From 556fa567a8c7807a8dfde13edb01eea49ec16d03 Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Tue, 2 Aug 2022 15:20:16 +0800 Subject: [PATCH 01/25] [Feature] Support MViT and add checkpoints. (#924) * [Feature] Support MViT. * Add MViT configs and docs * Add unit test * Fix unit tests. --- README.md | 1 + README_zh-CN.md | 1 + configs/_base_/models/mvit/mvitv2-base.py | 19 + configs/_base_/models/mvit/mvitv2-large.py | 23 + configs/_base_/models/mvit/mvitv2-small.py | 19 + configs/_base_/models/mvit/mvitv2-tiny.py | 19 + configs/mvit/README.md | 44 ++ configs/mvit/metafile.yml | 95 +++ configs/mvit/mvitv2-base_8xb256_in1k.py | 29 + configs/mvit/mvitv2-large_8xb256_in1k.py | 29 + configs/mvit/mvitv2-small_8xb256_in1k.py | 29 + configs/mvit/mvitv2-tiny_8xb256_in1k.py | 29 + docs/en/model_zoo.md | 4 + mmcls/models/backbones/__init__.py | 3 +- mmcls/models/backbones/mvit.py | 700 ++++++++++++++++++ model-index.yml | 1 + .../test_datasets/test_dataset_wrapper.py | 2 +- tests/test_models/test_backbones/test_mvit.py | 185 +++++ 18 files changed, 1230 insertions(+), 2 deletions(-) create mode 100644 configs/_base_/models/mvit/mvitv2-base.py create mode 100644 configs/_base_/models/mvit/mvitv2-large.py create mode 100644 configs/_base_/models/mvit/mvitv2-small.py create mode 100644 configs/_base_/models/mvit/mvitv2-tiny.py create mode 100644 configs/mvit/README.md create mode 100644 configs/mvit/metafile.yml create mode 100644 configs/mvit/mvitv2-base_8xb256_in1k.py create mode 100644 configs/mvit/mvitv2-large_8xb256_in1k.py create mode 100644 configs/mvit/mvitv2-small_8xb256_in1k.py create mode 100644 configs/mvit/mvitv2-tiny_8xb256_in1k.py create mode 100644 mmcls/models/backbones/mvit.py create mode 100644 tests/test_models/test_backbones/test_mvit.py diff --git a/README.md b/README.md index f47fc145c31..09e227339c1 100644 --- a/README.md +++ b/README.md @@ -142,6 +142,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea - [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer) - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet) - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer) +- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit) diff --git a/README_zh-CN.md b/README_zh-CN.md index 592a1d1ec11..f6235a0b85c 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -142,6 +142,7 @@ pip3 install -e . - [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer) - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet) - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer) +- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit) diff --git a/configs/_base_/models/mvit/mvitv2-base.py b/configs/_base_/models/mvit/mvitv2-base.py new file mode 100644 index 00000000000..c75e78ef706 --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-base.py @@ -0,0 +1,19 @@ +model = dict( + type='ImageClassifier', + backbone=dict(type='MViT', arch='base', drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=768, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/mvit/mvitv2-large.py b/configs/_base_/models/mvit/mvitv2-large.py new file mode 100644 index 00000000000..aa4a32503e1 --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-large.py @@ -0,0 +1,23 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='MViT', + arch='large', + drop_path_rate=0.5, + dim_mul_in_attention=False), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=1152, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/mvit/mvitv2-small.py b/configs/_base_/models/mvit/mvitv2-small.py new file mode 100644 index 00000000000..bb9329df0b3 --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-small.py @@ -0,0 +1,19 @@ +model = dict( + type='ImageClassifier', + backbone=dict(type='MViT', arch='small', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=768, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/mvit/mvitv2-tiny.py b/configs/_base_/models/mvit/mvitv2-tiny.py new file mode 100644 index 00000000000..7ca85dc3e64 --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-tiny.py @@ -0,0 +1,19 @@ +model = dict( + type='ImageClassifier', + backbone=dict(type='MViT', arch='tiny', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=768, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/mvit/README.md b/configs/mvit/README.md new file mode 100644 index 00000000000..6f5c5608d70 --- /dev/null +++ b/configs/mvit/README.md @@ -0,0 +1,44 @@ +# MViT V2 + +> [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf) + + + +## Abstract + +In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video +classification, as well as object detection. We present an improved version of MViT that incorporates +decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture +in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where +it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where +it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art +performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 boxAP on COCO object detection as +well as 86.1% on Kinetics-400 video classification. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :---------------------------------------------------------------------: | +| MViTv2-tiny\* | From scratch | 24.17 | 4.70 | 82.33 | 96.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-tiny_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth) | +| MViTv2-small\* | From scratch | 34.87 | 7.00 | 83.63 | 96.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) | +| MViTv2-base\* | From scratch | 51.47 | 10.20 | 84.34 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) | +| MViTv2-large\* | From scratch | 217.99 | 42.10 | 85.25 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/mvit). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@inproceedings{li2021improved, + title={MViTv2: Improved multiscale vision transformers for classification and detection}, + author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph}, + booktitle={CVPR}, + year={2022} +} +``` diff --git a/configs/mvit/metafile.yml b/configs/mvit/metafile.yml new file mode 100644 index 00000000000..8d46a0c8cdc --- /dev/null +++ b/configs/mvit/metafile.yml @@ -0,0 +1,95 @@ +Collections: + - Name: MViT V2 + Metadata: + Architecture: + - Attention Dropout + - Convolution + - Dense Connections + - GELU + - Layer Normalization + - Scaled Dot-Product Attention + - Attention Pooling + Paper: + URL: http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf + Title: 'MViTv2: Improved Multiscale Vision Transformers for Classification and Detection' + README: configs/mvit/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/mvit.py + Version: v0.24.0 + +Models: + - Name: mvitv2-tiny_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 4700000000 + Parameters: 24173320 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 82.33 + Top 5 Accuracy: 96.15 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_T_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-tiny_8xb256_in1k.py + + - Name: mvitv2-small_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 7000000000 + Parameters: 34870216 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 83.63 + Top 5 Accuracy: 96.51 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_S_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-small_8xb256_in1k.py + + - Name: mvitv2-base_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 10200000000 + Parameters: 51472744 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 84.34 + Top 5 Accuracy: 96.86 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_B_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-base_8xb256_in1k.py + + - Name: mvitv2-large_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 42100000000 + Parameters: 217992952 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 85.25 + Top 5 Accuracy: 97.14 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_L_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-large_8xb256_in1k.py diff --git a/configs/mvit/mvitv2-base_8xb256_in1k.py b/configs/mvit/mvitv2-base_8xb256_in1k.py new file mode 100644 index 00000000000..ea92cf40c2a --- /dev/null +++ b/configs/mvit/mvitv2-base_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/mvit/mvitv2-large_8xb256_in1k.py b/configs/mvit/mvitv2-large_8xb256_in1k.py new file mode 100644 index 00000000000..fbb81d69b7b --- /dev/null +++ b/configs/mvit/mvitv2-large_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-large.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs2048_AdamW.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/mvit/mvitv2-small_8xb256_in1k.py b/configs/mvit/mvitv2-small_8xb256_in1k.py new file mode 100644 index 00000000000..18038593de8 --- /dev/null +++ b/configs/mvit/mvitv2-small_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-small.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs2048_AdamW.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/mvit/mvitv2-tiny_8xb256_in1k.py b/configs/mvit/mvitv2-tiny_8xb256_in1k.py new file mode 100644 index 00000000000..f4b9bc48c21 --- /dev/null +++ b/configs/mvit/mvitv2-tiny_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-tiny.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs2048_AdamW.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md index 83e6ec5d59a..7a9a750b86b 100644 --- a/docs/en/model_zoo.md +++ b/docs/en/model_zoo.md @@ -141,6 +141,10 @@ The ResNet family models below are trained by standard data augmentations, i.e., | VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | | VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | | VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| MViTv2-tiny\* | 24.17 | 4.70 | 82.33 | 96.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-tiny_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth) | +| MViTv2-small\* | 34.87 | 7.00 | 83.63 | 96.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) | +| MViTv2-base\* | 51.47 | 10.20 | 84.34 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) | +| MViTv2-large\* | 217.99 | 42.10 | 85.25 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) | *Models with * are converted from other repos, others are trained by ourselves.* diff --git a/mmcls/models/backbones/__init__.py b/mmcls/models/backbones/__init__.py index 41e72f9d0d6..737356edb2a 100644 --- a/mmcls/models/backbones/__init__.py +++ b/mmcls/models/backbones/__init__.py @@ -12,6 +12,7 @@ from .mlp_mixer import MlpMixer from .mobilenet_v2 import MobileNetV2 from .mobilenet_v3 import MobileNetV3 +from .mvit import MViT from .poolformer import PoolFormer from .regnet import RegNet from .repmlp import RepMLPNet @@ -42,5 +43,5 @@ 'Conformer', 'MlpMixer', 'DistilledVisionTransformer', 'PCPVT', 'SVT', 'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c', 'ConvMixer', 'CSPDarkNet', 'CSPResNet', 'CSPResNeXt', 'CSPNet', 'RepMLPNet', - 'PoolFormer', 'DenseNet', 'VAN' + 'PoolFormer', 'DenseNet', 'VAN', 'MViT' ] diff --git a/mmcls/models/backbones/mvit.py b/mmcls/models/backbones/mvit.py new file mode 100644 index 00000000000..b9e67df95be --- /dev/null +++ b/mmcls/models/backbones/mvit.py @@ -0,0 +1,700 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Optional, Sequence + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import build_norm_layer +from mmcv.cnn.bricks import DropPath +from mmcv.cnn.bricks.transformer import PatchEmbed, build_activation_layer +from mmcv.cnn.utils.weight_init import trunc_normal_ +from mmcv.runner import BaseModule, ModuleList +from mmcv.utils import to_2tuple + +from ..builder import BACKBONES +from ..utils import resize_pos_embed +from .base_backbone import BaseBackbone + + +def resize_decomposed_rel_pos(rel_pos, q_size, k_size): + """Get relative positional embeddings according to the relative positions + of query and key sizes. + + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + resized = F.interpolate( + # (L, C) -> (1, C, L) + rel_pos.transpose(0, 1).unsqueeze(0), + size=max_rel_dist, + mode='linear', + ) + # (1, C, L) -> (L, C) + resized = resized.squeeze(0).transpose(0, 1) + else: + resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_h_ratio = max(k_size / q_size, 1.0) + k_h_ratio = max(q_size / k_size, 1.0) + q_coords = torch.arange(q_size)[:, None] * q_h_ratio + k_coords = torch.arange(k_size)[None, :] * k_h_ratio + relative_coords = (q_coords - k_coords) + (k_size - 1) * k_h_ratio + + return resized[relative_coords.long()] + + +def add_decomposed_rel_pos(attn, + q, + q_shape, + k_shape, + rel_pos_h, + rel_pos_w, + has_cls_token=False): + """Spatial Relative Positional Embeddings.""" + sp_idx = 1 if has_cls_token else 0 + B, num_heads, _, C = q.shape + q_h, q_w = q_shape + k_h, k_w = k_shape + + Rh = resize_decomposed_rel_pos(rel_pos_h, q_h, k_h) + Rw = resize_decomposed_rel_pos(rel_pos_w, q_w, k_w) + + r_q = q[:, :, sp_idx:].reshape(B, num_heads, q_h, q_w, C) + rel_h = torch.einsum('byhwc,hkc->byhwk', r_q, Rh) + rel_w = torch.einsum('byhwc,wkc->byhwk', r_q, Rw) + rel_pos_embed = rel_h[:, :, :, :, :, None] + rel_w[:, :, :, :, None, :] + + attn_map = attn[:, :, sp_idx:, sp_idx:].view(B, -1, q_h, q_w, k_h, k_w) + attn_map += rel_pos_embed + attn[:, :, sp_idx:, sp_idx:] = attn_map.view(B, -1, q_h * q_w, k_h * k_w) + + return attn + + +class MLP(BaseModule): + """Two-layer multilayer perceptron. + + Comparing with :class:`mmcv.cnn.bricks.transformer.FFN`, this class allows + different input and output channel numbers. + + Args: + in_channels (int): The number of input channels. + hidden_channels (int, optional): The number of hidden layer channels. + If None, same as the ``in_channels``. Defaults to None. + out_channels (int, optional): The number of output channels. If None, + same as the ``in_channels``. Defaults to None. + act_cfg (dict): The config of activation function. + Defaults to ``dict(type='GELU')``. + init_cfg (dict, optional): The config of weight initialization. + Defaults to None. + """ + + def __init__(self, + in_channels, + hidden_channels=None, + out_channels=None, + act_cfg=dict(type='GELU'), + init_cfg=None): + super().