvHRkZOjnJ2TbY2dlrtTrZ\nk3w4HGEY9q//6m8FQYIghKYZDMMgCIIhNJctxCPRZrOZOS6Pj40JgvST194YTEiajQ7r4jQNMBT3\n3rsfTk3OMgwLQUi/3y+XyxiGzczM7O7umqZ5cpLb3z8EDrK3ezg2OpVOl25+9PHY6MRf//XfAAAv\nLCz883/+LyRZGPh/YDgySNscHh5GUWx3dzeRSNTqlbm5udnZWVEUY7GY4zhTU1OhUMQ07WAwODY2\ngaL40VFaFFRFNp55+vkf/+gnKytnGJr3uAO6ZhYL5VAo7PcHU8lRBEEvX37y4YMNFKEMHZRK1YGz\nDsdxPM+TJOk4jqZpiqKoqjp4HngxmKb5K/4Lg1UOgGwALACZlm2KknL6zGyh2Pb6+E6nlUzF6/U6\nDCGsyx2PD/39t7/XbvVRlEyny6n4MAxhhXz55CRHklSn05ldSeE4ZtoGhCK/gkb/LQJ9+m3oumoY\n2qByBADAMGpbsCJrHo93b/cQhnAMw1dWzjAM3+kImmb4fL6JiYm33vqp3x8aGRnTNbtaaVqW4/d7\n79y5u79/UCpWnnzyyZOTk7GxMd7Nzs7OfvjhDVXVW62OKEo+X4DnPDs7e7Oz8wRBFQolSVQuXrg8\nPj5pGObDh+soiluOHQqFDg6OUqnh9z64rijaU0898+KLX7hz5x5Nuz66ddvt9tbr9WKxODs7SxAU\nQVAjIyOPH2+xLLO4OFOpVPz+II7jW1s7tVrts5998ejoKByOkiR5/97DmZnZXC6n63oymUokEgRO\nXXvqmVKpAsPohfOXtrcb09Pszs5eIV+mKdf+3uHIyNhnXvisplrJ5HAqObqxtnH+zHkcIx8/elyt\n1DGM8Lk9siz3esLo6FipVBrk0yuKFA4Hb926vrKyJCsCgCxB7B6nDxxgZrPZVCr105/+dGBoAjsg\nl8thMIJhWPrwqFar0QRJ0zQKg2azSaCY1+sNeH21Ws0xrVKhQODomTOnLctCEKjb6XQ6nQ8++AAB\n0JVLl0+dOgU50NTUVK1Wk2X56PAQBsCxYY51h0MRCEKCwTAEQblczu12wzAYkAsGUmsIgoADOzak\nKFqj0SoWi61WayDpZVw0gBwMwyAYWPYvuh8EwWAYtW0gK1K/3xcEQddVANm/NPjAYRiGIJggCIKg\nIAjBUJzASUXVc+VyudnuKoqDYTTH8T4/zfEERRq2xXBsp99zeziGoSVFNE29XC31FcHFuQACkSSZ\nK+Yoijo8PNRkRZZVRVJpgh4eHpmYmHTzPklUCvkygTPxWHJrcycYCNeqLZIEiqK2W91UcjgSifl8\nAZ/PR5F0q9XK5XLdVrdaaYmCVqu2jw6zzUaPdXltC9ndOTrJlFpNQVNt4GAkwZIEa1uIJKgkRlAU\nAwBEU65gMOz3B2mK6fWEra2darUuy6okys1Ga2/v4N69Dfjo6ABF4ZVTS+12m+W5nd1dX8DfbLV8\nPl/6JIMgiGVZly9eioZjJ4cZyIGuXX1qcnyUwom33njD0g2v2310cLi0sKQoGkO7CIIYHh4e7AM+\nvHFDUaRCIScpEkpguq6urT2wLIOmcQRxOM6FYajtGKomK4oEQQ5OoKapq6qMojCAHILACAKDIGfA\nuSJJHEC2Ayye5/x+n+PYiiLTNGkBC8EQF0sjCNwX+7dufZRKJU6dOc2yrMfDExReKpVu37792huv\n1VvNUCTc7/fdvHd6anZmeu706tlQMHLliSc/eP/6jes3bQtQJOPYUCQcMw3b6/FLovLKK6/ev/+w\n0WiRJF2t1huNFkOz/Z6Yy+VzuXyr1Tm1cnpne29AvDJ0p9XqaZr1+mtvWyZ0//7a7k722WeebzRa\nJEFHwjFBEG7e3D19+nQwGHzhhRd0Xa9UKl63r15toDA2sDA53DtMJaPNRtvj8ToO4Fk+EUuQOPGT\nn7z527/15UQ8xTBsrycUCiVRlNPpk5GRkfX19Zdeeunw8JCmXSzLb2xsZjLZer0JwxiOYRCEtVrd\nUqkS8IcgCJJl8PDhw0Qi8fDhw0ajUSoVIuFYs9nUdb3RaI2MjNTr9Wgs/O57Px9kiIyNTRwfZxAE\nu3r1qVqtsfbwkSJr2ZN8s9ny+fxra49cjBuGsPRx1rLs8dFxFEZxlHhwd93j8c7OLFYrzfv3Huma\nrSl2vdbudjrNer1aLlfL5Wat1mu3ZUHQZNm2TAg4KAxhCIwhMAwcaMAQgKABPECwAyDbNHXbMREU\nYBhkmuD06VOmCXien5waX19ff+qppx5vbfsCwe2d/URy5HAvQxPc7NSUm/Xdu/OQJpmTdDbg87/8\nyisIhq6snvr0dG5QEwwqsE+GhwOgGlAqLMuyHRNBoF+ag0GaZpiGBcOo48CKbBwfZQVB/uav/1Y8\nlqpVmzPTc4qikRiZSiQs3WjVG5ViJRwI723vNSotxMEgG+l3+mJPAJZdK1cunDu3tbG1u7X71JWn\nsum8JumGaqQP06l4Ckewk+MMx7Anx5mANyB0BWCBWrmROc6MjIxMT0/jOB6NRk3TrFSaBM4cHmR0\nzd58fOjYyGCNPDw83Ol08vk8TZAszbooV8gfCvlDN6/vvvgPXlQllUCJ0yunT45PUAgdGx6rFCtn\nV8/Ozs6iKLqzs3Px4kVFUarV2vz8fC6Xs0wHANjl4lQFYBhx796DkZGxk5NcMjlyfJR9+HCjmCsH\n/aFCrvDUk8/s7x+FQrFeT9AUPZlMqqpOoITUF4q5fDQULpfL3W7XxdJj4yO9fufevTseD8+yDEUR\nY2MjANiGqY+MDk9NT/r9/jNnzgiCpMkay3CbjzYlsW8bOgKD9OEBgaOry0ulYr7dqLs5DwIQnnUr\nklwoFB6trS0vLH744Ye9Xi8Ri48Oj8Tj8UePHsmyjGEYz3GjwyMwjOqamUoOuxju8CBNkq5atcFz\nbgBgFEUdx0EQhCAxDENgBKiKjqLoQFwIw+gn4VuGYQyQxrIMw9Q1TRtMpLvdrizLjuOIoijLMoAc\nBIEGOrZB/62q6iCPbvCWfD4fQRAYhjEuzoYxybDaguQguGE7um37An4UJy3HzhcLDEv3ZYlkqWKt\n3JMF2VAbnZZkKqIuq47eE/oUQ3/tG9+EMdQ0zUQiJfYloStQOHWwe6DKOsu4p6ZmPv74LorimUxG\n1/WZmQmCoBKJZKlU4jjOcSCGYUmSDgZCraZaLlU6bREBVLshYjAznJxsVHtiT/fyYdghCZS1dMTS\nERSiha4W8iVQmIIhHAJIvdauVOqSqLTbXYZhK+WqY4N4LCEK0qA84jh3Ih6HO912q900DF2SxIFE\nwLKMYCR849bN6amZarWqyLIiKsOpkeeeeV7sSw/vPcIgIhyKC30ZghBdsTTNaNRbsVgskUh4vd6/\n/uu/HjSzp0+f3tjY0DT1+PDgJH2EYWgsHm3WqwCyc/kTANntbsuyDF/ANzSaJEgMxmCWZVjepaqy\n3+92HMtxLEHseby8ooqGqRIEThC4JAntdtOyNQxDbNs2Tb3b7RaLBdM0VVXGcfzevXs/+9nPWq3G\nnTt3YBien5/P57PDw8MMwxweHn7zm9/K5XJbW9ter/eHP/zR2tpaPJ6Ynp6+cuVqLBYbHR1DUVTX\njUKhcHR03Kw3J8anT6+e297YEXtyp9Xpt4VzZy74/SECZ95/9/qFs5euf3BTkfS52aWx0Yk7d+59\n4XOfn5yYOn16NZ8vfv7FlxYXZ3jerSpGKjXEcXwmczI+7u92+s8998Ldu/cf3L2fzeTare7K8urG\nxsb46Nja2iOCoJYXF998862gP1DMFyLh2P7e4fz8/KNHj/LZQqPRzKSz42OTJEkGg8FAIBCNRiuV\nlmmafr+/Wq1KklQsFjXV8HkDzUa7VzEs0xEFeWlxRRBEv9+fTHJraw8wDKVpstmsT05OVqvVgZFX\nPB43DGNycvL4KBOPJTudzssvv3z69Om7d+8GAoHXXnuNIpnz58//xV98B0HQubm5SqVWLtUjwUg2\nU9AVff3B46HkMMPwmmZANni8vi30ZBwl52cW33rj5+sPNlv1XqPearVa3W5XEARFUT5J0vsVPhv0\nqfwIGAYoikIQhCAQTiCOY0mS5ABrfiHV7XZfeukyz/P7e4fZk/z77324v3cUDsVlSZ+bXeI5X7XS\nRBGyWm3YBkgf53LZgsvFnZyc/H/p+s8gybIsPQw8Vzzpz3V4aJlaZ2VnadHV3dO6Z4aDERjQgAUJ\nI7HLNS7IH7tmS/zYX7tmXNquwcDFEEvskiCxGA5mMNDdM9PT093VXV1dXboqtYzM0BGu1dNXnP1x\nPbyyhwa3tLBMz3D35/fde8R3vvOdq1evPn26qVAaryOlNDOEDI3bdd3/9fU88/h87h8hDJFoRdvt\nXrlcGw3jaqWxvbWfJvLWrbt5Ju/fv18sFrXWnU5ntjG/t7d/+/adTqdrWfbJE6e3tnbm5hZ2dvZ8\nP9jd3f36178eRQnn9tLisuN443H00ouv2LZjegnyXHYP1HPPXet0uic2Ts025i9duhKNxgXPT9P0\n7NnzAHTQhYsXLz9+/ORP//TPT57c2N7enWvM37//8O233z579uyDuw/iOI3juHnU8n2/3xt84xvP\nbz5+cuPGjSiMNzc3KWEffvjh40ebruvevnVnd2un1+leu3btRz96a3FxsVKpPHz4aDQar6+v3751\n5+TJ09euzR/sd567+rxW0G71Hz3cHAyGUuCZM+f+7//1/2N2drFarrmOf/vGrZPrJ5882Uqi5ODg\n4MGDBwsLi8PheHPzaRQlvhdoBZTSs2fPhtFIyKxaK7/y6gunTp0olQLPc27duuE4Tr1eHwwGgV+o\nV2vj8VhL9cYbbzSPjprN5qDfW1hY8Fy30Wg8f/36nTt3PMeN4/jixUvloNpqtrWGX/v2r6VJrhS+\n+uprB3uH5XI1jdLhcChy9cEHH42HoziOz5+/uLd3MDe38NOf/Mx1/Xa7vbe3t7y8nIuUUDSjFw3P\n07Ic13U9r8CZRQlTUpu+nMGg1+t1er1eliVxHA4Gg263OxqNkiSJwng0HB8dHbVarX6/PxoN4jgU\nMjO50dLS0tzcXKVSdR2fMQuAIhJNSCJkLwwP252ne3v7nc44ToRC5liEsTTPMyUfPnmYajm/snDn\n0YMc5N3tR/WVRe1wadFBHB502z95+6dr6yeiKJqZmWk0Gq7jf/ThZ+NB3O+Nq5X6p5/eqFbriCiE\nWl9fX5hfun//frFY9jx/0B8RYKbRuFptKAWEWJ5bShNV8Gq72y1OPZmTpYUTNi8uLa5vPT3kzF9c\nWL9983E4krs7zSgUnc4Akayvn6hW6oiQpSLPpdGHTZJsfn4xzyUiaR61ldI0SSIp86AcUIu6BbfW\nqI3jqNPrzC3Mc9v6+S/ezzM57I/2tvZlqtaXT2axzFONip3cOHPh7GWbu9E4KRZKjfrsnVu3f/Sj\nHzHGPv744/X11R+/9cOTpzbSJFpamIvGo/3trZPraxtra61W07Z5HIeOa/V6nX6/+/jxQ6/gcZtb\nLg+Kvus7lJPV9ZXBqM9tLrUUKs9llonUcnixHCDRg1E/SkKkCFqJPB2O+mkWj0ej1dXVoOifPrmh\ntZ6dnX33nZ9/97vfXV1dv3rpajgM69WZD9774Lf+ym+dOXVWCeW7hTiMP/no01/8/Bd/8ec/vHPr\nzmef3HAspxSUz589P+gNa7UZRHLz5u1z5y5wbg8H49dee+Pdd9/rdfq1Sl1JbDU741H0/PMv5kne\nbfdOnTr9j/77/3H7ye6oN16cW2ofdTdWTj5+8OTN199sHbYCL6BIr125Blr/yb/7bhJGS0tLR4et\nmerM7tZuq9VZWlr58Q/fdh1vcWHp8oXL9+49+OpXv771ZPv8+YuU8qWF5Q8//PjoqFWvzyDC9//s\nB6dPnxZCFIvFpaVGFEVxHD/33HPf//73v/Llr25snBBC9vvD8qxXLdXaR+1qqfrZx59wwt984w3X\nspsHh6+/+mrzoFUrV37w/R/90R/863Onz51YO/HTH/9UCz1bn5WZbB+1ZSa7re4br77x8Qcfriwu\n2Zzvbm9/51tviiwd9vrjwVDlMvBL3/s3fzLXWEiitHXY+tEPfnT31l1GuBZ62B3duXm/fdT/5IMb\n0SgjxIrjNEkSmeUolSELWJzblkUR6F8iKwAAgONYnHOlRZ6nSknGiOvanm8VCr6p5RaL5Xarc/v2\n3RdffFlpUqnNcNs7PGiH4ySK0n5vpCVksXQtN4mSOE5+9KMffe1rX/M877DVNGibZVlmkN1xC5Eg\n/6tpsJ+LqDKgDBgjjBFKOQCgplpDqVibqc+PhhEidexCvT7banaEkEJkg15XCjHsDw7299vN1vzs\nQuCV/+Zf/1s3Pr11auPM0UHTtd04TH7+9nurSxury2uD3kAJnadif/cgT/PmYUsLLBXKg+6wMsMe\n3HuYJfmffu9PF+cXW4et9dWNaDT+7OPPSoWgddiaqbEL5y5YzFqYW/Qcb2VppXnQTMJocW7x3u07\nq8srjLBHDx51270T6ydQQZZkd27dPXfm3OWLVwa9wcmNU3s7ey+/+IpjObVKfX5+MU3zbqd/7blr\n9+4+uHr12vb2bhCUAAjn9qOHjzm3hIBOp/vxx58iwt7e/isvv3Hj09uO5f/qt3/t9q27SZJtrJ1Y\nWlz58MNHhULwi3ffr1frX37zK+Fo3Gg0pFB3795/+nT3/v2HT59sr6+dOH36dK1Wu3fv7v7+3v7B\nLrdoqRScPn36T/7ku0GxMDs7m2VCpGJpYcFi7OnjzXJQ/MLVq1/9ype2nzzJk0QLCUqD1uFo1Kg1\n3vqLn7YOO5x6M/X5cqmexXkaZSvLG//Rf/Sf2Ja7vb2ztrxCEK9du7a2tnbq5Ml+tzceR4N+ePbs\n+fPnL7zzzruNRqNUKpktYaIi27Zt29Zaj0ahUX4yD0OwNKOKhch6vc54PByPh0mSAABqGI+jVquF\niJzToFhoNBrLy8snTpw4ffr0uXPnfD+wbRsA8jw3qLXh7GnCEoX9KDrqdPujcZyLRAhNiQQdZUk/\nHI3zNNV5Y2mO+NZY59XVuWY67KlIuRQKVrlRPep17ty/N7+4/OjRZqvZGfSGezv7J9ZPLi+uHB00\nXduxbT4YDB49enT58mVEfO3VNw4ODhqNuXa7c3TUnJtdeP76i+/94oPVlTnUtBLUomHW74yX5tco\n2ionh7utNBSDzvibX/3O9ube4/tPX3/lzaJfaR12S4Uqp7zb7m092U7jzObOpQuXhv3Rowe7c415\nLbEUlC1mi1RpCaAJjeIhEFmpBhpzQlWpUkzzZByFcZKkaTo/v5jnCpBfOHexVmmsLm/8ype++cbr\nX/nW138tHKbDfnTzxt3RMHzrrZ9+9OHHjx8/qVbrnud1ux0zO1JrFY+Gy/NznaP+uz/fZIB7u9vj\n0SBL4jAc2ZwtLC9Uq+WFpcVqvVKplKSWQgmlZZanGhVlZDQatFpHiKpQ8PI8HQ77SotqrTw3N4eg\nHm8+FEJwzmdqddu2i8XiyvKyzS1CSBJFlNJSqXJy4wRB2u/381z2er23fvzT9977YGtza3Pzqe/4\nlPJBd3D27PkvvfGljY2T9Uo9TfP9nf2jo9aVi1cunL3Q6/RBkyxVnLnjYZwlea/d23q6NzMzu7K4\nOugNqtXa7c/uuE5BazLoDb/+1W+cPn0WgKZJXqlUkySzuANAS6XK06dPFxaWwjDsdQelUuXx401O\nrUsXLgRBsLW15Vp2rVZPU/Bct1qtuq7bbbVXltcODg6Gw+HCwkKWic6+TJOcEPbkydbc3HzzqG1K\nHWEYHh4eLiwsuK67tLgyHI5efvnV3d19g3J3O/1+d5Cm2dFBc3d3txQUa7VanmVRND57+mSz2cxT\nsCwAjc1m88K5ix9+8PGJ9ZOFQmHrycGbb355f3dvNBoZ2fIsSZMkEVleLpf7vW40Dm3uFP3iaCCS\nKF1bXr1z627g+FuPnohULC0si0yGw2hv58BmBYo8GacWM/yiyc+J8gJjz9r9Z1ISTSnVKAE05xQA\nsiyTSjiOUyqVVldXZ2dnt57unDx5emP9VPOo9/jx1sLC0oOHj+eXlobDyHMLIpO7W/vxODk8bJ/a\nONVpxZRwAMhkFsbjS1cvheHYSAZY1oRWa9o+/v2J0S9nSUi0BiVBKzoexcPh+N7dR0FQ1AqiKO12\n+71O99HjB4xRy7J6vV65XF5aXBFCVSq13p7q9QaALM/0zs7u3NxCt9vPsrzd6jYaczs7e6+/9sat\nW3eODltpmnNud7v9LFPzc4tRmBweDuI49TyvVqtRSmdnGtVyrdfpXbxweevJ9sP7j1CTclCmSAt+\ncOPTm+ViqXl0VKvV0jQNx7FSamd7r1qtAtKFhYVwHNu2XavOPHnyRCs4OjpybM+M9A2C4OjoCACa\nzeb777/POW+324SwjY2NwWCUJrnv8zhKB4NhqViZn13od4cXL1wmQAt+cTQaHx40Acjq8tqJjdlT\n66cWFpak0O+++97q6vqNG7eWl5fjKFtcWAFkT5/uEsJGw/BnP/tZFEVpmgqRx3H8Fz/8c0Rl6FtZ\nmtqMX7hwaXtr9+zZs51OBwDLleKHH364urb8yacfcc4ePXr05ptvbm/tNhpzc3MLpVItiXNG3Vaz\nv7iwqiTZ2zvotLtJlMzMzFLKCSE3b95UStm2HUURo9be3v7LL7+SJvni4qIhCFBKbJubBN1wKdM0\nHQ6H7XZ3PI6SJFNKmRTHdV3fd4vFoiFeaq0RlWO7poPQiCxvbGycPXv2/IVzJ0+erFQqWZYeHR0N\nBoPhcDwcDsfjMAqTLBNSaqlQEYqMAbdSIZMsj7O0O+gPx1Gci0ESNfvdYqXcHPZGebJ+5lQz7KkC\nHzNx6tpl6fJeGo3yJKhX0WKHzSONxHG8Bw8ezdRmGNBuq7M4t2BZ7O7d2/1+f2lp4eHDh71e7+bN\nmyvLa4P+yPcDy3IYsxFpFGUnTpw+2G+5biAFaoUFP5ACUdN+f9jt9geDMEkyRp3hIAKkWsHpU+e2\nt3YP9o/iOEGEICh1Oj3bdtutDgDU6w1EEoYxZ1a/PywWi1Jq2pitMK6FSLMsFirPRUopXV9fX1tb\nW1pZe+mll8Nx/PDeo/t3Hh7uNUWqhv1x86D3/s8/jkf5Oz/5xXxj4YtvfPmv/e5fPzxsFotlz/MO\nDw8RcWvr6d/4m3+DgH7zjddnSuX/7D/9G//w//V/Hg/7/+X/4T9vHhyOBsNwPHq8+VBKOQyH5Vr5\n0ZNHcRoBA8u1hBaO7zx++vjU2VMHzQNN9OzC/O7Bvltw10+uc4c/evJoc2tToqxWK+12s3V4FI/D\no4PDcrE0Pzu3MLfoOX7BC3745z985cVX7t2+d2LtxK0bty9fvDJbn33tldcLbgE0Odw7jMaxZ3uP\nH26uLa89vP/o048+vXj+UjSKlhdXfvrjn549fe6jDz5ut7ulUiUM43q9sbS0IoW2LXdr8+mpjZMX\nL1ze2DhpJBjOnbvQanbiKP9v//7v3b/zEBUpBdWH9x/94R/8IBxFO1u787NzcZj8ype/+uMf/mJx\nfokglILiW2+93ZiZu3XrdqVSDcNo+8n2/FxJ5Mp3Cz/+8U+WllY6nV6n05O5Ak267R4wWF5eXVpc\n++zTm7btvvXWW/Pz8x999NGZM2eEEPPz8/v7+y+//PI77/xi0B/VqrNSINFkPIwsamVJPlOtHe4f\nhKPB1SuXe+1WnmaNen13e6daYqsrixTh6OCoHJTLxcp4MLaYvb6ylCW5ytW9O/dPnThx787dc2dO\nReNRu3lY8NyDvZ3xYPjitRdufXonjaBaqjrM/m//3v+7Xp0pFkrLiysF2xt2BnmUhYNY5kom6DCP\nUsopUAZGbQFQaS2VEqbJiJg/FAlFSpAQYkJOSmmxFLieLUQmhLBte35+4eKFyxcvXj59+szbP333\n8eOtXndUrcycv3Dl8LC5sX7yyebTbnf44MFmlqqFucWFuYX79x/OzBQ+++yzQqGwt7f34osvzszM\nTCmqUxzfZEvw73kQ8ksoImqiFWgN3W5vNIoJ8FazC0CVQiVxNAw1yjSNhcgODvc6nZbneZRSy3Ie\nPHhUW/I67cHi4vJHH34621jUimSp7PeGr732xnA4TNOUEDY/t5imuev6WSY+/vjTQqEwN7cQhrHr\nsO9+97tawcHevmPb1557rt/taYme43db3TRSnu198N6HSwvLzcNWnqRzjcYXrj53++bNQX/kun61\nWg/DuNGY6/eH5XL19u27SuH8/KLnFZrN9oMHj2zbTdP8yZOtubm5crn86NEjRHz48KnjOIeHh8vL\ny2trG7du3VlcXD5/7mKSZJ5buHr12uFB88MPP6ZI9vcP/+2//e71a8//2Z/8mRR6f//wm9/81h//\n8b+UWe5wq1QqPXnypN/tnTpx+vnrL9+7+2g0isul2s7OHgBtNBpXrlxpNpvtdlNK8dxzz4Vh+O1v\nf/PRowdpmgZBMD87l2fZwd4+p2x5aemjDz68fu25zUcPO6323/jrfz1L0tXllVdfeSVPs7nGnOcG\na6unolH24794+43Xv+K5wXvvvJ+m+Te+8a0rl66OhmG/N6yVK/ON2Uaj/vTpU9t2VlfXb9+6+5Of\nvP21r33jyZMng8HA+BKD6Jp5IlprQhjnPI5jo9xDnpHZJRSXlhcWFhZqtVq5XK7VapVKpV6vr66u\nl0rBaDT45JOP/uzP/uz73//+p59+amYUxHGcpqkBrj9PxCnPpUaghPEoz8dJOo6To063Px5GWSq0\nSkTaT0b77cOjflvYgJ7dlUlpZba8OncU9VrRMAJpB/7KifVyfSZJkmq1FvhBNIoGvaHOFCD2er3n\nrl1xXfvs2bO1Wg0RZ2dnhRC1Wu3osNXvDZeXVvd2jwp+6ZOPb66tbQCAX/BOnDhx//59RMU5XV1d\njeOYc37z5s0rVy8tLs0/fPjQdV3OWZIkvh/UKvWrl68szi/Mzy7Y3JmfXThzaoMRJnMpcxmOovFw\nFI1jgpRev37Nde1W+4AyaMzWNOjeoD8ej89dvPTxx5/+k//5n966dZcQtrtzEEdJp9k72Gne+Pjm\nndsPl5c2SsVavzf+kz/589/7vX/ImNXv91vNTqPRWFpaOnXq1Ozs7Ozs7NbjzZ/88EdZFP+d//1/\n84u33/now/fXlhbPnz+bpmkQFPqDLqV0c3OzVCohJcViwYwUarfb/X43SSKj2FaplM6fP9vuND+7\n8Umv17NtGwCFyBHRtJ4FQRCNxv1O99NPP3348OHu9s6Duw9t7rSOmrXajO/73/r6tz758KPlxWXX\ntu/cuvvXfuevnTl1dmtz6/VX31hfWR/2R6VC6Tvf+tV3f/buTK2x83Tn9Vff2NrcCrzgwtkLUZg4\nlv3xxx93O71yuZKm2YkTJ+7eud9ut5tH7W6nv7S09L3vfe/OzTsizVdW1v70T7//pTe/8tZbbw0H\n47W1MqVscWG51eqYyXtnzqzeuXPHaKWsri7duHFjMBi02+3l5eWf/OQnBc8zDRNm8uZ7771Xr9dL\npdLm5iYhpFQhm5tPB4PR/PxiHMeXL19ZXV394IOPCCGu6x4cHHBm371778qVy6NR6DheuVjhlBdc\njxHmcGtjfR2U7rTap0+cHA2Gq0vL4Wjo2nymVm/U6r1uN43iG5/eqJXrW0+2Bt3B81+4/uTRY9Bo\nM/7w/oPF+XmR5Q/v33/phRfD0ehgb//ExtrHH37Sa3cdCyql6n//3/0jkLA4vzDsj1xuHe4fDfvD\n3e29PBUUWalQ9myPAf0l4Ou4APPvs/6cEUO5NAfV87yFhYXTp08bJPajDz/Jc/nii69Y3N3a2u33\nxoPBaGtnz3X9SqU225jPM5HG6fvvf8iZPTe3kEbp3Nzcz3/+85OnTzx9ujk7P3fu3DnHccyVPGsL\nnr2Gv8Sv01pplMbfKKW0Rq2AEGaaJ5RC1OT0qbPlcnXY7y8vL4bREIgWIjclhFOnzhSDksVtkcti\nsdxq9s6du2BxN4oSx/GuXr3W7/ebzfa1577wb//Nd/NcXrhwaXd379TJ06Vi+X/7t/93N27csm17\nff3EcDC2OM/TtN/txVG6v7N34ey5/d1dkatGvSpzubyw3O90GZBTp04/3XyyurxGEDzbKRQK5XJZ\nKTUcDpMkMTmQ4zimafprX/vao0ePXnjhhf39/fn5+c3NzeFweOnSpX6/f/78qZWVlb29vTAMoygy\nZbbDw+a1a9c4t+/cuYNIWq1Wmub/5J/8i7/zd/7Le3cfnD17PkmyjY2N/b3Dl19+mXN7MBgZotPF\ni5f/4T/8n86fv+T7xcbMvG35ve6Qcxs1OTg4mJ2dLRaLBwcHFy6c3955enR0tLCwUK/Xut2uZVkb\n6ydfe+21paWlbre9sLDw/vvvnz59ul6v3rt3b2lpaXt7O03Tu3fvvfzSqwQsJdknn9ysVGbCMN3a\n2nvttTfbR21Keb1ev3z5crVaPXPmTLPZ5JwXi8VisXzp4pWtrR0p5XvvvWeY3IyxLMsAtOc5RhzI\nsiwhRDiOpJRaAyLRCg0JK07CKIo2NzcPDvf6/a6pJ21t7dy+fffhw4efffbZ4eGhYXtduHBudXXV\n933T9/ZMZxI3GbxCBEo0UAmQZJkGBEYzKRRiInNmW07g77ebihFwrUhkp66cs2tBL4+PwoFdKUYo\nnVKAFts92C+WS0vLKwf7R61WZ3amMVuf3dnaJYp4rv3uu++urC71B939/X3HcYQQjx8/7vUG6+sn\n5uYWnjzZ2draqZTr83NLeS7TNGWMUEa4xTQqy+ZxHALoNI09z9nb23Fdu1aveL6zt7/TmKnZ3DrY\nP+r3h48ePS4G5U6npzUc7B+lSY6IBb/YbrcRyeFhu1wu00sXzl27evnMmVPXr197/fXXr169fPLk\niUq9du/ePd/3X3vllauXLofjeGdnb9Ab9vtD1ExkuLS4+qMf/uTMmXOW5Xzlza/UyrVOuzceJfPz\n85TSxvyMW3De/tlP3n33naWFec+xd7aevvHaYjiO/+Cf/v5f+Q9+44P33t168ljlYjgcCpnt7Gxp\nLZ9uP/ns5qfN5iGA3tp6orW+deuWUuLhw/vb29sT8T4hbNtC1EmSSCltm9+7c/dgbz8cjSuViu8H\ng+4gCRPPLVy8ePH69eue55eC0q0bt/b3D4rFku8F2093rly88tO3fgoKXnvltX/1x//q29/8duuw\nZTErGkVpnD559OT0ydNL80uloFQMSpuPnyilsyxXUtfrja2tnSwVnXZvMBj1eoNKpTY7O//eex/s\n7Oy98sqr4/H48PCwXq///b//e+vrG4jE1HUePHhQKBR2d/cBKaOWEJIQGobx2TPnu91u4BcOD5vP\nP/+i8cSVSi1JssuXru7vHc7NND587/0vfekr7713Y3l5mVJqcbvdbtfr9cODphBif3//4sWLRimk\n1+sZiZeTJ04TQu7fv99oNPqHY8dyd7Z2be5QIDa3njzePDo49D0vTZJKuXzqxMlXXn7561/92l/8\n+Q+jcVwt1zqtbrvZqZarWZIxwqQQnVa7XCytLq/85Mc/Wl9deXDvrpbi9MkTJ9Y37ty8dXTQFBnU\nSjWt9XPPXey22hfPnX/04PGDe/eEUIzxSrEyOzNnMXs4HBl0zubWFLKzGbcZJwD0c/zr87oRABhv\nEcdhnqdBsVCrVXzfX1hYMGVwreAX777/0YefpYkEoEGhmOd5vz+M43Rzc7NSqXFuD/qjmzdvbmxs\nrKysXLt2fXNz9/Dw8MGDB2maXL582RDNTauH8Y5Gn+0vuaJnmd+TTtjjzl1EIMBGo3G32z84OOp2\n+7VavV5vmGb78aBfcJ2VpYW15aWtzSdzM404DJsHzWgkRv3hoDt47vJzj+4/unzhcvuoufnwEdHo\nO+7tGze/+pWvPH7w8JMPPyoHxdZha311VWTZuz9758Ynn7qWTQEASaFQQKkIIqN0plYPR9Hmo83O\nUf/e7Xu1Sg00WVpcfPTgoZbqn//hH73wwguc89bhkcrFxx98+ODuvSsXLxGNgee3j5r379w19bzl\nhcX33/2FFnI8HH388SftdpsQ4vt+FEWFQiEcx8PBWEq5t7enNTiOF0VJuVyVUkdR/LO3b926dStP\n4E/+3fcODo4OD5qdTmdne6/dauVp1ul0OOfD4fD69eu2bb/wwnP/3//P/7ixfrpem3v8eHNxcTlJ\nEpN7CSHK5fLs7Mzh4aHneUJmAPoP//APB/3+/u7e9tOt7/2779Yq1SuXLkfj8NTJk2mSXLp4sXXU\nvHTh4ng4Wl9dC/xCv9erlOq99qBaqTu21z7qvPT8yz/64VvD4fjpk+3xKLp39+7+3t7du3c9z3t0\n/0GaJLZt7+7ul0qVkydPf/jhrVqtNuVYmo5Ow70UQsRxPB6Ptf6lpjRTfTQSwEaIwbjVNE37/X6S\nJIhoWVYQBIVCgRCSJMlwOBwMBmQyvos9iwMDALcdyhm3HMtxS+VqvTZjux6zLKWB2BwYZY5tF7xR\nEgXVInGsQRY/bR3EkPv1Eguczd3tDJRd8MZJvHuwb+jvvXavediaa8zHUWRZ1nA46Hbbr732SrvT\n3HzyCBHn5uYMQ4cQ1u30atX6cDimlHuuPxr0kzjsdZtBwdFSgFbtVrPge5zB/t72g/t3pciUyAe9\nLqckjkMAcF3XzB6TUvZ6PaWU67phGFLCzRoaElOtOsM3Vs6/9tKvMMuOokhqoJRqnHBYwzBcmJ+N\n43A0HlQqFa2lw51okFrc1lp7QSFL4p29vcD3X7r+ptZ6OOrfuX2vVA7CKOLKtzH4yutfT6NRrTJb\nLlVff+UrjbnZX/zi/Xfeee8bX/3OwcFBlstqpb6/fTg3s3D/zsPZhXkg4HDHs735xmKSRDnJKXJO\n4HCvKWVeKpUbs/U8zfNErCyuAsDdu7fPnzkfZ/Gg12vMzRU822Lcc9y1tbU4Tebn53/01k82NjbO\nnDrt2u7Fs+f3t3devP78P/4f/tnf/Jt/9ac//en58+fXVtZee+21t99+mxBy/+791dXVs2fP3r9/\nf21tzfO8x48fDwaDRqOhte53u5zZvV4vjmPbstZXVsPhyLZYNBrfu3fvuStX9/Z25+bmvvOd73x2\n6+aDezAej0qBPxrFO0+3oigaD/unT59+urm5vr5+/+5dKWXR93a2nszPzd2/98B1bSGyEyfWoyjq\nd9u9XuezTz/+3d/93dGw/+7Pf2Fz+NIXrz99sn3m5In93a03Xn3lD//oD0SWuLYVjcZJOG4eHNYr\nVS1k6+iAU9LrtN5++53VlXUl0pl5f3114eH9O76H+wdPNk6sMmAFx9t5uvXlL5/cvP/w1KlTNmef\nfPBht9W+cPbMzVuf/fqv//onn3wyU6lsb2+LJNp8sPPctSsXzp/95JOPSoG/vrxw69atYrG4uDC3\nu/3EdcnSwszaCv/hX/zZxQvnXMt+/OhRY2bu7KmLg/6o3Rlb3BmMk3KNJmnaaDSyfEQpUjIZH46I\nmgA9JtWhGQxOAEADUEJwHIWlUuC6dpKiJpIQ7A/a3f5BY26mXHF3th6fPfeN93/x81q9cuPTByfP\nzOVxQhTO1mulQunD+5+c2jj12UcfOK6FiLdu3PK8QjgKr1w6/+je/eeuXP3jf/bP/vbf/NtxNCKE\n+n5AgTiWrYSWQhPybHqEiAhEEwSiuUYEIIASAJADaIWUEg1CiNlafdAf5rmTxGpxYa3faedZurS0\nOh5Ho2F7bfXUX/zgneeuvBD4/v7ejlawurziee4//8M/XliYe/p4e2d3a21tzebueBBVSvXPPrnh\n2p6WcPLsaSnEyvLGw/uPskQ4lnfrxh3HsbMwl7GcKTcIoZ9+ciPsJdWgbBNrrjF7sNdKonQ0GjQP\nOs9dvJqE6bUr195+5+euV377p7/47d/6LUqsONT7e0dPNjd/46/8ldu3bhXLRYI0ipJysfrJJ5/N\n1Bq3795ZW19jnN+/9eDNN9+8Nbz13s/e/8oXv7ywsPDOz34OOchUhIPx4/uPLl68+OD2vb3+/qkT\nM51O79zZxc8+vb20PDeOw4JX2tneXVpY/Pijj772K1+5dftGY25m5+nOxom1KIo2g62nm09OnzlZ\nLpayJO32WsuLKwd7R8P+8NGDR9/85jfv3LnDuW0z7y/+/Ifnz5y7fv36pzc+Ixy++vWv3blzS6O6\ncOHC7ds35xeWdveONGChGORS1GrlKEzOnD17cPD+f/Kf/mfv/vznv/rtX9/b29va2vrON75NCHnn\n3XfOXzj74rUX/vhf/vNisZjEschzQohIk7d+9N7lC8u2F3z7G1/c3d6qF6siEY5ry0waivY4yqSU\nrl8ICkUpc6WMH6KWZdk29wuu5zl5ntu2DUDyPJUy7fe7aRoWi8VC4Lqu7XkFy7KU1ISg53m2bSdJ\nijiV6GUGMmAEkCBQQhnnFivUat5MTfW7YRoTi0pNR2laXlroRYMHm4/Xr5772YfvWbVSKsXuwX6z\n0379tS/ef/jvqvWZF1544cd/8ZPxYHhq7eSjew9nZ+a6/V6tUm13OiMx4txaWznxve9+v1KsV+s1\ngvDw4ZNapcaoPTszv3ew32m1n/vC9U8++nh2dmY8DJXOapX6ndt3X3+9LoRYWlpijEkp2+32uXPn\nDg8PS6USs+j+/uHJjVM7W3sFv/j40fbq6srebjPN1KDfStNktpG7nr29vZfnmoCoVqqO4/D1+etK\nKJUqF4rcsjnnBqYQQlStXCe6YtVLlRWjLGuDpUhsAwcK6TCXqTtXOuk7ruN7ZiDbc2e+aPzh3t7e\n1b/yai7iLB/1z3aiKLZtGwW/fP7F4XA4HiTLC6eWllfn5+c9zzMwfbPZ3NnZGY1Gh3vta1dfqNUq\nRtG20WgcHBw0Gg0jmaW1DsPQSNgWvlCZmy+HcTgcDAztApR2LZsBOdrdL9jucmNWRnGhUChw6+TJ\nk51y9+7tB3/rb/3u8vLyb//2b77zzjtPnmz93b/7X62trSHqRmNmPA4PDg4c28szmaZhuVRl1CKE\nUM5efeEVo/nBOW922pbWZ9fXuwcHURR96fXXgiAAjaWi92Tzvmvjb/zGF3MhPnj/5xtr82kSN+rV\nsydPKNQW0KNW02I0DsfgyiAopmm8urZCCPng/fcANWfUdfjO1ub8bPXP/+zfpWl65dLJ7/7rP0bE\nmWohy0b3bn0kkkE87Io4+skPfiBlniSJhWTQ6vjcfnTnTpJkO48f2wS6zZ2Px521pVLn6NG4LzqH\nuzbQd35848qV1UGzN2y3nz54cO7kxt07t4bDIQA8d+FCa38vGQ6SUY+jGLQP9rceXbhw7mtfeo1Q\nuHfvzsp8jWNGZMohe+HaS91uV+YDkffrtXmb84sXN1zL3d89SOLYdf3d3f3Dw5ZfrA/C4cLqcioy\nt+xrAlTbBKXSSAihzIwhFkoqwgkiAgKaKBM0IACA4xUygcAgSpNavci4vnL1/Gjc7fVbrdbO9esX\nT6wv9Lpt27UbdfvSuXPZKEl6Q3sVth7dS8LuRx8dvvbq9U7rYKZWufXZjYuXr7YPWjuPt1yv4NVp\nreB+9N47841SFGcAilNQuWLACCVKIcCE3odEGiUICgwkIQiAFBgwphghDARBlmcZ416z2Ww06stL\nG3dubX7hhet3b94fdUfLy7ObD26cP3dx0BuWS2655D998vDll18t+IAiY64d9sev/8Zv/l/+q7/3\nG7/9xdF4LCJNFbt45tKdO3e3Dnf6/X7RLfX7/Y3lE8291tFBUylV9muMMRnD0XY7zaKV5dXTq+dK\nQfkH936Yj2WtUHsatmZKcyKWTx5svfzyi5989O5LL71IlX2w27l0/jpF78tf/ObH7/8ep96X3vja\nH/7Bv3zphRcH3eHO1u7GyfXf+PWvfPLxzYWZkssK+UgEgRN342yQ+bRAJY268dKl5Ue3N5cXFj1a\nsNEtOZXWbmd5du2jjz761e986/d//09mapnMNSh7ffl056hfrzSaB03Pdr73b/7t8vIilXplZW3z\nzqbjOxfOnhmPh2//8M9PnTqVh1jxSukwC4IgaBSfO3d97/FBozifJMk7P/r5/Px8sVC6eevWMB6v\nrKzcvHtPKo0Ie4etucW1x0/3lpeX79x/dPLMeWo7zW6Hc37z3p35hZVOe5jE8qc/eufihQuW4v/L\n//BPX3rpJRIrHYpbDz794ouvbW5vHTaPGnMzO/t7lmXNVmF+prI0N9877HjMLhYLB719AUwLjQod\n22bM0VpTwi1CNME0j7XWjh0UPJ9bFBUkUUoIsX2rXC4LIZ482RoMjhyHFItWfaZhWZbr+gDQ7w3M\nECnObYs7piRpapeEmHHDJM7jmbmZ0Wjk++76lUs/feutcqV4f6crVXbh8gUhIlEp3Lj7yXOvXPv/\n/es/+o3f/q2HW09kEh4e7K+trv/srZ88f+W5zz69vf1ws1KpS9+/c/s2s6yjZjMIgkjk43G4sLx0\n9+1b/QsRSmt3e8t1irs7O6tLJ0pBsdPrvv7yF3/yk7fnZ+ca9VnX9na39y5fPJ/E4Uxl7uSaaO53\nxsPk2tX1zc3NTrdbKlb7gxG3nDjJqMVnZuePmm3O/HgklFJb2WGrfbSwMDfqZcvLy4FbLfgF1aDx\naLNSrNZqta3NbS5TC5EDIgBoTUU+QfA595jmnBCb2cCAY6615oxarmfklQLb5z4nhEgpZS4lAGOO\nhVALAkKItVSoVquaCEJlb9gZj8cAYOSZLcsy2EgQBGZinklyGzXOwCsUCoPBYHZ21vf9OI4JIeVy\n2eHFacO81lo7XAWMaMd3i+3WoevRYlCzeLwwF8zNNQBor9e5eP7qwcGellRrORyOB9lo324SQr7+\ntW/3+30hRL1e+OY3v5NlmcUd13XNKEPbdj3Pc12/Wq3meR5FkdFfYowZhWOTrQ8GA8/zpqJqJn83\nMppKZVmeUMJzkd747JZfcMulqtKiMTNHGaAmWZ6kSa604My2LAuRaTWBpZIkiaKIMVYoFJIkUUqZ\n3u/PRdKUSqJ4bn621WpFUWSStjAMEbHX65lsw+jvZllmRk8aQmqz2fQ8Twhx/my4tLQEAN/42rfL\n5bLW+twZBIA0TcvlcrfbLl0OfNerV2fyVNQqdVRAgVECM7XGwcHe/OzC1vaTb3ztm0KI5cWV+/ce\nu461vrFSCUqOa+3v7O/s7MlMeZUACIuSjFqZ6/rctsIsQTST9ChBhjjJghBRK+N5CIJGQCTGDREg\nCEAp4VLmhUJJ6bRcLlIu7t2/tbQ0v7AwB6CazcOtp5uMg+e4juPWKrXW0e7CzGzRcx8Nu6iywHcZ\n14Ro13XK5TKndDwcDntJ5iZz9ZlqsdA62jt39sLHn9zMMjnXWI3jXCn0vSBKM0IIgAaigBAgCESD\nBlSMAgVKCGqCoCUiKtSEUqKUIqY5vzWkFI4O+0ks41B4bnHQHwVBsDC3+OnHt3Z3t4OiO1MrzTWK\nWoksjWfq1aPDw5kZerh7WKwWx6PRk82th/cfHR4eaa2LQeA6lmPZzcODcDS2uUUtN8/z8Xg8Goxt\nJhuzVc9yd5/uHMqjUiHotTtzMw2b3fcdt+gXGKDM5Kg/unPzdrfTP3HizP1Hj9dX1/M8pxSePH7a\nOmq/cP1FxlitUpeL6vTJM++8885cY77VaoksG3S6vu2cOXGaauTAKEAlKD+4c7fo+cXAl0IkcTjo\n9zc21giA7zm9XqdQgGKxSCjGcQxtbDTqjFApMsuySkXPdawsSTqtdrfd83zrG9/+le9//09ffP4L\njx49Wl9fT5LE9/08kbVaLYtFp9lbWFiwqDM3M1cpVmZqjR++9eNX33wjDMMz5872u73heLS8vLq7\nv/fzn//iq1//2vnzF9NcAEChGFTLlcODDuWyUqkVg3IajlHi/Oz83c9ufvjue9/+zjcVqmQcJcWo\n4LhPN7eKxcLq0mJvOFpZnhdJ/HTzsY3M8kuDwSAIAhRaEUUYaAUElNCCAGitOSGcM0RqROgZY4YI\nmiRRnssoiuIkMuoStmOZyZ+27RrqphCCEOScWxajFIAQADrFhAlhQLRt8/F4aLv28vpaomU/S+Kh\n2u71tM733333S1998/7O0/mN9ZWTJ/faR4VKKUlSIaQFrOD7DCFPxdWLl0ZhnIQxBZYncSaUY1nc\ncRARKc0TYduQRVJLahFX5mAR12JOGmVZInrtPiNW4BXv370feAVOKNM8cCs6w1qlsbm5WXCKR3ut\nNJGeEyABkQmNxHVd23UZzVUOuUDLsfq9Xm1lrt3sMuo6PKjXFgbdkHPHol69NlvwPNt287TDDw4O\nDBVkCknAMaV1Km9u1k4pxTktOO4zsijU8B1NwcNxnDzPC4VClmWmykcYMGKVio1yaZZSaoj5tm37\nvm9yUqWUVsTg9cXA8dxKEATVSqS1ti1HWsy2bdQsKNQNJmucQbXCcd5MoMna/X3GUCnsdFpS6tXl\njVptpt1uHh42y2dnu+13z507de7chb29HcfxBoPBhQuXDg+ODg4OCCEba/ONRmOmPjtTmTlsHxJC\nlMLxeGzbrsW8ct2vlISh8ZgWfWPrKTCbYzROAYBSi1KaJSocpYjIGLEsz7VdSni14rxwvVoIPCVR\nyMyxPcqAM5tblFGLUASkiCgEAhLTx2D8GSHEcZxpJ6bpuTNLRBnJktQIXqVpagoqo9HIJJfTJTK7\nmTFm23aSJPV6vd/vT9+kUCgcHR3V6/Xp7ahWq0YGcTTuJckYiOLcdl27ElSf7j7e3d395NOPSsF8\n4/JqvV6vVhYdy1MinGss/vZv/m98v0iZ0zo4KhbLV86/UCiUQVEp4Qd/8WPGXUJ5UCxa3KE0M905\nTFOC1DBfTQFGa601EEKAmLERihACMNmQaRqXy8V+v+t67MKFC3PztTDquZ7lF+woGqdpMxyn585e\n8NyAUl6rziTRqDFTqddqpWLR5g5jzHNsUi3Hcax9T+RJGkdBAXzf8z02Pz8b+OXnrr5w4+Zd3y9a\nlkWIQA1pmhNgZvo1IAFCAak5HAgKACgYGSGttUYNmoBl2ToTjuNQwpMknZ+f0xoWF5ddp/yTn/5g\nfWM1iscP7t9fWKwNet21leXt7aeUglHkK/rF2zc+u3T5nBBiPB5qLaN4OBqNCMVi4BeDUpKEaRZu\nPnlgJmEncWaY8UvLc7YlatWS79kiTw72m5VKZXfnyeXLl8+dXQ4Kzv5+3GruOzZlFB2bvfLS8z/+\nyc+WVpZ3th8JkZ09Pb+/fxSOumsrczO1uVarJfOw3dz9+OOPv/Wtb7VaLU71rZtPPb9VLHkPHt4e\nDNuVStlxyY2bHwHJ33v/7te+dj1Ohn6BZ3n48Scf+L4NRLkeNFt7QkjXtWynUCr7UqaFgksIoQyH\no15/0EnSiDJdr1f/+I//6Otf/+r3v//9Wq3Wbje/8IUvmEBKa53n2dmzp8MwrNdnfvGLd2u1SpJE\nSotyMdg8OvScs0dJlCXxeNhnBPI063XaF8+f7/e6FiMWZxT07vbTF76w8d7778RhhEI0mwezjfpL\nL1//F//iz95664df+fpXDKrWHbT9Al9YmPODQm84mp9bRCHD/jgT0q0WXMsVUiilAChM4kYFQLVC\nIEipUXxHziesHCDAGJudneWcS5nHUWJEyMrlcr1W9wsBY5YUKolTo1ljzrjjWIQQ1MTM1ZqcZU4r\njVp/NFBajcejzz77dDQezvlzvu9LySknUZjEcbq0NN9q9UrF2rs/f19KpYV2LNe3A88O4nG6tFDf\n32u5tlcuVHWGqGOLOShRgfYdXwk9Uw3iMImjOI2zve29cDRO07xcLBFNHj14XCqUsiTf3d2fa8xW\nSrX1tZMff/RBEARRHK8sLEdpAhq0UFqpTIosywilnuN53LEJR4+oKCZIQWmR5wzI4f6BY/E8zUSW\nGnrUTK0u88xitsoVv3PnzmS+OiFmCaZTxUzBzVTbjDeiFESWWYwhYhzHRu4iCAIzgcq27TiODUPf\neCNuWwbEKxXLjsvyVEVxplVq2TEBqlERoNxijHJCFSWMcSccCcbdLEttywLtAFqjQex6DgEqlVBS\nARGUMAQtcilkSllh2O9prcORevz48YN7W+vr667rVirVzc3N69deDYIgS+Ds6ec2Nzcf3Nv5+c8+\nW1xcXF5e3lg/sb6+johxnPaGkeuUEUmpXqpWctty+/0+I57tsWjcsnkREQkSVFIJTZBzWpifnTOJ\niylpZjSjlHqexy3qeZ6U0nf9UpCbmq0p5ZkyyS911SC1GJnwuAgDogkwJVWqkRAOjBFgBIDRifQI\no8wqFKIoqlQqtpUJISxuuQ7xPG+6sycBGoApqErBRE45KwQFl3MupXQcZ7Zhaa0dm3KmfN9HzfMs\nJwAUCosLs71eNwiC4bDfV8mwL0vFua986dcODvYWFhaUEi+/+KV2p+X7frPZdB2vUqu7rpunolqp\nV4oVABZHebcz+PO/+Hm5VLOdArGcPM8JYZ7nSymolIahoJSJAtEUgI3dRwQgFMzkCKCEYKHgZXmS\nZemLL73yd//u393Z3Tx9ZuPW7U+FyKWUnU6nWq09d/UlSvlwENYqxX6nGY5jx45WV9YtyzGTbyzL\nqlQqSZIwZmmtv/6Nr3me4/tBFMajfqYkdV3PtvxOp+M4hSAoxHEKBAApEDBe09SyAI9ZFoBIiAYk\nMMESbNvWgIiYpiLeP3JsLxwnUofNePu3f/s3/9W/+leXLl0oBcUf/ehH58+ffeeddwqFYmN2hhGW\n57nt0CSJr1y5cnR09PDxY6WE5zm2XXMcx5S1u92+X3CSOOWcABAEVSqXXNednan2+4dPnjweDvuM\nEcpQa0koqc9UV9eWKdPlcjHL60rnjUYVUUXx8PyFU8Viud/voxZSpGur88vLy1rL7a1N27avXL50\ncLAn8vzWzU89rwAoX3zplNFB7vaOyhXfL9it9v7DR08IgStX52r14nd+9Ru/8zu/84/+0T9680sv\nvfPO+5VqsLY+n2WZZbH5+QZjdDDoP3h4b25uznVtk6tnWZbnqVIiSaN2s7e3d/Crv/rrP/jBD55/\n/vk0zSllhDCjG93pdIym+PPPv8AYk5k8uXGqc9REBYNON09FJSi2jtqXL1/89je/aVlsptYAjXku\nkyQaS7QYT5NoZ2v7+rXnnjzePDjce/Tw/ne+9c1vfGO4v7+3u7u7srqaZHGxWPyv/2//1+/92Z8G\nfuHE2omd7b35mblfeeNrR3uH7cOm1qA1CqFQAdOUEEYpZ5QgQcZ5rsQ0fDfgBKFoLKfRwO71elLK\nIAiMrKrnB1LqKIxHo5GxnybctCyHc865kcHWxrAg1YNB3/E4EivLk1EY16s13/erxerT3adBqfDg\n7kPCYX/30LYdLcjTR7u/9mu/9uTJk263++ju49FgXAyorkPZr4yGY9/zdKEiUyVylYYh57zRaHQ7\nXZd6436klPbtACSUC9XAC3zbd7nfbw/XFze63e5sZa7olU6snbCYncVyplqIMVcCjnabFPnqwtoo\nDPM8j5JYCIE5RIM4FXkWCd/ytMRGfTYL44W5xe2dp8vzCzpTBCnmOk1TAN3r9giSOIz57u6uAcFM\ncG3yJM65oToYLgSl1AhZUgqolOsajlMchiGltF6v1+t1AzGZXpAwDA2NxLYdhcx1Xd8LLJuhJhol\no5bt8DyTCIpRy3Et1ETITEmkDCjhxVIhz2Qh8NIkpwzCcVwsFQp+UapcCm1MtkYpcpWLGKhO0qRS\nqdRry51yNBqNxiNJgP7Zn/7kxRdfPNg/KBbl+vp682g0N7v+0ovB3OwCY9xoto+GQikVhSnn0vO8\nNM1RuY7j5Jq2W+NiERqNxuNHu3k28dCWZQkhbZsC8E57nKaplNI4cq015wRQZ1laKnEppQoUAEPb\nIlgoFApCCBNOK6mmFB1KiZKICJSa3n6LgkUZfC7iqQCMVQZEhbmUru3IPNXSAg0iUwQZQZcRP/B9\n0wbBKCNAzGdIAMcqKYEEXQqelpAlUgnFaYEwUiyVtFKUsSSOGfEp2JzxYT+PQx347kxtlVJK1nxE\nrFZqVy7qOIm63TZBur4y6zjO4twZy7KAIqVU5gqAjiOZpens7HyxPL+zfei4geMFqVRpkhgcPI4j\nn3OqifE2WiNoQpACIgECBAkhiJQQAqhNEVdksWWxaBz//b/3/6zVyqdOrO7tb7/60qtJkgRBEIUJ\nAK1WZhizOu2ekNnFC2f29rfL5XK9Xje518LCQpYlJq9VSpntTSmkaT4aRgz8ne3D3/md3/lffv+P\njFBYtVaYggSIGoCgJkAIICMI3KKgUaMBFye8XEqJEAKBalQ0FVmWRVGiVBwEwAjtd3trK6v9bk9K\nfebMmW63W61Wy8USI6ByxYrFQqFQLldOnNhwXfuFF66b4QJG9VkIxRgb9EcA0O8PTYShJNbr9Var\n5Xr8pZcuP3x013XdjY2N2dnZVqtTLlWGw/7MTE0pNTvXKFeCJIlK5SCO48FgcObU6TRNbU57vV5l\nfXVubu7WrVvPP/+8QaR93793p3vm1Eav11lbWbHt2cOjvWrZ55xfPH+qXC4Ph8P9/X2CsLFev3Ll\nQrfbfXh/f3am3Dzc+ZVf+ZXD/Z0njx8k4TiOY24xz7EXF+evXr36heeu7O/vA+hqqSxVbqDyPE07\n7d7zz1+PxomW8B/+7l+/ceOG4zhCC8ehSiAj1rWr1x8+fDg7Mx+H6UsvveS4LiLpdrqU8O2nOyJX\nuWvtbO/5ridS2W13Cm6JMhj0Rxplpexdu/qFcCTPnj5VKBTSNF2Ya7SbrYODg7MXzmuCpu1paWXR\ncbzt7V2ZyUFv2O+NZhtLcZh+99/+aTKKzp48Nbe0+OThE0psJMAYIUAJUEAGlDBGUpEBaMYmGB2a\nZBrANPCawWMz9UatVqtUy7blCAlpkg2Hw9FolOeSc661yrJsOBxaluXYnuHsTRJxRNvh5Uoxy2WS\nJGkaA9DxfqiUXl1ZH0fhbGMuzkMp81MnL2xubp4+cb591NeC1itz4/F4vrFCKd1+uqsVKAHE5Y7l\nFwtKCp3xzLLsUlAJR7ETOIQQixFObSGEGW+RxtlkgJOdyFw3Gg0A4GAV7GBteSNJklqx3mw25xqL\n434UuCkDXrBt1/INAqmV9qnnFtxxL2GM2dySKi+WSjO1eq1Sz/O0Uqna3JFUISqRapkrTi3yD/7B\nf/Msc2iaGxk/Px1EOEXq0jimlBiagyHCTqUyDE3W4D9BEKRpaln2OBS27RgCgokgTO+9qRWZD5JS\nGm83rSoppTjnpv9DKVUsFg05cvpy4wCUkhoUoVgqlRhjhj5o27YQYmZmpt1ur66u5nkex/HS0pKx\n71GYUMqN4fY839R7KOHVanUwGDiO53lelmV7e3ulUmVtbeWHP/zhyZMbxiP4fiBlTggjBKXUUuaI\nhHPKuX0cyxPX9Rm1RqMRAIxGI1PX8TwvTVODLBNCnvFGVKrc+Dnn+GFGcx9jx4Rz7jiOwS6UUjZn\naRZ7bkFpkSY544QSbtmMUUvITCswz2iUWgGhaHGHcYKaKC2ML6eEA9HjUVStlbNUOK5lflrcyUXq\nec7BwYEhphcKvvkWlmUhKM55sViglBqRR8aYZVlpngCA73q+H1jcC8PIc4u9wfDXfvU3uOV4pSCM\n03EUM86ZbY/6g8B2AJXBJIUQUkpEZaJL85cpudtkIUokM43qX/2rv/l3/ov/3HH4YNhJ02RhcW48\nHler1TQRSZIVg7LjeHGUKZ1Xy95hc5dSWiwWwzAkFGu12t7eTqPRoGwSwCKiUkJKqSRYtChyEifp\niy+8Wi7N7O0d1eqzIlcKCCAFoo3eAgASigS1Y1nm1mgTJzDKqAWMpZmwLIcwWgzKmsDS0jIgrc5Y\nG+tep7tVKpUMQHpi49TSwkKWZcuLy0opotF0eCwtLRUKxTRNERVjDIDOzMxkWaa1dmzPFLrNSTG9\nL77vt5odIZOjo62Dwx3fC4rFcp7L4WC0trZxeHiY53m/3zeqo3EclkoBoRiG4aWLV9rtdr1aK1dr\njx48XFxeypJUKClz4RV80BjG0fVrX7h5+xYFMo5GQBS32cHBgYF8jVzF4uKiEKJQKNTr9cPDw2Kx\naHCR8XhcKddNdi6lHA6Hg2GfUhqG4crKilKTjjHLssychSAIUpETQvJMnj5zstcddLqtjfWTw1G/\nUq7lIh30R0oLizvVWlkKHUYjKfMsS0ynlKFcGUwbAMIwNM1AUspSqRQEgecV41DluQrHcRyOZmca\nGpWWSogsSSLGWLESRFEklBBK9QfdtfWTmaSUsl6z61i2z90/+oN/1tw7KvoFEIhSg0aT1gshkADn\nNBUZorJtOwgCy2bTMwugsyyTMjczjQoFI9XPRuNs0B+2Wq3RaEQIsyxLKZVlmRCKc25xx+ilTsAq\npiRJqAXFcslzC0JjEoskzRYWFg6brTiLbdsehSNKYWl1iRByeLQ/NzeHUhDCdnZ2OOeO442G4dLS\n0nA4ppQiAmPMsT0AMIPt+/2+gUwQ0YjoW5aFiEYVsFwuDwaDSqViigKe5104f3Z/Z7fdbo/jKAiC\nmZmZVqu1u7tbLJVs22a2pbU27cCcc8ty4nFccAthGAbFQpqmnucgokbZaNQti2utlRZHR0fFYqHf\n73Pf96eHf5ohGT9hbvMUxLMsi3PqOQ7A5wKCxqwrpaZFo+FwaEQnEfG4nuROm5mnNToDfFNKjUK7\nuQcGa0rTlDGWJInJySzLStPUtu3paw1ENun1oEApxFFrGvkqNU6SRCvg3Prs05uFQmF2dvbJ5pbW\nGpFkWWbbjsHNsrRlImUh1HA4JIQEQWlxcTHLssFgcLDfOjw8bLe7pVIpz6VSIghKWZZkmVBKaA2U\nAiJBVJRyxggiUUpl6WSObxAEeZ4b38Y57/V6RnvGVH2mC25ZfCoUbeJxkxgZcUYDmTqOY16lUWqp\nXNeWUhOCSqFSwnE8rSWlXCkBQC2LGe9orgqAIirLchCVmSGnNXBOEUkUReNx5HnOcDi2LMaYBaCl\nlJbNPM8bj6PBYDQajUy0YVlsPB7HcRwEgVG4YYwBoNEvIIQOh2OpUErdmJk/arbDKFtYmmWUEyIs\nblPONKK5iQQpGFraZB0YmXgg4yqOgyMCiMg5/Z3f+s2/9R//x6PBMIpHc3MNgtDvDvq9QTxOtaZK\nYr8zBqBKEte1D/f2OadpGoeF3Cj2Ex1pYXVbI8qMyII22j+MEcacVn/ftoKgWFpeXh4Mxp7nEAK2\nbadCAjHpDxBKJxIMRAMDQo1SBAISNPNhlTKzALJcSqkA6P7eIaV8PMJXX/7qG2+8KNLM9OS7rj/o\n9SuVSsEtmNZ327a77Y7v+0opVmOmkieEmKvPtXodY7vzPM+yrFwuj0YjzrnBIdZW1gqB+/y1K1Jl\nABSOh8qUipVms1mtVrXWGmWep1mWPN16kiRRtVpFxC++/ub+zu44Tl68/mJQLi3MzoVJXPQD1/cp\nQH84rFUqWSpdy63MlBXmlk2Nlr/p00zT1ExaM7v61Zdf63Q6Wmvf99M0U5IohdOSvmUxc8BHoxGA\nNh7IbG+lVCakBsoY8zz/wYP7q6trc7Oru7t78/Mr3W4PANZWz3Y6bcdxx8NxEBRtTnxbkwJxWMlE\ntKanx9jQJEniONZam+AYJHSOhjYvOlbBq5YLKyfazValXDMWUAhhLGC9Wi5VSmEYXr30hZ29A5vx\nICgVrapF2Wyl5rvFwI+CQjEajvC4UiikFtJMwNKEIKHAOGGckMk4Lg1AkyQ57om0pdSjUcg5t203\nFzgeR1kmKOWmYmQG6xEitNa5SBEUoR63KOMcKUFObJd/+9vf+sIXrnuFshSQ5cL3gzQTxUr58PCw\nUPQtyxqHw5PrJ7d2n2ZJWiwVXMc3kxKV0qPRCIASYEIIzysUi8WJMVEohEjTVGtthqwKIY4RIBHH\nsSl3tdvtcrlsqtR5mhJE+6Uv2ra9vbtTKBTG43Gj0Wi328aHGeOc5Jk5YoQQUGgmJgdBYTgcFovF\nXq/n+26WJ1pL13WllCsLq45jp2nKp1Zv6pDNRjlOGMGkSsY/2TYfD4ecs+mTpuRuWZbJbDzPC8PQ\n932ciPBrygkhoJRUSiLitNnYhHuccykJAHLOjOqG49hRFFmWA4Cu62qtPM9N01QpOcWvKCWMWYTY\nlNJcZmZfmhzOuHcz2dfI1uZ53uv1SqVSnufmPEyFyAhF2+EEmBkpZmrmvV4njlPGmJDZaIRCZEJk\nWZZLmbuum+dpHCd5nmoNlsUQSZYlhDDPcyzLMW1wURSNx2OTzRg+giEHKqWSJJlmn4wxSkkuErMg\nZsWmGPTUMwmZJSmDyeQ3mcbJTKM2HkXcopTwNIt9L0jSiFFLaUGAmecJRUYtkyeNxgPX8UvlgFEr\nSaM8k6VyIIWOkyCO0lI5aLe7QLTnFsqVYhiGJmiQUhaLxeFwWK1W4zjmnPb7/Xq93ul0hBC7u7u1\nWm0wGACAlBqQRklaKdcsx+n2Ru998Am3PdcL4izNcqFQK6EVoGVxyCU88yCTpr9JXo6/LOZNQcdR\neObsKUphb2/f6Cjnefrw4cPFxcXxOLG4UygEg34YhnGei1KpFIXDRqPR7w+KReV5znA4dNxhtVqW\nQkkpGc8Zm2guAGgpI98rtlpHo3H45a+8+fv/9A8rlZKQGCcRYdZxgY8CJYQgIYRQAqAIQdONpJGg\nBqEVagpKWhYoobQNtmXnWe66thCyVpllxFeEorY3H+/Nzs66tv/wwfZMtZalOs/zhYWFamXWBHNx\nHBOgBOxwHFp8vLtzMD8/7zhenqdSakri3Z2Der3OOZdCA0cpkAB4dimTQikkQDij3e6gUm5EYVyu\nFNM0Ra0KvtvrjrWWz1095dnOcDi8cvn6o0ePZmdnq9V6FEXlgluvN8bjcVCqKslcJyj6VZGrPFYL\ny4v9UTcolKMosq2C75Xtht3tdh27EIbh4sJalmqt2Pz8klJKybAUBEk8oSGgAmZ7SuelUsFzKkka\n2ZaLmmpNEKlruxZTYZQFxcpgMDh14gpjLImy+dkN13LnGqUoisKRKhbm4jiuVZazLPO4XfDd0Wiw\nMFMtBMHR4SHVrH