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diff --git a/LICENSE b/LICENSE
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--- /dev/null
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@@ -0,0 +1,203 @@
+Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ). All rights reserved.
+
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
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+ Copyright 2018 Simon Kohl.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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\ No newline at end of file
diff --git a/README.md b/README.md
index 3fa444e..5d515c7 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,106 @@
# Probabilistic U-Net
-Coming shortly: Re-implementation of the model described in `A Probabilistic U-Net for Segmentation of Ambiguous Images' ([arxiv.org/abs/1806.05034](https://arxiv.org/abs/1806.05034)).
+Re-implementation of the model described in `A Probabilistic U-Net for Segmentation of Ambiguous Images' ([arxiv.org/abs/1806.05034](https://arxiv.org/abs/1806.05034)).
+The architecture of the Probabilistic U-Net is depicted below: subfigure a) shows sampling and b) the training setup:
+
+
+Below see samples conditioned on held-out validation set images from the (stochastic) CityScapes data set:
+
+
+## Setup package in virtual environment
+
+```
+git clone https://phabricator.mitk.org/source/skohl/browse/master/prob_unet .
+cd prob_unet/
+virtualenv -p python3 venv
+source venv/bin/activate
+pip3 install -e .
+```
+
+## Install batch-generators for data augmentation
+```
+cd ..
+git clone https://github.com/MIC-DKFZ/batchgenerators
+cd batchgenerators
+pip3 install nilearn scikit-image nibabel
+pip3 install -e .
+cd prob_unet
+```
+
+## Download & preprocess the Cityscapes dataset
+
+1) Create a login account on the Cityscapes website: https://www.cityscapes-dataset.com/
+2) Once you've logged in, download the train, val and test annotations and images:
+ - Annotations: [gtFine_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=1) (241MB)
+ - Images: [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3) (11GB)
+3) unzip the data (unzip _trainvaltest.zip) and adjust `raw_data_dir` (full path to unzipped files) and `out_dir` (full path to desired output directory) in `preprocessing_config.py`
+4) bilinearly rescale the data to a resolution of 256 x 512 and save as numpy arrays by running
+```
+cd cityscapes
+python3 preprocessing.py
+cd ..
+```
+
+## Training
+
+[skip to evaluation in case you only want to use the pretrained model.]
+modify `data_dir` and `exp_dir` in `scripts/prob_unet_config.py` then:
+```
+cd training
+python3 train_prob_unet.py --config prob_unet_config.py
+```
+
+## Evaluation
+
+Load your own trained model or use a pretrained model. A set of pretrained weights can be downloaded from [zenodo.org](https://zenodo.org/record/1419051#.W5utoOEzYUE) (187MB). After down-loading, unpack the file via
+`tar -xvzf pretrained_weights.tar.gz`, e.g. in `/model`. In either case (using your own or the pretrained model), modify the `data_dir` and
+`exp_dir` in `evaluation/cityscapes_eval_config.py` to match you paths.
+
+then first write samples (defaults to 16 segmentation samples for each of the 500 validation images):
+```
+cd ../evaluation
+python3 eval_cityscapes.py --write_samples
+```
+followed by their evaluation (which is multi-threaded and thus reasonably fast):
+```
+python3 eval_cityscapes.py --eval_samples
+```
+The evaluation produces a dictionary holding the results. These can be visualized by launching an ipython notbook:
+```
+jupyter notebook evaluation_plots.ipynb
+```
+The following results are obtained from the pretrained model using above notebook:
+
+
+## Tests
+
+The evaluation metrics are under test-coverage. Run the tests as follows:
+```
+cd ../tests/evaluation
+python3 -m pytest eval_tests.py
+```
+
+## Deviations from original work
+
+The code found in this repository was not used in the original paper and slight modifications apply:
+
+- training on a single gpu (Titan Xp) instead of distributed training, which is not supported in this implementation
+- average-pooling rather than bilinear interpolation is used for down-sampling operations in the model
+- the number of conv kernels is kept constant after the 3rd scale as opposed to strictly doubling it after each scale (for reduction of memory footprint)
+- HeNormal weight initialization worked better than a orthogonal weight initialization
+
+
+## How to cite this code
+Please cite the original publication:
+```
+@article{kohl2018probabilistic,
+ title={A Probabilistic U-Net for Segmentation of Ambiguous Images},
+ author={Kohl, Simon AA and Romera-Paredes, Bernardino and Meyer, Clemens and De Fauw, Jeffrey and Ledsam, Joseph R and Maier-Hein, Klaus H and Eslami, SM and Rezende, Danilo Jimenez and Ronneberger, Olaf},
+ journal={arXiv preprint arXiv:1806.05034},
+ year={2018}
+}
+```
+
+## License
+The code is publihed under the [Apache License Version 2.0](LICENSE).
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..e69de29
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diff --git a/assets/architecture.png b/assets/architecture.png
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diff --git a/data/__init__.py b/data/__init__.py
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diff --git a/data/cityscapes/__init__.py b/data/cityscapes/__init__.py
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index 0000000..e69de29
diff --git a/data/cityscapes/cityscapes_labels.py b/data/cityscapes/cityscapes_labels.py
new file mode 100644
index 0000000..be3e5a9
--- /dev/null
+++ b/data/cityscapes/cityscapes_labels.py
@@ -0,0 +1,120 @@
+#!/usr/bin/python
+#
+# Cityscapes labels, code from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py
+#
+
+from collections import namedtuple
+
+
+#--------------------------------------------------------------------------------
+# Definitions
+#--------------------------------------------------------------------------------
+
+# a label and all meta information
+Label = namedtuple( 'Label' , [
+
+ 'name' , # The identifier of this label, e.g. 'car', 'person', ... .
+ # We use them to uniquely name a class
+
+ 'id' , # An integer ID that is associated with this label.
+ # The IDs are used to represent the label in ground truth images
+ # An ID of -1 means that this label does not have an ID and thus
+ # is ignored when creating ground truth images (e.g. license plate).
+ # Do not modify these IDs, since exactly these IDs are expected by the
+ # evaluation server.
+
+ 'trainId' , # Feel free to modify these IDs as suitable for your method. Then create
+ # ground truth images with train IDs, using the tools provided in the
+ # 'preparation' folder. However, make sure to validate or submit results
+ # to our evaluation server using the regular IDs above!
+ # For trainIds, multiple labels might have the same ID. Then, these labels
+ # are mapped to the same class in the ground truth images. For the inverse
+ # mapping, we use the label that is defined first in the list below.
+ # For example, mapping all void-type classes to the same ID in training,
+ # might make sense for some approaches.
+ # Max value is 255!
+
+ 'category' , # The name of the category that this label belongs to
+
+ 'categoryId' , # The ID of this category. Used to create ground truth images
+ # on category level.
+
+ 'hasInstances', # Whether this label distinguishes between single instances or not
+
+ 'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
+ # during evaluations or not
+
+ 'color' , # The color of this label
+ ] )
+
+
+#--------------------------------------------------------------------------------
+# A list of all labels
+#--------------------------------------------------------------------------------
+
+# Please adapt the train IDs as appropriate for your approach.
+# Note that you might want to ignore labels with ID 255 during training.
+# Further note that the current train IDs are only a suggestion. You can use whatever you like.
+# Make sure to provide your results using the original IDs and not the training IDs.
+# Note that many IDs are ignored in evaluation and thus you never need to predict these!
+
+labels = [
+ # name id trainId category catId hasInstances ignoreInEval color
+ Label( 'unlabeled' , 0 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
+ Label( 'ego vehicle' , 1 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
+ Label( 'rectification border' , 2 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
+ Label( 'out of roi' , 3 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
+ Label( 'static' , 4 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
+ Label( 'dynamic' , 5 , 255 , 'void' , 0 , False , True , (111, 74, 0) ),
+ Label( 'ground' , 6 , 255 , 'void' , 0 , False , True , ( 81, 0, 81) ),
+ Label( 'road' , 7 , 0 , 'flat' , 1 , False , False , (128, 64,128) ),
+ Label( 'sidewalk' , 8 , 1 , 'flat' , 1 , False , False , (244, 35,232) ),
+ Label( 'parking' , 9 , 255 , 'flat' , 1 , False , True , (250,170,160) ),
+ Label( 'rail track' , 10 , 255 , 'flat' , 1 , False , True , (230,150,140) ),
+ Label( 'building' , 11 , 2 , 'construction' , 2 , False , False , ( 70, 70, 70) ),
+ Label( 'wall' , 12 , 3 , 'construction' , 2 , False , False , (102,102,156) ),
+ Label( 'fence' , 13 , 4 , 'construction' , 2 , False , False , (190,153,153) ),
+ Label( 'guard rail' , 14 , 255 , 'construction' , 2 , False , True , (180,165,180) ),
+ Label( 'bridge' , 15 , 255 , 'construction' , 2 , False , True , (150,100,100) ),
+ Label( 'tunnel' , 16 , 255 , 'construction' , 2 , False , True , (150,120, 90) ),
+ Label( 'pole' , 17 , 5 , 'object' , 3 , False , False , (153,153,153) ),
+ Label( 'polegroup' , 18 , 255 , 'object' , 3 , False , True , (153,153,153) ),
+ Label( 'traffic light' , 19 , 6 , 'object' , 3 , False , False , (250,170, 30) ),
+ Label( 'traffic sign' , 20 , 7 , 'object' , 3 , False , False , (220,220, 0) ),
+ Label( 'vegetation' , 21 , 8 , 'nature' , 4 , False , False , (107,142, 35) ),
+ Label( 'terrain' , 22 , 9 , 'nature' , 4 , False , False , (152,251,152) ),
+ Label( 'sky' , 23 , 10 , 'sky' , 5 , False , False , ( 70,130,180) ),
+ Label( 'person' , 24 , 11 , 'human' , 6 , True , False , (220, 20, 60) ),
+ Label( 'rider' , 25 , 12 , 'human' , 6 , True , False , (255, 0, 0) ),
+ Label( 'car' , 26 , 13 , 'vehicle' , 7 , True , False , ( 0, 0,142) ),
+ Label( 'truck' , 27 , 14 , 'vehicle' , 7 , True , False , ( 0, 0, 70) ),
+ Label( 'bus' , 28 , 15 , 'vehicle' , 7 , True , False , ( 0, 60,100) ),
+ Label( 'caravan' , 29 , 255 , 'vehicle' , 7 , True , True , ( 0, 0, 90) ),
+ Label( 'trailer' , 30 , 255 , 'vehicle' , 7 , True , True , ( 0, 0,110) ),
+ Label( 'train' , 31 , 16 , 'vehicle' , 7 , True , False , ( 0, 80,100) ),
+ Label( 'motorcycle' , 32 , 17 , 'vehicle' , 7 , True , False , ( 0, 0,230) ),
+ Label( 'bicycle' , 33 , 18 , 'vehicle' , 7 , True , False , (119, 11, 32) ),
+ Label( 'license plate' , -1 , 255 , 'vehicle' , 7 , False , True , ( 0, 0,142) ),
+]
+
+
+#--------------------------------------------------------------------------------
+# Create dictionaries for a fast lookup
+#--------------------------------------------------------------------------------
+
+# Please refer to the main method below for example usages!
+
+# name to label object
+name2label = { label.name : label for label in labels }
+# id to label object
+id2label = { label.id : label for label in labels }
+# trainId to label object
+trainId2label = { label.trainId : label for label in reversed(labels) }
+# category to list of label objects
+category2labels = {}
+for label in labels:
+ category = label.category
+ if category in category2labels:
+ category2labels[category].append(label)
+ else:
+ category2labels[category] = [label]
diff --git a/data/cityscapes/data_loader.py b/data/cityscapes/data_loader.py
new file mode 100644
index 0000000..2c56f67
--- /dev/null
+++ b/data/cityscapes/data_loader.py
@@ -0,0 +1,353 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Script to serve the CityScapes dataset."""
+
+import os
+import sys, glob
+import numpy as np
+import imp
+import logging
+
+from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
+from batchgenerators.transforms.spatial_transforms import MirrorTransform
+from batchgenerators.transforms.abstract_transforms import Compose
+from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
+from batchgenerators.transforms.spatial_transforms import SpatialTransform
+from batchgenerators.transforms.crop_and_pad_transforms import CenterCropTransform
+from batchgenerators.transforms import AbstractTransform
+from .cityscapes_labels import labels as cityscapes_labels_tuple
+
+def loadFiles(label_density, split, input_path, cities=None, instance=False):
+ """
+ Assemble dict of file paths.
+ :param label_density: string in ['gtFine', 'gtCoarse']
+ :param split: string in ['train', 'val', 'test', 'train_extra']
+ :param input_path: string
+ :param cities: list of strings or None
+ :param instance: bool
+ :return: dict
+ """
+ input_dir = os.path.join(input_path, split)
+ logging.info("Assembling file dict from {}.".format(input_dir))
+ paths_dict = {}
+ for path, dirs, files in os.walk(input_dir):
+
+ skip_city = False
+ if cities is not None:
+ current_city = path.rsplit('/', 1)[-1]
+ if current_city not in cities:
+ skip_city = True
+
+ if not skip_city:
+ logging.info('Reading from {}'.format(path))
+ label_paths, img_paths = searchFiles(path, label_density, instance)
+ paths_dict = {**paths_dict, **zipPaths(label_paths, img_paths)}
+
+ return paths_dict
+
+
+def searchFiles(path, label_density, instance=False):
+ """
+ Get file paths via wildcard search.
+ :param path: path to files for each city
+ :param label_density: string in ['gtFine', 'gtCoarse']
+ :param instance: bool
+ :return: 2 lists
+ """
+ if (instance == True):
+ label_wildcard_search = os.path.join(path, "*{}_instanceIDs.npy".format(label_density))
+ else:
+ label_wildcard_search = os.path.join(path, "*{}_labelIds.npy".format(label_density))
+ label_paths = glob.glob(label_wildcard_search)
+ label_paths.sort()
+ img_wildcard_search = os.path.join(path, "*_leftImg8bit.npy")
+ img_paths = glob.glob(img_wildcard_search)
+ img_paths.sort()
+ return label_paths, img_paths
+
+
+def zipPaths(label_paths, img_paths):
+ """
+ zip paths in form of dict.
+ :param label_paths: list of strings
+ :param img_paths: list of strings
+ :return: dict
+ """
+ try:
+ assert len(label_paths) == len(img_paths)
+ except:
+ raise Exception('Missmatch: {} label paths vs. {} img paths!'.format(len(label_paths), len(img_paths)))
+
+ paths_dict = {}
+ for i, img_path in enumerate(img_paths):
+ img_spec = ('_').join(img_paths[i].split('/')[-1].split('_')[:-1])
+ try:
+ assert img_spec in label_paths[i]
+ except:
+ raise Exception('img and label name mismatch: {} vs. {}'.format(img_paths[i], label_paths[i]))
+
+ paths_dict[img_spec] = {"data": img_paths[i], "seg": label_paths[i], 'img_spec': img_spec}
+ return paths_dict
+
+
+def augment_gamma(data, gamma_range=(0.5, 2), invert_image=False, epsilon=1e-7, per_channel=False, retain_stats=False, p_per_sample=0.3):
+ """code by Fabian Isensee, see MIC_DKFZ/batch_generators on github."""
+ for sample in range(data.shape[0]):
+ if np.random.uniform() < p_per_sample:
+ if invert_image:
+ data = - data
+ if not per_channel:
+ if retain_stats:
+ mn = data[sample].mean()
+ sd = data[sample].std()
+ if np.random.random() < 0.5 and gamma_range[0] < 1:
+ gamma = np.random.uniform(gamma_range[0], 1)
+ else:
+ gamma = np.random.uniform(max(gamma_range[0], 1), gamma_range[1])
+ minm = data[sample].min()
+ rnge = data[sample].max() - minm
+ data[sample] = np.power(((data[sample] - minm) / float(rnge + epsilon)), gamma) * rnge + minm
+ if retain_stats:
+ data[sample] = data[sample] - data[sample].mean() + mn
+ data[sample] = data[sample] / (data[sample].std() + 1e-8) * sd
+ else:
+ for c in range(data.shape[1]):
+ if retain_stats:
+ mn = data[sample][c].mean()
+ sd = data[sample][c].std()
+ if np.random.random() < 0.5 and gamma_range[0] < 1:
+ gamma = np.random.uniform(gamma_range[0], 1)
+ else:
+ gamma = np.random.uniform(max(gamma_range[0], 1), gamma_range[1])
+ minm = data[sample][c].min()
+ rnge = data[sample][c].max() - minm
+ data[sample][c] = np.power(((data[sample][c] - minm) / float(rnge + epsilon)), gamma) * rnge + minm
+ if retain_stats:
+ data[sample][c] = data[sample][c] - data[sample][c].mean() + mn
+ data[sample][c] = data[sample][c] / (data[sample][c].std() + 1e-8) * sd
+ return data
+
+
+class GammaTransform(AbstractTransform):
+ """Augments by changing 'gamma' of the image (same as gamma correction in photos or computer monitors
+
+ Args:
+ gamma_range (tuple of float): range to sample gamma from. If one value is smaller than 1 and the other one is
+ larger then half the samples will have gamma <1 and the other >1 (in the inverval that was specified)
+
+ invert_image: whether to invert the image before applying gamma augmentation
+
+ retain_stats: Gamma transformation will alter the mean and std of the data in the patch. If retain_stats=True,
+ the data will be transformed to match the mean and standard deviation before gamma augmentation
+
+ """
+
+ def __init__(self, gamma_range=(0.5, 2), invert_image=False, per_channel=False, data_key="data", retain_stats=False,
+ p_per_sample=0.3, mask_channel_in_seg=None):
+ self.mask_channel_in_seg = mask_channel_in_seg
+ self.p_per_sample = p_per_sample
+ self.retain_stats = retain_stats
+ self.per_channel = per_channel
+ self.data_key = data_key
+ self.gamma_range = gamma_range
+ self.invert_image = invert_image
+
+ def __call__(self, **data_dict):
+ data_dict[self.data_key] = augment_gamma(data_dict[self.data_key], self.gamma_range, self.invert_image,
+ per_channel=self.per_channel, retain_stats=self.retain_stats,
+ p_per_sample=self.p_per_sample)
+ return data_dict
+
+
+def map_labels_to_trainId(arr):
+ """Remap ids to corresponding training Ids. Note that the inplace mapping works because id > trainId here!"""
+ id2trainId = {label.id:label.trainId for label in cityscapes_labels_tuple}
+ for id, trainId in id2trainId.items():
+ arr[arr == id] = trainId
+ return arr
+
+
+class AddLossMask(AbstractTransform):
+ """Splits one-hot segmentation into a segmentation array and a loss mask,
+ where the loss mask needs to be encoded as the next available integer larger than the last segmentation labels.
+
+ Args:
+ classes (tuple of int): All the class labels that are in the dataset
+
+ output_key (string): key to use for output of the one hot encoding. Default is 'seg' but that will override any
+ other existing seg channels. Therefore you have the option to change that. BEWARE: Any non-'seg' segmentations
+ will not be augmented anymore. Use this only at the very end of your pipeline!
+ """
+
+ def __init__(self, label2mask, output_key="loss_mask"):
+ self.output_key = output_key
+ self.label2mask = label2mask
+
+ def __call__(self, **data_dict):
+ seg = data_dict['seg']
+ if seg is not None:
+ data_dict[self.output_key] = (seg == self.label2mask).astype(np.uint8)
+ else:
+ from warnings import warn
+ warn("calling AddLossMask but there is no segmentation")
+ return data_dict
+
+
+class StochasticLabelSwitches(AbstractTransform):
+ """
+ Stochastically switches labels in a batch of integer-labeled segmentations.
+ """
+ def __init__(self, name2id, label_switches):
+ self._name2id = name2id
+ self._label_switches = label_switches
+
+ def __call__(self, **data_dict):
+
+ switched_seg = data_dict['seg']
+ batch_size = switched_seg.shape[0]
+
+ for c, p in self._label_switches.items():
+ init_id = self._name2id[c]
+ final_id = self._name2id[c + '_2']
+ switch_instances = np.random.binomial(1, p, batch_size)
+
+ for i in range(batch_size):
+ if switch_instances[i]:
+ switched_seg[i][switched_seg[i] == init_id] = final_id
+
+ data_dict['seg'] = switched_seg
+ return data_dict
+
+
+class BatchGenerator(SlimDataLoaderBase):
+ """
+ create the training/validation batch generator. Randomly sample n_batch_size patients
+ from the data set, (draw a random slice if 2D), pad-crop them to equal sizes and merge to an array.
+ :param data: data dictionary as provided by 'load_dataset'
+ :param batch_size: number of patients to sample for the batch
+ :param pre_crop_size: equal size for merging the patients to a single array (before the final random-crop in data aug.)
+ :return dictionary containing the batch data / seg / pids
+ """
+ def __init__(self, batch_size, data_dir, label_density='gtFine', data_split='train', resolution='quarter',
+ cities=None, gt_instances=False, n_batches=None, random=True):
+ super(BatchGenerator, self).__init__(data=None, batch_size=batch_size)
+
+ data_dir = os.path.join(data_dir, resolution)
+ self._data_dir = data_dir
+ self._label_density = label_density
+ self._gt_instances = gt_instances
+ self._data_split = data_split
+ self._random = random
+ self._n_batches = n_batches
+ self._batches_generated = 0
+ self._data = loadFiles(label_density, data_split, data_dir, cities=cities, instance=gt_instances)
+ logging.info('{} set comprises {} files.'.format(data_split, len(self._data)))
+
+ def generate_train_batch(self):
+
+ if self._random:
+ img_ixs = np.random.choice(list(self._data.keys()), self.batch_size, replace=True)
+ else:
+ batch_no = self._batches_generated % self._n_batches
+ img_ixs = [list(self._data.keys())[i] for i in\
+ np.arange(batch_no * self.batch_size, (batch_no + 1) * self.batch_size)]
+ img_batch, seg_batch, ids_batch = [], [], []
+
+ for b in range(self.batch_size):
+
+ img = np.load(self._data[img_ixs[b]]['data']) / 255.
+ seg = np.load(self._data[img_ixs[b]]['seg'])
+ seg = map_labels_to_trainId(seg)
+ seg = seg[np.newaxis]
+ ids_batch.append(self._data[img_ixs[b]]['img_spec'])
+
+ img_batch.append(img)
+ seg_batch.append(seg)
+
+ self._batches_generated += 1
+ batch = {'data': np.array(img_batch).astype('float32'), 'seg': np.array(seg_batch).astype('uint8'),
+ 'id': ids_batch}
+ return batch
+
+
+def create_data_gen_pipeline(cf, cities=None, data_split='train', do_aug=True, random=True, n_batches=None):
+ """
+ create mutli-threaded train/val/test batch generation and augmentation pipeline.
+ :param cities: list of strings or None
+ :param patient_data: dictionary containing one dictionary per patient in the train/test subset
+ :param test_pids: (optional) list of test patient ids, calls the test generator.
+ :param do_aug: (optional) whether to perform data augmentation (training) or not (validation/testing)
+ :param random: bool, whether to draw random batches or go through data linearly
+ :return: multithreaded_generator
+ """
+ data_gen = BatchGenerator(cities=cities, batch_size=cf.batch_size, data_dir=cf.data_dir,
+ label_density=cf.label_density, data_split=data_split, resolution=cf.resolution,
+ gt_instances=cf.gt_instances, n_batches=n_batches, random=random)
+ my_transforms = []
+ if do_aug:
+ mirror_transform = MirrorTransform(axes=(3,))
+ my_transforms.append(mirror_transform)
+ spatial_transform = SpatialTransform(patch_size=cf.patch_size[-2:],
+ patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
+ do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
+ alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
+ do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
+ angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
+ do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
+ random_crop=cf.da_kwargs['random_crop'],
+ border_mode_data=cf.da_kwargs['border_mode_data'],
+ border_mode_seg=cf.da_kwargs['border_mode_seg'],
+ border_cval_seg=cf.da_kwargs['border_cval_seg'])
+ my_transforms.append(spatial_transform)
+ else:
+ my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[-2:]))
+
+ my_transforms.append(GammaTransform(cf.da_kwargs['gamma_range'], invert_image=False, per_channel=True,
+ retain_stats=cf.da_kwargs['gamma_retain_stats'],
+ p_per_sample=cf.da_kwargs['p_gamma']))
+ my_transforms.append(AddLossMask(cf.ignore_label))
+ if cf.label_switches is not None:
+ my_transforms.append(StochasticLabelSwitches(cf.name2trainId, cf.label_switches))
+ all_transforms = Compose(my_transforms)
+ multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers,
+ seeds=range(cf.n_workers))
+ return multithreaded_generator
+
+
+def get_train_generators(cf):
+ """
+ wrapper function for creating the training batch generator pipeline. returns the train/val generators
+ """
+ batch_gen = {}
+ batch_gen['train'] = create_data_gen_pipeline(cf=cf, cities=cf.train_cities, data_split='train', do_aug=True,
+ n_batches=cf.n_train_batches)
+ batch_gen['val'] = create_data_gen_pipeline(cf=cf, cities=cf.val_cities, data_split='train', do_aug=False,
+ random=False, n_batches=cf.n_val_batches)
+ return batch_gen
+
+def main():
+ """Main entry point for the script."""
+ logging.info("start loading.")
+ cf = imp.load_source('cf', 'config.py')
+ dict = loadFiles("gtFine", "train", cf.out_dir, False)
+ logging.info('Contains {} elements.'.format(len(dict)))
+ logging.info(dict)
+ data_provider = BatchGenerator(8, cf.out_dir, data_split='val')
+ batch = next(data_provider)
+ logging.info(batch['data'].shape, batch['seg'].shape, batch['id'])
+
+
+if __name__ == '__main__':
+ sys.exit(main())
diff --git a/data/cityscapes/preprocessing.py b/data/cityscapes/preprocessing.py
new file mode 100644
index 0000000..1b17046
--- /dev/null
+++ b/data/cityscapes/preprocessing.py
@@ -0,0 +1,99 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Script to preprocess the Cityscapes dataset."""
+
+import os
+import numpy as np
+from tqdm import tqdm
+from PIL import Image
+import imp
+
+resolution_map = {1.0: 'full', 0.5: 'half', 0.25: 'quarter'}
+
+def resample(img, scale_factor=1.0, interpolation=Image.BILINEAR):
+ """
+ Resample PIL.Image objects.
+ :param img: PIL.Image object
+ :param scale_factor: float
+ :param interpolation: PIL.Image interpoaltion method
+ :return: PIL.Image object
+ """
+ width, height = img.size
+ basewidth = width * scale_factor
+ basewidth = int(basewidth)
+ wpercent = (basewidth / float(width))
+ hsize = int((float(height) * wpercent))
+ return img.resize((basewidth, hsize), interpolation)
+
+def recursive_mkdir(nested_dir_list):
+ """
+ Make the full nested path of directories provided. Order in list implies nesting depth.
+ :param nested_dir_list: list of strings
+ :return:
+ """
+ nested_dir = ''
+ for dir in nested_dir_list:
+ nested_dir = os.path.join(nested_dir, dir)
+ if not os.path.isdir(nested_dir):
+ os.mkdir(nested_dir)
+ return
+
+def preprocess(cf):
+
+ for set in list(cf.settings.keys()):
+ print('Processing {} set.'.format(set))
+
+ # image dir
+ image_dir = os.path.join(cf.raw_data_dir, 'leftImg8bit', set)
+ city_names = os.listdir(image_dir)
+
+ for city in city_names:
+ print('Processing {}'.format(city))
+ city_dir = os.path.join(image_dir, city)
+ image_names = os.listdir(city_dir)
+ image_specifiers = ['_'.join(img.split('_')[:3]) for img in image_names]
+
+ for img_spec in tqdm(image_specifiers):
+ for scale in cf.settings[set]['resolutions']:
+ recursive_mkdir([cf.out_dir, resolution_map[scale], set, city])
+
+ # image
+ img_path = os.path.join(city_dir, img_spec + '_leftImg8bit.png')
+ img = Image.open(img_path)
+ if scale != 1.0:
+ img = resample(img, scale_factor=scale, interpolation=Image.BILINEAR)
+ img_out_path = os.path.join(cf.out_dir, resolution_map[scale], set, city, img_spec + '_leftImg8bit.npy')
+ img_arr = np.array(img).astype(np.float32)
+
+ channel_axis = 0 if img_arr.shape[0] == 3 else 2
+ if cf.data_format == 'NCHW' and channel_axis != 0:
+ img_arr = np.transpose(img_arr, axes=[2,0,1])
+ np.save(img_out_path, img_arr)
+
+ # labels
+ for label_density in cf.settings[set]['label_densities']:
+ label_dir = os.path.join(cf.raw_data_dir, label_density, set, city)
+ for mod in cf.settings[set]['label_modalities']:
+ label_spec = img_spec + '_{}_{}'.format(label_density, mod)
+ label_path = os.path.join(label_dir, label_spec + '.png')
+ label = Image.open(label_path)
+ if scale != 1.0:
+ label = resample(label, scale_factor=scale, interpolation=Image.NEAREST)
+ label_out_path = os.path.join(cf.out_dir, resolution_map[scale], set, city, label_spec + '.npy')
+ np.save(label_out_path, np.array(label).astype(np.uint8))
+
+if __name__ == "__main__":
+ cf = imp.load_source('cf', 'preprocessing_config.py')
+ preprocess(cf)
\ No newline at end of file
diff --git a/data/cityscapes/preprocessing_config.py b/data/cityscapes/preprocessing_config.py
new file mode 100644
index 0000000..548ae40
--- /dev/null
+++ b/data/cityscapes/preprocessing_config.py
@@ -0,0 +1,28 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Cityscapes preprocessing config."""
+
+raw_data_dir = 'SET_INPUT_DIRECTORY_ABSOLUTE_PATH_HERE'
+out_dir = 'SET_OUTPUT_DIRECTORY_ABSOLUTE_PATH_HERE'
+
+# settings:
+settings = {
+ 'train': {'resolutions': [1.0, 0.5, 0.25], 'label_densities': ['gtFine'],
+ 'label_modalities': ['labelIds']},
+ 'val': {'resolutions': [1.0, 0.5, 0.25], 'label_densities': ['gtFine'],
+ 'label_modalities': ['labelIds']},
+ }
+
+data_format = 'NCHW'
diff --git a/evaluation/__init__.py b/evaluation/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/evaluation/cityscapes_eval_config.py b/evaluation/cityscapes_eval_config.py
new file mode 100644
index 0000000..5fa1792
--- /dev/null
+++ b/evaluation/cityscapes_eval_config.py
@@ -0,0 +1,92 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Cityscapes evaluation config."""
+
+import os
+from collections import OrderedDict
+from data.cityscapes.cityscapes_labels import labels as cs_labels_tuple
+from model import pretrained_weights
+
+config_path = os.path.realpath(__file__)
+
+#########################################
+# data #
+#########################################
+
+data_dir = 'PREPROCESSING_OUTPUT_DIRECTORY_ABSOLUTE_PATH'
+resolution = 'quarter'
+label_density = 'gtFine'
+num_classes = 19
+one_hot_labels = False
+ignore_label = 255
+cities = ['frankfurt', 'lindau', 'munster']
+
+#########################################
+# label-switches #
+#########################################
+
+color_map = {label.trainId:label.color for label in cs_labels_tuple}
+color_map[255] = (0.,0.,0.)
+
+trainId2name = {labels.trainId: labels.name for labels in cs_labels_tuple}
+name2trainId = {labels.name: labels.trainId for labels in cs_labels_tuple}
+
+label_switches = OrderedDict([('sidewalk', 8./17.), ('person', 7./17.), ('car', 6./17.), ('vegetation', 5./17.), ('road', 4./17.)])
+num_classes += len(label_switches)
+switched_Id2name = {19+i:list(label_switches.keys())[i] + '_2' for i in range(len(label_switches))}
+switched_name2Id = {list(label_switches.keys())[i] + '_2':19+i for i in range(len(label_switches))}
+trainId2name = {**trainId2name, **switched_Id2name}
+name2trainId = {**name2trainId, **switched_name2Id}
+
+switched_labels2color = {'road_2': (84, 86, 22), 'person_2': (167, 242, 242), 'vegetation_2': (242, 160, 19),
+ 'car_2': (30, 193, 252), 'sidewalk_2': (46, 247, 180)}
+switched_cmap = {switched_name2Id[i]:switched_labels2color[i] for i in switched_name2Id.keys()}
+color_map = {**color_map, **switched_cmap}
+
+exp_modes = len(label_switches)
+num_modes = 2 ** exp_modes
+
+#########################################
+# network #
+#########################################
+
+cuda_visible_devices = '0'
+cpu_device = '/cpu:0'
+gpu_device = '/gpu:0'
+
+patch_size = [256, 512]
+network_input_shape = (None, 3) +tuple(patch_size)
+network_output_shape = (None, num_classes) + tuple(patch_size)
+label_shape = (None, 1) + tuple(patch_size)
+loss_mask_shape = label_shape
+
+base_channels = 32
+num_channels = [base_channels, 2*base_channels, 4*base_channels,
+ 6*base_channels, 6*base_channels, 6*base_channels, 6*base_channels]
+
+num_convs_per_block = 3
+
+latent_dim = 6
+num_1x1_convs = 3
+analytic_kl = True
+use_posterior_mean = False
+
+#########################################
+# evaluation #
+#########################################
+
+num_samples = 16
+exp_dir = '/'.join(os.path.abspath(pretrained_weights.__file__).split('/')[:-1])
+out_dir = 'EVALUATION_OUTPUT_DIRECTORY_ABSOLUTE_PATH'
\ No newline at end of file
diff --git a/evaluation/eval_cityscapes.py b/evaluation/eval_cityscapes.py
new file mode 100644
index 0000000..68e5951
--- /dev/null
+++ b/evaluation/eval_cityscapes.py
@@ -0,0 +1,479 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Cityscapes evaluation script."""
+
+import tensorflow as tf
+import numpy as np
+import os
+import argparse
+from tqdm import tqdm
+from multiprocessing import Process, Queue
+from importlib.machinery import SourceFileLoader
+import logging
+import pickle
+
+from model.probabilistic_unet import ProbUNet
+from data.cityscapes.data_loader import loadFiles, map_labels_to_trainId
+from utils import training_utils
+
+
+def write_test_predictions(cf):
+ """
+ Write samples as numpy arrays.
+ :param cf: config module
+ :return:
+ """
+ # do not use all gpus
+ os.environ["CUDA_VISIBLE_DEVICES"] = cf.cuda_visible_devices
+
+ data_dir = os.path.join(cf.data_dir, cf.resolution)
+ data_dict = loadFiles(label_density=cf.label_density, split='val', input_path=data_dir,
+ cities=None, instance=False)
+ # prepare out_dir
+ if not os.path.isdir(cf.out_dir):
+ os.mkdir(cf.out_dir)
+
+ logging.info('Writing to {}'.format(cf.out_dir))
+
+ # initialize computation graph
+ prob_unet = ProbUNet(latent_dim=cf.latent_dim, num_channels=cf.num_channels,
+ num_1x1_convs=cf.num_1x1_convs,
+ num_classes=cf.num_classes, num_convs_per_block=cf.num_convs_per_block,
+ initializers={'w': training_utils.he_normal(),
+ 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0),
+ 'b': tf.contrib.layers.l2_regularizer(1.0)})
+ x = tf.placeholder(tf.float32, shape=cf.network_input_shape)
+
+ with tf.device(cf.gpu_device):
+ prob_unet(x, is_training=False, one_hot_labels=cf.one_hot_labels)
+ sampled_logits = prob_unet.sample()
+
+ saver = tf.train.Saver(save_relative_paths=True)
+ with tf.train.MonitoredTrainingSession() as sess:
+
+ print('EXP DIR', cf.exp_dir)
+ latest_ckpt_path = tf.train.latest_checkpoint(cf.exp_dir)
+ print('CKPT PATH', latest_ckpt_path)
+ saver.restore(sess, latest_ckpt_path)
+
+ for k, v in tqdm(data_dict.items()):
+ img = np.load(v['data']) / 255.
+ # add batch dimensions
+ img = img[np.newaxis]
+
+ for i in range(cf.num_samples):
+ sample = sess.run(sampled_logits, feed_dict={x: img})
+ sample = np.argmax(sample, axis=1)[:, np.newaxis]
+ sample = sample.astype(np.uint8)
+ sample_path = os.path.join(cf.out_dir, '{}_sample{}_labelIds.npy'.format(k, i))
+ np.save(sample_path, sample)
+
+
+def get_array_of_modes(cf, seg):
+ """
+ Assemble an array holding all label modes.
+ :param cf: config module
+ :param seg: 4D integer array
+ :return: 4D integer array
+ """
+ mode_stats = get_mode_statistics(cf.label_switches, exp_modes=cf.exp_modes)
+ switch = mode_stats['switch']
+
+ # construct ground-truth modes
+ gt_seg_modes = np.zeros(shape=(cf.num_modes,) + seg.shape, dtype=np.uint8)
+ for mode in range(cf.num_modes):
+ switched_seg = seg.copy()
+ for i, c in enumerate(cf.label_switches.keys()):
+ if switch[mode, i]:
+ init_id = cf.name2trainId[c]
+ final_id = cf.name2trainId[c + '_2']
+ switched_seg[switched_seg == init_id] = final_id
+ gt_seg_modes[mode] = switched_seg
+
+ return gt_seg_modes
+
+
+def get_array_of_samples(cf, img_key):
+ """
+ Assemble an array holding all segmentation samples for a given image.
+ :param cf: config module
+ :param img_key: string
+ :return: 5D integer array
+ """
+ seg_samples = np.zeros(shape=(cf.num_samples,1,1) + tuple(cf.patch_size), dtype=np.uint8)
+ for i in range(cf.num_samples):
+ sample_path = os.path.join(cf.out_dir, '{}_sample{}_labelIds.npy'.format(img_key, i))
+ try:
+ seg_samples[i] = np.load(sample_path)
+ except:
+ print('Could not load {}'.format(sample_path))
+
+ return seg_samples
+
+
+def get_mode_counts(d_matrix_YS):
+ """
+ Calculate image-level mode counts.
+ :param d_matrix_YS: 3D array
+ :return: numpy array
+ """
+ # assign each sample to a mode
+ mean_d = np.nanmean(d_matrix_YS, axis=-1)
+ sampled_modes = np.argmin(mean_d, axis=-2)
+
+ # count the modes
+ num_modes = d_matrix_YS.shape[0]
+ mode_count = np.zeros(shape=(num_modes,), dtype=np.int)
+ for sampled_mode in sampled_modes:
+ mode_count[sampled_mode] += 1
+
+ return mode_count
+
+
+def get_pixelwise_mode_counts(cf, seg, seg_samples):
+ """
+ Calculate pixel-wise mode counts.