__init__(init_cfg=init_cfg) + out_channels = out_channels or in_channels + hidden_channels = hidden_channels or in_channels + self.fc1 = nn.Linear(in_channels, hidden_channels) + self.act = build_activation_layer(act_cfg) + self.fc2 = nn.Linear(hidden_channels, out_channels) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + return x + + +def attention_pool(x: torch.Tensor, + pool: nn.Module, + in_size: tuple, + norm: Optional[nn.Module] = None): + """Pooling the feature tokens. + + Args: + x (torch.Tensor): The input tensor, should be with shape + ``(B, num_heads, L, C)`` or ``(B, L, C)``. + pool (nn.Module): The pooling module. + in_size (Tuple[int]): The shape of the input feature map. + norm (nn.Module, optional): The normalization module. + Defaults to None. + """ + ndim = x.ndim + if ndim == 4: + B, num_heads, L, C = x.shape + elif ndim == 3: + num_heads = 1 + B, L, C = x.shape + else: + raise RuntimeError(f'Unsupported input dimension {x.shape}') + + H, W = in_size + assert L == H * W + + # (B, num_heads, H*W, C) -> (B*num_heads, C, H, W) + x = x.reshape(B * num_heads, H, W, C).permute(0, 3, 1, 2).contiguous() + x = pool(x) + out_size = x.shape[-2:] + + # (B*num_heads, C, H', W') -> (B, num_heads, H'*W', C) + x = x.reshape(B, num_heads, C, -1).transpose(2, 3) + + if norm is not None: + x = norm(x) + + if ndim == 3: + x = x.squeeze(1) + + return x, out_size + + +class MultiScaleAttention(BaseModule): + """Multiscale Multi-head Attention block. + + Args: + in_dims (int): Number of input channels. + out_dims (int): Number of output channels. + num_heads (int): Number of attention heads. + qkv_bias (bool): If True, add a learnable bias to query, key and + value. Defaults to True. + norm_cfg (dict): The config of normalization layers. + Defaults to ``dict(type='LN')``. + pool_kernel (tuple): kernel size for qkv pooling layers. + Defaults to (3, 3). + stride_q (int): stride size for q pooling layer. Defaults to 1. + stride_kv (int): stride size for kv pooling layer. Defaults to 1. + rel_pos_spatial (bool): Whether to enable the spatial relative + position embedding. Defaults to True. + residual_pooling (bool): Whether to enable the residual connection + after attention pooling. Defaults to True. + input_size (Tuple[int], optional): The input resolution, necessary + if enable the ``rel_pos_spatial``. Defaults to None. + rel_pos_zero_init (bool): If True, zero initialize relative + positional parameters. Defaults to False. + init_cfg (dict, optional): The config of weight initialization. + Defaults to None. + """ + + def __init__(self, + in_dims, + out_dims, + num_heads, + qkv_bias=True, + norm_cfg=dict(type='LN'), + pool_kernel=(3, 3), + stride_q=1, + stride_kv=1, + rel_pos_spatial=False, + residual_pooling=True, + input_size=None, + rel_pos_zero_init=False, + init_cfg=None): + super().__init__(init_cfg=init_cfg) + self.num_heads = num_heads + self.in_dims = in_dims + self.out_dims = out_dims + + head_dim = out_dims // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(in_dims, out_dims * 3, bias=qkv_bias) + self.proj = nn.Linear(out_dims, out_dims) + + # qkv pooling + pool_padding = [k // 2 for k in pool_kernel] + pool_dims = out_dims // num_heads + + def build_pooling(stride): + pool = nn.Conv2d( + pool_dims, + pool_dims, + pool_kernel, + stride=stride, + padding=pool_padding, + groups=pool_dims, + bias=False, + ) + norm = build_norm_layer(norm_cfg, pool_dims)[1] + return pool, norm + + self.pool_q, self.norm_q = build_pooling(stride_q) + self.pool_k, self.norm_k = build_pooling(stride_kv) + self.pool_v, self.norm_v = build_pooling(stride_kv) + + self.residual_pooling = residual_pooling + + self.rel_pos_spatial = rel_pos_spatial + self.rel_pos_zero_init = rel_pos_zero_init + if self.rel_pos_spatial: + # initialize relative positional embeddings + assert input_size[0] == input_size[1] + + size = input_size[0] + rel_dim = 2 * max(size // stride_q, size // stride_kv) - 1 + self.rel_pos_h = nn.Parameter(torch.zeros(rel_dim, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(rel_dim, head_dim)) + + def init_weights(self): + """Weight initialization.""" + super().init_weights() + + if (isinstance(self.init_cfg, dict) + and self.init_cfg['type'] == 'Pretrained'): + # Suppress rel_pos_zero_init if use pretrained model. + return + + if not self.rel_pos_zero_init: + trunc_normal_(self.rel_pos_h, std=0.02) + trunc_normal_(self.rel_pos_w, std=0.02) + + def forward(self, x, in_size): + """Forward the MultiScaleAttention.""" + B, N, _ = x.shape # (B, H*W, C) + + # qkv: (B, H*W, 3, num_heads, C) + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, -1) + # q, k, v: (B, num_heads, H*W, C) + q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(0) + + q, q_shape = attention_pool(q, self.pool_q, in_size, norm=self.norm_q) + k, k_shape = attention_pool(k, self.pool_k, in_size, norm=self.norm_k) + v, v_shape = attention_pool(v, self.pool_v, in_size, norm=self.norm_v) + + attn = (q * self.scale) @ k.transpose(-2, -1) + if self.rel_pos_spatial: + attn = add_decomposed_rel_pos(attn, q, q_shape, k_shape, + self.rel_pos_h, self.rel_pos_w) + + attn = attn.softmax(dim=-1) + x = attn @ v + + if self.residual_pooling: + x = x + q + + # (B, num_heads, H'*W', C'//num_heads) -> (B, H'*W', C') + x = x.transpose(1, 2).reshape(B, -1, self.out_dims) + x = self.proj(x) + + return x, q_shape + + +class MultiScaleBlock(BaseModule): + """Multiscale Transformer blocks. + + Args: + in_dims (int): Number of input channels. + out_dims (int): Number of output channels. + num_heads (int): Number of attention heads. + mlp_ratio (float): Ratio of hidden dimensions in MLP layers. + Defaults to 4.0. + qkv_bias (bool): If True, add a learnable bias to query, key and + value. Defaults to True. + drop_path (float): Stochastic depth rate. Defaults to 0. + norm_cfg (dict): The config of normalization layers. + Defaults to ``dict(type='LN')``. + act_cfg (dict): The config of activation function. + Defaults to ``dict(type='GELU')``. + qkv_pool_kernel (tuple): kernel size for qkv pooling layers. + Defaults to (3, 3). + stride_q (int): stride size for q pooling layer. Defaults to 1. + stride_kv (int): stride size for kv pooling layer. Defaults to 1. + rel_pos_spatial (bool): Whether to enable the spatial relative + position embedding. Defaults to True. + residual_pooling (bool): Whether to enable the residual connection + after attention pooling. Defaults to True. + dim_mul_in_attention (bool): Whether to multiply the ``embed_dims`` in + attention layers. If False, multiply it in MLP layers. + Defaults to True. + input_size (Tuple[int], optional): The input resolution, necessary + if enable the ``rel_pos_spatial``. Defaults to None. + rel_pos_zero_init (bool): If True, zero initialize relative + positional parameters. Defaults to False. + init_cfg (dict, optional): The config of weight initialization. + Defaults to None. + """ + + def __init__( + self, + in_dims, + out_dims, + num_heads, + mlp_ratio=4.0, + qkv_bias=True, + drop_path=0.0, + norm_cfg=dict(type='LN'), + act_cfg=dict(type='GELU'), + qkv_pool_kernel=(3, 3), + stride_q=1, + stride_kv=1, + rel_pos_spatial=True, + residual_pooling=True, + dim_mul_in_attention=True, + input_size=None, + rel_pos_zero_init=False, + init_cfg=None, + ): + super().__init__(init_cfg=init_cfg) + self.in_dims = in_dims + self.out_dims = out_dims + self.norm1 = build_norm_layer(norm_cfg, in_dims)[1] + self.dim_mul_in_attention = dim_mul_in_attention + + attn_dims = out_dims if dim_mul_in_attention else in_dims + self.attn = MultiScaleAttention( + in_dims, + attn_dims, + num_heads=num_heads, + qkv_bias=qkv_bias, + norm_cfg=norm_cfg, + pool_kernel=qkv_pool_kernel, + stride_q=stride_q, + stride_kv=stride_kv, + rel_pos_spatial=rel_pos_spatial, + residual_pooling=residual_pooling, + input_size=input_size, + rel_pos_zero_init=rel_pos_zero_init) + self.drop_path = DropPath( + drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = build_norm_layer(norm_cfg, attn_dims)[1] + + self.mlp = MLP( + in_channels=attn_dims, + hidden_channels=int(attn_dims * mlp_ratio), + out_channels=out_dims, + act_cfg=act_cfg) + + if in_dims != out_dims: + self.proj = nn.Linear(in_dims, out_dims) + else: + self.proj = None + + if stride_q > 1: + kernel_skip = stride_q + 1 + padding_skip = int(kernel_skip // 2) + self.pool_skip = nn.MaxPool2d( + kernel_skip, stride_q, padding_skip, ceil_mode=False) + + if input_size is not None: + input_size = to_2tuple(input_size) + out_size = [size // stride_q for size in input_size] + self.init_out_size = out_size + else: + self.init_out_size = None + else: + self.pool_skip = None + self.init_out_size = input_size + + def forward(self, x, in_size): + x_norm = self.norm1(x) + x_attn, out_size = self.attn(x_norm, in_size) + + if self.dim_mul_in_attention and self.proj is not None: + skip = self.proj(x_norm) + else: + skip = x + + if self.pool_skip is not None: + skip, _ = attention_pool(skip, self.pool_skip, in_size) + + x = skip + self.drop_path(x_attn) + x_norm = self.norm2(x) + x_mlp = self.mlp(x_norm) + + if not self.dim_mul_in_attention and self.proj is not None: + skip = self.proj(x_norm) + else: + skip = x + + x = skip + self.drop_path(x_mlp) + + return x, out_size + + +@BACKBONES.register_module() +class MViT(BaseBackbone): + """Multi-scale ViT v2. + + A PyTorch implement of : `MViTv2: Improved Multiscale Vision Transformers + for Classification and Detection `_ + + Inspiration from `the official implementation + `_ and `the detectron2 + implementation `_ + + Args: + arch (str | dict): MViT architecture. If use string, choose + from 'tiny', 'small', 'base' and 'large'. If use dict, it should + have below keys: + + - **embed_dims** (int): The dimensions of embedding. + - **num_layers** (int): The number of layers. + - **num_heads** (int): The number of heads in attention + modules of the initial layer. + - **downscale_indices** (List[int]): The layer indices to downscale + the feature map. + + Defaults to 'base'. + img_size (int): The expected input image shape. Defaults to 224. + in_channels (int): The num of input channels. Defaults to 3. + out_scales (int | Sequence[int]): The output scale indices. + They should not exceed the length of ``downscale_indices``. + Defaults to -1, which means the last scale. + drop_path_rate (float): Stochastic depth rate. Defaults to 0.1. + use_abs_pos_embed (bool): If True, add absolute position embedding to + the patch embedding. Defaults to False. + interpolate_mode (str): Select the interpolate mode for absolute + position embedding vector resize. Defaults to "bicubic". + pool_kernel (tuple): kernel size for qkv pooling layers. + Defaults to (3, 3). + dim_mul (int): The magnification for ``embed_dims`` in the downscale + layers. Defaults to 2. + head_mul (int): The magnification for ``num_heads`` in the downscale + layers. Defaults to 2. + adaptive_kv_stride (int): The stride size for kv pooling in the initial + layer. Defaults to 4. + rel_pos_spatial (bool): Whether to enable the spatial relative position + embedding. Defaults to True. + residual_pooling (bool): Whether to enable the residual connection + after attention pooling. Defaults to True. + dim_mul_in_attention (bool): Whether to multiply the ``embed_dims`` in + attention layers. If False, multiply it in MLP layers. + Defaults to True. + rel_pos_zero_init (bool): If True, zero initialize relative + positional parameters. Defaults to False. + mlp_ratio (float): Ratio of hidden dimensions in MLP layers. + Defaults to 4.0. + qkv_bias (bool): enable bias for qkv if True. Defaults to True. + norm_cfg (dict): Config dict for normalization layer for all output + features. Defaults to ``dict(type='LN', eps=1e-6)``. + patch_cfg (dict): Config dict for the patch embedding layer. + Defaults to ``dict(kernel_size=7, stride=4, padding=3)``. + init_cfg (dict, optional): The Config for initialization. + Defaults to None. + + Examples: + >>> import torch + >>> from mmcls.models import build_backbone + >>> + >>> cfg = dict(type='MViT', arch='tiny', out_scales=[0, 1, 2, 3]) + >>> model = build_backbone(cfg) + >>> inputs = torch.rand(1, 3, 224, 224) + >>> outputs = model(inputs) + >>> for i, output in enumerate(outputs): + >>> print(f'scale{i}: {output.shape}') + scale0: torch.Size([1, 96, 56, 56]) + scale1: torch.Size([1, 192, 28, 28]) + scale2: torch.Size([1, 384, 14, 14]) + scale3: torch.Size([1, 768, 7, 7]) + """ + arch_zoo = { + 'tiny': { + 'embed_dims': 96, + 'num_layers': 10, + 'num_heads': 1, + 'downscale_indices': [1, 3, 8] + }, + 'small': { + 'embed_dims': 96, + 'num_layers': 16, + 'num_heads': 1, + 'downscale_indices': [1, 3, 14] + }, + 'base': { + 'embed_dims': 96, + 'num_layers': 24, + 'num_heads': 1, + 'downscale_indices': [2, 5, 21] + }, + 'large': { + 'embed_dims': 144, + 'num_layers': 48, + 'num_heads': 2, + 'downscale_indices': [2, 8, 44] + }, + } + num_extra_tokens = 0 + + def __init__(self, + arch='base', + img_size=224, + in_channels=3, + out_scales=-1, + drop_path_rate=0., + use_abs_pos_embed=False, + interpolate_mode='bicubic', + pool_kernel=(3, 3), + dim_mul=2, + head_mul=2, + adaptive_kv_stride=4, + rel_pos_spatial=True, + residual_pooling=True, + dim_mul_in_attention=True, + rel_pos_zero_init=False, + mlp_ratio=4., + qkv_bias=True, + norm_cfg=dict(type='LN', eps=1e-6), + patch_cfg=dict(kernel_size=7, stride=4, padding=3), + init_cfg=None): + super().