0wcj1PJtp2Sk5QSrOs4PvNVmumXq8WLUDLst1Ws9ltjeu1xSgaj8fR4uI8eDAc\nDRbnT+Yi63f6s/PzzYOjpbn1XON4HM5UGxRIFCWDzrDgFZMwoYQTpkxngkFoCCGcgxQ4FYt6tk8a\nAIwVNoYRAIrFYqFQDMNoCgKZeB2AGmM7kUnV2qAOjDEgyBjNsswYtDAMS8VasVQdjULP8fNEVIpV\ny3HiJCqXGtvbexb3vKBACQy6w3q9Ps5j1/FnVucPDg6yLGfAi16p5JeiKDJqWzLX1VI9jmPP8xAx\nkpHFLUDQQlDk841GlmXrKyfyPHe45zhOyqJiITBZ/rWr1zutdimo+K4311iQuTBJAufccuxp7mFZ\nVrvdrpTKCiWnTCgRh3G1XhV5mmRpuVjKRJolKbM4AU3+8T/+vakrMubeWAQT0ZjUYcr1siyWpqmR\nrcyyLM9zs+0KhYKUctqlbBZ0AqZJbRyeYVGbdCdNU5N1mQjLqBAa5UGjgjotxkwr/wYVnPZCG5dp\nwJJnAcZjL6imqYbJSADAXHChUJwaO63AEAKVUq7r27YthDRNRQ8fPuz3ho5r5Xm2trZiWU4ch4xZ\nWsssE5xTAMo5HY+j8Xi4sLCklPD9YDAYFvzy3t5Br9dzXbdcLhu2m1kWs/kMFGk+17Y5pROa1rQQ\naq7cwJUmgz4udVAAoABTzI0AM6xxBGWqbr4XZHkCSF3PzlJh6nBANCBFUBPoCSZ0iWefIRTNz2m2\nSgjRCsziTJ+cpnQGL6WUAcBoFNpOQeRqOAyjOK035n/2zrvAeH8wkFJSi2RZkuc5pYQAQCaVyMy7\nmS8IoE12GMVjALBta5qmS5V+65tfvnL1QrVa5ZxF8XhxcXE4HLbbbcZYUCilaeZ5BSVRCEUpG/RH\nnDGbcbOBCUHbti2bUwqcU9d1h6O+UnJ2dhZRJUlSqVSoRYvFspB6bm7+C9deWl1dTzNBwIrSrBiU\nbNvudHpewTfxo1K5xhxRIU6H0gISRoATwgjjlDBKOSWcMWZZDrPk669f+cqXXzEpRRony8vLnu2Y\nRLlUKpVLJcuyUEnDdSwUCkmWGtUyM97FmD5EVS5XCwXP8zwDKgDAeDweDgeFAnct2/AdlERCGOeW\nCXVN8pplSZrFRnjUdR0lZaVSUUo1m02l1Ey9YWoqeS7NNO44jm/fumvur8SsUPKZBeafBk82R97z\nPHMAzfUcRxXMtjxCmEGfTPRpMoBn8ANhcG+lMMsyjdTiE1zEmGnHcVzHzfJsqrButp/5LE4gTiJQ\nWqKulKtRHHJCqcW1kEgJA6IAiUYFSBGQUAAeJxmnrFD0TYSqlLJshohCZIQwy2LmeMpcIMFEpgDg\nMO7aTjwa/8N/8HvJOIrDUEulcqGVAgAziIQQUzdKKNOlUslwlwy6YwxaFEWUcjM9cnl5eX5+fjQM\nt3cOKeVCiCzLzJc1pskkxEkSmbZZAzlSm4zz3htvvv6bv/NX4yhj1Fld3eh2hqVyNctyI/mqtCaE\nICVayFykjuOg0tO4FhGl1Eops3MIsKlhN3Gk+e6EkKmCuEGYTIg/qVMqZXAyx3GicUhQaa0laptx\nwpkWMskz33GN4ZoG01PTrUCZ+0U0aqIpUqRoc65AE43AqEUZUiQa+VSk65ktRcw2mrCKj7t8Pg/q\nYQJ3Tj5MKZNLGRcCk5k001cJyhgAcItQhpQit4iNTEqJABqpRgSiKUNCCWMUjCEiCgE0KgQEAkCI\n7TAAYIxyTtAoJ2sFAJQdN68QBIKEAEFNAZ9ZTco4IKJlU0I5PZ5bgIiUASdAKNUaKNMIgjHCOdFa\nFgpur9e1bZdxi1vAuCJUaVQaNRABhI/H41KpBCRHEM3WrlKqUqloDVnOPc+amakYYFPrCe/Osjgi\nIAIhilLNOVDKHJdLKY6zBGCMTm8k52a12fFdAACNoCyLEwmmbRtRKgXTu45IEDKtc611nsssTw3U\nO3XqU7c93TFTn/3s45k3RCn19G5OkdJnEDbie8UwDFmaa0W5ZXkFaxSOR1FYqTaQUGXozwAAqJRW\nMmcKtRJSavMVlJIAgKCyOLMsrpTKsoQxSynhed7KylKlUrEsS2slhDYAgmGOeG5hNBpLqZMkyzPJ\nmEUpE3nuB2VDk81FrrVO05wxhqg4566XjcdjITKtiNY6TdM4zmqN8v7+fr0xe3h4+H/8P/0X//P/\n9PvjKF6YX3Z8RwgVxWGtVrEc+/CwmSSJ77s4mQI4KRMSYEgpAYpGzYgY2YZJhEwlHuy0Dva6hCIA\nEGQiBw5gW4UwDJUa56mmlMhcCJmhJqbONxj1pdRB4FuWI2XOGAuC4PCgCQBS5SasMYVYRmAnHBoo\nFYAKIY1DMgZRKaWUYJwYf2EQqmq5sru7myRJpVJTSrVaLdtyV1ZWZmYarusyZiVJcvvWAwBwHIdZ\n1ClwbhPObc6pZTmOY2aYSq1Ba2mwX85tM1+DEEKJTemkxkkIcRzPWBLP80wjuTGFxqUJoTi3CAVC\ncvOSqfyogU9MEGAMolIKNFqcopKoIYrDrj8ehyOL245rO7aLoJXUSkutUKNCbWhZPJeaEOK6LhJt\nGuoJIZTBtJvCsexj66lH8YBxAlIXPDePE9DEZjY6biIiPVFT1Mc/GQAyTihl00L7NCsyEblSmhBS\nLpcrlQoAhGEshABQxluYY2ViTbOxAcD4CVNSdV2nUqkGQZAmWZ7nBEi/308SgXqIhBKQAKA1UEop\nZ6BRS7LfPDKn26wbHnfgDPrh9OM8z/M8jxCS52KSkCkAotMkD6ORyBXjxHV8pQUgpQwIMMaJ7wV+\nwbW5GeBJplYCbM65rbU2XXnT4o6xsYRRw0o95qZOHkqpaW2bEAIatdbc87xnMZPpY1o9Ms9PScO2\n6whBCKUmSOacIwC3rGnIxgCIQQwBKEVCqOkmoXRCliUELYsBaEKMVQUDm0x5ZXBcXqLUmO9Jajy1\ny1obcHaiomHqDcepBQBM6jHT+w0AiGBZjHNqShSIBIAgIkXCuY2IlmWb9y8EThjGlWppa/uJ45YJ\nYY7LOedK20opRGrZlHNuO8yyrDgZlyuBCRU9z5NCj0Zjz3dKZc8kcHmeg9Baa893AYBxjYjcMoxh\n5npcCGW+4zQgOF55c5F8emsMrYFbhFBKKFPqeJWQaI2e5wKA7zvcIgBg27a57ONk0Szy5zLDSimz\nAjghj003ilZqQihHREIBkVBK8lwCAUKRUQAk0zDFYsA5YZymSvl+YCHb2TtkFgdKkBKkBCjlnAOg\nFJlUEqXWSmilAbRWCrUCAARiMUYJatAmRddKrywvfv0bX6FEAmqR54goMhmOIkS0uUOQNA+PKpWa\nSdydwEZUjmMBGC9tGhLoFDFIkgQICqGEUGEYm+Bdo+z0m0BUf9gbDEaLS8sXLp55vPlUo1BaWLbd\nbndS1y8EpSDwGWNRFDGbAyWAiPqYbqHARHGICEqDaYtFZbbdcDjqd0euazPGPMcZD6KIRrZtU2Ai\nE6NszBghhCglZSYymZdK5SRKsyyXueDcSpLYoAUIJv4wJdvcOCTXtVFJ2+aW5QCQPJN5buYHUkKI\n6YCRMs/yREoTafEH9x91u10hRKVScxxHKe25tNcfDUcGQ+ZCiPsPHkspC4ViseRLzB2XO7bLLWZb\njmVzAlRpKYUyFp9Q4MyyHYszixCS55rAZM0ZY47jGttk6PXP2l9ElAqV+hzYmNp0RDToiwlzTeZk\nUC/Hsilo1MQwHZQWju2ZzF6qXORKaTFFC0wNabKtCSjUQogsz4UQk7nmhLiu69oON0JQRNoF23Zo\nHEbFggdCjcdjqlBJSQghoE1IBqARtYmyDGYxCRZRIWqtTV7OOLe1zh3HmZtdKJeqw+FwOBxqDUoq\nRKSUoSZSaNOlnmWJ1mC+LKJSSmgtOS8QTiTC06fbUmpAq93qEbARCWMWAWZOLefc8VxOmQZtghXz\nMDUCU+71vAnn1kzeMR2faZpSwoUQSqHWMkmyMByZGoRtu1pLRAKgEQljxHV9z3MaMzUjZSSlVLkA\nAItzg2kBAIPJzT02ZZpwhuTz+SzPQlaIGpGA0lODPzFY01+a4kUTwvtxwjWNmhlnUhLDgjNHwpTZ\nnwmZn3ELhmp2TI6YQqiIaGBT855Tjtk0hYTPQdjJR5t8n/zyw9wJs14ARltMU8qPwR8wKIfWAKAp\nZWa4vfFGiKj1BPOllHmul+c559y2LUTlunaep4iaUFBKmLHoZk+aY2NSQ0ohCApxHPu+yzkDAErB\nspllMUQklDHuWJIJIVzPppRaNjMHzFyGbXPbZpOGymP7fmxx5PSQPuONjAnUlHKcKBrAdKlNZZ5z\nU89DU2JljDJGzC8/e+xNhPiX7hcAEIrTrNfcMtSEMZYk2TOfxaYXRrSulF3Prw7DmDArydUoGhWK\nQZiESgnjDzRKQpEQBKI1Sq3VhD4HCEQBAKFMKzUajV3XLZVKSZIwRubmGisry82jHRMjm080DBcT\nOPf7/ZWVFSllllHPs+I45RalDBEUpdqygXPKOdNaZ5nWqLSWnFNEbtbNhOdSZ65rKy0pw729nVdf\ne+n02dObj7feevtnly5eXd9YpYSPozhNM8/zGGO27UxyRKL1JCAAAMUYxc8FJiQAV0pQAFQ6SRLP\nc8wtjqJIiKxUKqGWWZbJXDBGzfEG0ARxOBxQShmjhrXoejYCDcMBpdSsp23blEKapUKmQtoERS4s\nizuIREolchOB0jRNzW5PsziOIyEyblHbtm3LrdVqeZ4PhwNK+cxMgzF2cHBQLJazTGitCdBut5vn\nMoqSPC8lIvY8x5ApDP4x3YpT42DAZ845ISzP5PHhBUqpoT4TQlzHBxAmn57SdrTWSZbS466SabGA\nPAPFTy2GSY/CLAalhRDD4ch1jJa2MpophlRFpjCvAcMtZlKrNMskaqAkFyJN0zRNAYASksaJTDI6\nORrQ6Xc83x71B55rc0SLWZRoDqjzzJwpIAbi0IgagTBGKSPTr4OIZMK+4cbmlkqlmZkZQshoNErT\nzOIeamEggckxp2jbdqFQMMQBQohSx7K8KDmwJE43N58SYMYPBYVqmuaUcEKoAcYcxykUCqZ/dhSO\np2bBFKrNkGWDBJp6SqFQMPOi8jz33KLJF80Km3qSEZ2ZrqQ+5kszxvq9nuc55uUqF4QQfpzpIiJF\nmN5fgoAEgJFp2fV4YxBERYhJSD5XiUVEPsVwJmnXLw89M0v8THiuQJNpNE0I4ZxPuG2m8+uZ0S+E\nEEoYojz2LjB9K0IIpcR8MiEmCaBgFMHkFBJk5Jfwx+kkAgagGZtAJYYJZr6heRNK+bETJGZQNE4Y\nAZwxcuxlyefuExglDIAggmnGnEjEa9AapVRJktm2y5hl4jjGOCJKKRzHMeMqhsPxYDAy0kFGuCHP\nU7MjHcdizMQX6hi709MIwIzGmC7m1OsQ0JkUE9yS8El2SAGIlrmg1IZnHtMXSik5t01ophRKqQnR\nJvQ2YML0qAOANS3SP3OvATRlhnKiCCFKacYYEmKqGmaPfL5hNGhQIou4xV2PIvOSHEWcDkb9JM/T\nNFMoOedAdC7SLEuUzPM84wqVFho1ACAgolFeQMuits2r1bLjWlE8Xl5ZWltf6fU6AKDyzOEWJ9S2\neZ7niIBAlMgJ6mq5FEURaIEql3nEOXd8S0qJWkqVI2ZCQBJnZv4Kox4hhIDSymxOJaXyA7fXby0s\nLifJ2POdOIpOnV63LOvl116+f//xWz/+aZbl9Zn5UjlwHd9xXKEQyZQaCqZWP719z/h1bfQahEwH\nw06t4lPKKdV5nkqRaeWKPM3SVIiMc25z0ExqraUSeZ5bloVaKpkAMi0VBeRU2TaL4ixN8jwzamaK\nMUbQYJ1Kqxw1KIVKISAlhPgej6IoSSPGyEy9yK1KkiRhFCGwOJdSSmQqE6LZOTJgGjAYjyMhlMWd\ncRxmWZ7kSSYzglILD6WybVsc4x+TwOi4sss510Ie03amyLDpFzalLxLiyLjJaQ6EmmiipVaUwtSa\nm/jSmPJpL8fUQGkNoAljTAjR7XYlTjxfGIamvG1yevPyPM9VLhhqxkgmRZTEEjW3LA0YZylntmNZ\nnLJcKiWE0QXgnGY619JKojiPI8filFJiYviJ9QHUGohGVHhsuCilhIJG9ewpNhfmOn6tOuO6bq/X\nC8OYc04pN/IjAMRgk8aJFosFg6waAS0AUFoKIYrVEiIb9Ee27XIGQihG/STOOGEAhpIH0slBaelY\nSIlSqFCr495yITUQRpmVZqZkAAgglSKGPmfbuRSjcGyU26YRP8smdX2zkhMHQwAJDEejJLUn8bQp\nhQDRWhuozTRcsektJlrRz8/GsUNBAJRSEWL0YT83SvzZgj8ci/1Mj9Z025nn4XgiGWPMdB6YzmpT\n45q+cLp7KKUE+LPvDwAa0WRK+pibTwglQAkQwMkcaFO/Od61xkBTQAJAABkljBKYcCqBIlJynO6A\nYeCafmhNCaWAhBhBaKCAFFFPP45MrDklhAJQxixjcF3Hj+O4GJRt283zVCughHPO80wCEEq4VJIz\nO88kJZxRq+AXtdZBoQQAdsUzlVLz1QzAaNovyDGRxtxjE1ROZfqmLp9MroyYP8cJnNn1nNK/vMjm\nn8fUDcM2ZIQQpdB13TzPCaGMTXjk0z1h+leejR4QEVFRiiYFnfCtJ1Ufo4/ACAECk0wXGADBeNzh\n3M9F5HpBKrM0T/I8DePYsV0knDEmlRBC5HmqlZQqB4lKHrM/AQA0ACGUWjYr20XKoNNpI+LZs6dO\nnToZhQPQSh6z8AzXnxBiepUsizFGpEq5RQlRrsdt20JIgQigApUw30thpjC2mQ8kZ5xTRiaps6JJ\nFrebfcu1hEykyl3XzfK42217viWVWF9f/rVf/86HH3wcJ3kUjbXWSDgiw0kYBJMapEY8zvun60kI\nUkoIlVKlvX5zfXVBI+YikSrVKKO4TxAo055lO47luxbjRCkglHDLSpJESGE7VGs5HLWllAaa1Zhp\nzLXSjDHKEEHkIncdawI+G+8OkwxtPIillAiaEaY0yEwlaZxmcSIixm0zRd2mjFvM92zHc2WeS51r\nrRQQIBqoFiIfhwPX4pZNLMkIRY2SKjqNNRljQABBaQQhM6mIEIJxopTCyRYy1o0xxrQylFFTVOAA\nAIRyClLlCKCRaS2VQgBNqEO0BKCMcyAglRDCJNMUEQky2y46jh9FYylzU/+3bU4pNf7PeDutNeeU\nArEIJaARiHQpR0ZtDgCEQ5qmwBilBLVCFMYZSkXTNJbKAq3DKOSlMmWglBIyM0xuUAamU8fVI0Lo\n59iPPmZvEULMfigWi0Zzst/vmzYYIWAKrsAxB8QAaJ7nISqDpyGiUZlizEqTPE1zAEZsVwqVpqmU\nGojRydPTXpc4JppAqVw3KZd50pgXcsyONjmK+V/DCFNKGUoFADiOQ44TAGNSpumm4zhmUKQ6brAB\nADrpmNRCCArEeCNCCJsWHYgWqKeQ9rOlgYlzp2hu6yS3UcfzLvGYkmB+dZqSG/ydHtPV4jQRSph+\nINu2bddRqBFRiMnXnpbOzMchEn2cTpmvapbJXAFj5ropgPGZBIAjglKmWDU1iwYZ0VMrqfXE+YE2\nJtsEqlM/SpVSWuMxZ8EcIZOi/tuyAAEAAElEQVT6TAw7pcS4SAMmUMI5o1kmtKKu6w8Go2KxTAm3\nLMdcu8GACCGcW4jAGIzHPfNZnFt5nnNupWlaLJa1Bs7tKYUfgHJup2lqxD+UUoaKhojHaRwaI3bs\n0ZEQ7XmFZ6IJfcwiZQSQEgOBokZtxAIASJpkQghGJ7EMpQSQEKAWtyfRBxy7fOPwjnPgyZUQolGD\n+SVqAgYKlAJXxk+naUrA1O2eCWeIsm1eCCyhJKXY7bWPmkfAwHVti9sStchFmkVpGkspAbVSiqIG\nVAQmQyWAgPGxcRi6rjuMIo3y7Nmza6vLBDTnPBwllILr+gZDMDmB1jpNY8dxhMziOCyVikopz7cZ\nIyKPNORIJFAJAJxxjxDGLc4JY8ri3OTonFMEJWQcJ+HizNJwNGCMRPFwplHp9ccWdxhHy3KXlxe/\n973vKU0KftmyrHGUAtpAKHl2Ih9+DgwwOD5sDDQQIErIeNhvSZUgMI0ZoiIExuGYArFs5lqWljKK\nMyATPNy0gEgpzehrbimNQqMcR2POmetRcxwAQKo8zXMpze1gWoFS2oA8qInW2nEty7KVEmHUz/KE\nELRdlkphew4FCOMEFGEOyWQ07AxKQYk7xLJtQhmzgCIqIXKJlu0hSkSpNQFQRinDAKdT3hOlCKCk\nVGmaWBY1FpBzDkCV0gQY51wplFICUBttpY4jbk6zPCJUE6CEAiWMUECNUgIlTEqmJBUy1woJBdSg\nlMpyneYxpXQY9oMgyPJME4dSmsUZPFOWMH9nqCUoQC2Vlig1EJGD1lpqzTgBIjRKjZIyxSigViLT\nxlgXHCeJwkKhAFowSnOG0Xg0taGGT4QICKYKCxPofkJtmNRoXdetVquU0k6nNxqFx6E8ZcwyLoQx\nbtsWtygAGJqxmbQplaCUGr6x1hBFCSKIXDGitIYkTAhQiZIQggqUUpIKJSShqLRutnpST1hzx8yy\nie1VSk2HVuhjFTFK7WmTzzTGndZETFH2GXPNTC3AbH5iCgRSqVzYJltSmhDC6XFSSyAnhk0yuS+I\naBoNTQJDjmGqSVpsLPXx7ftcEIVSIGSS+2ttyneoFPZ6gzRPUOkwjgK/QCmzGGcWl7lUqCkQbSFo\nlFpRIBK0kciccCeAIWglUSrBqIWgJy2QSLUWSiIQOdVz08dULrOUcjL9d7JexqVRSjmxjKdRalIH\nogQoISI35JzPc+fJihA07ogQMKISlHBGudYagJp6mBE3AoAsE5SCEErkSIgSOXILATkhuhiUEUlQ\nKAmZpYkcjXqLC5bnWgSYY3u+FxgK7Hg8JsBs29YKLO4wxlALk+0hmjI/R/icOT25TooWd8z2NbiT\naRclwDQIAOCEI1EEGWHACAeK9XojzRPfLSiUWqJCKaWe2ovpipFjTp3RjnumWGWSSwCgYKR6JsEL\nNxGGoaoDEgDU2uRzBIjiTuAXG+MwzRTZ3j3YenrAXb9SqYzHUZ4mSRTHcZjGISVICAghbM6RMAA0\nGSwAo5RQzgijtmvZrnXm9OlvfONrhUKh3W77vh/HCaXEdROttUklHccxh8pxLSnzPM8IDUSWIKJU\nqe87oAmAVihBIzKglDKLoVZSa6KIRlRCK+R5moXhyC8Wnm4/AYBCoUApTbOEMkvIxGOF4bBTry1w\nTjny0biXpbHnVeI0J4QRoIocQ6xaTQ+wJsSMQdJIgaJGjGJBGCRZbmrxts0dm4s8dlzH5pRSyEWc\nZZnQijFiIhi3YGcZRnHEOXUcJ1OpGeHocQcB8zxXShgIFClmIqWUm3AKKKBWBrFzXCcT6SgcGLUL\nyoCSiS+PohAMDmZRIWMhY0pZIpASTpkDoKXOpRRCSi2l79mGQKG00EgYTKAzJQ2djGpUWgGCyvM8\nTsaMo5QC0RR0wQzmMBxupRUASCUQ+AQXAUaZ0lprlIwwU0QQMjV3WemJKDOl1KIWglZalEulME7i\nKM1F7DplbjkEIRMpAYVkMhweNBJGLc4txlSWKy2QgUWJBhBKKpCUoJIotMi11lIxIIoQmeVxnCJj\nQilWqYbJSGJN5alj2YjKBLTHoS0F5EAANVDCKGUGuUFtlJANRMEKfrFUKiVJ1u/3jUxaEmeuZzNG\nOGdSCkqJZVmMEyGywWBgey7nVAEKrRgBTQARGbc0gs0dyk0FjuW5sLgjRE4IoUCAIuEUmEYABdrz\nLSGZcTCISkrjfLXjOKZ5hzKDkBEClDGGqBkjtm0TMmnmM531juMZ/qRtc6M4Qwg5zl8n+ZwpmaKa\nUPOVUuYZQwcmhndOiURtyi3GuxCkQAkqTRhlhGtAVKZlj/AslceFbfY50kBA5JM00HgtkRvfwIp+\nxfcCQJypU4tz0EQqTJMUEKVSlBBugamTcEaRwnA0MLoAlE4qN5Qxh1tKISUEiBYiAxATJgLqLIsm\nboZzI3lkED/LsgzamOf6uNkFhcikzKdufOLSQaOWWZqQX5YRMkU8x3EUk3LyDlRK2Rm2jfiN1jA7\nO2uXi8NBLwpHcqJAxbxCUSFkubRdj1IQShPCBqMxIWw4DoXIqvWZvYP9XCqlVJabBJxozAghjDvc\ncjnno/2jWn1WKRVGqRERSdOUUkuInJEJz20Kh5qvLISwHYcXuOGrAECaJ7bNgRoNaYoTrrtCRIXS\ndm0NSoNWqKSWlsMVKtueoKwmFWOMIRIppe1YRlCLEDDApoGaKeVCJLZtj0ajIAgOD/cWFhaGw74f\nFBhjRoKDUGJbFiJJM+0Wl2JVGMTde/cfdLqZ51fTNN1v7oxHkVS5zIXWklMGGpUSlFip0IQwwhAp\nWJxyTglqIfNiOTg42Lt06dLrX3zN84Nury+k7vYGUZI5jtPpDVdWlkZhVCwF3LHDMOUOL/mlTr/r\n+M44ighFQglnbi5BCNSKItiIKss0AyScKyUIoRZhUsssz7TIVC5SkcoktT0KALmKiSaUMAYWY04c\n9SxGB72j2ZlytzuoVoooMY77gDYAhal+BKKBiFHryVJSalFGGOOEaoqSUGqX91ujUyfXbUZlHqPM\nXMtVueiPI5OlKcAszwkhjmvZti2i0aRuhxCniVS6XKlyy86lMvg2AMulqaQyTRnlXCLmWabkJFyT\nWsksNbdTESI1ggTGgDGKChjjAChVHsWR0nLS3pdSy/Jcp2DxwomT66NhHIWpktKmoKWWuWKUatQE\nKSGQJAmlQIF4nsMIxHE86RdkpNdvai0Zs0wl3CArGq0oMXRwmsvJ0QMAyCYgOSEEGUM1cXUEMU9j\nMiWagpZ5avKJaDzIRC6yjDMY9luEM6JRojYdLSiVAuSEEs5QEEExjkaEINBJTCaOcS3ThSOE0GKi\nbc8ZI5Qy2/Ztuz08SFUodGR5NI4GSkrH5QhWLpSQCODYlqeBIqo0w7LnaCAKwXYKSqk4EQjUcf2g\nVA7j5PDgaDQO8zxXmtiuY9kEETUiZToXmcbcch3OKQFycLRv23alUiGUaQQgVBFSrtaREUY4ZUyh\nQkBuA6GSoJy6R4Eo5CRvE7lAwpCilBIVTmkIHCzuWIQQpYXWSChojXGWcG4RToBBLnKtAYlGikrL\nejk45u77lPIJF0xpAFBCCikppVpPUhzXKyAiIZMoFijVn/OiGYDBoRCU1oCgUaFGrcD0pDDKOEGg\nRCPPsoxSwrllXiGlMdrC9wuIxp9pU18w/lSjNDgsoJLSkLaJ2X9aCYVEyhyAmh43xsgxamnYdM/y\nssg075n+HUArLfGY8jTJNCnRqBh3CCFUkSzLsjydxvuO7ZpRBeq4V9T0oxiGaBiGphBlQFLGmNFW\nMa7b7HUj8FwqlYQQhiJs29y2eRRpAtq0uUgpGSNaT7SlGSNK6WO1OmkYKSZ7C0pFQ0U1ZZwpNajX\n65mpxiZLNYQfi3MKRGmhlDIKcghKK9AoLe4oLRAdpYXpY7Ut1+JcaqUBpVYTmOs4oqTHaRAoogFN\npZQgUsIYJ4AGpzbZEkNESphGQwWmANRIN+Z5qrWjFMZxOhiMDAeEc14ul4+7jJlhgpjOiVxxavvj\ndrfZbLY6405vPB6PszjOskyKDNGEi59n6ASAMM4YY5wQqkErIRUBDYSFSby0unbx4uX5hSWbW4PB\nKEkyIQQltshRyjzPtZRaCgUO4ZwLwbRSWkvDuCWEUAaEECl+CVswZp1JkFJjpqQNiJjm8vi/mBCp\nyDIlNWXE4jbnpsUq48whYGk1GYOCOgNkRCOgIgTIsXo3ATAtcRPE1SiQAyMagQEAFUqFqeiP4+Eo\n8m2OIiMoKUgtRZanCiVjRBPIhSCMKtRxmpjU8zjkohqQMCqUxGNGz7Pgu1FzQcRpnzL8MiF2uhUZ\nY0bCnBCCKJXOpMwJ1QCSEMu2XUSBqG2bB4VipTwjBaLWO0820zgW+VgIlzFmO9okPeNxNOkBAm0o\nakopYNqyGKIGEFk2USMkhBiKtoGD2HErNyGEEGZx/7jH4/N+DDiuaE4XAY85dZRmRiETEcXnmf3k\nXk9v/bGZ0ULk5BggMQGrmgwxmbA0mc3MazMhhFKQU6WEzAWjAFxTBrbPibYw11MnqhUoiQqVRlWu\nVbTSUk4AeaUyRGJZ9szMLAD0e4PhcJjn0rT6IqLnOYxxAJ0khqmvgAEir9ZrSCDJ0nEUEkKCIPCD\nguMVuO3AMfEMANAI5aHi9i/V48lEtYAQyhilpk/I/DQalVprQlErorQCNKjQZKmP+4vtae4BMGkm\nkzLXmhIipwieTaxnlveXGI+TZ2Ci7qW1VqiJEOpzfvTnDG1EJAqRIgXUx3eQ9/pNxohRNlMK0zRO\n0zzP01ptxggu/aWf9qRqStEA1MecdEKYEJlSaHIg46UAQElNn3lMd5uJ959dzckGNbdbKQAwE/AY\n41KqTqdlGhemp84QVOI4np5GcySm4kPmPR3HMuczDBMpZblcNsfJSDOQY67BcNgHgCSJjHqSbXMA\nTSkFI0mmpFBKSSQUtVSmIwxBiVzlIkWllcyTKEREmzNDvbMdh3MuhDAEyjSJszSxLc4ZBdRSSiWF\nUgy1NmtIKTBmaNCEAAqZaS1N2TAXOdOmnY0k2aS0ODVYZkk1m7QqAxJGODCiiZ5wKIHAcS+aGWan\nNaZ5KqU0rWCMWhMfqbRSSAnr9rqHB0daYZqm4zAzn6KO5RiE1FmmtAagbDQMD1q9o6Mjox0wHA6z\nOKaUaiXpscnBz5mZxvtbDmcISuhM6YwCYZyAwPNnzp86dQaRjEZhHCdSKou7nIs8zwkCozajWkpI\nU5llEjXVaGZc5Wa+mXlorZUCrScJnzkaiBSA5XmmtWCMSYmTdBO51kxkmOeKMcJ8BtSSQmWZsDin\nhAIQi3kWzaVUjHJGmNQTGvczD/LL/wQEBQTM2AmbcaVUHMdRFBFlg8wAJdE5aHXsjZgmWmlN0WJM\n4nE0hgqIRQkz1RRq7O+zRxqPz7wmRh9MIqJBvrVGdey9piYGFSADRi2llFS50sI0b4JmWhKBSisU\n6RiUXfAtzjxGOFK0LTeGWEnJlEQCOkOSSyGyKI4VSqmVQX2zLDXVTSklMOCcMyBKacPoQsrSLDfI\nOUOQeFy5BSDUlAMMb/ZzcH7iVlFTDSa3Py7XC6WUEHKKPJvNaXAOcTyd8nihJJBpzRWmCzKNWc0D\nEYTUeS6Ekg5xR8OYEvRtiwBDDZblqFwwzjk3ehxaUgCgDKhpgJzK/h4zpFmpVCqXy51Op91uD4dD\n84nGUz6rNTOhAzCqtfZ9PwiCwWg4HA4550EQuK7r+T6nlmm5nMTo5FjtU+pnKWZAQGutNGRCcs4Z\n4ybON/gHakItNtm2aOb6UWJUBpRizHIc26wJHtf+TRgBMAXwJzA+PsNxO65zfy7oRZ7RxCGEENOE\n98z5mNr/ae15UoIyDO9We3/qeE2Ybzhyh0d7JsNwXddM0uOcM0aieNLTq58hV5j3ms77mQZoSmKh\nUCbqcyRqeulGrW+6dSYXSpBzaox4lmVG3thIvRntUfM9n+1OoNQmYGzQ513cRpw0y7I4jo2k0LQn\nq9PpmP9Vx2NbjXMyX3Yq9oeIaZoiYppnSRJN13f6+41GQ5kgSuRaS0ohyyNCiOvNmNwoFyoXYNZT\nKVWuFLgFlGmLEEI0Rc0BKINxGJoqqNYmrZw8OOdaK6UyQgiiAOBa54iMAAPUJs9g1OJsQjYRuaIU\nTZ0MEVAT1ASR5kqYCrBZN0DQWgqhAEBKnaVCSglAjjNRo8pDwjCOogSROE5B5CpHHUUR5xalVGkA\nAMZci1NNrd6gNRqNkiQBANd1gyDghCBiniFjjAKaXX68/xhnLiWUEEqAESIBjUwR9f1gbXW9XC73\ner00Ts1dsywLkAJSxrjr+ohESRGFSZImlILR4daoAPTxd9eMMSn1tGtPSuPpFWNWnkmRI+cqSYRR\nFwSiFVKRg8hAc8gpotJC6CzTggrLopRwzy36nh4OIyAcUP//2frTWNu27DwMG2M2q9vtaW5/73uv\nXrGKrIaNaKtoFisSRKqJRMmC7ERAbCZBggSKhAAJEOVHgPwREFhCAgTQTyF/JMdRY6lsU1RD07Qo\nySQtymSxKRZZzat63X33vnvv6XazutmMkR9jzXnWffQu1MO55+y99mrmHM03vvENgPAHY8P8Q/5n\nTmCM1hQpjG4YBkUhhkFzVBiRY/A+cogxsig1oA6BjNKRXM5ysvvJaC2//sIkhhKT2FI2u/mVTUYM\nXNRW2P8hECqapKaIjS5zi4X3QhnXADAGHyb1ZJsNqHOOGYOnARwqQQUAQLGC0QcNRqFiQI6KADUb\nBkNRoVYQkQEpMmrWyKhYqxgocgRGUqAJIrJipAJLgsgRIwcVtVRDA3kgJIjMGEi0i6PVhTIIHgN5\nChw5AiGjaNoqjjRNZEEAAgJkUpGJIo4UvSNGhwyRCYgZFBESsdLKO5FZ0nVZtX6HwmHj4L0bQ2BC\n0TcaBt00lTFmGAaxHqvV6uTkZBiGw+Eg9FppeJKn0LatGKJ5BSHG2Pf96enpnTt3ViuZ+oha66Ko\ngmecSE+QHUCM0ZgSOEraZ5KOmorsxl4IIrkywoyCqgGwMLkARL3gtrtRFMKAFeLUvCyKYjLfGFhJ\n1d8ojj7MF3lebPNNcftXYkwJSE5hIXVGwwwYk9+bv/xX/nf0+it/jZh4cQzyijGG4LJarWBTOZAR\nZxYT1X0cRzfS8TACIAOFKPniFJtIyCBh/jxD9H4sCiP1f2aWGIGZz8/PITU6iOlExBh5szmRMxG4\nAKW5uqpEcF7QOSLq+/54PPZ9//jx435QGdbLbcPr9VokirXWy355bA+RgtJVWRYxxnF0RFQUhbWG\nmWKMz559JJcve7Vtj5eX2hgjhE5pZIuTOEpZVZW1+njcj2OfbxcixugYgtLWWiPuPBvuSEFMrTFG\nRI9C9MEPoyPhlWitp4IqAAAUxa2GRYyUbqxnCpKnaq3LsrRWhxCTriUzg/chCVVVZWmBHCCJ6uti\nsarrWmnbdf1u365WG6O1c4GZdaGD98du//LFRe8cJJbOYrHQAF3XTQNEeEpz9fQqS93EyBQjYJjU\nGpiRWYS8CmOvuysKsTDGj+M4dELmZubk1z0iejcpKTBHRNZaKy3UW2lC8MIbipGdG2JkqcoejwdR\nF5TsX2uLyJGdDxw8xAgcPWKIMYZACFwUymhdFk3T8PVVR4ghEGqVNptssNQo8Po+zN7Iex9BVIgG\nN3IYOgPBGlBAMQYSZtQEWgXjjLU6K0BKCCh7MAeSmJrS8t6WZz1LCG6lNPK2ki2PSACylX2Io1Jg\nrDSJY/BKoS4Lw6U0VAh/1xABaquNYdQ+BO8DEXmKDEwhOtHUIJJHrABClDG5FF2IPoidQ62QARQp\nQEYQD2GURmVGwwQMBIyspBQaOXIsbcnIyIga8+8DBY0aFMisr+ACAQXF2urgQuTIIkXFKGgOAVGI\nM28k38UEHP3tz0As7wGFw+7oRgKL49giamvLoigGZbfrlRCgiSi05ClE8sxM1OhpUq3T2q7X66ZZ\nIuKrV6/atlVK1XXtfZQ1rLUeBldVvq5NUVRlWYtCIAA8f/4CUZ+cnt45vycjm4lotdwEUEKIl9YU\npXSKPCgzpUNgREmII6JWrFDyZSQkBFAcGUHK9ICEABPfBhgqW8UY463upWZCooisGAD5tnsAAVlB\nsjy3KxCTSlCOxrLLYekNTNmRvCW5JsGAAJgRp7+Zt956S5gSM7zIKgXHY5fZBxmpY45df8wuOu+H\nfELZyouHcGNYLU+Z9XyLyvaQ2y3vz0guAEkfCTO3bSv5kJRD8/tjUnyRGO3i4kZcQpbLzSOCuq7b\n7XY3NzcSnkjMImHI8Xg8Ho8hTUYhIlGUCCForReLBSVq++D6cey7bpBoWmsMgcax325PZdaRTJSI\n0Xf9sSiKrj9GodhyCJ58GKUqt2hWN7tLYCWTyLVBmUfeHduiMEVRIUrkEiSiIYIQXFnWwmkRHbBx\n9IBGeoC01s69pg2YA2F5yV198fEzqUAqpaqqKopCIGyBNLWy2X4httqA1cxM19eXWuuuOzIzMe73\nBwBFESKwj+RcGPf97ubm5dV175yyJoQg48UgcZTLwkCCuSUzNsYYUxaqGAYXQgQU3I+MUVVlvvjF\nz2+2y647Xt+80miKwgASonZ+8D46P1xdXR0OO2NMVZUxxn5o5eNTbZIgBzqioiZVPeEIAdDx2A1D\nJzGjcwGRrQWiEGiqNBABRUGPkYiYPLMKCsuyXDRK61da2YiMCJmNnX1PRhc+6ZAiA5GPwbngnIuK\nhrZFdoVFBJL2b0CS/mI1KtRKa+3cICtTfFL2RnO4Oz9reZvEPZhaSeT32UZkVARADX0MIfjgmFnp\nqXwCAF17sKakRVFYb3RkExBYKb5//37f9xKGOuekMVwE0RluPaLEBKiV89EYyt+eIt+Yg7B85ilA\ncZ+4mTGJvEHqu9SzHlhKnWpiOiiVf3AaeBbzoaa9QJgD81sTCSCipQlWAhZmvCjIEUeAoQ8G9WHf\ntofj0B7LohCDYCtbEymjYyAGyrKnzLxer8/OzryP19fXYnaKoogxOjfF3HI3pMFZ9Be89+M4MPOr\nV6+YOcTIzKItWdd1VVW9U1pXOGPVA3tgSh1dAAyigUREzFDaSgKmGMXn6CQFMF2r3Bytp7k2Ck1k\nZg5MoDVIR6aMy0HQDBMhmwkBmUnmCk73f/Z8b1n1NJM4QET6AzlTDo/mn5IfjCxXrSXskjkuCgEW\niyVM4y0BAKchmEx1vZAgFBGncgRMaKxYxqlurDQDh0Bd61Pv5K32MyIWtkz+8jagk3K0sJXEx+TV\nJksBZ/qtNM1Yul3BPFMF1Wk8XTbTcjTRQRDXpZQqikKn0Q88y/ZSzgc3N1cy167rj24MITo3htH1\nV5c34mkYIhPKRLuiKJ49e4ZKxt6o4HzfHg+H1rlBKdO2B0RdFEZUMUR9crFYSBasNMy9lDUlcShs\nZaxyYxCOg/d+udxS5PnFyuv6+nr+S61zS5OSqe1ZBF0l5p5SSvqi9CToG2L0QB4weNfaevny1XOF\nWptqGNxytb28uSYCpYvg6Wq331/fdMMIZoIOhDPCzIpZYggiIuAM6yulANi5IQZH0aEiZG81LFfF\n9mS1XBY316+ury5fvfzY2rKuS6usrmqloy3Ae2rb/eXVS2NMXYuCmSciBlIqk4mnSCireRKB96Mw\nVgU9lYqm97EoRJl+JCLiSa1yKpiTyHBEjgQcEVVZGK2wsAhoiJlZyZqHmQDXvJjEzIiKmRhBKwWO\nog8xsLIcY4zeeUcApNLHVRJjjBD1pAIMiORvVXZYzHd+sqmIQjL9iNKImhBipqHm9ZDL78wEOWFi\nZFYhEkBkjkVRcYToI5CokWtm5EjNYmXSwBtbBFNY55w7+lkdi5KrC8wcUgcop/ExzDS3xdlnCMLB\n5HKInRYJMLMbJ8ZTdjbZJmTfP08QsxYDp2bPZFJyLsswM3zeSxulSMxJKEPMoNEyhxgIQBW2+uC9\n9w/767oor6+u8rnFwCGGEEi8gNxwKfwAwOFwkOnjzBwjZVRJTl46iowxuTF2mjc49Dc3N8S83++l\nUUlrfXV17b01RdDTAAtjLXgXxt5VVYXJHrpx4h5L+y8GysF0WcoENeOcm7ppokgPRFlFfRzyPRE5\nPk4kMp690r2Vgr0SE0ozPWWdxIHyc8TUXfqJQA1TXfATTwQRDZNGNEopnB4bR4rEjKjzLruN/CQD\nnohSKJQDZmKKWlmNFgBQE7OkyoxAq1UzXyVaa600AIbos/9UqFQGQCDG6JUy1hhrKoBIDArB+VBY\nA6BB5i8CIWgqhIfGc+8YKcYYhcYTZ1q5eY0abSYyFAAAEMtceiuFTczLF6jv+ydPHinpKgcv3ih4\niuSrsvFhBFaSA2mD4+Al5Mk6wTlxkUUjymOSgUkZQM5zGLuhdzKXfT6nlSEWtiIOx0MXyUv9WQYN\nOueE/g6JpyQOW/ZA3tUAsNvtiEj4BVLdkcQxHwGRRKgxxOhDF/2gDRaFbhZFPxyZ9Wqti8q66F5e\nXPT9uFyfKCzato3Am5OtCz5OXbFTfFAXxXq9VsgxRlK3FWkiijGMwxBkioQCAG8tLlfl2fn6w6ff\ns1a7sQ/BA4bYdopxGOuirLQyzjEqP45tP8Su14fDrqoqqRshCq4+AVYhBGZSShujhSMaI+WISint\nvXPOV1VpbdH3nSQfkAqHWsunAkVg1hS7sqyJdIwjoEWICjWlfIgFbX/9lf4UARBQA3H0NI4ueG+M\nIuDBOY4OFStghchMagL9JuFIXQj5zY+jl/xbeEZScI4xMmOMXnBi54Jzg6geaG1DcKJcUJbTfE/Z\n+2LYAaaBOjAFkXFy6szeMTO6ka2py6IROmXwtN/vZdg8EVVVZQvddd319S2lRSxDjvxQ6xjkyFHr\n6E2MMQqEnislcsOttcHfkiyUUrJ9JCwQ85ozIU6VMJXYsPn3cibiHbMDm+VSMGkUzQwfgDCtWPLm\nZA2VRmQKzgWDYJSuy2Z/s3v58Ys7d86CLWyhy7I01mqD5DGwhxBEo7IoChmXc3Nzc319PY6j3LFx\nHNu2HQbHWYYYUbZeRnFkPwaKRHQ4HITFgIhFURxfXIVQFNVaVqYMOfTeH4/Hi4urTIXouk60HBHR\nFNNNZmaRpBMJwcxjnKfOAhSJAmG+Y3/gBt7KPRAFRBQ9+IyvyDtFqjQ/yhwAZWLB3BVBkuHO3zh5\nI6ObyfaKtwHQqXqaoQCVPBMz5VRYmMEgkjtGTvr1Lk4ihOBTDDVdJLC09Gs1XQaTEGRFYTJoZbSW\nBYQAKlIAUIDKWp2QAUrJ1rTNRJYvX4VWRisrjZpa3ebpIUwIQOA4X83MoJWhyNYUKQuUBFGVZakQ\ngUMMAZCtslZrNigMbGwaAA0QOQIg4daITMRmtcp3b9o5SlGM9+/eFSXzfGMR0U/kC5NZi5I5MaNC\nJOZh6LyPWQEWKMAs0OMU6o7jKP22MnVQ9sMwDNWi2e/34zg+ePDg+fPnEr7JmoYp2PQhRMl9leLo\nezeOowsACrVVaEFpQLNYnQxjiAH/wT/66u9941v92DsXrnY3zWrFPA0Y9N5XVdXUtdaaoq/r2uoF\nM4/jKFMQI3mKrqysAghhWG2aN9969INf/Nw4tp96683lqlqtVlqj613bHsbRE8HoyY1BPvvZH3gz\nx/hd152fn65Wq67rRJS6bkqRnmSObdvvdtcxctNUZVkzx93uMI59348xGsGfEfVqXWlA54IkScG3\n3XEUEoQ1pdamLBYXLz+6c/7w9GSxXCzf++BZUS6ZFRFMemIskig0cWanvvcMoMe+GxXg2A/e+2EY\nm6qkurl4tbcKl6uqMNY7R94bY0pbMoL3XoO2yqA2GoxXWnS9FGMYvSMviIV4Vmtt1/XMBMCewdS2\nspXrD1dXV3fu3BWcgyKFcQCYlB6JQIrSAIQAGkRliA0aBqAQdlfXrveFrRAxMAjcJB3HMrNHRl/2\nbaeNMsYAYiRKqapyw4BwS1kKaQhnZmFkhGOKyXCaf1aWpSLoBw+Jc1SVpUrj0EIWXIZpI/OsNiZQ\nvEryEMQhhhhCiIFjZAEAJF65xbsAJBpTkySBijEG5xUgUKiaqu+Hw6G1tjzZnlKIYGHRrKqmFm6U\nj4GYCbioKxG9ZebLy8uXL18eDq2opkGCs+YpgvBax9G3bb/ZbO7evW+Kar/fn56cX19fd91Q1/Xx\n2O33x3v3Hrz//jv7fajKddM0fd8zs0xfu7y8XCwWAvDILp4E1Qqr7fS9crHikKbhRrNXtsne+6mR\n4HVsaZ4Y5QI/UZBFPjOeuU1e3eYbs2HW4hSVUvl8xLPCDMyQhRFjNMFDinFeOw9tjBY5ZwLiHJ6g\nLarbIJBymRS1tpnJJ7+PkYlQm5ISGzFGCZpYKemrn7y0SPtMp6WQSTRjNAADC4NZIwCARmSeAr3s\nIKck4PYIAABgjBXK/a2X1fK213w1wKS5m2/sLJAEEbgD1NqQlK1zzTo4pyS0nQRYUSlWSoEy0n2i\nszoT4vR/OZ5MspaGFQbRXAAAnlSngHlib6PRCrEomqKAiTvHQWGE1AdGxERRfj49PQXE9nhkJq3t\ner3SpsTdtQ9BJl5/97vf/aEv/vDVzaW1Vil0blRKWWusvcVRUXFlToiEgmsYrQ9xHOMQYtePm9Oz\nr/3Gb3/zW9/aHXaRWJni0B1uDgdZ0xIo5QRcAEDE2+eCiBqgWRUfffTByWa1WNQ+dP/Ov/tDf/an\n/6cffvj+Zt1YmwYNOOf9BgAUGtSlmqSgkZmlFti2rdKwWq1EiK/rHjGztTJ2y4szFjpTVVWS/eC8\nPzqRcZ1zfvCyq6VmMP1MSARa23EIV1c3J9s7d++e//zP/8J6e0a3udCtSE9+pcSIJWdiVqfb7eBc\nWdkQQozh8ub4qTcfLVf1i+fPQiA/tjEEyRf86JnZlkWMJB0emZA5LbmU+ELCYLXWy+VSfhAh56Io\nttuTk5OT58+f5/g0LUMU9Q2cHrcV/sLMsmAyAgAYGRAJKmsChcNNd+yPYzeOYdSIaNR2vSIkiODJ\na2DQWJqSFa7sWrrrfQwUog9CHafCWKmjC9uLUUm/e1WU3dAFF5RCozQoQEZGXi2WtrQatTFaZoYK\nKwEIyGiZdyj/E+aCcB+iD8JiQI2FsViqsQuggGNgBAXMyBR8jN6PTltTGAMKKfgArACLwoztAEBt\n23LwQsaxxgi2EqfhkIqV5kioodQ1JfXYYRgOh1amjwtsJXfVGGPMtAW01n3fZz8t61M8zdX19WKx\nKKpyt9sdj8e8VjnS8bh3bpBVfTzujTHS2CDpY4Z8JCMnMpSIrDmtkZHT+ZXXqqwxY5RoT0vdmhmZ\no7Vl5grMf+/9OBWg5unKpE4AMOH/Jof7IuiVz9Ak4V1B8uOMU83Mxuh64gVhXrUAANETETFPoL/W\nChRoLSqXefOBSv1+MYDWmTEBCgE1sAohDkllfApedHqfUgI4mnxzJrSdKbmQfOuUvCHR3m9f0voK\nAFq/9oc409/LPl/u2+zXDEm3TWuVf55tYBtnuR3MfLZCiykIFo1WaU+h4OdvTt9F2ljgW/+EClPJ\nLC8OhaCMnuYdDMMQgwSSArtpZo4BlNYg7EwgNT0P4ZhoYK+1reoKAGNwAKqqmmVRKVQ+jJ/9zA+8\nfPVK9G+Iw9nZuVJKQXbY03+Hbl9XK1CGmbrBKWXOT88I1Eg0uPg7v/uN9z/8oGmWgVhxDCH0vcuu\nyKZXURTAEQCIYgbQxHn2fXfnbHN6tt3trv6Dv/Dn/8//p/+jG/o337ivNQKywKwcJZM2SmtTVgDC\nwJos8hwIncd0AEAUiIKUEPLsUQDw0+jx28RdqBbjOGrQU6MCityiizECoDVl0yzdSMMwrlenu93h\nH3315x49We73EUCm2KbehtfWI0g9CQkBgBGvr6+DJ1ovjodusSwR1Wd/4PM/+IXvX62X43D0Q68B\nrdLIALItlVFG62kYnZ+TVClR18T8yc9C9FJpkrJgXNI+kVMQIenJI3j58qVYVRlHK8PWYoyHwyEf\n0xo9TRFDAlQcvBt7Pw4UQ2lNU9VlXQTvRf/AR6QQI6MCDQq7oWVQWimjlKmtqMcDYgwhs+Ah99Uz\nW6O1gsDEFBAKYzSyIohNXdnSaDRKs2IwEUXvyg1e2pIIiImZo0KtFEobmBwVQYkQIgKUhZLBnaDQ\nWiuKC4xQGNv2Xd92TFCVJWrlRzcM3XK51KjGvtWNHoaurmvXteJFpWxvjbUmjs6HQNpgJnxeX1/v\ndoe+7wUikrqLQm2MKYrbSMK5UBY1sOq7sSrdark5PS2tLatmcXl5OYzuzp17m83J1dXNu+++v16v\nX774MER0fsDEDpeITaw5ACS9R2aASHzcH7Pdk/8OM4v2evDBskFQsUKDioV7NaXO3ArDe2J+K5b/\n+knHRlOqxMs/hQI9t7TyLZigKXnnFG5mIeRbM6601uaf//NfgBl3JWdYTdPkFGxed6mrZp5ri1Eo\nikLQSYF9by9egfNuapVXSmsQCy7f4sYIALpSgEgxAiBqRZEAzISBgCh6AMBkuyG5Qkxpipu66pQ2\nZn55mTQC6d7LVcs/iSgfTC6NkopG5oqI666KEkSzLx1KfsYYc6o0c+WgTPGJeHn2KYZJP2Yq0UGm\nrKS4NH9isag+cQxE0BqJHJDcYenQFjlkDajGwRldAZQAMA5jszRFuQbAd9999+TkZLtdV3e2bbuv\nyuZmd10WhRBzmCV9mWxr1ZwAMMWgVLGoFwDKEx/afr3a/srXfvU3f+t3XeClKdkFH2m12Vo7yAAV\nSCCMmEg39jHGmNoDhLyOTN6P202lFD569Og/+U/+H5Ut236vDVqrAUAoPwgaOK1gWwQKed2bhTnb\nWgBOxQ+CqZoJkUMIjuGWTcMJH5eKYDbocsMnxkrMZBkAgKlrHTSiXi7XMTARLprV4dA+eXzXOZmi\njWktEUjAjbeHZYjAIJMykJXRWpe2mOJZLXPe/vJf+d+7oe+6I7mxLmyhDYQoyl2orXgjSFSFHAxR\nVs5Oq1S93n4o78yUgRT5vca3NLMBRbN6AEmddX5/iIgCI2hm5hg9eZkbXdlCF1oDggajlHQ5jcFx\niIFYGSs9IW3btm0rokHhdYFzmBEQcvcIIkrTuvyeiIRMK0idrB+ps+LrnDq50uvr608cnIgowsnJ\nWUzEXZ6xH588eXJ9fX11dSUJShoEPijQRVEcbq6t0VVpNVeHaYwXhRApsrYGlB4dtf1oFBVaS4Om\nDLVjZiIWrUsiMvp2UvvEOjGlJEbMfDweF4vFyfnZdruFj3C5XF7vbsZxXK1WElXcXO/u3DlnQNHY\n1prX6zUiphhlQjXk2couLq0mBGFFZR/DEHPPkHgX0XyRDD5Gz+wFrBH1aGutXAsqYGbpalKaERFw\nGqwq4X72RjhD/2DGjcSZxEbOhEIIUlDIn5JwykxWINWdsh8bxz4/OUpUbGaUuZ8SDshNkQMJ/mhu\np/6JXeGiUDJNbjLWybHJ7QYAwVJkG0i6h8ifqFUCwGazmdnl29disVKTwA9nblUm8GQXnVe/SU5r\n8rLJAUSfWRWAKDcElIrjEAqjUfLQGGMIABKJK5jFGskWRKXUdOmIkxsT5XCtgSffM/k2ZgJWtgQA\npsgsB05W2Bh+jSObmgt0Gm4k7plSaK61VSqGCKYc2haoZKefPX/+O7/7TQB4553/9o/80a/cOb/3\n8PGD777zzt275wqaCTxUpCYgmOUaQWuF5McQGWxpjbKb1eoXfvGX/sFX/8uPnr5s6m3Xem0KimGz\nPi3MMQdEmZ4nyy6jYbkbBpit1sH53/rad37zt/4VRxhhrOuFAs0CSYOQnDSgwsjIEQC0MqZINxk4\nRCaKhS3lwSIiADIwMGplEFmJQrkWR4WgwGjSaPJdm34/HZGiD8ysky5kuucaQTkXxiEaVSwb/ef/\n3H/wT/7pz4NMNmFMy2BKj/K+Shq4slJJKWUL2XgqhEDMv/GbXzvZnl7fXGqNBpZ1WVlUcXDAbK2d\neHizumZertnyZld6u9GSFc6ZYh5LiLNyNACN4yj7K+93mT/i/Zi3CQAQxxgjRTZoECWfIBLuRIwE\nkSMpo4zSjEAhVmSFlBYCUWnWq+bO+UneGoi43+/zjp5vGaNt3rPzLPB4PIrjyf344lHats2hcLY/\nSqk3nzzOx5x7XyKQuleu28v92e/3d86352cb8XNKqaqqyrKOLtZ1fdwftMaqtLury3QaPoTQdQOO\nse2H46EfQzRmmu8s+aWsf++j5OX5TOSpSZ2sKsuQegqHYXj16hUa/eDBg7ffflsp1fZd0sykV69e\nlUVlymq/OwY/bDYb73omP4yj1rqpy2ycc2wRiJP4pJZenaybk6fbwTQNTmqfUWkMYSJAYmIsG6Ox\nrnL+dLsCAZBIzyav4yxdmdvDGKPck6os66KUy88pETPn6RW33CsGczxOSulzNDA/Y3XLhhK3hkVR\nyfeFGIhjmlEX60a0tynSFKbJ8vJ+QpOkewkxCAVeOsdlWKR0OCHqHsYYo7CchdOsDSLoSP7Fx5fi\n1aVLS/6bLXXmHUnPYwiu70e5+9KTLN08zLGqGkTW2hqjrC2t1cYUSkHTLI1RwiYQ5BNRE5EFg6UR\nD+A9iYqBUqpaVMCvpUUIgGQBASa6ESJqQAClEThneQAABJN2DaLAC0TifUEmPWhUFEFqBGnwgthP\nxeOt9UEhQsqBAyhjhAxRmLUqlOvcL/93v3F1uX91ddk0zd/9z/7Lr3zlK7/3je/+6I/+aGHqOM2x\nnZyomE6Dyo9k6xJA2QLYRT8yoPIM/9nf/ervfP13z+7crRer997/cLkpY1QugNLTKMmcv3PC5bIF\noaxCxqqyzatXH/31v/5//+xnPldX9W5/VdqlD86aGpiAEIICNIAaAJVhII+K811GQISIYGJEIhY5\nYU5W22hkjsCQPyBrVSPklp15RMbMWhutAzALe0f0HoVBAwRFrQtLoCtt+C/+z/8X//gf/4J0quOU\nu/NcFQVuoy6RdmREPB6PTQMMsFgvELGqqo9ffPTi4sX56alGtqis5Ou1nIOGQDxfKrfPeho8BUoB\ns0kFeUyoHaSEDwC01pvNJlsr6SAWQ7zdrvO1JzsGNJvYDRP0SMyMxMgaEUUfgSP56ClQ5LhoGkap\n1wQgBMUalTI6xpj7TKWPX+pDTx7cZQT5jVSSKERi7LvOWGuUFbUFRgLCyCG4KKGFC2Nw4v8gkLe6\nkHfK7wN56Zode5fV68XqajSo1clmW9ZVVZQ+hsNuf3VzfdjtR+9OVgtQooE99cZKW05ZLKy1wXmj\nMYzDe97JnEkiDpHbtu/9oeuHwQdtCkkRcn2+qqzWehhcMkqTQo1S0yShYXAOXUiiRN77i4uLMXjv\n/ac/830hBGn2994Ls+7xk0fb07MPP/xwsS8/+9nPvnr1qu/7/X5/fn7etu088U0empxnRJyuZ3oC\niAooMkhXsEx0Aqk4U1la4TCXpQVQfd+OowegxaLOGqRZ2YQQSjCcJpzlLAIRJbvNfiSzfJd1I03c\n4ow5FT4lFcFZ+wEiGklcREuGeAo6Mkcl5xYAIHNthuGYx8eKzHj+OQQKITKDMQZRAZCgQOkkMenk\nGkQex84YDaDG0SnFIlfq3DCOo7VaqmpKSeqmpFMEphxcCGAkm7+u6xAkGWJjSGsEUFKC9n4MwSNy\nUaj0+9h1AyJrHbVGrUM+n6dPP/4f9UZx8GUxcWYkzJcCSTYTn/DZVV2EMKkwmDRtj4ikyQARJWqQ\ncExbGzx5ivn9GUgRIkpm+0gxsCzrUhWQqs0mvWRd1nVdVQAAblCVhstX7Xvfe37n/EFpowJ7un30\nr//l//DWp944P3t09+756enGGFBawFDFBERAEay1sQdmMDUUVo8O/sW/+NVf+u9++b3vPW/qEwW1\n0Qtrmhg0s+pab800KCxbeT8MUtrJJHKalf7H0X/2M5//v/7V/1sIAUBv1ndc31tbAhhABUoByUgL\nnCw8K4pTPzUiKqW1LrSWJRsBlEINiJxmXyHEKclLCaj8oJVRM8R1AlmZgYDjhBUAorRjgFIQ4zj6\nstnISCkA/MxnPnc4dPWySv5m5ipez2Ng4h0ppdSyLk1RjM4VRUHk3eBQq5/7uZ/7mf/oP1JWE5KL\nQREbuV7vwZbI/yPeSE5ZSKPTuGIFoG5nvcmlGdnkWo99ryfwXeuqqGoEpQCIQw+SCt8SdgAAvFgT\nBUnSgliQIBFlIyQlIk7T/8gHQlIMARRGBAOinB2lbWdWGZL/LppGolTJmn0IZGIkasqtKazVBUHU\naJRBIBRPI9G8j44Cg2JkFTkYZQN5PwYfHUcQ30MQFejIQX4jvlOjAYXt4VhWxWqxqJq6eOMJaiUe\nqD0cj13bHVtQWJcVKOzb7nDsiFUIwWhttfLDeHH5arlcxhhDiIag70ffDs4FRKtVgQjBx6wMKxMs\njTF1Xfd9r7WOgZxzZanrumbmYXDDMHCaMT2ZguubcXSvLi6fPHmyWC2/8Y1veO+/7/u+Tyvz7rvv\nvq21tWa9Xq3Xq2Hot9vNer3abrdiiCBxW1LGHAGt1lprK+MkYvTiFrPic/YxEp0zR+cHJixKA6y6\nvpTuRtHhFJRPdJyZMAIaXcY0EAdmmehqtcr2cF7xiaMTIzZ3PMxc13X2RjqJbcr4itcKsYgMQEVR\nZgsbU0MGYqGNbAkZSMoAgidx1x2T1rWuqkqGnMYYmmaZtysAxTjxC0JwxlTM0TmH6DNjCgC0RknS\nJIRNBIepdT1Fc8Lx4KZpmDmEqdtAqcIYEYH1MaoYR55K3FbwAGnxlo9PLWPMcncQJQRgohCjlvcr\nqa1ziDGG6BhYaWMLLc2kAJOEMzMTR+I4DFF6klCx0XK7VO4lkpzPexChAY2oSsOegUJkokBj9Byj\nj7GpKtQaOTLi2PeHtkXmwlYyZpCDzHFGNKhBg4bSlL3rS1NWi+rm8oYVu97t93tQhQvx1atXm5Pt\n2dmdl68u/+F//tW33v7Uw/sPisoumqasKqO1XDZzBIpVVWllBu8Ox+75i5ff+P1vPf3weVUtyqr5\n6PkzUzTr9bZ3IyIChOBGU1vFHMOUNTIDSf+58977EF1qAJ+6m994403nQl3VAHB5cXF2fg7MYfBa\n62keCQNEGWInQ50m+RoAYCIKCCBjWgwRMWpEoAiiWAGvF+E4OUKFBhjSpJVbWioAMVihHMCk6EcA\nCrRSagDW0YfgfFHWi2YtBMiJuJDoCzj9W0pZU/lNSpkImgFEVlyhYebgx4cP3vz1X//dn/mPC1AG\nGEMcNaMpCkAE74E0MUrVXezmBCsSE8t0cw3SJi+/D86HgACmKEAp1BO3pmzMbbGTCJiBZLLhrB4p\naSQxAVu7BCUUwSi5C3IkZj1B2UppBp4oCMQECq01ymirlGT3ooAmj2DynrMXxSjpv5Dr62lFsB9G\nies5RkAFrHzw40DWFkopRlaEaDjj/1pr5R2ww2DAysVRTr6zsRNbFEK4e+9BCMGHGA6d0IvLsjRo\njC2rkmPgYRiObS+n0zSLolwcDoeyMIWxXPu6rBZV7SbmmdGoBheCXL9G751HCsEKoQZSPW/6J2OE\nqe+lKIoQCPHoRNrKxzh0SmlrDRHtj/v1ZvPs+UfFVXU47rUyPkyB7wcfvvfgwYO6WRI7Bv/kjcc5\nw9BaZyOmtWYSnh5ZW0q4nFW1EoBGyRvdqseiVm17HMdRIlpjVzAbspz9TaovMupSusQ4lS0lO8RZ\nJ5lSKLkMAHChJVHURilVSFYQgiuKSowwIiNqEc80RVFJO0iMUcbNFkXlvbfWis8vywITOxamsF3r\n1DEOACFE511RFrbQcwdIRNrYuRLovA7UNFO5SFTpvB8BQHRQ66YcB+/DuLDWhzF4Wq4aoy2DcmMY\nxmBMUTerGLjrj8N4DMExMCp03g3jxCMkopubGwBYrVZKh0gBAEIMRdkICzO3ICBiWZY3u4Msmq7r\nZMqI9JTdO73bD33Xc1mWtjDM7PzB+YP49hijd/F2JyATA7Hrh75ZVM67ZdWMw6A0RAphiBTBh9G7\nKP4pRKkHBmDSMjEZwGhVVmZ3/coUNowWtQLiqmSNClT0zjECM/noSlsWdaGYurHzw1EX2pE7XF9o\npW1li9L+0T/+41/9L362Waxevny16y6268325PTd977z4fMPf/zHvgwKg4vnd+7EEIwtri4uI8ft\nSfPh0w+GfhxDvLy4Rm20Mr3zo3NdPzZNM/YHY3RleBzHSK4wPHTHkcHasrAlR/BjiC76wUcfgCg6\nx8gx+idvPPrut7/14P69P/KVr/zrf/mvU40Qr6939+/fXy03wkciT+PolFKFrWKMrHh0g9zeXDIV\nTl3f94KiLJrl9c3Varmu6lI2YbYIaXsoCTgErA8hiFWS35dlWdlCWeWH8dmLj7vDsWzq85PTtz79\ndrsfFpvGjQMaGNzoYghDt1gt9/u2qqoQgtZqv28329U4DrLGJyYDUwgemE1hkWi93h6Pg1IKoHaD\nffrB9dhb9qa0WmOJIv1BpLAGsKg06gn5c95rQF0YilEVGhjc6DWgLg0Q9MNQNyvFQSN6H41SaAww\nA2AYR1OWkjO5oS/qOjoHGjRgO/SLxfJ4PCybBWpF0Yu6my4sAIYwkg+EYFAhABgMwRVlBQbjMEam\noq6UQhqdKiwQE0WlDRgEhqE91ss1+0hEWmlQCpIhs1UVxpECFWUJSpHY0NKiHY3sRD91GumSTTXh\njRmHTGwUpxRo8gutxWJKR/lyuRRCvwgfr9drEVUaur6u6zC6rPsnJZkQQlluvfdV1bZtKxsfEV0M\n2hTr7RnH0B8OpydnFx+/ON+sj8c9ujIyMOn1wgLQEIJnD0yRgnOC3CgpZBjN4zjWVePcpLXmfby6\nuimL+tGTJ+++/4ForWFg58LY9YhaGfzw2YfD0N25c++HfuiL6/X22bOnF5cXROH09PTl82ef+tSn\nXn78rO97jfzpz366qqoQotZaoWEW2VyWWe+FlY77CZ6lJAwtrV0xyHCASS8jEklnw7zvh5ICdYZz\nM9uFmYfRZ1+Qq0RTGpRqctkxxxizTqb8V/KzqZ8y9VAKrg+gjPQwC5tF6nXyZXlwg5TmBHLx3jdN\nI4U7nLEMRFYnh4pyNlOylrqgJFqVBpQY4zD6iXRAERXKHElNk7CQ0mDAaIOKDUBQSvkwUVFvFbiZ\nisIMQy+l7ERyl3sERLGuK2OM1irGIGQ/ZjJGG6MRwVqjlNJaRMHVyclWZqsbo5l5GPpxHIqiGF0n\nRw5xHMaWk4L44XgDM+pIzrGmtFRRWRbDMHRdu9/vAUC81zwqCeBHB4KoUpILS1VEs1hWIYTRdTm0\nccyRqSoXRWGj4TjwEI6+05Ba2IAsM4foAgA5S0RD7z7zmbd//Wtfe+ONN16+uPjo+e7YHRh50VQ/\n90//8d2797/w+R8sy2pxumjb/smbby2X9ZNPPfjJxU+enKzff//5