+ :param cf: config module
+ :param seg: 4D array of integer labeled segmentations
+ :param seg_samples: 5D array of integer labeled segmentations
+ :return: array of shape (switchable classes, 3)
+ """
+ assert seg.shape == seg_samples.shape[1:]
+ num_samples = seg_samples.shape[0]
+ pixel_counts = np.zeros(shape=(len(cf.label_switches),3), dtype=np.int)
+
+ # iterate all switchable classes
+ for i,c in enumerate(cf.label_switches.keys()):
+ c_id = cf.name2trainId[c]
+ alt_c_id = cf.name2trainId[c+'_2']
+ c_ixs = np.where(seg == c_id)
+
+ total_num_pixels = np.sum((seg == c_id).astype(np.uint8)) * num_samples
+ pixel_counts[i,0] = total_num_pixels
+
+ # count the pixels of original class|original class and alternative class|original class
+ for j in range(num_samples):
+ sample = seg_samples[j]
+ sampled_original_pixels = np.sum((sample[c_ixs] == c_id).astype(np.uint8))
+ sampled_alternative_pixels = np.sum((sample[c_ixs] == alt_c_id).astype(np.uint8))
+ pixel_counts[i,1] += sampled_original_pixels
+ pixel_counts[i,2] += sampled_alternative_pixels
+
+ return pixel_counts
+
+
+def get_mode_statistics(label_switches, exp_modes=5):
+ """
+ Calculate a binary matrix of switches as well as a vector of mode probabilities.
+ :param label_switches: dict specifying class names and their individual sampling probabilities
+ :param exp_modes: integer, number of independently switchable classes
+ :return: dict
+ """
+ num_modes = 2 ** exp_modes
+
+ # assemble a binary matrix of switch decisions
+ switch = np.zeros(shape=(num_modes, 5), dtype=np.uint8)
+ for i in range(exp_modes):
+ switch[:,i] = 2 ** i * (2 ** (exp_modes - 1 - i) * [0] + 2 ** (exp_modes - 1 - i) * [1])
+
+ # calculate the probability for each individual mode
+ mode_probs = np.zeros(shape=(num_modes,), dtype=np.float32)
+ for mode in range(num_modes):
+ prob = 1.
+ for i, c in enumerate(label_switches.keys()):
+ if switch[mode, i]:
+ prob *= label_switches[c]
+ else:
+ prob *= 1. - label_switches[c]
+ mode_probs[mode] = prob
+ assert np.sum(mode_probs) == 1.
+
+ return {'switch': switch, 'mode_probs': mode_probs}
+
+
+def get_energy_distance_components(gt_seg_modes, seg_samples, eval_class_ids, ignore_mask=None):
+ """
+ Calculates the components for the IoU-based generalized energy distance given an array holding all segmentation
+ modes and an array holding all sampled segmentations.
+ :param gt_seg_modes: N-D array in format (num_modes,[...],H,W)
+ :param seg_samples: N-D array in format (num_samples,[...],H,W)
+ :param eval_class_ids: integer or list of integers specifying the classes to encode, if integer range() is applied
+ :param ignore_mask: N-D array in format ([...],H,W)
+ :return: dict
+ """
+ num_modes = gt_seg_modes.shape[0]
+ num_samples = seg_samples.shape[0]
+
+ if isinstance(eval_class_ids, int):
+ eval_class_ids = list(range(eval_class_ids))
+
+ d_matrix_YS = np.zeros(shape=(num_modes, num_samples, len(eval_class_ids)), dtype=np.float32)
+ d_matrix_YY = np.zeros(shape=(num_modes, num_modes, len(eval_class_ids)), dtype=np.float32)
+ d_matrix_SS = np.zeros(shape=(num_samples, num_samples, len(eval_class_ids)), dtype=np.float32)
+
+ # iterate all ground-truth modes
+ for mode in range(num_modes):
+
+ ##########################################
+ # Calculate d(Y,S) = [1 - IoU(Y,S)], #
+ # with S ~ P_pred, Y ~ P_gt #
+ ##########################################
+
+ # iterate the samples S
+ for i in range(num_samples):
+ conf_matrix = training_utils.calc_confusion(gt_seg_modes[mode], seg_samples[i],
+ loss_mask=ignore_mask, class_ixs=eval_class_ids)
+ iou = training_utils.metrics_from_conf_matrix(conf_matrix)['iou']
+ d_matrix_YS[mode, i] = 1. - iou
+
+ ###########################################
+ # Calculate d(Y,Y') = [1 - IoU(Y,Y')], #
+ # with Y,Y' ~ P_gt #
+ ###########################################
+
+ # iterate the ground-truth modes Y' while exploiting the pair-wise symmetries for efficiency
+ for mode_2 in range(mode, num_modes):
+ conf_matrix = training_utils.calc_confusion(gt_seg_modes[mode], gt_seg_modes[mode_2],
+ loss_mask=ignore_mask, class_ixs=eval_class_ids)
+ iou = training_utils.metrics_from_conf_matrix(conf_matrix)['iou']
+ d_matrix_YY[mode, mode_2] = 1. - iou
+ d_matrix_YY[mode_2, mode] = 1. - iou
+
+ #########################################
+ # Calculate d(S,S') = 1 - IoU(S,S'), #
+ # with S,S' ~ P_pred #
+ #########################################
+
+ # iterate all samples S
+ for i in range(num_samples):
+ # iterate all samples S'
+ for j in range(i, num_samples):
+ conf_matrix = training_utils.calc_confusion(seg_samples[i], seg_samples[j],
+ loss_mask=ignore_mask, class_ixs=eval_class_ids)
+ iou = training_utils.metrics_from_conf_matrix(conf_matrix)['iou']
+ d_matrix_SS[i, j] = 1. - iou
+ d_matrix_SS[j, i] = 1. - iou
+
+ return {'YS': d_matrix_YS, 'SS': d_matrix_SS, 'YY': d_matrix_YY}
+
+
+def calc_energy_distances(d_matrices, num_samples=None, probability_weighted=False, label_switches=None, exp_mode=5):
+ """
+ Calculate the energy distance for each image based on matrices holding the combinatorial distances.
+ :param d_matrices: dict holding 4D arrays of shape \
+ (num_images, num_modes/num_samples, num_modes/num_samples, num_classes)
+ :param num_samples: integer or None
+ :param probability_weighted: bool
+ :param label_switches: None or dict
+ :param exp_mode: integer
+ :return: numpy array
+ """
+ d_matrices = d_matrices.copy()
+
+ if num_samples is None:
+ num_samples = d_matrices['SS'].shape[1]
+
+ d_matrices['YS'] = d_matrices['YS'][:,:,:num_samples]
+ d_matrices['SS'] = d_matrices['SS'][:,:num_samples,:num_samples]
+
+ # perform a nanmean over the class axis so as to not factor in classes that are not present in
+ # both the ground-truth mode as well as the sampled prediction
+ if probability_weighted:
+ mode_stats = get_mode_statistics(label_switches, exp_modes=exp_mode)
+ mode_probs = mode_stats['mode_probs']
+
+ mean_d_YS = np.nanmean(d_matrices['YS'], axis=-1)
+ mean_d_YS = np.mean(mean_d_YS, axis=2)
+ mean_d_YS = mean_d_YS * mode_probs[np.newaxis, :]
+ d_YS = np.sum(mean_d_YS, axis=1)
+
+ mean_d_SS = np.nanmean(d_matrices['SS'], axis=-1)
+ d_SS = np.mean(mean_d_SS, axis=(1, 2))
+
+ mean_d_YY = np.nanmean(d_matrices['YY'], axis=-1)
+ mean_d_YY = mean_d_YY * mode_probs[np.newaxis, :, np.newaxis] * mode_probs[np.newaxis, np.newaxis, :]
+ d_YY = np.sum(mean_d_YY, axis=(1, 2))
+
+ else:
+ mean_d_YS = np.nanmean(d_matrices['YS'], axis=-1)
+ d_YS = np.mean(mean_d_YS, axis=(1,2))
+
+ mean_d_SS = np.nanmean(d_matrices['SS'], axis=-1)
+ d_SS = np.mean(mean_d_SS, axis=(1, 2))
+
+ mean_d_YY = np.nanmean(d_matrices['YY'], axis=-1)
+ d_YY = np.nanmean(mean_d_YY, axis=(1, 2))
+
+ return 2 * d_YS - d_SS - d_YY
+
+
+def eval(cf, cities, queue=None, ixs=None):
+ """
+ Perform evaluation w.r.t the generalized energy distance based on the IoU as well as image-level and pixel-level
+ mode frequencies (using samples written to file).
+ :param cf: config module
+ :param cities: string or list of strings
+ :param queue: instance of multiprocessing.Queue
+ :param ixs: None or 2-tuple of ints
+ :return: NoneType or numpy array
+ """
+ data_dir = os.path.join(cf.data_dir, cf.resolution)
+ data_dict = loadFiles(label_density=cf.label_density, split='val', input_path=data_dir, cities=cities,
+ instance=False)
+
+ num_modes = cf.num_modes
+ num_samples = cf.num_samples
+
+ # evaluate only switchable classes, so a total of 10 here
+ eval_class_names = list(cf.label_switches.keys()) + list(cf.switched_name2Id.keys())
+ eval_class_ids = [cf.name2trainId[n] for n in eval_class_names]
+ d_matrices = {'YS': np.zeros(shape=(len(data_dict), num_modes, num_samples, len(eval_class_ids)),
+ dtype=np.float32),
+ 'YY': np.ones(shape=(len(data_dict), num_modes, num_modes, len(eval_class_ids)),
+ dtype=np.float32),
+ 'SS': np.ones(shape=(len(data_dict), num_samples, num_samples, len(eval_class_ids)),
+ dtype=np.float32)}
+ sampled_mode_counts = np.zeros(shape=(num_modes,), dtype=np.int)
+ sampled_pixel_counts = np.zeros(shape=(len(cf.label_switches), 3), dtype=np.int)
+
+ logging.info('Evaluating class names: {} (corresponding to labels {})'.format(eval_class_names, eval_class_ids))
+
+ # allow for data selection by indexing via ixs
+ if ixs is None:
+ data_keys = list(data_dict.keys())
+ else:
+ data_keys = list(data_dict.keys())[ixs[0]:ixs[1]]
+ for k in d_matrices.keys():
+ d_matrices[k] = d_matrices[k][:ixs[1]-ixs[0]]
+
+ # iterate all validation images
+ for img_n, img_key in enumerate(tqdm(data_keys)):
+
+ seg = np.load(data_dict[img_key]['seg'])
+ seg = map_labels_to_trainId(seg)
+ seg = seg[np.newaxis, np.newaxis]
+ ignore_mask = (seg == cf.ignore_label).astype(np.uint8)
+
+ seg_samples = get_array_of_samples(cf, img_key)
+ gt_seg_modes = get_array_of_modes(cf, seg)
+
+ energy_dist = get_energy_distance_components(gt_seg_modes=gt_seg_modes, seg_samples=seg_samples,
+ eval_class_ids=eval_class_ids, ignore_mask=ignore_mask)
+ sampled_mode_counts += get_mode_counts(energy_dist['YS'])
+ sampled_pixel_counts += get_pixelwise_mode_counts(cf, seg, seg_samples)
+
+ for k in d_matrices.keys():
+ d_matrices[k][img_n] = energy_dist[k]
+
+ results = {'d_matrices': d_matrices, 'sampled_pixel_counts': sampled_pixel_counts,
+ 'sampled_mode_counts': sampled_mode_counts, 'total_num_samples': len(data_keys) * num_samples}
+
+ if queue is not None:
+ queue.put(results)
+ return
+ else:
+ return results
+
+
+def runInParallel(fns_args, queue):
+ """Run functions in parallel.
+ :param fns_args: list of tuples containing functions and a tuple of arguments each
+ :param queue: instance of multiprocessing.Queue()
+ :return: list of queue results
+ """
+ proc = []
+ for fn in fns_args:
+ p = Process(target=fn[0], args=fn[1])
+ p.start()
+ proc.append(p)
+ return [queue.get() for p in proc]
+
+
+def multiprocess_evaluation(cf):
+ """Evaluate the energy distance in multiprocessing.
+ :param cf: config module"""
+ q = Queue()
+ results = runInParallel([(eval, (cf, 'lindau', q)),
+ (eval, (cf, 'frankfurt', q, (0, 100))),
+ (eval, (cf, 'frankfurt', q, (100, 200))),
+ (eval, (cf, 'frankfurt', q, (200, 267))),
+ (eval, (cf, 'munster', q, (0, 100))),
+ (eval, (cf, 'munster', q, (100, 174)))],
+ queue=q)
+ total_num_samples = 0
+ sampled_mode_counts = np.zeros(shape=(cf.num_modes,), dtype=np.int)
+ sampled_pixel_counts = np.zeros(shape=(len(cf.label_switches), 3), dtype=np.int)
+ d_matrices = {'YS':[], 'SS':[], 'YY':[]}
+
+ # aggregate results from the queue
+ for result_dict in results:
+ for key in d_matrices.keys():
+ d_matrices[key].append(result_dict['d_matrices'][key])
+
+ sampled_pixel_counts += result_dict['sampled_pixel_counts']
+ sampled_mode_counts += result_dict['sampled_mode_counts']
+ total_num_samples += result_dict['total_num_samples']
+
+ for key in d_matrices.keys():
+ d_matrices[key] = np.concatenate(d_matrices[key], axis=0)
+
+ # calculate frequencies
+ print('pixel frequencies', sampled_pixel_counts)
+ sampled_pixelwise_mode_per_class = sampled_pixel_counts[:,1:]
+ total_num_pixels_per_class = sampled_pixel_counts[:,0:1]
+ sampled_pixel_frequencies = sampled_pixelwise_mode_per_class / total_num_pixels_per_class
+ sampled_mode_frequencies = sampled_mode_counts / total_num_samples
+
+ print('sampled pixel frequencies', sampled_pixel_frequencies)
+ print('sampled_mode_frequencies', sampled_mode_frequencies)
+
+ results_dict = {'d_matrices': d_matrices, 'pixel_frequencies': sampled_pixel_frequencies,
+ 'mode_frequencies': sampled_mode_frequencies}
+
+ results_file = os.path.join(cf.out_dir, 'eval_results.pkl')
+ with open(results_file, 'wb') as f:
+ pickle.dump(results_dict, f, pickle.HIGHEST_PROTOCOL)
+ logging.info('Wrote to {}'.format(results_file))
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description='Evaluation step selection.')
+ parser.add_argument('--write_samples', dest='write_samples', action='store_true')
+ parser.add_argument('--eval_samples', dest='write_samples', action='store_false')
+ parser.set_defaults(write_samples=True)
+ parser.add_argument('-c', '--config_name', type=str, default='cityscapes_eval_config.py',
+ help='name of the python file that is loaded as config module')
+ args = parser.parse_args()
+
+ # load evaluation config
+ cf = SourceFileLoader('cf', args.config_name).load_module()
+
+ # prepare evaluation directory
+ if not os.path.isdir(cf.out_dir):
+ os.mkdir(cf.out_dir)
+
+ # log to file and console
+ log_path = os.path.join(cf.out_dir, 'eval.log')
+ logging.basicConfig(filename=log_path, level=logging.INFO)
+ logging.getLogger().addHandler(logging.StreamHandler())
+ logging.info('Logging to {}'.format(log_path))
+
+ if args.write_samples:
+ logging.info('Writing samples to {}'.format(cf.out_dir))
+ write_test_predictions(cf)
+ else:
+ logging.info('Evaluating samples from {}'.format(cf.out_dir))
+ multiprocess_evaluation(cf)
\ No newline at end of file
diff --git a/evaluation/evaluation_plots.ipynb b/evaluation/evaluation_plots.ipynb
new file mode 100644
index 0000000..7eb0e37
--- /dev/null
+++ b/evaluation/evaluation_plots.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/utils/training_utils.py:21: UserWarning: \n",
+ "This call to matplotlib.use() has no effect because the backend has already\n",
+ "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
+ "or matplotlib.backends is imported for the first time.\n",
+ "\n",
+ "The backend was *originally* set to 'nbAgg' by the following code:\n",
+ " File \"/usr/local/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n",
+ " \"__main__\", mod_spec)\n",
+ " File \"/usr/local/lib/python3.6/runpy.py\", line 85, in _run_code\n",
+ " exec(code, run_globals)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in \n",
+ " app.launch_new_instance()\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
+ " app.start()\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 486, in start\n",
+ " self.io_loop.start()\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n",
+ " self.asyncio_loop.run_forever()\n",
+ " File \"/usr/local/lib/python3.6/asyncio/base_events.py\", line 422, in run_forever\n",
+ " self._run_once()\n",
+ " File \"/usr/local/lib/python3.6/asyncio/base_events.py\", line 1432, in _run_once\n",
+ " handle._run()\n",
+ " File \"/usr/local/lib/python3.6/asyncio/events.py\", line 145, in _run\n",
+ " self._callback(*self._args)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n",
+ " handler_func(fileobj, events)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n",
+ " return fn(*args, **kwargs)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n",
+ " self._handle_recv()\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n",
+ " self._run_callback(callback, msg)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n",
+ " callback(*args, **kwargs)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n",
+ " return fn(*args, **kwargs)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
+ " return self.dispatch_shell(stream, msg)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
+ " handler(stream, idents, msg)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
+ " user_expressions, allow_stdin)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
+ " res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
+ " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2662, in run_cell\n",
+ " raw_cell, store_history, silent, shell_futures)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2785, in _run_cell\n",
+ " interactivity=interactivity, compiler=compiler, result=result)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2901, in run_ast_nodes\n",
+ " if self.run_code(code, result):\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2961, in run_code\n",
+ " exec(code_obj, self.user_global_ns, self.user_ns)\n",
+ " File \"\", line 4, in \n",
+ " get_ipython().run_line_magic('matplotlib', 'notebook')\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2131, in run_line_magic\n",
+ " result = fn(*args,**kwargs)\n",
+ " File \"\", line 2, in matplotlib\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/magic.py\", line 187, in \n",
+ " call = lambda f, *a, **k: f(*a, **k)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/magics/pylab.py\", line 99, in matplotlib\n",
+ " gui, backend = self.shell.enable_matplotlib(args.gui)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3049, in enable_matplotlib\n",
+ " pt.activate_matplotlib(backend)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 311, in activate_matplotlib\n",
+ " matplotlib.pyplot.switch_backend(backend)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 231, in switch_backend\n",
+ " matplotlib.use(newbackend, warn=False, force=True)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1410, in use\n",
+ " reload(sys.modules['matplotlib.backends'])\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/importlib/__init__.py\", line 166, in reload\n",
+ " _bootstrap._exec(spec, module)\n",
+ " File \"/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 16, in \n",
+ " line for line in traceback.format_stack()\n",
+ "\n",
+ "\n",
+ " matplotlib.use('agg')\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /home/skohl/PycharmProjects/phabricator/skohl/prob_unet/utils/training_utils.py:270: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with distribution=normal is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "`normal` is a deprecated alias for `truncated_normal`\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import numpy as np\n",
+ "\n",
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.gridspec as gridspec\n",
+ "import seaborn as sns\n",
+ "import pandas as pd\n",
+ "import imp \n",
+ "import pickle\n",
+ "\n",
+ "from utils.training_utils import to_rgb, calc_confusion, metrics_from_conf_matrix\n",
+ "from data.cityscapes.data_loader import loadFiles, map_labels_to_trainId\n",
+ "from evaluation.eval_cityscapes import calc_energy_distances, get_mode_statistics\n",
+ "\n",
+ "from IPython.core.display import display, HTML\n",
+ "display(HTML(\"\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/tmp/prob_unet_1.0beta_analyticKLTrue_6latents_31x1s_32chan_lr0.0001PieceWise_bs16_7blocks_QMeanFalse_henormal_noBiasReg_OneHotMinus0.5_240kepochs/munster_000062_000019_sample7_labelIds.npy\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cf = imp.load_source('cf', 'cityscapes_eval_config.py')\n",
+ "path = cf.out_dir\n",
+ "\n",
+ "ix = 18\n",
+ "img_path = os.path.join(path, os.listdir(path)[ix])\n",
+ "print(img_path)\n",
+ "img = np.load(img_path)\n",
+ "\n",
+ "f = plt.figure(figsize=(8,4))\n",
+ "plt.imshow(to_rgb(img[0,0], cmap=cf.color_map))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# evaluate for different number of samples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/evaluation/eval_cityscapes.py:309: RuntimeWarning: Mean of empty slice\n",
+ " mean_d_YY = np.nanmean(d_matrices['YY'], axis=-1)\n",
+ "/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/evaluation/eval_cityscapes.py:309: RuntimeWarning: Mean of empty slice\n",
+ " mean_d_YY = np.nanmean(d_matrices['YY'], axis=-1)\n",
+ "/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/evaluation/eval_cityscapes.py:309: RuntimeWarning: Mean of empty slice\n",
+ " mean_d_YY = np.nanmean(d_matrices['YY'], axis=-1)\n",
+ "/home/skohl/PycharmProjects/phabricator/skohl/prob_unet/evaluation/eval_cityscapes.py:309: RuntimeWarning: Mean of empty slice\n",
+ " mean_d_YY = np.nanmean(d_matrices['YY'], axis=-1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "results_file = os.path.join(cf.out_dir, 'eval_results.pkl')\n",
+ "\n",
+ "with open(results_file, \"rb\") as f:\n",
+ " results = pickle.load(f)\n",
+ "\n",
+ "e_distances = []\n",
+ "e_means = []\n",
+ "samples_column = []\n",
+ " \n",
+ "for s in [1,4,8,16]:\n",
+ " e_dist = calc_energy_distances(results['d_matrices'], num_samples=s, probability_weighted=True, label_switches=cf.label_switches)\n",
+ " e_dist = e_dist[~np.isnan(e_dist)]\n",
+ " e_distances.extend(e_dist)\n",
+ " samples_column.extend([s] * len(e_dist))\n",
+ " e_means.append(np.mean(e_dist))\n",
+ "\n",
+ "energy = pd.DataFrame(data={'energy': e_distances, 'num_samples': samples_column})\n",
+ "means = pd.DataFrame(data={'energy': e_means, 'num_samples': [1,4,8,16]})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,0,'# samples')"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = plt.figure(figsize=(5,5), dpi=150)\n",
+ "gs = gridspec.GridSpec(1,1)\n",
+ "ax = plt.subplot(gs[0,0])\n",
+ "\n",
+ "sns.stripplot(x=\"num_samples\", y=\"energy\", data=energy, alpha=0.5, s=2, ax=ax)\n",
+ "sns.stripplot(x=\"num_samples\", y=\"energy\", data=means, s=18, marker='^', color='k', ax=ax, jitter=False)\n",
+ "sns.stripplot(x=\"num_samples\", y=\"energy\", data=means, s=14, marker='^', ax=ax, jitter=False)\n",
+ "ax.set_title('Probabilistic U-Net (validation set)', y=1.03)\n",
+ "fs=12\n",
+ "ax.set_ylabel(r'$D_{ged}^{2}$', fontsize=fs)\n",
+ "ax.set_xlabel('# samples', fontsize=fs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[0.39207232 0.4350246 ]\n",
+ " [0.44614304 0.31564506]\n",
+ " [0.61882485 0.30369349]\n",
+ " [0.72800224 0.20223276]\n",
+ " [0.7578801 0.22053149]]\n",
+ "[0.163625 0.038375 0.036625 0.00875 0.06 0.0365 0.012375 0.00575\n",