__init__(init_cfg) + + if isinstance(arch, str): + arch = arch.lower() + assert arch in set(self.arch_zoo), \ + f'Arch {arch} is not in default archs {set(self.arch_zoo)}' + self.arch_settings = self.arch_zoo[arch] + else: + essential_keys = { + 'embed_dims', 'num_layers', 'num_heads', 'downscale_indices' + } + assert isinstance(arch, dict) and essential_keys <= set(arch), \ + f'Custom arch needs a dict with keys {essential_keys}' + self.arch_settings = arch + + self.embed_dims = self.arch_settings['embed_dims'] + self.num_layers = self.arch_settings['num_layers'] + self.num_heads = self.arch_settings['num_heads'] + self.downscale_indices = self.arch_settings['downscale_indices'] + self.num_scales = len(self.downscale_indices) + 1 + self.stage_indices = { + index - 1: i + for i, index in enumerate(self.downscale_indices) + } + self.stage_indices[self.num_layers - 1] = self.num_scales - 1 + self.use_abs_pos_embed = use_abs_pos_embed + self.interpolate_mode = interpolate_mode + + if isinstance(out_scales, int): + out_scales = [out_scales] + assert isinstance(out_scales, Sequence), \ + f'"out_scales" must by a sequence or int, ' \ + f'get {type(out_scales)} instead.' + for i, index in enumerate(out_scales): + if index < 0: + out_scales[i] = self.num_scales + index + assert 0 <= out_scales[i] <= self.num_scales, \ + f'Invalid out_scales {index}' + self.out_scales = sorted(list(out_scales)) + + # Set patch embedding + _patch_cfg = dict( + in_channels=in_channels, + input_size=img_size, + embed_dims=self.embed_dims, + conv_type='Conv2d', + ) + _patch_cfg.update(patch_cfg) + self.patch_embed = PatchEmbed(**_patch_cfg) + self.patch_resolution = self.patch_embed.init_out_size + + # Set absolute position embedding + if self.use_abs_pos_embed: + num_patches = self.patch_resolution[0] * self.patch_resolution[1] + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches, self.embed_dims)) + + # stochastic depth decay rule + dpr = np.linspace(0, drop_path_rate, self.num_layers) + + self.blocks = ModuleList() + out_dims_list = [self.embed_dims] + num_heads = self.num_heads + stride_kv = adaptive_kv_stride + input_size = self.patch_resolution + for i in range(self.num_layers): + if i in self.downscale_indices: + num_heads *= head_mul + stride_q = 2 + stride_kv = max(stride_kv // 2, 1) + else: + stride_q = 1 + + # Set output embed_dims + if dim_mul_in_attention and i in self.downscale_indices: + # multiply embed_dims in downscale layers. + out_dims = out_dims_list[-1] * dim_mul + elif not dim_mul_in_attention and i + 1 in self.downscale_indices: + # multiply embed_dims before downscale layers. + out_dims = out_dims_list[-1] * dim_mul + else: + out_dims = out_dims_list[-1] + + attention_block = MultiScaleBlock( + in_dims=out_dims_list[-1], + out_dims=out_dims, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop_path=dpr[i], + norm_cfg=norm_cfg, + qkv_pool_kernel=pool_kernel, + stride_q=stride_q, + stride_kv=stride_kv, + rel_pos_spatial=rel_pos_spatial, + residual_pooling=residual_pooling, + dim_mul_in_attention=dim_mul_in_attention, + input_size=input_size, + rel_pos_zero_init=rel_pos_zero_init) + self.blocks.append(attention_block) + + input_size = attention_block.init_out_size + out_dims_list.append(out_dims) + + if i in self.stage_indices: + stage_index = self.stage_indices[i] + if stage_index in self.out_scales: + norm_layer = build_norm_layer(norm_cfg, out_dims)[1] + self.add_module(f'norm{stage_index}', norm_layer) + + def init_weights(self): + super().init_weights() + + if (isinstance(self.init_cfg, dict) + and self.init_cfg['type'] == 'Pretrained'): + # Suppress default init if use pretrained model. + return + + if self.use_abs_pos_embed: + trunc_normal_(self.pos_embed, std=0.02) + + def forward(self, x): + """Forward the MViT.""" + B = x.shape[0] + x, patch_resolution = self.patch_embed(x) + + if self.use_abs_pos_embed: + x = x + resize_pos_embed( + self.pos_embed, + self.patch_resolution, + patch_resolution, + mode=self.interpolate_mode, + num_extra_tokens=self.num_extra_tokens) + + outs = [] + for i, block in enumerate(self.blocks): + x, patch_resolution = block(x, patch_resolution) + + if i in self.stage_indices: + stage_index = self.stage_indices[i] + if stage_index in self.out_scales: + B, _, C = x.shape + x = getattr(self, f'norm{stage_index}')(x) + out = x.transpose(1, 2).reshape(B, C, *patch_resolution) + outs.append(out.contiguous()) + + return tuple(outs) diff --git a/model-index.yml b/model-index.yml index f0e0d75ccf3..a067de57261 100644 --- a/model-index.yml +++ b/model-index.yml @@ -27,3 +27,4 @@ Import: - configs/convmixer/metafile.yml - configs/densenet/metafile.yml - configs/poolformer/metafile.yml + - configs/mvit/metafile.yml diff --git a/tests/test_data/test_datasets/test_dataset_wrapper.py b/tests/test_data/test_datasets/test_dataset_wrapper.py index b6430f41a00..fc4e266ba1f 100644 --- a/tests/test_data/test_datasets/test_dataset_wrapper.py +++ b/tests/test_data/test_datasets/test_dataset_wrapper.py @@ -52,7 +52,7 @@ def construct_toy_single_label_dataset(length): dataset.data_infos.__len__.return_value = length dataset.get_cat_ids = MagicMock(side_effect=lambda idx: cat_ids_list[idx]) dataset.get_gt_labels = \ - MagicMock(side_effect=lambda: np.array(cat_ids_list)) + MagicMock(side_effect=lambda: cat_ids_list) dataset.evaluate = MagicMock(side_effect=mock_evaluate) return dataset, cat_ids_list diff --git a/tests/test_models/test_backbones/test_mvit.py b/tests/test_models/test_backbones/test_mvit.py new file mode 100644 index 00000000000..a37e93f55b2 --- /dev/null +++ b/tests/test_models/test_backbones/test_mvit.py @@ -0,0 +1,185 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from copy import deepcopy +from unittest import TestCase + +import torch + +from mmcls.models.backbones import MViT + + +class TestMViT(TestCase): + + def setUp(self): + self.cfg = dict(arch='tiny', img_size=224, drop_path_rate=0.1) + + def test_arch(self): + # Test invalid default arch + with self.assertRaisesRegex(AssertionError, 'not in default archs'): + cfg = deepcopy(self.cfg) + cfg['arch'] = 'unknown' + MViT(**cfg) + + # Test invalid custom arch + with self.assertRaisesRegex(AssertionError, 'Custom arch needs'): + cfg = deepcopy(self.cfg) + cfg['arch'] = { + 'embed_dims': 96, + 'num_layers': 10, + } + MViT(**cfg) + + # Test custom arch + cfg = deepcopy(self.cfg) + embed_dims = 96 + num_layers = 10 + num_heads = 1 + downscale_indices = (2, 5, 7) + cfg['arch'] = { + 'embed_dims': embed_dims, + 'num_layers': num_layers, + 'num_heads': num_heads, + 'downscale_indices': downscale_indices + } + model = MViT(**cfg) + self.assertEqual(len(model.blocks), num_layers) + for i, block in enumerate(model.blocks): + if i in downscale_indices: + num_heads *= 2 + embed_dims *= 2 + self.assertEqual(block.out_dims, embed_dims) + self.assertEqual(block.attn.num_heads, num_heads) + + def test_init_weights(self): + # test weight init cfg + cfg = deepcopy(self.cfg) + cfg['use_abs_pos_embed'] = True + cfg['init_cfg'] = [ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ] + model = MViT(**cfg) + ori_weight = model.patch_embed.projection.weight.clone().detach() + # The pos_embed is all zero before initialize + self.assertTrue(torch.allclose(model.pos_embed, torch.tensor(0.))) + + model.init_weights() + initialized_weight = model.patch_embed.projection.weight + self.assertFalse(torch.allclose(ori_weight, initialized_weight)) + self.assertFalse(torch.allclose(model.pos_embed, torch.tensor(0.))) + self.assertFalse( + torch.allclose(model.blocks[0].attn.rel_pos_h, torch.tensor(0.))) + self.assertFalse( + torch.allclose(model.blocks[0].attn.rel_pos_w, torch.tensor(0.))) + + # test rel_pos_zero_init + cfg = deepcopy(self.cfg) + cfg['rel_pos_zero_init'] = True + model = MViT(**cfg) + model.init_weights() + self.assertTrue( + torch.allclose(model.blocks[0].attn.rel_pos_h, torch.tensor(0.))) + self.assertTrue( + torch.allclose(model.blocks[0].attn.rel_pos_w, torch.tensor(0.))) + + def test_forward(self): + imgs = torch.randn(1, 3, 224, 224) + + cfg = deepcopy(self.cfg) + model = MViT(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (1, 768, 7, 7)) + + # test multiple output indices + cfg = deepcopy(self.cfg) + cfg['out_scales'] = (0, 1, 2, 3) + model = MViT(**cfg) + model.init_weights() + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for stride, out in zip([1, 2, 4, 8], outs): + self.assertEqual(out.shape, + (1, 96 * stride, 56 // stride, 56 // stride)) + + # test dim_mul_in_attention = False + cfg = deepcopy(self.cfg) + cfg['out_scales'] = (0, 1, 2, 3) + cfg['dim_mul_in_attention'] = False + model = MViT(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for dim_mul, stride, out in zip([2, 4, 8, 8], [1, 2, 4, 8], outs): + self.assertEqual(out.shape, + (1, 96 * dim_mul, 56 // stride, 56 // stride)) + + # test rel_pos_spatial = False + cfg = deepcopy(self.cfg) + cfg['out_scales'] = (0, 1, 2, 3) + cfg['rel_pos_spatial'] = False + cfg['img_size'] = None + model = MViT(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for stride, out in zip([1, 2, 4, 8], outs): + self.assertEqual(out.shape, + (1, 96 * stride, 56 // stride, 56 // stride)) + + # test residual_pooling = False + cfg = deepcopy(self.cfg) + cfg['out_scales'] = (0, 1, 2, 3) + cfg['residual_pooling'] = False + model = MViT(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for stride, out in zip([1, 2, 4, 8], outs): + self.assertEqual(out.shape, + (1, 96 * stride, 56 // stride, 56 // stride)) + + # test use_abs_pos_embed = True + cfg = deepcopy(self.cfg) + cfg['out_scales'] = (0, 1, 2, 3) + cfg['use_abs_pos_embed'] = True + model = MViT(**cfg) + model.init_weights() + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for stride, out in zip([1, 2, 4, 8], outs): + self.assertEqual(out.shape, + (1, 96 * stride, 56 // stride, 56 // stride)) + + # test dynamic inputs shape + cfg = deepcopy(self.cfg) + cfg['out_scales'] = (0, 1, 2, 3) + model = MViT(**cfg) + imgs = torch.randn(1, 3, 352, 260) + h_resolution = (352 + 2 * 3 - 7) // 4 + 1 + w_resolution = (260 + 2 * 3 - 7) // 4 + 1 + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + expect_h = h_resolution + expect_w = w_resolution + for i, out in enumerate(outs): + self.assertEqual(out.shape, (1, 96 * 2**i, expect_h, expect_w)) + expect_h = (expect_h + 2 * 1 - 3) // 2 + 1 + expect_w = (expect_w + 2 * 1 - 3) // 2 + 1 + + def test_structure(self): + # test drop_path_rate decay + cfg = deepcopy(self.cfg) + cfg['drop_path_rate'] = 0.2 + model = MViT(**cfg) + for i, block in enumerate(model.blocks): + expect_prob = 0.2 / (model.num_layers - 1) * i + if expect_prob > 0: + self.assertAlmostEqual(block.drop_path.drop_prob, expect_prob) From 6ec38fe742cb511699d81f0302fa178711dd848a Mon Sep 17 00:00:00 2001 From: Hubert <42952108+yingfhu@users.noreply.github.com> Date: Wed, 3 Aug 2022 17:33:08 +0800 Subject: [PATCH 02/25] [Feature] Support Swin Transform V2. (#799) * init rough try for modify * Init swin transform v2 * lint * reformat * init config * refactor * update config * fix test * add doc * refact * add model meta * rename config * add doc * fix meta model name * restruct * rename embed_bims to out_channels * fix ut and update model --- .../_base_/datasets/imagenet_bs64_swin_256.py | 71 +++ .../models/swin_transformer_v2/base_256.py | 25 + .../models/swin_transformer_v2/base_384.py | 17 + .../models/swin_transformer_v2/large_256.py | 16 + .../models/swin_transformer_v2/large_384.py | 16 + .../models/swin_transformer_v2/small_256.py | 25 + .../models/swin_transformer_v2/tiny_256.py | 25 + configs/swin_transformer_v2/README.md | 58 ++ configs/swin_transformer_v2/metafile.yml | 204 +++++++ .../swinv2-base-w16_16xb64_in1k-256px.py | 8 + ...v2-base-w16_in21k-pre_16xb64_in1k-256px.py | 13 + ...v2-base-w24_in21k-pre_16xb64_in1k-384px.py | 14 + .../swinv2-base-w8_16xb64_in1k-256px.py | 6 + ...2-large-w16_in21k-pre_16xb64_in1k-256px.py | 13 + ...2-large-w24_in21k-pre_16xb64_in1k-384px.py | 15 + .../swinv2-small-w16_16xb64_in1k-256px.py | 8 + .../swinv2-small-w8_16xb64_in1k-256px.py | 6 + .../swinv2-tiny-w16_16xb64_in1k-256px.py | 8 + .../swinv2-tiny-w8_16xb64_in1k-256px.py | 6 + mmcls/models/backbones/__init__.py | 11 +- mmcls/models/backbones/swin_transformer_v2.py | 560 ++++++++++++++++++ mmcls/models/utils/__init__.py | 4 +- mmcls/models/utils/attention.py | 181 +++++- mmcls/models/utils/embed.py | 136 +++-- model-index.yml | 1 + .../test_swin_transformer_v2.py | 243 ++++++++ tests/test_models/test_utils/test_embed.py | 29 +- 27 files changed, 1659 insertions(+), 60 deletions(-) create mode 100644 configs/_base_/datasets/imagenet_bs64_swin_256.py create mode 100644 configs/_base_/models/swin_transformer_v2/base_256.py create mode 100644 configs/_base_/models/swin_transformer_v2/base_384.py create mode 100644 configs/_base_/models/swin_transformer_v2/large_256.py create mode 100644 configs/_base_/models/swin_transformer_v2/large_384.py create mode 100644 configs/_base_/models/swin_transformer_v2/small_256.py create mode 100644 configs/_base_/models/swin_transformer_v2/tiny_256.py create mode 100644 configs/swin_transformer_v2/README.md create mode 100644 configs/swin_transformer_v2/metafile.yml create mode 100644 configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py create mode 100644 configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py create mode 100644 configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py create mode 100644 configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py create mode 100644 mmcls/models/backbones/swin_transformer_v2.py create mode 100644 tests/test_models/test_backbones/test_swin_transformer_v2.py diff --git a/configs/_base_/datasets/imagenet_bs64_swin_256.py b/configs/_base_/datasets/imagenet_bs64_swin_256.py new file mode 100644 index 00000000000..1f73683aa2f --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_swin_256.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=256, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(292, -1), # ( 256 / 224 * 256 ) + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=256), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/models/swin_transformer_v2/base_256.py b/configs/_base_/models/swin_transformer_v2/base_256.py new file mode 100644 index 00000000000..f711a9c8dce --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/base_256.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='base', + img_size=256, + drop_path_rate=0.5), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer_v2/base_384.py b/configs/_base_/models/swin_transformer_v2/base_384.py new file mode 100644 index 00000000000..5fb9aead2e9 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/base_384.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='base', + img_size=384, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/swin_transformer_v2/large_256.py b/configs/_base_/models/swin_transformer_v2/large_256.py new file mode 100644 index 00000000000..fe557c32058 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/large_256.py @@ -0,0 +1,16 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='large', + img_size=256, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer_v2/large_384.py b/configs/_base_/models/swin_transformer_v2/large_384.py new file mode 100644 index 00000000000..a626c40715d --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/large_384.py @@ -0,0 +1,16 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='large', + img_size=384, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer_v2/small_256.py b/configs/_base_/models/swin_transformer_v2/small_256.py new file mode 100644 index 00000000000..8808f097e18 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/small_256.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='small', + img_size=256, + drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer_v2/tiny_256.py b/configs/_base_/models/swin_transformer_v2/tiny_256.py new file mode 100644 index 00000000000..d40e39462d9 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/tiny_256.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='tiny', + img_size=256, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/swin_transformer_v2/README.md b/configs/swin_transformer_v2/README.md new file mode 100644 index 00000000000..31d1aff5739 --- /dev/null +++ b/configs/swin_transformer_v2/README.md @@ -0,0 +1,58 @@ +# Swin Transformer V2 + +> [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883.pdf) + + + +## Abstract + +Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle three major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google's billion-level visual models, which consumes 40 times less labelled data and 40 times less training time. + +