P/ov/vFvfu23x9FJIyqL0jKB\nipFjYAgKCIGtVlpbRO1HN46+bdth6IauA2CmcH52tttdKo0fvv+9tz/95o//4R+/e/duQvDAGCNy\n41dXN4KwI2Ig0oiEQEjeeSIy2goqEqeJVurq8lppjIF2+5s753eruiSi999/f73aMJBWxljNBKMb\ngo+owGg7DIM1RaTgXTgejxSZOAYfl6tF8PHYHrQyZVUo1MPgvv3Ouy8uLq8urxfL5nho79w99y40\ny9qY4vrmpmmqGD0Ax8hFaWJSzp4cEhCwrEXpvmIiZgKlrVJodK1V9Q/+3n/18NG9MPQcA4WxLJQB\n1FoX1fp46EXzt2kacZkSMAnwUJalmI+6rjeblfNjDqpUouSEEM7Ozpj55uZGHPDJyYkEl/LOoihy\n8Ns0TVEUT58+zUagrmsREdYa+7aN5ENoM9Egnxi4sSzLYQhKkTDB6sWZGzyARUQm6a4DVGgMBM+B\nCiKiEQFYBL/YKVMso+gXF0sAkGb5omoot4hxRERAbaxSpnSuH73jMS5XZb04rRdbJiAOJ2cbgAis\nLq9eOa8YrTY1Wmay9aJerC1FP46+H4O1ZbVY7G72iNqWqxoMgCpLWxQVatUNg7GqQjyUV6VWjx4+\n7vc33W7XlBUhaGuJOcRIY+TIrMCP3jlljJgXjjEiqKqq+n4EkIoLI2jJ6p0Ln/rUm5fXV5eXl5Na\nXdXkeQUxxg8/fP/p0w+01kVRnJ2d3blzx1r74MGnDofDze7qs5/97GLR9H3/+PHDsqwRFbCSllME\nNc1656AUaFQELKq1yIAGRLuWI7ngKXjhnvgY6mbpKcruzhwHojAMTijcIThJQ7LkneiaijZxCC7j\ngfJ7FvxcgRwNQMY/ReIIjMZqawpj9aJZyvxZ2YPTX3PKkigokzWE1GGXC7/yEpXA7AM59d/O4T7x\nnLJ2RYE7e6n8ttyuLMc3aRxRLsrlVsrMr49JElswvRCCMSpnXTp1FMs/Y4yr1aooCmGIygGdc5Lx\n5FRXrlQ+KDRlkbgQSuVqtRrbTiWRj1zs/UT/ikpdL8ws1UX5jfSLyHw/qTzNAxCpzy0Wi3mUgYlT\nlz+ulBLkM8bIwbdtW3Kdafs5Xchpsryym/zxL//YD//wD//tv/23vQ+73e75s2ePH70Rg/9DP/JD\nlxfXv/Pbv/HP/9k/WS7XhS2/8IUv3Ll/9t/80j9ru4PSdhzd82evhsExqmHonPOIaHAqFSqlrNKg\nOYZRyocxxq7tjsfj0HXe+6Io+v6oFTk3yCVVVfVn/syf+ZEv/sjJyakwviRDlTsjjNKy1JJiAoqM\nLyuNo3Pee2OU1C59cH3frtbLtj0Qx/V6WTeVVBwRWQZMgCgBal0UxlorTJZx7A+Hg+wiRLa2LEt7\nPB6VZudC1x3LskY17ca27feH674fd3s9jt6Hwfv4xS9+4Z133h3H8fz89HA4KKWcc7aw3otEQm4k\nz31pGtRUA8u7IMY4juN777335I2HnpmBpFCBgET09Nn3lovVcrlk5uvry3Ecm6bZbreXl69EOCCX\nMIuiWK1WL1+92G634zguFgsJ/JfL5X6///DDDyWIlPt8cXEhKaNsCtlK19fXx+OxruvVaiWt/oJu\nCdiutZZSrrFTQinpoBTbpVdps9mI67q4uHDOeSeW6DXZQClwZupslojUWiNy3/eInNtoMu50PB4/\nEZ8hIkNkmXaIChG0Ntaaum7KsmAGpVApfXHxqihKRFgslsPQY+S6roqiDMF3XR+CL4qyqsqbm521\nBlE5N3oflMKyrGxRrE63TdOsyqLdXUEI9WLZ7nZobF0WWusixMg0ihpW9DFCP4KM3BQb6L032pZl\nSdSnGgFgovORc/WiOj09XSwWbdsej0fRTFksFhcXF4vFYrPZKKXquj49Pd1ut1KRvb7effzxx5/9\n7Pc9ePDg29/+9tnZ2Ze+9KWiKJVSwLIpChGOEv1GpVGqAjIpQ3DXRd0ogwowUBSV7OC8C76TGcER\nhDUmjHBAsqYUTU4fRvmr6GeKYlmUObuBfRhFL010NWPgEB0TKg1GF8YqpcD70bmQuRJ1vShL630U\njG4+J28iZM+5T0VRpKrPxNPN/MWqqgBA1GWyLoUs4nk9KqdvsiJzJ1OG7HPh/RPpFKdynJpNQpKV\nnddr9nZ4q480scwzDKiUkn4pgezEecSkmCRfMe/G4MRQEKxWYhP5uE9TvEIITdOs1+sQwm63yw44\n7xk57SxDl0clyYCoqqrirPE4h7G5i00n6VK5RTc3N3LakpvKTR7c6MbICuVTmLgMkFSE5cxVmmkr\nD0Qb/GM/+Ud/9md/9vHjR1/72m8KDHs8HmOMwzDuD1fODWVZ/w+//m92h5sf/pEvXt5ctl13PHa7\nm6NSpqwXMRJwBKVQiXY9heCcH5gjQmSOolHdHfu+74PzRFGpkoiWi/r6+rKpCqXUV77ylZ/4iZ8w\nMLEzZMeWZWk0i1hWhtcmlACIQVJ40lpYkUL30MyRKGiN1tqytCG4/f4mRrZWG6NCCMPQAUx6w0oZ\nIr66ugKgplmenZ1bW3bdsW37vm/v3DmL0Wutl8tKKXM87p0Li0Vt7XoYutVqxRzH0VurY4x//I//\n8X/zb/76ZrMRAGTirejCOSet3HlJq+mlQWlglcsewXsfwzj2X/vN3/jc5z+rKBoNIQSvyIfMeUMf\nXFEUJ6dbAST2h51sASI6tgdpdA3Bf/j0A1nzXdfJvhMU+sWLFzJXJZv4rJn77NkzZt5sNgJyaq33\n+72IK+YygLyUUsKjXq0XEveIG5O4YbFYhBBevXpV1/X19bXAFcbYtu2lB2nqMEsIStd1siWzNr/W\nOkbftoeHDx82TXM4HEIIEpxJK2Ve23m/xIj9OK5Wi6Ko+r49HFpEdi42TdW2PSLX9WIcHbMax957\nAqDri2vgKP03wsUVS7poVsYqBO3DOA6eOGhltTVPP35hCrtq6jB0rt333TBGXm9OGKISEpe1RWEa\nLtGjc47Kkil6740xiDrGCIzGmNxBhYgx3N7VFy9enJ6f3b17V0KE6+trMa2SmIrbPj09PTs7A4Dj\n8SgY4FtvvX1+fve3f+vrzz/+6POf//w//If/aLvdVmVTFBWAUmi1tgJZgZ7MaV5yKSsoBLaZG1Xv\n/WazyVEyzkp9Yq9C4sdmIpJJ6rTzqhIRGA3AWilWZKO0N6FSaIgCgNFTDUcIzAbAbDbr+dfJOZgc\nXGcWBKSOqpwz5bVYVZV0bMnt9t4LkiAXJvtP7qlSSjJQ4ZbkTiBKHblZ5VDWZbbOkOYoi3WWY0pz\nmTxaKbitViullBTKc1iaDb3Y4q7rBNmTmE4syGq1yvFXzmBijCJ6WCTd+KqqpHdPUgG5IsGdxXMs\nl0ufhg3O86Tlctn3fdtOmJ5YBAkMMXW9ydWZP6A6LD+LmxG359OE3IzmWavyk8rbNc44kJ9IE1+9\nemFMcffu+V/8i/+zf/pP/3mzKLWGpim/+a3fbZqGCTebJRFstg2iWm7u//7vf91WZdMsN6s1APbd\n6Mc+EBhjcGKURCYKXtTDxs16GZzI0Lk02gC0xuNhZ41CJquRKDx4cP/Hf/zHtdbH3VEUfYqiaNse\nERlC13Wb9UkIMllcBttrRFYKDoejNlhVBSAdDx0gVWWzWNYvPn519955Yat+aN0YNtuVG8Nuf71Y\nPBpGpm4MMUAEBojj6PywWm3a7nA87kMcqrJRGqqqsEXtXYjknXPSPzC6XqGp6qLvxhBdWZajc947\nYbV+4Yufu7y6/MEffPLixfOmafIW1VqL4lRaBrdYAgNpo7VWqIAoRIreBWYeh/7q6uru6QkiF0Uh\n5R6tNQEPw3A8ehHitNbWdQkAwzCked6ktRLdJaIqxig/C31AKbBWi46nc0PbehnzwcxVVREZALq5\nuV6vl33fao1laa+uLvq+fvr0AxE0SyZsWkU5xBRipyi1i7iDMLsQ8YMPPnjw4IG1drPZSGcFUWSO\n1mrBEkIIbdtKniroDYrM/WzlO+eyqm9GFHMAd0scrRZFUZVFbYwpi4XUlW9u9otmNYxd8KS1BVZt\n21NUVV0sl2vBmrRGUbsRndlD2810TBRRBGB23pRVd+yC8xricddCiMaWi7ryYwdIzjmtoLQaoEBk\npqDLahxH76MxsSgmOza1FlVVVcm1kPegIkTm0dPNzdTieu/evfPz8/1+fzweHz9+LA5ss9ncuXOn\naRrvfVnW7bEri+rxoyfnd86ePn16fnb33t0HL1686Lvx9PR8vdYoJ8+OiAbv6kXDzAARcRKcnaJt\nPxozVYOydY2RPn5xCXBbaMhPBF4vC+XS0VwdVc94110/Zn8RJ412Z4wZ3a0okVJxGINqB0REvMo2\nM3+RES8yL/nmREGnEas5+5EfxApLqi5vkFA9G0H5rCzfdB6vDZUQvyKpVV5nctcEW5NTEiMrCT4l\n3Sf5IlmyWQEvW2eYQSIZ2ZB0Qf6aveAn0hRErOtafJg4HtkehdKS3EixRxSfpB4un5r7g6Ioci6Y\nT4mZh2GY50bzmyAICafuhAxUnpyciHaLnLCckjL6sO/mXy3AaZaknEUr02u5ahDR7Tpj1Je//KW/\n83d+/6233g6Blsu669qu65erJ+Tj/nB1PHTrzVJpcG4YhkEpg8oYqw77tuuGZrUEAEMWVWmMKUvL\nSBQ1MnjvRRQ1pWuylHm5bK4uXlR14X3/xR/8wuPHD4eulwvJ5RBO+l0Sv0vpaLaWgjbY9+0wDGVp\nRXqImQ6Hg9LQ923f92Vpi9IsFjXisKQmklcKysoWLJARDYNjF3f7awAqyipb6sNxNwwdgFqvl0pD\n27aIWrDf3e76eOxCcHVdI3ICHDxzXC4aAGjbVpaESsOIYwx5qQvvejLrFEUuOsYITIFipMhMCPzs\n2bN7Z6fOjZU1MQbkWJbVvu3KSi+XjbGKIhA7q+tmUa3XS2Hirta1zDQpi/r8/NFutxNlTFkJsqG2\n221Zljc3NxKeS6CtlMra0nVdt20rIZcs2rt3765WK5msk7tKERnVNBgib8/tdqu1zrC2wEqnp6ei\n+idtK5LWY2qEFG0CsSfSmpbHYJallTmZsgC6rqvrerlcLpdL2cJyKNlcktMHT1oF70PbtlI/k3mp\n4rSmnWirpmmaprm+vNTaWquISPBGZgbA5XIp6IXs1ikPIIiAkdToQ11YVVQxUlEtRj8qra2yZRms\ntYWxAhsGrzyrkF5ThwZjjrnFsqVAQUGMi8WiG3pR1i/Lcr1ey1yJvu/Lsmya5uTkxBiz3++HYWDG\noiiY8eZmb619++3vG8ceQN279+D58+erlagiGWCJgMgwm6KW2JxSl5DWODVGJslmBADQChGU4jgg\nGjEbMUbOw01iFGqcpCpa32qoM0dmMbw8nzetFCiFABpR+lbRORqHqLWWabfAKgb2FCmVybNHkH+a\n/O95epRNP0+qf0Y8hyxuWYgmtXmHNCjIJE0weZscJMMFYj05zaCVoCnbHXmcnPSXVOJHZBMvTG5O\nhAvxlyImCDOsL/sY8ZHZKIj5E/uuU2dW/qBJWhI5w5WNnb09pf4yScmFeifoR354ctrZQ8ubBeWX\nJBLT1KUceuR3Zr+l0zSg/X6fHT8mNX6g2DSNsiZvdfn2TwChcqNijMzRdyHGqBHrqnj08P5f+ct/\n6W/9rf/P+fn5wwd3Li8vmcLlxQtOGhA3NyNqVRSFj9T3o/cxeNLWbrfrY99JRBJ8WS8apVEhgIL9\n/mYcRz+OJI3WyMzAQHVpgXwkr1Tx+c997kv/7h9eNgvvvS1LSXAlPZJltlqtYozgiZiQheJNMXrv\nfVUV3g+iY43IiOC9G4be2mIcB+9DWZ46N97ceO/DYtFcXLy01iilmQnRC58FEbbbjfeu74eLi1fW\nFnVd1XVZVbZtO60VIheF1dporZzzIfi6LgGKui6JDBErhfv9Yb+/+amf+qmv/ebXJRiS1ShbgF/T\nQoTkaFlgwBCci44ICBgkHjTmo48+/MN/6Idi4Lop3UAcwVpbVYUk5U1TrddbAGrbw8tXz956622J\n8cvSVnWhNCmlQnQnJyfjOBaFIQrCMTscdmL6ZYAvAIxjLxmS7FytVd+3Mfq63jx7dvj0p992zr14\n8cL7cVLVn8YrG6314bijNBxP0MLVakVEZ2dnUrvd7XYnJycSojnn0rzaQrBNrTWRYo5lWRaFQWSZ\njS25kTHq9PT+MAzyzhBCHt+83+9VKreIa5TCLTNKaVhrKzS2qmru3Xvw7rvvIqLWMAwOUdf1oq4X\nMZI1pXiFEEIMMkAAtdZNvVQ4SOwIABSBKTKy8wG0HpxTEIwtwYdlvXjvnW+fn6xtU9lCj2PvnANH\nFFW0BTBBWTt0RBR81MoIlC0NlMMw5IUBQCE6QJ1Hqb169ep4PC6Xy7qu33jjDUk9x3Hc7XYSxxdF\nNfT+9PT0+vr661//7Xv37969e/fb335nt9udn59XVWNNZUyBoK0tmHB0nrAHo2SuOIVIwBqNOBNU\nCggDRWRQRjpXfGGVFv1PgjQpCFBhDBSBPMxjLCU/IbA0+SEiygeBFUIkikkEDkAachhYMymK0zsB\nkAiZlfMx21itJ6tlZPXATPkK06SsXKvPwREilmUpK0bEg4/Ho5BD5rmeMaaua6110zTzYkl2eBKn\niDUXD5QNK8+qUJR7+JMXJKKmaYwxEk/JKs9F47x2Y4x1XQv4JiUx2Z/DMGw2G5PmguQILt9rcR4C\nPIpbFUxS6HZz93Z6eirbQ8IxQQUlR8lFHdnVcoez85bTmzv7mJiROUmSna/TS26a994Ff3b3Lhod\nQsiAidwx6T6jpFFN0/RMRIS6KZVSCDrG+Oabb/yVv/KXfv3Xf+NXfuVXHj168oUvfv93v/vdvhvH\n0dV1fXP9CrUqyhpRg9J1XY7KH/bH/X5/eno6jn4cx9F1/dCBkoq3G/uBpNdkUroDYAYg52LbjScn\nG2v1n/yTf+ILX/iC0AeYWYgkEgNOpZdEdYmJSQVI3vth7IpCn5+fbzabcRyfPXu2290sFovz8/Ou\n6x4+fBhC6Pv+8vLyzp07Ips/jqPg4+M4ukQNL8tSGNh5Q0lME0K4f//e8Xg8HPayxmKMRWHPz89E\nWdl7fzz2RLRYLOq6urre/+RP/uS//Fe/st2uJfrRWkfy3nul5hHMrVqXhDIhBBeD95ERjHy1H58+\nfUppDs04HJ0fnR8F75JH2bYHiedOT58cj3ueBiCpO3dO1+snx+Px448/7vv+eOjkcoqikM+enp4C\nwGKxwERVuL6+Fv7CixcvHj58KCQdInr//felyf/09FQnVpiEgJLEhOjknxJoylLsuu54PA7D8ODB\nA+fcvXv3vve972mtu67TysoOlfsjW0bQ8hy5QhI11lrf3NzkWEq+tG3by8tLQSmVUkLizZAOp67E\nk5OT9XrddZ33/tWrVzc3N1nDTW5FCOH6+tqgyjo9OUoTk5X1KeC2XquXy0bb8rC7jsRFYQ3jarM8\ntMPp9qSwlbF13XX2sHdu0Bqt1YGUNlYpJRU7uXD5wXs/DC6mkRwhhHEclbbKiBSTF0F6qfB99NFH\nKYK8rf4eDu1mffqNb/z+OPZEYb87HA6Hp0+fbjabGKMb/TiOAMpOOZlxkXwMWhmlC2QGBCBiVABq\ndM4YA4CTdBUxE1LkIXiNt7Kc2f6UZZ2i4ZBcgNJate0tlqOUQoR0A11OnnLJKsfHiIToRGdAuHap\n9xaVIqVgQhD/9b/6JSl/EZGsaUHhxMHk+xInNXUoiqJtW2ut0HhEwUIaOHJ9SPaklOYELsDZ7HQJ\nVU5OToR+hojH41GW5vF4zMPnxc5mOy5aVbKUF4vF5eXl2dkZgAzKM8wsa074ArIJJeKW6fQPHjx4\n7733mqYR0MAkcUaYqfDVdS0QnLgiuYpCaQnNnHMyyESEpIZhEOhgnpSIB837Te5Yrk967wWRwFQ3\n0lpLUVfIM5lIIicjN1m4TLLcfQzr1Uk79PO5eRkRzZtBeApVVQFQd9wJs0S68FJZO37wwQd//+/9\ng4uLi3v37pVlfX19/eLFi+Vm7UMIxAAQAzOrqlkCqKvrnZRJiCjwNF9VBEYVq7Is2/ZgjPFuBKCz\nk8319WVZlgC0u7n5q3/1//Jjf/hLZWX7vl8u1ohqnHZRblecUkPzmlKWkTmtXdfuDzdEJIV3uUXG\nmJubGynjCRAkVrtpmv1+L4GwFCwlZhrHUVhqEprId0nyKoVASsXRKeBL1Vp5miaJhfvATXX2P/kj\nf+zTn35bkCWt9dX1xWazEaQuX1FK09FH0tqiMhGYCJTRRVkWRVEWhtj9+T/z03/yT/yx/c1VVVqM\n8fLylSmqxWIhJyyQ2vX1tayWm5sbyUIkztvtdjHSyclZVTZ93+d6+GKxWK/XJycn4quklVB8yXK5\nlP0o8aKQkuQ92+2WmZ8/f77ZbA6Hw6NHjyS1unf/zvPnz6VYG0IoimK9XkMqBUluIcDGycnJgwcP\nPn7+sixLIbJmECJvf1nzMiJI0ilZkH3fy8nIEAcx3Nkpwowz1TTLy8vLqqpWq9XNzc0wDGdnZ6en\np5eXl0KvWC6XmTQoWqgAMAyD9CRVVSV5uUjs3Llz5+XLl9J9EUIoyvrysFemUEAP7969ePGxRXh0\n7/xn/6uvPrp3DuTfePJguWi+9e1vPH36ASJHhmMfRxdlXyfLext5O+cOh4P0QimlQBtADepWn01O\n0qZhdxmp4okGQghmtztst+u33npjHMcPPnx/u93+8A//8Ha7rcp6HH2MfP/eo/v37zPpQ9f20aNW\nU75OE7GTiJzzzGxNKabm+fPnZVm7sbUGlZpsFCSytDyjmESP8lYVWy28Adkj4sAk2+NZeUl2jSTo\niJjVbaS7KAQnKm5ZnUf8k8kZAM1YE5LZ5INi6kHjNIA2hCDIr8C7nESvsxGn2WDBeSE0JmHEly9f\nhpk4uaxak4YeSqCakyR5rk3TQOqJS06LV6uV1koOKzSe4/Hovb93796rV6/W6/W9e/eeP39+fX0t\n2ILcSrHdlAQNtdbSMSd9HmVZCvFBtG3EteQCT15DOrHgxMbJhef6UPZGMYn+4azSI+5W6kmQBoRn\na6iU6rpOZuLJ103Bb1W2bdu7Mbu0fFgJLSUsFUeOiN4H59x2uxY33La9YJ7bbc3M/4e//Jd+/dd/\n/atf/ar3/gd/8AeLwnz08fOiKPw42rJoFvU4xPawI1DWKO+GKNETREm4ZchbCE6urqpKrbjvW+9H\na3UI7uz89I0nj99++y2lIcYoFy4VKfFtLlG3JcSRfZhul3TwObkulWjusgEQ8fz83MzIhJJhC1VP\nih/MLHWC09PTO3fuCCE7R8Rze4EJjM33P+Fst1CtPNMQwVq7XK6urq5OTk7E4M6XZUp5KQWGKDPu\nlFITsqnEXEXnAiC9//77z58/r0s7DH2hcLVaobYZwJRYJ3OyhWsjI+/CJGhdXF5eNvWYF5s4CWZ+\n9913RXhUohYJaCTJy9cl2IAsMOHUCaIAM0LN5eWlnEnev7JHpA5vrZWHqLWWKpTsFwGddGLuSHqU\nc3drrQQHEjlhwuTlW8SF5EI1zebjyRHEVhCRnIDkQLKiQpo+J/dHTkPCFOmVlGuRntntdktEMvNb\nKH8+htViCQoLY+u6Xq427N2x7bUpX13cMDki2m4WSpf37z90btjtj0qNxkwc19SjCUqpw+EgC3K9\nXtd1PYGox70yFpKymrzEyGRnAIktHGMkAop4crJZLBYvXrwQOuVisTgejy9fvtRaN/Xq/v2HWqvd\nbseERV1ZpZURzjDK0EilFIDa7XYibxEDEVFVNU3TMMemMRonQq9sAXn0Yg9zM0x+FpKz0sSomgAG\neYLiLF5PidDaMgX9oBQDKLHfACAz6kRSR0TAjJgJeaghyf8JFEZJJBgTrsXMTdOIwZVHK+POEFFm\nEcpatGnYn6wquUhIAo6ZmyHJOyLKD1Jcubm5kd46WUwSCIvhkK8QqFD2jEBkxkxPV0oR4jJvbm6Y\nOYdRQuffbrfZu8iZzHEwk7qCMnHAGFMUpWwS2SfL5VJwSxF6mIEzU1wsR8gBRS7QyXPNgKQ4VHHV\n2d/nTDlH5VVVUSIThxCQpwpzXrL5PCWLEkuULQsA1fWibfvd7iC/kRt7fX395Mmjp0+f/qk/9Sfe\neuuNv//3//5v/uZvnJycPXz4oG1bZXQIoT3su96XZb1ZLRj1brdjisSRKCACIMl43hijHx1RAGar\ncQTyYbCF8mP4oS9+4fOf+9yTx4/3+31VVZUtjvt9WTf5zgvVUJy+dCVrrZlJKaUNUgRZb0u1NEkb\nnogCkQ9hsVgoVDFGiiILBDFwDNy1QwxsrV00q9Vyo5Rqj/3V5Y1NU64pgncRcVpXovCfgTUxi1rr\nvu910tKOMcqkF61UXddf+MIXfuu3vnZ+fp5xthBC09QZl8tpdwLrggzcUQoBgTg4B0ahNvj+++93\nXddUa+89MTVNo22plLa2kCzh6upaFsDJyQkAeh9EBw4AZWaKMaYojVJF3ZQZ+TTG3Ll7tljWUQYX\ncdAGAbXSMIr3ZcQpcMaiNGVlJXe3hY7kjVXOD2Vlq6q6vLyUiFPweYnGxGFkk5QhZSLabFfy18Wy\nzgtbJ/kM2RQygVvCkb4bJT/LpTgJCiUczN5I3XJuqa5LIrJWn52dxRh3u533HoDmFSmZz0QUyrpA\nBYz08uLFer0W7x7IhxBWq+XV1dV6vR7HMXIoTRFdXDTLYRg0wnF/oBjLoogcP/OZ7/+VX/5XVjMg\na4OI3PW+61pGtV6vnee2bZ0LIfTimGWxAYAPDgCM1Uu7MFbbqtwdjmGWPajUYZmBxPxPpZRSZr2e\nIIH9fl+U9sGDe3Vdv3r1QhIOBEU0IUao2FilIyqNaVyOMhastQjaubKuF8zctp3SsFjU6/WK2JXF\npP1ElPSCGQi4rCtQk2KiTWqZ3vtmOU1zRsSiKgGAh4EBMLVv4oyGTUSbk60Q3CF118gy8GnS/NyU\nGdmTspJycCeWPQcyOVPJyZNAecMwSFaeLayeOCRZrB7zVs+fzfB0XtMS+glgJYlkXdcSUslfBR/L\nWEGYNeLVda21mp+8uDGBFK6urrz3d+7cuby8FMrKMAwqke5zXiwn1jSNiBv2fS/gjzGGnM83MTt8\nmNEfVCJcyA/iLShVjLINlWv8ROYrZ5Izp3xwscKZCaJSl7FsfjS3rU75U03TiKPNCWVRFFpj3x4F\nD5FQQADG9Xr93nvvrddrZn78+PHP/MzP/Oqv/uqv/dqvvf/udz/zA99/fbMHYLM0ytjg6XDYex9s\nWSIqICbSgMQMxISEpS2YORL1fVtaXRRGmrpX68WXfuxHP/f9n2eOIbiy3ChA770pYg7k0+riEEJZ\nVrI6RV9KbpQ2E1NAbKWYJ4Fx5g8lP9AY4/n5eW6vydsDAAQtybBSfmpyqPwscnghKZqesT0RUaHa\n7/df+tKXvvnN3xNFGUQEzsqlWdFriioAWGkjY31BBO4YZEZcWZTG6GO7v3v3LpArCuP7wXsfGY2x\nUvJcr9dSVtFaSzgYUmuqRIH7/X65XHPqClCpYUjWsNwugRMxNZDiLO0zaUCiStiy5FJJ9GjaxfLP\njETJDZdKnvCYxVdJ2B7Ca3VvSjIzklHlvWMSUzerCYsRV4luyq8ze/Pu2+/3SoMf/eG4a5oGkPq+\n92FUamULXZalKEOikuJIaBYL6QNBnOacxhiLQrbtpOPa973W6L23VmsFTKGwdfSTEo13/PanP/Nv\n/+2/GYf+Znesm6auiqv9fnd11TQNaoHAQUCOYeiks6VpqhDCOA4xdWouFouqWaA2owuZ3cep8ZH/\nQOXYWmttWRSFSIAaq5fLBoB2u+vD4XB+fr7ZrNbrrdYYyReqcM5dXLxanqxQIRIHCEAYIiEDalVY\nrTRxBGO01TrooBVURSH6TfKah2UZP8DZ6DuxaTm1yE8zW6dsyvKWEd+a/Wu2WuJiQ5o1I65rKjbS\njD8uO1B2b17EIY2ryhw5SkwbWcEC2mb3nq3kHFPSs+E3+Q0qgYGSpSVqI8tDzXZfDMSkjDvRTzHt\nvalFV6UCmtb67OxMpscbYy4vLxHx9PT0448/zis+O868FATsli0q+HLbthZVNp2YphEzs5j4HN3E\nmbA8zISbINWlsifLN0E223K51KmGNM+IxR/LU5DvKsuyrKsj93o6/K30Z77PQh0UAyFpR13XRmm5\naUAYY+RIHGlRN9GHxWKBa+BI/8v/+Ge+8hM//nf+0//0gw/eWy7X5+fnMZLeHWNkHyiEIPoIWqFC\nZNRErCNHjmVRI6IewYcei6qsiqE/gIHP/8D3f/rtTy0W9X6/XzWL0ui+H09PT693x0WzKtaii3Ec\nx16a+Z0bu/4IAFLp6ftWbvKrl9eLxVLgx4zlAuCrVxcSgeboMoQ4DBOTuChKZh5H13W9PM0QYvI3\naIzND04pmRChEJVMqiWSGcQwlXyZQ4jeB2ZGhR9//PGP/MiP/MqvfPo73/mO4KLR+aqqYgwZ0MuL\nFhG15MoMhJwmTSDA7TI4Pdu+fP7s7HTToVJK9aOPEbT2MhbK+2nUrFKmqprEAVMhEICShvYYfUKq\nJ91+Y4xzg6TyVVUYY6Tg75w7OzvxSUI+97kT0TAMMXpjdNPUAj90Xdd1x7knsEk5TLomiUgckk49\n70QiEhPl97KnxN8wM2IpUe8wSMsReh/kbXk7iO/s+369XtOMQpW/Yr1ZWquPx865ARXHGGyhq3qB\niGVZF0Ul3crEoA1CBKKgNFjUn/nsp7t2CNHt9zfGqkWzCtFtt2tUbK1erRdXlzeLxQLY12Vx5+w0\nuig0pePhsFgsvv8Hvvjs+Qcvnj/3T5/fPT8pyrpsmt6565srhcYYU1ZWIsi2PUhRytx2UpL3Ywgu\nAq5WqyqQmBef9E/nIXu+aoGmAVRRSsQAIbj94QYRF8t6t7/ebrdNUymN+/2NJIgEsNhWkoYbzZEi\nMFFEYFSIbuiB0BpljdrvR1+OqNgYncbKAMBtv4r30r0rYFoMISKiUhiCNO1MrFexc1rLCGlK/YLC\naZqo0Uozzvjck7+DyBABCRUDs0JAhWbu5XTqA2Dm8/PzkKZCSWI1N6k5BpdCkZSRslcMSeRca304\nHEyaCatSa5jUPHMQJL8XS3p+fp4HKguSk8djxEn8O0jtWg47DIMxOodgGc2UkpXQ4QTlOx6P4uQg\nCR/kKIATOpdD4wy72WlOK2SoR9ZKSDoU+VMww/rlYueZuLxyWiAH1GmEopphfTnYR0SxtkJNHMeR\nZL59CmSyyZMj5NqvJAEAwIzLxaJtj0I8SVXr9ubmRppLhmHYbrfb7fajjz56/Pjx3/ybf/PrX//6\n//fv/v9+7/d/73Of+8Lnv/ADHz9/eXWzW6/Xl5fXUWOMHgAZYuphRLF9xpgQJxiHmc/OTv/kn/oT\n2+02RG+sXq+WfTccj+2jR08GFyWA9aldmoicG0OUABaF+GuMYY42dY/nUlyObPIdy5m9hEfCDJ5C\n0arq00usZ16ZmaQg2bAccI6CyqpTs2KePGtr7Wc+8/infuqnPvzwQwGXcNYqnh4c58AoAoo3CkxM\nzCSDr1JFBOHly5eSCrMPZVmelQ0RxMBF4RSaqqoQdF2X+92xrusYmCEOvSNym/WJUqosp5F0+Txl\ny+RFnqvlQsherVbzXApntRkpqsktksWplLK2lERfyKjigWKMFxcXArgJiXGz2ch2U6mQmR+NSf2I\ndjbaVb69LO2d83tVVUkAmnerUioba57j3gaJ2DkR4qTjcR+CZwatZUhPoTUSoTFK64qIQzDKCOWa\nz85Otd45Z/q+LwoLSMH7k5OVc+Ny1VRVqTSgYguKmBXFAFNB3ppSK/vpz37/+b2772++896773z4\n/MX52Xa5PWn3h/Vade2w3+/jdbTFxPmq67ptQ1EUNklCyw1nnOjBElzmfJcS9QlS54z4YHmbGMlh\n6CJ5YUQhImIZyfd9G0K4urz2Pm42m8dvvtEebsqmrKpGK1CFwkkyTik03gfQYK0tS2s0KwWF0QwE\nMKFwOT0Sdo+e9Y/GNL9UHnfej5xad2jWD8oJ8UbEqqrnoXPeRxnCma9Dk3+VjVr+G6cJFtnaIqKU\niIVvLfCdnLH4EslgctphjNlut9lJiJ+TspNAyXJYaTiVS5VMMFtt8ROZ4yfRqAiZLJdLKRgKi0G6\nFuQGia8SdpBYImPMxcXFgwcPXrx4gbMW5fk1CmVObLpcUdM0/eEoiLmcszQEZDKJYPoqKVBwYnnk\nI+fUU4ZoUZpkmCM+4RNmg6sS+CY/S1Yk1xtjjEyFrXPbf/ZezLzZbIS+IXc+1/zbti2Kcr3eIGLX\ntX3fG1Pcvbt1bnj58qW4OuFZPXzwaHD9kydP/tpf+2tPnz77r3/+v/nab/1WUy8f3Lvf9t3JycZ7\nP3jnnAuBMflL8hy9LwprrSUKPgx3zk9/8id/8od+6IdCcAZBF6XsNwF1p4JB72TFi8KNCC5IqimX\nVlWF4Ip13Wgtw6eVtJEyo1L6/PxuXsfz0FJqhIfDgblrmqYs67KsZVnK3ANmmesjH6Rx9Nk/xcgh\nkNZIBMYU2cFYC/LVgHq5qAHgy1/+8s/93M9NIjfWdF1X19UsqrgNaBSg/CcwBZlUhxO4YYyprPnt\n3/7tr/z4vycepWmaq6sbJhxHv1jUTN6akgjKoo7xYLRYF6CoEFlmtfRDqzVmu5B/WC6XsjdlfS4W\nC+mQ3e/3YgFzJJ4DL6311dWV8IAePHgg3aPOBVnwEsqI5xaQUPaL7IKM7Ik7yTinxNqSCsjbZMWm\n0dr6/ffflzRIJeKunKfsLJ7NkgcAbXC9XoyuL4pisay8i5G8TGnZ746RvHMYyYuaBrAaHdii8tER\nwc3+OhAx0snZibW670erTOQYORRVMbh+tVkhgwXl/NAedoFgHL215XK9Qq0229Plcnl6ut1uN9/4\nva/vD8fVeqltef/e6fX1tRSq284VRVGWE9veWqsUZsjLGIXG+sDEr7XNZE2yuZ9GxFzIPxx2zjli\nkX4WQDvcv/+wLKvD4eD9TVFUTS36OP3zlx9uNqvt9rSqKmtKY60xqBQqxNKWzEiRjYKmKpqq6F10\nnhghMvkYENEAA0BkKo0GREYAhagVAjOzTNKYfoMgdAkiQq20nrKfuelTSsWZ/GkO1wDApBFxc0zI\nyIqRpZALHphy8+zistOSlSHmWGBiwYUyXjw38RJ7+vTKDj97V4GthZsgoTom/C1HvrJGbVKEZGbp\nJLfWWqv3+30pgoZFIetesDIJ35bLZdM0z58/F2rs8Xhcr9cmdenOq00CDEpwJzdIugHu370bSOYu\na2MMI2iti6rUXos+rhgXYSpiavvNDin/fDweMcHB86A1y1hw6joUzy2WRbgksufLstxsNsd2YMKs\n+U8ASBwoun7w3htUpixk7lkY3fFwKAqzPxwOh0NZVYW1RVX60V/vbjSqB48e923Xj26xWC3XG0T9\n8uXFozcevXr16s6dOz/zMz/z6c983y/+4n97dXXx5MmTtu77cTgejx3yCCEEAI3MaLT1YygLY3QR\nomPST568+af/1J82xux2u+1qHUI87NuTk7OmaS4vr/fHdrFYWauJZDTLRGschq5pmqIwqWfLEMHN\nzY3RdQhBFOe01lKUMkYNwyCawaIuLJN/BXNA5M1mY60OgQ6HHTNWVZFgeZnyIjPiTVJYAGNUWVpp\nkpH6x24nzFSV9wwiImgf4vOPPnzjjcdlWUpV8nx1utvtknj8rRaDvFbbE22MMTYQjN55HyMTIgNx\nXdeNLb77zrv//k//2eCHm/1RmE4KjdbDycmWCJSC/f4YY5SF7X0sCtFD88JGMXbyrDlHlMxGeHdS\n/SUiYbvl/JiTqEpOgyRLlpxSiJdyKO+j8Ckk+c5Zo0gBidnd7XZCF5IMKVsPSsq/gnz4pMgFCa4H\nUGVlJcAFAHFRGV3I4XYOECGC9+F46Oqaq2oTFYeAPnqZLSJFQeeCtQhcEpEbA6oQiUtb7HaH1WIZ\nQa2XCxd8XaK2pm+7EKmp9K4bHj142Pc9DQ60YoV1WSljEbRSE7d+v79Zr9Y/9CP/DgB881u/13Ye\nKAIMSim58H5oh2Fo2z5GXxSFJ66qApU2ZQUABMCBtC6Cc9GTMliXjS5tVZQujIfdMUIAAkBCVjF6\nmXXr3MDMVV0sFmsi8t41TXP//n0itta2x/5waB8+XN9/cO94aH/zN349sOvOT2Lk7Xbb1KhnXERb\nWQ7cDQNoBRqUVbGPWmthhIsXyLI1zDIaTIpYVqkJnRK8Sh5lZhJyatvHGfNAKYWorbWZxZBTHUgN\nZzmAmzzWb37t1+W7xUV/4og4q3nmJSJuPPMRMk0uV0Q/USmhRKTOsZUk9TFNrMi+Qd7jvZdOGkSU\nSvtHH32U56hTkgkBAO/HqqqkhTnfUPl2yd7E3Ge6DibytNa66zoRP879vBlLzDkWIh4Oh9VqtWya\nwbngXLNcWq2vd7vVYuFCUAASfAYikKcXSfylpAIZBjTGiLiyEFJFNhFnzXpqVoKG17Xv1EwSgiK6\n4DmSzD0KFBWgtqYuq7bvFKApbHBeJlqiVrvdDrSCSC4GxYAGIcLgh/ViiUYbVJ5idD4wWaWVNfv9\n7vzO6YuPX8klfOc73/l7f+/vP3369PHjx4EiM3ddt9/vQ5BGSKAQvY+5Znv//v3/zf/2f/3g7j3R\n62Nm0ZaOkSXVkwqBJLLW2rou5ZHtdrschdR1LSq/bduCkoKzYiatTVkWxlgAds6P4wCAVVUiqnEc\niNgYHUKU2ioAK6WVQpm6LH8FQO8dEReFFaXn9rAT+rsxZr3edl3Xdd2dO3fGweXATcZkEJG25ei4\nbpbf+ta3/sbf+Bt37p69fPmyKIpnz54tFo3WWoZGTlwVJIXGlNVqvVmtNsoYUBpRhxhDCEbrvm+b\nwhZW/b//X//P4PvTk5OuO0quBqxkNiNxcGNwfgietifrrh3Kyio0Ibq6WlxdXwi8JlyMk5OTzWbT\ndd3FxQURbTYbweVyp7mAKtLVZ62VRyzAw5hen1h+0kibaeUZbZMkRhYwJL3jpmkuLi4kvrTWbrfb\nvu93u51QkwQIlRYi2RSCP8tpYGrzyr5KJZhULkS6feu6vrh4mRlJTdOsVit5OjmQlS0j3LyyLH0M\nVVEWVRmc9zEgg4/BKE3AQ9ebwt45Oy+qsj0c277brNbH41H24/X1jgk3m41SZmg7H0YEvdtfI8Mw\nDP/kn/wTo6jQXBWaiA6Hw36/H8cxMBEFAEA9aXVmkCOSFwiBIxBEo6wtTVXUptDie4KLecI6EBJw\nTP3+Ysqk51eAoqurKyI4PT0ty3q32+12uxjjYr0oimJ7cvLgwYOzsztlWSljjCkWi0WgqEArPZlo\nmUdoza3uXDL+eZI1m9TribfyOjT3KDmBExFe/gPFDphNp4VU3zGJF55djBhtM/dOORKU88vvyz4g\nO6GMU+U0KF3hJJjGibQm3AlOtZCQxMLjTKMhryGJbsQZiP0VyrL3Pus55t0inzoej9LegTO6ToYQ\nMwWDZ1qBc5+crbz09FFqvxDkTSc+YmTOXXhRa2tt5q3rdCsmLD6lRGEmiy5HFg2uw+Fw584dcUIC\na+TLocSMBABRfBGHqrWu63qxWBRF8fzZSxnKhyzTRlFrXRjbdZ0bR3lGk0CD8wRcliUrDBgUEzMr\n1KRIKbNvu9Rvb6BQIFkGgVJ6GH3TNC740Ibt6cnnP/+5Zx8/67p2tVmP/QDAD+7d8zFcvHzV9V1d\n101VShH1wYMHP/iFL9y/c//+/QfDMACMzo1aO60nhqg0YcgEAdEYDCEgau9Ha0spgYbgxlEIKXax\nWLV9z9M0XhHIcLLShHackE9h00x9SxnvjjF6P91/yS/DazM4YnrWikiGeA0S5Sil6qY6Htqbmxtm\nFPWwoigYrTblr/73v/bP/tk/u3///s3Nzf3796+vr5OuhKALWfNXix03tlCmsNZqWxiDSmlrkWIs\nyxKBq7IOgbS2Q++MKbSeZE0SXAMhBAxSvbfNYgJDFCviICZ+tVptNpu2bff7fdu2UmyQBS8rQbZt\nzjPkTmbwRMKCkGSFTVITkFhKcvqYNALkDZA65PKnZPVmGnfGG2TrCUFJz+T55VM2KbzkLQ+pW1w6\nFrI1EJBAvsjaElETAaJ2LhwOrZSdUkFRW1sURSGkhnEcNYJSRqOJSAoYEBSw1tYgRksU6XjsdD92\nXedDCOGq6/u6qsqyBERbTlPfysrqgBRhsz4BwrryRlcUBk+DxqnqJjXIsWu7rq+amlwYvR+dV0qZ\noqyaclEuj7ubKTsHZI4U0OPIbJbLJbONRYxx6jIMIUQma8sQo7g0mSEgF3V9fS2WYbfbOXfZtq0Y\nW4UGWBGB99H7YExk1ADTaA9gJAo+JiqZQoaIqHItFqZBwdMcI2DFDEwIiEziI4SwAJAY22pW6s72\neb4MssnNP8uCzGlPfoPJTgxTiyvNJuVkl5O/GGcYffZsSqlc78lniQno49dxQ58mEn7ijNXrlXxZ\nxJlH+wmvm79Xay1uJScWnFpwdBr6IAs6437yQ5YjoiR/kJ3KPC5rmkbqMVkPX1KZzDUyiRA43Q0K\nPGNG5FtRFEXXdcvlUhKjjz76SII7O+sOxllBK4d7AuhjoifBjK81v28SQMkrV+O9SEFiBkamG5gr\npTlhzcHB4zeePP3oo+VisSq3bhyrxeKn/9yfe+NTn/rHP/uzX//db6yWy8dPnhz2+4vLy7Iotqen\ngvWNPvzYj/17P/3TP/3Zz36WiK53e++9scWyrGKMN/sDM4tY6jg4WxiFmhm9CwDA5Ec3VGWNqLVC\nUhQDOResUdqoRV3nxZkXoVyLFi7WMEASPOQYx76fIPukz6NS7cR7H71HAKt1IZW/GAWFUAqMsYfD\nYb3aAOM4uMPhUNeL7fa078dhcM4FpVRk/c533/vP/+FXP/jgg+12/aUvfen58+dNveyHdr/fOzeK\n0mVG+IgoC4V470GNWmtUhpkRoCytjyHG2Pd9Veqh69ebpcje5P0cQpDMRtpi7Kx7t+/7uq63260U\n2LTWIpYaQpD5xdmLiK3k1Cok5yf2ThCRXPXk1EQswVyOAsVjcQJkOE0dkx9ka8hHzs/PVeppzUs3\n48855Jr35cgizItfQgTJ6iT6jskcS24tRh8RxebILst167z3JZzPWybv7uTmg7Q8EpGgoHVda2O6\n/igMV5V6OYXXWhWFUir4sNlsOE4SAW6IGqMxSi5E5C1YieToNKpYIMrdbnc8HovCnG7WWufRoJOF\nVEllak7bkTpxjJPhs9auVitjzH6/v7q6ykZD1KsReblcbDZrHxkRg/ND13d1p7UuEQmTJ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R7mxEVGK72KQQr1PXJyIKezgHoyrhb5IeYdLyyLVrmr1wlhnEpLylZpUnSes59XsopZqm\ngVQPy4UAcVSUuKxx1sWYT0kCSn6dyS2fFbAupupyNlBz+5Yhwbz15r6EAXwYM9wilfzClNZaFngQ\np9JGsglSOzT+9fFvSink18Lo7KRVUqk4HA6icHj37t3T09O2bR8/fmyMkbblN9988/Ly8hvf+L39\n/qh1LyqxZVmjDL3UGGMEFDGz6P3onPFhjJ66Y880tcwTSJeepLBGa22KqkgCzSGEoWuVMmVdSSub\nVsIRuy1Op9v4moOYBw2Uypmc0p1P4Kg4I5XkO59z5bnrQkSTwascJsihpeknr+989MzgzN5ecGHn\nZLaHzNychgDJmsuBidh0gYaKNKkME3iVLf78oX7CzWbTTFPlaWLiz62wXJFgozHpbcj5y5nL9vv/\ns/VnwZZm2XkYttYe/uGcc6ccKiuzqqu7urt6IogW0IQBEgiGRIgERcqmRIsRinAEn+gHUU90+N0O\nvzrsBz7ZlCIceqBE0xESadIiJVCiOIAgQWJqAD2gu6uqu6ursirzTmf6hz0sP3z/XnffLJ6HjJv3\nnvOf/9977TV+61tqKpABMPdzXFTRzKiNhENXA15N6fytD2RtjeqzrW9wpd8ilfmBmopFdUpTnRrq\nyR00mdiIpbs4F7dtjPHeERG4GtgIG2GBUQH61unaGmPQIJlzFknGkDGUc5yjeGcccnPEknPOiTIZ\nkr7xMZFkss6QcESVydD5xWnJYWRaakcxztI40/qOehbJ4NJOKY+z32xWY5iNMc5vvPfHYU9imtYd\n9oOxJJlDnLquI84kJue8Xq+TEGVhEkk5pyjCZKy13jCZEhVx+VeEyFgSYuuScEiZhaw1ZxcPyXCp\ncBGmhKQUbm5uzi9O3/zMs0ePH5CYL37x8+fn59fXt+M4vnjx4r33fvj8+fNxHGNYcLGGUtv5Vb9p\nO59zPh73u93heNyLyMnJ+tmzZ1985wuPH73WdminSG+8/tQYQ2yTZBJmZ7tuhdTf648fXb580XcN\npbjZbM43J/vD0bMlc4ez8oXPCUzYprBBiwh4m05OTvRg1pZpriYH5qoIFKvWFl8aaQEa1iDMlJRy\nrjCKtiApNDms6j6VKYi4MVf60PUg4ODYgvFTPw99DlIS8prqMcYgBLRVAy/uB0xd+jhmaeFaaGHV\n3kghYsYv1ZdV3xe5DfVrF8tNy4i9gNFrclcSW6/XOWcSU6M/2tajZhGrwaRq+9u2bbo2Fahwycq0\nxi2x2jzPn7x8ebPddl339OnTs4sL6323WhHR21/4wpOnT3/6p7/+T//xP3/54upw3DEzc2ctW7cw\naNAySd1Yy76xKTWUmcWkOczxbsg3uztcYgqT2EVTpRCHYSDju1UPXGXfWWyHiC11fSMYa8SC3tgY\n74oRkEbVeFpxyBUCU531XCrcn87r6KK58vV3XI+xNB5r6kl9fymzTWHeuRryaIwBbgeZaHhPuKym\n9bSwlKpZEsYsvOswG3ANNPGlWl6vJtULDm7bNjnL8XgQodWqx3wB7wESxVy7JucU44LsVLia3t48\nz2gtTAWYaEpCDNVXHFR9Cmut3ucrwSUOm66vWiNNUeojpFL4hbAys84Zk1J91aBHLbS1hpgNO0wc\nIM4x5JRD1658Y2PIMcYI4moxkkMSYUxCYqKUk2QjlAxzFrLs2HDTWGJMnYgSU0qWmEhivKtvNX7h\nFSYiZm8ME3q3mfu2A/Un/CEutfcY78aCVK6niXOYxxHmvDlxjVvG/npj1TXGR/b7/c3NtjtZwUsl\nzpI55SCZ2cg0BlgsoaT/1g6BL/waxpi272a7sFHEsMyVN9Y0vn3zzTdvbq8ePXzt448/7vv+wYMH\n+/3+/Pw8Jfn6178eQkJKB4nri4sL58w8jymEmLO3VoxIlJBD55spBomBrPHGkjXeGuecpFzcESbm\ntm3X65Ou61ar7tnTp2+98Ya3LCnPYWzazs/JmZadl2pIMU4NYnEVKvweGDNTKiJYQFdwzPhepAFS\nQcTtdjtM0IClgXnQj6hasWV2DBLvVKXZYahc6YJRbwxERH3f22pYF3YBODE9LzggwHnaMhzWVNl7\nIrq4uFDvUN1WYwymBeLmFVOnrqq9AyUv6kszdYodlar8rMZJz2/MSw6fgKkzBl4R3GiM9g45qKfe\nutb7xrkgsoDKKOWco4j4tmmaJuY7qBeTiTGyLFCp1WqF9NUwHN9//71pGp2zf/Lf/ffOT0+HacpE\n73zpK+vNg9/73W9+61vfOhx2x3GOaWaWVds5j2y5GBZjrHOePTOzITscRzMMU5gpRGFC5paLy2KM\nybT40CnlJK5pusa1ROSto1Xrrc85sxAxG7Ji4GovPgoUNRYcyAAoN4hozpkox7gMnKytkQYAr3jq\n+t+UEv/g+3+o4XAJOBZZB4UaGjW0awH4camgLCklzHbEhqEhC/SO+/1ec3S1LsZ+q/HTmEaq4r+q\nYPwpV3UUFU1aYIURqGLUw41xbeuXCQmZUgqQOswo896hlxuNCwpsdQUUL6XWKiKIUTS/p/BWrtqe\npAKF55wl3E3VlCpT1/c98irYPxQSmfn09BQJd70HPCYQqOrrHY9HETk7O0spAUsd45ySGEMiGBAO\nsI2kFGC1vW+dM1liyhkm3RqTckYW9WSzESKMaiARYhb0MIVFg9cekCtQbKomW8MrhFZCdwuV2VTg\nScMwkc1mA9233W67VY++bkDtD4cDnKH1en11dTWO44MHD2D4YaQfvfba/njApAmiDOwD0qU3N9uc\no3ONSDoex5wjBoJJNswWkNSk43StxSmC6CKbzwVvCu9Z87pUUDC51NWoomfc7/ebzaZxfpjGFGK3\nalfd2jgeDsckGbOmgKqwbJw3RsgastYiZa/+4NPXX08pOabVatW1LRGFMMeYnG/F3EPTQZurUVd7\no/+N9+eN1d4MlbTH0u9V8nL1naiOEBH0zSgnt70PboK7Zu2d36AHNpcJEdrXqSpYz1SdeaOSaW+b\nHpVLLlgnHK5hGHLhwNYAyFrL5q73A8oHVpkqCAPCESoZe01O6P0QEerfSOqozSMiY0h7D4gMpYzp\nt4iCvG/Hw9G71nv/1//6Xz8/W9++/Phw3O7322maQpjmOKccxCytmSLStL33bYxxnkLM6XZ/EBFj\nqO/79cm6bRdf4eLRQ2vt2dnZX/s//B+Hw9CvV0TGuzZG8a598eLFv/j1f/47v/XbMYbW28Nu3ziz\n2aycsdM4ODbrTZ9Sut3tMhfXOWdnrfPeuQYnDnFC3/feLw18c5T16rzr129+5mm3bvrWn5ydta49\nOb+w4oY5NLZhxynNbJJ1lJO03dk4LOuPazaNg1LCOqccwpyyRGuACmbV7fUR+7e+nIYIuTC9032k\nli2EjFLIkWCckAqIZRCD9j3gizFxRP1c7btUi4JMnR4DtZM4LbUJTQXbpr9RP8guwARYLyQEBLU+\nnChN16bExmQiCiGqYeMqVa1nGJZS14RLGSyXV60mpEpmLo9fkQLo2WNmnVSEJWVmdD6BV0qvrGpC\nXTwNhNMy4Ad2i3JG3tIYw0SACDprc0o23WEumcRYcxcxwCOwWi0rgHKcYWv8PhzxX9CEyMKx1FQr\nKURGhGLMKU3jeNv3PWbZIfpxroE/Y60dx93hsHD/rFYbzExbrVbBN9ba4OaT9SbnbI093Zw4Yxvn\nnbHeOiKybCxza41brahQGVnbYVlOT9bQOCmlYbVorhhzimae4hyXZGyMMUlmZmpbLGMtbMYY9D9R\nxZ+Gt8GsNk2DbLP6ao8ePcIHu1WPD0IIpylw4Zzuug5Z67b10+E4TQMLtYvJCdaY1Wq16VcFlOyY\nrIgwLeDHXHkAd+Fd29YuvP6QCxpNA4Jaretv6k/Jp1Aw6vPhMRFOIa4yVa+bwi9V2muLnquBMiq0\nlb2/xzai/4Vt04eNpREbmUP9Xi0hG/squRkupRpGzbbmJPR5a/MJrhZYsjtRScmYCoUoZL1vmm61\nWs3jREQSl5ykMUZyPu4HIjJilhYfIqaMdY85iiQRJlnqsoYtGe77k2EepmmKMRyPR6Jute76VZtS\n8N66xlvHxtmUUkw5ZdP2m+3uuDk9+VO//KefPXv2O7/zO1effHJ20VAMImYeQ4pknYlTJuLOd+Io\n5uAKoiTGiEGXKSW0GTVN07YGAAQSezwe5ynenq5CaAbPRDI17TjOJ5sHDy5eCyHENMeYjE2rdTMM\nYbfbGe4AHC0zMy0zhzCR9j+ZpWkxi8RAkPNc6FQgGL4i4Nd9dHUGiSpHSYMkU5Gi5qq5qbYWsbCU\nahyjzr6m5jSix9dpK36+TyugVVAu2Tl8NepsVCGwNSIxpfVX0whcUXGraVGpNaWjSK2RjpOAHOOW\nNItdW0G4mQqiyxXkD3dyPOxUEeQqw6YpTa5A3vWfckkeQrshGJIq/1Cvsz6s3nYonBH8qXqbNXdj\nA4XEOOcdHQ4Hcdhfa9gyQV0lTB3UuDOU8Ym4Af1efcDj8egKLwPspYgg4+oLnSvcdpiT3W4Hdz7n\njAk90HFnZ2e5cNVoKgDS5Qili8RMSFyprc05G+NEyojFkGOU25sdSFqLNhdjTFM4y7nijWZmY5yu\nVX0QAAdVt4wKVeB6vUbv52az8d7joZxzb775ZiiTh6SMWx0G0/smxmhooSkxxvRd9+DBg8YuqDY1\nJNZatialhNn2dYYNRUrchnpFur+675qE0FhWJV+dGwT9egX1RYD2xuHVeMtau9vtciFfMAUH8YrM\nq6LPhYXIV+MSNI6p70df9dhl9cZc4TnV9ays5l0FAhdEnkPNWK3EuMoFqVTjeY/Ho6lYyfUgiySN\npTgvN8PMlo2IcOau67q2hxEdh6Mt5lAPY87CbLxBEwtRqZozGSKepkgMFBVzOfXYHTyIFLwr0VL+\nMMY4Z/u+//rXv/7kyZPf+63f+Te/+a+t5BCCFfbeisj+OFgm3/pskrPGusa6QllSgsNpGlAxt5bH\nMYQwMTkWG0KYhiPTzFa6rgkhXk+H3/3mH/78z//ixcVF125WdpVlcp7m7fDwwevb2xFpnlwgDDFG\n9D8ZskyL34+Xsh8g60sFPFJ31KgD4UJFAIpfcfEfpdD4a4UDGqdOT2s8AfRnLpyesXS/qgek2lwP\nhu6i3G9So/tejwpTLGy++CVupi0+L5UOifq45vtYnRiB/bOaN7CF21txGWrPcNt1Xl5xhk0hJq80\n4+KLoe1JqrKQXipXbUxwxIBthVmlKiUihWpISinbFT5WPedqmPEptJ7U+pRKvc1U8BVVXvq2un4W\nY+z7tlYx6lkjYVWbZyiR1WoFOJO+tNCiR1TDboT2+KySaULrweypPpKC1RThYRis8cayNT6lhCnd\nqCfnRGwEDC4kRiTV60NVBBxLkGGMoYINFhHmu4yTqiERWa1Wml6QQsKLbk1byqhctcfp4dSlY2Zj\nlp5zb52KB/of9rdbVaA5LmXaTGKMEcOqIlMhMar3Wnc2VwSGtVmiqn5Zn3Yp5OXq6OiGukIylKqa\nMRemRyoVLF3YWpuosNUrr3f1ynfpXyE/qQDkirFZ3ETtL6QKmsTMxt4LraSUJWoHUVejjgv1W0yp\n2WABEWHr6YtVpweVzjwRaX0D+2St9c5jK4fD3hWXVe26ISHKJIixKOecUK/KJEzjGI2HPqR5nud5\nDHFq23Zzeto0DfhrrGmICKSZ++PUd+sYpsvLy/Wqe+edd2ymjz/++OWLT+b5Zo7Re0/C8zwws/U2\nxmjcPagUCRtjQBdZVDGASybnbA3FGGMaU+powcLIfjd/8MGHX/vq/qtf+anDYffJi5/M6di05vHj\nJ5vVeQzbOYx6BOC0pwJlRDZVt6DW56Zq8PfVeDl9OVWa8I8gBMgd5wKAecU4qWzVl7OFYLEpHMB6\nRKVEM1wFGagzmdKLYCu6qk+LuCk4UT0htrQs1KfUVugDqjCv6puj5VNvozZaqbQF2FIvUROo6447\nr9yoO8JWVUau8LfqKundqsS7woCnyqX26fBcekLqCEyK7dfTq7uuZ163tn5/fd7Usqr/kSr0ERrx\nfKE10625vb3Vuosir6y1IL8IpUcYXwdUCOwNKm1wddG8BU2HqhJeqDvCIupFYAxOT093h4MI9d3a\nGHs47EOIzL5pfOM7ccRMhqMxTMQp5aZxbdvGvKhUZnaY/lJYMplZq0oiQnQXN9eG1lWtxFKlrTBO\n3pTUh+KetfypZXNjjLXsiL23zizZYGy9BpRLnsosCAJ0KxMtKTiF1cB91jvRjAWEU1FCtkIb6YnL\nhZtKJYQq9Je+YGlwxGD88NXaD6RXkCo2UpHDIXXOqXLnqm70yjfWvzk5OVF8kHq36pTUVgG3ZOlu\noK1UqXU1ZrVeylXqoj4UVACrar34/mtRVsWpy2WgJcmC4uNCpXjPDlWOYCFPssYYeEtJcsrp5OQk\nUULJ6jgOIqkLTQhhQJcxm8vLy4cPXiOiLMGYqbEmhYWgchqnF59cnp2d/cqv/Mp3vv2t733nuz98\n7/3b3bZvOzKOmOeUGPCD8hQ5Z5GUJZ749Wm7McYIpWkenLc9tdMU2CShME2jsWK9GcdxjjyNWYT/\n5//5n04zfelLX/z5X/jjZPLvfPM3v/f99x9djDFQ0zSmsPtY2xgDDVayNdSICDEqIwErb0pgqkbr\n32KNmmqeCpeslyrHXHmU+ADOpPZX15BQPb3qtuBuagupd+BKF7fKXCxo5k+7M6rl5T6XIpXWJVW1\n6s/qRfRBoDK0m1XNpCm4c00S1qJc2ycuYSkurp4pVRFGGEddXCoYBJXU2vbD+VUfTX26ZWPcHfhe\nS3+1T6C1gfomX9ld/Y3aeCn1MKqyK7VmUXVWi07OGeMP4ClDfeBLceqkQD/gxiIGsqU5VOUKDwIM\nIcCKgAnAMYcu09303ocUG986N6ckzIbZMpucEzK7MQbnGmMYfVHGOO8lJ2qaBp0WqP3EnEIIrrBv\niEiSXGlnqVfPlLYzmENXukR1B1FJYmbt9VHzoAuuHoAxdHFyutmsvHXgCfTeW2NCCF3X3xm5uwEz\nbIyxZWIQ4kVsH8Iy3cH6mKhKpcrhq7FtVDH52wpFXdsGPHJtbPAVgNvUMas6KGqEail6RQ7pfkgk\n99FJqr51GdVvS4W0V58OG7paL7mHWoZVfanSKLmQ+On7oYKAl8q063vAsYDr24VA24pImGZN6MWU\nYB6apjEy1aZInxTPgqpkFptzjgG0ql6MMCbDOQu2jnmep8PBGNN0/fF4fPa0FZGYeJ7nVb+epgAI\nviGOMfrGP3ny5OGDC+fc9fX18w8/iiH3q9aQnY9T0xtHS0YdffpYGTCIusLJ2fd915mmiVFIJM/h\nyGNa+804zuMsq+7iyWvNt779vX/xL3793ffe//D5T/74n/hjX/7S1/6dn/6Z7/3h+zfX29vbW5zx\nBw/PjTHH4+g9BIyI7jQVgHhYEF0iqXAGtUVYrFEswbK2ScOKqOOvm63XUqtDJeIO91l1uUoa6MdV\nUGxpglNLRgWADypi/aB+kUq2iEBnvXI89MHUtqkBUB2HeulUWqy59AD6Moyr1jt6JnURNV2AgoHe\nmMZY6kHr/eup0zVR1w+HX+FSqUBC9LTbAnNPpTSttQTVmyr6UnVOpAqnbi3YYHHglxWTJR7iuoyd\ncxLhzWZTWzvNK2r7Pd6d7qOBqeKkwnrqnSN+IqL9fg8QHawOxADQXle4YjGxhovVPxyOOXEiyYmu\nr28VO51SSDFkSUxCRGFOxpI1IpkTJedcZxgaPKW0Px4Oh4Otcqp1kJHzkuCl0v5lSt3FVsVLXSaE\n/kSEB+ECQMWgDSoYtrKJJoQwjqP4ZXA17qHrOmDG8F2GlvFawqSDhrHyXdfB5zscDpobx5tt4RZR\nX1CKd6UaVuVQnYxXMq760jDCVPk9U0YYu4oByBbePC5BthSYktYGaofGVHWCXHVW4E+oj9ry0vup\nLag+oMp87bbqseLSWaVnliqnTbWW3lttv3WhFE/By5Rem/OCFLXWximWs7Mwl/PdXI67Uy8izTJn\nB9kwqypumrbGGzDerr1LKUzzMM+zE8k5YyLfcs+RUlqSLnEOKURr7WazkRxvb28fP3j4ta/9kRDi\n7//uN58/fz5OwVCKMicSO3PTZOi6xjeYrX51dTXPc9uCbKlbr1fOubTKYU7e8BjGeZZTe5ZSioEe\nvvH4K1/5zG4X3n33/eub/e32JuTwi7/0C9bTO1/64nE/v/fee++99+5ut9PMMzyEUg6XapHv8jcq\nQmo79G2LK6CAMW1rgDhiHp2eUhVWeLJcmodMwR2oolEX2JQanZoNlU7nHKbJ4b9N4Vq11bAfVQQQ\nX1wcChqWADew2+1eOYp4wXRzwQriCAHGNs+TurGp1Lr6vof3Wh9mV0YTYUHrG473iUbuzIO5OwxY\nd4Upqp7N1dAEZHtMxagPSd1ut6+cauccJrKrb8hVRlG/Tk0aV6mPXLKIqo7VTEoVQlHl16t2wAWB\n0q5VM27j5OREStEeVwbKAwZpt9upQVLt5ssIvhACWu7V2mm0gdXY7Xb7/XG1OWVmnNKLi4u2bcZR\n1qd90obK6TBPyZgM8J733ptGSlXP3FjsrK1gx1RoFadp6YtCwkefUTN1OCCuvEIImIMANs9QiAzQ\n+qN5ibI7i6NmaDFjIQQDbl9aLDpio8XIGUYLTi6BPnpdh2EAWyt2EAcWwaiSqNaihfOoz6tbj0/V\nMlNceIPu2loGYHhyleyKZbZIDaCFu43Eg+aEpbzUk9PfY4XVkVLzpkdbPcX6JqHcU0rjdJQq5DIl\ni67LG6umIrUrXGWtcWPr9TqWKRVcZReMucMWOYNBduPhcHjt0WP4wcaYeV6YOVNK/s4mcq1h54C6\nnWFm5xvvfdvmOQYiF3KY5znn0Padc8swX+MctAGmhTVNY61nphhiCNH4BnnvYRjaxp2dnV1fX7/x\n5psXFxdd1/3ar/3aRx99ZDI570MYUoIZy957gAuEkoaDp6enm80G2WZr7fE4pBwO0z7PC5NLzubk\n5Ozx4yef/+I77//ww/1x/Obvfzsb+YU/8QtJaJ5lvW7eeeedly9ffv/737+5ufnSl7704OE5DE8h\nzSORJQnU90tCXqpX7bzWL6fgLvXB1aeoXSR1T2oFp5VMNCHRfUQ/ZAiZHDCaUKGHggsMiYSNQeuP\noh5MhTHzZdyWRg9qQU1poc2FZlQKbR1VDW6+8GGHMNVaSY8ilVjKFrQ31kFT0q50VkF73tzcqFbF\ne7Ca8zy7soBqlXEbsXRvvBJ5QAXUeflhGNCzRSWYUwuNa+qCVA7IssfII4EhMC3QEpz2hqpA0zk/\njqNIco41AnPOXVxsiO4Ix6gKiLFlpkpq4Xt1WBFXZDN4hNdff/3m5gbNpAqlgyHHly58JNbq4Fo8\nL2zkfr8XkbOz0yksEMRxHD/66CNYaywpLB84teBCLX5AWRDvPezlME1SWkpXq5WShujXMbPSHMD8\nqHMApY/ijStEnLr+kEZTzXOrtGF++fLlZrNqS2xkrfUlELQ6fygtkuycOzs72x72bdu+/vrr2ERs\nx4MHD9SHgKRhYBVuQJHooNojInRu1faVSzFMQysYVFyh73s8oBRaVSQAAHpMpXMLIB1ccBzH3W5n\nKpRpzhmrrd+LxUG/Bx6QS2CdyktKuF9rD2OMMosjtILI+UbJPVkPKfYLF0F/Ehe6MijxmpnClC7O\nuQxXIyL8t2kaY0i9scyZeRlQdNjtQwh900/TtF6fwPnu+z7N+6ZpmthM06Dp65xjERJnjKEK7piS\n5pawDpJBbewcduT58+fwzK6ud48fPznsDn3XOOeA8IsxjmN2znXrzX4YnfU/87N/zPn27//9v0+Z\n53BME7HneY7H4+icOzlZE9E4Dk3TbDadiIzjBDGDX8VMDx5cuNZfXV5ba1f9pg32yWuvj0P40jtf\n/dGPX/zGv/lXxOGbf/CtX/+Nf/Xv/6l/11rOmdrW/NzP/dyv//qv//CHP3z99dfPzs7G6dh1HSbI\nMHMIKaUMfJMaoVc8m1y1BCyuEuoWsWpK4oo8W71pKr5/KGRQtTfEzOv1WlUtV/h9XFO1WCoMRVoK\nVinMpXZNn2odNTo7pBwqLkgzjDVSR0nDlxohLfdfRK8iofWdqmL0yCFfn6pmydoBzBVconzkDlxk\nCljAlEGouSpuxRg10KT7eaFXXAZTZSpcQSdrZIONUJifsiZTAT7VbogUoASVcE2fjpaS4+IE6PvV\nJNd+CRXvRv+qXqq6zLp6UrC/phppg2o/XAdFN6i82jKF/fLyUgw72/jGOtfFNMcYj8NeREKcmrlr\nWkdinDfONlwoGCB78DDQ66pND7lkSssjLIhhuQ8BQuuiCl6dbtWVlyrClvvRqlmqIByGMZfhW2pr\nc85UFYG4jPOZYyCikBaCR1tKmJq+qx0CHATE0Llgw7hME0csq3ciJUmuW4mPYP01e6b9GGpl9Tao\n6nDCI+if1JwbY+BDqNyqxMJrUZfX3DH33zWTqEcLR0qjWK4wgU7u0J66HQjEpYAUYgErSpWV1XOt\nPi5VmQC5q4Etwp9zzowrkIggg93YBpGxttzqyujrlcMrIsx3GUtjLHpjjbl3V1LuFoU6pIv0Zird\ntbxwdg5hCiG99trrX/0jf/TlJ5985zvfcpZsNpKpbboQwoRJfa7xvnUFbnM8DsfjERKaCm/ner02\n7MZhjqmNWbxvJKa2WxnXhBhDit9/991f/MVfdI1DwdEuuES5vr559OgRkYK8CGOf8FIGJrmP4awV\nsv7GIX+dPlWKQHeYqbpb8EoFHa/yDaHUDJItsCJszPX1tcKBuIqXc2lrwFrUfp/Khwp6rlAVpuSg\nuKI80n3iqjpaL4FeJBUOm/p00f3AXyqcHrRnLIgsjYR01ClVYFMi8hWZP1cgAlf162DFIBlqabgg\nNV45P68oSr2gqXLo0Bp6wo0xAB0ws8ikV9BdTwWVRGVGli3jaI256w+trbWa+fqpqdjvOimEF7QJ\nJtVq8xmWWnuSiAjip9MkUynjLVvpXc55imEO4xwyWCcgR8aYeR7neXaTEeG29U3T4dmnaZrC3Vxn\ntO8gflqenUndLBxXFT8Fy6BfSvW4r2Z0atZLCj1aLo0NuYKiQB5XqxXoL2yhMMCC+MKua62lvLgU\nIcQQgmsbpI80bgajT6UxpfYnIE5gNNCFVXiRIlchPIqWrFVbLZy5pMex7+g3qt+MVVWCMj3RantU\nhtVu6Tt1i+9kle/1gXFJALiKrlu/V0R4vlMjNfQuV6WI+obrpCJVLpRKslTeg3Numu66aFOGwZOc\n88tPXlhrO99N07Tq7+YA6O3x/Zepko2qQq21QDHknJmxLMt9IteSc95utwjxqUxT1YOWUymHGyMh\nxSRZOBM/efrGL/yJX/z4o+dJ5P0ffDcGmcP84MGDEA4xyDzPJydryYbIiaRxmK+vbo0xXbtqWpdS\nNkZa19izpm37MafGdpKtZBNCRB93yEOW/Lu/97vH8S92tqFsnSNmu16fhBA++uijZ8+edl2XotCS\nlUCKkpityFzr53qh6H6+jpkdtIZU8BIVJpwQruoNIQTwTWk4zMU3wVJquqnmgqPKT1HdHQsE/BWF\nm+4De/S/6vurBOTSo/qKms73MRRqUOm+k/Vp01ifLj2uUMRqsDWBqQmuXIWfRORaF+/DYXE11bBq\nurjqVpHKcTCli4Lu+3f6OHpjdeiZCgaJCrGNKbksPdWmJLtF7gHJWCd/l1TbK9aofkDdLFM4ldWY\n1UEVMnIaCjTV3EVVE1yh+6BSbQVw8t6zNScnJ4dhGMcJ01RjDDkLkXRdIxJFqPDFW92LcZhjTvje\nEAJbU/O5CchXK+0ZC+VlHXOATIELJxaOAE6ELRxIuQzKqrO1tYARUd80KbGhajx2jPM8s2803Zor\nBhAqTFRoC9UApRZjqhwdvQeE2vpLWzByplS/8BSoS9VOHpa973tE1YpyBND0ld3XI6xCpcL5SpyE\nJ4rVCFq9lB4uY4xho2YeNwwwoVoU9YHwKexjrazwRcpBTvczHPoItTtlqgkvpqpB1IfCGGMJZWxj\nrX377bedc5x5v99jrgRGXqV0p16NMbl4fWVz7/rQ9VVcYWziXRICO3J7e4vZTiIyjmOMCzrDWpvT\ncnittSlh/m+f82C9e/LkyaNHj9br9X9/ON5cvtxud2HOkpnEhTDMczTGeU/orhuG6eZ63/dpve6N\nMW3jvPeO2Ns2tWR5/fLyyrq4288hi3GWDIvhH/3kRymjLJdCWILjaQovXryI8W5OsdCdW8CldF0/\ne+1AqPeznLvj8YgLqfaHUH7mM5+haliDKl9bXlSS5pq3VVHTrAsRwe2VKsmgOUBsWCiM2npmqLKf\nuWRd9Btru5IrxKqew1S4h6mKWmpDqEpQ35/KoCNVDXrDqHihXp0quI6WQON9mK9W+2u5V4uCnzWX\n6stko1Ty7Kp08v0coO5F/bCqRlHN0nXLOaNuP03TgwcPJZfWCWEikkKVz2RJMLLONL4jouQTCH7q\nb9RjnKuMrj5vrqrEuiNQqVB8anRVYSmonZkRPKWUFGVHxa9nZjacUnLenDYnvrGG3RzGaQwph75b\nt52LIcc0D8eJjQAiGMvgag24EQnN1UTBejt0kbkgsvAgqCHl+0G5ioeqzlyyc0DlaEmvvFAoZY1I\nQggxhBjjmDLqTBmIKWyfXVC5sKO6p1TKG6r9bSlwAtsGw6mrfS/4KBEwF/43ZeKpvRnUGhUFYAum\nAKZOt5irNIBaUFOF6Zgor//VBFquKru55OSttUx3MUquMDj5fsZCZd7YZb+w2lLmKtX7qIdIPQO6\nP6aAlB+hXFyfyBdSSmutEVhWa61FaTzN6Xg8Sl7S2tvtdt3d4+pV/VBwdCbfVzXMJlOd0blLouiG\n5oKyUZ9G/2Vma70xjtmO49h2Pst4HCbggN767Nu/9Et/8rvf+vbxOA7HkFI2TJLNOERmcG14MpwT\nHw7DPMcQwqrrV91ahFOSKQXXrinb733vPd+cHo7hZrvNmUDfYNgI55TScFj8SExvABkpMnVEJIIx\nSfd863+rNVJlpTrfbTabeheRsYV7Ukc2VOBnCPxV9aiixz35QsKmxV6NNKWChkuZPVqb0FyiNlMF\n0SoxcLTV0taqQTWjbhsV902fsygOcs6JZH0ovb7eDFS8njGgaHBy6lS1PqCrOGSttZLuuOlyFWPZ\nCi6o2g2X1WWpT1H6VF1XbXMtu6p94CioS6uaIsx3ac/alutj5mpCSc7ZmCbnO09H1ap6vvVD1ada\n70cfH29TT2KaJpS49VMIQerAojqlaLy3OUfJkiRQyMxRRJrWGdN0Xef8Aj9p24GQbooyjsFaQ+au\niTWT5JxR14RcYRIa/jrPCwVOLUKLoiz6lyrwN/p4dB/VymrXwV0EllLOMh2Oq1XXt52Gj8vWG6uS\noF9q3F3+mcusB7Ucai/VLNW5AX0blh1TXXS7QzXC2FQdEbqP+/0e71H4DBXgTG0MpOpGylWRT4Na\ndLXXJ1GqvkN7HwJTK9lXzr7eucoe1qrrm3qerC15XZUZqlL9UthaVZW98u21rqely9hoLc0IQigh\nomGcrLU53PUgnp+fn5yc5HBQa13HRrSEPszMUmk/5pzBZceLNdJVxRtiIYNofJdSYomoc1tr4UQ6\n1zRNI8Lb7bZp7wC0IURm8ws//4vrdh3m9P0f/GHONE3BGh9jPB5Gs7RSSM6Uhygix2PrjM2Z+vWK\nbWMNn3QXZP1HH34Y0uUYckgxU7KN921+63NfgIEAdCWEcH19PQzDet0DE8RsoFqliihgU+V+jKH6\nRwVp2Sz1hsq9LhoWpQ5lLiAiIKRRj1XvXqOcOlKWUgJNKaG+osKRSu+kdk3WuruW+FzFKNCwemOv\nKK/6+VWnaHCmTmLO2RhqmgbzENXe2DKMXJWRFG4F/BfYYgBdEPpg9V1pAKrDhelwrE+vageq6iX1\n6aL7xQaVWk3u6/OqXlATgpdajlx18iLPozNA9Za4OAo6YdpU4OBXhKZWWHcWoqRicknwyn0aTX2P\nLQBx7NowDA8fPsTWhzIlS5dIod5UoT/I8Pn56TAPCPWAD0SLwzAMiDmIGueW2hVydSQG9oZKui/n\nLBV2Q6rYSEMZVzg1uNDhqMm0ZQ6CItdFpLajEI8lGrrnQ2R1X9QdwYKvV2tNNnhbkPckKaXxeNRT\nimuicq6bwlWXur1PbaVY01xlI+sDCKZLSIWeglDYEDTBSyXfWwuP+qyIpVxhoZTK+UXgZavMrSp9\nlVtfjSxiviMe09OqxlW/jqokoS14B/Xe8IMvo3K1VUMvUt8k1qF2qelTPN+L8yEQ4ywiwBzGKXrv\nwxyHYRiGYb/fr9p7RB76sKkqTVUV/myMgBu3Ss4tmfxYCF5VQWMdWMigKpyBL2uaphEytiA8XdP6\ntktJ8hwa37311mdF6HA4Xl9fXV69WK/7cTyGEMZhNiYaS97HlBZojyFufHuW5fS8Z17yb+M43myn\nRFZYuq5rLfk+feMb33Cty0NGSfV4PMIaERnv2xAmfVImK5IXmr9qOmjteeQqVFVV4zb9apiHOMVE\nibOIyZY4Uj7u9u2qbew6U05zihL7pnddC8QnfBMp+XRTcagg/4DiP1o7jTHeWO89ZwkmcJac86rt\nuq7jLHOK3lhQvkZBeJRFbEoBExOc67y3OWfnFvSkiGCGQs7R+5YoW2KibMmJEfyb5pBzNOKyiUZM\nzpESCbMznDMbokSEMZDOGAzlyMZY5LtDmOZZUhLms5OT7XbbOHd+ft56PwyDpOTW65urK9c0rffC\nLCnFnC1zMuZwOPgybpmr+P14PCpFGIYYQXFAyutDm8vsSzUGmktk5tPT01hIaLRAHcugCix+KgWt\npmlimq1zli1bYjFkxFsPJqspjDmKb13ru0zJJOPYQDzUXqoY1bpAqrC47/tMYtkACYa8izDlnK13\n1topzFZkjmEcR+udc+4wHGOMbd+1bTvOU0oJMUFIcR4nYerbzjWembPI7X6XcwY1eF4K7BJCihH3\ng36XJZaVnFYrI5nHeRmvvlqtvPdJ8jiOAebHGBYWk3POhlhyIibLzhhKQXKOhiy5TFlSCoZs0zrK\nHNJs2TZNIynHGCVltiaFOEwjCzVdy0IiQmAjEzLGWDbMkkha36gLwlVhD4aBmT2GJswhzHOUrBNP\nYiFmxPk3BUJpSitCKDzNmieHsMGKSOlY4hKrqelV4aRSN1JwPGrD+CAMYR3pcoVNB8e5lEF/UvgG\nVTzU/NuCV1JXwxWiSNjCWM1RUztK98Mm/BcEx+olp2VOT0Kc7b2fpgkpRwWhSClr1VbZlAR+7SIw\nc9s26uRBnWLFpjDHOYzz6NqGPVtj+3W32vQUBqJoJDMLG+LMmPpKMeeFYN+yQdoZjyOIl5iZRLKI\nZM45r1ybkrBQTiQChKTJiVrf4WOGnRhRX6Rpus1mc7JZ7Xa7FOa2bYVyzPHDj3/SNe3n3/nid773\nh2T5anvVtu08j9Y1iwdJ4hxIxxMLtX3Dhpyxm1UXEs/zmLKd5kNMMxtHLKu+Ze+b1n31nS921gfK\nIKmbwrg/HEKaiZktUWKCVuVMRMSZhVhyrCrotUGSKsWlvoLb727JkLPWGScpxxAs282qj84nSfM0\nGGe8c449SZqHI7uF8YVLtAVtiGo5RFmqsMMSS0yWzTyMlk3nmxyTxNSuuhwiZSHJ0xQwi5OZ2lWb\ncxIh71vvG2ZKKccY2tZba4gykVjL3rfGWJHcWE8sKaSQQiZx1hmSlFPjrclCmUgyE7XesWMylGPy\n1hKbkCILGdg+yZKycCI23tpuc7IhkZRjTo3zT588yTENh0Ny/vz0lIXmGB6cX4QUc0xsyDvfGKYs\nOWdwYL8SKMCK1EcLARYVBpRa3eOkxTIMF6tNhcmbyhzousYmBZSx2+1QgIGrHkJYn6wzkRFOlFkk\ns8xxykla11h2zlFmOY6HKc6cyTrTtx3usI4yU+FDMuWlCUbrnWGmnCVz23dsrbfW913ftplIUjKN\nj/Pcr9enp6fjPOcYXdO0bUvGHMchpNT2Xdv30zB0dtW2bczZGSPMbI1hzll0IoZqkHlKTE5EpjEG\nk4k4ZxFDbdvlPBGRb1Ynm9U0TeM4pDi3XceS0QjIwo6ImBjkuYJUYIhE3nhuXJrTcNz3Te+d8cY1\nXUOJaI5GyDJNYXLGWOtizmyoa7whypK9tUnEGbK+MURzjBgwYZz1zqFowES+hLxzipnJYFyvZCNE\nhn3XeqKTkxMqDRkIIuGCaDAnJdaH0aojV9iw3W6nDgo48TDKeZqm8/NzxKbTNK3Xa1CSN02z3+9R\nNZnneb1eK0nz48ePb25uQgg6REALvUB81RqfK+aRVCYh4VnQDiVVdqRt29PT03EciS24UEuOhG3m\nOYzee2NsTHOId/lSrvggcml5hKa5vb1FEHN2dgZ6vVRaF/RlC6+EtvQpFhGitd3uVXdlRpw0z2m2\n1q5XPVuKOTRt07APFG1DTDlTMiZ6l3PkSBppuZxyiEkkCpOIZMpN60MIEoktG2uJOSWmzNb4YZi8\n847d7c3ND77//W984xtt08xzzFmsdZQphAUHn2M6hsM+72KKLz4+WMOGJYZJRJylzeOHcZqnafoT\nv/QL7777btPbH73/wyg5x3gYj411602PPXLwSdJ83F633l29dO369Pxisz/uXDOHmxcnZxci6cFZ\n//rrb7z12Wc/87Wvvvu9dx9ePHONtd78+MMff/TJR8M82s5tj3tnbcihb1trXErCkQxTCsE6F1O0\nxvhmmdRFTF3fHY9HNgQfTlKmLIbZrdcrLZZmk41ZMnWN85qKufMpmCSzFAdZfStTGOnX67UWopY0\n6DA559iVhFLKRAQSSYivN46sQ9FGeKmTiwiRGINOVcwLH3O+I5lPpew/HY6KuM2UmMl474ydKXnr\nyJYajIixC62nNRbjSOAdG2MMmXbV5oKAgGDiThKXWgIxM1OWmFIKkR1GX7NkiTmqjlZTVKcFcmGu\npPv1fw3n1UfQT6krretvCnqithau6gSkKr+xrA+JR0aCiYWFiYTEEAkN87g4DcbaxrWW4fbmEOuq\niXqOaF6uH42Z2ZpxHLnqlDI5ByIR+Xi7RV6LsX9ESSQDkksE1jkyBuPzeJ6t95xzMoZ1BsEypl1e\nWQfsu/celzEGbj5Kd55ooirxYgq5HFegDJyKGCOJtL6xOUWKMcZJllx267xzhpM1hjhLTDGHGHMm\nyiJEKcViACx6xcrsn7mkfLGDhrhfdZp4UZi4ShpVyShhthVOUr2ZWipMafeuMTVyvwRiypwFKo+s\nLgXSpEQEmhWYru12CxKzWJq0XOFrEBGw5aLdGC1NQP3q+zWSkJLnVLHX1GssJA7qn1FJnCLRoiUr\nU/iR1d2RkvLF1WBCNLWgaiTGqEUBGEIAKWvhwXJxKUaYwq+oyM9Qxs28cipx/UTCzsYY98PeOUeW\nMovlJBKIIuUklBBCwjHVeECVQEopYRon26UQS4CgGTLLTULP4CrLKolTj1P3d7GXbskbp5QAxwph\nYsvtqn3cPzk7P3n0+MFv9P/ym9/8ZphT27aYY0Jk+r5pfeMsz+POe+stpxzDNEzzaCw9uFgP4y7F\n/R/5I1+9vr6kNP2n/8lf/P73vvfk8WtXV1trbdM34zhu99vD8Xj+8KJd9ZJCJglxytaaTNZaK1m4\nzHSpglGukNIq2EumTXddt42K567K6O4zVZHK1K1bJQ+gzlG4Twhf1xL5U8VMFf1MqW1bkxdb5Up7\nHZRsXXFRxRTSgh5GvK9HtIYXqxnQz5oqtYg/odsxl8w4NKkULhBb0J+masjgKn2fS8EZJb5apnVh\n68NjSn48VyggqfqcXOnboCpfkatxBlLhaEMIyPghtKISaTnnQgjaalSfsbpyVk6O5JxNOasqNK8Y\nS91x/FdvSdEcXMrv/m6GJuktmar6jYWCFtCy+StSsV5v8JtXrFFdBeWSlbIV15EtI671rnSvpUzS\nIiJbiG0UWMylBwhBc+YE342FJGVr3D3JKSWBk/VG+RqUG8k5R5L0EHFx47iCKqiKxHVqC1Q/nf6L\ntIQeELnfAKCHi8tQBk3nptIQrerblCYbJermivhRRT2XMaZt24LrFgNwc8X7RRU/vWbPsMhchTWm\ngH1s6buqJYorNJPuF+wKUi+a+a9PLiKwYRgwBESHrKsq00WuRUgXXA8UTKb62VxcQLxg3eFPIK4q\nlUInxhnj2DrmSHcvQyTYSfOpzsh6r5fzZe5+5or9kvleB4v+AL1U+6PFFTam0G6dnp5+9atfRYno\nD37/m+hugsVt25bZEoEN2UABHod5jEJmWcO3Pve5OcX/6H/7F3/uZ7/xg/fetc69++673p+07Sql\ndHNzg5D3nXfeaZomTCnnHLPknFvrmdmwySQAf9cWSCrY/Sua8A5erM4CJOkV0CQOQMzJu06lhCvY\nGMQF2ASqQAQK4yG6VyrUkp3eSs45STTGRLnXVl0/Q76PdBARwK9VtvRStdLU86lgca4cf76jIfi3\nQCpSAX/XF6zVHxfesPoGqHiyqspNhWjUtU0VhFcqU4STXwNMaq2k51+XCJoFPV5wexdj42zOWe4D\nh/Q86H9zNZaxtU5/rg1huo+twnsy3bVt6VbiGRW6HQslhyKPa83rSrNwvYBcIeZThYaqbfanHwf3\noBdUrSr3XTAqUA5guKd5xl6AD0J9mhoOwGX8j7XWmLshLLWaUFuLoulCqcnMdOfjx6o7rXYp9N4A\nRlBTpC8YlVekRUrTm7lPDKFKqi43pgIHpWpSEcKCvu83m82LFy+4MFmoi4B0nwpqSgkNSYCV67bq\n93IFzrzTysaYMqJJf7mc9yJUtY8F1Q/DQyWEwgLCa9E1qaVC70clsP5BbZIuqWpzUyqvcEde0Qzq\nmeEj+rBYunme19aKs2zLCcW9lYB2OT6GjTHowhZPGaDpCrhhrc2l8Yir4nHO0jbe8B1USkqSti4W\nqjUyxsS4bN8wDJLCer1+6623rLU315e3t7f7223OyXA6HA7zOBmSi/NVSimkgW3IYrbH6ThO28PR\nNj7l8Mu//GdjjN/69h/c3l4bltceP9tuZ2Z3s93+5Cc/cc49fPjw7bffnqbJ4GGrNU+SkaLEK1XQ\ns9qhVO+QmZeOClugcbaQtaBaqO4PDnlM0XAKeYk/VK3j48gpa+RblPvSNsv3e91V9aiqzTknsSkl\nMsrHfmfY6g+qM4iQSOWVi+9Z54tNaUcH2IYKnEMV2aKFCyYQ18epCyHg93qHtmrxq1dAUaEXFxe5\n9DClwrBSi2Yq1JBUqtn6valq3a1dJNWtRHQ8Hlnn4jhXO5u5zKPSp0P6IlXYetWMAN1pMo0V9xWT\nmjd8BVe8A/XOikhGwbYUlqQkVaiQLKg3AyWiq6cK/RWDoUcOD+i9v73dq6rVXZASO6qeysXbxVfE\nisnwFWOpWkyPsZp8zGTC1psqs0oVS7oK1St2hYq5RRZLe4YML3KiaasamFdbytq23dNlRDkvlMy1\npOmzU+Vq6AexWfivWlljzHa7VXo6MJVdX1/f3NxcXFyICCiFAASA/cDRWK/XIrLf70GuenZ2BqSD\nEs4qFjFXUUttcW0ZSKYiIXfu/7IXygBrCkO2Hv+5TJ1XK6WfDWUmEwIChQXmaohtHeWrB6nCXJsi\n7euSyjeFkGBcLNxu8AQeDoeT800yTthmMpnQ7soiKBwQV16FE2uIXe9DCJi/tQj2gt6+Jw/FGi0N\nwvWf8PgAp9B955uIUDHpuqZtL1gEkvPs2bOf/Zk/9t0//PZ7x2Eccb6jZdM25upyZiPGudXJWdOv\njofxk5cvt7vdl7/21WE4/Bf/xf/zV37lV54bPj8/ffLao+9+97vn568zu6urq+fPn4vIw4cP+76/\nurrarDrnnLeGmSWkEEKWzBZwCVIxeOWe0/3ZvnfIFqpcPC6pgPr0GmMo0TAMc4pcKCb1/dhOtUxV\npo41TvJlxkwu9MC5CmBzzoWKYzF1dVrDVUwhahiccxTvykgq1vqNapDV3VM0Tq0coZ3VxKrTYcuL\nqzwP/lRf31SEKFTJn+osKnQDXEWTtiLqp2pCEm5DKhIa9R248l71YVVjmtLnqF4qhD5Xp4urmh/f\nz2AsSlNmVZRc4miVdf0I1sSQOOfYWin9mJor04kDyK9q5kdXpk61UTUHq9YdVHoApQoK745xpdHU\n/3jFStky56YuD0AklsSa92p+wI0PXquaOi+XVEP+1CA455wvzPe6oa6Qk+acYwq+sIXqwcPj61Po\n3tUnrjZOUkqPao30wKb7jNe1cVKZTAVjmXN+/Pgx6kChjAYXEag2aHPlTsQ6qCzB9uSSg60FFZ+i\nAu9WwAuXPg2u5pPVckhEAFnYChFuqpnL+hXqmw7DoItZr1iuEA1SJlxwlfrL99Mq9aHGyqjOqde8\n/qFWowCRn56eihEyzGwRD2US0KKmOiNHd9JrrRORHO+WQhWUVFa8/JVTSmTuOiNVL+mDy/36fc4o\nm3Hf95YZzmvTNF/5yldSSsft8fnHH47jmFL01hvrJSWybJM0fbQpHYf9dns7h3g8Ht97772f+7mf\na9tmPO6vr68P++3J5iKVwY/Y8fPzc9h1qjzLtMQ9YvjO7dN9l0IgIp/KqdwlwZVhk0tegu87XOWv\nS5kB+50qCkIF2GCyIfQvVdpWd13uF7VwrzHGkOaUEsUlqa3LLSJYAr4f/Xjve7+0RGi6DPcPIgA1\nGHrmNeZQ0wIhQyzoCr+yL5zEGhG/oiNgcdUldGUmzXa71YOqQlZrWKmmudjStao3po8MH1BNsm7Y\ner0OhfaNS7ORKf0ffd/rb/Bc4zhK0Z76LcwMkJWKvi6dKlPVF3ogVeNXp/euSGOqiFtE0J5VL7hK\nmi8joGp3BCn72tiHhaD67tzWS+TuEznrvqi2MlWtK9/vKam1vGG2xljcZM4ZljjnpmmSMakaRowV\nGIfZsAFJREpJUjaevXUiYtmwUJxDnAPMXgrR2Huujx5IW4CUatvwQ6yodF5RtZq65AK81ANYp81f\nkWdlNsKz42DudrthGMBCjXOqxCI5ZwSy6ABDzAE67aZpTk9Pc86Hw4FKpIKDrwnDWr1yiTu5ZAhN\nxQSD7VD2Cn1ePAtI39WsavVLvcP6W1Lh8uACdlANY6vpglJFHjUKQ79U3eta8+ilUEq3hS6k7/uv\n/ZGf+uH3v8M2GhvZejaBjCPjyEhxnrOIEL6XMxp6VG5VLaSUQMmo504dRLn/0tNRH4paexhDKeUQ\nsohY5hACs22a5uTk7Itf/GIOkYh+/OMfxxhTkuNhXHcOyKFhmKYg19fXMcaz89OLi/PVZvW1n/ra\nt7/7ndefPH7ztceWeBzni97bUqRv2/bJkychhPV6zbJgUvQ+qXKhdBlVgfgyiZwr//KOJ00zS69m\nY0oQgPecnZ7xaFVHqw9S8xMj0rcFCK6qJxZWR2utzg2ypW/GWmuzMQYd0PfqKHqMbYUZJSLv/X6/\nR2yON2v2IBdyIMT4qmRjxYxXW9nz83NVyvCaIaz6CLkqb6SUUMXFmTelz04qNIcpaUZtCqnVvSp6\n9K7rx1VYkXxQnk1NbakUUpVEstYOwwDXPpR5AdZalEbzpzBOVEIHVdCqMiimV7YMgqFPJ1UPo3P2\n9vbWNY12Qapt0/fXBkMKgax6pnXCypRacZ2xlMozra2RXvMVba5Ouq0YE7iKmKE3FWhABSSmy6JO\nhj6RLdj6lFKYb1X151INQq1IPRIpxQ/vfcpB5UEfB7osFj5Dql56t7V8Qk5AAwEh1/ksuSqh6ZuZ\nGYMrdXHwjFJFnBirgfq/Yj2wMn3fw27t9/uTkxM0Hu12u3EcN5uNLc1DmhhgZlQrvfcIGuqtwY2p\nJNxTwSUWyZ9C6yBzCGOGK0MOdV/0IKsGA1Mw0JKqBNL9Geeq+IAnjFXfgh5zvh944a6AklAhiTGe\nnJx86Utf+vCH70mOxiXnvPHeBh0R2wI+IIIGIyZmYkG1n4qfChnKOWu/WO0y8qecVF1M5e3U3+Bx\npHTl55zHacLqQTLfeOMz3vjb29vr61vZSZzTYTik6Pu+ZWtCHoTH4/HYNO7JsyfjPP3013/qgw8+\nePvzn00hfvjhh48fPNysz7z3IQSQ6bV9i35255yhu4k/RsgYY4TqupFUNhg6VhdZH9DFGCF86BPC\nQ+52u/V6nUp7NnKmImKcVYqRXFLtXFAAOAOoxCIhEEIYdwdVForOlIp1NBVKc9zZMAyu9VIKVzr7\nUg2pnjrUBqiAZXEy1d2WkjNFz3wIQc1S0aQLmAobtlytKDJY7/V6refEVnA+Zr69vdXbM6VtHnYR\nDixgSCCwwBdpiiNX+AWNJlVLikgIASgAtVhw+dfrNXJK0B0gfJumyVqLWARXhqU3xljvzs7OclUk\n19SllOyH3M/22GpePe4Qt50LTbUmbYwxMaemaUz5vanmhkBqQ6EWVTpq6DKuhtaYikocjrxWVoZh\n6Lq1vnme59PTU2PM8+fPQTJkSqYI34iqD+SZmff7fYwRfgMwh5/WgGkOWOEQQtd1p6enZm2wgChd\njOM4jmOIqeu6ruvtAwvqLK1pJ+0Yi5GI2noYbs4AodVoT9yzztfBQGs06tU3L1UAYQvjO4TKFZQK\nVjJWA5bqkAslBJg9jENERGVKuhI9Rjj7OvCJmRH6ID+B02etPTk5yaXaDzEDLny1WkH7M/PV1RV4\n9lQMNEkIU8qFakh/5kLNoK4SNgjPiH1f6hwFOycFEqlxDKRov9+rZOpERL4/b1DdCzAh6d6pFdxu\nt2+88UaMEcNSnz17dn19rVCOVEZAvfbaa+M4rlar/+DP/7m/9V//zdXmZLvfOd9O842xbtU219fX\nxlhnjYiQ4LBbZnYrN47jMC1j6du2DSkh1QlL/+DBg/Pzc0QeMQgRYVNQGUGcCnHS9t5cyha5AN81\n1lQDsEbD3+H47Nmb3/7295xrpuG4Xp+QxNvb3eb0RGzeHw+XV1fG2advPXv7C58/TscHjx+uT05a\n7zebzYPzi5cvrnPOWLqc85e+9KXXX38dktB6m3M2i+6lnHNOMUm21ksxNnrebcVcKhXdzDIMQvNI\n2HWoNq4yWkv44l0MMco9hnlsJPSylAweaqE5Z4x1ws9csVYzMwQXl1qcbkpd17nWUyF209vdbDZU\nwlKYHDzVfByo+LCmKhfDwNShIu4WZqmeNqSro2ZMdUGsBjipjsB1NJ5TG6AOI5cJYFx597ZUcWvn\nF+YfX+orQidof31MGB5N1ORquEtKCTlJdR7x8QXnTXJ7e8sluaEPSCW7oipDb+x8c6I7W5slDW1j\nhQNOkq21XIELYukv0QAI6wYZmOf57OysdnKphI8QOXWcsU3jOLbtSm8VjhEzbzabWjJhHiB1mEMD\nFmQoI1cmJXIBU+j1Qwit88453QVocGMM1C6eRX2RWBFCK0xA7ofaqUKvcZU9qz1u+G1UAB06bLDv\ne7hE+ll13pWbQ6+ALcA0OVhTfBZh0M3NTb2Pucrp1RpcY/1UOu1UhcG81agBKrgbTFKHKarH/LwS\nx6vfqbkQU2U+qbCUKsRUf0lVijuX/DzCHUWo29JXUC8yjoMuFBw1PUrqhtvSi2Kro4GPvPHGG7e3\nt1dXV8MwwONB1Ns0DQZvY8E//PDDt99+++rFJ//v/+ZvN13/8ccfnz94eHN1fXp6vt/vb65v0Hos\nkkIIInd1a9/cAeH0dHvvrXdad4fTEEIgQU5sIaGAVVaHUmM7rcdL1WKhooId2d7cElHOtNmcPnjw\nYJqm3e1xnnLjbcq83R3mm9uYIXt5u9/5zq9Wq65vjCX11Pu+99zudtcYNnh+ft51XciYQmlTSrQE\ne4t0EbExJtzHV0sJi+V+akRE3OnpKWbf2jJT0hgDB01FVo+fb5t5SqnwztqqmKaHUM2AXcYxLXk8\nNRUY8/pvFQVjzTzPYu6xh+HWt9utHpVUGLq8901xBtXxRKYF2jneJ9gmotPTUw2VUpmm472/uroy\nVTld3V4sQq6yyfqvhhHq/IoIYho9hAqIQN1YPReuMk6aTKirwZpSQ1Su+Ulb8uC2ZOHx1LHAJfVP\nyNS9ePHCeo8TpU6xlE5MUxXncNkwTlShFUxJGij4VfNO1lom472narQEFXt8dXVlCy5O7wc8hyh7\nQumrgMKxNaV9BOcfYgMZwKGF1Vmv1zpPiwpkGfuONV+tVtBcOWccG2gxfTSkqpxzn3z03BVQoka0\neKfuqS+UBzFG5jvEhy0oG65mO+UCXXWFQYfqtEyF19crQFNrFSdXFMbaHaK0UnpY8HFwZnNBRiAG\nUu8qFWS2Zs9sVYbUqCuXlGyuCA/xOL6aSoeb0RNh7nPj0v1CrAoV7rPGWEqVO7VVg5GpmOK4Sk/p\nR9Sm1sY+V8xY6gVCrnBXWuHGymMR9vu9uZ8zx/1fX1/f3t6enZ0hFvzoo4+stZvNpus6xILWWswy\nfu2113747nvv//jHX//6T//5//CP/52/93durq5SCvvD/s23PnN9dWmtjXGepolKMDdNk3Xdq7YW\n1O/eKee9KSU3awyRGGN1WdQ2a9CpzjRexpJQkmxEBDRfkjll2WxOvW/Pz/znPmsuX15b4x03bEgk\n9OMqSTzMA1k6s2aKkzGUUsAWjeORMztn4AVSdMfj8cWLF7FqN74LEuDT5zv33RiTQwhlbKOrGg/U\ne1bzDA/P2arWp+6JVLBalf6cg6Lpc+Hhh03G4VfnC1mC/fWtSqSKNSRDpbByHkUPM93PV6hJsKVb\nYlG4+c7x1EupptaDracIpxp6Xw+D6secM9SWwqI0ZFEtUOsCVQRqmWAFsZiasc2FlFbuNznVgTbu\nEN4Amo20zzGXkhWAf64giYv/Jc65w+EA1Z9L0rJt26Zr61OtD6JulOpQXUBbEhf6S5UErtovFm1F\nwrzgZnTBYbFOT09V6ahfrJ4KV1VlU/Ua673FggGz1nZdp1dQqbWlaVdTl7j5WDBvtbijFiIVlhd+\nZVPoBNWjUg/MVDDF2vBoUkDXU50GVW2pBuzkO3dVf6n+L1Wxl26l/iaV8bW+0IyqCsYboCKx70p+\nWHtjoTDxq9dVSyAXyFmuUl4q6ho042hw6WRAHIZ3DsOAA2Wq1JBeJFc1GM1X16dJf8NVJlzPr1od\nKU60OtR6/HEFPC/fZ8ev7ZCKIu4qVeVMfVL89/T09N133728vESczcxt215fX7/99ttd183z/NFH\nH11cXGw2m+12+/HLF+//8IcPHjz4xV/8JRF++PhJ07jvffe7H3/8onF3csWVvg5hErkPVa36TBCM\nDsOAEJ8pM2XDd9V9qaov+ht1NYwxQinnnNOd3suJcs5d207TxImttZvNiXMe3s80H0OKEsiwZWPY\niW2684sz713TlPZ/WVz8xnvXNMYYpViUqqcipSTLtgozJ8mZxBinBwovqUA3saDAFuG/vb2NhW4O\n0E91plQs1IrQwmB/ly5QCbYVYhK/R3bOl7EuSHcyM1SDKc1MehJyzinF9XpN9q7mrHuGdnERQemV\nFFgRZ70TtYt1D019QpAZcKUhNxbkXq5qffk+i6I+o6ngWFTN4FEdoWeAKkdPQ1H099SLqQ4yFdRQ\nLgM7mqa5vb1FyiWVki++7pUEoL5sBWxVNR1SdM5xhS3UjVMrq0uNT530q1qbaA7nFVDvInxMxhip\nnr02SKr1an0HLYz0DgwDSkpIgEjl9sIGoJyDU4pvV3e+1vJ6SqGgD4cDWuXxwZubm77vsfXQKdM0\n7ff7nPPF6RllSSmxkGVjiCXllDIvQmKJiIVyTEREWUiIsuSYUogLNVTKbIykzJacsZYNZYkSwcEV\nQ1RNpKZLlYgqWVMAb3V6WYVHKrCrKVlE5E+welgcW7oG1VzplrnSeoxDVEu4LRz2fL9pFDe83+8R\nGqovQpVuhSUAFlwq0JQqAVX0Z2dnWtnVN7gCx9doIJZGN2QI8XIFQG+rjLqqMAgeAgs1gfgBtT25\n3ziB2z47O1P1kivg/ieffJJSOj8/R6ElpTQMw9XV1UcfffTs2bO33nrrwYMHqKn/3b/7d7/97W+f\nXjz4hT/+i77p/tjP/a/+h//xH4qkzenpOB1zTklKIQA5AMnwMpktm7verGWj8zIz4XA43N7ePn78\nuBy04AuiXfNPuk26a0oZE+LifzAxUaP6Kic67AcjSBGbcZxTQjNZL7vt4sTMKXHoN/1mszk5OTk5\n22DlvG3Y2TkGw945Av3b6ekpsgticD8558yL6iuGn0SrjyrSVHoPFmNct2TVZBuqhrRnRZ98Cfkl\nd+0a7JMqcKb0l6RC/mFKAWkcx3XTmft0vFwY+/P9uFuPQSoaVuXeltlCUlgXcyEwbu0d83ztBiIv\nX7s/uObNzZLS1VpCSgnlcfW5qEo1xPsDAHVlQ5mpoarQFuwDVc51LGDTWLW81DpXjwEVyEBtCF2Z\nXQTPALSV+hXqSzIzkmCYCYaK8TzPx3FomobumO1JT3UdB9SGRNetvrjuqZTMJLRhJvHepwoYqecE\nlXD47OoT6J3XRxHX1wkXdQbPWnt9vdUkD5QLFhNK0FczKVTxucLNg/vk0lkJN98UGrfFDzA2yR3V\ntCoyLSDVViHnLHI3feNOlZSV0fXU/+YQU6Eg05VkZsCCcunDMyXLVJsiGG/cGHbWew9riqKamhar\nudlSToCe0lYnU/J4rrTEqoVTMeN/W5VLTT7+66rOKhHBrM6mcCibanYq/C09PvAAIF21mfRlphzf\nR/ekCuygN6M3xlUEJhWogasMtpRYSs0k35/GkgoGTD/onLu4uIDwXF5ejuN4enr65MmTr371q4fD\nAb7R8Xh8+fLl2dkZM/+Fv/AX/upf/ave+x+//8Pf+70/ePnicr1ZrS9Wx2Hv2OaciIz33pplJfu+\nj9sD8Z3+FRFITyoOsU4iV3Wktr8+9bUV1xDWVQSDXGJokbuGxb7rnXOnp6c4mE3TiaT1et30TRf6\nw3i8vH1xeXn5wYcfvv3O52M8FWG8czlosoD7+75/8ODBQkkqFGNs/V1OxeAGxIjklJLWrTUrZu5z\nrOjmLmebmVEmhf9iqsyYanNmtoaHYYiyOCCmUnMoBZkyy1WlTU+RKoKcM+RSr1Av7jRNcYGg3CVz\nVJfpUVcby3SXsvdlRkiqIMJqWrgMTVcKyFxIUOCkw79wZeZ0bSZVNeu/mN4mFQwd96A1A1NgQiro\nKh/1U8O66FbZMhNIg1RbodTQpcgFXqEG1ZVx2po8pBJemBK71IeZiLbbrb5HoSVUTUWrPRo8US1A\ni0qy91SVvpCpg+rBdiBQRqCTq5hYbcBqtYJBwo4cDgc8UdP0UATY3FD4NOucGBd3KpUB3qg2Ax3Q\n9z3IqrHFCqjBC4Uiut+/pcpUn1eU9TLJK/GBnjGqgPL1FmQmXTQuQTw2FA8Vq6YcKqyM2AgldEc0\nqXKuhVjIDxIGesjVzqlQuYIg9dW0zFQVexQ9QZXjaApKIlacRnpZGCF8JJWqUq5SHblKCWLloTRV\nEUsJ96UKpvW46X/1nfrtfB81gwXJFUmHpmFiYWHOFUWQFBhLrBDetsxuhsnpuu7k5OTk5OTFixcA\n0MIODcNweXn55MkTxNls3O12vz3sf+1f/vp0PFxdM0s+OV3Pw5hyFgiqpHmeOSeMJBJeWIJ0EUTE\nmjtaGY1irbFN01hzx0ZYO4541YdXfTuVWCLCVKQYQQfuQ0iGnWQGA16SbI033okjbuxE04vLT54/\nf359fX12dmbtHTxyGIYUyUR/PB7B1OecOx6P7KyCgBZvpkBhDJuURJPaqidNIcuo9zrn7FLKx3Hw\n1vfrlSEzhdlad3J2srvdZcqUJaTIwkmyY0fCZI3JpCdN1R/WJaV0OByQQPDLxHEPHZEkWzau8d46\n46xN1reNty5JnsdpCjNlYWt819q7RmYSyYhqYkzw/4goRrD8uqZpckjCOWdha5xxwhTnOMfAhBnH\nJi8zZ8gaa5w57o+ZhIXHeZQkrnEsPMeZMsUcMQDAsEkSKJP1xjmXSSTlTCJZkmRKRIbDNLM1LJQJ\nQzwyZREmQ8zWELEwxRDnGCxb46y3PuYoMQuLZSuMq6UYk/VOhJIkErLesXCm3HV9zDGlLEw5S8zJ\nZ2GgPJimYYw5YfUMsXF2v925xjdNkyTv9/sk2RnrGj+VMJeqWIdLpo6qjA0kplv1zGzIZMpYk5xy\nkuR9kylKFjJCxGxN67xr3DDNIBwWkYyUFyM/HsdxTCk7ZxGPdV2/WvXG2HmeiKBNSCSHEHNOMaYQ\nZiKGmjUmGWNFpO/by8t92/brdX885mkaRBpm6ft1SmEc5xhn55qmQJVyzsMwYHQCMPo4M4fDASfB\nl+lTOAOHw9EY0/pGd1NEQBqWJEsSMuyNY2tYBJkHSLJx1lpbGu9zZ1vMyhOmO0lg9l37ihuonixm\nL+HeYCdcwbDlQmYIywQEtiJcqJSp4LRS6Q3Kpc0AmwvjPc8zrlwHqVJlwqH+NCY21UsKel7THnr/\nqG1olgL1JFtm4N4ppoq0Xj1CGB7NjiCtol2DsMSg4dHbUO9B485cgBW2YmyRAgtCGK0ecH0/uA6K\nBQgumVVfoxWs9d57w8fddjoeri4v33jjjXmeveHGmvb81Fp+6603u1U/z/OPP/jwM5/5zH/73/29\nj55/Yoj7VStkb292bedTzELZkMmJpmniLL4zrmlzkkQlJ2TJCGUhZ+3hMMQYjXdt07GxMZG1luiO\nRk8K+CgV+nMqmQb1AHR/c86S1TwYhp52nDlnE8Um11jfuoZ9lDSMh3me+3X3uQefe/DowjaOl/IB\n+KjSMEw55NbNDy5eH8N0HA7EwszjdFyZzelmdTgcJOWckqSckYs0xMSZhD/lZNSyd88azSG5prXs\ndoejEWOc73yXhNg4a6xj12/6F89fnJyfUKLDOKzXSxJzLpzHtiAllBix73t0Iex2O/yJUhbDxpgo\nWWLIKTTWjWGepomsabzrGpfmMKcYQmoax2zneYwxW8vGOGaJES1HiVmYrfdNjHm73fX9WtjkzOMc\nGstkjW+6KDFOUSjPMYUcvPG2sdMch/3QunaY5rPNme+7/e0+iRhhPLttrLN2CtFIFsONb9jxMI27\nwyHHuNpszk9PY87H/X5/PD568CCJGMMkkiSM89x63/b9T370websVFozHQ8SxXe+a7pMst0djDcS\nJVHqfBclpjmxY990vm1zlGGeLdk4T5TINnbdrSE7c5rjHMcw7XfDRy8+OTvZhJRONusk4oxhaw+7\n3WEYjLMzipnGCJN1vvE+FgYmLuDmULqVQdkH5xd7tNvtDofDkyevz/OEe+ub3jaNYclpnuYYJTt2\nxjkjJgZJiVKIItx1vbU+50iEg2Fyjtvt3jnTtr1zxvvWWo4x7/eHlGSzWTVNl1IyxomkGHNK0ftO\nhAAv8L611jBTCOPLly9DmDebzcuXL2JMfd+N4/H8/CzG+ebm+unTZ/M8vXjxcrNZp5SHYXj48LF2\n8ECbKyZYa9q5lNlEhKwxzpHhkFNjXdetKOUxzI11IaccYmYSpsxkvWu89T5NcTJCrm1CmucU2r7x\nXbu7uZ2nSCmTNa3zxjvOEufsvUeD88nJibX2cDggKso573Y7ZoaLjWwBWlaRxEPkhON6cnIyTRP4\n5WCB5nne7XYvXrxYr9emwrmpddntdvozfESk4/DVQHCcnp5O07Tb7UIhd3klLuGq8gojVKs/LgmV\nVFrlhmFArwzCICj9k5OT9Xr9/vvvu4rRx5dJtQA31uAL2M7Hjx8jdtHYC/uFZgBAKHMhDDsejw8e\nPEAMR6WMDasWC4XdPM/K9cfMx+NRdaLmu2KMcRo3q/aw3Q0pbvpV4/1Zv6I5fPDDd5umubr65I/+\n9E//yx//YJh3X3znnReXtyK9/eTqD77zg8994Svbm+tpPN5sj13vGnLGdSwh5zLht/ON74zJbN3x\nuB+nwbXOGIoxseGr65cnZxeJxDqfhF5e7s5Oz0XkcDwC8em8yTnHNGsAvWq6lNI4HtNxQdDILMip\nwHrB9yKhMIfLmxenm81+uNkddrvj9Wc//8b3v/dud9LFcbq5vUK+bpyHQHG12tzsrs9OL1jMZrU2\nxlDKp+tN33bWtNN8EM5znJrOW0MPL86Z7XG/YyJnKGYm1BetQ0bEe+/bOzJDEiJZRnFConJOZNga\n6xrvMO9LCA54lCjzHJccRSJu7DjOmWmelz67q6urOpBXSF6uqspcUsBJB9STsFCUbDOmHvKs48Dz\n4vukBasuITBRmmeEcs5aArnFfa8KSZ5VXuDtYsVEySabmKOITFHRqCYzGWGy7H2biQzbYZ5yzlGy\nEZOZ2rZH7/ed6U7pOI15iKvVCtAaa+0wTSLSr9cXDx/CFU1wcEAw6r33fnN2bq0lazbdKdYhSubM\nUXJDTkx2prGN52xFZhzIdBzh9p6drdbNCSocU1TeNiZrGu6oIaLcrzdyPESkubz33o/jKDGitob8\nUWQmkZhzrAiWUjUXSkTQ2Cslp58K30yMmcgYz8YYMUxk2FHr+nmeJXOQlGciIoxetWJTiilZ5lzo\nEe/9KyIpiTHJGG+tbVvMQJNhGKYplDQmt21vrW2azpgl3wjtyszr9draE6gtIpqmCf2k+O/xeJjn\nOWdtyL2Xcaq1c9d1tYtdpz7GccxMp5uNtXaaJjJ8fn5+fX1NRGSNZSb0dZWRQgvqhxf+00XmnXVM\nkSIzZyYq2SG8IcYIyiiuarQas6bCFam56FyA4HgQZobKHsfxWOaUY3GA50YyHO4g8pywIr5qYuOS\n+uaC5dNCDhKGXOUSqSqd6lppUEJVAj+VFjfoAb097eO+vb0FpwP+i5dmbPTjtmq7DiGguFvnY6X0\nFHLBU0iVR9UClYZxGmLaCgANiwUtWQuDlL7D6Xg47I+S88OHj463W9f1Z6enP/nJT7765a/85m//\n1ttvffbJkyfr9foLX3rn8uqFc93NzeGf/dpvnF2cD4fjBx/8/mrVn12cxwgOVkFmxjjv2bORJDnG\nTDmADhV3G1LkLE+ePmW2CJLYOCYzpyxR6uRTrCDdpiou2sIVkiu4oBbnRMTYZIlDDsR0cn7yYHpw\ns7uZ0nA43jrTXlxcpBSmPJeyJa3anip8Y2N91zVd2zHb6+vDfr+NcV6ve+cgydkYg9Flhlhwt1RK\nem4ZJ5ZLZl7dC41l1WTcsXbWeVsulQkEs3AukCIAPQbfb+KRQjoA9UqlzAC/jO+/IFLI72toqUKG\nPL4p5U1boah1S7TcraeIqm5qPT8qu/qApoBwENjhCInIZrOBL8klkwPllVIYhgE1lGmaAHnSMZp1\nJl3FGsM0QT1pSye55srr5ANeKSV0M0AUtOyBOUmqGsrnCP065j7qxBS+rxoKrBuvp1T/Vf2CAFkK\nVfNqtcKRV32keBZNwdf6qNhL0aBbL64jFeAixapnVmvvrnAPGmNg3VX8aEmOp5RS2za73Q7QjOPx\n2Pe9dgVhebWGr+UuqaoO0Fzb7daVJqdavR4Oh4uLC2vtJ598wsyoTj9//lxjBTxmKigYKlgb7QSC\nOQfXg5YbuWqUxmOC6g2Vv3SflyEXvBxQ2q/EOnWII/czYKag6lNpSoNkYn10Qerr+Ir4QMMa9DPZ\nQiKsWyz3odtqLXRhX5FPqVJzVKC5oRANqMzrp2oNoNfHV19dXeniL2kc55xzkBOcvlQa9Uw1tCUX\n2ipmDiHM8+gc/kp24QwUojxNg34wlSK0Mf7hwyciwuKYfNet9/vBEA/H+eL80Ze/9LXj8fDgwWuv\nP3nzrbc+/y/+xb/85u9/+0/98n+w7v3vffO3P/e5z33+858Ncfq1X/tnX/ji2689eGAMSUohzobY\ntJ6JQggnJ+chRWNMTCbkICII9djacZyFzOnpBcJfzpJybNvOVNVrqrKpsZrVonXBVPrK9XhiZZCs\nyjmv1+tHjx6hHTCEwAvKlEKY1+vVEMcYY7/uQDMGTdg3Xd+vG9dK5s1m8eSQ+EViGZSGrmqwU3mr\nb8xWBIOqjWtBcrbqt6pFjUrfdUppvV4j0ldwhRTi2FTI6qHW8YRUmH6ICPgQW0EzTWmEVMeqNlTI\nIGlpLhccnQrlKydE+ZqkcgCpYKbr79WlsaWTA91bKSUdLSiFp6C8Zu891tY5p2Ntj8cjfC5bIZFw\nPGDGULONZciCnnPVHbE0G6pZBUSqBndwwX/r9aWQ66zXay3PAjGvnNm2ICb03vQOdde4km8unuYS\n7Mtyk3p9rMnJyYkiIXWR5VODHqgi6zMV4CeXKj2gbqYMk0XjQqoYYvQOmZkoHw6HYSDVjJp1oeKP\nl9teWmH6fgEl6grjUjBmtqJ3wkY8fvx4u91aax8/fpxzfvnyZc754uICLf25SlihsIGCRC4ULOpS\naCdgrbLVOcM26ULpQdDlrdeQ76NA1djo4qQK+Q32z1xhlHHUY9V3ZUtvFgwnDqlUSDPd0Fc0gMqJ\nHi59hcKx7SqGxhACZk/E0i5GxXNXFmNTdQgoqiUXeK0p7Rm1DwpxAvUDsOy1quVCHKXWhQpsDz40\nPAZ1WxV7xiV0UNihtT4MabfbPTh/OI7T/nr32c9+9vz0rOtW4xBPT87/6T/552+/887v/d63/pu/\n/bc/+OCDN976zN/4G/+PP//n/9f/2X/2v/9bf+tvdY1j484vTuM8E2VrrTBzNEtLDFkiur69ISLn\njLCZp5gltmzJLznz9ebss5/93NnZWdP6nMgQuwq2rkqV7iOzVK2reH9aJQIOA/Ypa+3p6el6vZ6O\nwzge53ne7m9d6zanm2k7Hg6Hft2pX2uMda4pbpOsVistUqo6qs8sF1iTqTK6deCL+wfqpHatmNn5\nwndJ911mV8aLQQ6AN4Po186RFBglzidiDipVVhHZbDZ60lTg6tj5FUdby3TqKKnnqHU8/TisUa46\nybl0J2hmT0ollgoRS32ekWC11irzSir8gzHGlDDJeFkHRbLOZa4dzpImuHEF0KXAP1X2Rnw7Tmmq\nZuCGELTAjsLAOI5oZa2tXazYS9VUYBdSQazhDVKR96iscGF80HWGO5MrjB+u0zRLl6hU+XS49vpZ\n/KB7J/djLLxhHEclgFAzo8dDTQIVXxjavHZBoCS7rhvHAd1mWCKwfKaUwNylIQIXKnfd6FTxW5vC\n5xur3lJsPTgVb29vReTRo0cxxpcvX4JTgytYvOIkmzLwTe2oLS1Qug61LlCeMSjNnDMKP7U+pWr2\nqOpxXZ/aG1M3E3qWS7+ElBgLwq8ouFesS62quOTK4AKqCaTqpVZZJdZVXQemwvTjPeAI1xhRncJU\nkcHghSP2SgQvxatDBoJKW2UqZBzO3TnQtjQ5Oee0D09fuOxqteq6Fl+Y0rL1XdcWkGdUqQO5gbft\nPEcifvLa02/97h984xs/Nw3jo4ev3WxvD8PhZ37mG//4H/+T3/rN39mPexH+Z//s1/7kn/yTrz0+\nu729/Xf/vV86Wa9ee+21/8v/+f90fra6vr5crbvWN2BESLJEitb6zNT0ZZYmpY45zMn6drXqHz16\n9Prrr1s2krIzTbPpA1opCvUMF1rC2vxQyU9qKKxaPhfWc0XHHA4HZt5sNuv1+rjbT8O83W5fXr1Y\nn66TpA8/+nAcxwePLtBC0PnWGzeToyyWbBKTZf7kk0+ur6+hil3pY1tsUgHQGWPYMIRf2wz0RNSb\nrufFWutUL+sGq1wCN2xKO3osQ+lfkW/cB7S5Vh194aVG06seVCpBPd6v91S0DwOoo/aDii8TCrMy\nhBKOVX0nardUg9RughpCLnOyFRGL/dvv97WHWCyZm6apaTycYp10xxUVty4xzN7hMALHRSVTkUvy\nWnUBFQICmFhXNR6q7gsVUWyt41ar1X6/A+pfKWXRGKFGQpcuVR1U+Kt6UtAy+jhq5IjmVMoYWHks\nI7ogbcXZgfdrRrjeXFOR/+tX4ynwyxijqjn81ReaACipcpwySD+RSkI2mIjOz88vLy81LMDXIXVw\nOIyvrCTuGV6LfhciMyS1dIqltfbq6irGCCFUzasnok6DaKbeOYeLqyjqLktphZHCQxgKbw2sux5d\nU/qN6iNmqmQ4SkH6OFDT+X4VAUunaqg2KlxFG7Zyt6n0cauvoO+nkrHXx9Hd59JxmUoPA7ImsIJS\nEuyKauMqC5cKigRPCnrTurUrFQaK+mZqwc5VMlMfTX+jig/yM02RaHEx+77v+x4ZacBJ1DXhxV3O\nFyePvG9//5u/96f+1J96+PDhT37yk//hH/z3f+Wv/JW/9//7//63f+e/+y//y7/xD/7RP3x5+cnX\nf/ZnfumXfulLX/2Sc/ZHP35/u91+8uKjX/2H/+DJa6/93/9v/9f/6r/6f737/e83TdO3XdM0lklE\nUogxp+vbrQiv133btqvN6hv/zs9+4xvf2Gw2pxfnhq31jXftarUZx3GIoxsbLq6zOl61HOpZy6Uj\nWDfO3k9HgeMVGBYkV9br9VXjT5pOOI9psN4exwMCI3ChhjIHOaUUY2ZjDdHheIQahD2T0rHjEOjn\nKroojM9c0h65YpPSjaMq839njUyV5CGi3W4HOBByI3gehSqpVOlF4aCFwhWm/hF8cN312lVXlSFV\neVnvVc0JlW44qYJrLWtprk93RV0JtUBGp8OV1gocHpxDKKZ6uhoecJ7nGOezs7Om8VxoLrmiwFJf\nW0q3RAhBhPSX0Hf1sZH7wYFU9TYUpfBQfd/DVU8KA1kexLVtG2MwFTEdbJIt2LnatJuSWeIyn1DF\nFyRD6qRQ6b0NIaqUS2EtshXj3Cvaqg5Va29A8cSql9WB4CqY0C+qVaSaMaKMfjUU+RBbaBQLz8tU\nCFcpqcj8qSnv4CWLBYiMdYbfg1rRfr9v2xaxkTZj6XqqcrclCUZVBIan4E9l6qigFtN9SKvyNEoV\nuPP98Y+1tKvNUINUYvcEQ6hPrd+iUlH7Z8yM4a0ouNaKIN8vgqrx4PsBhz5pbb24dPKZKvyFKeKq\n4VR9wdpLUCi26oRasVhrVYFgo5XjnCvuhrpiYUsLMJZimqaUIpyVi4uL8/NznAJFLUlVxxKh8TC7\ng/nD732rX/k8h9/93d/cH26ub1785m/9K+uk7fzJyfrnf/6P/bn/zX/49OnTy5vLF88/mubhD37v\n9/7Yz/70zcuPf/CDH/zkJz/+yle/vN/ebrfbm5tra62reu271fo4DsdxmGNYbdaPH7/2+rM3mDnM\n0RiKccxeJJH3vm877/12u5cCW4e0SxXUquyp6tOshivMMmqrpESrXOi1NpvNhz/+AB0RcYgxhxDC\nxq6nGBTcaK0nMpyFmaxbCuG+8DInndKCmSx50VTMLKW2WvvHUmLfWiHrwVkGh6gLnwr7EH6A4sbx\nCxW7sCoaU/obgOlUn9oXssXtdqsomtrZ1MK+qgwqPfyxcHNx4X5WBKpeWSVeNZrmAXJVW8oVaRWX\nLtFQKPzAvIkV0HyaLQAVa+00ATUwmzLEBWUqIDvUKGLn8NVnZ2cIF5oysRjWBek4Peq62sjpoU9z\ns9mgnAYYK1WxHS9eLX/88cdEslqtkAJGcu/i4kJbR6WUpvFdczUQ01RFIyUFr+23cy6laKv0YCij\n5ZFypIr8GxnLk5MT1Qu5SiGqclHFBH0BpLJS8OGG53m+ubnB6aISDdvSCLzZrMH0k1K6vr4+OTm5\nvr62ZfK69qlgGY25i7FU0cQYHz586AoFHAQSP2w2G4TvAHGh98WXNupQGDdqT3+hvLo/HERd0Vyl\nTIlIETGxDPdKKR2Px5OTEy3BwtFxhR3KVKRZqQABTk9PY0EEqJQqUSFeVBVcEXOoaqCSzIHMqGHg\nQsQA+IytcEmqQfh+izSeEY+PlCl+sPepoep8AM6FHmG4a/BckaGVKkOOBzkcDqZgc/DUUAtnZ2dq\nBVGdtdZCqNQvVCklwlTJjC86PT3dnJykgn42xbXKmkJg433zyScfPn32+J/+s/9pd3X7+utP/uJ/\n/B//7f/P33z782/9p/+7/2R/uP0L/9Gfu7y5vrz6aLe7Pk5Hlvz8+fO33/7c+++/96f/9L//i7/4\nJ/7J//K/fOELX+AsP/jBD3743vvjOIYi2Nb7l1eXRPTo0aPXXnvtnXfe+amf+qk333zrxYsXZ2e9\nCMcYwUrXNE1KGfkD9dggtNA2ppq2rv5xKqSxelpzVQfBcsFKQc32ff+FL70TwrQ/7jMl21hhOjk/\nefPNN0Wytc4Yx0ISU0xMLjK525vd4XAApEv5VhCKEBG4siBdMaUQQr9eUZV7pzJFwRYWfFVZIsK/\n/Vv/JucM/KiUUAYnf7vd5pxBERjLFiIvjGVCVMHMUI5Uyra5jJO5urp6+PBhKNBYKlP/YOH0yEmV\nMkqlvAHzttlsvPeYqIGzp6V+vB+cCK60HTx58uTq6gr3YyrKAz11SC9gPsXV1VXOGVWxs7Mzay3y\ndWdnZyD/SClYa2MMKaVxHPu+B/oO2kpdRSpJ/xjjen2qKql2A6VMK1CNI2WesUY2UIt934PU63g8\nWmtRmpKFXmi+urq6uDiPMaLUARcPehYHW20hfHBTyjahdBpBINq2RbWgZqZwzhnj6X4pSGMCW0gQ\nMMEBkm0qUhZdCmzuyclJSglqBeZH/bV6u6UaPq0EGSU/Bs7TO8pOSALkE3ZItTyO3zwv9qzve23H\nSSWrDicAAnY8Hm9uboZhePDgAcKjWErr8CFSGXGEXYNh3m63YKbHX2FR1MyrVlVrVKjJFlAiEnSo\nOOIw26rmhEBB7Zwe9ToUUEnDRoDOHPKAe8bvkSGRKlejMWid+dF4FE/HpStL46oiFUZ3KhW+JVPo\nbhFx4hlBXpUKBB+GVqFG6qebAscw91MjufTSYsS1NsZCYwzDgI2AttW/6hFTiVWd2DRORH71V3+1\nbdu/+p//51eXl+fn58baqSh67z0bQ8tns2UhyinGGOeGPRsaj8fb25vnn3y82vQpye1he5zGw3QY\njlNmev3x63EO8zz/5Cc/emnlpgAAkzNJREFUmcax7/vdbnc8HsM4geDuww8//PDDD2+uruHNt31/\n8fDhF77whRjjZnP6y7/8yyL85S9/ebvdOts0zjEVSIIz1rj9MHJJCwORj03HmaWqcwvBLhQ4FYx0\nLLx/tky0gTfw7rvvvv/++/M8pxROzk7Pzk7Z0hjGkKJtbNs2Dx8+evz4sSU7HgfHThJJkn61+Re/\n8a9/9X/6R23b/pk/82e++PkvoIvu+vq6Q9c63/kr4LNuurt6vFRY0Ff0J/ZORw7e8T5RycA8ePAA\nz3N5ealBD5xTaHO04M3zvFqtwEoJpXM4HJDWg+PMFdGZ2iTNR2tgpKEJF5g1NOwrKD78XkNUzb1g\ns3Eyh2FQX1JTn+DUgg+lPEYoIaCqbCtsBYxHjDO8P1cQgzBmVF51sMmlnqwBAVdZjlDGPgGizQXC\n8Prrr2OqJtJEkDmMz8B2ILgE9cs8T/jBVtVLeBnX19eqH6HXhmFAV6Bz7nA4rNfrq6urBw8eoLE/\nVOyriL5BhXJ1dVufZ031xIKMUMdWRJoyORSB4FRmTcIX0WYA1W5N0+x2u1gNVVONBgQ28qVq6pwz\n2+12teo1T6hINoACVHJgy+d5xnQ+VeuQFmvtRx99hDGmVKaet2378OHDm5ub9Xqtc9m5lNBwmOsi\nhJTZM+rO46lBPuJLq5DKMO7tcDgA1Y3gGCEpkoGQRkRLGtaA7BU3mUqZRGMvfZsmOqD6qeRhfJnf\nAbfPl1m6KEpDkjVy1QyYL3RZUuUkNThLhc5RIw/cBkJM3QJ87+3tLXQinjqUuVZNmRKiBhu7htWo\ncyd4HY9HpJLUy5EqnauxoN6hL/1D5j7ibprC2dnZj370wTRNP/7RB595661pHCmktlshKgohEgEC\napmJTCZia6x1LbEhSYA6OU/rdd80zcmDlfEuEXo86PrldiIjQm3bsXDbdJOfYxMdG02oPHv2LISQ\nYxKmpusvLy+JqO/7p0+frtcnTHa3PVrTEHGKGeJGRFlS5AWcFQscSYEMmu+pAkGyZXIHl7qR5roU\nw4a3weWNMfbrDuw/iVLn+7XldtV1XWeMHfYHQ7axzapbS5LD9nD58uXz58+ttc+ePXv69Ck6tbGt\ni5rjmlp6AaHQfUymxiS16cGN3U0HeMWzGIZBObJwtvF4T548qb0hPNXFxQUOmzEGeQn8sFqtdrud\nSk9teNTsLTdejgFiLBTcYox42hgjeMZUQagon56eoiSAiMcYg75OyKgqR1NSUul+E2guxVjNzule\nFmmeUopwzWwhUEmp9FcXzkc9Hgg41A7pckM1qNU3Jdl9c3NjjMGpA8gbR10nrAOkoP4yAHvqnyrv\nC7oLc9VlpQwxyO0AYqv5k1wwwXq3MMDAjofCBZ4KzhhqVDcrlR5DqXLTtRlDQwOyKJpCQbnOlHqm\nlFQnXH7VJpoUkmW89OLWAcu+Wq0QG9W+f0oJjgg0GmrUAPVBesFcp+g+W6ANEBhNH+nWq/V1Zb64\ntjfALY1l8h7MHlAG6jPpCYxlap+SX0CegcvAO23V6BPLCCu5j55XDa6ZGQ0ytOlVlfs4jtBK5lPN\nFa6MmcmluQ2LgzoilVqRrUDhd9pFRMoQGiQqdBHwBi65O7g4UlKdsJq+kFGpI6/3pmlVFSHAGsES\nnQuYOJcqpi0VUE0PIpxV4Vdt5l17fXX7+pNnv/mbv/lf/82/9Zf/8l9++uzZx8+fX1wAsu+dNURk\nluskipGtUBbJmSlJjMMw7A9biMo0Dzf7XWYSQ8MwDMMs0Q6HeRzH4YAu7DTPMYY0z2EOMQn5tmv6\nLuec5hBC2A9H661vm7btV+uTOaST9erdd9//2pe/oovMtBDGZ74bomEquIr6Aaa0jqWK4lIlSuVH\nVW4uiWWECkTElowzTeNc61zjnTNiiIg634YQDFlIlCQ5Ho8//slHP/rRj6y1jx49AvUqih1d10k1\nRHHR9kRUzTrArqkw643VtsrZis5ZVSoOj9LUbzYbeP1zIeLNZVA3RB8mB2kubexQlwdaxhb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j14cjebze3tLQRGoQEQm81moyhqRNgajquBwdeZUuPRMCKXojR2+Xg8rtfrk5MTeFdoHpqroeNU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\ndXDAK5LaR5/ggLwKiPuotfFZiDXaPiKxaRTl1hqsOBVrDoN5YG+YJ8G+U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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 3 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "cFHKCvCTxiff", - "colab_type": "code", - "outputId": "5a78c0ef-9ad8-4106-f699-81eec13f33f8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 553 - } - }, - "source": [ - "mask = Image.open('PennFudanPed/PedMasks/FudanPed00001_mask.png')\n", - "# each mask instance has a different color, from zero to N, where\n", - "# N is the number of instances. In order to make visualization easier,\n", - "# let's adda color palette to the mask.