+ " 0.065375 0.0125 0.02025 0.002375 0.024125 0.009625 0.007875 0.001875\n",
+ " 0.136125 0.03225 0.0425 0.006875 0.051125 0.032 0.01175 0.007\n",
+ " 0.0735 0.01125 0.03325 0.003125 0.02725 0.008625 0.014 0.003375]\n",
+ "[0.004732871, 0.0053244797, 0.0067612445, 0.0076063997, 0.00867693, 0.009761547, 0.011358891, 0.012395615, 0.012778752, 0.013945066, 0.015381831, 0.016226986, 0.01730456, 0.01825536, 0.020824632, 0.021974044, 0.023427712, 0.0247208, 0.028200023, 0.029749475, 0.031725027, 0.03346816, 0.036916394, 0.040285747, 0.041530944, 0.045321465, 0.052737705, 0.05932992, 0.06768005, 0.07614006, 0.09668579, 0.10877152]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(results['pixel_frequencies'])\n",
+ "print(results['mode_frequencies'])\n",
+ "\n",
+ "stats = get_mode_statistics(cf.label_switches, exp_modes=5)\n",
+ "print(sorted(stats['mode_probs']))\n",
+ "\n",
+ "log_mode_frequencies = np.log(results['mode_frequencies'])\n",
+ "log_gt_mode_frequencies = np.log(stats['mode_probs'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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z58615cuXuzc/sCCAAAIIIFBIAQJ6ITXZFwIlFEgP6Loo1QWjes91Ea6hrXpGWKFbS3CxfOqpp7oL9/QluABWT9grr7xS5ffBRanC5AUXXOB+HwROXSxr+Hf688jqOb7zzjtDPwuvMKreKfVS6gI4WKIepxABXT2C6jHUcNlcS9SAruCnHvlMN05yHTP196UM6LqhonMr6OXX6/20ZAro6hH/wQ9+4IaWP/HEE1WqpMClody6QaOeVi01Cei6YaAAmCk0p563vXv3Nj1WkLoE8zdoKLcmwguz6MaVelbVk5y65BPQNfJEj3wo5KvcqYvKeMopp7gfhX19nW58KETLVUE4WKIG9KB8Gt7+s5/9rFL5NNxdN5V0c05hORjqHnzm6PNCnxupi/6+9ZkS9rWGurFz7bXX2m9/+1sbPHhwtc1S7PNN58XDDz9selxC5zULAggggAAChRQgoBdSk30hUEKB6t5xvssuu7ies9SLx+BiOdMFtoodBJP03vOgSrow1nOmGuY6YcIE9+MgOKt3PDVQB9toeLYu3DXrsd6NnbroeVnNMq/eTg1DVm+klscff9wNydXPgiXqcQoR0DWkX72D8tFQXQ3Bz7ZECegKuBo2rzkAdBOgJu+uL2VAl4Gel9dIi9QQmCmg6xVnel5XoUY9u+mLgq2et9ajDxr5oaUmAV3n8KBBg1x76Znl9EXnmkZGrFu3zj23HQxjV8hWD7ludqlHNhgNEmyvORn0yIh6szVHgNbTop529XTr982bN684XNiArpnT9UiHht1rJEvqMPFgZ9qvhq1nCuga1aLHTxTKtb3qpxEeGiGgGycK0MESJaCnlk+PDaS7aN/6O9ffu9pP7agl+MxJ78XX79TLr5txmT4bMv19BSMb9LeiXnrdaNTnXKal2OebbiRpBEm2z9KsHxD8AgEEEEAAgRACBPQQSKyCQBwF0t+Drl5sXfDqglyBadddd61U7OBiWWEi9ZneYCX9TBPKqXddF7/pS9ArpeGsCtZaguCsGwHqTUpfNPxVwVPhQxfkwaIJuNS7rgv/bEvq6+OiHqcQAV3hRkNzFYy0KMCp10/GGvIbPN+r30UJ6JoQToEm202OfM49BSG1c65eSYVTDSmXq9bVoue/03uAU5/3DoJd6psBFCj1fLSexdccAgppmQK6XvuXOlw8W520L4VNLTUJ6JrkUBO3ZXvXt/Y/dOhQ99z72LFjXZjXot59DdnXTQTdTEhdHnnkEddzrICabVGZZRssYQO6bhToOXbdpFDQz7ToEQE9ApAa0PU3onbTIyjpr1sM9qG/geDc1c+iBPSgfC1atHA95JkW/Y1ouL0MgwkNg8+cbO8Nz3eWe91w0Y1C1VXPn+tvRo9+6OaZ5kQIlmKfbzq3dI5pdJDOIxYEEEAAAQQKKUBAL6Qm+0KghALVTRKXqRipMyqnPjcdrJsroOviWxfhNQ3omuhKw+j1/LmGM6ssmnApmG1az7hqsri4BHT5qGdVw4zVS6lQG4RI1UP/P3jHdm0HdI0+0OzkRxxxhHtmPtuiIKgwrrkBgufqM81CnzpjeqaArv1rNMXPf/5z92ywzpFMAT04t/S/qc9wp5dPATB4hrkmAT1XD7qOqxsvmnQstddeN5p0I0rzACjkBcu7777renq1aKi1bmCpJzd47lvPuqvnOL13u9gBXY9EaGI5TZqmkK7zUW2rCeV080R/U7phIMtgKVZAD+wyBfTgLQbp7Z1vQNf2Gomj80wT0ml+Ac1xofrqBkpwY6DY55tu6Ogcowe9hF94HAoBBBAoIwECehk1NlX1S6DQAT0Y4q5ZyzVENH1RmNZr0/IZ4h6EoNRhrHqOVCEn01B6PYOu4bMK55kCerZe5kzHUfkL0YOe6azRs/3qZdVxU72iBPRCDnFXSFRYrK6nU48OBJPz6UZImBm0ZZAtoGsUhJ51VojVTOZ63lghKjWsBpOypYa3XH+NNQnowTPouimluRWyLRptouHqepOBRpzIQjZqk9SZ3zU8X38T6q1Wr3v6ou00JD5qQE8dQq7AmTrhW3AszXiuRyBSj6Hn7FXXTKNegonyChHQwwxxD3r4Mw1xL2RAT7XXjTN9jgwZMqTS6INin288g57rr5ffI4AAAgjURICAXhM9tkWgFgUKHdCDSeI0rFnvKU9f1CurAJZtkjgFgmBm52BbvfpI70dX+P/DH/7gfqxhwr///e9dT+UZZ5xR6TB6Zla9tloyBXQN4w97nGIGdO1bs83rZoV6j/Ustpbg9XaqX6ZXgWWbxT2YJE4zeasHPOqiwKIeVA3D1kiFo446qsquNHRbASN1OHmY42UL6NpW9VW7qmdZz0GnB/RghvJMk4VlO3Zw40JvJNAcAPksOr568rNNeBjsS68du/766917xtWzrzrolXp6vjh1Cdo10wRlmtgteEwgakDXsb7zne+4vzs5B38DQRn0qjRNaKcl9RiavV2vSNTQd40GSF00M7qG+KcHdL3LXe2Q+oaH1O2y3RgJJonTDP56zCF10U0OTeyYbZK4YgX0oAzBqBvdJNF8AsU+3/SKNz3SwSzu+fxVsi4CCCCAQFgBAnpYKdZDIGYChQ7oqa9ZSx+6qed0NXOyhvUqgAWzNKe+/kwBQs+hBxNcadK4448/3oXF1FeUaQizerz07nTNLq3hqVo0C7xeJ6XnXbVkCuj6edjjaN2a9qCrN1OBRK+p0rP0waJJuPT8uWYfV3hTyNMShNhsPa3ZAnpwY0K+GkavsBMsCt0KbmFes6ZtdGxNjKbnwfXcd2oPuYKrXiGmZ8azTQaY7TSvLqCrh1Uz+Cs8qvdXIyFSg+TWrVtdgFQbKzjqud3gkYagrYM3ByiQa9EQbc1doJ5jlTf1Wf8wf4oK3Jr3IP096Knb6jl8nSMK8wp2er1Zag9wsG7wbLoeB1AbBpOkaYI5tYtm4NdSk4CuIdoalaFArdAfvGtdjyLo7yKY6C31GLqZEIzgUHsGnwk6XzREX3976QE9OAd1/uqRiPQlW0APDDRjvZyCZ75lrL9l/Szba9YKEdB1Q0/ncjABXVBufc4ceeSR7nNHk/TpcZNinm+8Bz3MXx/rIIAAAgjURICAXhM9tkWgFgUKHdBVFU3mpIttBSz1Emlounqs582b58KSwqNmNQ+W4GJfz6VrBmcFNfUEanK4F1980f1/TaikHspg0UW03lesIK53CKtnXkN3dYGvZ9wV5jVcOlNAz+c4Ol5NA7pm6tbQZ91EkIf2p+CoZ7f1bnT9f40qUIjUol5fvXpM9VZwU8+e2kk3JBRgswV0bRvMDK0gqp5fPVesIejqHVXI0v+GWdR2eiWXPBVaNCJCveoKk3pmV2XT+7bVk5rPjPHVBXSVS68HS+1ZTQ+rem5fN1f0c4Vh3YTQ/yqAqm6aHE3PUesximDRc+2akFAz5+vxBoUvBfj015BlctE6KrN634PQn2m9oGdYv1Pb6QZU+qI21/H1aINCovanoKbgGbyzXjcYahLQdUyNnlBo1g0AhXINs9ffkcKw6q5REanHePvtt125dHNNozBkqnZOfSd5ekBXufUzeavu2rdGpmiUix6PqO7RAo0Y0cgR3YTp1auXO790TmviOAVnfX6kzkCfa96LfJ5B13mhx2x0E0vD6TUhpv7edDNCs+nrppQmkQuWYp1vwWzyAwYMcG8mYEEAAQQQQKDQAgT0QouyPwRKJFCMgK6iK4RohmIFAwVnPc+ssKCgnf6e89TAqZ5kPV+umeDVa6leSV1U6yI9fVGPqNZViFRgV1DXsF71/iokZQvoWief49Q0oKsnToFEF+W6AaHArKCk3k1NSKUezNTXaqmeGo6sXmKtH8z4HfQgVhfQta1e4aXh8roBoG0VYBUAFZ7yed+yQrgeKVAI1/Bj9XJqBIDCnIKrnl3OJ5yrbLkCuqzU5nqeW0um14Hpxo16ejXSQuFS2yjw6rxSGFdA1fkWLAqROic0jFshUEEsdeK66v7UFJgVpPXsuHqZsy3Bowr6/U033WQjR47MuKpecaZh4RqVoPNAQVEzvmsb9VbrXK5pQJeH3nCgtlNQloVuWumc1+MgmY6hG2gakaC3AeickaUeu/jpT3/q2jg9oKtyuqmkx090M0x/q7oZpkdc9Lea69l/vR9eEwOqR1/l1d+rev71Csb0Z+cLGdB1E0eP12h0gT4fdC7p3NFNCY3u0WdU+lKM8003oXQzSj33+ntiQQABBBBAoNACBPRCi7I/BBBAAIFYCKinVTeD9C91SH0sCkchEieg0SkaFaORRbohwoIAAggggEAxBAjoxVBlnwgggAACtS6gRzI0cZ0mfdNoBxYEaiKgRzA0jD7be91rsm+2RQABBBBAIBAgoHMuIIAAAgh4K6BnpTWcXkPv6UX3tpmLXjH1nut5fY3K0I0fFgQQQAABBIolQEAvliz7RQABBBBAAAEEEEAAAQQQQCAPAQJ6HlisigACCCCAAAIIIIAAAggggECxBAjoxZJlvwgggAACCCCAAAIIIIAAAgjkIUBAzwOLVRFAAAEEEEAAAQQQQAABBBAolgABvViy7BcBBBBAAAEEEEAAAQQQQACBPAQI6HlgsSoCCCCAAAIIIIAAAggggAACxRIgoBdLlv0igAACCCCAAAIIIIAAAgggkIcAAT0PLFZFAAEEEEAAAQQQQAABBBBAoFgCBPRiybJfBBBAAAEEEEAAAQQQQAABBPIQIKDngcWqCCCAAAIIIIAAAggggAACCBRLgIBeLFn2iwACCCCAAAIIIIAAAggggEAeAgT0PLBYFQEEEEAAAQQQQAABBBBAAIFiCRDQiyXLfhFAAAEEEEAAAQQQQAABBBDIQ4CAngcWqyKAAAIIIIAAAggggAACCCBQLAECerFk2S8CCCCAAAIIIIAAAggggAACeQgQ0PPAYlUEEEAAAQQQQAABBBBAAAEEiiVAQC+WLPtFAAEEEEAAAQQQQAABBBBAIA8BAnoeWKyKAAIIIIAAAggggAACCCCAQLEECOhmtm3bNhs9erRNmjTJVq1aZW3atLH+/fvbkCFDrF69elntN2/ebA888IBNnz7dFi5caBs2bLC2bdvaaaedZsOGDbNmzZpV2faNN96woUOH2ssvv+x+17NnTxs1apQdeuihNVq3WCcI+0UAAQQQQAABBBBAAAEEECiNAAHdzAYOHGjjx4+3fv36Wffu3W3WrFk2ZcoU9/Nx48ZlbQmF7U6dOtkxxxxjvXv3tt13393mzp3rgr6Cuv67SZMmFdsvXbrUjjjiCGvevLkNGjTI/Xzs2LH2ySef2Kuvvmr77bdfpHVLc6pwFAQQQAABBBBAAAEEEEAAgWIKlH1AX7RokXXu3NkF5jFjxlRYDx482O655x5bsGCBdezYMWMbfPjhh7ZmzRoX0lOX+++/3y655BK766677Kqrrqr41dlnn23PPvusLV682PXSa1GP/UEHHWSnnHKKPf7445HWLeYJwr4RQAABBBBAAAEEEEAAAQRKI1D2AX348OF2++232/Lly61du3YV6itWrLD27dubfn/rrbfm1RobN250PecK6epN1/L555/bbrvtZueff75NnTq10v769u1r06ZNs48++sgaN26c17p5FYyVEUAAAQQQQAABBBBAAAEEYitQ9gFdQ9PVS75u3boqjdSyZUvr0qWL6/XOZ1myZIkdeOCBdt1119kdd9zhNp09e7YbPq+h9AMGDKi0O/1Mw+m1ztFHH53XuvmUi3URQAABBBBAAAEEEEAAAQTiK1D2AV3D1xs0aOCeF09funbtalu2bDENg89nueiii+yhhx6yefPmueHzWp588knTEHdNKNenT59Ku9PPzjjjDHviiSfsrLPOymvd6sq1du1a07/U5eOPP3ZD7HXjoVGjRvlUi3URQAABBBBAAAFvBL744gtbuXKlm0eoRYsW3tSLiiCAQLIFyj6gd+jQwdRT/sorr1RpSfV4r1+/3t55553QrXzffffZpZdealdccYXdfZV/Ra8AACAASURBVPfdFds9+OCDpuD+3HPP2UknnVRpfzNmzHBfDlrnwgsvdP8bdt3qCjZixAgbOXJk6LKzIgIIIIAAAgggUG4C6lS54IILyq3a1BcBBGIqUPYBvZA96E8//bTrJT/55JNN/12/fv2KZo9LD/r8+fPds/H6MtLkdCwIIIAAAggggEA5CmhEoTpG9OrbHj16lCMBdUYAgRgKlH1AL9Qz6OoFP/300+2oo45yz6ynDx+PyzPo//nPf6xbt25uSL+G8LMggAACCCCAAALlKMA1UTm2OnVGIP4CZR/Qhw0b5iZyyzaLu35/2223VduSM2fOdK9JO+SQQ+yFF16o9O7zYMNcs7g/+uijbhb3nXfeOecs7qnr5nuK8WWUrxjrI4AAAggggICPAlwT+diq1AmB5AuUfUDXDO6aMC3be9A1JFzvOddkccuWLbOmTZtaq1atKlp+zpw5dsIJJ1jbtm1NQb158+ZZzwpNAKdn0N966y1r3bq1Wy94D7p68jUMPljyWTef05Avo3y0WBcBBBBAAAEEfBXgmsjXlqVeCCRboOwDuppPrz2bOHGi9evXzz2DNGvWLJs8ebKb7G3ChAmuhTXLp96TrneWT5kyxf3s3XffdeFe7z1XL/wee+xR6WzQ5HMnnnhixc/0+rUjjzzSvQ/98ssvdz8fO3as6zl/9dVX7YADDoi0bj6nIF9G+WixLgIIIIAAAgj4KsA1ka8tS70QSLYAAd3Mtm7daqNGjbJJkybZ6tWrba+99rL+/fvb0KFDKyZ6yxTQX3rpJTv++OOzngHHHnusaZ3UZeHChXbttde6CUm09OzZ0+68807XS5++5LNu2NOQL6OwUqyHAAIIIIAAAj4LcE3kc+tSNwSSK0BAT27bRSo5X0aR2NgIAQQQQAABBDwT4JrIswalOgh4IkBA96Qhw1aDL6OwUqyHAAJJEti2bZt98MEH9uWXX9o333yTpKJTVgQQKKBA3bp13ejHJk2auHmD6tSpk3XvXBMVEJ5dIYBAwQQI6AWjTMaO+DJKRjtRSgQQCC+gcP7ee++5cK6L83r16oXfmDURQMArAd2g02eClh133NHatGlT8bhiekW5JvKq6akMAt4IENC9acpwFeHLKJwTayGAQHIE1qxZY59++qk1a9bMTdZZXY9ZcmpFSRFAIKqA3ryzfv16++yzz9zEvLvvvnvGXXFNFFWY7RBAoJgCBPRi6sZw33wZxbBRKBICCNRIYPny5e5VmPvvvz/hvEaSbIyAPwLqSV+6dKntsMMO1r59ewK6P01LTRDwXoCA7n0TV64gAb3MGpzqIlAGAu+8846r5b777lsGtaWKCCAQViDXZwPXRGElWQ8BBEopQEAvpXYMjsWXUQwagSIggEBBBXJdhBf0YOwMAQQSI5Drs4FrosQ0JQVFoKwECOhl1dxmfBmVWYNTXQTKQCDXRXgZEFBFBBDIIJDrs4FrIk4bBBCIowABPY6tUsQy8WVURFx2jQACtSKQ6yK8VgrFQRFAoNYFcn02cE1U601EARBAIIMAAb3MTgu+jMqswakuAmUgkOsivAwIClrFESNG2MiRI23FihXWtm3bgu47LjvTTP8333yzqa4sxRXYunWrm6jtkksusUmTJhX3YGl7z/XZwDVRSZuDgyGAQEgBAnpIKF9W48vIl5akHgggEAjkugivM7JObLC237w9UlleeuklO/7447Nue8YZZ9jTTz8det+a+f6BBx6w73//+3bYYYdV2q62A7pm377llltcuVS+YixhA/rFF19sU6dOzViESy+91CZMmFCM4nm1TwK6V81JZRBAoAQCBPQSIMfpEAT0OLUGZUEAgUIIlFNAVy/kcccdV4WtTZs2duyxx4bmfP755+3EE0+0yZMnm0Jo6qJApX8NGzasldfWBYGub9++NmXKlNB1ymfFfAO6QnrdunUrHUKv9TvyyCPzOWzZrvvll19a/fr13b9SLrk+G7gmKmVrcCwEEAgrQEAPK+XJenwZedKQVAMBBCoEcl2E+9SD/vvf/9769+9f49avLqDXeOc13EEcA/qWLVvyDpebNm2yxo0b11CDzWsikOuzgWuimuiyLQIIFEuAgF4s2Zjuly+jmDYMxUIAgcgCuS7Cyy2gb9682W699VZ7/PHH7f3337cdd9zRPUuuYP+LX/zC9Ur369evinfwTHamIe7BUO81a9bYNddcY3/729/c9uedd56NHTvW9bgPHTrUHXPjxo32ve99z3QzYdddd604zpw5c+zee++1V155xVavXu3C63e+8x2744477MADD3TrrVy50tq1a1elbBodoGH+WrZt2+aOqd7/t99+2xo1auRGFdx+++120EEHVdr23XfftSuuuMJ0Q0K9tyeffLL99re/tT322CPUM+hBvXMF9J49ezrr5557zq688kp7+eWX7YADDrDXXnvNlefDDz90w/b//Oc/29q1a2333Xe3H/zgB66dmjZtWqnML774ol133XW2cOFC+9a3vuXaqnv37nbKKafYv/71L9OxtFx44YX273//24LzP9hJcPMldd18yqD9Pvzww/a///3PBg8ebH/9619Njx2ceuqpNn78eGvWrFml8mquAs1ZMGPGDPvoo4+sZcuWbjTHqFGjrFWrVu7cyPYM+v333+/2+d///te1z9FHH22//OUv7aijjqo4htp79OjR7lEDtaf21bp1a3fu3XTTTdV+buT6bOCaKPLHLhsigEARBQjoRcSN4675Mopjq1AmBBCoiUCui3CfAvrdd9/tgln6svPOO7sgrkWh8o9//KMNGDDAOnXqZOrJVQD67LPP7NFHHzU9f67w/Ktf/cp+9rOf2THHHOO207r6V11A79q1qwue2uaf//ynC+QKpG+99VZFAJ43b56bDCx9iPr111/vwvl3v/td23PPPe29996ziRMnuvCn8im0qqxPPvmk21bHUPm0KPRpSL6Wc845xz1v/+Mf/9gNMVco/N3vfue2VSDeb7/93Hoff/yxde7c2davX+9uTCj4K2x+8MEHpjKGmSQuCOjr1q2zevXqVWJv0aJFxf9XaJaBHgs46aSTXNBUvX7+85+78ilwqjw//elPrX379rZ48WIXTDt27GizZs2yBg0auH0pVMtHNxD0jLt+rhsRaluVOWpAz6cMQUDv1q2bK6vmPlB5x40bZxdddJErT7C8+eabrp00hF110w0SeT/zzDP2m9/8xnr06JE1oOvGiW60nH322S7Q68aSzhuFcJ1b3/72t91hbrjhBnfzRTcq5Pj111/bkiVLTMd+4YUXqv3oyPXZwDVRTT552RYBBIolQEAvlmxM98uXUUwbhmIhgEBkgVwX4T4F9GxI99xzj1122WXu1+q1/tGPfuQCVbaluiHu1QV0hSrdJAiWLl262IIFC1xonjZtWsXP1dv6j3/8w4XSYJi3AthOO+1UqUjqAVdI1TEV4LVUN8RdNwTOPfdcd2NAwS5Y1CN/8MEHu577Rx55xP1YvdB33nmnPfXUUxWTzW3fvt2VVTcB8gnomRw1UkA3RrQooCto33bbbTZs2LBKqytoP/bYYy5gp86Kr5sM6kVXKNXcAloOP/xwNypAYV83MbR8+umnzmjVqlWRA3o+ZQgCenpb6/zSDZVPPvmkok01ckE3XV5//XV3cyd1kbWe9c/Ugz579mw3KkDnko4TLDI95JBDbJ999nF11aK660aBRh/ku+T6bOCaKF9R1kcAgVIIENBLoRyjY/BlFKPGoCgIIFAQgVwX4T4F9KuuusoNdU5fNERcw361qKe4efPm9qc//ckFnUxL1ICunktNjhYsCm26EaBh2amzzGvo9o033uh6OdOHnWvbzz//3L766itTiOvVq5ftu+++rrxaqgvoZ555pmmovG4KpC8XXHCB6TtOQ7O16Lja19KlSyutqmHh6p3NJ6Br6Hr6JHGqb9CrroCuoKowvcsuu1QcT73oagu1mW6ipC6qu9pHM/DrpoJuMqgNNfJBveupS+AZpQc9nzLomEFA199Vhw4dKooR3BwJ2lQjEdTTr1EGqb3q6e2SKaBffvnldt9999myZcvcqIPURY9QPPjgg+5RCd3QUQ+9Hh/Q6AfdhMlnyfXZwDVRPpqsiwACpRIgoJdKOibH4csoJg1BMRBAoGACuS7CfQroYSaJC4Z/KwQfeuihLgCrtzkYyi74qAFdQ5lTA5WGH6vXOD3M6VnzQYMGuWexNcxZi569Vq/2X/7yF9eznroo7Crka6kuoCugabh1dYueWVaY1rBwDYvX8VIXhWg9R51PQA/zDLpuBCi0pi56Zn+vvfaqtrxqHw3VlpXaSM/I69nv1EU9/mrDKAE9nzLomEFAVzukDusPzpmgTTViQDcmNJRdjzlkWzIFdD0GoBEW1S16BEJvJ9DxNNJAz/Hr8QWdK3r9XqYbVen7y/XZwDVRwT6G2RECCBRQgIBeQMwk7IovoyS0EmVEAIF8BHJdhJdbQJedwoyeA9bEapq8S+E49b3dUQN6elANAromCksdvh0E9CBQqhdXz4OrrTQKQM+yq6dZQVpDnPU8dzAJXHUBXb3i6nlX72u2Rc9wa2h1qQO6enk1yV3qEvSKn3XWWa5nPNOiRxL0vHe+AV3P4GuoePokcert12R4gX0+ZUgN6OrhT13SJ5+rSUDXjRONhAhGTWRyUfgP5lXQ/Al///vf3Y0MBXs59+nTx81FkD6yIXVfuT4buCbK55OWdRFAoFQCBPRSScfkOHwZxaQhKAYCCBRMINdFeDkG9FRcBV4N/9Zz0BpSrOd5FXROOOGEjO9Br+4Z9KgBXUPSDzvsMPesuXquUxf1MKtnNHWWds3onek96AplWm/Dhg1uNu/qFoV59abrme7UJcoQ9zA96JkCuuwVwDUJmm6YVLfkO8Rdw8Q127omgEtdNDx+4MCBFQE9nzLkE9CDIe653lefqQddk+dNmDDBPY6QOtlemA8F3ThQj/2YMWNs5syZ7k0A2ZZcnw1cE4URZx0EECi1AAG91OK1fDy+jGq5ATg8AggUXCDXRXg5BXQFUj27m/4qrOAZZk3mpd7aIKRmGp5cjID+xhtvuMm+9FosvZIrWB566CE3G3vqa9T0O/Wc9u7du8rEYHpWWxPgZRuerhnENRu8lmuvvda96qsQk8RFDegqhyaA06vtNDN5ephUeFXvsJ5T16K20VD51Eni9Hs9qpA+SZyeaVdInz9/vhudoEWjC4444ghbtGhRpeHw+ZQhGOKeqwddx9MkcerF1+z5+UwSp959WejVfxoNoREPqUtqO2o0SHqID14VqB50PcNPQC/4xyo7RACBWhQgoNcifm0cmoBeG+ocEwEEiilQTgFdQUuhKH3Ru7TVu6wZtjX7t57RVY/1brvt5sKeJnJTz7l6svVcsUK8Xl2mScmGDBnihpsrBOpfMQK6gqj2rVdoKVRq8jHdLNCz1bqZoGeNgx501U3DmzXrucK8yqjQrWe1FRp/+MMfuhnj9Ryzhkqr7NqvhkBrBnCFfi3qWZaBemk1mZ0mz1MvdpTXrNUkoCtg6jl8vd5OrylTCNf+dN6q/nqFmCZa06IeYY1s0PvDNSRer1nTu8IzvWZNowj0WIF66GWq5YEHHnBzBCgwpz6vnk8Z8gnoej2e2kqvPtNr1jRHQPB4hd5dXt1r1q6++mr3/Lom7Dv99NPduarnznUeaASFbmho0c0L3cDRjQedsxrertfqyUTzETRp0oSAXswPWPaNAAIlFyCgl5y8dg9IQK9df46OAAKFFyingJ5NT+8mVxBXUFIvtZ4XViDUq80UcBWA9Bqzb33rWxW70KzcCuPqsVVgDHqlixHQdVANr9fQZD1rrePpXeEKccFrtlIDunqA9e7yuXPnujqk9rArpKvXVcFVAVH/X8PkFQYVEvX6rmDRs/Hav4b0a0i8ns3WJGyafbzQk8RlGuIelEM3TvTeeT1zrZsJevXc3nvv7UYJqJ7672BR26mtZKCeY73/W3XTpGipoVvrK8Qq6Abvkdc8Awqyqc+g51uGfAK69q2/P1mq3KqnbHUTSa+4039nGuIelEk3WnTzSKMAdE7oxoTeba86y0aLJiHUDO56VEE3lrSObs5o/oNUt0x/G7k+G7gmKvznMXtEAIGaCxDQa26YqD3wZZSo5qKwCCAQQiDXRXiIXbAKArEWSJ+gLdaFjVHhcn02cE0Uo8aiKAggUCFAQC+zk4EvozJrcKqLQBkI5LoILwMCqui5AAE9WgPn+mzgmiiaK1shgEBxBQjoxfWN3d75Mopdk1AgBBCooUCui/Aa7p7NEah1AQJ6tCbI9dnANVE0V7ZCAIHiChDQi+sbu73zZRS7JqFACCBQQ4FcF+E13D2bI1DrAgT0aE2Q67OBa6JormyFAALFFSCgF9c3dnvnyyh2TUKBEECghgK5LsJruHs2RwCBhArk+mzgmiihDUuxEfBcgIDueQOnV48vozJrcKqLQBkI5LoILwMCqogAAhkEcn02cE3EaYMAAnEUIKDHsVWKWCa+jIqIy64RQKBWBHJdhNdKoTgoAgjUukCuzwauiWq9iSgAAghkECCgl9lpwZdRmTU41UWgDARyXYSXAQFVRAABetA5BxBAwBMBAronDRm2GgT0sFKshwACSREgoCelpSgnAqUVyPXZwDVRaduDoyGAQDgBAno4J2/W4svIm6akIggg8H8Fcl2EA4UAAuUpkOuzgWui8jwvqDUCcRcgoMe9hQpcPr6MCgzK7hBAoNYFcl2E13oBKQACCNSKQK7PBq6JaqVZOCgCCOQQIKCX2SnCl1GZNTjVRaAMBHJdhJcBAVVEAIEMArk+G7gm4rRBAIE4ChDQ49gqRSwTX0ZFxGXXCCBQKwK5LsJrpVActEYCI0aMsJEjR9r27dtrtJ9ibxyUc+nSpbbvvvsW+3DsP0+BXJ8NXBPlCcrqCCBQEgECekmY43MQvozi0xaUBAEECiNQ3UX4sccea6tWrSrMgQqwlzZt2tjMmTMj7+mzzz6zcePG2dNPP21LliyxzZs3W/Pmze2www6z73//+9a3b19r1KhR5P3HZcOwAX358uX2wAMPuLrLoBhLdccgoBdDvHD7JKAXzpI9IYBA6QQI6KWzjsWRCOixaAYKgQACBRSo7iK8ffv29t5779nee+9dwCNG21VQDgW+KMubb75p3/ve9+z99993gbRnz57WtGlTW79+vQv9zz33nPv5k08+GWX3sdombEB//vnn7cQTT7TJkyfbxRdfXJQ6VHcMAnpRyAu2UwJ6wSjZEQIIlFCAgF5C7DgcioAeh1agDAggUEiBXAFdx4oaigtZTt0siFqWzz//3Dp16mSffvqpC+KHH354laLJ4c9//rNdffXV1RZ706ZN1rhx40JWreD7KlZA/+KLL6xhw4ZWt27d0GUmoIemit2KBPTYNQkFQgCBEAIE9BBIPq1CQPepNakLAghIoBwC+l133WXXXHNN3j3Fbdu2tdatW9tvfvMbGzp0qL322muux1lD5LW8/vrrdtNNN9msWbPs66+/tkMPPdQd57zzzqt0ctWpU8duvvlmU3BOXdRr/dJLL9nKlSsrfhwcU0PxBw8ebK+++qo1adLE+vfvb7fcckuVcPy73/3O7r77bvcoQocOHey6666zZcuW5XwGfcqUKdavX78qfwRBOYOQr++93//+925kwf/+9z/bsGGDq7+2XbFiham8qUtqXcMeY8GCBTZp0iR79NFHTTdT9GjFhAkTbJ999uGPtBYFCOi1iM+hEUAgsgABPTJdMjckoCez3Sg1AghkFyiHgH7MMce4MP3JJ5+4HuCwSxA+9ez6BRdc4Hrhd9xxR/vxj39ss2fPtl69elmzZs1swIABLkQ//PDDNnfuXBs7dqwNGjSo4jD5BvR69eqZeurPP/98O/DAA+3vf/+7TZ8+3YXYSy65pGK/v/71r23IkCF21FFH2Q9/+EP76KOP7N5773Whed68edVOEqdREQrev/rVr+xnP/uZyUiL6qh/QUDXTYcWLVrYmWeeaRs3brQrrrjCHnvssVABPewxunXr5hz1iMG6devcDRH97F//+lfYpmK9IggQ0IuAyi4RQKDoAgT0ohPH6wAE9Hi1B6VBAIGaC5RDQNdEcHqOfv78+ZXANGRbQTh1URgNFgXdd99916ZOnWoXXXRRpfUUihctWmR6tj0I8l9++aV9+9vftrfffts9677rrru6bfIN6DrmE088YWeddVbFMTWJW4MGDVyPupaPP/7Y9tprL9dr//LLL7vfaVFvdNeuXe2bb77JOYt7mOHnJ5xwgnssIHVYe9AznqsHXeUJc4w+ffq4xwvkpOW3v/2tXXnllfbf//7XDj744Jqf5OwhkgABPRIbGyGAQC0LENBruQFKfXgCeqnFOR4CCBRboBwCev369V1wTu+RDXqgU423bNliWl+Lgrd6zz/88MNKAfWDDz6wPfbYw37yk5/YH/7wh0pNpFnRNRv8tGnT7Nxzz40U0L/66itbu3Ztpf1quPtDDz3kesm1aP/qYdfP1Lufupx88skuVOd6zVqY8PzUU0+5nu3UpdAB/dlnn7XevXtXHEK9/7rJoFEDCu8stSNAQK8dd46KAAI1EyCg18wvcVsT0BPXZBQYAQRyCJRDQM/Wg66Z4dXbrWX06NE2Y8YMSw/ou+22mxu2nrr8+9//doFfAT99UjkNpT/iiCPc0PFrr702UkBv1aqVG0KfuqRP/Kb9X3/99W7ovoaDpy7qfVYvdCEC+sKFC61jx45FDehvvfWWHXDAARXH0DP57dq1M90I0M0OltoRIKDXjjtHRQCBmgkQ0Gvml7itCeiJazIKjAACBHT3SjWF7OqeQdeEbRrKnh7QNUmchpAXI6DrWXb16meaJC79mLUV0JcuXWr77rtvpfrLSV7pQ9y3bdvmRh+kTogXppc+/RhBQC/m69/4YMgtQEDPbcQaCCAQPwECevzapKglIqAXlZedI4BALQiUQw96MJQ907PkAXk+Ab26Ie4PPvige15dE6mdc845bvfqwVcYHzNmTKUW7tGjh61evTpSQC/EEPcXXnjB9Ix5piBc3TvKNfT8jDPOMH0ndunSpaJOCtr7779/pYAe5RgE9Fr4IMhwSAJ6PNqBUiCAQH4CBPT8vBK/NgE98U1IBRBAIE2gHAK6Zh/XMG29wkvD2PV8c/qiodR6fjxMD7q2PfrooysmiQteB6Znx7t3724asp06SZwmlNOEdBouHixz5sxxw+Q1eV2UHnRNErfnnnu6ekWdJC4Yqq9Z0zUsPnWpLqAvXrzYTd6WPsT/F7/4hem1b6k96FGOQUCPx8cUAT0e7UApEEAgPwECen5eiV+bgJ74JqQCCCBQhgFdVdaM66eeeqqbfE2TnmnYu16Ntn79ejfMXK8y06zoCofBjOXBO8nTh5trf8Fr1jRT+89//nPbZZdd3GvW9Ex4+mvWgknVdPzTTjvNHUOvONPw+U8//TRSQFcZ7rzzTvfec90s0IRxwWvWdMNAM9bnegZdNy5atmzpyqHXtakOmhVe/6oL6Dr2cccdZ7rJoGCv7XXjY82aNe5d8akBPcoxCOjx+JgioMejHSgFAgjkJ0BAz88r8WsT0BPfhFQAAQTyDOiaSE29vLW9BOXQu7WjLgrDek+4Xum1ZMkS27x5sxt+3rlzZ/eebw1N32mnnSp2X11A10oKozfddJPNmjXL9bwfcsghds0117iwnLooKN9yyy02ceJE27Bhg3vPuMK1hty/9NJLkQO6jqH6aEK4VatWWYcOHdzEccuWLbORI0fmDOja/vHHH3dhXMPTVYcgXOcK6DrewIED7cUXX3SveNNs63fffbd7Z3pqQI9yDAJ61DO8sNsR0Avryd4QQKA0AgT00jjH5igE9Ng0BQVBAIECCVR3EX7ssce64BeXpU2bNjZz5sy4FIdyIOC1AAHd6+alcgh4K0BA97ZpM1eMgF5mDU51ESgDgVwX4WVAQBURQCCDQK7PBq6JOG0QQCCOAgT0OLZKEcvEl1ERcdk1AgjUikCui/BaKRQHRQCBWhfI9dnANVGtNxEFQACBDAIE9DI7LfgyKrMGp7oIlIFArovwMiCgigggQA865wACCHgiQED3pCHDVoOAHlaK9RBAICkCBPSktBTlRKC0Ark+G7gmKm17cDQEEAgnQEAP5+TNWnwZedOUVAQBBP6vQK6LcKAQQKA8BXJ9NnBNVJ7nBbVGIO4CBPS4t1CBy8eXUYFB2R0CCNS6QK6L8FovIAVAAIFaEcj12cA1Ua00CwdFAIEcAgT0MjtF+DIqswanugiUgUCui/AyIKCKCCCQQSDXZwPXRJw2CCAQRwECehxbpYhl4suoiLjsGgEEakVg+fLltmXLFtt///2tTp06tVIGDooAAvES+Oabb2zp0qW2ww47WPv27TMWjmuieLUZpUEAgf9fgIBuZtu2bbPRo0fbpEmTbNWqVdamTRvr37+/DRkyxOrVq1ftuTJjxgx74oknbO7cubZo0SJ3kbhixQpr27Ztpe1Wrlxp7dq1y7qv+vXru22D5eKLL7apU6dmXF9lbN26daRzmC+jSGxshAACMRZYs2aNffrpp9asWTPbY489COkxbiuKhkApBHQ9tX79evvss89st912s913352AXgp4joEAAgURIKCb2cCBA238+PHWr18/6969u82aNcumTJnifj5u3LhqoRWkH3nkEevYsaN99dVX9sYbb2QM6Js2bbKnnnqqyr4UtocNG2Z9+vSx6dOnVwnoCul169attN2ZZ55pO+20U6QTgIAeiY2NEEAgxgK6yfree+/Zl19+6T4vc91YjXFVKBoCCNRQQD3n+kzQsuOOO7pOF3WCZFq4JqohNpsjgEBRBMo+oKvXu3PnzjZo0CAbM2ZMBfLgwYPtnnvusQULFrjwnW1Zmg3ZTAAAIABJREFUvXq1tWjRwho2bGg33HCD3XbbbRkDerbtb731VrvxxhvtySefNAXvYAl60HUXONsXS5Qzgi+jKGpsgwACcRfQBfkHH3zgQrou0FkQQKA8BXSTTtdNTZo0saZNm1Y7ooZrovI8R6g1AnEXKPuAPnz4cLv99ttNzzCmDkHXMHU9s6TfK0SHWaIEdD0zuWHDBtMQzQYNGlQJ6F9//bV98cUXtvPOO1fpSQ9TpvR1+DKKosY2CCCAAAIIIOCbANdEvrUo9UHAD4GyD+i9e/d2veTr1q2r0qItW7a0Ll262LPPPhuqtfMN6K+88or16NHDLrvsMtdbn7oEPei77LKLbdy40Ro1amSnnHKKjRo1yjp06BCqPJlW4ssoMh0bIoAAAggggIBHAlwTedSYVAUBjwTKPqBr+Lp6rjXJW/rStWtXN3GbhsGHWfIN6Jdeeqndd9999tprr9nhhx9e6RDXXXedbd261bp16+bKpzB/7733uiFbr7/+uu2zzz45i7R27VrTv9Rl8eLFduGFF7r6qn4sCCCAAAIIIIBAOQoQ0Mux1akzAvEXKPuArt5o9ZQrAKcvmjBOs4AG79HM1Zz5BHQ9J9mqVSvba6+93MRyYRb15KsXvW/fvm4Su1zLiBEjbOTIkRlXI6Dn0uP3CCCAAAIIIOCzAAHd59albggkV6DsA3pt9aA/9thjdt5557kh63qdW9hFPerqFdcz67kWetBzCfF7BBBAAAEEEChXAQJ6ubY89UYg3gJlH9Br6xn0U0891Z577jn33nX1pIddNNP7M888Y5o8LsrCl1EUNbZBAAEEEEAAAd8EuCbyrUWpDwJ+CJR9QNc7yO+4446ss7jr93p1Wpgl7BB3TUjXunVrO+mkk+xvf/tbmF1XrNOpUyf7+OOPXbCPsvBlFEWNbRBAAAEEEEDANwGuiXxrUeqDgB8CZR/QNYO7ZmrP9h70+fPnm0KxJotbtmyZe6dmth7vsAH9rrvusmuuucamTZtm5557bpUzadOmTe5njRs3rvQ7rX/++eebJpebMGFCpDOQL6NIbGyEAAIIIIAAAp4JcE3kWYNSHQQ8ESj7gK52HDBggE2cONH69evnXns2a9Ysmzx5cqUgvHLlSvee9PQJ2hYuXGjTp093p8Pzzz9vM2fOtKuvvtqaNWvm/ukVaumLAr96wNWT3rBhwyq/102BXr16uWfUDzjgADeL++zZs+3hhx+2vffe2+bMmeMmtouy8GUURY1tEEAAAQQQQMA3Aa6JfGtR6oOAHwIEdDP3OjNN1jZp0iRbvXq1m1m9f//+NnToUKtfv75r6WwBXbOpK9hnWvQqNG2XusybN8+93kw3BcaPH59xOwV3hXy9fk2Twan3vk2bNtanTx8bPny4tWjRIvLZx5dRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLzZdRZDo2RAABBBBAAAGPBLgm8qgxqQoCHgkQ0D1qzDBV4csojBLrIIAAAggggIDvAlwT+d7C1A+BZAoQ0JPZbpFLnfpl1O0v3SLtZ/vN2yNtx0YIIIAAAggggEBcBAjocWkJyoEAAqkCBPQyOx8I6GXW4FQXAQQQQAABBDIKENA5MRBAII4CBPQ4tkoRy0RALyIuu0YAAQQQQACBxAgQ0BPTVBQUgbISIKCXVXObEdDLrMGpLgIIIIAAAgjQg845gAACiREgoCemqQpTUAJ6YRzZCwIIIIAAAggkW4Ae9GS3H6VHwFcBArqvLZulXgT0MmtwqosAAggggAAC9KBzDiCAQGIECOiJaarCFJSAXhhH9oIAAggggAACyRagBz3Z7UfpEfBVgIDua8vSg15mLUt1EUAAAQQQQCAfAQJ6PlqsiwACpRIgoJdKOibHoQc9Jg1BMRBAAAEEEECgVgUI6LXKz8ERQCCLAAG9zE4NAnqZNTjVRQABBBBAAIGMAgR0TgwEEIijAAE9jq1SxDIR0IuIy64RQAABBBBAIDECBPTENBUFRaCsBAjoZdXcvAe9zJqb6iKAAAIIIIBAFgECOqcGAgjEUYCAHsdWKWKZ6EEvIi67RgABBBBAAIHECBDQE9NUFBSBshIgoJdVc9ODXmbNTXURQAABBBBAgB50zgEEEEiQAAE9QY1ViKLSg14IRfaBAAIIIIAAAkkXoAc96S1I+RHwU4CA7me7Zq0VAb3MGpzqIoAAAggggEBGAQI6JwYCCMRRgIAex1YpYpkI6EXEZdcIIIAAAgggkBgBAnpimoqCIlBWAgT0smpunkEvs+amuggggAACCCCQRYCAzqmBAAJxFCCgx7FVilgmetCLiMuuEUAAAQQQQCAxAgT0xDQVBUWgrAQI6GXV3PSgl1lzU10EEEAAAQQQoAedcwABBBIkQEBPUGMVoqj0oBdCkX0ggAACCCCAQNIF6EFPegtSfgT8FCCg+9muWWtFQC+zBqe6CCCAAAIIIJBRgIDOiYEAAnEUIKDHsVWKWCYCehFx2TUCCCCAAAIIJEaAgJ6YpqKgCJSVAAG9rJqbZ9DLrLmpLgIIIIAAAghkESCgc2oggEAcBQjocWyVIpaJHvQi4rJrBBBAAAEEEEiMAAE9MU1FQREoKwECelk1Nz3oZdbcVBcBBBBAAAEE6EHnHEAAgQQJENAT1FiFKCo96IVQZB8IIIAAAgggkHQBetCT3oKUHwE/BQjofrZr1loR0MuswakuAggggAACCGQUIKBzYiCAQBwFCOhxbJUilomAXkRcdo0AAggggAACiREgoCemqSgoAmUlQEAvq+bmGfQya26qiwACCCCAAAJZBAjonBoIIBBHAQJ6HFuliGWiB72IuOwaAQQQQAABBBIjQEBPTFNRUATKSoCAXlbNTQ96mTU31UUAAQQQQAABetA5BxBAIEECBPQENVYhikoPeiEU2QcCCCCAAAIIJF2AHvSktyDlR8BPAQK6mW3bts1Gjx5tkyZNslWrVlmbNm2sf//+NmTIEKtXr161LT9jxgx74oknbO7cubZo0SLbsmWLrVixwtq2bVtlu4svvtimTp2acX86buvWrSv97o033rChQ4fayy+/7H7es2dPGzVqlB166KGRz0YCemQ6NkQAAQQQQAABjwQI6B41JlVBwCMBArqZDRw40MaPH2/9+vWz7t2726xZs2zKlCnu5+PGjau2uRW6H3nkEevYsaN99dVXplCdK6ArpNetW7fSfs8880zbaaedKn62dOlSO+KII6x58+Y2aNAg9/OxY8faJ598Yq+++qrtt99+kU5DAnokNjZCAAEEEEAAAc8ECOieNSjVQcATgbIP6Or17ty5swvBY8aMqWjWwYMH2z333GMLFixw4Tvbsnr1amvRooU1bNjQbrjhBrvttttyBnT1stevX7/aU+jss8+2Z5991hYvXux69LWol/2ggw6yU045xR5//PFIpyABPRIbGyGAAAIIIICAZwIEdM8alOog4IlA2Qf04cOH2+23327Lly+3du3aVTSresHbt29v+v2tt94aqrnDBvSvv/7avvjiC9t5552r9KTrQJ9//rnttttudv7551cZEt+3b1+bNm2affTRR9a4ceNQ5UpdiYCeNxkbIIAAAggggICHAgR0DxuVKiHggUDZB/TevXu7XvJ169ZVac6WLVtaly5dXE92mCVsQN9ll11s48aN1qhRI9cbrufKO3ToUHGI2bNnu6H2GnY/YMCASofWzzT0XuscffTRYYpVaR0Cet5kbIAAAggggAACHgoQ0D1sVKqEgAcCZR/QNXy9QYMGbpK39KVr165u0jcNgw+z5Aro1113nW3dutW6devmjvnKK6/Yvffea02aNLHXX3/d9tlnH3eYJ5980jTEffr06danT59Kh9bPzjjjDDcx3VlnnVVtsdauXWv6l7poyPyFF17o6tvtL93CVKvKOttv3h5pOzZCAAEEEEAAAQTiIkBAj0tLUA4EEEgVKPuArp5r9ZQrLKcv6sVev369vfPOO6HOmlwBPdNO1DuvXnQNXdfEdFoefPBBu+iii+y5556zk046qdJmmjVevf5aR0G7umXEiBE2cuTIjKsQ0EM1KSshgAACCCCAgKcCBHRPG5ZqIZBwgbIP6KXsQc92rqhHXT3da9ascavQg57wvyqKjwACCCCAAAKxFyCgx76JKCACZSlQ9gG9lM+gZzvD9Iq1Z555xjR5nBaeQS/Lv0UqjQACCCCAAAIlFCCglxCbQyGAQGiBsg/ow4YNszvuuCPrLO76vV6dFmaJMsRd++3UqZN9/PHH7jVqWnLN4v7oo4+6Wdw1C3y+C5PE5SvG+ggggAACCCDgowAB3cdWpU4IJF+g7AO6ZnDXTO3Z3oM+f/58F6A1WdyyZcusadOm1qpVq4wtX11A37Rpk9sm/dVoemWaXqd26aWX2oQJEyr2qwng9Az6W2+9Za1bt3Y/D96Drl5/DYOPshDQo6ixDQIIIIAAAgj4JkBA961FqQ8CfgiUfUBXM+pVZhMnTrR+/fpZjx49bNasWTZ58uRKoXnlypXuPempk7lp24ULF7rZ1rU8//zzNnPmTLv66qutWbNm7t9ll13mfqeg36tXLzvvvPPsgAMOcLO4ayj7ww8/bHvvvbfNmTPHTVYXLEuWLLEjjzzSvQ/98ssvdz8eO3as6zl/9dVX3T6iLAT0KGpsgwACCCCAAAK+CRDQfWtR6oOAHwIEdDP36jO9i3zSpEm2evVq22uvvax///42dOhQq1+/vmvpbAFdM68r2Gda9No0badF71lXcH/ttdfcZHDqkW/Tpo17jdrw4cOtRYsWVXah8H/ttdfayy+/7H7Xs2dPu/POO12PftSFgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqgkBPRQTKyGAAAIIIICA5wIEdM8bmOohkFABAnpCGy5qsQnoUeXYDgEEEEAAAQR8EiCg+9Sa1AUBfwQI6P60ZaiaENBDMbESAggggAACCHguQED3vIGpHgIJFSCgJ7ThohabgB5Vju0QQAABBBBAwCcBArpPrUldEPBHgIDuT1uGqkkxAnqdkXVCHTvTSttv3h55WzZEAAEEEEAAAQSiChDQo8qxHQIIFFOAgF5M3Rjum4Aew0ahSAgggAACCCBQcgECesnJOSACCIQQIKCHQPJpFQK6T61JXRBAAAEEEEAgqgABPaoc2yGAQDEFCOjF1I3hvgnoMWwUioQAAggggAACJRcgoJecnAMigEAIAQJ6CCSfViGg+9Sa1AUBBBBAAAEEogoQ0KPKsR0CCBRTgIBeTN0Y7puAHsNGoUgIIIAAAgggUHIBAnrJyTkgAgiEECCgh0DyaRUCuk+tSV0QQAABBBBAIKoAAT2qHNshgEAxBQjoxdSN4b4J6DFsFIqEAAIIIIAAAiUXIKCXnJwDIoBACAECeggkn1YhoPvUmtQFAQQQQAABBKIKENCjyrEdAggUU4CAXkzdGO6bgB7DRqFICCCAAAIIIFByAQJ6yck5IAIIhBAgoIdA8mkVArpPrUldEEAAAQQQQCCqAAE9qhzbIYBAMQUI6MXUjeG+CegxbBSKhAACCCCAAAIlFyCgl5ycAyKAQAgBAnoIJJ9WIaD71JrUBQEEEEAAAQSiChDQo8qxHQIIFFOAgF5M3Rjum4Aew0ahSAgggAACCCBQcgECesnJOSACCIQQIKCHQPJpFQK6T61JXRBAAAEEEEAgqgABPaoc2yGAQDEFCOjF1I3hvgnoMWwUioQAAggggAACJRcgoJecnAMigEAIAQJ6CCSfViGg+9Sa1AUBBBBAAAEEogoQ0KPKsR0CCBRTgIBeTN0Y7puAHsNGoUgIIIAAAgggUHIBAnrJyTkgAgiEECCgh0DyaRUCuk+tSV0QQAABBBBAIKoAAT2qHNshgEAxBQjoxdSN4b4J6DFsFIqEAAIIIIAAAiUXIKCXnJwDIoBACAECeggkn1YhoPvUmtQFAQQQQAABBKIKENCjyrEdAggUU4CAXkzdGO6bgB7DRqFICCCAAAIIIFByAQJ6yck5IAIIhBAgoIdA8mkVArpPrUldEEAAAQQQQCCqAAE9qhzbIYBAMQUI6MXUjeG+CegxbBSKhAACCCCAAAIlFyCgl5ycAyKAQAgBAnoIJJ9WIaD71JrUBQEEEEAAAQSiChDQo8qxHQIIFFOAgF5M3Rjum4Aew0ahSAgggAACCCBQcgECesnJOSACCIQQIKCb2bZt22z06NE2adIkW7VqlbVp08b69+9vQ4YMsXr16lXLOGPGDHviiSds7ty5tmjRItuyZYutWLHC2rZtW2m7zZs32wMPPGDTp0+3hQsX2oYNG9w6p512mg0bNsyaNWtWaf2LL77Ypk6dmvHYKmPr1q1DNG/VVQjokdjYCAEEEEAAAQQ8EyCge9agVAcBTwQI6GY2cOBAGz9+vPXr18+6d+9us2bNsilTprifjxs3rtqmVpB+5JFHrGPHjvbVV1/ZG2+8kTGg6+edOnWyY445xnr37m277767C/W6KaCgrv9u0qRJxbGCgK6QXrdu3UplOPPMM22nnXaKdAoS0COxsRECCCCAAAIIeCZAQPesQakOAp4IlH1AV693586dbdCgQTZmzJiKZh08eLDdc889tmDBAhe+sy2rV6+2Fi1aWMOGDe2GG26w2267LWNA//DDD23NmjUupKcu999/v11yySV211132VVXXVUloKtHvn79+gU73QjoBaNkRwgggAACCCCQYAECeoIbj6Ij4LFA2Qf04cOH2+23327Lly+3du3aVTS1hqm3b9/e9Ptbb7011ClQXUDPtoONGze6nnOFdPWmB0vQg/7111/bF198YTvvvHOVnvRQhUpbiYAeRY1tEEAAAQQQQMA3AQK6by1KfRDwQ6DsA7qGm6uXfN26dVVatGXLltalSxd79tlnQ7V2lIC+ZMkSO/DAA+26666zO+64o0pA32WXXUwhvlGjRnbKKafYqFGjrEOHDqHKk2klAnpkOjZEAAEEEEAAAY8ECOgeNSZVQcAjgbIP6Bq+3qBBA/cMePrStWtXN+mbhsGHWaIE9IsuusgeeughmzdvnhtqHywK7Fu3brVu3bq58r3yyit27733ut72119/3fbZZ5+cRVq7dq3pX+qyePFiu/DCC119u/2lW859ZFph+83bK/24zsg6kfajjdL3FXlHbIgAAggggAACCOQhQEDPA4tVEUCgZAJlH9DVG62ecgXg9EUTxq1fv97eeeedUA2Sb0C/77777NJLL7UrrrjC7r777pzHUE++etH79u3rJrHLtYwYMcJGjhyZcTUCei49fo8AAggggAACPgsQ0H1uXeqGQHIFyj6g11YP+tNPP21nn322nXzyyab/DjsRnHrU1SuuCedyLfSg5xLi9wgggAACCCBQrgIE9HJteeqNQLwFyj6g18Yz6Hp3+umnn25HHXWUe75dz5eHXfSKtWeeecY0eVyUhWfQo6ixDQIIIIAAAgj4JkBA961FqQ8CfgiUfUAfNmyYm5wt2yzu+r1enRZmCTPEfebMmW6Y+iGHHGIvvPBCpXefhzmGXtP28ccf26pVq8KsXmUdAnokNjZCAAEEEEAAAc8ECOieNSjVQcATgbIP6JrBXTO1Z3sP+vz58927yzVZ3LJly6xp06bWqlWrjM2fK6DPmTPHTjjhBGvbtq0pqDdv3jzjfjZt2uR+3rhx40q/nzZtmp1//vnuufUJEyZEOgUJ6JHY2AgBBBBAAAEEPBMgoHvWoFQHAU8Eyj6gqx0HDBhgEydOtH79+lmPHj1s1qxZNnny5EpBeOXKle496ekTtC1cuNCmT5/uTofnn3/eBe+rr77amjVr5v5ddtll7nfvvvuuuxGgV6apx36PPfaodApporoTTzzR/Uw3BXr16mXnnXeeHXDAAW4W99mzZ9vDDz9se++9tynoa/0oCwE9ihrbIIAAAggggIBvAgR031qU+iDghwAB3cy9zkzvF580aZKtXr3a9tprL+vfv78NHTq0YvK2bAFds6kr2Gda9Co0baflpZdesuOPPz7rWXPssce6dbTonewK+a+99pqbDE69923atLE+ffrY8OHDrUWLFpHPPgJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1LOpm9DAAAgAElEQVQVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqkJAD6PEOggggAACCCDguwAB3fcWpn4IJFOAgJ7MdotcagJ6ZDo2RAABBBBAAAGPBAjoHjUmVUHAIwECukeNGaYqBPQwSqyDAAIIIIAAAr4LENB9b2Hqh0AyBQjoyWy3yKUmoEemY0MEEEAAAQQQ8EiAgO5RY1IVBDwSIKB71JhhqhJ8GXXp0sXmrZgXZpMq63yn03ds5syZFT+vM7JOpP1oo+03b4+8LRsigAACCCCAAAJRBQjoUeXYDgEEiilAQC+mbgz3HXwZ7bnnnrZm7RqzpnkW8lOzdm3b2fLlywnoedKxOgIIIIAAAgjER4CAHp+2oCQIIPD/BAjoZXY2VArom9eYXZEnwG/N2u1KQM9TjdURQAABBBBAIGYCBPSYNQjFQQABJ0BAL7MTgYBeZg1OdRFAAAEEEEAgowABnRMDAQTiKEBAj2OrFLFMBPQi4rJrBBBAAAEEEEiMAAE9MU1FQREoKwECelk1txkBvcwanOoigAACCCCAAD3onAMIIJAYAQJ6YpqqMAUloBfGkb0ggAACCCCAQLIF6EFPdvtRegR8FSCg+9qyWepFQC+zBqe6CCCAAAIIIEAPOucAAggkRoCAnpimKkxBCeiFcWQvCCCAAAIIIJBsAXrQk91+lB4BXwUI6L62LD3oZdayVBcBBBBAAAEE8hEgoOejxboIIFAqAQJ6qaRjchx60GPSEBQDAQQQQAABBGpVgIBeq/wcHAEEsggQ0Mvs1CCgl1mDU10EEEAAAQQQyChAQOfEQACBOAoQ0OPYKkUsEwG9iLjsGgEEEEAAAQQSI0BAT0xTUVAEykqAgF5Wzc170MusuakuAggggAACCGQRIKBzaiCAQBwFCOhxbJUiloke9CLismsEEEAAAQQQSIwAAT0xTUVBESgrAQJ6WTU3Pehl1txUFwEEEEAAAQToQeccQACBBAkQ0BPUWIUoKj3ohVBkHwgggAACCCCQdAF60JPegpQfAT8FCOh+tmvWWhHQy6zBqS4CCCCAAAIIZBQgoHNiIIBAHAUI6HFslSKWiYBeRFx2jQACCCCAAAKJESCgJ6apKCgCZSVAQC+r5uYZ9DJrbqqLAAIIIIAAAlkECOicGgggEEcBArqZbdu2zUaPHm2TJk2yVatWWZs2bax///42ZMgQq1evXrXtNmPGDHviiSds7ty5tmjRItuyZYutWLHC2rZtm3G7N954w4YOHWovv/yy+33Pnj1t1KhRduihh1ZZP591w55c9KCHlWI9BBBAAAEEEPBZgIDuc+tSNwSSKxCbgP7NN9/Yn/70J3vhhRfs/ffft+3bt9uee+5pxx13nJ1zzjm2ww47FE154MCBNn78eOvXr591797dZs2aZVOmTDH9fNy4cdUe9+KLL7ZHHnnEOnbsaF999ZUpVGcL6EuXLrUjjjjCmjdvboMGDXL7HTt2rH3yySf26quv2n777VdxrHzWzQeGgJ6PFusigAACCCCAgK8CBHRfW5Z6IZBsgVgE9GXLltkZZ5xhixcvzqip3uinn37aheBCL+r17ty5swvMY8aMqdj94MGD7Z577rEFCxZUe9zVq1dbixYtrGHDhnbDDTfYbbfdljWgn3322fbss8+6eqqXXot67A866CA75ZRT7PHHH684fj7r5mNCQM9Hi3URQAABBBBAwFcBArqvLUu9EEi2QK0H9E2bNtlhhx1my5cvd73m2Rb1pitM77rrrgUVHz58uN1+++3u+O3atavYt3rB27dvb/r9rbfeGuqY1QX0zz//3HbbbTc7//zzberUqZX217dvX5s2bZp99NFH1rhxY8tn3VAFS1mJgJ6vGOsjgAACCCCAgI8CBHQfW5U6IZB8gVoP6Oq1vvLKK61OnTr23e9+1/VCH3LIIe7Zb/U033HHHfbMM8+4348YMcJuvPHGgqr37t3b9ZKvW7euyn5btmxpXbp0cb3eYZbqAvrs2bPd8HkNpR8wYECl3elnGk6vdY4++mj3v2HXDVOu1HUI6PmKsT4CCCCAAAII+ChAQPexVakTAskXqPWA3qtXL5s5c6adddZZ9thjj2UU/clPfuKeCe/atau9/vrrBVXXsPkGDRq4Sd7SFx1Pk76p5z7MUl1Af/LJJ03D1qdPn259+vSptDv9TEP8NdmcHPJZt7pyrV271vQvddFNjwsvvNA9379m8xqzK8LULGWd35q127WdG3EQLHVG1slzJ/9v9e03Zx81EXmnbIgAAggggAACCOQQIKBziiCAQBwFaj2gq5f6ww8/tDlz5tjhhx+e0eitt96ygw8+2HbccUfbvHlzQR07dOhgKsMrr7xSZb/qxV6/fr298847oY5ZXUB/8MEH7aKLLrLnnnvOTjrppEr700zw6snXOgrP+axbXcE04mDkyJEZVyGgh2pSVkIAAQQQQAABTwUI6J42LNVCIOECtR7Q1Xut15xt2LDBmjZtmpFTv9cs7hrm/sUXX7ge70It9KDnKUkPep5grI4AAggggAACcRQgoMexVSgTAgjUekCvW7euC94bN260nXbaKWuLhF0v3yblGfQ8xQjoeYKxOgIIIIAAAgjEUYCAHsdWoUwIIFD2AX3YsGFuIrpss7jr93p1WpilJrO4P/roo24W95133jnnLO6p64YpV+o6TBKXrxjrI4AAAggggICPAgR0H1uVOiGQfIHYBPQ333zTGjVqlFVU70JXT3uu9fbee++8WkUzuGum9mzvQZ8/f7516tTJTRan97VrGH6rVq0yHiPXe9A1AZyeQdcz9a1bt3b7CN6Drp58TQ4XLPmsm0+FCej5aLEuAggggAACCPgqQED3tWWpFwLJFohNQC8EowL81q1b896VXns2ceJE69evn/Xo0cNmzZplkydPtksvvdQmTJjg9rdy5Ur3nnS9s1wzygfLwoUL3czsWp5//nk3I/3VV19tzZo1c/8uu+yyinWXLFli/x97ZwJlRXE97qsSFUURNSKyyLjEJQIqblEUlwgxrlHjEo1AxGiMCImKRmLU/BEVV3ABDRFwi2tiFI1rIgoaEY4CGncGIe4LLiju/M+t/N44M7z3qrpfdb/q7q/P8agz3bdvfbf6TX+vqqu322478z70E0880fx87NixZuR8xowZsskmm8TaN0qDEfQotNgXAhCAAAQgAIG8EkDQ81pZ2gWBbBMIQtB9IVRB1wXlom4q9aNHj5YJEybIa6+9Jp07d5bBgwfL8OHDpU2bNlUFXWVdxb7ctv766xuxb76p0J966qkybdo08+M+ffrI+eefb0bpW29R9nVtM4LuSor9IAABCEAAAhDIMwEEPc/VpW0QyC6Bugt6pdeAxUV65plnxj20EMch6IUoM42EAAQgAAEIQMBCAEGni0AAAiESqLughwglzzn5FvS+ffvKI3MeiY1sl567mMcC2CAAAQhAAAIQgECaBBD0NGlzLghAwJUAgu5KKif7+Rb0DTbYQBrnN4qUf4V9dWofijR0bzAr6LNBAAIQgAAEIACBNAkg6GnS5lwQgIArAQTdlVRO9ktE0Bc1igyLAajMO9VjROEQCEAAAhCAAAQgEJkAgh4ZGQdAAAIpEEDQU4Ac0ikQ9JCqQS4QgAAEIAABCNSLAIJeL/KcFwIQqEYAQS9Y/0DQC1ZwmgsBCEAAAhCAQFkCCDodAwIQCJEAgh5iVRLMCUFPEC6hIQABCEAAAhDIDAEEPTOlIlEIFIoAgl6ocosg6AUrOM2FAAQgAAEIQIARdPoABCCQGQIIemZK5SdRBN0PR6JAAAIQgAAEIJBtAoygZ7t+ZA+BvBJA0PNa2QrtQtALVnCaCwEIQAACEIAAI+j0AQhAIDMEEPTMlMpPogi6H45EgQAEIAABCEAg2wQYQc92/cgeAnklgKDntbKMoBessjQXAhCAAAQgAIEoBBD0KLTYFwIQSIsAgp4W6UDOwwh6IIUgDQhAAAIQgAAE6koAQa8rfk4OAQhUIICgF6xrIOgFKzjNhQAEIAABCECgLAEEnY4BAQiESABBD7EqCeaEoCcIl9AQgAAEIAABCGSGAIKemVKRKAQKRQBBL1S5eQ96wcpNcyEAAQhAAAIQqEAAQadrQAACIRJA0EOsSoI5MYKeIFxCQwACEIAABCCQGQIIemZKRaIQKBQBBL1Q5WYEvWDlprkQgAAEIAABCDCCTh+AAAQyRABBz1CxfKTKCLoPisSAAAQgAAEIQCDrBBhBz3oFyR8C+SSAoOezrhVbhaAXrOA0FwIQgAAEIACBsgQQdDoGBCAQIgEEPcSqJJgTgp4gXEJDAAIQgAAEIJAZAgh6ZkpFohAoFAEEvVDl5hn0gpWb5kIAAhCAAAQgUIEAgk7XgAAEQiSAoIdYlQRzYgQ9QbiEhgAEIAABCEAgMwQQ9MyUikQhUCgCCHqhys0IesHKTXMhAAEIQAACEGAEnT4AAQhkiACCnqFi+UiVEXQfFIkBAQhAAAIQgEDWCTCCnvUKkj8E8kkAQc9nXSu2CkEvWMFpLgQgAAEIQAACZQkg6HQMCEAgRAIIeohVSTAnBD1BuISGAAQgAAEIQCAzBBD0zJSKRCFQKAIIeqHKzTPoBSs3zYUABCAAAQhAoAIBBJ2uAQEIhEgAQQ+xKgnmxAh6gnAJDQEIQAACEIBAZggg6JkpFYlCoFAEEPRClZsR9IKVm+ZCAAIQgAAEIMAIOn0AAhDIEAEEPUPF8pEqI+g+KBIDAhCAAAQgAIGsE2AEPesVJH8I5JMAgp7PulZsFYJesILTXAhAAAIQgAAEyhJA0OkYEIBAiAQQ9BCrkmBOCHqCcAkNAQhAAAIQgEBmCCDomSkViUKgUAQQ9EKVm2fQC1ZumgsBCEAAAhCAQAUCCDpdAwIQCJEAgh5iVRLMiRH0BOESGgIQgAAEIACBzBBA0DNTKhKFQKEIIOiFKjcj6AUrN82FAAQgAAEIQIARdPoABCCQIQIIeoaK5SNVRtB9UCQGBCAAAQhAAAJZJ8AIetYrSP4QyCcBBD2fda3YKgS9YAWnuRCAAAQgAAEIlCWAoNMxIACBEAkg6CFWJcGcEPQE4RIaAhCAAAQgAIHMEEDQM1MqEoVAoQgg6IUqN8+gF6zcNBcCEIAABCAAgQoEEHS6BgQgECIBBD3EqiSYEyPoCcIlNAQgAAEIQAACmSGAoGemVCQKgUIRQNALVW5G0AtWbpoLAQhAAAIQgAAj6PQBCEAgQwQQ9AwVy0eqjKD7oEgMCEAAAhCAAASyToAR9KxXkPwhkE8CCHo+61qxVQh6wQpOcyEAAQhAAAIQKEsAQadjQAACIRJA0EOsSoI5IegJwiU0BCAAAQhAAAKZIYCgZ6ZUJAqBQhFA0AtVbp5BL1i5aS4EIAABCEAAAhUIIOh0DQhAIEQCCHqIVUkwJ0bQE4RLaAhAAAIQgAAEMkMAQc9MqUgUAoUigKAXqtyMoBes3DQXAhCAAAQgAAFG0OkDEIBAhggg6Bkqlo9UGUH3QZEYEIAABCAAAQhknQAj6FmvIPlDIJ8EEPR81rViqxD0ghWc5kIAAhCAAAQgUJYAgk7HgAAEQiSAoIdYlQRzQtAThEtoCEAAAhCAAAQyQwBBz0ypSBQChSKAoBeq3DyDXrBy01wIQAACEIAABCoQQNDpGhCAQIgEEPQQq5JgToygJwiX0BCAAAQgAAEIZIYAgp6ZUpEoBApFAEEvVLkZQS9YuWkuBCAAAQhAAAKMoNMHIACBDBFA0DNULB+pMoLugyIxIAABCEAAAhDIOgFG0LNeQfKHQD4JIOj5rGvFViHoBSs4zYUABCAAAQhAoCwBBJ2OAQEIhEgAQQ+xKgnmhKAnCJfQEIAABCAAAQhkhgCCnplSkSgECkUAQS9UuXkGvWDlprkQgAAEIAABCFQggKDTNSAAgRAJIOgi8vXXX8sFF1wgEyZMkIULF0rXrl1l8ODBcsopp8gKK6xgrdszzzwjw4cPl2nTppl9+/TpI6NHj5Ytttii6dj58+dLQ0NDxVht2rSRL7/8sun3AwcOlMmTJ5fdX3Ps0qWLNa9yOzCCHgsbB0EAAhCAAAQgkDMCCHrOCkpzIJATAgi6iBx//PEybtw4GTRokOy4444yffp0mTRpkvn5FVdcUbXUL730kmy77bay5pprypAhQ8y+Y8eOlQ8++EBmzJghG2+8sfnZJ598In/729+WiaWyffrpp8u+++4rd9555zKCrpK+/PLLtzjuwAMPlFVWWSVWFwxV0Pv27Wu+HIm76ZcqU6dOjXs4x0EAAhCAAAQgUDACCHrBCk5zIZARAoUX9Llz50qvXr2MXI8ZM6apbEOHDpXLLrtMZs+eLT169KhYzoMPPljuvfdeee6558zIu24qmptttpnstddecuutt1btCiNHjpQzzjhDbr/9dlHxLm2lEXQdVdfRdV9bqIK+wQYbyIIFC6Rbt26Rm1o6bt68eZGP5QAIQAACEIAABIpJAEEvZt1pNQRCJ1B4QR8xYoSMGjVKVO6aT0FvbGwUlUb9vUp0uW3x4sWy1lpryWGHHbbMdPQBAwbIzTffLO+9956suuqqFfvB9773PXn//ffl9ddflxVXXHEZQf/iiy9kyZIl0q5du2VG0uN0rpAFXdsTR7K1TnGPjcOQYyAAAQhAAAIQyD4BBD37NaQFEMgjgcILev/+/c0o+ZtvvrlMfTt27ChbbbWVGSEvtz3++ONmSrxOjz/uuONa7KI/0ynyus8OO+xQ9vjHHntMdtppJznhhBPMaH3zrTSCvtpqq8nHH38sbdu2NSPy+mz7hhtuGLsvIuix0XEgBCAAAQhAAAI5IoCg56iYNAUCOSJQeEHX6es6cj1r1qxlyrr11lubhdt0Gny5Tael6xR3fXZcnyFvvunP9t9/f7ntttvkoIMOKnv8scceK1dffbU8+eSTss0227TY57TTTpOvvvpKevfubfJTmb/88stl9dVXl5kzZ8r6669v7YZvvPGG6D/NN52Kf+SRR8p6660nr3/6usgwa5iWO1wq0tChoWmkW0evGxc1Ro+jUZvFqmUUvJZjI7ae3SEAAQhAAAIQyAkBBD0nhaQZEMgZgcILuo5G60i5CnDrTUfH3377bXn55ZfLlv26666To446Su677z7p169fi33uv/9+0dF53UeFuPX22WefSadOnaRz586iq8C7bDqSr6PoOn1eF7GzbWeddZacffbZZXdD0G30+D0EIAABCEAAAnkmgKDnubq0DQLZJVB4Qa/XCPott9wihx56qJmyrq9zc910RF1HxfWZddvGCLqNEL+HAAQgAAEIQKCoBBD0olaedkMgbAKFF/R6PYO+9957m5F3XfFdR9JdN13pfcqUKaKLx8XZeAY9DjWOgQAEIAABCEAgbwQQ9LxVlPZAIB8ECi/o+g7yc889t+Iq7vr7c845p2y1bau433TTTWYVd12BvfmmC9J16dLFTIu/5557IvWknj17yqJFi2K/MxxBj4SbnSEAAQhAAAIQyCkBBD2nhaVZEMg4gcILuq7griu1V3oP+tNPPy0qxbpY3CuvvCLt27dvMeKtC8DpSPjzzz9vpFu30nvQdXReF5JrvV100UVy8sknm9ewHXLIIcv8/pNPPjE/a/16Nt1fX+mmi8uNHz8+VtcrgqAvd/ZysdjoQUvPXBr7WA6EAAQgAAEIQCA7BBD07NSKTCFQJAKFF3Qttr4i7aqrrpJBgwaZ155Nnz5dJk6c2EKE58+fb96T3nqBthdeeEG222478z70E0880fSdsWPHmpHzGTNmyCabbLJMf1LhV4nXkfSVVlppmd/rlwK77767eUZdj9dV3PV1bTfccIN069ZNnnjiCbOwXZwNQa9ODUGP06s4BgIQgAAEIJA9Agh69mpGxhAoAgEEXcS8zkwXa5swYYK89tprZmX1wYMHy/Dhw6VNmzamH1QSdP3dnDlz5NRTT5Vp06aZffv06SPnn3++GXlvvT311FOir2/TLwX0XenlNhX3k046ybx+TReD09H7rl27mle5jRgxQtZee+3YfRNBR9Bjdx4OhAAEIAABCOSIAIKeo2LSFAjkiACCnqNiujQFQUfQXfoJ+0AAAhCAAATyTgBBz3uFaR8EskkAQc9m3WJnjaAj6LE7DwdCAAIQgAAEckQAQc9RMWkKBHJEAEHPUTFdmoKgI+gu/YR9IAABCEAAAnkngKDnvcK0DwLZJICgZ7NusbNG0BH02J2HAyEAAQhAAAI5IoCg56iYNAUCOSKAoOeomC5NQdARdJd+wj4QgAAEIACBvBNA0PNeYdoHgWwSQNCzWbfYWSPoCHrszsOBEIAABCAAgRwRQNBzVEyaAoEcEUDQc1RMl6Yg6Ai6Sz9hHwhAAAIQgEDeCSDoea8w7YNANgkg6NmsW+ysEXQEPXbn4UAIQAACEIBAjggg6DkqJk2BQI4IIOg5KqZLUxB0BN2ln7APBCAAAQhAIO8EEPS8V5j2QSCbBBD0bNYtdtYIOoIeu/NwIAQgAAEIQCBHBBD0HBWTpkAgRwQQ9BwV06UpCDqC7tJP2AcCEIAABCCQdwIIet4rTPsgkE0CCHo26xY7awQdQY/deTgQAhCAAAQgkCMCCHqOiklTIJAjAgh6jorp0pS8C3rfvn3lkTmPuKAou88uPXeRqVOnxj6eAyEAAQhAAAIQyAYBBD0bdSJLCBSNAIJesIrnXdA32GADaZzfKNI+RmE/FGno3iDz5s2LcTCHQAACEIAABCCQJQIIepaqRa4QKA4BBL04tTYtLYSgL2oUGRajsJeKNHRA0GOQ4xAIQAACEIBA5ggg6JkrGQlDoBAEEPRClPnbRiLoVQqOoBfsaqC5EIAABCBQZAIIepGrT9shEC4BBD3c2iSSGYKOoCfSsQgKAQhAAAIQyBgBBD1jBSNdCBSEAIJekEKXmomgI+gF6/I0FwIQgAAEIFCWAIJOx4AABEIkgKCHWJUEc0LQEfQEuxehIQABCEAAApkhgKBnplQkCoFCEUDQC1VuFomrWm6eQS/Y1UBzIQABCECgyAQQ9CJXn7ZDIFwCCHq4tUkks5BH0Bs9rL5uXrPmIU4i8AkKAQhAAAIQgEAwBBD0YEpBIhCAQDMCCHrBugOCXqXgjKAX7GqguRCAAAQgUGQCCHqRq0/bIRAuAQQ93NokkhmCjqAn0rEICgEIQAACEMgYAQQ9YwUjXQgUhACCXpBCl5qJoCPoBevyNBcCEIAABCBQlgCCTseAAARCJICgh1iVBHNC0BH0BLsXoSEAAQhAAAKZIYCgZ6ZUJAqBQhFA0AtV7rBXcfexuBuLxBWsQ9NcCEAAAhCAQEwCCHpMcBwGAQgkSgBBTxRveMEZQWcEPbxeSUYQgAAEIACB9Akg6Okz54wQgICdAIJuZ5SrPRB0BD1XHZrGQAACEIAABGISQNBjguMwCEAgUQIIeqJ4wwuOoCPo4fVKMoIABCAAAQikTwBBT585Z4QABOwEEHQ7o1ztgaAj6Lnq0DQGAhCAAAQgEJMAgh4THIdBAAKJEkDQE8UbXnAEHUEPr1eSEQQgAAEIQCB9Agh6+sw5IwQgYCeAoNsZ5WoPBB1Bz1WHpjEQgAAEIACBmAQQ9JjgOAwCEEiUAIKeKN7wgiPoCHp4vZKMIAABCEAAAukTQNDTZ84ZIQABOwEE3c4oV3sg6Ah6rjo0jYEABCAAAQjEJICgxwTHYRCAQKIEEPRE8YYXHEFH0MPrlWQEAQhAAAIQSJ8Agp4+c84IAQjYCSDodka52gNBdxf05c5eLnbtl565NPaxHAgBCEAAAhCAQPIEEPTkGXMGCEAgOgEEPTqzTB+BoLsJet++feWROY/ErvUuPXeRqVOnxj6eAyEAAQhAAAIQSJYAgp4sX6JDAALxCCDo8bhl9igE3U3QN9hgA2mc3yjSPkapPxRp6N4g8+bNi3Ewh0AAAhCAAAQgkAYBBD0NypwDAhCISgBBj0os4/sj6BEEfVGjyLAYBb9UpKEDgh6DHIdAAAIQgAAEUiOAoKeGmhNBAAIRCCDoEWDlYVcEHUHPQz+mDRCAAAQgAIFaCSDotRLkeAhAIAkCCHoSVAOOiaAj6AF3T1KDAKZHz6YAACAASURBVAQgAAEIpEYAQU8NNSeCAAQiEEDQI8DKw64IOoKeh35MGyAAAQhAAAK1EkDQayXI8RCAQBIEEPQkqAYcE0FH0APunqQGAQhAAAIQSI0Agp4aak4EAQhEIICgR4CVh10RdAQ9D/2YNkAAAhCAAARqJYCg10qQ4yEAgSQIIOhJUA04JoKOoAfcPUkNAhCAAAQgkBoBBD011JwIAhCIQABBjwArD7si6Ah6HvoxbYAABCAAAQjUSgBBr5Ugx0MAAkkQQNCToBpwTAQdQQ+4e5IaBCAAAQhAIDUCCHpqqDkRBCAQgQCCHgFWHnZF0BH0PPRj2gABCEAAAhColQCCXitBjocABJIggKAnQTXgmAg6gh5w9yQ1CEAAAhCAQGoEEPTUUHMiCEAgAgEEPQKsPOyKoCPoeejHtAECEIAABCBQKwEEvVaCHA8BCCRBAEFPgmrAMRF0BD3g7klqEIAABCAAgdQIIOipoeZEEIBABAIIegRYedgVQUfQ89CPaQMEIAABCECgVgIIeq0EOR4CEEiCAIKeBNWAYyLoCHrA3ZPUIAABCEAAAqkRQNBTQ82JIACBCAQQ9Aiw8rArgo6g56Ef0wYIQAACEIBArQQQ9FoJcjwEIJAEAQQ9CaoBx0TQEfSAuyepQQACEIAABFIjgKCnhpoTQQACEQgg6BFg5WFXBD19Qe/bt68sXLgwVvfp2rWrTJ06NdaxHAQBCEAAAhCAQGUCCDq9AwIQCJEAgh5iVRLMCUFPX9A32GADWbBggXTr1i1SZUvHzJs3L9Jx7AwBCEAAAhCAgJ0Agm5nxB4QgED6BBD09JnX9YwIen0EXc8aVbRV7OMcV9cOxskhAAEIQAACGSGAoGekUKQJgYIRQNBF5Ouvv5YLLrhAJkyYYKYi67TiwYMHyymnnCIrrLCCtUs888wzMnz4cJk2bZrZt0+fPjJ69GjZYostWhw7cOBAmTx5ctl4et4uXbq0+J1rXGuCzXZA0BH0KP2FfSEAAQhAAAJ5JYCg57WytAsC2SaAoIvI8ccfL+PGjZNBgwbJjjvuKNOnT5dJkyaZn19xxRVVK/zSSy/JtttuK2uuuaYMGTLE7Dt27Fj54IMPZMaMGbLxxhs3HV8SdJX05ZdfvkXcAw88UFZZZZWmn0WJG6ULIugIepT+wr4QgAAEIACBvBJA0PNaWdoFgWwTKLygz507V3r16mXkesyYMU3VHDp0qFx22WUye/Zs6dGjR8UqH3zwwXLvvffKc889Z0beddPR8M0220z22msvufXWW5cR9C+//FLatGlTtedEiRulCyLo9RH0xkWNIsOiVEpELhVp6NAQeWp8xLOwOwQgAAEIQKCQBBD0QpadRkMgeAKFF/QRI0bIqFGjjAQ1NDQ0FayxsVH0GWD9/ciRI8sWcvHixbLWWmvJYYcdtszU9QEDBsjNN98s7733nqy66qrm+NII+hdffCFLliyRdu3aLTOSrvtFjRullyHoCHqU/sK+EIAABCAAgbwSQNDzWlnaBYFsEyi8oPfv39+Mkr/55pvLVLJjx46y1VZbmRHyctvjjz9upsTr9PjjjjuuxS76M50ir/vssMMOLQR9tdVWk48//ljatm1rRtn1efUNN9yw6fiocaN0QQQdQY/SX9gXAhCAAAQgkFcCCHpeK0u7IJBtAoUXdJ2+vuKKK8qsWbOWqeTWW28tOh1dp8GX226//XbRqeh33nmn7Lvvvi120Z/tv//+ctttt8lBBx1kfnfaaafJV199Jb179zbnfOyxx+Tyyy+X1VdfXWbOnCnrr7++2S9q3Epd8I033hD9p/mmU/GPPPJIWW+99eT1T1+vedq1zjKINX1bk2o2hTuvcbSZsdvGFPdsf7qSPQQgAAEIBE0AQQ+6PCQHgcISKLyg68i1jpSrLLfedHT87bfflpdffrlsB7nuuuvkqKOOkvvuu0/69evXYp/7779fdHRe91EhrrTp6LyOouuUeF2YTjcfcTXOWWedJWeffXbZUyPoZbAk8IUBgl7Yz1YaDgEIQAACgRNA0AMvEOlBoKAECi/oaY6gV+pjOqKuI92vv/662YUR9IhXYwJiHXvUW1NvNfIdOxYj6BE7ArtDAAIQgAAE3Akg6O6s2BMCEEiPQOEFPc1n0CuVVV+xNmXKFNHF43TjGfSIFwCCHhEYu0MAAhCAAAQggKDTByAAgRAJFF7QTz/9dDn33HMrruKuvz/nnHPK1s622vpNN91kVnHX1dqrbT179pRFixaZ17Pp5ituuXOySFyVSiQg+no2RtBD/OgjJwhAAAIQKDoBBL3oPYD2QyBMAoUXdF3BXVdqr/Qe9KefflpUoHWxuFdeeUXat28vnTp1aqqmLgCnz6A///zz0qVLF/Pz0nvQdXRep6vr9sknn5h/l165Vgqgr2LT17Qde+yxMn78+Mhxo3YrBB1Bj9pn2B8CEIAABCCQRwIIeh6rSpsgkH0ChRd0LaG+Iu2qq66SQYMGyU477STTp0+XiRMntpDm+fPnm/ekN1/MTY994YUXZLvttjPvQz/xxBNNjxg7dqwZOZ8xY4Zssskm5mcq+rvvvrsceuih5me6irtOZb/hhhukW7du8sQTT5jF6kqba9yoXRBBR9Cj9hn2hwAEIAABCOSRAIKex6rSJghknwCCLmJefabvIp8wYYK89tpr0rlzZxk8eLAMHz5c2rRpY6pcSdD1d3PmzJFTTz1Vpk2bZvbt06ePnH/++WbkvbTpe9ZPOukkefLJJ81icDoi37VrV/N6thEjRsjaa6+9TG9yiRu1CyLoCHrUPsP+EIAABCAAgTwSQNDzWFXaBIHsE0DQs1/DSC1A0BH0SB2GnSEAAQhAAAI5JYCg57SwNAsCGSeAoGe8gFHTR9AR9Kh9hv0hAAEIQAACeSSAoOexqrQJAtkngKBnv4aRWoCgI+iROgw7QwACEIAABHJKAEHPaWFpFgQyTgBBz3gBo6aPoCPoUfsM+0MAAhCAAATySABBz2NVaRMEsk8AQc9+DSO1AEFH0CN1GHaGAAQgAAEI5JQAgp7TwtIsCGScAIKe8QJGTR9Bz66g9+3bVxYuXBi15GZ/fWPA1KlTYx3LQRCAAAQgAIE8EkDQ81hV2gSB7BNA0LNfw0gtQNCzK+gbbLCBLFiwQLp16xap5qVj5s2bF+k4doYABCAAAQjkmQCCnufq0jYIZJcAgp7d2sXKHEHPtqBr9lFFW8U+znGxOhgHQQACEIAABDJCAEHPSKFIEwIFI4CgF6zgCDqCXrAuT3MhAAEIQAACZQkg6HQMCEAgRAIIeohVSTAnBB1BT7B7ERoCEIAABCCQGQIIemZKRaIQKBQBBL1Q5RZB0BH0gnV5mgsBCEAAAhBgBJ0+AAEIZIYAgp6ZUvlJFEFH0P30JKJAAAIQgAAEsk2AEfRs14/sIZBXAgh6XitboV0IOoJesC5PcyEAAQhAAAKMoNMHIACBzBBA0DNTKj+JIugIup+eRBQIQAACEIBAtgkwgp7t+pE9BPJKAEHPa2UZQY9e2UtFGjo0mNeY6avJGhc1igyLHkaaxdGjY8cqE0fjhfSateXOXi4GoP8dsvTMpbGP5UAIQAACEIBArQQQ9FoJcjwEIJAEAQQ9CaoBx2QEvUpxEPTIPRdBj4yMAyAAAQhAIBACCHoghSANCECgBQEEvWAdAkFH0Pv27SsLFy6M1fO7du0qU6dObToWQY+FkYMgAAEIQCAAAgh6AEUgBQhAYBkCCHrBOgWCjqDrlPsFCxZIt27dIvX+0jHNp9gj6JEQsjMEIAABCAREAEEPqBikAgEIfDsAtnTpUh4ELVCHQNARdBV03Xw8y46gF+jDg6ZCAAIQyBkBBD1nBaU5EMgJAUbQc1JI12Yg6Ai6L0HXqfKPzHnEtests98uPXdpMV0+diAOhAAEIAABCMQggKDHgMYhEIBA4gQQ9MQRh3UCBB1B9yXoZnX6+Y0i7WP08Q9FGrr/b8V8NghAAAIQgEA9CCDo9aDOOSEAARsBBN1GKGe/R9ARdK+C7ulVdDm7zGgOBCAAAQhkgACCnoEikSIECkgAQS9Y0RF0BB1BL9hFT3MhAAEIQKAsAQSdjgEBCIRIAEEPsSoJ5oSgI+gIeoIXGKEhAAEIQCAzBBD0zJSKRCFQKAIIeqHKLYKgI+gIesEuepoLAQhAAAKMoNMHIACBzBBA0DNTKj+JIugIOoLu51oiCgQgAAEIZJsAI+jZrh/ZQyCvBBD0vFa2QrsQdAQ9z4LOe9kL9oFGcyEAAQjUQABBrwEeh0IAAokRQNATQxtmYAQdQUfQy/eBpWcuDfOiJSsIQAACEEiEAIKeCFaCQgACNRJA0GsEmLXDEXQEHUFH0LP2uUW+EIAABJIggKAnQZWYEIBArQQQ9FoJZux4BB1BD03Q+/btKwsXLox9JXXt2lWmTp1qjmeKe2yMHAgBCECgcAQQ9MKVnAZDIBMEEPRMlMlfkgh6tgW9cVGjyLCI/eFSkYYODTJv3jxzYGiCrvksWLBAunXrFrFh0nRcqW0IemSEHAABCECgsAQQ9MKWnoZDIGgCCHrQ5fGfHIKOoIco6FqVkmRH6fWt24KgR6HHvpUIxO1HrGNAn4JAtggg6NmqF9lCoCgEEPSiVPr/2omgI+gIevk+gFwV7MOwSnMRdPoCBIpBAEEvRp1pJQSyRgBBz1rFaswXQUfQEXQEvcaPkdwfjqDnvsQ0EAKGAIJOR4AABEIkgKCHWJUEc0LQEXQEHUFP8CMmF6ER9FyUkUZAwEoAQbciYgcIQKAOBBD0OkCv5ykRdAQdQUfQ6/kZlIVzI+hZqBI5QqB2Agh67QyJAAEI+CeAoPtnGnREBB1BR9AR9KA/pAJIDkEPoAikAIEUCCDoKUDmFBCAQGQCCHpkZNk+AEFH0BF0BD3bn2LJZ4+gJ8+YM0AgBAIIeghVIAcIQKA1AQS9YH0CQUfQ8yroffv2lUfmPBL7it6l5y4yderU2MdzYH4IIOj5qSUtgUA1Agg6/QMCEAiRAIIeYlUSzAlBR9DzKujarsb5jSLtY1xAH4o0dG+I9S72GGfjkMAJIOiBF4j0IOCJAILuCSRhIAABrwQQdK84ww+GoCPouRb0RY0iw2Jch5eKNHQIW9CRxhh1jXkIrGOC4zAIZIwAgp6xgpEuBApCAEEvSKFLzUTQEXQEvUwfQNAL9klYvbkIOt0BAsUggKAXo860EgJZI4CgZ61iNeaLoCPoCDqCXuPHSO4PR9BzX2IaCAFDAEGnI0AAAiESQNBDrEqCOSHoCDqCbhd0XXBu4cKFsa7Erl27tlhszpfs+YoTq1EFOwjWBSs4zS0sAQS9sKWn4RAImgCCHnR5/CeHoCPoCLpd0JXRggULpFu3bpEuwtIx8+bNazrOl+z5ihOpQQXdOTTWcfPR8i09c2lBq0izIWAngKDbGbEHBCCQPgEEPX3mdT0jgo6gI+hugq57NRdtlwu3HNu4ctVarHzFcWlH0fcJjXXcfBD0ovdk2m8jgKDbCPF7CECgHgQQ9HpQr+M5EXQEHUFH0Ov4EZSJU8cV4qRGq+Pmg6BnoruRZB0JIOh1hM+pIQCBigQQ9IJ1DgQdQUfQEfSCfexFbm5cIUbQI6PmAAjUlQCCXlf8nBwCEKhAAEEvWNdA0BF0BB1BL9jHXuTmIuiRkXEABDJJAEHPZNlIGgK5J4Cg577ELRuIoCPoCHp6gq6rwT8y55FYnzK79NwlkdXgYyVTsIMQ9IIVnOYWlgCCXtjS03AIBE0AQQ+6PP6TQ9ARdAQ9PUFX1o3zG0XaR7yWPxRp6N7QYpG60KQxYosytXtorOPmo9CTmnafqYKSLAQqEEDQ6RoQgECIBBD0EKuSYE4IOoKOoKcs6IsaRYZFvKgvFWnogKBHpOZt97hCnJQMx80HQffWJQiUUwIIek4LS7MgkHECCHrGCxg1fQQdQUfQEfSonxtF2z+uECPoResptDfrBBD0rFeQ/CGQTwIIej7rWrFVCDqCjqBnT9B9PstesI+8WM1F0GNh4yAIZI4Agp65kpEwBApBAEEvRJm/bSSCjqAj6NkTdJ/PshfsIy9WcxH0WNg4CAKZI4CgZ65kJAyBQhBA0AtRZgTdqczNnvs1QhTn2WE9Uavnh2PHSjCOpjlv3jwnLKWdWot97Ha1YhT3CwMN0/xYX/m0jhsFkjdGCdU+SluKvC+CXuTq0/YiEUDQi1Rt2gqB7BBA0LNTKy+ZMoJeBSOCXrWPeZNPBN1+LSPodkYJ7oGgJwiX0BAIiACCHlAxSAUCEGgigKCLyNdffy0XXHCBTJgwQRYuXChdu3aVwYMHyymnnCIrrLCCtbs888wzMnz4cJk2bZrZt0+fPjJ69GjZYostmo799NNP5dprr5U777xT5syZI++//750795d9tlnHzn99