+ +
+ +## Results and models + +### ImageNet-21k + +The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. + +| Model | resolution | Params(M) | Flops(G) | Download | +| :------: | :--------: | :-------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------: | +| Swin-B\* | 192x192 | 87.92 | 8.51 | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth) | +| Swin-L\* | 192x192 | 196.74 | 19.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-large-w12_3rdparty_in21k-192px_20220803-d9073fee.pth) | + +### ImageNet-1k + +| Model | Pretrain | resolution | window | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------: | :----------: | :--------: | :----: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------: | :----------------------------------------------------------------: | +| Swin-T\* | From scratch | 256x256 | 8x8 | 28.35 | 4.35 | 81.76 | 95.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w8_3rdparty_in1k-256px_20220803-e318968f.pth) | +| Swin-T\* | From scratch | 256x256 | 16x16 | 28.35 | 4.4 | 82.81 | 96.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w16_3rdparty_in1k-256px_20220803-9651cdd7.pth) | +| Swin-S\* | From scratch | 256x256 | 8x8 | 49.73 | 8.45 | 83.74 | 96.6 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w8_3rdparty_in1k-256px_20220803-b01a4332.pth) | +| Swin-S\* | From scratch | 256x256 | 16x16 | 49.73 | 8.57 | 84.13 | 96.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w16_3rdparty_in1k-256px_20220803-b707d206.pth) | +| Swin-B\* | From scratch | 256x256 | 8x8 | 87.92 | 14.99 | 84.2 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w8_3rdparty_in1k-256px_20220803-8ff28f2b.pth) | +| Swin-B\* | From scratch | 256x256 | 16x16 | 87.92 | 15.14 | 84.6 | 97.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_3rdparty_in1k-256px_20220803-5a1886b7.pth) | +| Swin-B\* | ImageNet-21k | 256x256 | 16x16 | 87.92 | 15.14 | 86.17 | 97.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_in21k-pre_3rdparty_in1k-256px_20220803-8d7aa8ad.pth) | +| Swin-B\* | ImageNet-21k | 384x384 | 24x24 | 87.92 | 34.07 | 87.14 | 98.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w24_in21k-pre_3rdparty_in1k-384px_20220803-44eb70f8.pth) | +| Swin-L\* | ImageNet-21k | 256X256 | 16x16 | 196.75 | 33.86 | 86.93 | 98.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w16_in21k-pre_3rdparty_in1k-256px_20220803-c40cbed7.pth) | +| Swin-L\* | ImageNet-21k | 384x384 | 24x24 | 196.75 | 76.2 | 87.59 | 98.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w24_in21k-pre_3rdparty_in1k-384px_20220803-3b36c165.pth) | + +*Models with * are converted from the [official repo](https://github.com/microsoft/Swin-Transformer#main-results-on-imagenet-with-pretrained-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +*ImageNet-21k pretrained models with input resolution of 256x256 and 384x384 both fine-tuned from the same pre-training model using a smaller input resolution of 192x192.* + +## Citation + +``` +@article{https://doi.org/10.48550/arxiv.2111.09883, + doi = {10.48550/ARXIV.2111.09883}, + url = {https://arxiv.org/abs/2111.09883}, + author = {Liu, Ze and Hu, Han and Lin, Yutong and Yao, Zhuliang and Xie, Zhenda and Wei, Yixuan and Ning, Jia and Cao, Yue and Zhang, Zheng and Dong, Li and Wei, Furu and Guo, Baining}, + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {Swin Transformer V2: Scaling Up Capacity and Resolution}, + publisher = {arXiv}, + year = {2021}, + copyright = {Creative Commons Attribution 4.0 International} +} +``` diff --git a/configs/swin_transformer_v2/metafile.yml b/configs/swin_transformer_v2/metafile.yml new file mode 100644 index 00000000000..cef839234c2 --- /dev/null +++ b/configs/swin_transformer_v2/metafile.yml @@ -0,0 +1,204 @@ +Collections: + - Name: Swin-Transformer-V2 + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - AdamW + - Weight Decay + Training Resources: 16x V100 GPUs + Epochs: 300 + Batch Size: 1024 + Architecture: + - Shift Window Multihead Self Attention + Paper: + URL: https://arxiv.org/abs/2111.09883.pdf + Title: "Swin Transformer V2: Scaling Up Capacity and Resolution" + README: configs/swin_transformer_v2/README.md + +Models: + - Name: swinv2-tiny-w8_3rdparty_in1k-256px + Metadata: + FLOPs: 4350000000 + Parameters: 28350000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.76 + Top 5 Accuracy: 95.87 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w8_3rdparty_in1k-256px_20220803-e318968f.pth + Config: configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-tiny-w16_3rdparty_in1k-256px + Metadata: + FLOPs: 4400000000 + Parameters: 28350000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.81 + Top 5 Accuracy: 96.23 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w16_3rdparty_in1k-256px_20220803-9651cdd7.pth + Config: configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-small-w8_3rdparty_in1k-256px + Metadata: + FLOPs: 8450000000 + Parameters: 49730000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.74 + Top 5 Accuracy: 96.6 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w8_3rdparty_in1k-256px_20220803-b01a4332.pth + Config: configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-small-w16_3rdparty_in1k-256px + Metadata: + FLOPs: 8570000000 + Parameters: 49730000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.13 + Top 5 Accuracy: 96.83 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w16_3rdparty_in1k-256px_20220803-b707d206.pth + Config: configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w8_3rdparty_in1k-256px + Metadata: + FLOPs: 14990000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.2 + Top 5 Accuracy: 96.86 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w8_3rdparty_in1k-256px_20220803-8ff28f2b.pth + Config: configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w16_3rdparty_in1k-256px + Metadata: + FLOPs: 15140000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.6 + Top 5 Accuracy: 97.05 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_3rdparty_in1k-256px_20220803-5a1886b7.pth + Config: configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w16_in21k-pre_3rdparty_in1k-256px + Metadata: + Training Data: ImageNet-21k + FLOPs: 15140000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.17 + Top 5 Accuracy: 97.88 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_in21k-pre_3rdparty_in1k-256px_20220803-8d7aa8ad.pth + Config: configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w24_in21k-pre_3rdparty_in1k-384px + Metadata: + Training Data: ImageNet-21k + FLOPs: 34070000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 87.14 + Top 5 Accuracy: 98.23 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w24_in21k-pre_3rdparty_in1k-384px_20220803-44eb70f8.pth + Config: configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-large-w16_in21k-pre_3rdparty_in1k-256px + Metadata: + Training Data: ImageNet-21k + FLOPs: 33860000000 + Parameters: 196750000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.93 + Top 5 Accuracy: 98.06 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w16_in21k-pre_3rdparty_in1k-256px_20220803-c40cbed7.pth + Config: configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-large-w24_in21k-pre_3rdparty_in1k-384px + Metadata: + Training Data: ImageNet-21k + FLOPs: 76200000000 + Parameters: 196750000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 87.59 + Top 5 Accuracy: 98.27 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w24_in21k-pre_3rdparty_in1k-384px_20220803-3b36c165.pth + Config: configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w12_3rdparty_in21k-192px + Metadata: + Training Data: ImageNet-21k + FLOPs: 8510000000 + Parameters: 87920000 + In Collections: Swin-Transformer-V2 + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-large-w12_3rdparty_in21k-192px + Metadata: + Training Data: ImageNet-21k + FLOPs: 19040000000 + Parameters: 196740000 + In Collections: Swin-Transformer-V2 + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-large-w12_3rdparty_in21k-192px_20220803-d9073fee.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth + Code: https://github.com/microsoft/Swin-Transformer diff --git a/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py new file mode 100644 index 00000000000..5f375ee1fc9 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py new file mode 100644 index 00000000000..0725f9e739a --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + window_size=[16, 16, 16, 8], + drop_path_rate=0.2, + pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py b/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py new file mode 100644 index 00000000000..3dd4e5fd935 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_384.py', + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + img_size=384, + window_size=[24, 24, 24, 12], + drop_path_rate=0.2, + pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py new file mode 100644 index 00000000000..23fc4070147 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py new file mode 100644 index 00000000000..62a2a29b843 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py @@ -0,0 +1,13 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer_v2/large_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + window_size=[16, 16, 16, 8], pretrained_window_sizes=[12, 12, 12, 6]), +) diff --git a/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py b/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py new file mode 100644 index 00000000000..d97d9b2b869 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py @@ -0,0 +1,15 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer_v2/large_384.py', + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + img_size=384, + window_size=[24, 24, 24, 12], + pretrained_window_sizes=[12, 12, 12, 6]), +) diff --git a/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py new file mode 100644 index 00000000000..f87265dd199 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/small_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py new file mode 100644 index 00000000000..f1001f1b6e1 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/small_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py new file mode 100644 index 00000000000..7e1f290f371 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/tiny_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py new file mode 100644 index 00000000000..2cdc9a25ae8 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/tiny_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/mmcls/models/backbones/__init__.py b/mmcls/models/backbones/__init__.py index 737356edb2a..f1e772dec11 100644 --- a/mmcls/models/backbones/__init__.py +++ b/mmcls/models/backbones/__init__.py @@ -27,6 +27,7 @@ from .shufflenet_v1 import ShuffleNetV1 from .shufflenet_v2 import ShuffleNetV2 from .swin_transformer import SwinTransformer +from .swin_transformer_v2 import SwinTransformerV2 from .t2t_vit import T2T_ViT from .timm_backbone import TIMMBackbone from .tnt import TNT @@ -39,9 +40,9 @@ 'LeNet5', 'AlexNet', 'VGG', 'RegNet', 'ResNet', 'ResNeXt', 'ResNetV1d', 'ResNeSt', 'ResNet_CIFAR', 'SEResNet', 'SEResNeXt', 'ShuffleNetV1', 'ShuffleNetV2', 'MobileNetV2', 'MobileNetV3', 'VisionTransformer', - 'SwinTransformer', 'TNT', 'TIMMBackbone', 'T2T_ViT', 'Res2Net', 'RepVGG', - 'Conformer', 'MlpMixer', 'DistilledVisionTransformer', 'PCPVT', 'SVT', - 'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c', 'ConvMixer', - 'CSPDarkNet', 'CSPResNet', 'CSPResNeXt', 'CSPNet', 'RepMLPNet', - 'PoolFormer', 'DenseNet', 'VAN', 'MViT' + 'SwinTransformer', 'SwinTransformerV2', 'TNT', 'TIMMBackbone', 'T2T_ViT', + 'Res2Net', 'RepVGG', 'Conformer', 'MlpMixer', 'DistilledVisionTransformer', + 'PCPVT', 'SVT', 'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c', + 'ConvMixer', 'CSPDarkNet', 'CSPResNet', 'CSPResNeXt', 'CSPNet', + 'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT' ] diff --git a/mmcls/models/backbones/swin_transformer_v2.py b/mmcls/models/backbones/swin_transformer_v2.py new file mode 100644 index 00000000000..c26b4e6c227 --- /dev/null +++ b/mmcls/models/backbones/swin_transformer_v2.py @@ -0,0 +1,560 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from copy import deepcopy +from typing import Sequence + +import numpy as np +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp +from mmcv.cnn import build_norm_layer +from mmcv.cnn.bricks.transformer import FFN, PatchEmbed +from mmcv.cnn.utils.weight_init import trunc_normal_ +from mmcv.runner.base_module import BaseModule, ModuleList +from mmcv.utils.parrots_wrapper import _BatchNorm + +from ..builder import BACKBONES +from ..utils import (PatchMerging, ShiftWindowMSA, WindowMSAV2, + resize_pos_embed, to_2tuple) +from .base_backbone import BaseBackbone + + +class SwinBlockV2(BaseModule): + """Swin Transformer V2 block. Use post normalization. + + Args: + embed_dims (int): Number of input channels. + num_heads (int): Number of attention heads. + window_size (int): The height and width of the window. Defaults to 7. + shift (bool): Shift the attention window or not. Defaults to False. + extra_norm (bool): Whether add extra norm at the end of main branch. + ffn_ratio (float): The expansion ratio of feedforward network hidden + layer channels. Defaults to 4. + drop_path (float): The drop path rate after attention and ffn. + Defaults to 0. + pad_small_map (bool): If True, pad the small feature map to the window + size, which is common used in detection and segmentation. If False, + avoid shifting window and shrink the window size to the size of + feature map, which is common used in classification. + Defaults to False. + attn_cfgs (dict): The extra config of Shift Window-MSA. + Defaults to empty dict. + ffn_cfgs (dict): The extra config of FFN. Defaults to empty dict. + norm_cfg (dict): The config of norm layers. + Defaults to ``dict(type='LN')``. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Defaults to False. + pretrained_window_size (int): Window size in pretrained. + init_cfg (dict, optional): The extra config for initialization. + Defaults to None. + """ + + def __init__(self, + embed_dims, + num_heads, + window_size=8, + shift=False, + extra_norm=False, + ffn_ratio=4., + drop_path=0., + pad_small_map=False, + attn_cfgs=dict(), + ffn_cfgs=dict(), + norm_cfg=dict(type='LN'), + with_cp=False, + pretrained_window_size=0, + init_cfg=None): + + super(SwinBlockV2, self).