\n", - "mask.putpalette([\n", - " 0, 0, 0, # black background\n", - " 255, 0, 0, # index 1 is red\n", - " 255, 255, 0, # index 2 is yellow\n", - " 255, 153, 0, # index 3 is orange\n", - "])\n", - "mask" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAIYAgMAAADYxeTCAAAADFBMVEUAAAD/AAD//wD/mQBMHVIj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\n", - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 4 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C9Ee5NV54Dmj", - "colab_type": "text" - }, - "source": [ - "So each image has a corresponding segmentation mask, where each color correspond to a different instance. Let's write a `torch.utils.data.Dataset` class for this dataset." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "mTgWtixZTs3X", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import os\n", - "import numpy as np\n", - "import torch\n", - "import torch.utils.data\n", - "from PIL import Image\n", - "\n", - "\n", - "class PennFudanDataset(torch.utils.data.Dataset):\n", - " def __init__(self, root, transforms=None):\n", - " self.root = root\n", - " self.transforms = transforms\n", - " # load all image files, sorting them to\n", - " # ensure that they are aligned\n", - " self.imgs = list(sorted(os.listdir(os.path.join(root, \"PNGImages\"))))\n", - " self.masks = list(sorted(os.listdir(os.path.join(root, \"PedMasks\"))))\n", - "\n", - " def __getitem__(self, idx):\n", - " # load images and masks\n", - " img_path = os.path.join(self.root, \"PNGImages\", self.imgs[idx])\n", - " mask_path = os.path.join(self.root, \"PedMasks\", self.masks[idx])\n", - " img = Image.open(img_path).convert(\"RGB\")\n", - " # note that we haven't converted the mask to RGB,\n", - " # because each color corresponds to a different instance\n", - " # with 0 being background\n", - " mask = Image.open(mask_path)\n", - "\n", - " mask = np.array(mask)\n", - " # instances are encoded as different colors\n", - " obj_ids = np.unique(mask)\n", - " # first id is the background, so remove it\n", - " obj_ids = obj_ids[1:]\n", - "\n", - " # split the color-encoded mask into a set\n", - " # of binary masks\n", - " masks = mask == obj_ids[:, None, None]\n", - "\n", - " # get bounding box coordinates for each mask\n", - " num_objs = len(obj_ids)\n", - " boxes = []\n", - " for i in range(num_objs):\n", - " pos = np.where(masks[i])\n", - " xmin = np.min(pos[1])\n", - " xmax = np.max(pos[1])\n", - " ymin = np.min(pos[0])\n", - " ymax = np.max(pos[0])\n", - " boxes.append([xmin, ymin, xmax, ymax])\n", - "\n", - " boxes = torch.as_tensor(boxes, dtype=torch.float32)\n", - " # there is only one class\n", - " labels = torch.ones((num_objs,), dtype=torch.int64)\n", - " masks = torch.as_tensor(masks, dtype=torch.uint8)\n", - "\n", - " image_id = torch.tensor([idx])\n", - " area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n", - " # suppose all instances are not crowd\n", - " iscrowd = torch.zeros((num_objs,), dtype=torch.int64)\n", - "\n", - " target = {}\n", - " target[\"boxes\"] = boxes\n", - " target[\"labels\"] = labels\n", - " target[\"masks\"] = masks\n", - " target[\"image_id\"] = image_id\n", - " target[\"area\"] = area\n", - " target[\"iscrowd\"] = iscrowd\n", - "\n", - " if self.transforms is not None:\n", - " img, target = self.transforms(img, target)\n", - "\n", - " return img, target\n", - "\n", - " def __len__(self):\n", - " return len(self.imgs)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J6f3ZOTJ4Km9", - "colab_type": "text" - }, - "source": [ - "That's all for the dataset. Let's see how the outputs are structured for this dataset" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ZEARO4B_ye0s", - "colab_type": "code", - "outputId": "03974749-b9ba-4d03-b3a6-9a566078b320", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 326 - } - }, - "source": [ - "dataset = PennFudanDataset('PennFudanPed/')\n", - "dataset[0]" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(,\n", - " {'area': tensor([35358., 36225.]), 'boxes': tensor([[159., 181., 301., 430.],\n", - " [419., 170., 534., 485.]]), 'image_id': tensor([0]), 'iscrowd': tensor([0, 0]), 'labels': tensor([1, 1]), 'masks': tensor([[[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - " \n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]]], dtype=torch.uint8)})" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lWOhcsir9Ahx", - "colab_type": "text" - }, - "source": [ - "So we can see that by default, the dataset returns a `PIL.Image` and a dictionary\n", - "containing several fields, including `boxes`, `labels` and `masks`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RoAEkUgn4uEq", - "colab_type": "text" - }, - "source": [ - "## Defining your model\n", - "\n", - "In this tutorial, we will be using [Mask R-CNN](https://arxiv.org/abs/1703.06870), which is based on top of [Faster R-CNN](https://arxiv.org/abs/1506.01497). Faster R-CNN is a model that predicts both bounding boxes and class scores for potential objects in the image.\n", - "\n", - "![alt text](https://)\n", - "\n", - "Mask R-CNN adds an extra branch into Faster R-CNN, which also predicts segmentation masks for each instance.\n", - "\n", - "![alt text](https://)\n", - "\n", - "There are two common situations where one might want to modify one of the available models in torchvision modelzoo.\n", - "The first is when we want to start from a pre-trained model, and just finetune the last layer. The other is when we want to replace the backbone of the model with a different one (for faster predictions, for example).\n", - "\n", - "Let's go see how we would do one or another in the following sections.\n", - "\n", - "\n", - "### 1 - Finetuning from a pretrained model\n", - "\n", - "Let's suppose that you want to start from a model pre-trained on COCO and want to finetune it for your particular classes. Here is a possible way of doing it:\n", - "```\n", - "import torchvision\n", - "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n", - "\n", - "# load a model pre-trained pre-trained on COCO\n", - "model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n", - "\n", - "# replace the classifier with a new one, that has\n", - "# num_classes which is user-defined\n", - "num_classes = 2 # 1 class (person) + background\n", - "# get number of input features for the classifier\n", - "in_features = model.roi_heads.box_predictor.cls_score.in_features\n", - "# replace the pre-trained head with a new one\n", - "model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) \n", - "```\n", - "\n", - "### 2 - Modifying the model to add a different backbone\n", - "\n", - "Another common situation arises when the user wants to replace the backbone of a detection\n", - "model with a different one. For example, the current default backbone (ResNet-50) might be too big for some applications, and smaller models might be necessary.\n", - "\n", - "Here is how we would go into leveraging the functions provided by torchvision to modify a backbone.\n", - "\n", - "```\n", - "import torchvision\n", - "from torchvision.models.detection import FasterRCNN\n", - "from torchvision.models.detection.rpn import AnchorGenerator\n", - "\n", - "# load a pre-trained model for classification and return\n", - "# only the features\n", - "backbone = torchvision.models.mobilenet_v2(pretrained=True).features\n", - "# FasterRCNN needs to know the number of\n", - "# output channels in a backbone. For mobilenet_v2, it's 1280\n", - "# so we need to add it here\n", - "backbone.out_channels = 1280\n", - "\n", - "# let's make the RPN generate 5 x 3 anchors per spatial\n", - "# location, with 5 different sizes and 3 different aspect\n", - "# ratios. We have a Tuple[Tuple[int]] because each feature\n", - "# map could potentially have different sizes and\n", - "# aspect ratios \n", - "anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),\n", - " aspect_ratios=((0.5, 1.0, 2.0),))\n", - "\n", - "# let's define what are the feature maps that we will\n", - "# use to perform the region of interest cropping, as well as\n", - "# the size of the crop after rescaling.\n", - "# if your backbone returns a Tensor, featmap_names is expected to\n", - "# be [0]. More generally, the backbone should return an\n", - "# OrderedDict[Tensor], and in featmap_names you can choose which\n", - "# feature maps to use.\n", - "roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],\n", - " output_size=7,\n", - " sampling_ratio=2)\n", - "\n", - "# put the pieces together inside a FasterRCNN model\n", - "model = FasterRCNN(backbone,\n", - " num_classes=2,\n", - " rpn_anchor_generator=anchor_generator,\n", - " box_roi_pool=roi_pooler)\n", - "```\n", - "\n", - "### An Instance segmentation model for PennFudan Dataset\n", - "\n", - "In our case, we want to fine-tune from a pre-trained model, given that our dataset is very small. So we will be following approach number 1.\n", - "\n", - "Here we want to also compute the instance segmentation masks, so we will be using Mask R-CNN:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "YjNHjVMOyYlH", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import torchvision\n", - "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n", - "from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n", - "\n", - " \n", - "def get_instance_segmentation_model(num_classes):\n", - " # load an instance segmentation model pre-trained on COCO\n", - " model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)\n", - "\n", - " # get the number of input features for the classifier\n", - " in_features = model.roi_heads.box_predictor.cls_score.in_features\n", - " # replace the pre-trained head with a new one\n", - " model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n", - "\n", - " # now get the number of input features for the mask classifier\n", - " in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n", - " hidden_layer = 256\n", - " # and replace the mask predictor with a new one\n", - " model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n", - " hidden_layer,\n", - " num_classes)\n", - "\n", - " return model" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-WXLwePV5ieP", - "colab_type": "text" - }, - "source": [ - "That's it, this will make model be ready to be trained and evaluated on our custom dataset.\n", - "\n", - "## Training and evaluation functions\n", - "\n", - "In `references/detection/,` we have a number of helper functions to simplify training and evaluating detection models.\n", - "Here, we will use `references/detection/engine.py`, `references/detection/utils.py` and `references/detection/transforms.py`.\n", - "\n", - "Let's copy those files (and their dependencies) in here so that they are available in the notebook" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "UYDb7PBw55b-", - "colab_type": "code", - "outputId": "45309f6e-2fed-4c49-a2c0-381da0fb4aca", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 68 - } - }, - "source": [ - "%%shell\n", - "\n", - "# Download TorchVision repo to use some files from\n", - "# references/detection\n", - "git clone https://github.com/pytorch/vision.git\n", - "cd vision\n", - "git checkout v0.3.0\n", - "\n", - "cp references/detection/utils.py ../\n", - "cp references/detection/transforms.py ../\n", - "cp references/detection/coco_eval.py ../\n", - "cp references/detection/engine.py ../\n", - "cp references/detection/coco_utils.py ../" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "fatal: destination path 'vision' already exists and is not an empty directory.\n", - "Already on 'v0.3.0'\n", - "Your branch is up to date with 'origin/v0.3.0'.\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 8 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2u9e_pdv54nG", - "colab_type": "text" - }, - "source": [ - "\n", - "\n", - "Let's write some helper functions for data augmentation / transformation, which leverages the functions in `refereces/detection` that we have just copied:\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "l79ivkwKy357", - "colab_type": "code", - "colab": {} - }, - "source": [ - "from engine import train_one_epoch, evaluate\n", - "import utils\n", - "import transforms as T\n", - "\n", - "\n", - "def get_transform(train):\n", - " transforms = []\n", - " # converts the image, a PIL image, into a PyTorch Tensor\n", - " transforms.append(T.ToTensor())\n", - " if train:\n", - " # during training, randomly flip the training images\n", - " # and ground-truth for data augmentation\n", - " transforms.append(T.RandomHorizontalFlip(0.5))\n", - " return T.Compose(transforms)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FzCLqiZk-sjf", - "colab_type": "text" - }, - "source": [ - "#### Note that we do not need to add a mean/std normalization nor image rescaling in the data transforms, as those are handled internally by the Mask R-CNN model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3YFJGJxk6XEs", - "colab_type": "text" - }, - "source": [ - "### Putting everything together\n", - "\n", - "We now have the dataset class, the models and the data transforms. Let's instantiate them" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "a5dGaIezze3y", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# use our dataset and defined transformations\n", - "dataset = PennFudanDataset('PennFudanPed', get_transform(train=True))\n", - "dataset_test = PennFudanDataset('PennFudanPed', get_transform(train=False))\n", - "\n", - "# split the dataset in train and test set\n", - "torch.manual_seed(1)\n", - "indices = torch.randperm(len(dataset)).tolist()\n", - "dataset = torch.utils.data.Subset(dataset, indices[:-50])\n", - "dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:])\n", - "\n", - "# define training and validation data loaders\n", - "data_loader = torch.utils.data.DataLoader(\n", - " dataset, batch_size=2, shuffle=True, num_workers=4,\n", - " collate_fn=utils.collate_fn)\n", - "\n", - "data_loader_test = torch.utils.data.DataLoader(\n", - " dataset_test, batch_size=1, shuffle=False, num_workers=4,\n", - " collate_fn=utils.collate_fn)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "L5yvZUprj4ZN", - "colab_type": "text" - }, - "source": [ - "Now let's instantiate the model and the optimizer" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zoenkCj18C4h", - "colab_type": "code", - "outputId": "44c71ea4-7778-40ec-c838-99ee45aace4d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 71 - } - }, - "source": [ - "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n", - "\n", - "# our dataset has two classes only - background and person\n", - "num_classes = 2\n", - "\n", - "# get the model using our helper function\n", - "model = get_instance_segmentation_model(num_classes)\n", - "# move model to the right device\n", - "model.to(device)\n", - "\n", - "# construct an optimizer\n", - "params = [p for p in model.parameters() if p.requires_grad]\n", - "optimizer = torch.optim.SGD(params, lr=0.005,\n", - " momentum=0.9, weight_decay=0.0005)\n", - "\n", - "# and a learning rate scheduler which decreases the learning rate by\n", - "# 10x every 3 epochs\n", - "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n", - " step_size=3,\n", - " gamma=0.1)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Downloading: \"https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth\" to /root/.cache/torch/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth\n", - "100%|██████████| 178090079/178090079 [00:02<00:00, 61358754.67it/s]\n" - ], - "name": "stderr" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XAd56lt4kDxc", - "colab_type": "text" - }, - "source": [ - "And now let's train the model for 10 epochs, evaluating at the end of every epoch." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "at-h4OWK0aoc", - "colab_type": "code", - "outputId": "80d9fbf0-100b-46b5-ea7d-fad8bd8bd4e5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 7517 - } - }, - "source": [ - "# let's train it for 10 epochs\n", - "num_epochs = 10\n", - "\n", - "for epoch in range(num_epochs):\n", - " # train for one epoch, printing every 10 iterations\n", - " train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n", - " # update the learning rate\n", - " lr_scheduler.step()\n", - " # evaluate on the test dataset\n", - " evaluate(model, data_loader_test, device=device)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Epoch: [0] [ 0/60] eta: 0:01:54 lr: 0.000090 loss: 3.5688 (3.5688) loss_classifier: 0.7563 (0.7563) loss_box_reg: 0.1544 (0.1544) loss_mask: 2.6350 (2.6350) loss_objectness: 0.0167 (0.0167) loss_rpn_box_reg: 0.0064 (0.0064) time: 1.9101 data: 0.4269 max mem: 3175\n", - "Epoch: [0] [10/60] eta: 0:00:35 lr: 0.000936 loss: 1.5702 (2.1186) loss_classifier: 0.4521 (0.4978) loss_box_reg: 0.1846 (0.1915) loss_mask: 0.9227 (1.3971) loss_objectness: 0.0173 (0.0219) loss_rpn_box_reg: 0.0087 (0.0103) time: 0.7126 data: 0.0450 max mem: 4552\n", - "Epoch: [0] [20/60] eta: 0:00:26 lr: 0.001783 loss: 0.8643 (1.4273) loss_classifier: 0.2400 (0.3407) loss_box_reg: 0.1589 (0.1740) loss_mask: 0.3977 (0.8806) loss_objectness: 0.0173 (0.0201) loss_rpn_box_reg: 0.0090 (0.0119) time: 0.5888 data: 0.0067 max mem: 4552\n", - "Epoch: [0] [30/60] eta: 0:00:19 lr: 0.002629 loss: 0.5349 (1.1192) loss_classifier: 0.0967 (0.2569) loss_box_reg: 0.1289 (0.1610) loss_mask: 0.2496 (0.6734) loss_objectness: 0.0102 (0.0163) loss_rpn_box_reg: 0.0101 (0.0116) time: 0.6026 data: 0.0066 max mem: 5310\n", - "Epoch: [0] [40/60] eta: 0:00:12 lr: 0.003476 loss: 0.4128 (0.9479) loss_classifier: 0.0634 (0.2091) loss_box_reg: 0.1078 (0.1524) loss_mask: 0.2095 (0.5601) loss_objectness: 0.0059 (0.0138) loss_rpn_box_reg: 0.0113 (0.0125) time: 0.6283 data: 0.0066 max mem: 5310\n", - "Epoch: [0] [50/60] eta: 0:00:06 lr: 0.004323 loss: 0.3223 (0.8260) loss_classifier: 0.0453 (0.1783) loss_box_reg: 0.0899 (0.1395) loss_mask: 0.1734 (0.4838) loss_objectness: 0.0038 (0.0118) loss_rpn_box_reg: 0.0113 (0.0126) time: 0.6311 data: 0.0070 max mem: 5310\n", - "Epoch: [0] [59/60] eta: 0:00:00 lr: 0.005000 loss: 0.2608 (0.7366) loss_classifier: 0.0390 (0.1564) loss_box_reg: 0.0574 (0.1245) loss_mask: 0.1512 (0.4334) loss_objectness: 0.0020 (0.0103) loss_rpn_box_reg: 0.0102 (0.0121) time: 0.6404 data: 0.0075 max mem: 5310\n", - "Epoch: [0] Total time: 0:00:38 (0.6413 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:28 model_time: 0.3890 (0.3890) evaluator_time: 0.0042 (0.0042) time: 0.5795 data: 0.1848 max mem: 5310\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1158 (0.1223) evaluator_time: 0.0046 (0.0088) time: 0.1289 data: 0.0036 max mem: 5310\n", - "Test: Total time: 0:00:07 (0.1421 s / it)\n", - "Averaged stats: model_time: 0.1158 (0.1223) evaluator_time: 0.0046 (0.0088)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.682\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.982\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.872\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.406\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.693\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.742\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.742\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.745\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.704\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.982\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.910\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.434\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.717\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.323\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.748\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.750\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.688\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.754\n", - "Epoch: [1] [ 0/60] eta: 0:01:04 lr: 0.005000 loss: 0.3601 (0.3601) loss_classifier: 0.0550 (0.0550) loss_box_reg: 0.1111 (0.1111) loss_mask: 0.1692 (0.1692) loss_objectness: 0.0032 (0.0032) loss_rpn_box_reg: 0.0217 (0.0217) time: 1.0794 data: 0.4107 max mem: 5310\n", - "Epoch: [1] [10/60] eta: 0:00:37 lr: 0.005000 loss: 0.2678 (0.2754) loss_classifier: 0.0542 (0.0497) loss_box_reg: 0.0572 (0.0565) loss_mask: 0.1510 (0.1532) loss_objectness: 0.0016 (0.0019) loss_rpn_box_reg: 0.0096 (0.0142) time: 0.7499 data: 0.0436 max mem: 5310\n", - "Epoch: [1] [20/60] eta: 0:00:28 lr: 0.005000 loss: 0.2345 (0.2438) loss_classifier: 0.0400 (0.0421) loss_box_reg: 0.0362 (0.0445) loss_mask: 0.1347 (0.1429) loss_objectness: 0.0014 (0.0022) loss_rpn_box_reg: 0.0095 (0.0121) time: 0.6846 data: 0.0068 max mem: 5310\n", - "Epoch: [1] [30/60] eta: 0:00:20 lr: 0.005000 loss: 0.1942 (0.2286) loss_classifier: 0.0235 (0.0373) loss_box_reg: 0.0205 (0.0355) loss_mask: 0.1293 (0.1434) loss_objectness: 0.0005 (0.0017) loss_rpn_box_reg: 0.0068 (0.0106) time: 0.6301 data: 0.0066 max mem: 5310\n", - "Epoch: [1] [40/60] eta: 0:00:13 lr: 0.005000 loss: 0.1951 (0.2253) loss_classifier: 0.0277 (0.0361) loss_box_reg: 0.0173 (0.0324) loss_mask: 0.1331 (0.1450) loss_objectness: 0.0005 (0.0016) loss_rpn_box_reg: 0.0074 (0.0102) time: 0.6304 data: 0.0066 max mem: 5310\n", - "Epoch: [1] [50/60] eta: 0:00:06 lr: 0.005000 loss: 0.2011 (0.2242) loss_classifier: 0.0348 (0.0370) loss_box_reg: 0.0207 (0.0309) loss_mask: 0.1337 (0.1438) loss_objectness: 0.0007 (0.0016) loss_rpn_box_reg: 0.0080 (0.0109) time: 0.6441 data: 0.0068 max mem: 5310\n", - "Epoch: [1] [59/60] eta: 0:00:00 lr: 0.005000 loss: 0.2162 (0.2248) loss_classifier: 0.0381 (0.0382) loss_box_reg: 0.0253 (0.0307) loss_mask: 0.1325 (0.1437) loss_objectness: 0.0008 (0.0016) loss_rpn_box_reg: 0.0085 (0.0106) time: 0.6245 data: 0.0067 max mem: 5310\n", - "Epoch: [1] Total time: 0:00:39 (0.6548 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:17 model_time: 0.1625 (0.1625) evaluator_time: 0.0040 (0.0040) time: 0.3575 data: 0.1894 max mem: 5310\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1113 (0.1127) evaluator_time: 0.0037 (0.0072) time: 0.1226 data: 0.0034 max mem: 5310\n", - "Test: Total time: 0:00:06 (0.1306 s / it)\n", - "Averaged stats: model_time: 0.1113 (0.1127) evaluator_time: 0.0037 (0.0072)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.750\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.983\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.959\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.440\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.803\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.803\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.809\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.734\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.974\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.889\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.413\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.749\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.339\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.776\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.776\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.662\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.784\n", - "Epoch: [2] [ 0/60] eta: 0:00:57 lr: 0.005000 loss: 0.2592 (0.2592) loss_classifier: 0.0457 (0.0457) loss_box_reg: 0.0357 (0.0357) loss_mask: 0.1604 (0.1604) loss_objectness: 0.0005 (0.0005) loss_rpn_box_reg: 0.0169 (0.0169) time: 0.9603 data: 0.4132 max mem: 5310\n", - "Epoch: [2] [10/60] eta: 0:00:31 lr: 0.005000 loss: 0.1793 (0.1962) loss_classifier: 0.0260 (0.0358) loss_box_reg: 0.0164 (0.0199) loss_mask: 0.1189 (0.1288) loss_objectness: 0.0010 (0.0015) loss_rpn_box_reg: 0.0086 (0.0102) time: 0.6373 data: 0.0410 max mem: 5310\n", - "Epoch: [2] [20/60] eta: 0:00:25 lr: 0.005000 loss: 0.2012 (0.2086) loss_classifier: 0.0270 (0.0358) loss_box_reg: 0.0164 (0.0226) loss_mask: 0.1226 (0.1372) loss_objectness: 0.0010 (0.0015) loss_rpn_box_reg: 0.0102 (0.0115) time: 0.6289 data: 0.0052 max mem: 5310\n", - "Epoch: [2] [30/60] eta: 0:00:18 lr: 0.005000 loss: 0.1754 (0.1926) loss_classifier: 0.0270 (0.0320) loss_box_reg: 0.0133 (0.0183) loss_mask: 0.1226 (0.1313) loss_objectness: 0.0005 (0.0011) loss_rpn_box_reg: 0.0082 (0.0099) time: 0.6294 data: 0.0067 max mem: 5310\n", - "Epoch: [2] [40/60] eta: 0:00:12 lr: 0.005000 loss: 0.1664 (0.1907) loss_classifier: 0.0313 (0.0322) loss_box_reg: 0.0121 (0.0178) loss_mask: 0.1240 (0.1294) loss_objectness: 0.0005 (0.0014) loss_rpn_box_reg: 0.0079 (0.0098) time: 0.6273 data: 0.0067 max mem: 5310\n", - "Epoch: [2] [50/60] eta: 0:00:06 lr: 0.005000 loss: 0.1771 (0.1862) loss_classifier: 0.0285 (0.0308) loss_box_reg: 0.0145 (0.0170) loss_mask: 0.1263 (0.1278) loss_objectness: 0.0005 (0.0013) loss_rpn_box_reg: 0.0086 (0.0094) time: 0.6417 data: 0.0068 max mem: 5310\n", - "Epoch: [2] [59/60] eta: 0:00:00 lr: 0.005000 loss: 0.1771 (0.1900) loss_classifier: 0.0257 (0.0316) loss_box_reg: 0.0158 (0.0180) loss_mask: 0.1269 (0.1291) loss_objectness: 0.0009 (0.0014) loss_rpn_box_reg: 0.0077 (0.0099) time: 0.6555 data: 0.0073 max mem: 5310\n", - "Epoch: [2] Total time: 0:00:38 (0.6433 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:18 model_time: 0.1615 (0.1615) evaluator_time: 0.0041 (0.0041) time: 0.3662 data: 0.1992 max mem: 5310\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1143 (0.1142) evaluator_time: 0.0035 (0.0059) time: 0.1230 data: 0.0034 max mem: 5310\n", - "Test: Total time: 0:00:06 (0.1307 s / it)\n", - "Averaged stats: model_time: 0.1143 (0.1142) evaluator_time: 0.0035 (0.0059)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.803\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.958\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.814\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.363\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.840\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.840\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.846\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.764\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.922\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.776\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.345\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.803\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.803\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.725\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.809\n", - "Epoch: [3] [ 0/60] eta: 0:01:31 lr: 0.000500 loss: 0.2188 (0.2188) loss_classifier: 0.0488 (0.0488) loss_box_reg: 0.0222 (0.0222) loss_mask: 0.1369 (0.1369) loss_objectness: 0.0002 (0.0002) loss_rpn_box_reg: 0.0106 (0.0106) time: 1.5300 data: 0.7803 max mem: 5310\n", - "Epoch: [3] [10/60] eta: 0:00:35 lr: 0.000500 loss: 0.1462 (0.1512) loss_classifier: 0.0216 (0.0238) loss_box_reg: 0.0074 (0.0093) loss_mask: 0.1066 (0.1111) loss_objectness: 0.0005 (0.0006) loss_rpn_box_reg: 0.0048 (0.0064) time: 0.7073 data: 0.0738 max mem: 5310\n", - "Epoch: [3] [20/60] eta: 0:00:27 lr: 0.000500 loss: 0.1462 (0.1572) loss_classifier: 0.0216 (0.0257) loss_box_reg: 0.0068 (0.0108) loss_mask: 0.1055 (0.1123) loss_objectness: 0.0004 (0.0007) loss_rpn_box_reg: 0.0052 (0.0077) time: 0.6362 data: 0.0049 max mem: 5310\n", - "Epoch: [3] [30/60] eta: 0:00:20 lr: 0.000500 loss: 0.1587 (0.1656) loss_classifier: 0.0256 (0.0275) loss_box_reg: 0.0095 (0.0120) loss_mask: 0.1156 (0.1169) loss_objectness: 0.0005 (0.0009) loss_rpn_box_reg: 0.0082 (0.0083) time: 0.6555 data: 0.0066 max mem: 5310\n", - "Epoch: [3] [40/60] eta: 0:00:13 lr: 0.000500 loss: 0.1624 (0.1694) loss_classifier: 0.0229 (0.0269) loss_box_reg: 0.0107 (0.0128) loss_mask: 0.1235 (0.1206) loss_objectness: 0.0007 (0.0010) loss_rpn_box_reg: 0.0076 (0.0082) time: 0.6545 data: 0.0068 max mem: 5310\n", - "Epoch: [3] [50/60] eta: 0:00:06 lr: 0.000500 loss: 0.1547 (0.1647) loss_classifier: 0.0229 (0.0262) loss_box_reg: 0.0094 (0.0123) loss_mask: 0.1069 (0.1176) loss_objectness: 0.0003 (0.0009) loss_rpn_box_reg: 0.0057 (0.0077) time: 0.6276 data: 0.0068 max mem: 5310\n", - "Epoch: [3] [59/60] eta: 0:00:00 lr: 0.000500 loss: 0.1461 (0.1655) loss_classifier: 0.0218 (0.0258) loss_box_reg: 0.0084 (0.0123) loss_mask: 0.1061 (0.1185) loss_objectness: 0.0003 (0.0009) loss_rpn_box_reg: 0.0056 (0.0079) time: 0.6126 data: 0.0068 max mem: 5310\n", - "Epoch: [3] Total time: 0:00:39 (0.6519 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:18 model_time: 0.1630 (0.1630) evaluator_time: 0.0038 (0.0038) time: 0.3705 data: 0.2021 max mem: 5310\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1125 (0.1124) evaluator_time: 0.0037 (0.0057) time: 0.1215 data: 0.0036 max mem: 5310\n", - "Test: Total time: 0:00:06 (0.1294 s / it)\n", - "Averaged stats: model_time: 0.1125 (0.1124) evaluator_time: 0.0037 (0.0057)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.814\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.991\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.953\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.543\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.823\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.371\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.855\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.855\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.860\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.760\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.991\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.918\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.478\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.345\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.801\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.801\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.750\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.805\n", - "Epoch: [4] [ 0/60] eta: 0:01:06 lr: 0.000500 loss: 0.1322 (0.1322) loss_classifier: 0.0270 (0.0270) loss_box_reg: 0.0052 (0.0052) loss_mask: 0.0926 (0.0926) loss_objectness: 0.0006 (0.0006) loss_rpn_box_reg: 0.0069 (0.0069) time: 1.1061 data: 0.5225 max mem: 5310\n", - "Epoch: [4] [10/60] eta: 0:00:32 lr: 0.000500 loss: 0.1779 (0.1740) loss_classifier: 0.0281 (0.0279) loss_box_reg: 0.0109 (0.0119) loss_mask: 0.1205 (0.1249) loss_objectness: 0.0003 (0.0020) loss_rpn_box_reg: 0.0063 (0.0073) time: 0.6471 data: 0.0514 max mem: 5310\n", - "Epoch: [4] [20/60] eta: 0:00:25 lr: 0.000500 loss: 0.1608 (0.1713) loss_classifier: 0.0286 (0.0280) loss_box_reg: 0.0116 (0.0126) loss_mask: 0.1129 (0.1215) loss_objectness: 0.0003 (0.0015) loss_rpn_box_reg: 0.0059 (0.0077) time: 0.6207 data: 0.0055 max mem: 5345\n", - "Epoch: [4] [30/60] eta: 0:00:19 lr: 0.000500 loss: 0.1483 (0.1668) loss_classifier: 0.0242 (0.0268) loss_box_reg: 0.0076 (0.0124) loss_mask: 0.1040 (0.1189) loss_objectness: 0.0004 (0.0012) loss_rpn_box_reg: 0.0059 (0.0075) time: 0.6336 data: 0.0070 max mem: 5345\n", - "Epoch: [4] [40/60] eta: 0:00:12 lr: 0.000500 loss: 0.1355 (0.1625) loss_classifier: 0.0154 (0.0258) loss_box_reg: 0.0067 (0.0115) loss_mask: 0.1040 (0.1165) loss_objectness: 0.0003 (0.0011) loss_rpn_box_reg: 0.0070 (0.0075) time: 0.6434 data: 0.0075 max mem: 5345\n", - "Epoch: [4] [50/60] eta: 0:00:06 lr: 0.000500 loss: 0.1472 (0.1608) loss_classifier: 0.0202 (0.0249) loss_box_reg: 0.0074 (0.0112) loss_mask: 0.1040 (0.1161) loss_objectness: 0.0003 (0.0011) loss_rpn_box_reg: 0.0060 (0.0076) time: 0.6428 data: 0.0071 max mem: 5345\n", - "Epoch: [4] [59/60] eta: 0:00:00 lr: 0.000500 loss: 0.1477 (0.1613) loss_classifier: 0.0225 (0.0251) loss_box_reg: 0.0092 (0.0113) loss_mask: 0.1126 (0.1163) loss_objectness: 0.0003 (0.0010) loss_rpn_box_reg: 0.0065 (0.0076) time: 0.6340 data: 0.0069 max mem: 5345\n", - "Epoch: [4] Total time: 0:00:38 (0.6423 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:17 model_time: 0.1500 (0.1500) evaluator_time: 0.0040 (0.0040) time: 0.3557 data: 0.2002 max mem: 5345\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1121 (0.1121) evaluator_time: 0.0034 (0.0057) time: 0.1219 data: 0.0034 max mem: 5345\n", - "Test: Total time: 0:00:06 (0.1286 s / it)\n", - "Averaged stats: model_time: 0.1121 (0.1121) evaluator_time: 0.0034 (0.0057)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.820\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.991\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.953\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.537\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.376\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.860\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.860\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.775\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.866\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.769\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.991\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.910\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.447\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.779\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.347\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.806\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.806\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.738\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.811\n", - "Epoch: [5] [ 0/60] eta: 0:00:52 lr: 0.000500 loss: 0.1502 (0.1502) loss_classifier: 0.0202 (0.0202) loss_box_reg: 0.0114 (0.0114) loss_mask: 0.1132 (0.1132) loss_objectness: 0.0002 (0.0002) loss_rpn_box_reg: 0.0052 (0.0052) time: 0.8670 data: 0.2655 max mem: 5345\n", - "Epoch: [5] [10/60] eta: 0:00:32 lr: 0.000500 loss: 0.1636 (0.1717) loss_classifier: 0.0243 (0.0294) loss_box_reg: 0.0138 (0.0136) loss_mask: 0.1141 (0.1192) loss_objectness: 0.0003 (0.0006) loss_rpn_box_reg: 0.0084 (0.0090) time: 0.6526 data: 0.0301 max mem: 5345\n", - "Epoch: [5] [20/60] eta: 0:00:25 lr: 0.000500 loss: 0.1494 (0.1601) loss_classifier: 0.0224 (0.0261) loss_box_reg: 0.0092 (0.0117) loss_mask: 0.1076 (0.1138) loss_objectness: 0.0003 (0.0005) loss_rpn_box_reg: 0.0083 (0.0080) time: 0.6330 data: 0.0066 max mem: 5345\n", - "Epoch: [5] [30/60] eta: 0:00:18 lr: 0.000500 loss: 0.1496 (0.1594) loss_classifier: 0.0195 (0.0251) loss_box_reg: 0.0092 (0.0113) loss_mask: 0.1076 (0.1146) loss_objectness: 0.0002 (0.0005) loss_rpn_box_reg: 0.0075 (0.0079) time: 0.6204 data: 0.0066 max mem: 5345\n", - "Epoch: [5] [40/60] eta: 0:00:12 lr: 0.000500 loss: 0.1606 (0.1639) loss_classifier: 0.0249 (0.0260) loss_box_reg: 0.0108 (0.0124) loss_mask: 0.1124 (0.1169) loss_objectness: 0.0003 (0.0005) loss_rpn_box_reg: 0.0072 (0.0081) time: 0.6338 data: 0.0067 max mem: 5345\n", - "Epoch: [5] [50/60] eta: 0:00:06 lr: 0.000500 loss: 0.1578 (0.1641) loss_classifier: 0.0230 (0.0257) loss_box_reg: 0.0093 (0.0117) loss_mask: 0.1112 (0.1180) loss_objectness: 0.0004 (0.0006) loss_rpn_box_reg: 0.0055 (0.0080) time: 0.6592 data: 0.0070 max mem: 5345\n", - "Epoch: [5] [59/60] eta: 0:00:00 lr: 0.000500 loss: 0.1517 (0.1626) loss_classifier: 0.0220 (0.0252) loss_box_reg: 0.0081 (0.0111) loss_mask: 0.1121 (0.1179) loss_objectness: 0.0003 (0.0007) loss_rpn_box_reg: 0.0053 (0.0078) time: 0.6494 data: 0.0070 max mem: 5345\n", - "Epoch: [5] Total time: 0:00:38 (0.6447 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:17 model_time: 0.1581 (0.1581) evaluator_time: 0.0041 (0.0041) time: 0.3526 data: 0.1888 max mem: 5345\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1133 (0.1119) evaluator_time: 0.0036 (0.0058) time: 0.1216 data: 0.0035 max mem: 5345\n", - "Test: Total time: 0:00:06 (0.1288 s / it)\n", - "Averaged stats: model_time: 0.1133 (0.1119) evaluator_time: 0.0036 (0.0058)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.818\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.959\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.531\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.828\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.374\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.858\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.858\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.863\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.764\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.916\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.484\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.350\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.806\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.806\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.809\n", - "Epoch: [6] [ 0/60] eta: 0:01:15 lr: 0.000050 loss: 0.1268 (0.1268) loss_classifier: 0.0136 (0.0136) loss_box_reg: 0.0076 (0.0076) loss_mask: 0.0992 (0.0992) loss_objectness: 0.0001 (0.0001) loss_rpn_box_reg: 0.0063 (0.0063) time: 1.2659 data: 0.4840 max mem: 5345\n", - "Epoch: [6] [10/60] eta: 0:00:34 lr: 0.000050 loss: 0.1542 (0.1612) loss_classifier: 0.0221 (0.0240) loss_box_reg: 0.0120 (0.0117) loss_mask: 0.1061 (0.1164) loss_objectness: 0.0002 (0.0004) loss_rpn_box_reg: 0.0063 (0.0087) time: 0.6829 data: 0.0505 max mem: 5345\n", - "Epoch: [6] [20/60] eta: 0:00:25 lr: 0.000050 loss: 0.1531 (0.1596) loss_classifier: 0.0212 (0.0233) loss_box_reg: 0.0076 (0.0108) loss_mask: 0.1123 (0.1169) loss_objectness: 0.0003 (0.0006) loss_rpn_box_reg: 0.0059 (0.0080) time: 0.6122 data: 0.0072 max mem: 5345\n", - "Epoch: [6] [30/60] eta: 0:00:18 lr: 0.000050 loss: 0.1465 (0.1650) loss_classifier: 0.0202 (0.0256) loss_box_reg: 0.0058 (0.0120) loss_mask: 0.1123 (0.1186) loss_objectness: 0.0004 (0.0009) loss_rpn_box_reg: 0.0055 (0.0078) time: 0.5959 data: 0.0069 max mem: 5345\n", - "Epoch: [6] [40/60] eta: 0:00:12 lr: 0.000050 loss: 0.1378 (0.1603) loss_classifier: 0.0256 (0.0268) loss_box_reg: 0.0068 (0.0109) loss_mask: 0.0993 (0.1142) loss_objectness: 0.0005 (0.0009) loss_rpn_box_reg: 0.0052 (0.0074) time: 0.6272 data: 0.0066 max mem: 5345\n", - "Epoch: [6] [50/60] eta: 0:00:06 lr: 0.000050 loss: 0.1372 (0.1623) loss_classifier: 0.0256 (0.0264) loss_box_reg: 0.0075 (0.0115) loss_mask: 0.1033 (0.1161) loss_objectness: 0.0003 (0.0009) loss_rpn_box_reg: 0.0058 (0.0075) time: 0.6603 data: 0.0066 max mem: 5345\n", - "Epoch: [6] [59/60] eta: 0:00:00 lr: 0.000050 loss: 0.1372 (0.1619) loss_classifier: 0.0204 (0.0260) loss_box_reg: 0.0082 (0.0116) loss_mask: 0.1074 (0.1159) loss_objectness: 0.0004 (0.0008) loss_rpn_box_reg: 0.0070 (0.0075) time: 0.6463 data: 0.0067 max mem: 5345\n", - "Epoch: [6] Total time: 0:00:38 (0.6395 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:17 model_time: 0.1552 (0.1552) evaluator_time: 0.0040 (0.0040) time: 0.3581 data: 0.1974 max mem: 5345\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1129 (0.1116) evaluator_time: 0.0035 (0.0057) time: 0.1212 data: 0.0034 max mem: 5345\n", - "Test: Total time: 0:00:06 (0.1282 s / it)\n", - "Averaged stats: model_time: 0.1129 (0.1116) evaluator_time: 0.0035 (0.0057)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.820\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.960\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.378\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.861\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.861\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.867\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.767\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.916\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.462\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.776\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.349\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.809\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.809\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.813\n", - "Epoch: [7] [ 0/60] eta: 0:00:53 lr: 0.000050 loss: 0.1048 (0.1048) loss_classifier: 0.0119 (0.0119) loss_box_reg: 0.0030 (0.0030) loss_mask: 0.0879 (0.0879) loss_objectness: 0.0000 (0.0000) loss_rpn_box_reg: 0.0020 (0.0020) time: 0.8976 data: 0.3350 max mem: 5345\n", - "Epoch: [7] [10/60] eta: 0:00:31 lr: 0.000050 loss: 0.1401 (0.1356) loss_classifier: 0.0189 (0.0191) loss_box_reg: 0.0068 (0.0071) loss_mask: 0.1048 (0.1042) loss_objectness: 0.0002 (0.0003) loss_rpn_box_reg: 0.0039 (0.0049) time: 0.6264 data: 0.0366 max mem: 5345\n", - "Epoch: [7] [20/60] eta: 0:00:25 lr: 0.000050 loss: 0.1401 (0.1490) loss_classifier: 0.0189 (0.0204) loss_box_reg: 0.0068 (0.0087) loss_mask: 0.1070 (0.1134) loss_objectness: 0.0003 (0.0007) loss_rpn_box_reg: 0.0044 (0.0057) time: 0.6145 data: 0.0077 max mem: 5345\n", - "Epoch: [7] [30/60] eta: 0:00:19 lr: 0.000050 loss: 0.1424 (0.1498) loss_classifier: 0.0211 (0.0213) loss_box_reg: 0.0081 (0.0087) loss_mask: 0.1076 (0.1126) loss_objectness: 0.0005 (0.0008) loss_rpn_box_reg: 0.0058 (0.0065) time: 0.6514 data: 0.0081 max mem: 5370\n", - "Epoch: [7] [40/60] eta: 0:00:12 lr: 0.000050 loss: 0.1435 (0.1523) loss_classifier: 0.0230 (0.0232) loss_box_reg: 0.0090 (0.0091) loss_mask: 0.1069 (0.1127) loss_objectness: 0.0004 (0.0007) loss_rpn_box_reg: 0.0057 (0.0065) time: 0.6590 data: 0.0071 max mem: 5370\n", - "Epoch: [7] [50/60] eta: 0:00:06 lr: 0.000050 loss: 0.1519 (0.1545) loss_classifier: 0.0251 (0.0239) loss_box_reg: 0.0092 (0.0099) loss_mask: 0.1141 (0.1131) loss_objectness: 0.0003 (0.0007) loss_rpn_box_reg: 0.0056 (0.0069) time: 0.6489 data: 0.0068 max mem: 5370\n", - "Epoch: [7] [59/60] eta: 0:00:00 lr: 0.000050 loss: 0.1533 (0.1590) loss_classifier: 0.0280 (0.0257) loss_box_reg: 0.0101 (0.0109) loss_mask: 0.1141 (0.1143) loss_objectness: 0.0003 (0.0008) loss_rpn_box_reg: 0.0084 (0.0073) time: 0.6595 data: 0.0072 max mem: 5370\n", - "Epoch: [7] Total time: 0:00:38 (0.6479 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:18 model_time: 0.1607 (0.1607) evaluator_time: 0.0041 (0.0041) time: 0.3618 data: 0.1955 max mem: 5370\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1134 (0.1118) evaluator_time: 0.0037 (0.0058) time: 0.1218 data: 0.0036 max mem: 5370\n", - "Test: Total time: 0:00:06 (0.1283 s / it)\n", - "Averaged stats: model_time: 0.1134 (0.1118) evaluator_time: 0.0037 (0.0058)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.821\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.960\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.832\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.380\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.862\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.862\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.868\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.768\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.917\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.776\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.350\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.809\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.809\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.813\n", - "Epoch: [8] [ 0/60] eta: 0:00:56 lr: 0.000050 loss: 0.1368 (0.1368) loss_classifier: 0.0151 (0.0151) loss_box_reg: 0.0078 (0.0078) loss_mask: 0.1098 (0.1098) loss_objectness: 0.0004 (0.0004) loss_rpn_box_reg: 0.0038 (0.0038) time: 0.9355 data: 0.2996 max mem: 5370\n", - "Epoch: [8] [10/60] eta: 0:00:31 lr: 0.000050 loss: 0.1555 (0.1604) loss_classifier: 0.0233 (0.0258) loss_box_reg: 0.0086 (0.0107) loss_mask: 0.1098 (0.1175) loss_objectness: 0.0002 (0.0003) loss_rpn_box_reg: 0.0047 (0.0061) time: 0.6251 data: 0.0334 max mem: 5370\n", - "Epoch: [8] [20/60] eta: 0:00:25 lr: 0.000050 loss: 0.1418 (0.1505) loss_classifier: 0.0192 (0.0216) loss_box_reg: 0.0067 (0.0087) loss_mask: 0.1037 (0.1131) loss_objectness: 0.0002 (0.0004) loss_rpn_box_reg: 0.0054 (0.0068) time: 0.6100 data: 0.0068 max mem: 5370\n", - "Epoch: [8] [30/60] eta: 0:00:19 lr: 0.000050 loss: 0.1427 (0.1506) loss_classifier: 0.0195 (0.0224) loss_box_reg: 0.0062 (0.0088) loss_mask: 0.1037 (0.1120) loss_objectness: 0.0003 (0.0004) loss_rpn_box_reg: 0.0065 (0.0070) time: 0.6461 data: 0.0068 max mem: 5370\n", - "Epoch: [8] [40/60] eta: 0:00:12 lr: 0.000050 loss: 0.1516 (0.1536) loss_classifier: 0.0240 (0.0236) loss_box_reg: 0.0096 (0.0103) loss_mask: 0.1037 (0.1117) loss_objectness: 0.0003 (0.0006) loss_rpn_box_reg: 0.0072 (0.0074) time: 0.6651 data: 0.0068 max mem: 5370\n", - "Epoch: [8] [50/60] eta: 0:00:06 lr: 0.000050 loss: 0.1570 (0.1595) loss_classifier: 0.0253 (0.0244) loss_box_reg: 0.0110 (0.0114) loss_mask: 0.1060 (0.1156) loss_objectness: 0.0003 (0.0006) loss_rpn_box_reg: 0.0074 (0.0074) time: 0.6429 data: 0.0067 max mem: 5370\n", - "Epoch: [8] [59/60] eta: 0:00:00 lr: 0.000050 loss: 0.1545 (0.1596) loss_classifier: 0.0253 (0.0247) loss_box_reg: 0.0092 (0.0111) loss_mask: 0.1118 (0.1157) loss_objectness: 0.0003 (0.0007) loss_rpn_box_reg: 0.0074 (0.0074) time: 0.6498 data: 0.0066 max mem: 5370\n", - "Epoch: [8] Total time: 0:00:38 (0.6474 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:17 model_time: 0.1581 (0.1581) evaluator_time: 0.0041 (0.0041) time: 0.3566 data: 0.1928 max mem: 5370\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1125 (0.1125) evaluator_time: 0.0036 (0.0058) time: 0.1217 data: 0.0036 max mem: 5370\n", - "Test: Total time: 0:00:06 (0.1290 s / it)\n", - "Averaged stats: model_time: 0.1125 (0.1125) evaluator_time: 0.0036 (0.0058)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.826\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.960\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.837\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.865\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.865\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.870\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.772\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.917\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.782\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.812\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.812\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.815\n", - "Epoch: [9] [ 0/60] eta: 0:00:52 lr: 0.000005 loss: 0.1495 (0.1495) loss_classifier: 0.0158 (0.0158) loss_box_reg: 0.0093 (0.0093) loss_mask: 0.1086 (0.1086) loss_objectness: 0.0054 (0.0054) loss_rpn_box_reg: 0.0105 (0.0105) time: 0.8829 data: 0.2796 max mem: 5370\n", - "Epoch: [9] [10/60] eta: 0:00:32 lr: 0.000005 loss: 0.1645 (0.1607) loss_classifier: 0.0271 (0.0248) loss_box_reg: 0.0093 (0.0111) loss_mask: 0.1086 (0.1156) loss_objectness: 0.0007 (0.0020) loss_rpn_box_reg: 0.0077 (0.0072) time: 0.6560 data: 0.0323 max mem: 5370\n", - "Epoch: [9] [20/60] eta: 0:00:25 lr: 0.000005 loss: 0.1444 (0.1535) loss_classifier: 0.0218 (0.0224) loss_box_reg: 0.0078 (0.0095) loss_mask: 0.1078 (0.1142) loss_objectness: 0.0004 (0.0013) loss_rpn_box_reg: 0.0036 (0.0060) time: 0.6136 data: 0.0072 max mem: 5370\n", - "Epoch: [9] [30/60] eta: 0:00:18 lr: 0.000005 loss: 0.1361 (0.1559) loss_classifier: 0.0218 (0.0234) loss_box_reg: 0.0078 (0.0101) loss_mask: 0.1026 (0.1149) loss_objectness: 0.0004 (0.0010) loss_rpn_box_reg: 0.0052 (0.0065) time: 0.6150 data: 0.0069 max mem: 5370\n", - "Epoch: [9] [40/60] eta: 0:00:12 lr: 0.000005 loss: 0.1613 (0.1622) loss_classifier: 0.0243 (0.0252) loss_box_reg: 0.0092 (0.0118) loss_mask: 0.1054 (0.1169) loss_objectness: 0.0003 (0.0010) loss_rpn_box_reg: 0.0072 (0.0075) time: 0.6652 data: 0.0075 max mem: 5370\n", - "Epoch: [9] [50/60] eta: 0:00:06 lr: 0.000005 loss: 0.1473 (0.1602) loss_classifier: 0.0232 (0.0251) loss_box_reg: 0.0084 (0.0116) loss_mask: 0.1102 (0.1151) loss_objectness: 0.0004 (0.0009) loss_rpn_box_reg: 0.0070 (0.0075) time: 0.6760 data: 0.0074 max mem: 5370\n", - "Epoch: [9] [59/60] eta: 0:00:00 lr: 0.000005 loss: 0.1391 (0.1572) loss_classifier: 0.0203 (0.0244) loss_box_reg: 0.0067 (0.0109) loss_mask: 0.1049 (0.1136) loss_objectness: 0.0004 (0.0010) loss_rpn_box_reg: 0.0066 (0.0072) time: 0.6440 data: 0.0068 max mem: 5370\n", - "Epoch: [9] Total time: 0:00:38 (0.6447 s / it)\n", - "creating index...\n", - "index created!\n", - "Test: [ 0/50] eta: 0:00:17 model_time: 0.1590 (0.1590) evaluator_time: 0.0039 (0.0039) time: 0.3443 data: 0.1797 max mem: 5370\n", - "Test: [49/50] eta: 0:00:00 model_time: 0.1123 (0.1119) evaluator_time: 0.0035 (0.0057) time: 0.1212 data: 0.0034 max mem: 5370\n", - "Test: Total time: 0:00:06 (0.1280 s / it)\n", - "Averaged stats: model_time: 0.1123 (0.1119) evaluator_time: 0.0035 (0.0057)\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "Accumulating evaluation results...\n", - "DONE (t=0.01s).\n", - "IoU metric: bbox\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.828\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.960\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.839\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.867\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.867\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.873\n", - "IoU metric: segm\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.771\n", - " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.990\n", - " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.917\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.780\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.811\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.811\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.762\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.814\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Z6mYGFLxkO8F", - "colab_type": "text" - }, - "source": [ - "Now that training has finished, let's have a look at what it actually predicts in a test image" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "YHwIdxH76uPj", - "colab_type": "code", - "colab": {} - }, - "source": [ - "# pick one image from the test set\n", - "img, _ = dataset_test[0]\n", - "# put the model in evaluation mode\n", - "model.eval()\n", - "with torch.no_grad():\n", - " prediction = model([img.to(device)])" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DmN602iKsuey", - "colab_type": "text" - }, - "source": [ - "Printing the prediction shows that we have a list of dictionaries. Each element of the list corresponds to a different image. As we have a single image, there is a single dictionary in the list.\n", - "The dictionary contains the predictions for the image we passed. In this case, we can see that it contains `boxes`, `labels`, `masks` and `scores` as fields." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Lkmb3qUu6zw3", - "colab_type": "code", - "outputId": "fe5616ea-7e27-4a29-d070-358bee6a1be8", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 527 - } - }, - "source": [ - "prediction" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[{'boxes': tensor([[ 61.6491, 35.3001, 197.0657, 327.6245],\n", - " [276.3604, 21.6470, 291.0668, 73.5886],\n", - " [ 78.8921, 43.7346, 201.9858, 207.4100]], device='cuda:0'),\n", - " 'labels': tensor([1, 1, 1], device='cuda:0'),\n", - " 'masks': tensor([[[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]]], device='cuda:0'),\n", - " 'scores': tensor([0.9995, 0.8236, 0.0713], device='cuda:0')}]" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RwT21rzotFbH", - "colab_type": "text" - }, - "source": [ - "Let's inspect the image and the predicted segmentation masks.\n", - "\n", - "For that, we need to convert the image, which has been rescaled to 0-1 and had the channels flipped so that we have it in `[C, H, W]` format." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "bpqN9t1u7B2J", - "colab_type": "code", - "outputId": "13b60c23-dce3-4a0c-fdf0-54eae39e5cc6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 366 - } - }, - "source": [ - "Image.fromarray(img.mul(255).permute(1, 2, 0).byte().numpy())" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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CsRKAL/MEZEY4paVYcbud6BpLa5YX1rRHo9vtiVu1X/CT2pGm8Bsx/P+g1toG/leZ0MzC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\n7wKjSfns1R0ZoeYhcLfrT3lWVQtsyAjsZTpr3pQZ8cmTYu292G5L273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fxqHv375BRA/q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\n/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dYiEpmdZMYQVTXX6gmP1jqVorUWEQIAhhCIYkgcgIkBgSkgYeA2JgHzV8igqDAgBr56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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 15 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M58J3O9OtT1G", - "colab_type": "text" - }, - "source": [ - "And let's now visualize the top predicted segmentation mask. The masks are predicted as `[N, 1, H, W]`, where `N` is the number of predictions, and are probability maps between 0-1." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "5v5S3bm07SO1", - "colab_type": "code", - "outputId": "502433ff-ac0d-4388-d79c-1d94cf26dc38", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 366 - } - }, - "source": [ - "Image.fromarray(prediction[0]['masks'][0, 0].mul(255).byte().cpu().numpy())" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 19 - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0EZCVtCPunrT", - "colab_type": "text" - }, - "source": [ - "Looks pretty good!\n", - "\n", - "## Wrapping up\n", - "\n", - "In this tutorial, you have learned how to create your own training pipeline for instance segmentation models, on a custom dataset.\n", - "For that, you wrote a `torch.utils.data.Dataset` class that returns the images and the ground truth boxes and segmentation masks. You also leveraged a Mask R-CNN model pre-trained on COCO train2017 in order to perform transfer learning on this new dataset.\n", - "\n", - "For a more complete example, which includes multi-machine / multi-gpu training, check `references/detection/train.py`, which is present in the [torchvision GitHub repo](https://github.com/pytorch/vision/tree/v0.3.0/references/detection). \n", - "\n" - ] - } - ] -} \ No newline at end of file diff --git a/_static/tv-training-code.py b/_static/tv-training-code.py index 890f8fe9223..1f3c1a0b761 100644 --- a/_static/tv-training-code.py +++ b/_static/tv-training-code.py @@ -1,21 +1,126 @@ -# Sample code from the TorchVision 0.3 Object Detection Finetuning Tutorial -# http://pytorch.org/tutorials/intermediate/torchvision_tutorial.html +# -*- coding: utf-8 -*- +""" +TorchVision Object Detection Finetuning Tutorial +==================================================== +""" + +###################################################################### +# For this tutorial, we will be finetuning a pre-trained `Mask +# R-CNN `__ model on the `Penn-Fudan +# Database for Pedestrian Detection and +# Segmentation `__. It contains +# 170 images with 345 instances of pedestrians, and we will use it to +# illustrate how to use the new features in torchvision in order to train +# an object detection and instance segmentation model on a custom dataset. +# +# +# .. note :: +# +# This tutorial works only with torchvision version >=0.16 or nightly. +# +# +# Defining the Dataset +# -------------------- +# +# The reference scripts for training object detection, instance +# segmentation and person keypoint detection allows for easily supporting +# adding new custom datasets. The dataset should inherit from the standard +# ``torch.utils.data.Dataset`` class, and implement ``__len__`` and +# ``__getitem__``. +# +# The only specificity that we require is that the dataset ``__getitem__`` +# should return a tuple: +# +# - image: :class:`torchvision.tv_tensors.Image` of shape ``[3, H, W]``, a pure tensor, or a PIL Image of size ``(H, W)`` +# - target: a dict containing the following fields +# +# - ``boxes``, :class:`torchvision.tv_tensors.BoundingBoxes` of shape ``[N, 4]``: +# the coordinates of the ``N`` bounding boxes in ``[x0, y0, x1, y1]`` format, ranging from ``0`` +# to ``W`` and ``0`` to ``H`` +# - ``labels``, integer :class:`torch.Tensor` of shape ``[N]``: the label for each bounding box. +# ``0`` represents always the background class. +# - ``image_id``, int: an image identifier. It should be +# unique between all the images in the dataset, and is used during +# evaluation +# - ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used +# during evaluation with the COCO metric, to separate the metric +# scores between small, medium and large boxes. +# - ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with ``iscrowd=True`` will be +# ignored during evaluation. +# - (optionally) ``masks``, :class:`torchvision.tv_tensors.Mask` of shape ``[N, H, W]``: the segmentation +# masks for each one of the objects +# +# If your dataset is compliant with above requirements then it will work for both +# training and evaluation codes from the reference script. Evaluation code will use scripts from +# ``pycocotools`` which can be installed with ``pip install pycocotools``. +# +# .. note :: +# For Windows, please install ``pycocotools`` from `gautamchitnis `__ with command +# +# ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI`` +# +# One note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class, +# you should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can +# define ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has both +# classes, your ``labels`` tensor should look like ``[1, 2]``. +# +# Additionally, if you want to use aspect ratio grouping during training +# (so that each batch only contains images with similar aspect ratios), +# then it is recommended to also implement a ``get_height_and_width`` +# method, which returns the height and the width of the image. If this +# method is not provided, we query all elements of the dataset via +# ``__getitem__`` , which loads the image in memory and is slower than if +# a custom method is provided. +# +# Writing a custom dataset for PennFudan +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Let’s write a dataset for the PennFudan dataset. After `downloading and +# extracting the zip +# file `__, we +# have the following folder structure: +# +# :: +# +# PennFudanPed/ +# PedMasks/ +# FudanPed00001_mask.png +# FudanPed00002_mask.png +# FudanPed00003_mask.png +# FudanPed00004_mask.png +# ... +# PNGImages/ +# FudanPed00001.png +# FudanPed00002.png +# FudanPed00003.png +# FudanPed00004.png +# +# Here is one example of a pair of images and segmentation masks +# +# .. image:: ../../_static/img/tv_tutorial/tv_image01.png +# +# .. image:: ../../_static/img/tv_tutorial/tv_image02.png +# +# So each image has a corresponding +# segmentation mask, where each color correspond to a different instance. +# Let’s write a :class:`torch.utils.data.Dataset` class for this dataset. +# In the code below, we are wrapping images, bounding boxes and masks into +# ``torchvision.TVTensor`` classes so that we will be able to apply torchvision +# built-in transformations (`new Transforms API `_) +# for the given object detection and segmentation task. +# Namely, image tensors will be wrapped by :class:`torchvision.tv_tensors.Image`, bounding boxes into +# :class:`torchvision.tv_tensors.BoundingBoxes` and masks into :class:`torchvision.tv_tensors.Mask`. +# As ``torchvision.TVTensor`` are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain +# :class:`torch.Tensor` API. For more information about torchvision ``tv_tensors`` see +# `this documentation `_. import os import torch -import torchvision -from torchvision.models.detection.faster_rcnn import FastRCNNPredictor -from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor from torchvision.io import read_image from torchvision.ops.boxes import masks_to_boxes -from torchvision import datapoints as dp +from torchvision import tv_tensors from torchvision.transforms.v2 import functional as F -from torchvision.transforms import v2 as T - - -from engine import train_one_epoch, evaluate -import utils class PennFudanDataset(torch.utils.data.Dataset): @@ -54,12 +159,12 @@ def __getitem__(self, idx): # suppose all instances are not crowd iscrowd = torch.zeros((num_objs,), dtype=torch.int64) - # Wrap sample and targets into torchvision datapoints: - img = dp.Image(img) + # Wrap sample and targets into torchvision tv_tensors: + img = tv_tensors.Image(img) target = {} - target["boxes"] = dp.BoundingBoxes(boxes, format="XYXY", canvas_size=F.get_size(img)) - target["masks"] = dp.Mask(masks) + target["boxes"] = tv_tensors.BoundingBoxes(boxes, format="XYXY", canvas_size=F.get_size(img)) + target["masks"] = tv_tensors.Mask(masks) target["labels"] = labels target["image_id"] = image_id target["area"] = area @@ -73,6 +178,115 @@ def __getitem__(self, idx): def __len__(self): return len(self.imgs) +###################################################################### +# That’s all for the dataset. Now let’s define a model that can perform +# predictions on this dataset. +# +# Defining your model +# ------------------- +# +# In this tutorial, we will be using `Mask +# R-CNN `__, which is based on top of +# `Faster R-CNN `__. Faster R-CNN is a +# model that predicts both bounding boxes and class scores for potential +# objects in the image. +# +# .. image:: ../../_static/img/tv_tutorial/tv_image03.png +# +# Mask R-CNN adds an extra branch +# into Faster R-CNN, which also predicts segmentation masks for each +# instance. +# +# .. image:: ../../_static/img/tv_tutorial/tv_image04.png +# +# There are two common +# situations where one might want +# to modify one of the available models in TorchVision Model Zoo. The first +# is when we want to start from a pre-trained model, and just finetune the +# last layer. The other is when we want to replace the backbone of the +# model with a different one (for faster predictions, for example). +# +# Let’s go see how we would do one or another in the following sections. +# +# 1 - Finetuning from a pretrained model +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# Let’s suppose that you want to start from a model pre-trained on COCO +# and want to finetune it for your particular classes. Here is a possible +# way of doing it: + + +import torchvision +from torchvision.models.detection.faster_rcnn import FastRCNNPredictor + +# load a model pre-trained on COCO +model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") + +# replace the classifier with a new one, that has +# num_classes which is user-defined +num_classes = 2 # 1 class (person) + background +# get number of input features for the classifier +in_features = model.roi_heads.box_predictor.cls_score.in_features +# replace the pre-trained head with a new one +model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) + +###################################################################### +# 2 - Modifying the model to add a different backbone +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +import torchvision +from torchvision.models.detection import FasterRCNN +from torchvision.models.detection.rpn import AnchorGenerator + +# load a pre-trained model for classification and return +# only the features +backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features +# ``FasterRCNN`` needs to know the number of +# output channels in a backbone. For mobilenet_v2, it's 1280 +# so we need to add it here +backbone.out_channels = 1280 + +# let's make the RPN generate 5 x 3 anchors per spatial +# location, with 5 different sizes and 3 different aspect +# ratios. We have a Tuple[Tuple[int]] because each feature +# map could potentially have different sizes and +# aspect ratios +anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), + aspect_ratios=((0.5, 1.0, 2.0),)) + +# let's define what are the feature maps that we will +# use to perform the region of interest cropping, as well as +# the size of the crop after rescaling. +# if your backbone returns a Tensor, featmap_names is expected to +# be [0]. More generally, the backbone should return an +# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which +# feature maps to use. +roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], + output_size=7, + sampling_ratio=2) + +# put the pieces together inside a Faster-RCNN model +model = FasterRCNN(backbone, + num_classes=2, + rpn_anchor_generator=anchor_generator, + box_roi_pool=roi_pooler) + +###################################################################### +# Object detection and instance segmentation model for PennFudan Dataset +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# In our case, we want to finetune from a pre-trained model, given that +# our dataset is very small, so we will be following approach number 1. +# +# Here we want to also compute the instance segmentation masks, so we will +# be using Mask R-CNN: + + +import torchvision +from torchvision.models.detection.faster_rcnn import FastRCNNPredictor +from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor + def get_model_instance_segmentation(num_classes): # load an instance segmentation model pre-trained on COCO @@ -88,15 +302,42 @@ def get_model_instance_segmentation(num_classes): hidden_layer = 256 # and replace the mask predictor with a new one model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, - hidden_layer, - num_classes) + hidden_layer, + num_classes) return model +###################################################################### +# That’s it, this will make ``model`` be ready to be trained and evaluated +# on your custom dataset. +# +# Putting everything together +# --------------------------- +# +# In ``references/detection/``, we have a number of helper functions to +# simplify training and evaluating detection models. Here, we will use +# ``references/detection/engine.py`` and ``references/detection/utils.py``. +# Just download everything under ``references/detection`` to your folder and use them here. +# On Linux if you have ``wget``, you can download them using below commands: + +os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/engine.py") +os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/utils.py") +os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_utils.py") +os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_eval.py") +os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/transforms.py") + +# Since v0.15.0 torchvision provides `new Transforms API `_ +# to easily write data augmentation pipelines for Object Detection and Segmentation tasks. +# +# Let’s write some helper functions for data augmentation / +# transformation: + +from torchvision.transforms import v2 as T + + def get_transform(train): transforms = [] - transforms.append(T.ToImage()) if train: transforms.append(T.RandomHorizontalFlip(0.5)) transforms.append(T.ToDtype(torch.float, scale=True)) @@ -104,57 +345,166 @@ def get_transform(train): return T.Compose(transforms) -def main(): - # train on the GPU or on the CPU, if a GPU is not available - device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') - - # our dataset has two classes only - background and person - num_classes = 2 - # use our dataset and defined transformations - dataset = PennFudanDataset('PennFudanPed', get_transform(train=True)) - dataset_test = PennFudanDataset('PennFudanPed', get_transform(train=False)) - - # split the dataset in train and test set - indices = torch.randperm(len(dataset)).tolist() - dataset = torch.utils.data.Subset(dataset, indices[:-50]) - dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:]) - - # define training and validation data loaders - data_loader = torch.utils.data.DataLoader( - dataset, batch_size=2, shuffle=True, num_workers=4, - collate_fn=utils.collate_fn) - - data_loader_test = torch.utils.data.DataLoader( - dataset_test, batch_size=1, shuffle=False, num_workers=4, - collate_fn=utils.collate_fn) - - # get the model using our helper function - model = get_model_instance_segmentation(num_classes) - - # move model to the right device - model.to(device) - - # construct an optimizer - params = [p for p in model.parameters() if p.requires_grad] - optimizer = torch.optim.SGD(params, lr=0.005, - momentum=0.9, weight_decay=0.0005) - # and a learning rate scheduler - lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, - step_size=3, - gamma=0.1) - - # let's train it for 10 epochs - num_epochs = 10 - - for epoch in range(num_epochs): - # train for one epoch, printing every 10 iterations - train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10) - # update the learning rate - lr_scheduler.step() - # evaluate on the test dataset - evaluate(model, data_loader_test, device=device) - - print("That's it!") - -if __name__ == "__main__": - main() +# Testing ``forward()`` method (Optional) +# --------------------------------------- +# +# Before iterating over the dataset, it's good to see what the model +# expects during training and inference time on sample data. +import utils + + +model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") +dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True)) +data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=2, + shuffle=True, + num_workers=4, + collate_fn=utils.collate_fn +) + +# For Training +images, targets = next(iter(data_loader)) +images = list(image for image in images) +targets = [{k: v for k, v in t.items()} for t in targets] +output = model(images, targets) # Returns losses and detections +print(output) + +# For inference +model.eval() +x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] +predictions = model(x) # Returns predictions +print(predictions[0]) + + +###################################################################### +# Let’s now write the main function which performs the training and the +# validation: + + +from engine import train_one_epoch, evaluate + +# train on the GPU or on the CPU, if a GPU is not available +device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + +# our dataset has two classes only - background and person +num_classes = 2 +# use our dataset and defined transformations +dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True)) +dataset_test = PennFudanDataset('data/PennFudanPed', get_transform(train=False)) + +# split the dataset in train and test set +indices = torch.randperm(len(dataset)).tolist() +dataset = torch.utils.data.Subset(dataset, indices[:-50]) +dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:]) + +# define training and validation data loaders +data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=2, + shuffle=True, + num_workers=4, + collate_fn=utils.collate_fn +) + +data_loader_test = torch.utils.data.DataLoader( + dataset_test, + batch_size=1, + shuffle=False, + num_workers=4, + collate_fn=utils.collate_fn +) + +# get the model using our helper function +model = get_model_instance_segmentation(num_classes) + +# move model to the right device +model.to(device) + +# construct an optimizer +params = [p for p in model.parameters() if p.requires_grad] +optimizer = torch.optim.SGD( + params, + lr=0.005, + momentum=0.9, + weight_decay=0.0005 +) + +# and a learning rate scheduler +lr_scheduler = torch.optim.lr_scheduler.StepLR( + optimizer, + step_size=3, + gamma=0.1 +) + +# let's train it for 5 epochs +num_epochs = 5 + +for epoch in range(num_epochs): + # train for one epoch, printing every 10 iterations + train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10) + # update the learning rate + lr_scheduler.step() + # evaluate on the test dataset + evaluate(model, data_loader_test, device=device) + +print("That's it!") + + + +###################################################################### +# So after one epoch of training, we obtain a COCO-style mAP > 50, and +# a mask mAP of 65. +# +# But what do the predictions look like? Let’s take one image in the +# dataset and verify +# +# .. image:: ../../_static/img/tv_tutorial/tv_image05.png +# +import matplotlib.pyplot as plt + +from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks + + +image = read_image("../_static/img/tv_tutorial/tv_image05.png") +eval_transform = get_transform(train=False) + +model.eval() +with torch.no_grad(): + x = eval_transform(image) + # convert RGBA -> RGB and move to device + x = x[:3, ...].to(device) + predictions = model([x, ]) + pred = predictions[0] + + +image = (255.0 * (image - image.min()) / (image.max() - image.min())).to(torch.uint8) +image = image[:3, ...] +pred_labels = [f"pedestrian: {score:.3f}" for label, score in zip(pred["labels"], pred["scores"])] +pred_boxes = pred["boxes"].long() +output_image = draw_bounding_boxes(image, pred_boxes, pred_labels, colors="red") + +masks = (pred["masks"] > 0.7).squeeze(1) +output_image = draw_segmentation_masks(output_image, masks, alpha=0.5, colors="blue") + + +plt.figure(figsize=(12, 12)) +plt.imshow(output_image.permute(1, 2, 0)) + +###################################################################### +# The results look good! +# +# Wrapping up +# ----------- +# +# In this tutorial, you have learned how to create your own training +# pipeline for object detection models on a custom dataset. For +# that, you wrote a ``torch.utils.data.Dataset`` class that returns the +# images and the ground truth boxes and segmentation masks. You also +# leveraged a Mask R-CNN model pre-trained on COCO train2017 in order to +# perform transfer learning on this new dataset. +# +# For a more complete example, which includes multi-machine / multi-GPU +# training, check ``references/detection/train.py``, which is present in +# the torchvision repository. +# diff --git a/intermediate_source/torchvision_tutorial.rst b/intermediate_source/torchvision_tutorial.rst index c6166e1c5b6..9869be322d0 100644 --- a/intermediate_source/torchvision_tutorial.rst +++ b/intermediate_source/torchvision_tutorial.rst @@ -2,9 +2,11 @@ TorchVision Object Detection Finetuning Tutorial ==================================================== .. tip:: - To get the most of this tutorial, we suggest using this - `Colab Version `__. - This will allow you to experiment with the information presented below. + + To get the most of this tutorial, we suggest using this + `Colab Version `__. + This will allow you to experiment with the information presented below. + For this tutorial, we will be finetuning a pre-trained `Mask R-CNN `__ model on the `Penn-Fudan @@ -14,6 +16,12 @@ Segmentation `__. It contains illustrate how to use the new features in torchvision in order to train an object detection and instance segmentation model on a custom dataset. + +.. note :: + + This tutorial works only with torchvision version >=0.16 or nightly. + + Defining the Dataset -------------------- @@ -26,38 +34,38 @@ adding new custom datasets. The dataset should inherit from the standard The only specificity that we require is that the dataset ``__getitem__`` should return a tuple: -- image: :class:`torchvision.datapoints.Image` of shape ``[3, H, W]``, a pure tensor, or a PIL Image of size ``(H, W)`` +- image: :class:`torchvision.tv_tensors.Image` of shape ``[3, H, W]``, a pure tensor, or a PIL Image of size ``(H, W)`` - target: a dict containing the following fields - - ``boxes``, :class:`torchvision.datapoints.BoundingBoxes` of shape ``[N, 4]``: - the coordinates of the ``N`` bounding boxes in ``[x0, y0, x1, y1]`` format, ranging from ``0`` - to ``W`` and ``0`` to ``H`` - - ``labels``, integer :class:`torch.Tensor` of shape ``[N]``: the label for each bounding box. - ``0`` represents always the background class. - - ``image_id``, int: an image identifier. It should be - unique between all the images in the dataset, and is used during - evaluation - - ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used - during evaluation with the COCO metric, to separate the metric - scores between small, medium and large boxes. - - ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with iscrowd=True will be - ignored during evaluation. - - (optionally) ``masks``, :class:`torchvision.datapoints.Mask` of shape ``[N, H, W]``: the segmentation - masks for each one of the objects + - ``boxes``, :class:`torchvision.tv_tensors.BoundingBoxes` of shape ``[N, 4]``: + the coordinates of the ``N`` bounding boxes in ``[x0, y0, x1, y1]`` format, ranging from ``0`` + to ``W`` and ``0`` to ``H`` + - ``labels``, integer :class:`torch.Tensor` of shape ``[N]``: the label for each bounding box. + ``0`` represents always the background class. + - ``image_id``, int: an image identifier. It should be + unique between all the images in the dataset, and is used during + evaluation + - ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used + during evaluation with the COCO metric, to separate the metric + scores between small, medium and large boxes. + - ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with ``iscrowd=True`` will be + ignored during evaluation. + - (optionally) ``masks``, :class:`torchvision.tv_tensors.Mask` of shape ``[N, H, W]``: the segmentation + masks for each one of the objects If your dataset is compliant with above requirements then it will work for both training and evaluation codes from the reference script. Evaluation code will use scripts from ``pycocotools`` which can be installed with ``pip install pycocotools``. .. note :: - For Windows, please install ``pycocotools`` from `gautamchitnis `__ with command + For Windows, please install ``pycocotools`` from `gautamchitnis `__ with command - ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI`` + ``pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI`` One note on the ``labels``. The model considers class ``0`` as background. If your dataset does not contain the background class, you should not have ``0`` in your ``labels``. For example, assuming you have just two classes, *cat* and *dog*, you can define ``1`` (not ``0``) to represent *cats* and ``2`` to represent *dogs*. So, for instance, if one of the images has both -classes, your ``labels`` tensor should look like ``[1,2]``. +classes, your ``labels`` tensor should look like ``[1, 2]``. Additionally, if you want to use aspect ratio grouping during training (so that each batch only contains images with similar aspect ratios), @@ -77,18 +85,18 @@ have the following folder structure: :: - PennFudanPed/ - PedMasks/ - FudanPed00001_mask.png - FudanPed00002_mask.png - FudanPed00003_mask.png - FudanPed00004_mask.png - ... - PNGImages/ - FudanPed00001.png - FudanPed00002.png - FudanPed00003.png - FudanPed00004.png + PennFudanPed/ + PedMasks/ + FudanPed00001_mask.png + FudanPed00002_mask.png + FudanPed00003_mask.png + FudanPed00004_mask.png + ... + PNGImages/ + FudanPed00001.png + FudanPed00002.png + FudanPed00003.png + FudanPed00004.png Here is one example of a pair of images and segmentation masks @@ -100,80 +108,81 @@ So each image has a corresponding segmentation mask, where each color correspond to a different instance. Let’s write a :class:`torch.utils.data.Dataset` class for this dataset. In the code below, we are wrapping images, bounding boxes and masks into -``torchvision.datapoints`` classes so that we will be able to apply torchvision +``torchvision.TVTensor`` classes so that we will be able to apply torchvision built-in transformations (`new Transforms API `_) for the given object detection and segmentation task. -Namely, image tensors will be wrapped by :class:`torchvision.datapoints.Image`, bounding boxes into -:class:`torchvision.datapoints.BoundingBoxes` and masks into :class:`torchvision.datapoints.Mask`. -As datapoints are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain -:class:`torch.Tensor` API. For more information about torchvision datapoints see -`this documentation `_. +Namely, image tensors will be wrapped by :class:`torchvision.tv_tensors.Image`, bounding boxes into +:class:`torchvision.tv_tensors.BoundingBoxes` and masks into :class:`torchvision.tv_tensors.Mask`. +As ``torchvision.TVTensor`` are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain +:class:`torch.Tensor` API. For more information about torchvision ``tv_tensors`` see +`this documentation `_. .. code:: python - import os - import torch - - from torchvision.io import read_image - from torchvision.ops.boxes import masks_to_boxes - from torchvision import datapoints as dp - from torchvision.transforms.v2 import functional as F - - - class PennFudanDataset(torch.utils.data.Dataset): - def __init__(self, root, transforms): - self.root = root - self.transforms = transforms - # load all image files, sorting them to - # ensure that they are aligned - self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages")))) - self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks")))) - - def __getitem__(self, idx): - # load images and masks - img_path = os.path.join(self.root, "PNGImages", self.imgs[idx]) - mask_path = os.path.join(self.root, "PedMasks", self.masks[idx]) - img = read_image(img_path) - mask = read_image(mask_path) - # instances are encoded as different colors - obj_ids = torch.unique(mask) - # first id is the background, so remove it - obj_ids = obj_ids[1:] - num_objs = len(obj_ids) - - # split the color-encoded mask into a set - # of binary masks - masks = (mask == obj_ids[:, None, None]).to(dtype=torch.uint8) - - # get bounding box coordinates for each mask - boxes = masks_to_boxes(masks) - - # there is only one class - labels = torch.ones((num_objs,), dtype=torch.int64) - - image_id = idx - area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]) - # suppose all instances are not crowd - iscrowd = torch.zeros((num_objs,), dtype=torch.int64) - - # Wrap sample and targets into torchvision datapoints: - img = dp.Image(img) - - target = {} - target["boxes"] = dp.BoundingBoxes(boxes, format="XYXY", canvas_size=F.get_size(img)) - target["masks"] = dp.Mask(masks) - target["labels"] = labels - target["image_id"] = image_id - target["area"] = area - target["iscrowd"] = iscrowd - - if self.transforms is not None: - img, target = self.transforms(img, target) - - return img, target - - def __len__(self): - return len(self.imgs) + import os + import torch + + from torchvision.io import read_image + from torchvision.ops.boxes import masks_to_boxes + from torchvision import tv_tensors + from torchvision.transforms.v2 import functional as F + + + class PennFudanDataset(torch.utils.data.Dataset): + def __init__(self, root, transforms): + self.root = root + self.transforms = transforms + # load all image files, sorting them to + # ensure that they are aligned + self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages")))) + self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks")))) + + def __getitem__(self, idx): + # load images and masks + img_path = os.path.join(self.root, "PNGImages", self.imgs[idx]) + mask_path = os.path.join(self.root, "PedMasks", self.masks[idx]) + img = read_image(img_path) + mask = read_image(mask_path) + # instances are encoded as different colors + obj_ids = torch.unique(mask) + # first id is the background, so remove it + obj_ids = obj_ids[1:] + num_objs = len(obj_ids) + + # split the color-encoded mask into a set + # of binary masks + masks = (mask == obj_ids[:, None, None]).to(dtype=torch.uint8) + + # get bounding box coordinates for each mask + boxes = masks_to_boxes(masks) + + # there is only one class + labels = torch.ones((num_objs,), dtype=torch.int64) + + image_id = idx + area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]) + # suppose all instances are not crowd + iscrowd = torch.zeros((num_objs,), dtype=torch.int64) + + # Wrap sample and targets into torchvision tv_tensors: + img = tv_tensors.Image(img) + + target = {} + target["boxes"] = tv_tensors.BoundingBoxes(boxes, format="XYXY", canvas_size=F.get_size(img)) + target["masks"] = tv_tensors.Mask(masks) + target["labels"] = labels + target["image_id"] = image_id + target["area"] = area + target["iscrowd"] = iscrowd + + if self.transforms is not None: + img, target = self.transforms(img, target) + + return img, target + + def __len__(self): + return len(self.imgs) + That’s all for the dataset. Now let’s define a model that can perform predictions on this dataset. @@ -197,7 +206,7 @@ instance. There are two common situations where one might want -to modify one of the available models in torchvision modelzoo. The first +to modify one of the available models in TorchVision Model Zoo. The first is when we want to start from a pre-trained model, and just finetune the last layer. The other is when we want to replace the backbone of the model with a different one (for faster predictions, for example). @@ -211,63 +220,72 @@ Let’s suppose that you want to start from a model pre-trained on COCO and want to finetune it for your particular classes. Here is a possible way of doing it: + .. code:: python - import torchvision - from torchvision.models.detection.faster_rcnn import FastRCNNPredictor + import torchvision + from torchvision.models.detection.faster_rcnn import FastRCNNPredictor + + # load a model pre-trained on COCO + model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") - # load a model pre-trained on COCO - model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") + # replace the classifier with a new one, that has + # num_classes which is user-defined + num_classes = 2 # 1 class (person) + background + # get number of input features for the classifier + in_features = model.roi_heads.box_predictor.cls_score.in_features + # replace the pre-trained head with a new one + model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) - # replace the classifier with a new one, that has - # num_classes which is user-defined - num_classes = 2 # 1 class (person) + background - # get number of input features for the classifier - in_features = model.roi_heads.box_predictor.cls_score.in_features - # replace the pre-trained head with a new one - model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) 2 - Modifying the model to add a different backbone ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: python - import torchvision - from torchvision.models.detection import FasterRCNN - from torchvision.models.detection.rpn import AnchorGenerator - - # load a pre-trained model for classification and return - # only the features - backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features - # FasterRCNN needs to know the number of - # output channels in a backbone. For mobilenet_v2, it's 1280 - # so we need to add it here - backbone.out_channels = 1280 - - # let's make the RPN generate 5 x 3 anchors per spatial - # location, with 5 different sizes and 3 different aspect - # ratios. We have a Tuple[Tuple[int]] because each feature - # map could potentially have different sizes and - # aspect ratios - anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), - aspect_ratios=((0.5, 1.0, 2.0),)) - - # let's define what are the feature maps that we will - # use to perform the region of interest cropping, as well as - # the size of the crop after rescaling. - # if your backbone returns a Tensor, featmap_names is expected to - # be [0]. More generally, the backbone should return an - # OrderedDict[Tensor], and in featmap_names you can choose which - # feature maps to use. - roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], - output_size=7, - sampling_ratio=2) - - # put the pieces together inside a FasterRCNN model - model = FasterRCNN(backbone, - num_classes=2, - rpn_anchor_generator=anchor_generator, - box_roi_pool=roi_pooler) + import torchvision + from torchvision.models.detection import FasterRCNN + from torchvision.models.detection.rpn import AnchorGenerator + + # load a pre-trained model for classification and return + # only the features + backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features + # ``FasterRCNN`` needs to know the number of + # output channels in a backbone. For mobilenet_v2, it's 1280 + # so we need to add it here + backbone.out_channels = 1280 + + # let's make the RPN generate 5 x 3 anchors per spatial + # location, with 5 different sizes and 3 different aspect + # ratios. We have a Tuple[Tuple[int]] because each feature + # map could potentially have different sizes and + # aspect ratios + anchor_generator = AnchorGenerator( + sizes=((32, 64, 128, 256, 512),), + aspect_ratios=((0.5, 1.0, 2.0),) + ) + + # let's define what are the feature maps that we will + # use