NNljTXWaHGegQMHyuTJk8ueW3Ps0qWLNa9yOyDoCHrcEeskBd3HbAVG0Mv07TKrwcf64CjYQQh6wQpOcwtLAEEvbOlpOASCJoCgi8jxxx8v48aNk0GDBsmOO+4o06dPl0mTJpmfX3HFFVUL+NJLL8m2224ra665pgwZMsTsO3bsWPnggw9kxowZsvHGG5ufqcT37NlTdt55Z+nfv7+ss846MmvWLPOlgIq6/vfqq6/edK6SoKukL7/88i1yOPDAA2WVVVaJ1bEQdAQdQbeLbN0ZMYIe6/PN10EIui+SxIFA2AQQ9LDrQ3YQKCqBwgv63LlzpVevXkaux4wZ09QPhg4dKpdddpnMnj1bevToUbF/HHzwwXLvvffKc889Z0beddMR7s0220z22msvufXWW83P3n33XXn99deNpDffrrnmGjn66KPloosukt/+9rfLCPqXX34pbdq08dY/EXQEve7yqSVI4HECRtDtXzx4+yDJeSAEPecFpnkQ+D8CCDpdAQIQCJFA4QV9xIgRMmrUKLNgVkNDQ1ONGhsbzQJU+vuRI0eWrd3ixYtlrbXWksMOO2yZ6egDBgyQm2++Wd577z1ZddVVK9b+448/NiPnKuk6ml7aSiPoX3zxhSxZskTatWu3zEh6nA6FoCPoCLpdZOvOiBH0OB9v3o5B0L2hJBAEgiaAoAddHpKDQGEJFF7Qdbq5jpK/+eaby3SCjh07ylZbbWVGyMttjz/+uJkSr9PjjzvuuBa76M90irzus8MOO1TsYC+88IJsuummctppp8m55567jKCvttpqohLftm1bMyKvz7ZvuOGGsTssgo6g110+tQSMoFe/hhH02J9xPg5E0H1QJAYEwieAoIdfIzKEQBEJFF7Qdfr6iiuuaJ4Bb71tvfXWolPMdRp8ue32228XneKuC7/tu+++LXbRn+2///5y2223yUEHHVSxbx111FFy/fXXy1NPPWWm2pc2FfavvvpKevfubfJ77LHH5PLLLzej7TNnzpT111/f2l/feOMN0X+abzoV/8gjj5T11ltPXv/0dZFh1jAtd/AlDoFLmu/p0rEWQfPFukwcxR/Sa9Zi8UmoD2nYun+JkVDtI17thd0dQS9s6Wl4wQgg6AUrOM2FQEYIFF7QdTRaR8pVgFtvOjr+9ttvy8svv1y2nNddd52oYN93333Sr1+/Fvvcf//9ZjE43UeFuNx29dVXy7HHHivDhg2TSy65xNpldCRfR9F1+rwuYmfbzjrrLDn77LPL7oagl8GSwKhuSfZiCWhCklZ3+UxIrH1/qVLXLzESqn3fvn3NGhlxN11nY+rUqXEPz8xxCHpmSkWiEKiJAIJeEz4OhgAEEiJQeEGv1wj6HXfcYUbff/SjH4n+t+tCcDqirqPiuuCcbWME3Uao1e8R9KrAeM2avT95Y5SQoGt+CxYskG7dutkb02qP0nFRZ15EPlEAByDoARSBFCCQAgEEPQXInAICEIhMoPCCXo9n0HV0fb/99pPtt9/ePN+uz5e7bvqKtSlTpoguHhdn4xn0KtQQdAR93jzDoO6zDBIUdG1fHMmOyyTO51S9j0HQ610Bzg+BdAgg6Olw5iwQgEA0AoUX9NNPP90szlZpFXf9/TnnnFOWqm0V95tuusms4q4rsJc2nR6q09S///3vy0MPPdTi3ecupdPXtC1atCj2NFUEHUGPPRXclzRqCRL4MiR2u1rlg6CXv0YQdPsn9NIzl9p3irFH3C8M9FRJ5RSjGRwCgeAIIOjBlYSEIAABESm8oOsK7rpSe6X3oD/99NPm3eW6WNwrr7wi7du3l06dOjV1Hl0ATp9Bf/7556VLly7m56X3oOvovC4kV9qeeOIJ+eEPfyjdu3c3z3GuueaaZTvhJ598Yn7e+vVs+to2faWbPrc+fvz4WB0YQUfQY4ssgm695rIwxV0bUesIet6fZY8rxEnJcNx8EHTrJcsOBSeAoBe8A9B8CARKoPCCrnXRV6RdddVVMmjQINlpp51k+vTpMnHixBYiPH/+fPOe9NYLtOlr0rbbbjvzPvQTTzzRlHns2LFm5HzGjBmyySabmJ+9+uqr5osAfWWajtivu+66LbqELlS35557mp/plwK77767HHrooeZ4XcVdX9d2ww03mGdHVfR1/zgbgo6gI+hl+kBoK937+jLEU7uUWPMvH/L+LHtcIUbQ4/xV4hgI1I8Agl4/9pwZAhCoTABBFzGvM9P3i0+YMEFee+016dy5swwePFiGDx/etHhbJUFXtHPmzJFTTz1Vpk2bZkj36dNHzj//fDPyXtoefvhh2W233SpWQkekdB/d9J3sJ510kjz55JNmMTgdvdfVk/VVbiNGjJC11147dp9G0BF0BB1Br3UEvZbp7rUcG/uDL+KBCHpEYOwOgYwSQNAzWjjShkDOCSDoOS9w6+Yh6Ag6go6gI+jVP/gR9IL9YaS5hSWAoBe29DQcAkETQNCDLo//5BB0BB1BR9BDEfRQn2VH0P3/7SEiBEIkgKCHWBVyggAEEPSC9QEEHUFH0BH0UAQ91GfZEfSC/WGkuYUlgKAXtvQ0HAJBE0DQgy6P/+QQdAQdQUfQQxJ0rUat+fj+pETQfRMlHgTCJICgh1kXsoJA0Qkg6AXrAQg6go6gI+i1CnEtC721Xg3el6DXMl1eF+HUV1+WNgS9YH8YaW5hCSDohS09DYdA0AQQ9KDL4z85BB1BR9AR9DwKetzp8gsWLDCvr2zOBEH3/7eHiBAIkQCCHmJVyAkCEEDQC9YHEHQEHUFH0PMq6HFG48vNBkDQC/aHkeYWlgCCXtjS03AIBE0AQQ+6PP6TQ9ARdATdTdAbFzWKDIt4DV4q0tChoWk0tu6sy+QTR2L1mCSmpvuaKt86vyhVQ9Cj0GJfCOSLAIKer3rSGgjkhQCCnpdKOrYDQUfQ6y6NWoJm4hg7n4TilGQPQW95rSDo9g/ZpWcute8UY4+4I/p6qqRyitEMDoFAcAQQ9OBKQkIQgICIIOgF6wYIOoIeW4h9jQ4nJNax29UqHwS9/DWCoNv/WCQlwwi6nT17QCAOAQQ9DjWOgQAEkiaAoCdNOLD4CDqCHltkEXT71eyLUYJxtBE8g/5tKZnibu/W7AGBvBJA0PNaWdoFgWwTQNCzXb/I2SPoCDqCXqYPJCjEoU2VR9Bb1h9Bj/xnhAMgkBsCCHpuSklDIJArAgh6rsppbwyCjqAj6Ag6I+iVR9D1feqPzHnE/mFaZo9deu7S4n3qsYKUOYgp7r5IEgcCLQkg6PQICEAgRAIIeohVSTAnBB1BR9ARdAS9sqCb62N+o0j7iB/EH4o0dP92Bf+IR1fdHUH3SZNYEPiWAIJOb4AABEIkgKCHWJUEc0LQEXQEHUFH0C2C7uEVezoSv3Dhwlif5l27dm0xEo+gx8LIQRCwEkDQrYjYAQIQqAMBBL0O0Ot5SgQdQUfQEXQEPXlB1+tswYIF0q1bt0gf+aVjmtcIQY+EkJ0h4EwAQXdGxY4QgECKBBD0FGGHcCoEHUFH0BF0BD0dQdezRGXtc9E6PX9Sr34L4e8ZOUCgVgIIeq0EOR4CEEiCAIKeBNWAYyLoCDqCjqBHlUYlVpT3oPu8PnwIei2L1un5k1q4LuA/c6QGAWcCCLozKnaEAARSJICgpwg7hFMh6Ai6TwGJ9QoxLUGz15rFziehOCUZjdW2DLyuLY40IugOn95lah+HdesR9NiL1unJE1y4zoEIu0AgeAIIevAlIkEIFJIAgl6wsiPoCHpsIfYlnwmJdex2tcoHQS9/jTCCbvljkaSgx1m0rky/LtifO5oLASsBBN2KiB0gAIE6EEDQ6wC9nqdE0BH02CKLoNsvXV+MEowTZ1S39KVF6dhyz0nb4fxvjyREv3Vc11zKHefz+ojDuuwIugdBr2VVeW1H65XlozBmXwiESgBBD7Uy5AWBYhNA0AtWfwQdQfcpILGmgWsJmOJe/ZMHQbd+MpcT2boKceAj6HFXlVem5VaWtxaIHSCQAQIIegaKRIoQKCABBL1gRUfQEXQEvUwfSFCIY32JkWA+cSS29UgzI+hufSgO66RG0H3VrGB/Mmluzgkg6DkvMM2DQEYJIOgZLVzctBF0BB1Bd5Or0MTaVz5xpBFBd/jEzcAIuo/aO5BgFwhkhgCCnplSkSgECkUAQS9UuUUQdAQdQUfQec3at33A24h1QQSdZ9kLdtOQ8+Yi6DkvMM2DQEYJIOgZLVzctBF0BB1BR9AR9GIKeqxZGIqq1ZoRpWfSo/4d4ln2qMTYP2kCCHrShIkPAQjEIYCgx6GW4WMQdAQdQUfQEXQEPdKfsVaCrsfW2ocinZ+dIZAQAQQ9IbCEhQAEaiKAoNeEL3sHI+gIOoKOoNcqV74WHPMVRysaN1aRprj7GkH3Jei1TJfntW/Zu/8IMWMEPcSqkBMEIICgF6wPIOgIOoKOoCPojKBH+tOX0Ah63Fe/tZ4qj+hHqiY7NyOAoNMdIACBEAkg6CFWJcGcEHQEHUEvtqD7GkX1FcfXaCwjIaig3AAAIABJREFU6NX7dezrXsMmKOhx6l9u1kOc5+J5Jj7Bm42MhEbQM1Io0oRAwQgg6AUrOIKOoMe+Uff1bu4yN/y+ZM9HHE2v7ox8sfYVJ8GaxRG0Uo2aH4ugF1vQ4/SjuH2mYLcNuW4ugp7r8tI4CGSWAIKe2dLFSxxBR9DrLp8Jyh6C3qp/Z0DQfdbMh6T5vD7qmk+C11mcdiX9pUqcnFoLOlPl491XZPkoBD3L1SN3COSXAIKe39qWbRmCjqD7FBAfchU7n4QEpCQSsdrmS4hDi5MQa1+1D7VmPqTRFyOfceK0KwuC7uuZ+ILdVmS6uQh6pstH8hDILQEEPbelLd8wBB1Bj32j7ksakT37p44v1r7iULNYNYsjst5WlU+wZnHalRVBj9M2psrbL49Q90DQQ60MeUGg2AQQ9ILVH0FH0BH0Mn3Al8jmNU6CshdrpkKrfEryFytWgjXzIXuxr9cEaxanXQh6wW42MtJcBD0jhSJNCBSMAIJesIIj6Ah67Bt+XyKToDjEErSMyF6stlEz+ye8L0Zl4sQRWUbQ7SUrx8gXax9x7C1gj1AIIOihVII8IACB5gQQ9IL1BwQdQUfQy/SBBCWtrmLtq118qWL/S4GgWxmFLtZxp6r7XGxOIXbt2lWmTp1q5ckOtRNA0GtnSAQIQMA/AQTdP9OgIyLoCDqCjqDLsBgfU63ehR3ri4eERF/D1r1fI+jWTuVtdkCCrH2MoMddbE7PzbvZrd3I6w4IulecBIMABDwRQNA9gcxKGAQdQa+7yCQkabHb1SqfUGUvlhAzgm7/aPbFKEFpjFX7BK+zOBJbuq6aHxv7mk2QdZy2+ZoZUI6RvQOzRy0EEPRa6HEsBCCQFAEEPSmygcZF0BF0nzfFoYmDj3wQ9ArXCCPo1T/VE5RGH/069nVfRvTjSCyC7nZT0Fz2a3kvu56NqfJ25gi6nRF7QAAC6RNA0NNnXtczIugIeuwbdV8jjWVu+EMSEAQdQY/VHwsk6LH4tLrufV9ncb40CG3KfesvMZgqn/ztEoKePGPOAAEIRCeAoEdnlukjEHQEHUEv0wd8ffmQ1zh8qWL/3EfQYzGKJfuhsfaUTzlBj/PFQ+s49sIUdw8Evbi1p+UQCJkAgh5ydRLIDUFH0BF0BJ1F4pr1Ab5Usf+lSeDxBkbQy2NvPqofd1X51oLOVPnKXRxBt1/+7AEBCKRPAEFPn3ldz4igI+gIOoKOoCPokf4QIejVcQU+gs5UeQQ90vXOzhCAQN0JIOh1L0G6CSDoCDqCjqAj6Ah6pL88CHrmBV0bMG/evEhlbz0Sr/9fy2h8iIvWMYIeuUtwAAQgkAIBBD0FyCGdAkFH0BF0BB1BR9Aj/V3KgKD7epbdVxwfQuxriruvOCVhb5zfKNI+Ug8S+VCkoXtDrC8JIp4p0u4IeiRc7AwBCKREAEFPCXQop0HQEXQEHUFH0BH0SH+TEHRG0P9v9N3X349I/S/BnWsR9OWWW0569eolX331lay11lpyzTXXyIYbbiiDBw+WE044QbbccsvImT/88MMycuRIefDBByMfqwdss802MnPmzFjHNj+oe/fusuqqq8p3vvMd8+NTTjlFjjjiiJrjhhagdTvXW289ueeeeyqmeemll8ovfvELWX311c0+tdS69Unmz58vjzzyiBx11FHmV6+//rocd9xxcuedd4aGjXxSIICgpwA5pFMg6Ai6rxus2HG0BAnc8PvKR9OLHYsFx+wfdwnUnppVwJ4A69jXRqvrPu810/bVOqXc18i3rzg+a2b/oEhnj1oEvU2bNkbOdTv11FPlhRdekDvuuKOmxGsV9JpO3uxgFddp06ZJly5dKobUtiuDLG8u7Wzevqj7R2ETSu2j5My+yRFA0JNjG2RkBB1Bj32D7Us+EXT7Z4Mv1r7iUDNq1uF/05Njf34g6PY+9H9fDpbk3pdYh1izUJ5l9yXo//jHP+Tkk0+WZ599VnbddVczCq4j6Dqifd1118m2224rf/jDH+Tdd9+VK6+8Ut5//305/vjjzTX1xRdfyO9//3s5+OCDpZKkffPNN0aWGxsbzaj22muvLf/v//0/+fWvfy1nnXWW+X8dtS99afDpp5+aEe9XXnlF9Nj9999fzjnnHFmyZIn85je/EW23/vexxx5rjmu9VRJRjX/aaafJ3XffLb/73e/khz/8Ydl2LF26VE466SSZMmWKdOzYUbp16yabbbaZaWeJT58+fcxpm3/Roe3XfTQ3nZXw5z//WXTtgoEDB0q7du3k6aefltdee82w1rbr9s9//tPk9Pnnn8uKK64o9913nxxyyCGG9y677GL2+eUvfyk/+MEPZNCgQS2aWqmd+uXE0KFD5euvvzZfwmgeOro9YsQI2XjjjU0NZsyYIf369TO11rZou7beemuZPn26vP322+aYv/3tb6amykBHwldZZRWZOHGijBs3Tr788ktp3769+f+GhgbZaqut5OWXXzazMH70ox+Z0XPlqz/TTY+54oorRGdu6LkuueQSw07/e7vttjP56Xkvuugi+clPfuL0ecNO4RJA0MOtTSKZIegIeuybNWTPfk36YhRaHAQ9e7WnZnWtWUmy7Um03COJ16zF/sxP+EuVBQsWGHGLspWOiTM7odx5fAi6yqjK9ocffig33nhjCwF9/PHH5eijj5bLLrvMCJ9KnUqaTmM+8sgjjeB98MEHRuCfeOIJmTNnTsUp7irZKr0qdSqc66+/vtxyyy3mfJdffrlsscUWTbL717/+1YjqVVddZZq9aNEi6dChg5FWFX09XoV2p512kmuvvVY233zzZcS1+RT3+++/X9ZZZx0jhyqeOs1bt0rtmDp1quh0cJ2q/8knnxhx1WOqCbp+abHPPvuIftmhbbz11lvltttuk5tvvtkIun658fe//13eeustk+8777xjmOtjBg899JBsuumm8vHHH8tKK61k9rvrrrtM2/T8m2yyiZnhoG1qvrWe4q5CP3bsWNlvv/3MFxAq9Sron332mfmCoLXQN/+yQf9ba6C1UBn/2c9+Jsptxx13NF8Y7L333jJgwAB57733zJcPummdtJ1/+ctflvlyRqe8lwRdv5jQL3CefPJJw+bAAw+U/v37y69+9StTf237+PHjzeMNhx9+uLz00ktRLiv2DZAAgh5gUZJMCUFH0GPfrPmSRsTBfon7Yu0rDjWjZoygV+4DGXjNWqzF7xIW9DhfYrSeVVDrSLyOQvbu3VtmzZplJDLKVnoGXQVdBUmFtFOnTsuMEKuUXnjhhWbKuI6o6/bd735XOnfu3HQ6Fejbb79dFi9eXFHQL774YiObKmgq+Tqaql8AbLTRRrJw4UIjz6XRaB05V/lXqd9zzz3Nf6+wwgrm/Do6XXq2/KOPPio74lppZFnPocevvPLKVdtxww03mJFm/eJCNx21VymtJug62q4CqyPmuunI/2qrrWZGpFXQd955Z/Nlh2464vzoo4+aEXUdgVZ2zTcdnf7e974nTz31lPmdiq0KbOutUjt1FPqmm24yYr3vvvua+upmE/QzzjhD9thjD9Evkrbffnt54403zHGjRo0yMyV0tsNjjz1mRuL1CwcdoV9++eXlmWeeqSroY8aMkf/+979ywQUXmHj6KIV+GVT6gqZ0Xu2L+gWFnost2wQQ9GzXL3L2CHoVZAk8r6lnq7sQ+5I0X3Fa3fTF5pNQHGpW4RpJ4PrwVXtqRs1iCaivzzQE3X4v4olRa0GP+4730ki8jtDGFfTmU7ObA2g9hVtHY3XkXEeedRRVN5VVleg11lijBbvmU9x1ATIdEdXz6L/1/m348OFG0M8//3xRKdMvKHSUVkdidWuek44uP/DAA2YUWkeWdVq6tlXzsC1gV22Ke+m5+2rtUCGvJOg6Klyafq4irUKpMq4j3ppbuef4VdD1OJ11oJt+KaGj83PnzpVJkyYtI+i6z+mnny666Jt+WaCj2tp2V0HX/XTEXdnq1PIzzzxTDj30UKugl6a7q0zrtHcdBdftvPPOa/ryRb9cULnW0XnNX6ej6zT21o83NB9B11F9/RKmJOg6Q0DbVRL00nlb9wH7hckeoRJA0EOtTEJ5IegIemwp8nUzm5BYx25Xq3yQPWQvNNmLlQ/Xmf2vqK/PNE/yWfrs0X+Xnvcv/be9MS33aD1V3kcf8v3ZGKdt5QS9ljhJC7pOMVcx1hFZfa743//+txk9V9HUZ43PPvtsUzgd6VVp1qnhlVZxLz2HrlPNdeRYY+vIrE57P/HEE02ckqCrIK655ppmpF1XA9dp4DolXEew9RnuCRMmmBF1nQqtz0eXViUv9SJXQa/UDhVInVGgXxDo8/A6O0Gf/9bzH3PMMWaKukq8jlLrlGwd+dUR5Z49exop1qniKu/PP/+89OjRw4yglxN0nXZeboq7Pouuz+vrlHXlrfe+5bZK7VQu+gWDbspYZzioHGsuOuW+9EhA6ynuLoKuX85ovfXxjiFDhpgp/SroOotDp6zrlzm6tZ7i/tOf/tTMBNBa6XR3nRWhz6lXe6Y/6ucG+4dDAEEPpxapZIKgI+ixRdbXzSziYL/WfbH2FYeaUTOmuFfuAwh6rOujFrEuPYMedyG90nFJCrqKr0qUPluuUn311VcbWVd51eetVapnz55tRo91Wre+3ksX+qr2mjV9RltH3a+//nojr7rwmsq6SmpzQVfp04XTdEq6yq8+T33YYYeZZ6n1lWn/+te/zP66uJwy0H8331wFvVI79LyVFol78cUXRWVT99HRY13srjQqr19Q6Ir4Oo1ef6ZT5HUxuEqCrnnqInE6s0CFXqfea9v1ywnddIaBjnyXptq37qitn0HXLzR0CrqeU0e0VfT12f3JkyebGukCfzrdvG3btmUXiXMRdJ0loPtpjjqjQkfTVdC1vXvttZd5xv7HP/5xxUXitA0q5foFSGmROEbQ7R9BWdsDQc9axWrMF0FH0BH0Mn3Al8jmNQ6Cbv/kDa321KyuNYsjn3pMkRaJi8MopBF0ewdjjxIBffZaZVJH0NPadIq/jsjrwnv6WAAbBLJEAEHPUrU85IqgI+gIOoIuw2J8mPAMenVoCLq9U4XGKMF84sgngm7vQuUEPdb0/f+rfS0j6PZs2aNegq7T0HWmgM4iqDR6TnUgEDIBBD3k6iSQG4KOoCPoCDqC3qwPJChptYhD8ym8seJoE/lSpW5fqvgS9JBqX/oCIVZOnh4DQNATuDEkJAQgEBwBBD24kiSbEIKOoCPoCDqCjqBH+kuTgOj7lj1f0ugrDoLeqoch6JEuOXaGAASKTQBBL1j9EXQEHUFH0BF0BD3Snz4EPZ2R+ILNeojzJQYj6JGuXHaGAAQySgBBz2jh4qaNoCPoCDqCjqAj6JH+hiDoCPq8eYZB3f9+eH4khWfQI30SsDMEIJASAQQ9JdChnAZBR9DrfoNVsFEiX1Nm6xqHmtk/wj2LA8+gu31W50oauc5Sv84QdDty9oAABNIngKCnz7yuZ0TQ3W76YktsqxusXN08+hIQbkLtnwG+WPuKQ82oGe9Br9wHuM6yd32wiru9ZuwBAQjUjQCCXjf09Tkxgo6gx/7ygZtQ+0Xri1FocRD07NWemlEzvlSxfqnCCLr9MmEPCEAgfQIIevrMUzvjG2+8IfpP8+3RRx+VYcOGSfv27eXDDz8UaRsxnSUia6+9tlx66aXmQI317rvvRo+jBzeLldc4NTHyxTq0OIHXnppV+EwI+HqlZtQs1t8hPhvtNwChMfKcz8CBA+XCCy+U0aNHyx577GHnwR4QyCGBJUuWyPz586V///7mHp+t/gQiCXrfvn1l4cKF9c+aDJwILFq0SD744AOnfdkJAhCAAAQgAAEIQAACECgmgeuvv16OOOKIYjY+sFZHEnSdGrtgwQLp1q1bYM0gHVcCjY2NZlf9hmzFFVd0PWyZ/V5//XVZfvnlZd11140dQw8MLU6IOYXGKLR8qJn9EqRm2WNEzahZrX9f+Wx060O6l4rJZpttZj+APSCQQwLPPfecHHnkkTJt2jTZaaedctjC7DUpsqBrE0ury2avuWTcpk0b+frrr2XWrFmy9dZbxwbyne98R7p27VpzXwgtjgIJLSfysXdTGFVnFBofrrPs9WlqRs3y+Df/u9/9rnlMr9Z7InvvYA8IhEugtD4V10E4NQpG0B9++GHZbbfdZOLEiaLPBNm25ZZbTs4880w566yzbLvW5fea19lnny06Yt29e3eTg7Zr8uTJ8uWXX4qKcj02BN1OPTSZIR9qVuuXoqH1IWQve32amlEzBN3eB9gDAlkkgKCHVzUEPaGaZFXQdXT9ggsukAkTJpj1BvQP8uDBg+WUU06RFVZYoYmW7YZ/8eLFZuEV/TZu5syZ8uabb8qAAQNk0qRJLYjb4ujOLjm5xPGVk0s+LjezoeWTNuss1iy02vvqQz5rn1dGvtrlk7VL/bnOav875MLZ5TM/7dq75OSrX/tilHY+pRH09dZbT1ZaaaWE7ggJWyKg95RTp04FSGAEEPTACiIiwQj6N998I1988YWZXtxcBCshYwQ9XmeyjaAff/zxMm7cOBk0aJDsuOOOMn36dCPV+vMrrrjCWdB1NciGhgbp1KmT9O7dW6ZMmRJb0F1ycrkJ9ZWTSz4uN0ah5aM5u7TNF2tfcVxYu7QrzTi+au8rjs/a+2Ltq22+4vhql0/WLm3jOqv975ALZ5fPj7Rr75KTr37ti1Ha+ZQEXdqJSH0mNsa7mcvAUQ0dGlpkWVrDqtYZYRloeuZSRNDDK1kwgh4VDYIeldj/9q8m6HPnzpVevXrJkCFDZMyYMU0nGDp0qFx22WUye/Zs6dGjh/m57abv888/N891de7cWb766iuzf5wRdNecbPlozj5ycs0nLUY+83GN5Yu1rzg21q7tSiuOr77oM44rI1vNXOPYWPtsW2jXvSsjG2tXRr7i2Grm2q604rjyseXjM44ro6zVzBcjVz4+a9Yk6L8UkfXi3VdxVHkCS89c2uIXutC0bgh6eD0GQQ+vJqkJ+qeffiojR46UW2+9Vf773//KyiuvbJ7N1unTv/71r6XSM+ivvvqqedf2gw8+aOTyRz/6kXkHt65u2voZdJ0aNXbsWPMc+4svviht27aVXXfdVUaNGtW0OufLL78sG2+8sRHOE044wVRER+91VXN9LZmuZLjpppuan+vU7G233Vauu+46s7qh7qex7rnnHhNfp3TpKPExxxwjv/nNb0S/NChtrlPc33vvPdlrr73MB9Y//vEPc74kt2qCPmLECNM+zUXbVdr0OXr9YNXfaw1d/jg2b0Mtgu6ak8sNjY+cXPNJi5HPfFxj+WLtK46NtWu70orT+vqu5frw0ac1hisjW81c49hYh8bIZ7tcY9lYuzLyFcdWM9d2pRXHlY8tH59xXBllrWa+GLny8VkzBD25O04EPTm2viMj6L6J1h4vNUHXBdJuvPFGOe6446Rnz57yySefyLPPPisfffSR3HTTTWUFXYVZR3TffvttI/EqjXfffbe89dZb8tRTTy0j6D/96U/ljjvukJ///Oey3XbbicrvlVdeac715JNPGjHXrUuXLrLDDjvIbbfdZv7/6aeflq222sq8NkyncWuOuukz1PrstT6Lrcd89tlnZkT40EMPNRKv+99///1y1113yRlnnCF//OMfmyriIuj6RUW/fv0MgwceeCCVV3xUE/T+/fubUXJ9Xrz11rFjR8Po3nvvNb+KcgNRi4C45hQlH80/bk6u+aTFyGc+rrF8sfYVx8batV1pxfF1M+szjisjW81c49hY+2xb81ghXPeujGysXRn5imOrmWu70orjyseWj884royyVjNfjFz5+KwZgl67SFSKgKAnx9Z3ZATdN9Ha46Um6B06dJCf/exnLZ5jbp5+uRH00047Tc4//3z529/+JgcccIDZfenSpaIifvvtt7cQdB2ZP+SQQ8wI/cEHH9wU+rXXXpPNN99cfvzjH8tf/vIX8/MjjjjCCLGKvo5663RulettttlGNE/9wkC3fffd14yo66h76dwq6Toy33zT57VV9vULgdK7xW2CrqPUKue6v+ay/vrr115NhwjVBF2nr2s+urBb601fyaarz+sUNJ9/HG03Iq452eL4uoFwzSctRj7zcY3li7WvODbWru1KK46vvugzjisjW81c49hY+2xb81hxBd1nu1xj2Vi7MvIVx1Yz13alFceVjy0fn3FcGWWtZr4YufLxWTME3eHGMeYuCHpMcHU4DEGvA3TLKVMTdB39XnPNNeWvf/1rWRktJ+ibbbaZGel86aWXWjTj3//+t/zgBz9oIegHHnigPPHEE2YEuPWmQq6d75133jG/uvrqq+XYY481srnFFlvIT37yE/Pz7bff3sj6G2+8Yaaza74HHXSQ/PnPf14mpub18ccfmxXGdVRZR+313Do7QLdqgj5jxgzZZ599RFcNve+++2SdddZJrWdUE/QNN9xQdKT8scceWyYfXTBOZzKUvqyIcgMR96ZYk3DNKUo+GjduTq75+LyBqNY2n/m4xvLF2lccG2vXdqUVx9fNrM84roxsNXONY2Pts23NY4Vw3bsysrF2ZeQrjq1mru1KK44rH1s+PuO4MspazXwxcuXjs2YIenK3nwh6cmx9R0bQfROtPV5qgl6aeq7PbasU77777make+eddzatKCfo+pz6nnvuaaaQN98+/PBDWWONNVoIuo6S62h3tU1lWqel6/Pjm2yyiXkOXafO6/Pnf/jDH4ygq/hrHJ0WryPq1157rZHv0nb99dfLRRddZORe4zXf9NURu+yyi/lRNUFfbbXVZK211jJT69u3b197FSNEYAT9f7Di3qjX4xv+ajdrPvNxjeXr5tFXHNvNmmu70orj62bWZxxXRraaucaxsfbZtuaxQrjuXRnZWLsy8hXHVjPXdqUVx5WPLR+fcVwZZa1mvhi58vFZMwQ9wg1kxF0R9IjA6rg7gl5H+BVOnZqg6/l1VW993ZbKuD67rSPVOpI9fvz4mgVdR9t1tV4dHa+07bHHHk0LuenotY4Kq5jrc+4qy9///vfNFHd9D7guanfSSSeJLlLXrVs3E/KWW24xz5/vtNNOZkVyfR5dp4Rrxz711FPlX//6l1mUTrdqgq4L4+l7xvU8J598cqq9gmfQaxP0ejwjV+1mzWc+rrF83Tz6imO7WXNtV1pxfN3M+ozjyshWM9c4NtY+2+ZD0H22yzWWjbUrI19xbDVzbVdacVz52PLxGceVUdZq5ouRKx+fNUPQk7sFRdCTY+s7MoLum2jt8VIV9NY3Sjr1XKX3lVdeEX0/4m677WZWYNcF5XRT6dZRah3xbr6Vm+Kuz4ur+L///vtmATPbdvjhh8tDDz1kVi/W58/1ywN9Hl1XidfReRX0Z555psXrIHQqvE5n18XrdHS/tJWmzLsKur7vXd/1qZJ+ySWXmFXq09qqCfrpp58u5557bsVV3PX355xzjkk1yg1E3FErPY9rTlHy0bhxc3LNJy1GPvNxjeWLta84Ntau7Uorjq+bWZ9xXBnZauYax8baZ9ta/93RNkR95aPPdrnGsrF2ZeQrjq1mru1KK44rH1s+PuO4MspazXwxcuXjs2YIenJ3nwh6cmx9R0bQfROtPV4qgq6Src9rq/g23/SVXbr6ub7OTH/fWtB1VHr06NFOi8TpAnC6CF3rV6+VzqfPTzd/1vuqq64yq7Xrauz6jy5Ep9t5551nXuOmo/Eq5Ndcc01TyjolX1+xps+yr7rqqubnumicvhpNZd5V0HWxtRVWWEGOPvpo84WEvhpO3z2exlZN0PUZel2pvdJ70HWWQekZ+yg3EHFlWHm45hQlH40bNyfXfHzeQFRrm898XGP5Yu0rjo21a7vSiuPrZtZnHFdGtpq5xrGx9tm25rFCuO5dGdlYuzLyFcdWM9d2pRXHlY8tH59xXBllrWa+GLny8VkzBD25O08EPTm2viMj6L6J1h4vFUH/4IMPzIJouhL7lltuaZ6/fv75582K7vp+bf1QfvTRR5cRdF0VXfdXIdZ3lutCczpFvtxr1nR1dx0Vv/nmm83q6Prsuj7rrVPU9f3iOn1dnx8vbXp+HaHXTYV86NCh5r9Lo/P635MmTTIjLaVNj9fn0fUVbfpv/VJh8uTJZlV37dxRBF1FWRei0xXg9Tl3fR3cr371q9oraolQTdD1UP3SQr+80Lx0Kv/06dPNlwilRxFK4V1uIC6//HLR2ms79YsTlX9dzE+3/fbbz8i+SxyXnFzi6Hl95OSSj+sNRGj5uLTNF+vevXtL165dW8xSKdd9Q2LkwifN2vvq067Xvkvt88rIZ7tcYrmwdqk/15mfv0M+Pod8XmcutXf5G+vSF9P8TEs7HwQ9udtOBD05tr4jI+i+idYeLxVB1ynd+qz3gw8+aG7Gdfq4vldcJe13v/ud6AdkuUXitHmNjY1mCrhOR9cbFp2CrkK97rrrLjNarpKu08111Fvfsa7/r8+Jq2gec8wx5pnz5lunTp3MO791ZFifQ9dNR1j0OXRdzG7+/PnLrDivC8vpPzolX49XkdW4+oVAVEHX86m8HnXUUeYd8fos/i9/+cvaq1olgk3Qtf06a0Gn3+sr6pSfPjM/fPhw0WOjCHr37t3NFyTlttKjDC43oS45ucTRPHzk5JKP6w1NaPm4tM0Xa70mXQQ9JEYufNKsva8+Xfrss137LrXPKyOf7XKJ5cLapf5cZ37+Dvn4HPJ5nbnUXh8XtPUjl76Y5mda2vkg6MndciLoybH1HRlB90209nipCHrtaRLBFwGboLuex/ZHP6txXG9EXNqXV0ahtYua2XsjNcseI2pGzXRAo9YttH4UWj4Ieq09rPLxCHpybH1HRtB9E609HoJeO8NMRUDQ7eUK7QaCfKhZrTfqofUhvlTJXp+mZtTMZcaTjVJon0UIuq1i8X+FWPrQAAAgAElEQVSPoMdnl/aRCHraxO3nQ9DtjHK1B4JuL2doNxDkQ80Q9Mp9gOuj+vXhiw+Cnr3PIWpmrxmCbmcUdw8EPS659I9D0NNnbjsjgm4jlLPfI+j2gvq6oSUO4pA3sfbVpxGH7H0OUTNqlusR9HYi8u0yO/Zis4eVQEOHhhb76NpN3bp1sy4Maw3MDt4JIOjekdYcMJKg62rl+lqx5ouF1ZwBAVIloAuw6KavedP3vsfdSnFq7QuhxVEeoeVEPvZeCqPqjELjw3WWvT5NzaiZEsjb3/zSZ6O+aWillVayF5k9aiKgX/JMnTq1phgc7J8Agu6faa0RIwu6vsNbvwFjyyYBXRVft1r/GGkclfxa+0JocZRNaDmRj/1ag1F1RqHx4TrLXp+mZtQsj3/z9W09+safWbNmydZbb20vMntAIIcEEPTwihpJ0PWd5brVOm0zPAzFyYgp7vZa+5rGS5zqrH3x0bP4ikUcalbr37e89iGus+z97aBm9pqVnkFH0O2s2CO/BBD08GqLoIdXk0QzQtDtePN6g53XdnETmr0+Tc2oWR6fZw7tM5brzH6dlQS91lmF9jOxR9IEmD4fnzCCHp9dUkci6EmRDTSuTdC//vprueCCC2TChAmycOFC0Q+8wYMHyymnnGKmtJc2lxsRl1gucRYvXiwXXnihmYI2c+ZMefPNN2XAgAEyadKkSPm4xHG5oXFpVxbj+OKjbXdh5Kv2abL2xciFj0u7fOVDzeyzMKhZ9hiFVjOf16uvWL4Y+Yrjq12ucZpWcWeRuFh3ra0XgosVxMNBLEBXG0QEvTZ+SRyNoCdBNeCYNkE//vjjZdy4cTJo0CDZcccdZfr06UaE9edXXHFFJCF2ieUiafqMWENDg3Tq1El69+4tU6ZMiSXoLnFcpMilXVmM44uPtt2Fka/ap8naFyMXPi7t8pUPNbPLJzXLHqPQaubzevUVyxcjX3F8tcs1Dq9Zq+2GtfWr1GqLFv9oHsGNz06PRNBr45fE0bkSdF3AThf7WHHFFZNglYuY1QR97ty50qtXLxkyZIiMGTOmqb1Dhw6Vyy67TGbPni09evQwP7fJlWssWxw91+effy7vvvuudO7c2aywrsfEGUF3iWNrm2u7shbHlbOtXfp7V0a+am/LyTUfWxxfjELLh5r976OuWn+kZtljFGLNfPwNKv1h9hHLFyNfcXx9xkaJg6DXdmuLoNfGL5SjEfRQKvFtHqkIuo7A6ojsPffcI48++qgZkV20aJFss802cskll5h/lzadJjV27FiZOHGivPjii6Kvdtt1111l1KhRstlmmzXtV4p59913m1Hea6+9Vl5//XUzDXrLLbc0U7R1xPfll1820q6jr7vvvrtcddVVTTH055dffrlcffXV8tJLL0m7du1kjz32kHPOOUc22mijpv0efvhh2W233eRPf/qTec2c5vzf//7X5KP/rb/LylZN0EeMGGE46yJJOmJd2nQFaP12Un8/cuRI682s7uAay0XSmrOtRdBd4thu1F3blbU4rftvJc62dtWj9racfNXMF6PQ8qFmdvmkZtljFGLNfPwNKnevEffz2hcjX3F8fcZGiYOg13b3iqDXxi+UoxH0UCpRJ0FXcdY/JAMHDhR9PkjlWIX3ySeflE033dRk9dOf/lTuuOMO+fnPfy7bbbedvPfee3LllVfKJ598YvbbeOONzX4lQd9iiy3MyMcRRxwhyy+/vBx00EHyz3/+03whsP/++0v//v3N+75VOu+66y557rnnmlqvI8P6ZYCK+wEHHCBvvPGGGSnWd2Hqs87du3c3+5YEXadXf/TRR3L00UebUfpLL73UfNHw6quvSocOHcKrbpmMqgm6stJRcn3Gu/XWsWNH2WqrreTee+81v7KJtWssWxzXP7S+4tja5tqurMVx5Wxrl/7elVHWauaLkSsfF9a+bvhdc6JmtX82UjP73w8fjFz7dJrXmY92lbvRiCvovhj5iuPrMzZKHAS9tltXBL02fqEcjaCHUolv80h1BF2Fd86cObLaaquZDJ555hkzpVrl+Pbbb5dbb71VDjnkEPPvgw8+uCnL1157TTbffHP58Y9/LH/5y1/Mz0uCrj/XUfOVV165af+f/OQnZvT92WefrUj8P//5j3z/+9+XvffeW+68804j97r9+9//Ns9eH3rooU3nKgm6rvKpgr/66qubfZ9++mkjrTpSr89fZWGrJug6fV2/eFCerTd9P6g+QqBT2VxuaFxj+brh9xXH1jbXdmUtTpQbGhtrV0a2OL5ycs3HVrO85qPtcmVEzWr/bGzej+KKFTX7H8Vq/dG1T9vi+Lruk4qjceP2I1+MfMVJilE1Pgh6bXeuCHpt/EI5GkEPpRLf5pGqoOvU8dNPP70FBZVuFWAdmVY5f+KJJ8wobutNR8i1A73zzjvmVyVB1ynmw4YNa7G7jp7/9a9/NVPqd9ppp7LUzz//fDnttNPkkUcekZ133rnFPjqirqP1H374oRH3kqDr/ueee26Lfdu3b29WOb/ooovCq26ZjKoJ+oYbbig6Uv7YY48tc6R+afH222+bRwZcbmhcY/m64fcVx9Y213ZlLY7PGyNXRlmrmS9GrnxsfchXPhrHNSdqVvtnY/O6xRUravY/itX6o2uftsXxeZ35qr2vnHwx8hXHV7uixEHQa7t1RdBr4xfK0Qh6KJX4No9UBV1HyQ888MAWFH7zm9+YqeL6TPeee+7ZYgp6OVz6jLpKc0nQdfR73333bbGrPk+uU6702ekuXbqYZ9h1Hz23Cqpuxx13nHkeXRcfW2uttVocf8IJJ5hRcZ3qrcJaEvTx48fLscce22JfnRWgz6DrM/NZ2BhB/1+V4t4Y+xopCC1OlBsam6S5ts0Wx1dOrvmkdaMeWj7abtecqBkj6JXeX+7ah4p8nYUm6L5q5iuOr8/8KHEQ9NruXBH02viFcjSCHkolAhX0H/7wh2bFbl20rdKmi7jpM+UlQX/ggQdEj2u96bPt+rsHH3xQHnroITPdXadp64j5qquuGkvQdZE4HS1vvqmg6xcAzd/JHV6Zv82IZ9BrE3Rfz9qFFifKDY1N0lzbZovjKyfXfNISh9Dy0Xa75kTNeAa9kqC79qEiX2ehCbqvmvmK4+szP0ocBL22O1YEvTZ+oRyNoIdSiToJum2Kuz47rqPV77//vpm6Vm2zCXrrY3XxtxNPPFH+/Oc/yy9+8QupNsVdvwTQKe4ffPBBiynueRd0ffxAp/BXWsVdf681dLnBco3l64bfVxxb21zblbU4UW5obKxdGdni+MrJNR9bzfKaj7bLlRE1K/+Giyifjb4kjZpVn+Luyiet697X50e5+6K4M8J8MfIVJylGPIOenHwg6MmxTTMygp4mbbdzpTrFvdIicbrauj4zrgvA/exnP5MzzzxTzjrrrGVaoM9Ar7POOubn1QRdp62vvfbaLY6fNm2aedb8wgsvlJNOOslMpdcF5nTqu64aX1okbsaMGbLDDjvIYYcdJjfeeKOJ0fw1a3keQddn/3XRu0rvQddF8Xr27Okk6K6xfN3w+4pju1lzbVfW4vi8MXJllLWa+WLkysfWh3zlo3Fcc6JmQ2TMmDFN6PVNIPrlb5TPRl+CTs2qC7ornzSvM1+193Xt+2LkK46vdkWJwwi6myxU2gtBr41fKEcj6KFU4ts8UhX00mvWdBE3fc2a3tgsWbLEjFbrO8X1veSHH3643HzzzdKvXz/zTLqu+K6vMfvHP/5hVl2//vrrrYKuU9n1ufI+ffqYZ9Dfeust87y5vrJNV5HXd3rrVnrNmo6Y65cE+sy55qQrmZd7zVreR9CVSenZfK2RLrCn75jX5+v12Xt9Br+0udyou8RyiaPn1Ffy6YyGb775xnyBo18klNYz2G+//URfgVdp6mXzy84WR7+AsOXk0i6Xm77Q4rhwduHj2o9snEt1C6lmvhj5qr2vfKhZetc9NXN7zVoer3uftfcVy9dnka84vtrlGgdBr01MEPTa+IVyNIIeSiXqJOi6qro+Az558mQzjX2bbbaRiy++2LzvvLSppOsz6Ndcc415blz/v3PnzkYWjznmGPMKNN2qjaCrSN90003mNW4qdfoBrMf//ve/N4shNT+X3gSovOvCcu3atROV9VGjRslGG23UtF9RRtC1wToVbPTo0TJhwgTR19spe501MHz48KYF9lzk0zWWq6Tp7Av9oqbcpl8gaN9wEXRbnIEDB1oF3Rej0OIoWx980q59mjXzxchX7X3lQ83Su+6pmZug+/gsCvE689Gu0t9BH7F8MfIVx+f14cIHQa9NTBD02viFcjSCHkol6iTolRZ0Cw9LfjOqtkhclFa7irUtZmhxXL98sLWLOHZCvmoP6/RYU7PssaZm1Mzly2sbJV/9KLQ4CLqt8tV/j6DXxi+UoxH0UCqBoIdXiZQyQtDtoEO7gSAfaqYLN9ayhdaH+FLFXk1qlj1G1Cx7NUPQ7TWrtgeCXhu/UI5G0EOpBIIeXiVSyghBt4MO7SaLfKgZgl65D3B9VL8+fPHhS5XsfQ5RM3vNmgS9nYi0se/PHi0JNHRoCALJggULpFu3buYtRGzRCSDo0ZklfUSqi8QxxT3pctrjI+h2Rr5uaImDONR6s5DXPoQ4ZO9ziJpRszxOlS8J+nrrrScrrbSSvcjsESwB7Z9Tp04NNr+QE0PQw6tOJEFv27atfPbZZy0WCwuvSWRUjYAu5KLbCiusIMstt1xsWKU4Kvy1bKHF0baElhP52HsYjKozCo0P11n2+jQ1o2ZKIG9/80ufjbNmzRJ9AxAbBIpIAEEPr+qRBf3LL78000jYskmgsbHRJF7rt8UaRyW/1r4QWhxlE1pO5GO/1mBUnVFofLjOstenqRk1y+Pf/Pnz55u3BSHo9v7NHvklgKCHV9tIgl56f3it0zbDw1CcjJjibq91XqcV57VdWtG8ti2v7aJm2fscombULM9T3BF0e/9mj/wSQNDDqy2CHl5NEs0IQbfjzasU5bVdiEP2+jQ1o2Z5lL3QPmO5zuzXGc+g2xmxh0jen29H0MPr5Qh6eDVJNCOboH/99ddywQUXyIQJE2ThwoXmQ2nw4MFyyimnmCntpc3lRsQlVmhxXG5oFi9eLBdeeKGZEjdz5kx58803ZcCAATJp0qQWtbO1zVccF85ptkvP5ZKTjY/GCY2RS7tcWPuKkzZrl5r5altotfeVj684rteHS8185UTtv2P+ZlabZejCyKVmLnFcPot81T6rcVjFPdFbzroG97XCfBFWiEfQ69pVy54cQQ+vJolmZBP0448/XsaNGyeDBg2SHXfcUaZPn27EU39+xRVXRBJ0l1guNyJpxnG5odFn1hoaGqRTp07Su3dvmTJlSixB9xXHhU+a7dJzueTkUvvQGLm0y4W1rzhps3apma+2hVZ7X/n4iqO1d4nlUjOXOGn267zm4/N6De0681WztOPwHvREbznrGtzXO9qL8Hgvgl7XrppvQddFPpYsWSKrrLJKeJQDyqiaoM+dO1d69eolQ4YMkTFjxjRlPXToULnssstk9uzZ0qNHD/Nz202fa6zQ4ri07fPPP5d3331XOnfubFZ81zbEGUH3EceVc5rtcs3JVnvNOSRGru2ysfYVR8/jGssXa1sc13xsjEKrva98fMZxjWWrmWscW82ofXh/F20181X7LMdB0AO6QfWcCoLuDhRBd2eV1p6pjaB/8skncv7558stt9xivvVfffXVZcstt5Q//OEP0qdPH9Pe++67T66++mp58skn5a233pIOHTpIv3795LzzzjOrjpe20jesI0aMkO9973tmSvYLL7wgo0ePlmHDhqXFLpPnqSboynPUqFFmep6OEJc2XQFav0HU348cOdJJ0F1j2W4e047jckPTvPC1CLqPOK