__init__(init_cfg) + self.with_cp = with_cp + self.extra_norm = extra_norm + + _attn_cfgs = { + 'embed_dims': embed_dims, + 'num_heads': num_heads, + 'shift_size': window_size // 2 if shift else 0, + 'window_size': window_size, + 'dropout_layer': dict(type='DropPath', drop_prob=drop_path), + 'pad_small_map': pad_small_map, + **attn_cfgs + } + # use V2 attention implementation + _attn_cfgs.update( + window_msa=WindowMSAV2, + msa_cfg=dict( + pretrained_window_size=to_2tuple(pretrained_window_size))) + self.attn = ShiftWindowMSA(**_attn_cfgs) + self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1] + + _ffn_cfgs = { + 'embed_dims': embed_dims, + 'feedforward_channels': int(embed_dims * ffn_ratio), + 'num_fcs': 2, + 'ffn_drop': 0, + 'dropout_layer': dict(type='DropPath', drop_prob=drop_path), + 'act_cfg': dict(type='GELU'), + 'add_identity': False, + **ffn_cfgs + } + self.ffn = FFN(**_ffn_cfgs) + self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1] + + # add extra norm for every n blocks in huge and giant model + if self.extra_norm: + self.norm3 = build_norm_layer(norm_cfg, embed_dims)[1] + + def forward(self, x, hw_shape): + + def _inner_forward(x): + # Use post normalization + identity = x + x = self.attn(x, hw_shape) + x = self.norm1(x) + x = x + identity + + identity = x + x = self.ffn(x) + x = self.norm2(x) + x = x + identity + + if self.extra_norm: + x = self.norm3(x) + + return x + + if self.with_cp and x.requires_grad: + x = cp.checkpoint(_inner_forward, x) + else: + x = _inner_forward(x) + + return x + + +class SwinBlockV2Sequence(BaseModule): + """Module with successive Swin Transformer blocks and downsample layer. + + Args: + embed_dims (int): Number of input channels. + depth (int): Number of successive swin transformer blocks. + num_heads (int): Number of attention heads. + window_size (int): The height and width of the window. Defaults to 7. + downsample (bool): Downsample the output of blocks by patch merging. + Defaults to False. + downsample_cfg (dict): The extra config of the patch merging layer. + Defaults to empty dict. + drop_paths (Sequence[float] | float): The drop path rate in each block. + Defaults to 0. + block_cfgs (Sequence[dict] | dict): The extra config of each block. + Defaults to empty dicts. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Defaults to False. + pad_small_map (bool): If True, pad the small feature map to the window + size, which is common used in detection and segmentation. If False, + avoid shifting window and shrink the window size to the size of + feature map, which is common used in classification. + Defaults to False. + extra_norm_every_n_blocks (int): Add extra norm at the end of main + branch every n blocks. Defaults to 0, which means no needs for + extra norm layer. + pretrained_window_size (int): Window size in pretrained. + init_cfg (dict, optional): The extra config for initialization. + Defaults to None. + """ + + def __init__(self, + embed_dims, + depth, + num_heads, + window_size=8, + downsample=False, + downsample_cfg=dict(), + drop_paths=0., + block_cfgs=dict(), + with_cp=False, + pad_small_map=False, + extra_norm_every_n_blocks=0, + pretrained_window_size=0, + init_cfg=None): + super().__init__(init_cfg) + + if not isinstance(drop_paths, Sequence): + drop_paths = [drop_paths] * depth + + if not isinstance(block_cfgs, Sequence): + block_cfgs = [deepcopy(block_cfgs) for _ in range(depth)] + + if downsample: + self.out_channels = 2 * embed_dims + _downsample_cfg = { + 'in_channels': embed_dims, + 'out_channels': self.out_channels, + 'norm_cfg': dict(type='LN'), + **downsample_cfg + } + self.downsample = PatchMerging(**_downsample_cfg) + else: + self.out_channels = embed_dims + self.downsample = None + + self.blocks = ModuleList() + for i in range(depth): + extra_norm = True if extra_norm_every_n_blocks and \ + (i + 1) % extra_norm_every_n_blocks == 0 else False + _block_cfg = { + 'embed_dims': self.out_channels, + 'num_heads': num_heads, + 'window_size': window_size, + 'shift': False if i % 2 == 0 else True, + 'extra_norm': extra_norm, + 'drop_path': drop_paths[i], + 'with_cp': with_cp, + 'pad_small_map': pad_small_map, + 'pretrained_window_size': pretrained_window_size, + **block_cfgs[i] + } + block = SwinBlockV2(**_block_cfg) + self.blocks.append(block) + + def forward(self, x, in_shape): + if self.downsample: + x, out_shape = self.downsample(x, in_shape) + else: + out_shape = in_shape + + for block in self.blocks: + x = block(x, out_shape) + + return x, out_shape + + +@BACKBONES.register_module() +class SwinTransformerV2(BaseBackbone): + """Swin Transformer V2. + + A PyTorch implement of : `Swin Transformer V2: + Scaling Up Capacity and Resolution + `_ + + Inspiration from + https://github.com/microsoft/Swin-Transformer + + Args: + arch (str | dict): Swin Transformer architecture. If use string, choose + from 'tiny', 'small', 'base' and 'large'. If use dict, it should + have below keys: + + - **embed_dims** (int): The dimensions of embedding. + - **depths** (List[int]): The number of blocks in each stage. + - **num_heads** (List[int]): The number of heads in attention + modules of each stage. + - **extra_norm_every_n_blocks** (int): Add extra norm at the end + of main branch every n blocks. + + Defaults to 'tiny'. + img_size (int | tuple): The expected input image shape. Because we + support dynamic input shape, just set the argument to the most + common input image shape. Defaults to 224. + patch_size (int | tuple): The patch size in patch embedding. + Defaults to 4. + in_channels (int): The num of input channels. Defaults to 3. + window_size (int | Sequence): The height and width of the window. + Defaults to 7. + drop_rate (float): Dropout rate after embedding. Defaults to 0. + drop_path_rate (float): Stochastic depth rate. Defaults to 0.1. + use_abs_pos_embed (bool): If True, add absolute position embedding to + the patch embedding. Defaults to False. + interpolate_mode (str): Select the interpolate mode for absolute + position embeding vector resize. Defaults to "bicubic". + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Defaults to False. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. Defaults to -1. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Defaults to False. + pad_small_map (bool): If True, pad the small feature map to the window + size, which is common used in detection and segmentation. If False, + avoid shifting window and shrink the window size to the size of + feature map, which is common used in classification. + Defaults to False. + norm_cfg (dict): Config dict for normalization layer for all output + features. Defaults to ``dict(type='LN')`` + stage_cfgs (Sequence[dict] | dict): Extra config dict for each + stage. Defaults to an empty dict. + patch_cfg (dict): Extra config dict for patch embedding. + Defaults to an empty dict. + pretrained_window_sizes (tuple(int)): Pretrained window sizes of + each layer. + init_cfg (dict, optional): The Config for initialization. + Defaults to None. + + Examples: + >>> from mmcls.models import SwinTransformerV2 + >>> import torch + >>> extra_config = dict( + >>> arch='tiny', + >>> stage_cfgs=dict(downsample_cfg={'kernel_size': 3, + >>> 'padding': 'same'})) + >>> self = SwinTransformerV2(**extra_config) + >>> inputs = torch.rand(1, 3, 224, 224) + >>> output = self.forward(inputs) + >>> print(output.shape) + (1, 2592, 4) + """ + arch_zoo = { + **dict.fromkeys(['t', 'tiny'], + {'embed_dims': 96, + 'depths': [2, 2, 6, 2], + 'num_heads': [3, 6, 12, 24], + 'extra_norm_every_n_blocks': 0}), + **dict.fromkeys(['s', 'small'], + {'embed_dims': 96, + 'depths': [2, 2, 18, 2], + 'num_heads': [3, 6, 12, 24], + 'extra_norm_every_n_blocks': 0}), + **dict.fromkeys(['b', 'base'], + {'embed_dims': 128, + 'depths': [2, 2, 18, 2], + 'num_heads': [4, 8, 16, 32], + 'extra_norm_every_n_blocks': 0}), + **dict.fromkeys(['l', 'large'], + {'embed_dims': 192, + 'depths': [2, 2, 18, 2], + 'num_heads': [6, 12, 24, 48], + 'extra_norm_every_n_blocks': 0}), + # head count not certain for huge, and is employed for another + # parallel study about self-supervised learning. + **dict.fromkeys(['h', 'huge'], + {'embed_dims': 352, + 'depths': [2, 2, 18, 2], + 'num_heads': [8, 16, 32, 64], + 'extra_norm_every_n_blocks': 6}), + **dict.fromkeys(['g', 'giant'], + {'embed_dims': 512, + 'depths': [2, 2, 42, 4], + 'num_heads': [16, 32, 64, 128], + 'extra_norm_every_n_blocks': 6}), + } # yapf: disable + + _version = 1 + num_extra_tokens = 0 + + def __init__(self, + arch='tiny', + img_size=256, + patch_size=4, + in_channels=3, + window_size=8, + drop_rate=0., + drop_path_rate=0.1, + out_indices=(3, ), + use_abs_pos_embed=False, + interpolate_mode='bicubic', + with_cp=False, + frozen_stages=-1, + norm_eval=False, + pad_small_map=False, + norm_cfg=dict(type='LN'), + stage_cfgs=dict(downsample_cfg=dict(is_post_norm=True)), + patch_cfg=dict(), + pretrained_window_sizes=[0, 0, 0, 0], + init_cfg=None): + super(SwinTransformerV2, self).__init__(init_cfg=init_cfg) + + if isinstance(arch, str): + arch = arch.lower() + assert arch in set(self.arch_zoo), \ + f'Arch {arch} is not in default archs {set(self.arch_zoo)}' + self.arch_settings = self.arch_zoo[arch] + else: + essential_keys = { + 'embed_dims', 'depths', 'num_heads', + 'extra_norm_every_n_blocks' + } + assert isinstance(arch, dict) and set(arch) == essential_keys, \ + f'Custom arch needs a dict with keys {essential_keys}' + self.arch_settings = arch + + self.embed_dims = self.arch_settings['embed_dims'] + self.depths = self.arch_settings['depths'] + self.num_heads = self.arch_settings['num_heads'] + self.extra_norm_every_n_blocks = self.arch_settings[ + 'extra_norm_every_n_blocks'] + self.num_layers = len(self.depths) + self.out_indices = out_indices + self.use_abs_pos_embed = use_abs_pos_embed + self.interpolate_mode = interpolate_mode + self.frozen_stages = frozen_stages + + if isinstance(window_size, int): + self.window_sizes = [window_size for _ in range(self.num_layers)] + elif isinstance(window_size, Sequence): + assert len(window_size) == self.num_layers, \ + f'Length of window_sizes {len(window_size)} is not equal to '\ + f'length of stages {self.num_layers}.' + self.window_sizes = window_size + else: + raise TypeError('window_size should be a Sequence or int.') + + _patch_cfg = dict( + in_channels=in_channels, + input_size=img_size, + embed_dims=self.embed_dims, + conv_type='Conv2d', + kernel_size=patch_size, + stride=patch_size, + norm_cfg=dict(type='LN'), + ) + _patch_cfg.update(patch_cfg) + self.patch_embed = PatchEmbed(**_patch_cfg) + self.patch_resolution = self.patch_embed.init_out_size + + if self.use_abs_pos_embed: + num_patches = self.patch_resolution[0] * self.patch_resolution[1] + self.absolute_pos_embed = nn.Parameter( + torch.zeros(1, num_patches, self.embed_dims)) + self._register_load_state_dict_pre_hook( + self._prepare_abs_pos_embed) + + self._register_load_state_dict_pre_hook(self._delete_reinit_params) + + self.drop_after_pos = nn.Dropout(p=drop_rate) + self.norm_eval = norm_eval + + # stochastic depth + total_depth = sum(self.depths) + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, total_depth) + ] # stochastic depth decay rule + + self.stages = ModuleList() + embed_dims = [self.embed_dims] + for i, (depth, + num_heads) in enumerate(zip(self.depths, self.num_heads)): + if isinstance(stage_cfgs, Sequence): + stage_cfg = stage_cfgs[i] + else: + stage_cfg = deepcopy(stage_cfgs) + downsample = True if i > 0 else False + _stage_cfg = { + 'embed_dims': embed_dims[-1], + 'depth': depth, + 'num_heads': num_heads, + 'window_size': self.window_sizes[i], + 'downsample': downsample, + 'drop_paths': dpr[:depth], + 'with_cp': with_cp, + 'pad_small_map': pad_small_map, + 'extra_norm_every_n_blocks': self.extra_norm_every_n_blocks, + 'pretrained_window_size': pretrained_window_sizes[i], + **stage_cfg + } + + stage = SwinBlockV2Sequence(**_stage_cfg) + self.stages.append(stage) + + dpr = dpr[depth:] + embed_dims.append(stage.out_channels) + + for i in out_indices: + if norm_cfg is not None: + norm_layer = build_norm_layer(norm_cfg, embed_dims[i + 1])[1] + else: + norm_layer = nn.Identity() + + self.add_module(f'norm{i}', norm_layer) + + def init_weights(self): + super(SwinTransformerV2, self).init_weights() + + if (isinstance(self.init_cfg, dict) + and self.init_cfg['type'] == 'Pretrained'): + # Suppress default init if use pretrained model. + return + + if self.use_abs_pos_embed: + trunc_normal_(self.absolute_pos_embed, std=0.02) + + def forward(self, x): + x, hw_shape = self.patch_embed(x) + + if self.use_abs_pos_embed: + x = x + resize_pos_embed( + self.absolute_pos_embed, self.patch_resolution, hw_shape, + self.interpolate_mode, self.num_extra_tokens) + x = self.drop_after_pos(x) + + outs = [] + for i, stage in enumerate(self.stages): + x, hw_shape = stage(x, hw_shape) + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + out = norm_layer(x) + out = out.view(-1, *hw_shape, + stage.out_channels).permute(0, 3, 1, + 2).contiguous() + outs.append(out) + + return tuple(outs) + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + for i in range(0, self.frozen_stages + 1): + m = self.stages[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + for i in self.out_indices: + if i <= self.frozen_stages: + for param in getattr(self, f'norm{i}').parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(SwinTransformerV2, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() + + def _prepare_abs_pos_embed(self, state_dict, prefix, *args, **kwargs): + name = prefix + 'absolute_pos_embed' + if name not in state_dict.keys(): + return + + ckpt_pos_embed_shape = state_dict[name].shape + if self.absolute_pos_embed.shape != ckpt_pos_embed_shape: + from mmcls.utils import get_root_logger + logger = get_root_logger() + logger.info( + 'Resize the absolute_pos_embed shape from ' + f'{ckpt_pos_embed_shape} to {self.absolute_pos_embed.shape}.') + + ckpt_pos_embed_shape = to_2tuple( + int(np.sqrt(ckpt_pos_embed_shape[1] - self.num_extra_tokens))) + pos_embed_shape = self.patch_embed.init_out_size + + state_dict[name] = resize_pos_embed(state_dict[name], + ckpt_pos_embed_shape, + pos_embed_shape, + self.interpolate_mode, + self.num_extra_tokens) + + def _delete_reinit_params(self, state_dict, prefix, *args, **kwargs): + # delete relative_position_index since we always re-init it + relative_position_index_keys = [ + k for k in state_dict.keys() if 'relative_position_index' in k + ] + for k in relative_position_index_keys: + del state_dict[k] + + # delete relative_coords_table since we always re-init it + relative_position_index_keys = [ + k for k in