to perform the region of interest cropping, as well as + # the size of the crop after rescaling. + # if your backbone returns a Tensor, featmap_names is expected to + # be [0]. More generally, the backbone should return an + # ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which + # feature maps to use. + roi_pooler = torchvision.ops.MultiScaleRoIAlign( + featmap_names=['0'], + output_size=7, + sampling_ratio=2, + ) + + # put the pieces together inside a Faster-RCNN model + model = FasterRCNN( + backbone, + num_classes=2, + rpn_anchor_generator=anchor_generator, + box_roi_pool=roi_pooler, + ) + Object detection and instance segmentation model for PennFudan Dataset ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -280,29 +298,32 @@ be using Mask R-CNN: .. code:: python - import torchvision - from torchvision.models.detection.faster_rcnn import FastRCNNPredictor - from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor + import torchvision + from torchvision.models.detection.faster_rcnn import FastRCNNPredictor + from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor - def get_model_instance_segmentation(num_classes): - # load an instance segmentation model pre-trained on COCO - model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights="DEFAULT") + def get_model_instance_segmentation(num_classes): + # load an instance segmentation model pre-trained on COCO + model = torchvision.models.detection.maskrcnn_resnet50_fpn(weights="DEFAULT") - # get number of input features for the classifier - in_features = model.roi_heads.box_predictor.cls_score.in_features - # replace the pre-trained head with a new one - model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) + # get number of input features for the classifier + in_features = model.roi_heads.box_predictor.cls_score.in_features + # replace the pre-trained head with a new one + model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) - # now get the number of input features for the mask classifier - in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels - hidden_layer = 256 - # and replace the mask predictor with a new one - model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, - hidden_layer, - num_classes) + # now get the number of input features for the mask classifier + in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels + hidden_layer = 256 + # and replace the mask predictor with a new one + model.roi_heads.mask_predictor = MaskRCNNPredictor( + in_features_mask, + hidden_layer, + num_classes, + ) + + return model - return model That’s it, this will make ``model`` be ready to be trained and evaluated on your custom dataset. @@ -313,7 +334,17 @@ Putting everything together In ``references/detection/``, we have a number of helper functions to simplify training and evaluating detection models. Here, we will use ``references/detection/engine.py`` and ``references/detection/utils.py``. -Just copy everything under ``references/detection`` to your folder and use them here. +Just download everything under ``references/detection`` to your folder and use them here. +On Linux if you have ``wget``, you can download them using below commands: + +.. code:: python + + os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/engine.py") + os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/utils.py") + os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_utils.py") + os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/coco_eval.py") + os.system("wget https://raw.githubusercontent.com/pytorch/vision/main/references/detection/transforms.py") + Since v0.15.0 torchvision provides `new Transforms API `_ to easily write data augmentation pipelines for Object Detection and Segmentation tasks. @@ -323,16 +354,16 @@ transformation: .. code:: python - from torchvision.transforms import v2 as T + from torchvision.transforms import v2 as T - def get_transform(train): - transforms = [] - if train: - transforms.append(T.RandomHorizontalFlip(0.5)) - transforms.append(T.ToDtype(torch.float, scale=True)) - transforms.append(T.ToPureTensor()) - return T.Compose(transforms) + def get_transform(train): + transforms = [] + if train: + transforms.append(T.RandomHorizontalFlip(0.5)) + transforms.append(T.ToDtype(torch.float, scale=True)) + transforms.append(T.ToPureTensor()) + return T.Compose(transforms) Testing ``forward()`` method (Optional) @@ -343,178 +374,145 @@ expects during training and inference time on sample data. .. code:: python - model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") - dataset = PennFudanDataset('PennFudanPed', get_transform(train=True)) - data_loader = torch.utils.data.DataLoader( - dataset, batch_size=2, shuffle=True, num_workers=4, - collate_fn=utils.collate_fn) - # For Training - images, targets = next(iter(data_loader)) - images = list(image for image in images) - targets = [{k: v for k, v in t.items()} for t in targets] - output = model(images, targets) # Returns losses and detections - # For inference - model.eval() - x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] - predictions = model(x) # Returns predictions - -Let’s now write the main function which performs the training and the -validation: - -.. code:: python - - from engine import train_one_epoch, evaluate - import utils - - - def main(): - # train on the GPU or on the CPU, if a GPU is not available - device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') - - # our dataset has two classes only - background and person - num_classes = 2 - # use our dataset and defined transformations - dataset = PennFudanDataset('PennFudanPed', get_transform(train=True)) - dataset_test = PennFudanDataset('PennFudanPed', get_transform(train=False)) + import utils - # split the dataset in train and test set - indices = torch.randperm(len(dataset)).tolist() - dataset = torch.utils.data.Subset(dataset, indices[:-50]) - dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:]) - # define training and validation data loaders - data_loader = torch.utils.data.DataLoader( - dataset, batch_size=2, shuffle=True, num_workers=4, - collate_fn=utils.collate_fn) + model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") + dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True)) + data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=2, + shuffle=True, + num_workers=4, + collate_fn=utils.collate_fn + ) - data_loader_test = torch.utils.data.DataLoader( - dataset_test, batch_size=1, shuffle=False, num_workers=4, - collate_fn=utils.collate_fn) + # For Training + images, targets = next(iter(data_loader)) + images = list(image for image in images) + targets = [{k: v for k, v in t.items()} for t in targets] + output = model(images, targets) # Returns losses and detections + print(output) - # get the model using our helper function - model = get_model_instance_segmentation(num_classes) + # For inference + model.eval() + x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] + predictions = model(x) # Returns predictions + print(predictions[0]) - # move model to the right device - model.to(device) - # construct an optimizer - params = [p for p in model.parameters() if p.requires_grad] - optimizer = torch.optim.SGD(params, lr=0.005, - momentum=0.9, weight_decay=0.0005) - # and a learning rate scheduler - lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, - step_size=3, - gamma=0.1) - - # let's train it for 10 epochs - num_epochs = 10 +Let’s now write the main function which performs the training and the +validation: - for epoch in range(num_epochs): - # train for one epoch, printing every 10 iterations - train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10) - # update the learning rate - lr_scheduler.step() - # evaluate on the test dataset - evaluate(model, data_loader_test, device=device) +.. code:: python - print("That's it!") + from engine import train_one_epoch, evaluate + + # train on the GPU or on the CPU, if a GPU is not available + device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + + # our dataset has two classes only - background and person + num_classes = 2 + # use our dataset and defined transformations + dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True)) + dataset_test = PennFudanDataset('data/PennFudanPed', get_transform(train=False)) + + # split the dataset in train and test set + indices = torch.randperm(len(dataset)).tolist() + dataset = torch.utils.data.Subset(dataset, indices[:-50]) + dataset_test = torch.utils.data.Subset(dataset_test, indices[-50:]) + + # define training and validation data loaders + data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=2, + shuffle=True, + num_workers=4, + collate_fn=utils.collate_fn + ) + + data_loader_test = torch.utils.data.DataLoader( + dataset_test, + batch_size=1, + shuffle=False, + num_workers=4, + collate_fn=utils.collate_fn + ) + + # get the model using our helper function + model = get_model_instance_segmentation(num_classes) + + # move model to the right device + model.to(device) + + # construct an optimizer + params = [p for p in model.parameters() if p.requires_grad] + optimizer = torch.optim.SGD( + params, + lr=0.005, + momentum=0.9, + weight_decay=0.0005 + ) + + # and a learning rate scheduler + lr_scheduler = torch.optim.lr_scheduler.StepLR( + optimizer, + step_size=3, + gamma=0.1 + ) + + # let's train it for 5 epochs + num_epochs = 5 + + for epoch in range(num_epochs): + # train for one epoch, printing every 10 iterations + train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10) + # update the learning rate + lr_scheduler.step() + # evaluate on the test dataset + evaluate(model, data_loader_test, device=device) + + print("That's it!") + + +So after one epoch of training, we obtain a COCO-style mAP > 50, and +a mask mAP of 65. -You should get as output for the first epoch: +But what do the predictions look like? Let’s take one image in the +dataset and verify -:: +.. image:: ../../_static/img/tv_tutorial/tv_image05.png - Epoch: [0] [ 0/60] eta: 0:01:18 lr: 0.000090 loss: 2.5213 (2.5213) loss_classifier: 0.8025 (0.8025) loss_box_reg: 0.2634 (0.2634) loss_mask: 1.4265 (1.4265) loss_objectness: 0.0190 (0.0190) loss_rpn_box_reg: 0.0099 (0.0099) time: 1.3121 data: 0.3024 max mem: 3485 - Epoch: [0] [10/60] eta: 0:00:20 lr: 0.000936 loss: 1.3007 (1.5313) loss_classifier: 0.3979 (0.4719) loss_box_reg: 0.2454 (0.2272) loss_mask: 0.6089 (0.7953) loss_objectness: 0.0197 (0.0228) loss_rpn_box_reg: 0.0121 (0.0141) time: 0.4198 data: 0.0298 max mem: 5081 - Epoch: [0] [20/60] eta: 0:00:15 lr: 0.001783 loss: 0.7567 (1.1056) loss_classifier: 0.2221 (0.3319) loss_box_reg: 0.2002 (0.2106) loss_mask: 0.2904 (0.5332) loss_objectness: 0.0146 (0.0176) loss_rpn_box_reg: 0.0094 (0.0123) time: 0.3293 data: 0.0035 max mem: 5081 - Epoch: [0] [30/60] eta: 0:00:11 lr: 0.002629 loss: 0.4705 (0.8935) loss_classifier: 0.0991 (0.2517) loss_box_reg: 0.1578 (0.1957) loss_mask: 0.1970 (0.4204) loss_objectness: 0.0061 (0.0140) loss_rpn_box_reg: 0.0075 (0.0118) time: 0.3403 data: 0.0044 max mem: 5081 - Epoch: [0] [40/60] eta: 0:00:07 lr: 0.003476 loss: 0.3901 (0.7568) loss_classifier: 0.0648 (0.2022) loss_box_reg: 0.1207 (0.1736) loss_mask: 0.1705 (0.3585) loss_objectness: 0.0018 (0.0113) loss_rpn_box_reg: 0.0075 (0.0112) time: 0.3407 data: 0.0044 max mem: 5081 - Epoch: [0] [50/60] eta: 0:00:03 lr: 0.004323 loss: 0.3237 (0.6703) loss_classifier: 0.0474 (0.1731) loss_box_reg: 0.1109 (0.1561) loss_mask: 0.1658 (0.3201) loss_objectness: 0.0015 (0.0093) loss_rpn_box_reg: 0.0093 (0.0116) time: 0.3379 data: 0.0043 max mem: 5081 - Epoch: [0] [59/60] eta: 0:00:00 lr: 0.005000 loss: 0.2540 (0.6082) loss_classifier: 0.0309 (0.1526) loss_box_reg: 0.0463 (0.1405) loss_mask: 0.1568 (0.2945) loss_objectness: 0.0012 (0.0083) loss_rpn_box_reg: 0.0093 (0.0123) time: 0.3489 data: 0.0042 max mem: 5081 - Epoch: [0] Total time: 0:00:21 (0.3570 s / it) - creating index... - index created! - Test: [ 0/50] eta: 0:00:19 model_time: 0.2152 (0.2152) evaluator_time: 0.0133 (0.0133) time: 0.4000 data: 0.1701 max mem: 5081 - Test: [49/50] eta: 0:00:00 model_time: 0.0628 (0.0687) evaluator_time: 0.0039 (0.0064) time: 0.0735 data: 0.0022 max mem: 5081 - Test: Total time: 0:00:04 (0.0828 s / it) - Averaged stats: model_time: 0.0628 (0.0687) evaluator_time: 0.0039 (0.0064) - Accumulating evaluation results... - DONE (t=0.01s). - Accumulating evaluation results... - DONE (t=0.01s). - IoU metric: bbox - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.606 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.984 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.780 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.313 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.582 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.612 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.270 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.672 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.672 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.755 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.664 - IoU metric: segm - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.704 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.979 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.871 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.325 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.488 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.727 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.316 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.748 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.749 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.650 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.673 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.758 - -So after one epoch of training, we obtain a COCO-style mAP of 60.6, and -a mask mAP of 70.4. - -After training for 10 epochs, I got the following metrics +.. code:: python -:: + import matplotlib.pyplot as plt + from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks - IoU metric: bbox - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.799 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.969 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.935 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.349 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.592 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.324 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.844 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.844 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.777 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.870 - IoU metric: segm - Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.761 - Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.969 - Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.919 - Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.341 - Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464 - Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.788 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.303 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.799 - Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.799 - Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400 - Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.769 - Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818 + image = read_image("../_static/img/tv_tutorial/tv_image05.png") + eval_transform = get_transform(train=False) -But what do the predictions look like? Let’s take one image in the -dataset and verify + model.eval() + with torch.no_grad(): + x = eval_transform(image) + # convert RGBA -> RGB and move to device + x = x[:3, ...].to(device) + predictions = model([x, ]) + pred = predictions[0] -.. image:: ../../_static/img/tv_tutorial/tv_image05.png + image = (255.0 * (image - image.min()) / (image.max() - image.min())).to(torch.uint8) + image = image[:3, ...] + pred_labels = [f"pedestrian: {score:.3f}" for label, score in zip(pred["labels"], pred["scores"])] + pred_boxes = pred["boxes"].long() + output_image = draw_bounding_boxes(image, pred_boxes, pred_labels, colors="red") -The trained model predicts 9 -instances of person in this image, let’s see a couple of them: + masks = (pred["masks"] > 0.7).squeeze(1) + output_image = draw_segmentation_masks(output_image, masks, alpha=0.5, colors="blue") -.. image:: ../../_static/img/tv_tutorial/tv_image06.png + plt.figure(figsize=(12, 12)) + plt.imshow(output_image.permute(1, 2, 0)) -.. image:: ../../_static/img/tv_tutorial/tv_image07.png -The results look pretty good! +The results look good! Wrapping up ----------- @@ -526,11 +524,6 @@ images and the ground truth boxes and segmentation masks. You also leveraged a Mask R-CNN model pre-trained on COCO train2017 in order to perform transfer learning on this new dataset. -For a more complete example, which includes multi-machine / multi-gpu +For a more complete example, which includes multi-machine / multi-GPU training, check ``references/detection/train.py``, which is present in -the torchvision repo. - -You can download a full source file for this tutorial -`here `__. - - +the torchvision repository. 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@@ expects during training and inference time on sample data. predictions = model(x) # Returns predictions print(predictions[0]) +:: + + {'loss_classifier': tensor(0.0820, grad_fn=), 'loss_box_reg': tensor(0.0278, grad_fn=), 'loss_objectness': tensor(0.0027, grad_fn=), 'loss_rpn_box_reg': tensor(0.0036, grad_fn=)} + {'boxes': tensor([], size=(0, 4), grad_fn=), 'labels': tensor([], dtype=torch.int64), 'scores': tensor([], grad_fn=)} + Let’s now write the main function which performs the training and the validation: @@ -474,6 +479,102 @@ validation: print("That's it!") +:: + + Epoch: [0] [ 0/60] eta: 0:02:43 lr: 0.000090 loss: 2.8181 (2.8181) loss_classifier: 0.5218 (0.5218) loss_box_reg: 0.1272 (0.1272) loss_mask: 2.1324 (2.1324) loss_objectness: 0.0346 (0.0346) loss_rpn_box_reg: 0.0022 (0.0022) time: 2.7332 data: 0.4483 max mem: 1984 + Epoch: [0] [10/60] eta: 0:00:24 lr: 0.000936 loss: 1.3190 (1.6752) loss_classifier: 0.4611 (0.4213) loss_box_reg: 0.2928 (0.3031) loss_mask: 0.6962 (0.9183) loss_objectness: 0.0238 (0.0253) loss_rpn_box_reg: 0.0074 (0.0072) time: 0.4944 data: 0.0439 max mem: 2762 + Epoch: [0] [20/60] eta: 0:00:13 lr: 0.001783 loss: 0.9419 (1.2621) loss_classifier: 0.2171 (0.3037) loss_box_reg: 0.2906 (0.3064) loss_mask: 0.4174 (0.6243) loss_objectness: 0.0190 (0.0210) loss_rpn_box_reg: 0.0059 (0.0068) time: 0.2108 data: 0.0042 max mem: 2823 + Epoch: [0] [30/60] eta: 0:00:08 lr: 0.002629 loss: 0.6349 (1.0344) loss_classifier: 0.1184 (0.2339) loss_box_reg: 0.2706 (0.2873) loss_mask: 0.2276 (0.4897) loss_objectness: 0.0065 (0.0168) loss_rpn_box_reg: 0.0059 (0.0067) time: 0.1650 data: 0.0051 max mem: 2823 + Epoch: [0] [40/60] eta: 0:00:05 lr: 0.003476 loss: 0.4631 (0.8771) loss_classifier: 0.0650 (0.1884) loss_box_reg: 0.1924 (0.2604) loss_mask: 0.1734 (0.4084) loss_objectness: 0.0029 (0.0135) loss_rpn_box_reg: 0.0051 (0.0063) time: 0.1760 data: 0.0052 max mem: 2823 + Epoch: [0] [50/60] eta: 0:00:02 lr: 0.004323 loss: 0.3261 (0.7754) loss_classifier: 0.0368 (0.1606) loss_box_reg: 0.1424 (0.2366) loss_mask: 0.1479 (0.3599) loss_objectness: 0.0022 (0.0116) loss_rpn_box_reg: 0.0051 (0.0067) time: 0.1775 data: 0.0052 max mem: 2823 + Epoch: [0] [59/60] eta: 0:00:00 lr: 0.005000 loss: 0.3261 (0.7075) loss_classifier: 0.0415 (0.1433) loss_box_reg: 0.1114 (0.2157) loss_mask: 0.1573 (0.3316) loss_objectness: 0.0020 (0.0103) loss_rpn_box_reg: 0.0052 (0.0066) time: 0.2064 data: 0.0049 max mem: 2823 + Epoch: [0] Total time: 0:00:14 (0.2412 s / it) + creating index... + index created! + Test: [ 0/50] eta: 0:00:25 model_time: 0.1576 (0.1576) evaluator_time: 0.0029 (0.0029) time: 0.5063 data: 0.3452 max mem: 2823 + Test: [49/50] eta: 0:00:00 model_time: 0.0335 (0.0701) evaluator_time: 0.0025 (0.0038) time: 0.0594 data: 0.0025 max mem: 2823 + Test: Total time: 0:00:04 (0.0862 s / it) + Averaged stats: model_time: 0.0335 (0.0701) evaluator_time: 0.0025 (0.0038) + Accumulating evaluation results... + DONE (t=0.01s). + Accumulating evaluation results... + DONE (t=0.01s). + IoU metric: bbox + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.722 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.987 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.938 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.359 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.752 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.730 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.762 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.762 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.775 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.769 + IoU metric: segm + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.726 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.993 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.913 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.344 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.593 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.743 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.360 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.760 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.760 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.633 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.662 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772 + + ... + + Epoch: [4] [ 0/60] eta: 0:00:32 lr: 0.000500 loss: 0.1593 (0.1593) loss_classifier: 0.0194 (0.0194) loss_box_reg: 0.0272 (0.0272) loss_mask: 0.1046 (0.1046) loss_objectness: 0.0044 (0.0044) loss_rpn_box_reg: 0.0037 (0.0037) time: 0.5369 data: 0.3801 max mem: 3064 + Epoch: [4] [10/60] eta: 0:00:10 lr: 0.000500 loss: 0.1609 (0.1870) loss_classifier: 0.0194 (0.0236) loss_box_reg: 0.0272 (0.0383) loss_mask: 0.1140 (0.1190) loss_objectness: 0.0005 (0.0023) loss_rpn_box_reg: 0.0029 (0.0037) time: 0.2016 data: 0.0378 max mem: 3064 + Epoch: [4] [20/60] eta: 0:00:08 lr: 0.000500 loss: 0.1652 (0.1826) loss_classifier: 0.0224 (0.0242) loss_box_reg: 0.0286 (0.0374) loss_mask: 0.1075 (0.1165) loss_objectness: 0.0003 (0.0016) loss_rpn_box_reg: 0.0016 (0.0029) time: 0.1866 data: 0.0044 max mem: 3064 + Epoch: [4] [30/60] eta: 0:00:06 lr: 0.000500 loss: 0.1676 (0.1884) loss_classifier: 0.0245 (0.0264) loss_box_reg: 0.0286 (0.0401) loss_mask: 0.1075 (0.1175) loss_objectness: 0.0003 (0.0013) loss_rpn_box_reg: 0.0018 (0.0030) time: 0.2106 data: 0.0055 max mem: 3064 + Epoch: [4] [40/60] eta: 0:00:03 lr: 0.000500 loss: 0.1726 (0.1884) loss_classifier: 0.0245 (0.0265) loss_box_reg: 0.0283 (0.0394) loss_mask: 0.1187 (0.1184) loss_objectness: 0.0003 (0.0011) loss_rpn_box_reg: 0.0020 (0.0029) time: 0.1897 data: 0.0056 max mem: 3064 + Epoch: [4] [50/60] eta: 0:00:01 lr: 0.000500 loss: 0.1910 (0.1938) loss_classifier: 0.0273 (0.0280) loss_box_reg: 0.0414 (0.0418) loss_mask: 0.1177 (0.1198) loss_objectness: 0.0003 (0.0010) loss_rpn_box_reg: 0.0022 (0.0031) time: 0.1623 data: 0.0056 max mem: 3064 + Epoch: [4] [59/60] eta: 0:00:00 lr: 0.000500 loss: 0.1732 (0.1888) loss_classifier: 0.0273 (0.0278) loss_box_reg: 0.0327 (0.0405) loss_mask: 0.0993 (0.1165) loss_objectness: 0.0003 (0.0010) loss_rpn_box_reg: 0.0023 (0.0030) time: 0.1732 data: 0.0056 max mem: 3064 + Epoch: [4] Total time: 0:00:11 (0.1920 s / it) + creating index... + index created! + Test: [ 0/50] eta: 0:00:21 model_time: 0.0589 (0.0589) evaluator_time: 0.0032 (0.0032) time: 0.4269 data: 0.3641 max mem: 3064 + Test: [49/50] eta: 0:00:00 model_time: 0.0515 (0.0521) evaluator_time: 0.0020 (0.0031) time: 0.0579 data: 0.0024 max mem: 3064 + Test: Total time: 0:00:03 (0.0679 s / it) + Averaged stats: model_time: 0.0515 (0.0521) evaluator_time: 0.0020 (0.0031) + Accumulating evaluation results... + DONE (t=0.01s). + Accumulating evaluation results... + DONE (t=0.01s). + IoU metric: bbox + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.846 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.997 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.978 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.412 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.689 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.864 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.417 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.876 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.876 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.567 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.750 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.896 + IoU metric: segm + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.777 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.997 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.961 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.424 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.631 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.791 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.373 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.814 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.814 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.633 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.713 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827 + + That's it! + So after one epoch of training, we obtain a COCO-style mAP > 50, and a mask mAP of 65. @@ -512,6 +613,9 @@ dataset and verify plt.imshow(output_image.permute(1, 2, 0)) +.. image:: ../../_static/img/tv_tutorial/tv_image06.png + + The results look good! Wrapping up From 7bab5ce8027084f08609bf1c1770c728e3d6b8f8 Mon Sep 17 00:00:00 2001 From: vfdev-5 Date: Tue, 5 Sep 2023 16:18:22 +0200 Subject: [PATCH 3/3] Addressed review comments --- _static/tv-training-code.py | 44 ++++++++++++++------ intermediate_source/torchvision_tutorial.rst | 5 +++ 2 files changed, 37 insertions(+), 12 deletions(-) diff --git a/_static/tv-training-code.py b/_static/tv-training-code.py index 1f3c1a0b761..bdd93760a7d 100644 --- a/_static/tv-training-code.py +++ b/_static/tv-training-code.py @@ -5,6 +5,14 @@ """ ###################################################################### +# +# .. tip:: +# +# To get the most of this tutorial, we suggest using this +# `Colab Version `__. +# This will allow you to experiment with the information presented below. +# +# # For this tutorial, we will be finetuning a pre-trained `Mask # R-CNN `__ model on the `Penn-Fudan # Database for Pedestrian Detection and @@ -17,6 +25,8 @@ # .. note :: # # This tutorial works only with torchvision version >=0.16 or nightly. +# If you're using torchvision<=0.15, please follow +# `this tutorial instead `_. # # # Defining the Dataset @@ -252,8 +262,10 @@ def __len__(self): # ratios. We have a Tuple[Tuple[int]] because each feature # map could potentially have different sizes and # aspect ratios -anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),), - aspect_ratios=((0.5, 1.0, 2.0),)) +anchor_generator = AnchorGenerator( + sizes=((32, 64, 128, 256, 512),), + aspect_ratios=((0.5, 1.0, 2.0),) +) # let's define what are the feature maps that we will # use to perform the region of interest cropping, as well as @@ -262,15 +274,19 @@ def __len__(self): # be [0]. More generally, the backbone should return an # ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which # feature maps to use. -roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'], - output_size=7, - sampling_ratio=2) +roi_pooler = torchvision.ops.MultiScaleRoIAlign( + featmap_names=['0'], + output_size=7, + sampling_ratio=2 +) # put the pieces together inside a Faster-RCNN model -model = FasterRCNN(backbone, - num_classes=2, - rpn_anchor_generator=anchor_generator, - box_roi_pool=roi_pooler) +model = FasterRCNN( + backbone, + num_classes=2, + rpn_anchor_generator=anchor_generator, + box_roi_pool=roi_pooler +) ###################################################################### # Object detection and instance segmentation model for PennFudan Dataset @@ -301,9 +317,11 @@ def get_model_instance_segmentation(num_classes): in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels hidden_layer = 256 # and replace the mask predictor with a new one - model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, - hidden_layer, - num_classes) + model.roi_heads.mask_predictor = MaskRCNNPredictor( + in_features_mask, + hidden_layer, + num_classes + ) return model @@ -508,3 +526,5 @@ def get_transform(train): # training, check ``references/detection/train.py``, which is present in # the torchvision repository. # +# You can download a full source file for this tutorial +# `here `__. \ No newline at end of file diff --git a/intermediate_source/torchvision_tutorial.rst b/intermediate_source/torchvision_tutorial.rst index 05ed0d68681..a3856c16a11 100644 --- a/intermediate_source/torchvision_tutorial.rst +++ b/intermediate_source/torchvision_tutorial.rst @@ -20,6 +20,8 @@ an object detection and instance segmentation model on a custom dataset. .. note :: This tutorial works only with torchvision version >=0.16 or nightly. + If you're using torchvision<=0.15, please follow + `this tutorial instead `_. Defining the Dataset @@ -631,3 +633,6 @@ perform transfer learning on this new dataset. For a more complete example, which includes multi-machine / multi-GPU training, check ``references/detection/train.py``, which is present in the torchvision repository. + +You can download a full source file for this tutorial +`here `__. \ No newline at end of file

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