580myXa0622re+wOKyds3Hxii0OJqva06+WNviuOZjYx1a7X3lk1QcjVvp+rDVzFdO1N4u6K6MbDVzjZPWdearD9UjDoKeyVtZp6QRdCdMZicE3Z1VWnumIug6st23b18j3occcojsvPPO5n3qjz32mJkirH9sdDviiCPkww8/NFOr9UNTpVuFXacS6+jtyiuvbPYrCfoWW2xhRF6neuk+Kuu77bZbWuwyeZ5qgt6/f3/DWZ+pbr117NhRttpqK7n33nudBN01lu1GJO04WbuhceWTZrtcc7LV3tfNmms+NkahxdF8XXPyxdoWxzUfG+vQau8rn6TihCDo1N4u6K6MsnadJdWv434pGyUfBD2Tt7JOSSPoTpgQdHdMqe6ZiqDrqOsZZ5xhpkmfcMIJLRqoU9OXW24587NPP/10mSnqU6dOlV133VVuvPFGOfzww1sIuv4R+89//iMbbbRRqtCyfLJqgq7T11dccUWz+Fnrbeutt5Yvv/zSTKl1ubl2jWW7EUk7jkvbmrNJ4waiGiNXPmm2yzUnW+2j3GSlwci1XTbWvuLoeVxj+WJti+Oaj41RaLX3lU9ScUIQdGpvF3RXRlm7zpLq12n8fUXQs3xHWz13BN29toygu7NKa89UBL1nz55mZFynSi+//PLWtqm0f/zxx/LFF1+YfTfeeGOzaNnFF1/cQtD3339/ueOOO6zx2OFbAtUEfcMNNxQdKdeZDa03ndXw9ttvy8svv+wk6K6xbDciacdJSxx83dC48kmzXa452WofGiPXdtlY+4qj53GN5Yu1LY5rPjZGodXeVz5JxQlB0Km9XdBdGWXtOkuqXyPo3L3WQgBBd6eHoLuzSmvPVAS9bdu2svvuu8vdd99dtV0vvviinHbaafLAAw+Y1ys131TQr7nmmhaC/tvf/lYuuuiitFjl4jyMoNc+O6B5R0jjBiKN0WGfN1i+Rol85eSaj00aQ4uj+brmZLvhd2Vti+Oaj421az62OKHl46td5f4Y1fsZ9NBYh5aPz+s1xLaF9HcxynXGCHoubm3LNgJBd68tgu7OKq09gxH0jz76yDxDrtOoVbw333xzadeunZn+fthhh8k+++zT9J7p5ovElRYtSwtY1s/DM+i1P18f0o2I6zONNpGJckNjkzTXnGxxfOXkmo+NUWhxNF/XnHyxtsVxzcfGOrTa+8onqTgat96CTu3tI+iujLJ2nSXVr9P4AhxBz/pdbeX8EXT32iLo7qzS2jMVQXeZ4v73v/9dDjjgACPh+sqq0qYLzKmo//znP0fQPfSKaoJ++umny7nnnltxFXf9/TnnnGOysN1AuMYKLY5L20ISdFfOabbLNSdb7X3d9LnmY2MUWhzN1zUnX6xtcVzzsbEOrfa+8kkqTgiCTu3D+7uY1nWWVL9G0D3c9BU4BILuXnwE3Z1VWnumIugui8RNmTJF9t13XzONXaezl7bSsc3fM80IevzuUU3QdQV3Xam90nvQn376adEvW1z+6LvGst3wpx3HpW0hCbornzTb5ZqTrfa+bvpc87ExCi2O5uuaky/Wtjiu+dhYh1Z7X/kkFScEQaf2dkF3ZZS16yypfo2gx7/X40gRBN29FyDo7qzS2jMVQddRcH3XuXaA0mvWdCq7LkamQqjfvC9atEg22WQTM01v6NChZrGyhx9+WB5//HHRd6gzxd1Pl6gm6HqG4447Tq666irzJclOO+0k06dPl4kTJ8qxxx4r48ePb0rCdgPhGiu0OK7icPnll8sHH3wg33zzjZx55pmmHx944IGGz3777We+yHBpm484PmvmIx+ftddYPnLyxSi0OGmz1tdidu3a1cyyqbT5YhRa7X3l4zOOSyyXmrnEcflMo/bf8XJ9uPztCI21rz6UdhymuPu5twwxCoLuXhUE3Z1VWnumIujaGF2VXadH33rrrbJw4UJZY401RF/dpa9fUxHUTTvIySefLDNnzpQVVljBvNNcF4HTf+ur1nT6u26MoMfvHjZB1y9IRo8eLRMmTJDXXntNOnfuLIMHD5bhw4eLHlvaXG4gXGKFFsdV0Lt37y6vvvpq2ULoFxoDBw50EnQfcVw4p90ul5xcaq95h8TIpV0urH3F0XO5xPLF+phjjrEKiEs+LoxCq72vfHzGcYnlUjOXOC6fadTeLugujFyuV5c4aV5nvvpQ2nEQ9Pj3k6EfiaC7VwhBd2eV1p6pCXpaDeI81QnYBN2Vn8sNhEus0OK43tBksW2hsfaVDzWz90ZfrH3FoWbUzDYTw07IPqXcJUae+2Ke2+brswhBd71Ksrcfgu5eMwTdnVVaeyLoaZEO5DwIur0Qvv7wE6c6a198uAnNXp+mZtQMQa/cB/hsTO/6QNDtrLO6B4LuXjkE3Z1VWnsi6GmRDuQ8CLq9EL5ujoiDoFd7VtveE/M7Qoig26sf2ucHNaNmefxSpUnQ24nIt0/x2YvNHsETaOjQ4CXHBQsWSLdu3aquveLlRHUMgqDXEX6FUyPo4dUk0YwQdDve0G6MyYea5U30kb3s9WlqRs3yLOjrrbeerLTSSvYis0chCWjfnzp1am7bjqCHV9pIgt62bVv57LPPWiwWFl6TyKgaAV1YRjddhG+55ZaLDasUp/nCcXGChRZH2xBaTuRj71kwqs4oND5cZ9nr09SMmimBvP3NL302zpo1yyxczAaBIhJA0MOremRB19ej6VQPtmwSaGxsNInX+m2xxlHJr7UvhBZH2YSWE/nYrzUYVWcUGh+us+z1aWpGzfL4N1/fCrR06VJB0O39mz3ySwBBD6+2kQR9gw02MC2odbpleBjSyaj0ejh9b/ZZZ52VzklbnYUp7nbsTCmvzig0PpptaDmRD9dZrX8nQ+tDXGfZ69PUzF6z0jPotQ5a2M/EHhAoTyCE6fMIeni9E0FvVhO9obr22mvlgAMOkC233DJ2tS6++GJZc801zbuwm29ZEPSvv/5aLrjgAvMedH1fvX5w6HvQTznlFDNiXtpcbh5dYoUWx+WGxqVdacZZvHixXHjhhWYEYObMmfLmm2/KgAEDZNKkSS36X5qsXXLylU8WWfvqQy6cXfjoPi6x0qyZSz4ubfPF2lccX+3Strvk5FIzXzn5iuPSLpfa+8rHF2uf+fiKVfQ4LBIX+1Y3kwf6WjjOV+NDWYAOQfdVUX9xEPRmLB988EHZc889ZeLEicvIdRTkXbp0kY022kgefvjhFofpNKrPP//cPMNV63NcUfJpvq9tBP3444+XcePGyaBBg2THHXeU6dOnG9HTn19xxRWRBN0llsvNY5pxXG76XPJJM07pi59OnTpJ7969ZcqUKbEF3aVtLjVzyckljks+WWTtq10unF346D4usdKsmUs+Lm3zxdpXHF/t0ra75ORSM185+Yrj0i6X2vvKxxdrn/n4ilX0OLxmLe7dZDaP8/XqNV+tD2VmMoLuq6L+4iDoKQq6v7LFj1RN0OfOnSu9evWSIUOGyJgxY5pOMnToULnssstk9uzZ0qNHD/Nz202fa6zQ4tja5tqutOLoefRLn3fffVc6d+5sFrhTpnFG0F3bZquZa062OK75ZI21r3a5crbxKV3oLv0orZr5apsv1r7i+GqXxnHNyVYznzm59CFbf3Rtly2Oz3a55mRj7YNPlOs1TUa+2pZ2HAQ9/v1kFo9E0MtXDUEPrzcnLuh33nmn7L///nLjjTfK4Ycf3oLAO++8YxYrO+aYY+TKK680v5s8ebIZqX322Wdl+eWXlx122EH++Mc/yg9+8IMWx7799tty0kknmdFClZI+ffoYqezXr5/suuuuy0zvtcXVUWIdNW69lZ4Xf/755+WSSy4xo+I69Vv/EG+//fYmN82xtJVbGX399dc3o1OVprh/9NFH5pn022+/Xd544w3RkdCDDz7Y/Gy11VZriq3/f/bZZxtR1inoN910k5mW2rdvXxk/frzoeWxbNUEfMWKEjBo1yqwx0NDw7fsjdYEp/ZZPfz9y5EhzCtuNiGus0OLY2ubarrTitK53LYLu2jZbzVxzssVxzSdrrH21y5WzjU+5z4xK/Sitmvlqmy/WvuL4apfGcc3JVjOfOTWPFfezyLVdUft13HySYl1LPqHVLMv5IOi2u8Z8/R5BL19PBD28fp64oOuq7+uuu66ZLn3XXXe1IHD55Zeb0VqdRq2/P/nkk0Wf3z7ooIOMZH/66adyzTXXGGF86KGHjITrpt+wbrPNNvKf//xHfvnLX0rPnj1NjEceeUSWLFkie++9dwtBd4mr5/jTn/4k5513nom58847m3NpbP1HBfjqq6+WffbZxzyXrV8QqCTr87767O/mm29u9r/++uvlxBNPlI4dO5obKN3atWtnnmsvJ+hffPGFaZc+O6zPrGu79L91mr2Kv7ZJb7B0Kwm6TmNeY401TEw9vzLTnz366KPWHlZN0Pv372/kX2O23rQ9W221ldx7773mV7abPtdYocWxtc21XWnF8Xlj5No2W81cc7LFcc0na6x9tcuVs41PuQ+NuIIeWtt85eMrjs+aueZku8585tQ8VlwBdW1X1H4dNx89j2tO/7+9cwGbczj/8CTaRFCSSJ3SIqIkjhHHSktIXCQRJeocdUjSBHEuSZCmcUgqKGkIglJnpZQKcqljIyiaOBdxiKAoEZLUqYn/9Zv+36/7bfbbmf129v3eefee63IheffZ57mf2d35zTwzUwnravzJWs5i9geB7hw25uoBBHrpdCLQs9fNay7QFfKIESOs0Nbq8Oqrr95AQaviH3zwgRXgTz31lNluu+3sAWUS1EnTCvFmm21mV5Uff/xx+8daYR85cqT9t/aqJW3MmDFWYBeW91Zit9wedE0WrLTSSo0yqLJiCfN99tnHXH755Q1/19Qe9FICXfu9FYP8HjVqVIONc88914wePdpODAwfPtz+eSLQBw4caO68886Ge8wvuugic+KJJ9qqg2SioKmuVk6gq3y9TZs2dsKhuOl+UE22qNTPZ2Dka8s1oEnbjis2X3/SshNyYOQbmytnvj657Pj6ExvrUBLoTTMAACAASURBVHH5cnbxKfVd0VyBnrXYQvkTyk7InPn65PqchfSp0FZzBahvXJX26+b6o/fx9akS1tX4k7WcxewPAr2p0WI+/xyBXjqvCPTs9fdUBLpWdnfaaSd7+JjEuppEedeuXRvKpk866SSjFfXXX3/dtGvXrhEpCVUJfJWCazV6jz32MLNmzbL7biUok6aS+TXWWKORQK/Eru8hcRLrWqnXoW9a9X7vvfeMOnfSKhHo/fr1syvf8r0wbtnv1KmTLV+/5557rOlEoGsVWzP6SZs9e7aRgNZ2Aon3cq2cQFc+tFIutsVNFQ6qGpg7d679K9dAxNdW1uy4YvONKy07IQdGvrG5cubrk8uOrz+xsQ4Vly9nF59S3xfNFehZiy2UP6HshMyZr0+uz1lInwptNVeA+sZVab9urj96H1+fKmFdjT9Zy1nM/iDQyw4Zc/eXCPTSKUWgZ6+rpyLQJWS1p1ml4UkZtvYyjx071papd+/e3fTv39/ce++9ZQkle6O7detmhflzzz233PMdOnSwe96TK6YqsVtOoGty4IwzzjC33nrrciXgiq3wzttKBLpi0Y96sjJdGJAqB3TdzMsvv9xIoGs//MYbb9zwaLIyr5hVPdBcge67SuAzMPK15RrQpG3HFZuvP2nZCTkw8o3NlTNfn1x2fP2JjXWouHw5u/iEFOhZiy2UP6HshMyZr0+uz1lIn0IIdN+4Ku3X1QhiX58qYV2NP1nLWcz+INCzJ0xq6RECHYFey/4V0nYqAl0On3baabaMW2Jy3XXXtaXYK664YsPKs1aStd9apdtNtV69etlV5koEeiV2ywl0Cf0ZM2aYY445xsgPTQToELuJEyfaVX/FlbRaC/TXXnvNXuOWtESg+1wPxx706vbX++5FdA0eQ9kJOTDy9amSQaj8a+5qrK8/sbEOFVfI3PvacuU+a7GF8ieUHV/Orj6tv/f1yZWzkD6FEOi+cfkwCuFPrVgj0N9wjmVDMSpnB4HuTEOuHkCgI9Bj6dCpCXTtj9aKsAStfoBVkn3BBRcYlaCr6bA4lbhrn7oOlSvXKilxr8SuDqLr27fvcvegL1y40ApylbNLBBc2lX6rxL1QoKtSQCVxxfegl9qDnpS4q1xfExZJ++KLLxpK3KdPn27/OClxr5VA1ySK8tPUKe76+3POOcf64hr0+drKmh1XbL5xpWUn5ODaNzZXznx9ctnx9Sc21qHi8uXs4lPqu7a5kypZiy2UP6HshMyZr0+uz1lIn0IIYt+4Ku3X1Yg9X58qYV2NP1nLWcz+INBjkSth/ESgI9DD9KTaW0lNoCuUHj162JJtCXRdWabrynTNmpoOgJPY1VVnV111VcMBaAkC7X/W/nK15PR3/Vsr2kkrdUhcJXafeOIJe52bTkXXoWtJW7RokVlttdXMoYceaq+BS5rK9bVHXBUBhQJdK/xt27a1J6IXtlICXYfAHXXUUcsdjqfD8k499VR7+JxOlVertUCXvzqpval70OfMmWNPtPcZGPnacg1o0rbjis3Xn7TshBwY+cbmypmvTy47vv7ExjpUXL6cXXxK/cw0V6BnLbZQ/oSyEzJnvj65PmchfSq01VwB6htXpf26uf7ofXx9qoR1Nf5kLWcx+4NAL/ULkN8/Q6CXzi170LPX51MV6Ino1N3eOrFdJeWFTaeYT5o0yd4vrn3kOiRNIl4r0drHnuxf1+qyrhXTXmydcK79YTrYTCXyOsBNV6EVrnT72pUQ1yFpKlE/5ZRT7B3kWvXXP1pZf+ihh+whd5po0L5wTSRInOt1hQJ98ODB9t533aG+0UYb2YPtdHib65o1TU4UXrMmDqWuWavVCrpyofg0KSBfVMqv6+vEUpw1mZA0n4GIj62s2fEZ9PnElaYdvZcmq1TpsWzZMtvvNNEyaNAgm6699trLTqykydrHJ32GVW1SeH5D8VdkXlmHisuHs2/ufWylmTMff3xiC8U6lJ1Qcfl+X/t87kP6FOK7KM+sQ/BJvidD2apnOwj07AmTWnqEQEeg17J/hbSdqkB/5513zHrrrWdFhE5llwgsbjqETden6WRy3RGu69W23XZbW16ucvCk6a7uk08+2dx9993WnlayJ0+ebMX9/vvvb6ZOndrItK9dPaeVaolgXSsmsaP/1wq+3k/70JcsWWK23HJLc9ZZZ5nrrrvOTiAUCnRNKkjQzpw504p3xay/LyXQ5aQOoNP73HbbbfYAOpX477fffmb8+PF2kiBptV5B1/toVl+TJLrj/d133zWdO3c2Q4cOtav52r9eiUD3seUzeEzTjo+w9vEnTTt6r/XXX9/Mmzev5HdDcjZBmqx9fBo2bJhToOeVdai4fDjru9Mn9z620syZjz8+sYViHcpOqLh8v69D5d6HdajY8sw61Hd1KNb1bgeBHlJSZN8WAh2Bnv1e+l8PUxXotYayYMECe8/6hAkTjMrdacsTKHdIXCW8fAd9LptZs+MjrF0xVTKJ4WMra4yy5g85c/cichYfI3JGzspVF7np/PeJrPWjrPmDQPftSfl4DoGOQI+lJ0cr0HVPePF96cke9CeffNKW0NMQ6M3pA1kbQOCPO4swKs8oa3wQDvH1aXJGzlxbktyEsjdhgED3yVp+nkGgI9Bj6c3RCnTtr1155ZVt+bvagw8+aK9o23vvvc0dd9wRC//U/WQF3Y08a2IGf8hZtStpWetDiL34+jQ5I2e5FuirGGP+t4vPnWyeiJJAlw5dMuX322+/bc+yqvY3vtqgOCSuWoLhXx+tQJ82bZo9tEx3kGs1XR38wAMPNGPHjrUnqNNKE0Cgu3tG1sQM/pCzan+8s9aHEHvx9WlyRs7yLNB1oxBjR3cf54nwBPS5euSRR8IbrsAiAr0CWCk9Gq1AT4lP7t4Gge5OadbEDP6QMwR6032Az0f5z0coPgj0+L6HyJk7Z0mJ+zPPPGN69uzpfgFPQCCHBBDo2UtqRQJde751xVnhad7ZCwmPyhHQ6bhqK6ywwnJ3zVdCLrFTbV/Imh0xyJpP+OPumTAqzyhrfPicxdenyRk5E4G8/eYn340IdHf/5on8EkCgZy+3FQt0XT2mcnJanATefPNN63i15VyyI5FfbV/Imh2xyZpP+OP+rMGoPKOs8eFzFl+fJmfkLI+/+br+9ptvvql6TOTuHTwBgfQJ+JbPI9DTz43rHSsS6BtssIG1V225pcupWv39NddcY+9e12BVd5HWY6PE3Z31UCWh2CnPOhQfvUsoW9ghZ9X+vuW1D/E5i++3g5y5c9ZwijuHxLlh8USLEWjO4XaVHECHQG+x1Db5xgj07OWkph65BPrSpUvNeeedZ6688kozf/58o9m3oUOHmlNOOcWumCfNZxDqYytrdnwGND5xxWhHPvvEFmPOFi9ebM4//3yjMsann37avP/+++awww4zmrQrbK7YfPj45D6UP1nMWajYssY6VFwhc+bjk6tP+/qTZr/2iaue/VHsMPq2HZ80NaHmy4dr1mo65MR4IALNuR6ukkVVBHqgRAU0g0APCDMGUy6BfvTRR5tLL73UVhrsuOOO5rHHHrMiRn9+ySWXVCTQfWz5DB7TtOMz6PPxJ0Y78tknthhzpjLGLl26mLXXXttsvfXW5u67726WQPfh45P7UP5kMWehYssa61BxhcyZj0+hPq9p9mufuOrZH8UOo/IC3ZcPAj2GkSs+ItDrrw+0mEDXXnbt+2nTpk1q1Clx/+8BL1qZKnUgyvPPP2+23HJLc+yxx5rJkyc35OX44483U6ZMMc8++6zZfPPN7Z+7Bn2+trJmxxWbb1yx2ZG/vrHFljPF9uWXX5qPPvrIdO7c2R4CqBgqXUH35ePKfSh/spizULFlkXWIPhQyZ76sQ31e0+zXoVjn1Y5v7smZ+zsfgZ7aEJw3qoIAAr0KeJG+NBWBngjj6dOn2xXZa6+91rz33ntWJG6yySZm4sSJ5vrrrzfaL9GxY0czYMAAc84555g111yzAauenzRpknnggQfszLHEvcTk6aefbvr3778c/ttuu82ceeaZ5tVXX7WD8pEjR5r27dubI488kj3oTQh0sZwwYYItGdNqY9K0Z1+lMvr7s88+20ug+9pyDR7TtuMa0Pj6E5sd+esbW2w5K/5yaK5A9+Xjyn0of7KYs1CxZZF1YWzN7UMhc+bLOtTnNc1+HYp1PdhRjNX0x3pgVI4PAj1S9VJnbiPQ6yzhxphUBfpmm21mV64OOeQQ07p1a7Pvvvua4447ztx5551mn332MX379jWvvPKKLbHW6eDaKypRrXbffffZZ/UaHfD22WefWVGvlZb777/f9OnTpyF7t956qznggANMt27drCDXLPrll19uOnXqZGbPno1Ab0Kg77777naVXPtzi5smS7baaiubB5+Bmq8t1+AxbTuu2Hz9ic2O/PWNLbac+QqZtHIWyp8s5ixUbL590ZWzUP6EtOMbm+tz5uuTy46vPzGyrgfxiUB/wzlyR6A7EfFAxgkg0DOeoBq4l6pA12q5Vs1XXHFFG8q9995rV79HjBhhRXnSbrnlFnPggQea0aNH29V1tc8//9y+rlWrVg3P6U52iUaJ+RkzZtg/V/n2euutZycAXnjhBbPqqqvaP9cKvAT7okWLEOhNCHSVr2vLgXJU3Hr27Gm0LUETIj4DNV9brsFj2nZcsfn6E5sd+esbW2w58xUyaeUslD9ZzFmo2Hz7oitnofwJacc3NtfnzNcnlx1ff2JkXcgor6vMijGvsaURFyvoNVAWmAxOAIEeHGnmDaYq0C+88EJzwgknNEA56qijzGWXXWZL23UaZ9JUvr7hhhuatm3bmpdeemk5iBLmS5YssWXuY8eONRL0CxYssM89+eSTZocddrCl2CqTLGyaCNBKOtesld6D3rVrV7utYNasWcsx14FxH374oZk7d66XQPe15Ro8pm3HNQj19Sc2O/LXN7bYcuYrZNLKWSh/spizULH59kVXzkL5E9KOb2yuz5mvTy47vv7EyBqB7j4vph4YsYKeeS2Cgw4CCPT66yKpCvS77rrLDBw4sIHyHnvsYfeka1W7uO255552v7lWztW++uorK7q1f33evHmNHteq+rJly+yf3Xzzzeaggw4y2oOucvjCpoPPNEGAQC8t0EOupPjacg0e07bjGoT6+hObHfnrG1tsOfMVMmnlLJQ/WcxZqNh8+6IrZ6H8CWnHNzbX58zXJ5cdX39iZF0P4lMxprHS7OpHsbJmBb3+hE+MESPQY8xadT6nKtC1V1z7zJNWiUBPrtw59NBD7V5Z7SfXvdxXX321ufHGG+1qOgLd3RnKneIeci+iry3Xj37adlyDUF9/YrMjf31jiy1nvkImrZyF8ieLOQsVm29fdOUslD8h7fjG5vqc+frksuPrT4ysYxWNrpz55p6c/ZcUK+jusSFPZJsAAj3b+amFdy0q0JsqcVegSYn7iy++aOPu0KGD6dGjh3nooYcacTj44IPNTTfd1CDQKXEv303KCfTTTjvN7vlv6hR3/b1O1/f50fe15RqIpG3HFZuvP7HZkb++scWWs1CDWV8+rtyH8ieLOQsVWxZZhxJ7vrG5Pme+rF12fP1Js1+HYl0PdlwC1JX/emCEQK+FfMBmmgQQ6GnSzsZ7tahA14ng/fr1M8ccc4y5+OKLG4joFPb999/fjBkzxl77paYV80033dQ88sgjDc+99tprZosttjDak56soOuQOB0ap9V1DolbvpOVE+g6wV2H7jV1D/qcOXMsb5+Bmq8t1+AhbTuu2Hz9ic2O/PWNLbac+QqZtHIWyp8s5ixUbL590ZWzUP6EtOMbm+tz5uuTy46vPzGyrgfxiUDnFPdsyAm8qCUBBHot6WbTdosKdCHZe++97TVr2i++66672nvLp06dag+N02niyTVrQ4cONVdddZXRinnv3r3tPnQ9pyvXdHVaItBlM9mH3r17d3vNmvav6zA6rlkzppxAF7vkIL0jjjjC9OrVy54RoG0Ew4cPtwyT5hr0+drKmh2fQWgoRlmzk+ecKTZNAi5cuNCeVzFu3Dg7GTVo0CDbpffaay87+eTqj6FyFsqfLOYsVGxZZB2iD4XMmQ/rrbfe2v6eqjKqqZZn1qFyljU7Prn3+U6rdzvsQc+mOMGrxgQQ6PXXI1pcoEs8q6z6uuuus6e5d+zY0QwYMMCWUq+11loNGVm8eLFdUb/99tvtie26Mk3XsL388stm/PjxjQS6XvSHP/zBnHnmmUar7J07dzYjR460ZfIS7BwSV/qQuGQmftKkSebKK6807777rmWnyZFTTz3VivtKBLrKyly2XILI16dQdnwEuk9cMdpJm3WaOVNsmswrPmAy6c+ahDr88MOdAj1U7kP5k8WchYoti6xD9KGQOfNhPWzYMKdAzzPrUDnLmh2f3Pt8p9W7HQR6/QmfGCNGoMeYtep8TkWgV+cirw5JwLWC7vtePuLKx1bW7PgIa5+4sOOmFCr3sE6PNTmLjzU5I2euCgo3ocquaytnL1R/DGUHge6TfZ5paQII9JbOQPrvj0BPn3mLviMC3Y0/1A8/dsqzDsUHgR5fnyZn5AzR2HQf4Lsxvc9Hg0BfxRjzvyJBtwM8AYEUCXTp0KXid1NVss7kKre9KTH697//3Wg7lLYW9+zZs+L34gXhCSDQwzPNtEUEujs9oQZH2EGg+/wwxrTaFKpPI9Dj+x4iZ+Qsj5MqiUBfZ511TNu2bd1J5gkIRERAn9nCw7Wbch2Bnr2kItCzl5OaeoRAd+MNJUKwg0BHoNd+lZDPGZ8zPmd8zty/7KWfSAQ6K4fNJcjr8kAAgZ69LCLQs5eTmnqEQHfjZcCfzoA/FGdW9uLr0+SMnOVxNTbUd1ooO3zO3J8zBLqbEU/knwACPXs5rligJ3sashcKHvkQ0An2atWWc8mO7prX/pZqWtbsKJas+YQ/7h4Go/KMssaHz1l8fZqckbM8/ua/9dZb9hYgVtDd/Zsn8ksAgZ693FYk0HfeeWczf/787EWBR94E3nnnHfP111/bO+HbtGnj/briBz/66CP7R7JTTcuaHcWSNZ/wx93DYFSeUdb48DmLr0+TM3KWx9/8JUuWmE8//dRcf/31pnv37u4k8wQEckhAV1YPHjzYzJw50/Tq1SuHEcYXUkUCPb7w8LiYwA033GA/hDQIQAACEIAABCAAAQhAAAIiMGXKFDNy5EhgZIAAAj0DSUjTBa2kzZgxw6y//vqmXbt2ab417wUBCEAAAhCAAAQyQ+Cf//ynmT59uhkwYIBZe+21M+OXHElWNbO4uo9vlXeVLDObM2eOGTJkiPnLX/5i+vTpU3lwvCI4AQR6cKQYhAAEIAABCEAAAhCAQPMJZHlfML5VnleYVc6snl+BQK/n7BM7BCAAAQhAAAIQgEDmCCDompeSrHLLql+inGXfmtcL4n8VAj3+HBIBBCAAAQhAAAIQgECOCGRZNOFb5R0NZpUzq+dXINDrOfvEDgEIQAACEIAABCCQOQIIuualJKvcsuoXK+jN62e1fhUCvdaEsQ8BCEAAAhCAAAQgAIEKCOgAu8svv9wMHz48cwfY4VsFifz/R2FWObN6fgUCvZ6zT+wQgEA0BFq1amUOO+wwc80110Tjc94c1e0XvXv3Jgd5SyzxQAACEIAABDJEAIGeoWTgCgQgkH8CDz/8sNlll10aAv3Wt75lVlttNbPBBhuYXr162atONttss+VAxCDQ33jjDXPttdeavffe2/To0SN4MsVArW/fvub+++9fzv57771n1l13XbN06dKaTGYg0IOnFIMQgAAEIAABCBQRQKDTJSAAAQikSCAR6BLiWo1dtmyZ+fTTT81zzz1n/vjHP9r/Hjt2rPnVr37VyKsvvvjCrLDCCubb3/52it5W9la6Q3W33XYzV199tTn88MMre7HH0xLoK664ovnqq6/M22+/bTp37tzoVZMmTTLjxo0zYlWLagMEukeSeAQCEIAABCAAgaoIINCrwseLIQABCFRGIBHoV1xxhRk6dGijF0ucH3DAAWbGjBk1E7m+3n799dfmm2++MW3atPF9iUlDoA8cONC+zy9/+UszevToRr5tuummZvPNNze33HILAt07azwIAQhAAAIQgECWCCDQs5QNfIEABHJPoJxAV/AS6VqpVdn7W2+91cCjVIn7lVdeaS655BIzd+5cK6bXXntts+uuu9qDhQrbXXfdZX7zm9/Yu05V/q1y+kMOOaRB4Gpf+xFHHGGmT59uHnvsMVumrnLxZ555pqFU/fe//719rxdffNG0bt3a7LDDDubMM880P/zhD+1bJTaKE6gV7aQa4OOPPzZnnXWW+dOf/mTtf/e73zX77LOPOfvss0379u2duRcD+a2qgzlz5piXXnqp4TVPP/202Xbbbc0999xj+vfvv5xAF5+LL77YTJs2zbz22mtmlVVWMX369DHnnHOO2XDDDRu9t/w8+eSTzZ133mn+85//mB//+Mfmt7/9rS2tL7UH3cVGxsX9ggsusJyUV21t+P73v29++tOfmvHjxztj5wEIQAACEIAABOqDAAK9PvJMlBCAQEYIuAS63DzyyCPtCroEaPfu3a3nxQI9EcQ/+clPzO67727/XnvA//znP5uXX365Idrzzz/fnHLKKWaTTTYxBx54oBXF+vsnn3zSPPHEE43Etfa+q4ReIlgifN9997V7un/xi19Yga//l0D997//bX73u9/Z93vggQfMj370I/vfqgr49a9/bX7+859bUau2xRZb2H8++eQTs/3225uPPvrI/r0mCf7xj3+YSy+91Mb4+OOPm7Zt25bNUiLQBw8ebPr162f+9re/WVGuduyxx5o77rjD+iE7xSXuxx9/vBXZmsDQHnmdqDtlyhT7rMS9JkXUVD6vSYfZs2ebYcOGmS233NL89a9/tRMXn3/+uRkwYECjQ+J82MiuJikkxFX6L/t6n1dffdU8++yz5pFHHslI78QNCEAgbQL6TTjmmGPMl19+ab9fNcGq7UyFbf/99zcPPfSQnbjVhGxazeWbJkrl+8KFC+1vkL7bjzvuuFTcc/m2ZMkSe96LvmtVEaYzXqZOnWonR2vdXL4Vvr9+U1555ZVU8urjl/Ko372k6Td+9dVXrzUy7BcRQKDTJSAAAQikSMBHoF944YXmpJNOsiu4e+21l/WuWKBr5VkCTyvaTTWt1P7gBz+wwlhl4dq/nTStKCeHriViXyJeq+aFzz311FNmu+22M+edd54V6klbvHixPcxOq/YS12rlStw1iLv++uvtKn7Xrl0b7Nx9991GZeuXXXaZvU6oXEsEulastfqsCQOJbA3A1llnHbtlQKvxmmQoFOia6FD5uwZCqibQ5IOaJih23HFHu63gpptusn8mP4466iijHJxwwgkN7mhFXZMUhXYrYbPVVltZH1WlQIMABCAgAqoG2mijjez3kr5/JcT1PaXvmcL24IMP2ioj/X1aAt3HN/0G6bdk4403Np999pnZeuutza233lqTQ0ILefj4pmc0maxqKf23qpX0e1qL81Eq9S15/oYbbjD33Xef/Q2tdV59mMkvTWCocozWsgQQ6C3Ln3eHAATqjICPQFfpulZvJWi1mq1WLNBVkn777bfbkm6tDJRqEpQSlhLOKuduqiUCvViU6nlNFKg0/PXXXzft2rVrZEJ7wLWSroGZBkFNCXQN4Dp16mR90ApGcdPqtVbENbAr1xKBLi6qClCVgVbCJfIHDRpkJys02C0W6Oeee64t53/00UcbVvaT99GKuoS2thZIuMuPmTNn2pX+whX9999/305GFAr0SthoJUer+8qXJgtoEIAABFTJpO8yfTep6fwRbSWSYC9umnDVNptaC7nkfSvxLXmNBLB+uzTpWstWqW+axNWktiY4iic/Qvvp65t+Y8RLv6F77rlnzfPq6xcCPXSPaJ49BHrzuPEqCEAAAs0i4CPQkxV0DdKSgU6xQNc+apW2v/nmm+Z73/ueLY3UsxKqSQmfVoK1Ivzhhx/a0naXQC98v+RZ7ee+9957y8Yq4dmlS5cmBbref8011yxrY6eddnKWehcK9BdeeMEeCKeydk0wvPvuu1Zoa+a/WKCPGDHClo1qQFRcqjdy5Eg7IJYAl4/dunWzr3/++eeX87dDhw5GWwqSu+grYaMVEr32X//6l93zLsGuUnvZoEEAAvVJQDd3aKJVK6lq2n508MEH2y02LS3QK/FNvmoSV1ub9N3csWPHmia0Et9UQabtVPquve6662pe4u7rm7ZqaTubtnulMfHi65cmqlUJoRV3LRBoIpqWPgEEevrMeUcIQKCOCfgIdK2OSwRqsCbBqFbqkDhdJ6b7wLVyrX1iWkHu2bOnXY1ZeeWVbal2JQJdtjRQKGxaUZY9lds31bSCr9X1plbQP/jgA7PWWmtZQapS91JN5ZvbbLNN2Z5RKND1oAYRWrmX+FW1gMR2mgK9Ejbyd9GiRXayQ7kSa02uyIYqAJKy+zr+aBA6BOqOwG233WYnGbMo0CvxTXvQJc51u8Z+++1X8zxW4puc0ZYsbYlStULxb1xoZ3180+/AzTffbLRdK63KCB+/xGL+/Pl2C5kOS9Vv9tFHH20OOuig0Jiw5yCAQKeLQAACEEiRgEugq9R6vfXWsysQWplOWimBXuy29mPrgJ6rrrrKzswnJe6lhHfha5MS91LP6fA1lbirlFwiu1yT8NTgp/gedM3EKx6d/K79ds1txQJ98uTJdp+4Vrzln1bHSwn0ciXuKrvXyrsGmJWWuFfCpjhmlf1rsKiT3cVNpfY0CECgvgiUKjvW960O+yxuaQm55H19fdM+7912282e5ZHWAXG+vhUyVKWU9szrd6OWzce3MWPGNKzm6zdLk9g6LPW5556rmWs+fhW/uSb4NfGvsQUtXQII9HR5824QgECdEygn0LWXW3vktA9RM+s/+9nPmhToOUgmTwAAC41JREFUKtfWvu7Cpr3TWsXQye3ae64BncqpJYx9DokrJdC1Oq2D1LSqL+GfHCyXvK/K19dYYw37vzp0TSeUa2LgxBNPbOSbDoDTKe8SoyrvLmy6gkwTE66yyGKBLgYazGq2f8iQIdZkKYGuSgQdwKQtALriLVmt1inwYqPT7W+88Ub7ep0qrxUDn0PiKmFTKl/aS3/ooYcarWxodYcGAQjUFwF99+kgT1XRJIfEqapG37fFLW2B7uObTkfX96q+R5PrNNPIoI9v+m1q06aNPVxPJ+Rr+5e2Gemk+Vo2H98K3z+tvPr4pdtWVA2ng2JVoSdm2ruvcwVo6RJAoKfLm3eDAATqnEAi0CUotW9cK6kSp5o5l1DTf2ugM3bs2EakilfQVcquFWNdcaY96JqB1z5rlaXJlva1qSWrxzqYTEJUYlpXusyaNavh9PVyK+iyMWrUKDNp0iR7GrwGOJoYUBmcYpH/uoZMTSXc2sctf7Q6/J3vfMee9K5/FixYYA+z0wFHEqUqT9eAQf+vPZiKWaewl2vFAr3Us6UEup5LrlnTirli0J5zrQpoAFd8zZpOrRdDDUp69OhhS/w1+aEBS/E1a75sdAaA4te1cDpsToMyHZin1X9NIPjcA1/nHx3Ch0AuCeiEdm3PkYjceeedzbRp0+xhkjoTRAeGqul7Rzdg6AwLVTJpq5BWYWvdXL6pNF8TyToPJGlnnHGGPTG91s3lm66A00SHfmf0j/ag6/cwje1ELt9aQqDrPV1+adJZExhipN9SHV43ceLEVJjVur/EZh+BHlvG8BcCEIiaQCLQkyB03+2qq65qBbXEtkSqBG1xKxboWo3WHjYdyKPy7EQAanBUOFiSHQl/rQhrwCI7uuZMh7+ceuqp9m1cAl3P6IR1lQjq8CKdiCuRKbGpK2u04pM0PSexrUPstLoybty4hpUVTT5ogKTDaubNm2dn6nXPug6704BTpf3lWjUCXRMJWm3XJIZ80951ifUJEybYKoPCpkGwKhA0QNbATlUJKotUGacmVZJD4gpjdrHRIEerZJoc0USG+Mne6aef3nAHe9QdG+chAAEIQAACEAhCAIEeBCNGIAABCEAAAhCAAAQgAAEIQAAC1RFAoFfHj1dDAAIQgAAEIAABCEAAAhCAAASCEECgB8GIEQhAAAIQgAAEIAABCEAAAhCAQHUEEOjV8ePVEIAABCAAAQhAAAIQgAAEIACBIAQQ6EEwYgQCEIAABCAAAQhAAAIQgAAEIFAdAQR6dfx4NQQgAAEIQAACEIAABCAAAQhAIAgBBHoQjBiBAAQgAAEIQAACEIAABCAAAQhURwCBXh0/Xg0BCEAAAhCAAAQgAAEIQAACEAhCAIEeBCNGIAABCEAAAhCAAAQgAAEIQAAC1RFAoFfHj1dDAAIQgAAEIAABCEAAAhUQePjhh80uu+zS8IpWrVqZ9u3bm2222caMGjXK9OnTpwJrPAqBfBFAoOcrn0QDAQhAAAIQgAAEIACBTBNIBPqQIUNM7969zdKlS83cuXPNZZddZj755BNz3333mb59+2Y6BpyDQK0IINBrRRa7EIAABCAAAQhAAAIQgMByBBKBfsUVV5ihQ4c2/P3s2bNNz549ze67725FOg0C9UgAgV6PWSdmCEAAAhCAAAQgAAEItBCBpgS63OnUqZPp2LGjefXVV1vIO94WAi1LAIHesvx5dwhAAAIQgAAEIAABCNQVgaYE+scff2zWWGMNs/3225tZs2bVFROChUBCAIFOX4AABCAAAQhAAAIQgAAEUiOQCPQLL7zQDB482CxbtszuQR8zZox59NFHzUUXXWSOP/741PzhjSCQJQII9CxlA18gAAEIQAACEIAABCCQcwLFp7gn4a600krmuOOOMxMmTDA62Z0GgXokgECvx6wTMwQgAAEIQAACEIAABFqIQCLQTzrpJNOvXz/TunVrs9pqq5lNNtnEtGvXroW84m0hkA0CCPRs5AEvIAABCEAAAhCAAAQgUBcEyh0SVxcACBICZQgg0OkeEIAABCAAAQhAAAIQgEBqBBDoqaHmjSIkgECPMGm4DAEIQAACEIAABCAAgVgJINBjzRx+p0EAgZ4GZd4DAhCAAAQgAAEIQAACELAEEOh0BAg0TQCBTu+AAAQgAAEIQAACEIAABFIjgEBPDTVvFCEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIHwEEev5ySkQQgAAEIAABCEAAAhCAAAQgECEBBHqEScNlCEAAAhCAAAQgAAEIQAACEMgfAQR6/nJKRBCAAAQgAAEIQAACEIAABCAQIQEEeoRJw2UIQAACEIAABCAAAQhAAAIQyB8BBHr+ckpEEIAABCAAAQhAAAIQgAAEIBAhAQR6hEnDZQhAAAIQgAAEIAABCEAAAhDIH4H/A6vvy2ICsZVpAAAAAElFTkSuQmCC\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f = plt.figure(figsize=(8,7))\n",
+ "gs = gridspec.GridSpec(2, 2, wspace=0.0, hspace=0.0, height_ratios=[32,6], width_ratios=[3,1])\n",
+ "\n",
+ "ax = plt.subplot(gs[0, 0])\n",
+ "sort_ixs = np.argsort(stats['mode_probs'])[::-1]\n",
+ "plt.bar(x=range(len(log_gt_mode_frequencies)), height=results['mode_frequencies'][sort_ixs], align='center', color='g', label='Estimated Frequencies')\n",
+ "plt.bar(x=range(len(log_gt_mode_frequencies)), height=stats['mode_probs'][sort_ixs], align='center', edgecolor='k', fc=(0,0,0,0), label='Ground truth')\n",
+ "plt.tick_params(\n",
+ " axis='x', # changes apply to the x-axis\n",
+ " which='both', # both major and minor ticks are affected\n",
+ " bottom=False, # ticks along the bottom edge are off\n",
+ " top=False, # ticks along the top edge are off\n",
+ " labelbottom=False) # labels along the bottom edge are off\n",
+ "\n",
+ "ax.legend()\n",
+ "ax.set_title('Probabilistic U-Net (validation set)', y=1.03)\n",
+ "ax = plt.gca()\n",
+ "ax.set_xlim([-.5,31.5])\n",
+ "ax.set_ylim([0,0.2])\n",
+ "yticks = ax.yaxis.get_major_ticks()\n",
+ "yticks[0].label1.set_visible(False)\n",
+ "ax.set_ylabel('P', fontdict={'size': 15})\n",
+ "\n",
+ "rows = list(cf.label_switches.keys())\n",
+ "cell_text = np.transpose(stats['switch'][sort_ixs], axes=[1,0])\n",
+ "\n",
+ "ax = plt.subplot(gs[1, 0])\n",
+ "the_table = ax.table(cellText=cell_text,\n",
+ " rowLabels=rows,\n",
+ " loc='center')\n",
+ "\n",
+ "ax.get_yaxis().set_visible(False)\n",
+ "plt.tick_params(\n",
+ " axis='x', # changes apply to the x-axis\n",
+ " which='both', # both major and minor ticks are affected\n",
+ " bottom=False, # ticks along the bottom edge are off\n",
+ " top=False, # ticks along the top edge are off\n",
+ " labelbottom=False) # labels along the bottom edge are off\n",
+ "ax.set_xlabel('Discrete Modes')\n",
+ "\n",
+ "ax = plt.subplot(gs[1, 1])\n",
+ "ax.set_title('Pixel-wise Frequency Estimation',fontdict={'size': 6})\n",
+ "plt.barh(y=range(5), width=results['pixel_frequencies'][:,1], align='center', color='g')\n",
+ "plt.barh(y=range(5), width=list(cf.label_switches.values()), align='center', edgecolor='k', fc=(0,0,0,0))\n",
+ "ax.get_yaxis().set_visible(False)\n",
+ "ax.set_xticks([0.1,0.2,0.3,0.4,0.5])\n",
+ "ax.set_xlabel('P')\n",