state_dict.keys() if 'relative_coords_table' in k + ] + for k in relative_position_index_keys: + del state_dict[k] diff --git a/mmcls/models/utils/__init__.py b/mmcls/models/utils/__init__.py index 53e3917a0d8..09d7273593c 100644 --- a/mmcls/models/utils/__init__.py +++ b/mmcls/models/utils/__init__.py @@ -1,5 +1,5 @@ # Copyright (c) OpenMMLab. All rights reserved. -from .attention import MultiheadAttention, ShiftWindowMSA +from .attention import MultiheadAttention, ShiftWindowMSA, WindowMSAV2 from .augment.augments import Augments from .channel_shuffle import channel_shuffle from .embed import (HybridEmbed, PatchEmbed, PatchMerging, resize_pos_embed, @@ -15,5 +15,5 @@ 'to_ntuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'PatchEmbed', 'PatchMerging', 'HybridEmbed', 'Augments', 'ShiftWindowMSA', 'is_tracing', 'MultiheadAttention', 'ConditionalPositionEncoding', 'resize_pos_embed', - 'resize_relative_position_bias_table' + 'resize_relative_position_bias_table', 'WindowMSAV2' ] diff --git a/mmcls/models/utils/attention.py b/mmcls/models/utils/attention.py index 155127f7877..b94c4679c15 100644 --- a/mmcls/models/utils/attention.py +++ b/mmcls/models/utils/attention.py @@ -1,6 +1,7 @@ # Copyright (c) OpenMMLab. All rights reserved. import warnings +import numpy as np import torch import torch.nn as nn import torch.nn.functional as F @@ -122,6 +123,176 @@ def double_step_seq(step1, len1, step2, len2): return (seq1[:, None] + seq2[None, :]).reshape(1, -1) +class WindowMSAV2(BaseModule): + """Window based multi-head self-attention (W-MSA) module with relative + position bias. + + Based on implementation on Swin Transformer V2 original repo. Refers to + https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer_v2.py + for more details. + + Args: + embed_dims (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to q, k, v. + Defaults to True. + attn_drop (float, optional): Dropout ratio of attention weight. + Defaults to 0. + proj_drop (float, optional): Dropout ratio of output. Defaults to 0. + pretrained_window_size (tuple(int)): The height and width of the window + in pre-training. + init_cfg (dict, optional): The extra config for initialization. + Defaults to None. + """ + + def __init__(self, + embed_dims, + window_size, + num_heads, + qkv_bias=True, + attn_drop=0., + proj_drop=0., + cpb_mlp_hidden_dims=512, + pretrained_window_size=(0, 0), + init_cfg=None, + **kwargs): # accept extra arguments + + super().__init__(init_cfg) + self.embed_dims = embed_dims + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + + # Use small network for continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear( + in_features=2, out_features=cpb_mlp_hidden_dims, bias=True), + nn.ReLU(inplace=True), + nn.Linear( + in_features=cpb_mlp_hidden_dims, + out_features=num_heads, + bias=False)) + + # Add learnable scalar for cosine attention + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + + # get relative_coords_table + relative_coords_h = torch.arange( + -(self.window_size[0] - 1), + self.window_size[0], + dtype=torch.float32) + relative_coords_w = torch.arange( + -(self.window_size[1] - 1), + self.window_size[1], + dtype=torch.float32) + relative_coords_table = torch.stack( + torch.meshgrid([relative_coords_h, relative_coords_w])).permute( + 1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= ( + pretrained_window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= ( + pretrained_window_size[1] - 1) + else: + relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) + relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + torch.abs(relative_coords_table) + 1.0) / np.log2(8) + self.register_buffer('relative_coords_table', relative_coords_table) + + # get pair-wise relative position index + # for each token inside the window + indexes_h = torch.arange(self.window_size[0]) + indexes_w = torch.arange(self.window_size[1]) + coordinates = torch.stack( + torch.meshgrid([indexes_h, indexes_w]), dim=0) # 2, Wh, Ww + coordinates = torch.flatten(coordinates, start_dim=1) # 2, Wh*Ww + # 2, Wh*Ww, Wh*Ww + relative_coordinates = coordinates[:, :, None] - coordinates[:, + None, :] + relative_coordinates = relative_coordinates.permute( + 1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + + relative_coordinates[:, :, 0] += self.window_size[ + 0] - 1 # shift to start from 0 + relative_coordinates[:, :, 1] += self.window_size[1] - 1 + relative_coordinates[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coordinates.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer('relative_position_index', + relative_position_index) + + self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(embed_dims)) + self.v_bias = nn.Parameter(torch.zeros(embed_dims)) + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(embed_dims, embed_dims) + self.proj_drop = nn.Dropout(proj_drop) + + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + + x (tensor): input features with shape of (num_windows*B, N, C) + mask (tensor, Optional): mask with shape of (num_windows, Wh*Ww, + Wh*Ww), value should be between (-inf, 0]. + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + (self.q_bias, + torch.zeros_like(self.v_bias, + requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, + C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[ + 2] # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = ( + F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp( + self.relative_coords_table).view(-1, self.num_heads) + relative_position_bias = relative_position_bias_table[ + self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, + N) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + @ATTENTION.register_module() class ShiftWindowMSA(BaseModule): """Shift Window Multihead Self-Attention Module. @@ -146,6 +317,8 @@ class ShiftWindowMSA(BaseModule): avoid shifting window and shrink the window size to the size of feature map, which is common used in classification. Defaults to False. + version (str, optional): Version of implementation of Swin + Transformers. Defaults to `v1`. init_cfg (dict, optional): The extra config for initialization. Defaults to None. """ @@ -163,6 +336,8 @@ def __init__(self, pad_small_map=False, input_resolution=None, auto_pad=None, + window_msa=WindowMSA, + msa_cfg=dict(), init_cfg=None): super().__init__(init_cfg) @@ -177,7 +352,10 @@ def __init__(self, self.window_size = window_size assert 0 <= self.shift_size < self.window_size - self.w_msa = WindowMSA( + assert issubclass(window_msa, BaseModule), \ + 'Expect Window based multi-head self-attention Module is type of' \ + f'{type(BaseModule)}, but got {type(window_msa)}.' + self.w_msa = window_msa( embed_dims=embed_dims, window_size=to_2tuple(self.window_size), num_heads=num_heads, @@ -185,6 +363,7 @@ def __init__(self, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=proj_drop, + **msa_cfg, ) self.drop = build_dropout(dropout_layer) diff --git a/mmcls/models/utils/embed.py b/mmcls/models/utils/embed.py index 7dd27cd51a3..ff65fc43583 100644 --- a/mmcls/models/utils/embed.py +++ b/mmcls/models/utils/embed.py @@ -1,11 +1,13 @@ # Copyright (c) OpenMMLab. All rights reserved. import warnings +from typing import Sequence import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.cnn.bricks.transformer import AdaptivePadding from mmcv.runner.base_module import BaseModule from .helpers import to_2tuple @@ -272,93 +274,147 @@ def forward(self, x): class PatchMerging(BaseModule): - """Merge patch feature map. + """Merge patch feature map. Modified from mmcv, which uses pre-norm layer + whereas Swin V2 uses post-norm here. Therefore, add extra parameter to + decide whether use post-norm or not. - This layer use nn.Unfold to group feature map by kernel_size, and use norm - and linear layer to embed grouped feature map. + This layer groups feature map by kernel_size, and applies norm and linear + layers to the grouped feature map ((used in Swin Transformer)). + Our implementation uses `nn.Unfold` to + merge patches, which is about 25% faster than the original + implementation. However, we need to modify pretrained + models for compatibility. Args: - input_resolution (tuple): The size of input patch resolution. in_channels (int): The num of input channels. - expansion_ratio (Number): Expansion ratio of output channels. The num - of output channels is equal to int(expansion_ratio * in_channels). + to gets fully covered by filter and stride you specified. + out_channels (int): The num of output channels. kernel_size (int | tuple, optional): the kernel size in the unfold layer. Defaults to 2. stride (int | tuple, optional): the stride of the sliding blocks in the - unfold layer. Defaults to be equal with kernel_size. - padding (int | tuple, optional): zero padding width in the unfold - layer. Defaults to 0. + unfold layer. Defaults to None. (Would be set as `kernel_size`) + padding (int | tuple | string ): The padding length of + embedding conv. When it is a string, it means the mode + of adaptive padding, support "same" and "corner" now. + Defaults to "corner". dilation (int | tuple, optional): dilation parameter in the unfold - layer. Defaults to 1. + layer. Default: 1. bias (bool, optional): Whether to add bias in linear layer or not. Defaults to False. norm_cfg (dict, optional): Config dict for normalization layer. Defaults to dict(type='LN'). + is_post_norm (bool): Whether to use post normalization here. + Defaults to False. init_cfg (dict, optional): The extra config for initialization. Defaults to None. """ def __init__(self, - input_resolution, in_channels, - expansion_ratio, + out_channels, kernel_size=2, stride=None, - padding=0, + padding='corner', dilation=1, bias=False, norm_cfg=dict(type='LN'), + is_post_norm=False, init_cfg=None): - super().__init__(init_cfg) - warnings.warn('The `PatchMerging` in mmcls will be deprecated. ' - 'Please use `mmcv.cnn.bricks.transformer.PatchMerging`. ' - "It's more general and supports dynamic input shape") - - H, W = input_resolution - self.input_resolution = input_resolution + super().__init__(init_cfg=init_cfg) self.in_channels = in_channels - self.out_channels = int(expansion_ratio * in_channels) + self.out_channels = out_channels + self.is_post_norm = is_post_norm - if stride is None: + if stride: + stride = stride + else: stride = kernel_size + kernel_size = to_2tuple(kernel_size) stride = to_2tuple(stride) - padding = to_2tuple(padding) dilation = to_2tuple(dilation) - self.sampler = nn.Unfold(kernel_size, dilation, padding, stride) + + if isinstance(padding, str): + self.adaptive_padding = AdaptivePadding( + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + padding=padding) + # disable the padding of unfold + padding = 0 + else: + self.adaptive_padding = None + + padding = to_2tuple(padding) + self.sampler = nn.Unfold( + kernel_size=kernel_size, + dilation=dilation, + padding=padding, + stride=stride) sample_dim = kernel_size[0] * kernel_size[1] * in_channels + self.reduction = nn.Linear(sample_dim, out_channels, bias=bias) + if norm_cfg is not None: - self.norm = build_norm_layer(norm_cfg, sample_dim)[1] + # build pre or post norm layer based on different channels + if self.is_post_norm: + self.norm = build_norm_layer(norm_cfg, out_channels)[1] + else: + self.norm = build_norm_layer(norm_cfg, sample_dim)[1] else: self.norm = None - self.reduction = nn.Linear(sample_dim, self.out_channels, bias=bias) + def forward(self, x, input_size): + """ + Args: + x (Tensor): Has shape (B, H*W, C_in). + input_size (tuple[int]): The spatial shape of x, arrange as (H, W). + Default: None. - # See https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html - H_out = (H + 2 * padding[0] - dilation[0] * - (kernel_size[0] - 1) - 1) // stride[0] + 1 - W_out = (W + 2 * padding[1] - dilation[1] * - (kernel_size[1] - 1) - 1) // stride[1] + 1 - self.output_resolution = (H_out, W_out) + Returns: + tuple: Contains merged results and its spatial shape. - def forward(self, x): + - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out) + - out_size (tuple[int]): Spatial shape of x, arrange as + (Merged_H, Merged_W). """ - x: B, H*W, C - """ - H, W = self.input_resolution B, L, C = x.shape + assert isinstance(input_size, Sequence), f'Expect ' \ + f'input_size is ' \ + f'`Sequence` ' \ + f'but get {input_size}' + + H, W = input_size assert L == H * W, 'input feature has wrong size' x = x.view(B, H, W, C).permute([0, 3, 1, 2]) # B, C, H, W + if self.adaptive_padding: + x = self.adaptive_padding(x) + H, W = x.shape[-2:] + # Use nn.Unfold to merge patch. About 25% faster than original method, # but need to modify pretrained model for compatibility - x = self.sampler(x) # B, 4*C, H/2*W/2 + # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2) + x = self.sampler(x) + + out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] * + (self.sampler.kernel_size[0] - 1) - + 1) // self.sampler.stride[0] + 1 + out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] * + (self.sampler.kernel_size[1] - 1) - + 1) // self.sampler.stride[1] + 1 + + output_size = (out_h, out_w) x = x.transpose(1, 2) # B, H/2*W/2, 4*C - x = self.norm(x) if self.norm else x - x = self.reduction(x) + if self.is_post_norm: + # use post-norm here + x = self.reduction(x) + x = self.norm(x) if self.norm else x + else: + x = self.norm(x) if self.norm else x + x = self.reduction(x) - return x + return x, output_size diff --git a/model-index.yml b/model-index.yml index a067de57261..9b779055898 100644 --- a/model-index.yml +++ b/model-index.yml @@ -7,6 +7,7 @@ Import: - configs/shufflenet_v1/metafile.yml - configs/shufflenet_v2/metafile.yml - configs/swin_transformer/metafile.yml + - configs/swin_transformer_v2/metafile.yml - configs/vgg/metafile.yml - configs/repvgg/metafile.yml - configs/tnt/metafile.yml diff --git a/tests/test_models/test_backbones/test_swin_transformer_v2.py b/tests/test_models/test_backbones/test_swin_transformer_v2.py new file mode 100644 index 00000000000..1fd43140c3d --- /dev/null +++ b/tests/test_models/test_backbones/test_swin_transformer_v2.py @@ -0,0 +1,243 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math +import os +import tempfile +from copy import deepcopy +from itertools import chain +from unittest import TestCase + +import torch +from mmcv.runner import load_checkpoint, save_checkpoint +from mmcv.utils.parrots_wrapper import _BatchNorm + +from mmcls.models.backbones import SwinTransformerV2 +from mmcls.models.backbones.swin_transformer import SwinBlock +from .utils import timm_resize_pos_embed + + +def check_norm_state(modules, train_state): + """Check if norm layer is in correct train state.""" + for mod in modules: + if