+ "ax.tick_params(axis='both', which='major', labelsize=6)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# prepare animated gif from samples"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ 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AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEIgIG4iJFiEGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEDcX6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECEQEDcZEixCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgICBOD9AgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBiICBuEgRYhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQMBAnB8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBARMBAXKQIMQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIGAgzg8QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgImAgLlKEGAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEDAQ5wcIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAQETAQFylCDAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBiI8wMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCICBiIixQhBgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAzE+QECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhEBAzERYoQgwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgbi/AABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQiAgbiIkWIQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQNxfoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIRAQNxkSLEIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgIE4P0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGIgIG4SBFiECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAwECcHyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBEwEBcpAgxCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgYCDODxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAiYCAuUoQYBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQMBDnBwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIBARMBAXKUIMAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIGIjzAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEIgIGIiLFCEGAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEDMT5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEQEDMRFihCDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBuL8AAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCICBuIiRYhBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABA3F+gAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhEBA3GRIsQgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICAgTg/QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYiAgbhIEWIQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEDAQJwfIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQETAQFykCDEIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBgIM4PECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAICJgIC5ShBgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIA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+ "metadata": {},
+ "output_type": "execute_result"
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+ "text": [
+ "Saved to /home/skohl/PycharmProjects/phabricator/skohl/prob_unet/tmp//prob_unet_0.0005beta_analyticKLTrue_6latents_31x1s_32chan_lr0.0001PieceWise_bs16_7blocks_QMeanFalse_henormal_noBiasReg_OneHotMinus0.5_240kepochs_TFP/gif_panel_sample_0.png\n"
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+ "text": [
+ "Saved to /home/skohl/PycharmProjects/phabricator/skohl/prob_unet/tmp//prob_unet_0.0005beta_analyticKLTrue_6latents_31x1s_32chan_lr0.0001PieceWise_bs16_7blocks_QMeanFalse_henormal_noBiasReg_OneHotMinus0.5_240kepochs_TFP/gif_panel_sample_1.png\n"
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+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlegend_handles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_to_anchor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.03\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlegend_handles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0mout_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mout_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'gif_panel_sample_{}.png'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 58\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Saved to {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 709\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 711\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, **kwargs)\u001b[0m\n\u001b[1;32m 2033\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2034\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2035\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2036\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2037\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m 2210\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2211\u001b[0m \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2212\u001b[0;31m **kwargs)\n\u001b[0m\u001b[1;32m 2213\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2214\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/PycharmProjects/phabricator/skohl/prob_unet/venv/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 526\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 527\u001b[0m _png.write_png(renderer._renderer, fh,\n\u001b[0;32m--> 528\u001b[0;31m self.figure.dpi, metadata=metadata)\n\u001b[0m\u001b[1;32m 529\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moriginal_dpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ],
+ "output_type": "error"
+ }
+ ],
+ "source": [
+ "from matplotlib.lines import Line2D\n",
+ "from data.cityscapes.data_loader import map_labels_to_trainId\n",
+ "\n",
+ "# get dictionary of image files\n",
+ "data_dir = os.path.join(cf.data_dir, cf.resolution)\n",
+ "data_dict = loadFiles(label_density=cf.label_density, split='val', input_path=data_dir,\n",
+ " cities=None, instance=False)\n",
+ "\n",
+ "# list of image indices in data_dict\n",
+ "ixs = [0,2,75,85,100,145,350,390,460,470]\n",
+ "num_samples = 16\n",
+ "y_dim = 256\n",
+ "x_dim = 512\n",
+ "\n",
+ "img_arr = np.zeros(shape=(y_dim, len(ixs) * x_dim, 3)) \n",
+ "gt_arr = np.zeros(shape=(y_dim, len(ixs) * x_dim))\n",
+ "sample_arr = np.zeros(shape=(num_samples, y_dim, len(ixs) * x_dim))\n",
+ "\n",
+ "for i,ix in enumerate(ixs):\n",
+ "\n",
+ " img_key = list(data_dict.keys())[ix]\n",
+ " img_path = data_dict[img_key]['data']\n",
+ " seg_path = data_dict[img_key]['seg']\n",
+ " \n",
+ " # load images\n",
+ " img_arr[:, i * x_dim: (i + 1) * x_dim] = np.transpose(np.load(img_path), axes=[1,2,0]) / 255.\n",
+ " \n",
+ " # load gt segmentation\n",
+ " gt_arr[:, i * x_dim: (i + 1) * x_dim] = map_labels_to_trainId(np.load(seg_path))\n",
+ " \n",
+ " # load samples\n",
+ " for sample_num in range(num_samples):\n",
+ " sample_path = os.path.join(cf.out_dir, '{}_sample{}_labelIds.npy'.format(img_key, sample_num))\n",
+ " sample_arr[sample_num, :, i * x_dim: (i + 1) * x_dim] = np.load(sample_path)[0,0]\n",
+ "\n",
+ "for sample_num in range(num_samples):\n",
+ " f = plt.figure(figsize=(len(ixs) * 4, 9))\n",
+ "\n",
+ " arr = np.concatenate([img_arr, to_rgb(gt_arr, cmap=cf.color_map), to_rgb(sample_arr[sample_num], cmap=cf.color_map)], axis=0)\n",
+ " \n",
+ " plt.imshow(arr)\n",
+ " plt.text(-80,170, 'image', fontsize=12, rotation=90, rotation_mode='anchor')\n",
+ " plt.text(-80,470, 'deterministic', fontsize=12, rotation=90, rotation_mode='anchor')\n",
+ " plt.text(-40,460, 'groundtruth', fontsize=12, rotation=90, rotation_mode='anchor')\n",
+ " plt.text(-80,690, 'sample {}'.format(sample_num + 1), fontsize=12, rotation=90, rotation_mode='anchor')\n",
+ " \n",
+ " ax = plt.gca()\n",
+ " ax.get_xaxis().set_visible(False)\n",
+ " ax.get_yaxis().set_visible(False)\n",
+ " \n",
+ " # custom legend\n",
+ " trainId2name = cf.trainId2name\n",
+ " trainId2name[255] = 'ignore'\n",
+ " legend_handles = [Line2D([0], [0], color=tuple([c / 255. for c in cf.color_map[trainID]]), lw=4, label=trainId2name[trainID]) for trainID in list(cf.color_map.keys())] \n",
+ " ax.legend(handles=legend_handles, loc=9, bbox_to_anchor=(0.5, -0.03), ncol=len(legend_handles), frameon=False)\n",
+ " out_dir = os.path.join(cf.out_dir, 'gif_panel_sample_{}.png'.format(sample_num))\n",
+ " plt.savefig(out_dir, dpi=200, bbox_inches='tight', pad_inches=0.0)\n",
+ " plt.close()\n",
+ " print('Saved to {}'.format(out_dir))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "language": "python",
+ "name": "venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3.0
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/model/__init__.py b/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/model/probabilistic_unet.py b/model/probabilistic_unet.py
new file mode 100644
index 0000000..cb5d93b
--- /dev/null
+++ b/model/probabilistic_unet.py
@@ -0,0 +1,478 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Probabilistic U-Net model."""
+
+import tensorflow as tf
+import sonnet as snt
+import tensorflow_probability as tfp
+tfd = tfp.distributions
+from utils.training_utils import ce_loss, he_normal
+
+def down_block(features,
+ output_channels,
+ kernel_shape,
+ stride=1,
+ rate=1,
+ num_convs=2,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers=None,
+ nonlinearity=tf.nn.relu,
+ down_sample_input=True,
+ down_sampling_op=lambda x, size: tf.image.resize_images(x, size,
+ method=tf.image.ResizeMethod.BILINEAR, align_corners=True),
+ data_format='NCHW',
+ name='down_block'):
+ """A block made up of a down-sampling step followed by several convolutional layers."""
+ with tf.variable_scope(name):
+ if down_sample_input:
+ features = down_sampling_op(features, data_format)
+
+ for _ in range(num_convs):
+ features = snt.Conv2D(output_channels, kernel_shape, stride, rate, data_format=data_format,
+ initializers=initializers, regularizers=regularizers)(features)
+ features = nonlinearity(features)
+
+ return features
+
+
+def up_block(lower_res_inputs,
+ same_res_inputs,
+ output_channels,
+ kernel_shape,
+ stride=1,
+ rate=1,
+ num_convs=2,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers=None,
+ nonlinearity=tf.nn.relu,
+ up_sampling_op=lambda x, size: tf.image.resize_images(x, size,
+ method=tf.image.ResizeMethod.BILINEAR, align_corners=True),
+ data_format='NCHW',
+ name='up_block'):
+ """A block made up of an up-sampling step followed by several convolutional layers."""
+ with tf.variable_scope(name):
+ spatial_shape = same_res_inputs.get_shape()[2:]
+
+ if data_format=='NHWC':
+ features = up_sampling_op(lower_res_inputs, spatial_shape)
+ features = tf.concat([features, same_res_inputs], axis=-1)
+ else:
+ lower_res_inputs = tf.transpose(lower_res_inputs, perm=[0,2,3,1])
+ features = up_sampling_op(lower_res_inputs, spatial_shape)
+ features = tf.transpose(features, perm=[0,3,1,2])
+ features = tf.concat([features, same_res_inputs], axis=1)
+
+ for _ in range(num_convs):
+ features = snt.Conv2D(output_channels, kernel_shape, stride, rate, data_format=data_format,
+ initializers=initializers, regularizers=regularizers)(features)
+ features = nonlinearity(features)
+
+ return features
+
+
+class VGG_Encoder(snt.AbstractModule):
+ """A quasi VGG-style convolutional net, made of M x (down-sampling, N x conv)-operations,
+ where M = len(num_channels), N = num_convs_per_block."""
+
+ def __init__(self,
+ num_channels,
+ nonlinearity=tf.nn.relu,
+ num_convs_per_block=3,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0), 'b': tf.contrib.layers.l2_regularizer(1.0)},
+ data_format='NCHW',
+ down_sampling_op=lambda x, df: tf.nn.avg_pool(x, ksize=[1,1,2,2], strides=[1,1,2,2],
+ padding='SAME', data_format=df),
+ name="vgg_enc"):
+ super(VGG_Encoder, self).__init__(name=name)
+ self._num_channels = num_channels
+ self._nonlinearity = nonlinearity
+ self._num_convs = num_convs_per_block
+ self._initializers = initializers
+ self._regularizers = regularizers
+ self._data_format = data_format
+ self._down_sampling_op = down_sampling_op
+
+ def _build(self, inputs):
+ """
+ :param inputs: 4D tensor of shape NCHW or NWHC
+ :return: a list of 4D tensors of shape NCHW or NWHC
+ """
+ features = [inputs]
+
+ # iterate blocks (`processing scales')
+ for i, n_channels in enumerate(self._num_channels):
+
+ if i == 0:
+ down_sample = False
+ else:
+ down_sample = True
+ tf.logging.info('encoder scale {}: {}'.format(i, features[-1].get_shape()))
+ features.append(down_block(features[-1],
+ output_channels=n_channels,
+ kernel_shape=(3,3),
+ num_convs=self._num_convs,
+ nonlinearity=self._nonlinearity,
+ initializers=self._initializers,
+ regularizers=self._regularizers,
+ down_sample_input=down_sample,
+ data_format=self._data_format,
+ down_sampling_op=self._down_sampling_op,
+ name='down_block_{}'.format(i)))
+ # return all features except for the input images
+ return features[1:]
+
+
+class VGG_Decoder(snt.AbstractModule):
+ """A quasi VGG-style convolutional net, made of M x (up-sampling, N x conv)-operations,
+ where M = len(num_channels), N = num_convs_per_block."""
+
+ def __init__(self,
+ num_channels,
+ num_classes,
+ nonlinearity=tf.nn.relu,
+ num_convs_per_block=3,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0), 'b': tf.contrib.layers.l2_regularizer(1.0)},
+ data_format='NCHW',
+ up_sampling_op=lambda x, size: tf.image.resize_images(x, size,
+ method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, align_corners=True),
+ name="vgg_dec"):
+ super(VGG_Decoder, self).__init__(name=name)
+ self._num_channels = num_channels
+ self._num_classes = num_classes
+ self._nonlinearity = nonlinearity
+ self._num_convs = num_convs_per_block
+ self._initializers = initializers
+ self._regularizers = regularizers
+ self._data_format = data_format
+ self._up_sampling_op = up_sampling_op
+
+ def _build(self, input_list):
+ """
+ :param input_list: a list of 4D tensors of shape NCHW or NHWC
+ :return: 4D tensor
+ """
+ try:
+ assert len(self._num_channels) == len(input_list)
+ except:
+ raise AssertionError('Missmatch: {} blocks vs. {} incoming feature-maps!')
+
+ n = len(input_list) - 2
+ lower_res_features = input_list[-1]
+
+ # iterate input features & channels in reverse order, starting from the second last (here, =nth) features
+ for i in range(n, -1, -1):
+ same_res_features = input_list[i]
+ n_channels = self._num_channels[i]
+
+ tf.logging.info('decoder scale {}: {}'.format(i, lower_res_features.get_shape()))
+ lower_res_features = up_block(lower_res_features,
+ same_res_features,
+ output_channels=n_channels,
+ kernel_shape=(3, 3),
+ num_convs=self._num_convs,
+ nonlinearity=self._nonlinearity,
+ initializers=self._initializers,
+ regularizers=self._regularizers,
+ data_format=self._data_format,
+ up_sampling_op=self._up_sampling_op,
+ name='up_block_{}'.format(i))
+ return lower_res_features
+
+
+class UNet(snt.AbstractModule):
+ """A quasi standard U-Net, similar to `U-Net: Convolutional Networks for Biomedical Image Segmentation',
+ https://arxiv.org/abs/1505.04597."""
+
+ def __init__(self,
+ num_channels,
+ num_classes,
+ nonlinearity=tf.nn.relu,
+ num_convs_per_block=3,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers=None,
+ data_format='NCHW',
+ down_sampling_op=lambda x, df: tf.nn.avg_pool(x, ksize=[1,1,2,2], strides=[1,1,2,2],
+ padding='SAME', data_format=df),
+ up_sampling_op=lambda x, size: tf.image.resize_images(x, size,
+ method=tf.image.ResizeMethod.BILINEAR, align_corners=True),
+ name="unet"):
+ super(UNet, self).__init__(name=name)
+ with self._enter_variable_scope():
+ tf.logging.info('Building U-Net.')
+ self._encoder = VGG_Encoder(num_channels, nonlinearity, num_convs_per_block, initializers, regularizers,
+ data_format=data_format, down_sampling_op=down_sampling_op)
+ self._decoder = VGG_Decoder(num_channels, num_classes, nonlinearity, num_convs_per_block, initializers,
+ regularizers, data_format=data_format, up_sampling_op=up_sampling_op)
+
+ def _build(self, inputs):
+ """
+ :param inputs: 4D tensor of shape NCHW or NHWC
+ :return: 4D tensor
+ """
+ encoder_features = self._encoder(inputs)
+ predicted_logits = self._decoder(encoder_features)
+ return predicted_logits
+
+
+class Conv1x1Decoder(snt.AbstractModule):
+ """A stack of 1x1 convolutions that takes two tensors to be concatenated along their channel axes."""
+
+ def __init__(self,
+ num_classes,
+ num_channels,
+ num_1x1_convs,
+ nonlinearity=tf.nn.relu,
+ initializers={'w': tf.orthogonal_initializer(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0), 'b': tf.contrib.layers.l2_regularizer(1.0)},
+ data_format='NCHW',
+ name='conv_decoder'):
+ super(Conv1x1Decoder, self).__init__(name=name)
+ self._num_classes = num_classes
+ self._num_channels = num_channels
+ self._num_1x1_convs = num_1x1_convs
+ self._nonlinearity = nonlinearity
+ self._initializers = initializers
+ self._regularizers = regularizers
+ self._data_format = data_format
+
+ if data_format == 'NCHW':
+ self._channel_axis = 1
+ self._spatial_axes = [2,3]
+ else:
+ self._channel_axis = -1
+ self._spatial_axes = [1,2]
+
+ def _build(self, features, z):
+ """
+ :param features: 4D tensor of shape NCHW or NHWC
+ :param z: 4D tensor of shape NC11 or N11C
+ :return: 4D tensor
+ """
+ shp = features.get_shape()
+ spatial_shape = [shp[axis] for axis in self._spatial_axes]
+ multiples = [1] + spatial_shape
+ multiples.insert(self._channel_axis, 1)
+
+ if len(z.get_shape()) == 2:
+ z = tf.expand_dims(z, axis=2)
+ z = tf.expand_dims(z, axis=2)
+
+ # broadcast latent vector to spatial dimensions of the image/feature tensor
+ broadcast_z = tf.tile(z, multiples)
+ features = tf.concat([features, broadcast_z], axis=self._channel_axis)
+ for _ in range(self._num_1x1_convs):
+ features = snt.Conv2D(self._num_channels, kernel_shape=(1,1), stride=1, rate=1,
+ data_format=self._data_format,
+ initializers=self._initializers, regularizers=self._regularizers)(features)
+ features = self._nonlinearity(features)
+ logits = snt.Conv2D(self._num_classes, kernel_shape=(1,1), stride=1, rate=1,
+ data_format=self._data_format,
+ initializers=self._initializers, regularizers=None)
+ return logits(features)
+
+
+class AxisAlignedConvGaussian(snt.AbstractModule):
+ """A convolutional net that parametrizes a Gaussian distribution with axis aligned covariance matrix."""
+
+ def __init__(self,
+ latent_dim,
+ num_channels,
+ nonlinearity=tf.nn.relu,
+ num_convs_per_block=3,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0), 'b': tf.contrib.layers.l2_regularizer(1.0)},
+ data_format='NCHW',
+ down_sampling_op=lambda x, df:\
+ tf.nn.avg_pool(x, ksize=[1,1,2,2], strides=[1,1,2,2], padding='SAME', data_format=df),
+ name="conv_dist"):
+ self._latent_dim = latent_dim
+ self._initializers = initializers
+ self._regularizers = regularizers
+ self._data_format = data_format
+
+ if data_format == 'NCHW':
+ self._channel_axis = 1
+ self._spatial_axes = [2,3]
+ else:
+ self._channel_axis = -1
+ self._spatial_axes = [1,2]
+
+ super(AxisAlignedConvGaussian, self).__init__(name=name)
+ with self._enter_variable_scope():
+ tf.logging.info('Building ConvGaussian.')
+ self._encoder = VGG_Encoder(num_channels, nonlinearity, num_convs_per_block, initializers, regularizers,
+ data_format=data_format, down_sampling_op=down_sampling_op)
+
+ def _build(self, img, seg=None):
+ """
+ Evaluate mu and log_sigma of a Gaussian conditioned on an image + optionally, concatenated one-hot segmentation.
+ :param img: 4D array
+ :param seg: 4D array
+ :return: snt.AbstractModule object
+ """
+ if seg is not None:
+ seg = tf.cast(seg, tf.float32)
+ img = tf.concat([img, seg], axis=self._channel_axis)
+ encoding = self._encoder(img)[-1]
+ encoding = tf.reduce_mean(encoding, axis=self._spatial_axes, keepdims=True)
+
+ mu_log_sigma = snt.Conv2D(2 * self._latent_dim, (1,1), stride=1, rate=1, data_format=self._data_format,
+ initializers=self._initializers, regularizers=self._regularizers)(encoding)
+
+ mu_log_sigma = tf.squeeze(mu_log_sigma, axis=self._spatial_axes)
+ mu = mu_log_sigma[:, :self._latent_dim]
+ log_sigma = mu_log_sigma[:, self._latent_dim:]
+
+ return tfd.MultivariateNormalDiag(loc=mu, scale_diag=tf.exp(log_sigma))
+
+
+class ProbUNet(snt.AbstractModule):
+ """Probabilistic U-Net."""
+
+ def __init__(self,
+ latent_dim,
+ num_channels,
+ num_classes,
+ num_1x1_convs=3,
+ nonlinearity=tf.nn.relu,
+ num_convs_per_block=3,
+ initializers={'w': he_normal(), 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0), 'b': tf.contrib.layers.l2_regularizer(1.0)},
+ data_format='NCHW',
+ down_sampling_op=lambda x, df:\
+ tf.nn.avg_pool(x, ksize=[1,1,2,2], strides=[1,1,2,2], padding='SAME', data_format=df),
+ up_sampling_op=lambda x, size:\
+ tf.image.resize_images(x, size, method=tf.image.ResizeMethod.BILINEAR, align_corners=True),
+ name='prob_unet'):
+ super(ProbUNet, self).__init__(name=name)
+ self._data_format = data_format
+ self._num_classes = num_classes
+
+ with self._enter_variable_scope():
+ self._unet = UNet(num_channels=num_channels, num_classes=num_classes, nonlinearity=nonlinearity,
+ num_convs_per_block=num_convs_per_block, initializers=initializers,
+ regularizers=regularizers, data_format=data_format,
+ down_sampling_op=down_sampling_op, up_sampling_op=up_sampling_op)
+
+ self._f_comb = Conv1x1Decoder(num_classes=num_classes, num_1x1_convs=num_1x1_convs,
+ num_channels=num_channels[0], nonlinearity=nonlinearity,
+ data_format=data_format, initializers=initializers, regularizers=regularizers)
+
+ self._prior =\
+ AxisAlignedConvGaussian(latent_dim=latent_dim, num_channels=num_channels,
+ nonlinearity=nonlinearity, num_convs_per_block=num_convs_per_block,
+ initializers=initializers, regularizers=regularizers, name='prior')
+
+ self._posterior =\
+ AxisAlignedConvGaussian(latent_dim=latent_dim, num_channels=num_channels,
+ nonlinearity=nonlinearity, num_convs_per_block=num_convs_per_block,
+ initializers=initializers, regularizers=regularizers, name='posterior')
+
+ def _build(self, img, seg=None, is_training=True, one_hot_labels=True):
+ """
+ Evaluate individual components of the net.