isinstance(mod, _BatchNorm): + if mod.training != train_state: + return False + return True + + +class TestSwinTransformerV2(TestCase): + + def setUp(self): + self.cfg = dict( + arch='b', img_size=256, patch_size=4, drop_path_rate=0.1) + + def test_arch(self): + # Test invalid default arch + with self.assertRaisesRegex(AssertionError, 'not in default archs'): + cfg = deepcopy(self.cfg) + cfg['arch'] = 'unknown' + SwinTransformerV2(**cfg) + + # Test invalid custom arch + with self.assertRaisesRegex(AssertionError, 'Custom arch needs'): + cfg = deepcopy(self.cfg) + cfg['arch'] = { + 'embed_dims': 96, + 'num_heads': [3, 6, 12, 16], + } + SwinTransformerV2(**cfg) + + # Test custom arch + cfg = deepcopy(self.cfg) + depths = [2, 2, 6, 2] + num_heads = [6, 12, 6, 12] + cfg['arch'] = { + 'embed_dims': 256, + 'depths': depths, + 'num_heads': num_heads, + 'extra_norm_every_n_blocks': 2 + } + model = SwinTransformerV2(**cfg) + for i, stage in enumerate(model.stages): + self.assertEqual(stage.out_channels, 256 * (2**i)) + self.assertEqual(len(stage.blocks), depths[i]) + self.assertEqual(stage.blocks[0].attn.w_msa.num_heads, + num_heads[i]) + self.assertIsInstance(model.stages[2].blocks[5], torch.nn.Module) + + def test_init_weights(self): + # test weight init cfg + cfg = deepcopy(self.cfg) + cfg['use_abs_pos_embed'] = True + cfg['init_cfg'] = [ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ] + model = SwinTransformerV2(**cfg) + ori_weight = model.patch_embed.projection.weight.clone().detach() + # The pos_embed is all zero before initialize + self.assertTrue( + torch.allclose(model.absolute_pos_embed, torch.tensor(0.))) + + model.init_weights() + initialized_weight = model.patch_embed.projection.weight + self.assertFalse(torch.allclose(ori_weight, initialized_weight)) + self.assertFalse( + torch.allclose(model.absolute_pos_embed, torch.tensor(0.))) + + pretrain_pos_embed = model.absolute_pos_embed.clone().detach() + + tmpdir = tempfile.TemporaryDirectory() + # Save checkpoints + checkpoint = os.path.join(tmpdir.name, 'checkpoint.pth') + save_checkpoint(model, checkpoint) + + # test load checkpoint + cfg = deepcopy(self.cfg) + cfg['use_abs_pos_embed'] = True + model = SwinTransformerV2(**cfg) + load_checkpoint(model, checkpoint, strict=False) + + # test load checkpoint with different img_size + cfg = deepcopy(self.cfg) + cfg['img_size'] = 384 + cfg['use_abs_pos_embed'] = True + model = SwinTransformerV2(**cfg) + load_checkpoint(model, checkpoint, strict=False) + resized_pos_embed = timm_resize_pos_embed( + pretrain_pos_embed, model.absolute_pos_embed, num_tokens=0) + self.assertTrue( + torch.allclose(model.absolute_pos_embed, resized_pos_embed)) + + tmpdir.cleanup() + + def test_forward(self): + imgs = torch.randn(1, 3, 256, 256) + + cfg = deepcopy(self.cfg) + model = SwinTransformerV2(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (1, 1024, 8, 8)) + + # test with window_size=12 + cfg = deepcopy(self.cfg) + cfg['window_size'] = 12 + model = SwinTransformerV2(**cfg) + outs = model(torch.randn(1, 3, 384, 384)) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (1, 1024, 12, 12)) + with self.assertRaisesRegex(AssertionError, r'the window size \(12\)'): + model(torch.randn(1, 3, 256, 256)) + + # test with pad_small_map=True + cfg = deepcopy(self.cfg) + cfg['window_size'] = 12 + cfg['pad_small_map'] = True + model = SwinTransformerV2(**cfg) + outs = model(torch.randn(1, 3, 256, 256)) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (1, 1024, 8, 8)) + + # test multiple output indices + cfg = deepcopy(self.cfg) + cfg['out_indices'] = (0, 1, 2, 3) + model = SwinTransformerV2(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for stride, out in zip([1, 2, 4, 8], outs): + self.assertEqual(out.shape, + (1, 128 * stride, 64 // stride, 64 // stride)) + + # test with checkpoint forward + cfg = deepcopy(self.cfg) + cfg['with_cp'] = True + model = SwinTransformerV2(**cfg) + for m in model.modules(): + if isinstance(m, SwinBlock): + self.assertTrue(m.with_cp) + model.init_weights() + model.train() + + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (1, 1024, 8, 8)) + + # test with dynamic input shape + imgs1 = torch.randn(1, 3, 224, 224) + imgs2 = torch.randn(1, 3, 256, 256) + imgs3 = torch.randn(1, 3, 256, 309) + cfg = deepcopy(self.cfg) + cfg['pad_small_map'] = True + model = SwinTransformerV2(**cfg) + for imgs in [imgs1, imgs2, imgs3]: + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + expect_feat_shape = (math.ceil(imgs.shape[2] / 32), + math.ceil(imgs.shape[3] / 32)) + self.assertEqual(feat.shape, (1, 1024, *expect_feat_shape)) + + def test_structure(self): + # test drop_path_rate decay + cfg = deepcopy(self.cfg) + cfg['drop_path_rate'] = 0.2 + model = SwinTransformerV2(**cfg) + depths = model.arch_settings['depths'] + blocks = chain(*[stage.blocks for stage in model.stages]) + for i, block in enumerate(blocks): + expect_prob = 0.2 / (sum(depths) - 1) * i + self.assertAlmostEqual(block.ffn.dropout_layer.drop_prob, + expect_prob) + self.assertAlmostEqual(block.attn.drop.drop_prob, expect_prob) + + # test Swin-Transformer V2 with norm_eval=True + cfg = deepcopy(self.cfg) + cfg['norm_eval'] = True + cfg['norm_cfg'] = dict(type='BN') + cfg['stage_cfgs'] = dict(block_cfgs=dict(norm_cfg=dict(type='BN'))) + model = SwinTransformerV2(**cfg) + model.init_weights() + model.train() + self.assertTrue(check_norm_state(model.modules(), False)) + + # test Swin-Transformer V2 with first stage frozen. + cfg = deepcopy(self.cfg) + frozen_stages = 0 + cfg['frozen_stages'] = frozen_stages + cfg['out_indices'] = (0, 1, 2, 3) + model = SwinTransformerV2(**cfg) + model.init_weights() + model.train() + + # the patch_embed and first stage should not require grad. + self.assertFalse(model.patch_embed.training) + for param in model.patch_embed.parameters(): + self.assertFalse(param.requires_grad) + for i in range(frozen_stages + 1): + stage = model.stages[i] + for param in stage.parameters(): + self.assertFalse(param.requires_grad) + for param in model.norm0.parameters(): + self.assertFalse(param.requires_grad) + + # the second stage should require grad. + for i in range(frozen_stages + 1, 4): + stage = model.stages[i] + for param in stage.parameters(): + self.assertTrue(param.requires_grad) + norm = getattr(model, f'norm{i}') + for param in norm.parameters(): + self.assertTrue(param.requires_grad) diff --git a/tests/test_models/test_utils/test_embed.py b/tests/test_models/test_utils/test_embed.py index 8dba06065d1..eb7356b1f09 100644 --- a/tests/test_models/test_utils/test_embed.py +++ b/tests/test_models/test_utils/test_embed.py @@ -36,28 +36,26 @@ def test_hybrid_embed(): def test_patch_merging(): - settings = dict( - input_resolution=(56, 56), in_channels=16, expansion_ratio=2) + settings = dict(in_channels=16, out_channels=32, padding=0) downsample = PatchMerging(**settings) # test forward with wrong dims with pytest.raises(AssertionError): inputs = torch.rand((1, 16, 56 * 56)) - downsample(inputs) + downsample(inputs, input_size=(56, 56)) # test patch merging forward inputs = torch.rand((1, 56 * 56, 16)) - out = downsample(inputs) - assert downsample.output_resolution == (28, 28) + out, output_size = downsample(inputs, input_size=(56, 56)) + assert output_size == (28, 28) assert out.shape == (1, 28 * 28, 32) # test different kernel_size in each direction downsample = PatchMerging(kernel_size=(2, 3), **settings) - out = downsample(inputs) + out, output_size = downsample(inputs, input_size=(56, 56)) expected_dim = cal_unfold_dim(56, 2, 2) * cal_unfold_dim(56, 3, 3) assert downsample.sampler.kernel_size == (2, 3) - assert downsample.output_resolution == (cal_unfold_dim(56, 2, 2), - cal_unfold_dim(56, 3, 3)) + assert output_size == (cal_unfold_dim(56, 2, 2), cal_unfold_dim(56, 3, 3)) assert out.shape == (1, expected_dim, 32) # test default stride @@ -66,18 +64,25 @@ def test_patch_merging(): # test stride=3 downsample = PatchMerging(kernel_size=6, stride=3, **settings) - out = downsample(inputs) + out, output_size = downsample(inputs, input_size=(56, 56)) assert downsample.sampler.stride == (3, 3) assert out.shape == (1, cal_unfold_dim(56, 6, stride=3)**2, 32) # test padding - downsample = PatchMerging(kernel_size=6, padding=2, **settings) - out = downsample(inputs) + downsample = PatchMerging( + in_channels=16, out_channels=32, kernel_size=6, padding=2) + out, output_size = downsample(inputs, input_size=(56, 56)) assert downsample.sampler.padding == (2, 2) assert out.shape == (1, cal_unfold_dim(56, 6, 6, padding=2)**2, 32) + # test str padding + downsample = PatchMerging(in_channels=16, out_channels=32, kernel_size=6) + out, output_size = downsample(inputs, input_size=(56, 56)) + assert downsample.sampler.padding == (0, 0) + assert out.shape == (1, cal_unfold_dim(56, 6, 6, padding=2)**2, 32) + # test dilation downsample = PatchMerging(kernel_size=6, dilation=2, **settings) - out = downsample(inputs) + out, output_size = downsample(inputs, input_size=(56, 56)) assert downsample.sampler.dilation == (2, 2) assert out.shape == (1, cal_unfold_dim(56, 6, 6, dilation=2)**2, 32) From b5bb86a3575599a3fce95f91989d4ec257019813 Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Wed, 3 Aug 2022 19:32:29 +0800 Subject: [PATCH 03/25] [Fix] Fix the output position of Swin-Transformer. (#947) * [Fix] Fix the output position of Swin-Transformer. * Rename `downsample` argument to `do_downsample`. --- mmcls/models/backbones/swin_transformer.py | 25 ++++++++++++++----- .../test_backbones/test_swin_transformer.py | 2 +- 2 files changed, 20 insertions(+), 7 deletions(-) diff --git a/mmcls/models/backbones/swin_transformer.py b/mmcls/models/backbones/swin_transformer.py index 0ab82f19009..962d41d6e08 100644 --- a/mmcls/models/backbones/swin_transformer.py +++ b/mmcls/models/backbones/swin_transformer.py @@ -183,11 +183,11 @@ def __init__(self, else: self.downsample = None - def forward(self, x, in_shape): + def forward(self, x, in_shape, do_downsample=True): for block in self.blocks: x = block(x, in_shape) - if self.downsample: + if self.downsample is not None and do_downsample: x, out_shape = self.downsample(x, in_shape) else: out_shape = in_shape @@ -232,6 +232,8 @@ class SwinTransformer(BaseBackbone): window_size (int): The height and width of the window. Defaults to 7. drop_rate (float): Dropout rate after embedding. Defaults to 0. drop_path_rate (float): Stochastic depth rate. Defaults to 0.1. + out_after_downsample (bool): Whether to output the feature map of a + stage after the following downsample layer. Defaults to False. use_abs_pos_embed (bool): If True, add absolute position embedding to the patch embedding. Defaults to False. interpolate_mode (str): Select the interpolate mode for absolute @@ -301,6 +303,7 @@ def __init__(self, drop_rate=0., drop_path_rate=0.1, out_indices=(3, ), + out_after_downsample=False, use_abs_pos_embed=False, interpolate_mode='bicubic', with_cp=False, @@ -329,6 +332,7 @@ def __init__(self, self.num_heads = self.arch_settings['num_heads'] self.num_layers = len(self.depths) self.out_indices = out_indices + self.out_after_downsample = out_after_downsample self.use_abs_pos_embed = use_abs_pos_embed self.interpolate_mode = interpolate_mode self.frozen_stages = frozen_stages @@ -392,9 +396,15 @@ def __init__(self, dpr = dpr[depth:] embed_dims.append(stage.out_channels) + if self.out_after_downsample: + self.num_features = embed_dims[1:] + else: + self.num_features = embed_dims[:-1] + for i in out_indices: if norm_cfg is not None: - norm_layer = build_norm_layer(norm_cfg, embed_dims[i + 1])[1] + norm_layer = build_norm_layer(norm_cfg, + self.num_features[i])[1] else: norm_layer = nn.Identity() @@ -421,14 +431,17 @@ def forward(self, x): outs = [] for i, stage in enumerate(self.stages): - x, hw_shape = stage(x, hw_shape) + x, hw_shape = stage( + x, hw_shape, do_downsample=self.out_after_downsample) if i in self.out_indices: norm_layer = getattr(self, f'norm{i}') out = norm_layer(x) out = out.view(-1, *hw_shape, - stage.out_channels).permute(0, 3, 1, - 2).contiguous() + self.num_features[i]).permute(0, 3, 1, + 2).contiguous() outs.append(out) + if stage.downsample is not None and not self.out_after_downsample: + x, hw_shape = stage.downsample(x, hw_shape) return tuple(outs) diff --git a/tests/test_models/test_backbones/test_swin_transformer.py b/tests/test_models/test_backbones/test_swin_transformer.py index 90d7db7175b..33947304bd8 100644 --- a/tests/test_models/test_backbones/test_swin_transformer.py +++ b/tests/test_models/test_backbones/test_swin_transformer.py @@ -167,7 +167,7 @@ def test_forward(self): outs = model(imgs) self.assertIsInstance(outs, tuple) self.assertEqual(len(outs), 4) - for stride, out in zip([2, 4, 8, 8], outs): + for stride, out in zip([1, 2, 4, 8], outs): self.assertEqual(out.shape, (1, 128 * stride, 56 // stride, 56 // stride)) From 1a3d51acc23a02ff8bb106ccd4f622494a22e98a Mon Sep 17 00:00:00 2001 From: JiayuXu <84259897+JiayuXu0@users.noreply.github.com> Date: Thu, 4 Aug 2022 18:15:51 +0800 Subject: [PATCH 04/25] [Feature] Support CSRA head. (#881) * Support CSRA head. * Add CSRA config. * Improve training scheduler and Update cfg, ckpt, log * Update metafile * Rename config files and checkpoints Co-authored-by: Ezra-Yu <1105212286@qq.com> Co-authored-by: mzr1996 --- configs/csra/README.md | 36 ++++++ configs/csra/metafile.yml | 29 +++++ .../csra/resnet101-csra_1xb16_voc07-448px.py | 75 +++++++++++ mmcls/datasets/voc.py | 31 ++++- mmcls/models/heads/__init__.py | 3 +- mmcls/models/heads/multi_label_csra_head.py | 121 ++++++++++++++++++ model-index.yml | 1 + tests/test_models/test_heads.py | 28 +++- 8 files changed, 318 insertions(+), 6 deletions(-) create mode 100644 configs/csra/README.md create mode 100644 configs/csra/metafile.yml create mode 100644 configs/csra/resnet101-csra_1xb16_voc07-448px.py create mode 100755 mmcls/models/heads/multi_label_csra_head.py diff --git a/configs/csra/README.md b/configs/csra/README.md new file mode 100644 index 00000000000..fa677cfca1b --- /dev/null +++ b/configs/csra/README.md @@ -0,0 +1,36 @@ +# CSRA + +> [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://arxiv.org/abs/2108.02456) + + + +## Abstract + +Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. To effectively capture different spatial regions occupied by objects from different categories, we propose an embarrassingly simple module, named class-specific residual attention (CSRA). CSRA generates class-specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature. CSRA achieves state-of-the-art results on multilabel recognition, and at the same time is much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads to consistent improvement across many diverse pretrained models and datasets without any extra training. CSRA is both easy to implement and light in computations, which also enjoys intuitive explanations and visualizations. + +