+ :param img: 4D image array
+ :param seg: 4D segmentation array
+ :param is_training: if False, refrain from evaluating the posterior
+ :param one_hot_labels: bool, if False expects integer labeled segmentation of shape N1HW or NHW1
+ :return: None
+ """
+ if is_training:
+ if seg is not None:
+ if not one_hot_labels:
+ if self._data_format == 'NCHW':
+ spatial_shape = img.get_shape()[-2:]
+ class_axis = 1
+ one_hot_shape = (-1, self._num_classes) + tuple(spatial_shape)
+ elif self._data_format == 'NHWC':
+ spatial_shape = img.get_shape()[-3:-1]
+ one_hot_shape = (-1,) + tuple(spatial_shape) + (self._num_classes,)
+ class_axis = 3
+
+ seg = tf.reshape(seg, shape=[-1])
+ seg = tf.one_hot(indices=seg, depth=self._num_classes, axis=class_axis)
+ seg = tf.reshape(seg, shape=one_hot_shape)
+ seg -= 0.5
+ self._q = self._posterior(img, seg)
+
+ self._p = self._prior(img)
+ self._unet_features = self._unet(img)
+
+ def reconstruct(self, use_posterior_mean=False, z_q=None):
+ """
+ Reconstruct a given segmentation. Default settings result in decoding a posterior sample.
+ :param use_posterior_mean: use posterior_mean instead of sampling z_q
+ :param z_q: use provided latent sample z_q instead of sampling anew
+ :return: 4D logits tensor
+ """
+ if use_posterior_mean:
+ z_q = self._q._mu
+ else:
+ if z_q is None:
+ z_q = self._q.sample()
+ return self._f_comb(self._unet_features, z_q)
+
+ def sample(self):
+ """
+ Sample a segmentation by reconstructing from a prior sample.
+ Only needs to re-evaluate the last 1x1-convolutions.
+ :return: 4D logits tensor
+ """
+ z_p = self._p.sample()
+ return self._f_comb(self._unet_features, z_p)
+
+ def kl(self, analytic=True, z_q=None):
+ """
+ Calculate the Kullback-Leibler divergence KL(Q||P) between 2 axis-aligned gaussians,
+ i.e. the variance sigma is assumed diagonal.
+ :param analytic: bool, if False, approximate the KL via sampling from the posterior
+ :param z_q: None or 2D tensor, if analytic=False the posterior sample can be provided instead of sampling anew
+ :return: 4D tensor
+ """
+ if analytic:
+ kl = tfd.kl_divergence(self._q, self._p)
+ else:
+ if z_q is None:
+ z_q = self._q.sample()
+ log_q = self._q.log_prob(z_q)
+ log_p = self._p.log_prob(z_q)
+ kl = log_q - log_p
+ return kl
+
+ def elbo(self, seg, beta=1.0, analytic_kl=True, reconstruct_posterior_mean=False, z_q=None, one_hot_labels=True,
+ loss_mask=None):
+ """
+ Calculate the evidence lower bound (elbo) of the log-likelihood of P(Y|X).
+ :param seg: 4D tensor
+ :param analytic_kl: bool, if False calculate the KL via sampling
+ :param z_q: 4D tensor
+ :param one_hot_labels: bool, if False expects integer labeled segmentation of shape N1HW or NHW1
+ :param loss_mask: 4D tensor, binary
+ :return: 1D tensor
+ """
+ if z_q is None:
+ z_q = self._q.sample()
+
+ self._kl = tf.reduce_mean(self.kl(analytic_kl, z_q))
+
+ self._rec_logits = self.reconstruct(use_posterior_mean=reconstruct_posterior_mean, z_q=z_q)
+ rec_loss = ce_loss(labels=seg, logits=self._rec_logits, n_classes=self._num_classes,
+ loss_mask=loss_mask, one_hot_labels=one_hot_labels)
+ self._rec_loss = rec_loss['sum']
+ self._rec_loss_mean = rec_loss['mean']
+
+ return -(self._rec_loss + beta * self._kl)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..6dd1342
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,12 @@
+tensorflow_probability==0.3.0
+tensorflow-gpu==1.10.0
+dm-sonnet==1.23
+matplotlib==2.2.2
+numpy==1.14.5
+Pillow==5.1.0
+scipy==1.1.0
+SimpleITK==1.1.0
+tensorboard==1.10.0
+tqdm==4.23.4
+seaborn==0.9.0
+pytest==3.8.0
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..79ea0a1
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,33 @@
+#!/usr/bin/env python
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from distutils.core import setup
+from setuptools import find_packages
+
+req_file = "requirements.txt"
+
+def parse_requirements(filename):
+ lineiter = (line.strip() for line in open(filename))
+ return [line for line in lineiter if line and not line.startswith("#")]
+
+install_reqs = parse_requirements(req_file)
+
+setup(name='model',
+ version='latest',
+ packages=find_packages(exclude=['test', 'test.*']),
+ install_requires=install_reqs,
+ dependency_links=[],
+ )
\ No newline at end of file
diff --git a/tests/evaluation/eval_tests.py b/tests/evaluation/eval_tests.py
new file mode 100644
index 0000000..6b6c322
--- /dev/null
+++ b/tests/evaluation/eval_tests.py
@@ -0,0 +1,152 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Evaluation metrics tests."""
+
+import pytest
+import numpy as np
+from evaluation.eval_cityscapes import get_energy_distance_components, get_mode_counts, get_pixelwise_mode_counts
+from utils.training_utils import calc_confusion, metrics_from_conf_matrix
+
+
+def nan_save_array_equal(a, b, nan_replacement=-1.):
+ """Replace NANs to savely compare arrays elementwise."""
+ a_nan_ixs = np.where(np.isnan(a))
+ a[a_nan_ixs] = nan_replacement
+
+ b_nan_ixs = np.where(np.isnan(b))
+ b[b_nan_ixs] = nan_replacement
+
+ return (a == b).all()
+
+
+@pytest.mark.parametrize("test_input,expected", [
+ ([np.zeros(shape=(1,1,10,10)), np.zeros(shape=(1,1,10,10))],
+ {'tp_0': 100, 'tp_1': 0, 'fp_0': 0, 'fp_1': 0, 'fn_0': 0, 'fn_1': 0}),
+ ([np.ones(shape=(1,1,10,10)), np.ones(shape=(1,1,10,10))],
+ {'tp_0': 0, 'tp_1': 100, 'fp_0': 0, 'fp_1': 0, 'fn_0': 0, 'fn_1': 0}),
+ ([np.ones(shape=(1,1,10,10)), np.zeros(shape=(1,1,10,10))],
+ {'tp_0': 0, 'tp_1': 0, 'fp_0': 100, 'fp_1': 0, 'fn_0': 0, 'fn_1': 100}),
+ ([np.concatenate([np.ones(shape=(1,1,10,5)), np.zeros(shape=(1,1,10,5))], axis=-1), np.zeros(shape=(1,1,10,10))],
+ {'tp_0': 50, 'tp_1': 0, 'fp_0': 50, 'fp_1': 0, 'fn_0': 0, 'fn_1': 50}),
+])
+def test_confusion(test_input, expected):
+
+ gt_seg_modes = test_input[0]
+ seg_samples = test_input[1]
+
+ conf_matrix = calc_confusion(gt_seg_modes, seg_samples, class_ixs=[0,1])
+
+ tp_0 = conf_matrix[0,0]
+ tp_1 = conf_matrix[1,0]
+ fp_0 = conf_matrix[0,1]
+ fp_1 = conf_matrix[1,1]
+ fn_0 = conf_matrix[0,3]
+ fn_1 = conf_matrix[1,3]
+
+ assert tp_0 == expected['tp_0']
+ assert tp_1 == expected['tp_1']
+ assert fp_0 == expected['fp_0']
+ assert fp_1 == expected['fp_1']
+ assert fn_0 == expected['fn_0']
+ assert fn_1 == expected['fn_1']
+
+
+@pytest.mark.parametrize("test_input,expected,eval_f", [
+ ([np.zeros(shape=(1,1,10,10)), np.zeros(shape=(1,1,10,10))], {'iou_0': 1., 'iou_1': np.nan},
+ [lambda x,y: x == y, lambda x,y: np.isnan(x) and np.isnan(y)]),
+ ([np.ones(shape=(1,1,10,10)), np.ones(shape=(1,1,10,10))], {'iou_0': np.nan, 'iou_1': 1.},
+ [lambda x,y: np.isnan(x) and np.isnan(y), lambda x,y: x == y]),
+ ([np.ones(shape=(1,1,10,10)), np.zeros(shape=(1,1,10,10))], {'iou_0': 0., 'iou_1': 0.},
+ [lambda x,y: x == y, lambda x,y: x == y]),
+ ([np.concatenate([np.ones(shape=(1, 1, 10, 5)), np.zeros(shape=(1, 1, 10, 5))], axis=-1),
+ np.zeros(shape=(1, 1, 10, 10))], {'iou_0': 0.5, 'iou_1': 0.},
+ [lambda x,y: x == y, lambda x,y: x == y])
+])
+def test_metrics_from_confusion(test_input, expected, eval_f):
+
+ gt_seg_modes = test_input[0]
+ seg_samples = test_input[1]
+
+ conf_matrix = calc_confusion(gt_seg_modes, seg_samples, class_ixs=[0,1])
+ iou = metrics_from_conf_matrix(conf_matrix)['iou']
+
+ assert eval_f[0](iou[0], expected['iou_0'])
+ assert eval_f[1](iou[1], expected['iou_1'])
+
+
+@pytest.mark.parametrize("test_input,expected,eval_f", [
+ ([np.zeros(shape=(1,1,1,10,10)), np.zeros(shape=(1,1,1,10,10)), [0]], {'YS': 0., 'SS': 0. , 'YY': 0.},
+ 3 * [lambda x,y: x == y]),
+ ([np.zeros(shape=(1,1,1,10,10)), np.zeros(shape=(1,1,1,10,10)), [1]], {'YS': np.nan, 'SS': np.nan , 'YY': np.nan},
+ 3 * [lambda x,y: np.isnan(x) and np.isnan(y)]),
+ ([np.ones(shape=(1,1,1,10,10)), np.zeros(shape=(1,1,1,10,10)), [0]], {'YS': 1., 'SS': 0. , 'YY': np.nan},
+ 2 * [lambda x,y: x == y] + [lambda x,y: np.isnan(x) and np.isnan(y)]),
+ ([np.concatenate([np.ones(shape=(1,1,1,10,10)), np.zeros(shape=(1,1,1,10,10))], axis=0),
+ np.concatenate([np.zeros(shape=(1,1,1,10,10)), np.ones(shape=(1,1,1,10,10))], axis=0), [0]],
+ {'YS': np.array([[[1.],[np.nan]], [[0.],[1.]]]), 'SS': np.array([[[0.],[1.]], [[1.],[np.nan]]]),
+ 'YY': np.array([[[np.nan],[1.]], [[1.],[0.]]])},
+ 3 * [lambda x,y: nan_save_array_equal(x,y)]),
+ ([np.concatenate([np.ones(shape=(1,1,1,10,10)), np.zeros(shape=(1,1,1,10,10))], axis=0),
+ np.concatenate([np.zeros(shape=(1,1,1,10,10)), np.ones(shape=(1,1,1,10,10))], axis=0), 2],
+ {'YS': np.array([[[1.,1.], [np.nan,0.]], [[0.,np.nan], [1.,1.]]]),
+ 'SS': np.array([[[0.,np.nan], [1.,1.]], [[1.,1.], [np.nan,0.]]]),
+ 'YY': np.array([[[np.nan,0.], [1.,1.]], [[1.,1.], [0.,np.nan]]])},
+ 3 * [lambda x,y: nan_save_array_equal(x, y)]),
+])
+def test_energy_distance_components(test_input, expected, eval_f):
+
+ gt_seg_modes = test_input[0]
+ seg_samples = test_input[1]
+ eval_class_ids = test_input[2]
+
+ results = get_energy_distance_components(gt_seg_modes, seg_samples, eval_class_ids=eval_class_ids)
+
+ assert eval_f[0](results['YS'], expected['YS'])
+ assert eval_f[1](results['SS'], expected['SS'])
+ assert eval_f[2](results['YY'], expected['YY'])
+
+
+@pytest.mark.parametrize("test_input,expected,eval_f", [
+ (np.concatenate([np.zeros(shape=(1,5,5)), 0.1 * np.ones(shape=(1,5,5))]), [5,0], lambda x,y: (x==y).all())
+])
+def test_get_mode_counts(test_input, expected, eval_f):
+ mode_counts = get_mode_counts(test_input)
+
+ assert eval_f(mode_counts, expected)
+
+
+@pytest.mark.parametrize("test_input,expected,eval_f", [
+ ([np.zeros(shape=(1,1,10,10)),
+ np.concatenate([np.ones(shape=(1,1,1,10,5)), np.zeros(shape=(1,1,1,10,5))], axis=-1), 1],
+ [[100, 50, 50]], lambda x,y: (x==y).all()),
+ ([np.concatenate([np.ones(shape=(1,1,10,5)), np.zeros(shape=(1,1,10,5))], axis=-1),
+ np.concatenate([np.ones(shape=(1,1,1,10,10)), np.zeros(shape=(1,1,1,10,10))], axis=0), 2],
+ [[100, 50, 0],[100, 50, 0]], lambda x,y: (x==y).all()),
+ ([np.concatenate([np.ones(shape=(1, 1, 10, 5)), np.zeros(shape=(1, 1, 10, 5))], axis=-1),
+ np.concatenate([3 * np.ones(shape=(1, 1, 1, 10, 10)), 2 * np.ones(shape=(1, 1, 1, 10, 10)),
+ np.ones(shape=(1, 1, 1, 10, 10)), np.zeros(shape=(1, 1, 1, 10, 10))], axis=0), 2],
+ [[200, 50, 50], [200, 50, 50]], lambda x, y: (x == y).all()),
+])
+def test_pixelwise_mode_counts(test_input, expected, eval_f):
+
+ class cf():
+ def __init__(self, num_classes):
+ self.label_switches = {'class_{}'.format(i): np.random.uniform(0.0,1.0) for i in range(num_classes)}
+ self.name2trainId = {**{'class_{}'.format(i): i for i in range(num_classes)},
+ **{'class_{}_2'.format(i): i + num_classes for i in range(num_classes)}}
+ num_classes = test_input[2]
+ pixelwise_mode_counts = get_pixelwise_mode_counts(cf(num_classes), seg=test_input[0], seg_samples=test_input[1])
+
+ assert eval_f(pixelwise_mode_counts, expected)
\ No newline at end of file
diff --git a/training/prob_unet_config.py b/training/prob_unet_config.py
new file mode 100644
index 0000000..50a653a
--- /dev/null
+++ b/training/prob_unet_config.py
@@ -0,0 +1,127 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""CityScapes training config."""
+
+import os
+import numpy as np
+from collections import OrderedDict
+from data.cityscapes.cityscapes_labels import labels as cs_labels_tuple
+
+config_path = os.path.realpath(__file__)
+
+#########################################
+# data-loader #
+#########################################
+
+data_dir = 'PREPROCESSING_OUTPUT_DIRECTORY_ABSOLUTE_PATH'
+resolution = 'quarter'
+label_density = 'gtFine'
+gt_instances = False
+train_cities = ['aachen', 'bochum', 'bremen', 'cologne', 'dusseldorf', 'erfurt', 'hamburg', 'hanover',
+ 'jena', 'krefeld', 'stuttgart', 'strasbourg', 'tubingen', 'weimar', 'zurich']
+val_cities = ['darmstadt', 'monchengladbach', 'ulm', ]
+
+num_classes = 19
+batch_size = 10
+pre_crop_size = [256, 512]
+patch_size = [256, 512]
+n_train_batches = None
+n_val_batches = 274 // batch_size
+n_workers = 5
+ignore_label = 255
+
+da_kwargs = {
+ 'random_crop': True,
+ 'rand_crop_dist': (patch_size[0] / 2., patch_size[1] / 2.),
+ 'do_elastic_deform': True,
+ 'alpha': (0., 800.),
+ 'sigma': (25., 35.),
+ 'do_rotation': True,
+ 'angle_x': (-np.pi / 8., np.pi / 8.),
+ 'angle_y': (0., 0.),
+ 'angle_z': (0., 0.),
+ 'do_scale': True,
+ 'scale': (0.8, 1.2),
+ 'border_mode_data': 'constant',
+ 'border_mode_seg': 'constant',
+ 'border_cval_seg': ignore_label,
+ 'gamma_retain_stats': True,
+ 'gamma_range': (0.7, 1.5),
+ 'p_gamma': 0.3
+}
+
+data_format = 'NCHW'
+one_hot_labels = False
+
+#########################################
+# label-switches #
+#########################################
+
+color_map = {label.trainId:label.color for label in cs_labels_tuple}
+color_map[255] = (0.,0.,0.)
+
+trainId2name = {labels.trainId: labels.name for labels in cs_labels_tuple}
+name2trainId = {labels.name: labels.trainId for labels in cs_labels_tuple}
+
+label_switches = OrderedDict([('sidewalk', 8./17.), ('person', 7./17.), ('car', 6./17.), ('vegetation', 5./17.), ('road', 4./17.)])
+num_classes += len(label_switches)
+switched_Id2name = {19+i:list(label_switches.keys())[i] + '_2' for i in range(len(label_switches))}
+switched_name2Id = {list(label_switches.keys())[i] + '_2':19+i for i in range(len(label_switches))}
+trainId2name = {**trainId2name, **switched_Id2name}
+name2trainId = {**name2trainId, **switched_name2Id}
+
+switched_labels2color = {'road_2': (84, 86, 22), 'person_2': (167, 242, 242), 'vegetation_2': (242, 160, 19),
+ 'car_2': (30, 193, 252), 'sidewalk_2': (46, 247, 180)}
+switched_cmap = {switched_name2Id[i]:switched_labels2color[i] for i in switched_name2Id.keys()}
+color_map = {**color_map, **switched_cmap}
+
+#########################################
+# network & training #
+#########################################
+
+cuda_visible_devices = '0'
+cpu_device = '/cpu:0'
+gpu_device = '/gpu:0'
+
+network_input_shape = (None, 3) + tuple(patch_size)
+network_output_shape = (None, num_classes) + tuple(patch_size)
+label_shape = (None, 1) + tuple(patch_size)
+loss_mask_shape = label_shape
+
+base_channels = 32
+num_channels = [base_channels, 2*base_channels, 4*base_channels,
+ 6*base_channels, 6*base_channels, 6*base_channels, 6*base_channels]
+
+num_convs_per_block = 3
+
+n_training_batches = 240000
+validation = {'n_batches': n_val_batches, 'every_n_batches': 2000}
+
+learning_rate_schedule = 'piecewise_constant'
+learning_rate_kwargs = {'values': [1e-4, 0.5e-4, 1e-5, 0.5e-6],
+ 'boundaries': [80000, 160000, 240000],
+ 'name': 'piecewise_constant_lr_decay'}
+initial_learning_rate = learning_rate_kwargs['values'][0]
+
+regularizarion_weight = 1e-5
+latent_dim = 6
+num_1x1_convs = 3
+beta = 1.0
+analytic_kl = True
+use_posterior_mean = False
+save_every_n_steps = n_training_batches // 3 if n_training_batches >= 100000 else n_training_batches
+disable_progress_bar = False
+
+exp_dir = "EXPERIMENT_OUTPUT_DIRECTORY_ABSOLUTE_PATH"
diff --git a/training/train_prob_unet.py b/training/train_prob_unet.py
new file mode 100644
index 0000000..82c2e92
--- /dev/null
+++ b/training/train_prob_unet.py
@@ -0,0 +1,188 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Probabilistic U-Net training script."""
+
+import tensorflow as tf
+import numpy as np
+import os
+import time
+from tqdm import tqdm
+import shutil
+import logging
+import argparse
+from importlib.machinery import SourceFileLoader
+
+from data.cityscapes.data_loader import get_train_generators
+from model.probabilistic_unet import ProbUNet
+import utils.training_utils as training_utils
+
+
+def train(cf):
+ """Perform training from scratch."""
+
+ # do not use all gpus
+ os.environ["CUDA_VISIBLE_DEVICES"] = cf.cuda_visible_devices
+
+ # initialize data providers
+ data_provider = get_train_generators(cf)
+ train_provider = data_provider['train']
+ val_provider = data_provider['val']
+
+ prob_unet = ProbUNet(latent_dim=cf.latent_dim, num_channels=cf.num_channels,
+ num_1x1_convs=cf.num_1x1_convs,
+ num_classes=cf.num_classes, num_convs_per_block=cf.num_convs_per_block,
+ initializers={'w': training_utils.he_normal(),
+ 'b': tf.truncated_normal_initializer(stddev=0.001)},
+ regularizers={'w': tf.contrib.layers.l2_regularizer(1.0)})
+
+ x = tf.placeholder(tf.float32, shape=cf.network_input_shape)
+ y = tf.placeholder(tf.uint8, shape=cf.label_shape)
+ mask = tf.placeholder(tf.uint8, shape=cf.loss_mask_shape)
+
+ global_step = tf.train.get_or_create_global_step()
+
+ if cf.learning_rate_schedule == 'piecewise_constant':
+ learning_rate = tf.train.piecewise_constant(x=global_step, **cf.learning_rate_kwargs)
+ else:
+ learning_rate = tf.train.exponential_decay(learning_rate=cf.initial_learning_rate, global_step=global_step,
+ **cf.learning_rate_kwargs)
+ with tf.device(cf.gpu_device):
+ prob_unet(x, y, is_training=True, one_hot_labels=cf.one_hot_labels)
+ elbo = prob_unet.elbo(y, reconstruct_posterior_mean=cf.use_posterior_mean, beta=cf.beta, loss_mask=mask,
+ analytic_kl=cf.analytic_kl, one_hot_labels=cf.one_hot_labels)
+ reconstructed_logits = prob_unet._rec_logits
+ sampled_logits = prob_unet.sample()
+
+ reg_loss = cf.regularizarion_weight * tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
+ loss = -elbo + reg_loss
+ rec_loss = prob_unet._rec_loss_mean
+ kl = prob_unet._kl
+
+ mean_val_rec_loss = tf.placeholder(tf.float32, shape=(), name="mean_val_rec_loss")
+ mean_val_kl = tf.placeholder(tf.float32, shape=(), name="mean_val_kl")
+
+ optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss, global_step=global_step)
+
+ # prepare tf summaries
+ train_elbo_summary = tf.summary.scalar('train_elbo', elbo)
+ train_kl_summary = tf.summary.scalar('train_kl', kl)
+ train_rec_loss_summary = tf.summary.scalar('rec_loss', rec_loss)
+ train_loss_summary = tf.summary.scalar('train_loss', loss)
+ reg_loss_summary = tf.summary.scalar('train_reg_loss', reg_loss)
+ lr_summary = tf.summary.scalar('learning_rate', learning_rate)
+ beta_summary = tf.summary.scalar('beta', cf.beta)
+ training_summary_op = tf.summary.merge([train_loss_summary, reg_loss_summary, lr_summary, train_elbo_summary,
+ train_kl_summary, train_rec_loss_summary, beta_summary])
+ batches_per_second = tf.placeholder(tf.float32, shape=(), name="batches_per_sec_placeholder")
+ timing_summary = tf.summary.scalar('batches_per_sec', batches_per_second)
+ val_rec_loss_summary = tf.summary.scalar('val_loss', mean_val_rec_loss)
+ val_kl_summary = tf.summary.scalar('val_kl', mean_val_kl)
+ validation_summary_op = tf.summary.merge([val_rec_loss_summary, val_kl_summary])
+
+ tf.global_variables_initializer()
+
+ # Add ops to save and restore all the variables.
+ saver_hook = tf.train.CheckpointSaverHook(checkpoint_dir=cf.exp_dir, save_steps=cf.save_every_n_steps,
+ saver=tf.train.Saver(save_relative_paths=True))
+ # save config
+ shutil.copyfile(cf.config_path, os.path.join(cf.exp_dir, 'used_config.py'))
+
+ with tf.train.MonitoredTrainingSession(hooks=[saver_hook]) as sess:
+ summary_writer = tf.summary.FileWriter(cf.exp_dir, sess.graph)
+ logging.info('Model: {}'.format(cf.exp_dir))
+
+ for i in tqdm(range(cf.n_training_batches), disable=cf.disable_progress_bar):
+
+ start_time = time.time()
+ train_batch = next(train_provider)
+ _, train_summary = sess.run([optimizer, training_summary_op],
+ feed_dict={x: train_batch['data'], y: train_batch['seg'],
+ mask: train_batch['loss_mask']})
+ summary_writer.add_summary(train_summary, i)
+ time_delta = time.time() - start_time
+ train_speed = sess.run(timing_summary, feed_dict={batches_per_second: 1. / time_delta})
+ summary_writer.add_summary(train_speed, i)
+
+ # validation
+ if i % cf.validation['every_n_batches'] == 0:
+
+ train_rec = sess.run(reconstructed_logits, feed_dict={x: train_batch['data'], y: train_batch['seg']})
+ image_path = os.path.join(cf.exp_dir,
+ 'batch_{}_train_reconstructions.png'.format(i // cf.validation['every_n_batches']))
+ training_utils.plot_batch(train_batch, train_rec, num_classes=cf.num_classes,
+ cmap=cf.color_map, out_dir=image_path)
+
+ running_mean_val_rec_loss = 0.
+ running_mean_val_kl = 0.
+
+ for j in range(cf.validation['n_batches']):
+ val_batch = next(val_provider)
+ val_rec, val_sample, val_rec_loss, val_kl =\
+ sess.run([reconstructed_logits, sampled_logits, rec_loss, kl],
+ feed_dict={x: val_batch['data'], y: val_batch['seg'], mask: val_batch['loss_mask']})
+ running_mean_val_rec_loss += val_rec_loss / cf.validation['n_batches']
+ running_mean_val_kl += val_kl / cf.validation['n_batches']
+
+ if j == 0:
+ image_path = os.path.join(cf.exp_dir,
+ 'batch_{}_val_reconstructions.png'.format(i // cf.validation['every_n_batches']))
+ training_utils.plot_batch(val_batch, val_rec, num_classes=cf.num_classes,
+ cmap=cf.color_map, out_dir=image_path)
+ image_path = os.path.join(cf.exp_dir,
+ 'batch_{}_val_samples.png'.format(i // cf.validation['every_n_batches']))
+
+ for _ in range(3):
+ val_sample_ = sess.run(sampled_logits, feed_dict={x: val_batch['data'], y: val_batch['seg']})
+ val_sample = np.concatenate([val_sample, val_sample_], axis=1)
+
+ training_utils.plot_batch(val_batch, val_sample, num_classes=cf.num_classes,
+ cmap=cf.color_map, out_dir=image_path)
+
+ val_summary = sess.run(validation_summary_op, feed_dict={mean_val_rec_loss: running_mean_val_rec_loss,
+ mean_val_kl: running_mean_val_kl})
+ summary_writer.add_summary(val_summary, i)
+
+ if cf.disable_progress_bar:
+ logging.info('Evaluating epoch {}/{}: validation loss={}, kl={}'\
+ .format(i, cf.n_training_batches, running_mean_val_rec_loss, running_mean_val_kl))
+
+ sess.run(global_step)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description='Training of the Probabilistic U-Net')
+ parser.add_argument('-c', '--config', type=str, default='prob_unet_config.py',
+ help='name of the python script defining the training configuration')
+ parser.add_argument('-d', '--data_dir', type=str, default='',
+ help="full path to the data, if empty the config's data_dir attribute is used")
+ args = parser.parse_args()
+
+ # load config
+ cf = SourceFileLoader('cf', args.config).load_module()
+ if args.data_dir != '':
+ cf.data_dir = args.data_dir
+
+ # prepare experiment directory
+ if not os.path.isdir(cf.exp_dir):
+ os.mkdir(cf.exp_dir)
+
+ # log to file and console
+ log_path = os.path.join(cf.exp_dir, 'train.log')
+ logging.basicConfig(filename=log_path, level=logging.INFO)
+ logging.getLogger().addHandler(logging.StreamHandler())
+ logging.info('Logging to {}'.format(log_path))
+ tf.logging.set_verbosity(tf.logging.INFO)
+
+ train(cf)
\ No newline at end of file
diff --git a/utils/training_utils.py b/utils/training_utils.py
new file mode 100644
index 0000000..3571de6
--- /dev/null
+++ b/utils/training_utils.py
@@ -0,0 +1,249 @@
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Training utilities."""
+
+import tensorflow as tf
+import numpy as np
+import matplotlib
+
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from tensorflow.python.ops.init_ops import VarianceScaling
+
+
+def ce_loss(labels, logits, n_classes, loss_mask=None, data_format='NCHW', one_hot_labels=True, name='ce_loss'):
+ """
+ Cross-entropy loss.
+ :param labels: 4D tensor
+ :param logits: 4D tensor
+ :param n_classes: integer for number of classes
+ :param loss_mask: binary 4D tensor, pixels to mask should be marked by 1s
+ :param data_format: string
+ :param one_hot_labels: bool, indicator for whether labels are to be expected in one-hot representation
+ :param name: string
+ :return: dict of (pixel-wise) mean and sum of cross-entropy loss
+ """
+ with tf.variable_scope(name):
+ # permute class channels into last axis
+ if data_format == 'NCHW':
+ labels = tf.transpose(labels, [0,2,3,1])
+ logits = tf.transpose(logits, [0,2,3,1])
+ elif data_format == 'NCDHW':
+ labels = tf.transpose(labels, [0,2,3,4,1])
+ logits = tf.transpose(logits, [0,2,3,4,1])
+
+ batch_size = tf.cast(tf.shape(labels)[0], tf.float32)
+
+ if one_hot_labels:
+ flat_labels = tf.reshape(labels, [-1, n_classes])
+ else:
+ flat_labels = tf.reshape(labels, [-1])
+ flat_labels = tf.one_hot(indices=flat_labels, depth=n_classes, axis=-1)
+ flat_logits = tf.reshape(logits, [-1, n_classes])
+
+ # do not compute gradients wrt the labels
+ flat_labels = tf.stop_gradient(flat_labels)
+
+ ce_per_pixel = tf.nn.softmax_cross_entropy_with_logits_v2(labels=flat_labels, logits=flat_logits)
+
+ # optional element-wise masking with binary loss mask
+ if loss_mask is None:
+ ce_sum = tf.reduce_sum(ce_per_pixel) / batch_size
+ ce_mean = tf.reduce_mean(ce_per_pixel)
+ else:
+ loss_mask_flat = tf.reshape(loss_mask, [-1,])
+ loss_mask_flat = (1. - tf.cast(loss_mask_flat, tf.float32))
+ ce_sum = tf.reduce_sum(loss_mask_flat * ce_per_pixel) / batch_size
+ n_valid_pixels = tf.reduce_sum(loss_mask_flat)
+ ce_mean = tf.reduce_sum(loss_mask_flat * ce_per_pixel) / n_valid_pixels
+
+ return {'sum': ce_sum, 'mean': ce_mean}
+
+
+def softmax_2_onehot(arr):
+ """Transform a numpy array of softmax values into a one-hot encoded array. Assumes classes are encoded in axis 1.
+ :param arr: ND array
+ :return: ND array
+ """
+ num_classes = arr.shape[1]
+ arr_argmax = np.argmax(arr, axis=1)
+
+ for c in range(num_classes):
+ arr[:,c] = (arr_argmax == c).astype(np.uint8)
+ return arr
+
+
+def numpy_one_hot(label_arr, num_classes):
+ """One-hotify an integer-labeled numpy array. One-hot encoding is encoded in additional last axis.
+ :param label_arr: ND array
+ :param num_classes: integer
+ :return: (N+1)D array
+ """
+ # replace labels >= num_classes with 0
+ label_arr[label_arr >= num_classes] = 0
+
+ res = np.eye(num_classes)[np.array(label_arr).reshape(-1)]
+ return res.reshape(list(label_arr.shape)+[num_classes])
+
+
+def calc_confusion(labels, samples, class_ixs, loss_mask=None):
+ """
+ Compute confusion matrix for each class across the given arrays.
+ Assumes classes are given in integer-valued encoding.
+ :param labels: 4/5D array
+ :param samples: 4/5D array
+ :param class_ixs: integer or list of integers specifying the classes to evaluate
+ :param loss_mask: 4/5D array
+ :return: 2D array
+ """
+ try:
+ assert labels.shape == samples.shape
+ except:
+ raise AssertionError('shape mismatch {} vs. {}'.format(labels.shape, samples.shape))
+
+ if isinstance(class_ixs, int):
+ num_classes = class_ixs
+ class_ixs = range(class_ixs)
+ elif isinstance(class_ixs, list):
+ num_classes = len(class_ixs)
+ else:
+ raise TypeError('arg class_ixs needs to be int or list, not {}.'.format(type(class_ixs)))
+
+ if loss_mask is None:
+ shp = labels.shape
+ loss_mask = np.zeros(shape=(shp[0], 1, shp[2], shp[3]))
+
+ conf_matrix = np.zeros(shape=(num_classes, 4), dtype=np.float32)
+ for i,c in enumerate(class_ixs):
+
+ pred_ = (samples == c).astype(np.uint8)
+ labels_ = (labels == c).astype(np.uint8)
+
+ conf_matrix[i,0] = int(((pred_ != 0) * (labels_ != 0) * (loss_mask != 1)).sum()) # TP
+ conf_matrix[i,1] = int(((pred_ != 0) * (labels_ == 0) * (loss_mask != 1)).sum()) # FP
+ conf_matrix[i,2] = int(((pred_ == 0) * (labels_ == 0) * (loss_mask != 1)).sum()) # TN
+ conf_matrix[i,3] = int(((pred_ == 0) * (labels_ != 0) * (loss_mask != 1)).sum()) # FN
+
+ return conf_matrix
+
+
+def metrics_from_conf_matrix(conf_matrix):
+ """
+ Calculate IoU per class from a confusion_matrix.
+ :param conf_matrix: 2D array of shape (num_classes, 4)
+ :return: dict holding 1D-vectors of metrics
+ """
+ tps = conf_matrix[:,0]
+ fps = conf_matrix[:,1]
+ fns = conf_matrix[:,3]
+
+ metrics = {}
+ metrics['iou'] = np.zeros_like(tps, dtype=np.float32)
+
+ # iterate classes
+ for c in range(tps.shape[0]):
+ # unless both the prediction and the ground-truth is empty, calculate a finite IoU
+ if tps[c] + fps[c] + fns[c] != 0:
+ metrics['iou'][c] = tps[c] / (tps[c] + fps[c] + fns[c])
+ else:
+ metrics['iou'][c] = np.nan
+
+ return metrics
+
+
+def he_normal(seed=None):
+ """He normal initializer.
+ It draws samples from a truncated normal distribution centered on 0
+ with `stddev = sqrt(2 / fan_in)`
+ where `fan_in` is the number of input units in the weight tensor.
+ Arguments:
+ seed: A Python integer. Used to seed the random generator.
+ Returns:
+ An initializer.
+ References:
+ He et al., http://arxiv.org/abs/1502.01852
+ Code:
+ https://github.com/tensorflow/tensorflow/blob/r1.9/tensorflow/python/keras/initializers.py
+ """
+ return VarianceScaling(scale=2., mode='fan_in', distribution='normal', seed=seed)
+
+
+def plot_batch(batch, prediction, cmap, num_classes, out_dir=None, clip_range=True):
+ """
+ Plots a batch of images, segmentations & samples and optionally saves it to disk.
+ :param batch: dict holding images and gt labels for a batch
+ :param prediction: logit prediction of the corresponding batch
+ :param cmap: dictionary as colormap
+ :param out_dir: full path to save png image to
+ :return:
+ """
+ img_arr = batch['data']
+ seg_arr = batch['seg']
+
+ num_predictions = prediction.shape[1] // num_classes
+ num_y_tiles = 2 + num_predictions
+ batch_size = img_arr.shape[0]
+
+ f = plt.figure(figsize=(batch_size * 4, num_y_tiles * 2))
+ gs = gridspec.GridSpec(num_y_tiles, batch_size, wspace=0.0, hspace=0.0)
+
+ # suppress matplotlib range warnings
+ if clip_range:
+ img_arr[img_arr < 0.] = 0.
+ img_arr[img_arr > 1.] = 1.
+
+ for tile in range(batch_size):
+ # image
+ ax = plt.subplot(gs[0, tile])
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+ plt.imshow(np.transpose(img_arr[tile], axes=[1,2,0]))
+
+ # (here sampled) gt segmentation
+ ax = plt.subplot(gs[1, tile])
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+ if seg_arr.shape[1] == 1:
+ gt_seg = np.squeeze(seg_arr[tile], axis=0)
+ else:
+ gt_seg = np.argmax(seg_arr[tile], axis=0)
+ plt.imshow(to_rgb(gt_seg, cmap))
+
+ # multiple predictions can be concatenated in channel axis, iterate all predictions
+ for i in range(num_predictions):
+ ax = plt.subplot(gs[2 + i, tile])
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+ single_prediction = prediction[tile][i * num_classes: (i+1) * num_classes]
+ pred_seg = np.argmax(single_prediction, axis=0)
+ plt.imshow(to_rgb(pred_seg, cmap))
+ if out_dir is not None:
+ plt.savefig(out_dir, dpi=200, bbox_inches='tight', pad_inches=0.0)
+ plt.close(f)
+
+
+def to_rgb(arr, cmap):
+ """
+ Transform an integer-labeled segmentation map using an rgb color-map.
+ :param arr: img_arr w/o a color-channel
+ :param cmap: dictionary mapping from integer class labels to rgb values
+ :return:
+ """
+ new_arr = np.zeros(shape=(arr.shape)+(3,))
+ for c in cmap.keys():
+ ixs = np.where(arr == c)
+ new_arr[ixs] = [cmap[c][i] / 255. for i in range(3)]
+ return new_arr