+ +
+ +## Results and models + +### VOC2007 + +| Model | Pretrain | Params(M) | Flops(G) | mAP | OF1 (%) | CF1 (%) | Config | Download | +| :------------: | :------------------------------------------------: | :-------: | :------: | :---: | :-----: | :-----: | :-----------------------------------------------: | :-------------------------------------------------: | +| Resnet101-CSRA | [ImageNet-1k](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) | 23.55 | 4.12 | 94.98 | 90.80 | 89.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/csra/resnet101-csra_1xb16_voc07-448px.py) | [model](https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.log.json) | + +## Citation + +```bibtex +@misc{https://doi.org/10.48550/arxiv.2108.02456, + doi = {10.48550/ARXIV.2108.02456}, + url = {https://arxiv.org/abs/2108.02456}, + author = {Zhu, Ke and Wu, Jianxin}, + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {Residual Attention: A Simple but Effective Method for Multi-Label Recognition}, + publisher = {arXiv}, + year = {2021}, + copyright = {arXiv.org perpetual, non-exclusive license} +} +``` diff --git a/configs/csra/metafile.yml b/configs/csra/metafile.yml new file mode 100644 index 00000000000..f1fa62289c1 --- /dev/null +++ b/configs/csra/metafile.yml @@ -0,0 +1,29 @@ +Collections: + - Name: CSRA + Metadata: + Training Data: PASCAL VOC 2007 + Architecture: + - Class-specific Residual Attention + Paper: + URL: https://arxiv.org/abs/1911.11929 + Title: 'Residual Attention: A Simple but Effective Method for Multi-Label Recognition' + README: configs/csra/README.md + Code: + Version: v0.24.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/heads/multi_label_csra_head.py + +Models: + - Name: resnet101-csra_1xb16_voc07-448px + Metadata: + FLOPs: 4120000000 + Parameters: 23550000 + In Collections: CSRA + Results: + - Dataset: PASCAL VOC 2007 + Metrics: + mAP: 94.98 + OF1: 90.80 + CF1: 89.16 + Task: Multi-Label Classification + Weights: https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.pth + Config: configs/csra/resnet101-csra_1xb16_voc07-448px.py diff --git a/configs/csra/resnet101-csra_1xb16_voc07-448px.py b/configs/csra/resnet101-csra_1xb16_voc07-448px.py new file mode 100644 index 00000000000..5dc5dd62aad --- /dev/null +++ b/configs/csra/resnet101-csra_1xb16_voc07-448px.py @@ -0,0 +1,75 @@ +_base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py'] + +# Pre-trained Checkpoint Path +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth' # noqa +# If you want to use the pre-trained weight of ResNet101-CutMix from +# the originary repo(https://github.com/Kevinz-code/CSRA). Script of +# 'tools/convert_models/torchvision_to_mmcls.py' can help you convert weight +# into mmcls format. The mAP result would hit 95.5 by using the weight. +# checkpoint = 'PATH/TO/PRE-TRAINED_WEIGHT' + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(3, ), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint, prefix='backbone')), + neck=None, + head=dict( + type='CSRAClsHead', + num_classes=20, + in_channels=2048, + num_heads=1, + lam=0.1, + loss=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))) + +# dataset setting +img_norm_cfg = dict(mean=[0, 0, 0], std=[255, 255, 255], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=448, scale=(0.7, 1.0)), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=448), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + # map the difficult examples as negative ones(0) + train=dict(pipeline=train_pipeline, difficult_as_postive=False), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +# the lr of classifier.head is 10 * base_lr, which help convergence. +optimizer = dict( + type='SGD', + lr=0.0002, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10)})) + +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='step', + step=6, + gamma=0.1, + warmup='linear', + warmup_iters=1, + warmup_ratio=1e-7, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/mmcls/datasets/voc.py b/mmcls/datasets/voc.py index be8c0a05a5b..e9c8bceb18a 100644 --- a/mmcls/datasets/voc.py +++ b/mmcls/datasets/voc.py @@ -11,14 +11,29 @@ @DATASETS.register_module() class VOC(MultiLabelDataset): - """`Pascal VOC `_ Dataset.""" + """`Pascal VOC `_ Dataset. + + Args: + data_prefix (str): the prefix of data path + pipeline (list): a list of dict, where each element represents + a operation defined in `mmcls.datasets.pipelines` + ann_file (str | None): the annotation file. When ann_file is str, + the subclass is expected to read from the ann_file. When ann_file + is None, the subclass is expected to read according to data_prefix + difficult_as_postive (Optional[bool]): Whether to map the difficult + labels as positive. If it set to True, map difficult examples to + positive ones(1), If it set to False, map difficult examples to + negative ones(0). Defaults to None, the difficult labels will be + set to '-1'. + """ CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') - def __init__(self, **kwargs): + def __init__(self, difficult_as_postive=None, **kwargs): + self.difficult_as_postive = difficult_as_postive super(VOC, self).__init__(**kwargs) if 'VOC2007' in self.data_prefix: self.year = 2007 @@ -55,9 +70,19 @@ def load_annotations(self): labels.append(label) gt_label = np.zeros(len(self.CLASSES)) + # set difficult example first, then set postivate examples. # The order cannot be swapped for the case where multiple objects # of the same kind exist and some are difficult. - gt_label[labels_difficult] = -1 + if self.difficult_as_postive is None: + # map difficult examples to -1, + # it may be used in evaluation to ignore difficult targets. + gt_label[labels_difficult] = -1 + elif self.difficult_as_postive: + # map difficult examples to positive ones(1). + gt_label[labels_difficult] = 1 + else: + # map difficult examples to negative ones(0). + gt_label[labels_difficult] = 0 gt_label[labels] = 1 info = dict( diff --git a/mmcls/models/heads/__init__.py b/mmcls/models/heads/__init__.py index b81106fbe96..ad520ce4ca5 100644 --- a/mmcls/models/heads/__init__.py +++ b/mmcls/models/heads/__init__.py @@ -3,6 +3,7 @@ from .conformer_head import ConformerHead from .deit_head import DeiTClsHead from .linear_head import LinearClsHead +from .multi_label_csra_head import CSRAClsHead from .multi_label_head import MultiLabelClsHead from .multi_label_linear_head import MultiLabelLinearClsHead from .stacked_head import StackedLinearClsHead @@ -11,5 +12,5 @@ __all__ = [ 'ClsHead', 'LinearClsHead', 'StackedLinearClsHead', 'MultiLabelClsHead', 'MultiLabelLinearClsHead', 'VisionTransformerClsHead', 'DeiTClsHead', - 'ConformerHead' + 'ConformerHead', 'CSRAClsHead' ] diff --git a/mmcls/models/heads/multi_label_csra_head.py b/mmcls/models/heads/multi_label_csra_head.py new file mode 100755 index 00000000000..f28ba42bdb6 --- /dev/null +++ b/mmcls/models/heads/multi_label_csra_head.py @@ -0,0 +1,121 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# Modified from https://github.com/Kevinz-code/CSRA +import torch +import torch.nn as nn +from mmcv.runner import BaseModule, ModuleList + +from ..builder import HEADS +from .multi_label_head import MultiLabelClsHead + + +@HEADS.register_module() +class CSRAClsHead(MultiLabelClsHead): + """Class-specific residual attention classifier head. + + Residual Attention: A Simple but Effective Method for Multi-Label + Recognition (ICCV 2021) + Please refer to the `paper `__ for + details. + + Args: + num_classes (int): Number of categories. + in_channels (int): Number of channels in the input feature map. + num_heads (int): Number of residual at tensor heads. + loss (dict): Config of classification loss. + lam (float): Lambda that combines global average and max pooling + scores. + init_cfg (dict | optional): The extra init config of layers. + Defaults to use dict(type='Normal', layer='Linear', std=0.01). + """ + temperature_settings = { # softmax temperature settings + 1: [1], + 2: [1, 99], + 4: [1, 2, 4, 99], + 6: [1, 2, 3, 4, 5, 99], + 8: [1, 2, 3, 4, 5, 6, 7, 99] + } + + def __init__(self, + num_classes, + in_channels, + num_heads, + lam, + loss=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=1.0), + init_cfg=dict(type='Normal', layer='Linear', std=0.01), + *args, + **kwargs): + assert num_heads in self.temperature_settings.keys( + ), 'The num of heads is not in temperature setting.' + assert lam > 0, 'Lambda should be between 0 and 1.' + super(CSRAClsHead, self).__init__( + init_cfg=init_cfg, loss=loss, *args, **kwargs) + self.temp_list = self.temperature_settings[num_heads] + self.csra_heads = ModuleList([ + CSRAModule(num_classes, in_channels, self.temp_list[i], lam) + for i in range(num_heads) + ]) + + def pre_logits(self, x): + if isinstance(x, tuple): + x = x[-1] + return x + + def simple_test(self, x, post_process=True, **kwargs): + logit = 0. + x = self.pre_logits(x) + for head in self.csra_heads: + logit += head(x) + if post_process: + return self.post_process(logit) + else: + return logit + + def forward_train(self, x, gt_label, **kwargs): + logit = 0. + x = self.pre_logits(x) + for head in self.csra_heads: + logit += head(x) + gt_label = gt_label.type_as(logit) + _gt_label = torch.abs(gt_label) + losses = self.loss(logit, _gt_label, **kwargs) + return losses + + +class CSRAModule(BaseModule): + """Basic module of CSRA with different temperature. + + Args: + num_classes (int): Number of categories. + in_channels (int): Number of channels in the input feature map. + T (int): Temperature setting. + lam (float): Lambda that combines global average and max pooling + scores. + init_cfg (dict | optional): The extra init config of layers. + Defaults to use dict(type='Normal', layer='Linear', std=0.01). + """ + + def __init__(self, num_classes, in_channels, T, lam, init_cfg=None): + + super(CSRAModule, self).__init__(init_cfg=init_cfg) + self.T = T # temperature + self.lam = lam # Lambda + self.head = nn.Conv2d(in_channels, num_classes, 1, bias=False) + self.softmax = nn.Softmax(dim=2) + + def forward(self, x): + score = self.head(x) / torch.norm( + self.head.weight, dim=1, keepdim=True).transpose(0, 1) + score = score.flatten(2) + base_logit = torch.mean(score, dim=2) + + if self.T == 99: # max-pooling + att_logit = torch.max(score, dim=2)[0] + else: + score_soft = self.softmax(score * self.T) + att_logit = torch.sum(score * score_soft, dim=2) + + return base_logit + self.lam * att_logit diff --git a/model-index.yml b/model-index.yml index 9b779055898..a57802a85f0 100644 --- a/model-index.yml +++ b/model-index.yml @@ -28,4 +28,5 @@ Import: - configs/convmixer/metafile.yml - configs/densenet/metafile.yml - configs/poolformer/metafile.yml + - configs/csra/metafile.yml - configs/mvit/metafile.yml diff --git a/tests/test_models/test_heads.py b/tests/test_models/test_heads.py index 392afe74cf5..4ab18c332c4 100644 --- a/tests/test_models/test_heads.py +++ b/tests/test_models/test_heads.py @@ -4,8 +4,8 @@ import pytest import torch -from mmcls.models.heads import (ClsHead, ConformerHead, DeiTClsHead, - LinearClsHead, MultiLabelClsHead, +from mmcls.models.heads import (ClsHead, ConformerHead, CSRAClsHead, + DeiTClsHead, LinearClsHead, MultiLabelClsHead, MultiLabelLinearClsHead, StackedLinearClsHead, VisionTransformerClsHead) @@ -317,3 +317,27 @@ def test_deit_head(): # test assertion with pytest.raises(ValueError): DeiTClsHead(-1, 100) + + +@pytest.mark.parametrize( + 'feat', [torch.rand(4, 20, 20, 30), (torch.rand(4, 20, 20, 30), )]) +def test_csra_head(feat): + head = CSRAClsHead(num_classes=10, in_channels=20, num_heads=1, lam=0.1) + fake_gt_label = torch.randint(0, 2, (4, 10)) + + losses = head.forward_train(feat, fake_gt_label) + assert losses['loss'].item() > 0 + + # test simple_test with post_process + pred = head.simple_test(feat) + assert isinstance(pred, list) and len(pred) == 4 + with patch('torch.onnx.is_in_onnx_export', return_value=True): + pred = head.simple_test(feat) + assert pred.shape == (4, 10) + + # test pre_logits + features = head.pre_logits(feat) + if isinstance(feat, tuple): + torch.testing.assert_allclose(features, feat[0]) + else: + torch.testing.assert_allclose(features, feat) From b3668978896174d99369465425470c3e6a951ba0 Mon Sep 17 00:00:00 2001 From: Timothy Lim Date: Wed, 10 Aug 2022 18:17:36 +0800 Subject: [PATCH 05/25] [Docs] Refine the docstring of RegNet (#935) * Update regnet.py In the example comment to print out the different layers of outputs, we need to indicate the `out_indices` to (0,1,2,3) to see all backbone layers output as the default argument is (3,) * Update regnet.py following changes proposal of maintainer * fix linting * fix blank space for docs * fix blank space for docs * fix blank space for docs --- mmcls/models/backbones/regnet.py | 85 ++++++++++++++++++-------------- 1 file changed, 48 insertions(+), 37 deletions(-) diff --git a/mmcls/models/backbones/regnet.py b/mmcls/models/backbones/regnet.py index 1dce86aa632..036b699c4d5 100644 --- a/mmcls/models/backbones/regnet.py +++ b/mmcls/models/backbones/regnet.py @@ -45,24 +45,31 @@ class RegNet(ResNet): Example: >>> from mmcls.models import RegNet >>> import torch - >>> self = RegNet( - arch=dict( - w0=88, - wa=26.31, - wm=2.25, - group_w=48, - depth=25, - bot_mul=1.0)) - >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) - >>> level_outputs = self.forward(inputs) + >>> # use str type 'arch' + >>> # Note that default out_indices is (3,) + >>> regnet_cfg = dict(arch='regnetx_4.0gf') + >>> model = RegNet(**regnet_cfg) + >>> model.eval() + >>> level_outputs = model(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) - (1, 96, 8, 8) - (1, 192, 4, 4) - (1, 432, 2, 2) - (1, 1008, 1, 1) + (1, 1360, 1, 1) + >>> # use dict type 'arch' + >>> arch_cfg =dict(w0=88, wa=26.31, wm=2.25, + >>> group_w=48, depth=25, bot_mul=1.0) + >>> regnet_cfg = dict(arch=arch_cfg, out_indices=(0, 1, 2, 3)) + >>> model = RegNet(**regnet_cfg) + >>> model.eval() + >>> level_outputs = model(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 96, 8, 8) + (1, 192, 4, 4) + (1, 432, 2, 2) + (1, 1008, 1, 1) """ + arch_settings = { 'regnetx_400mf': dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0), @@ -82,31 +89,33 @@ class RegNet(ResNet): dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0), } - def __init__(self, - arch, - in_channels=3, - stem_channels=32, - base_channels=32, - strides=(2, 2, 2, 2), - dilations=(1, 1, 1, 1), - out_indices=(3, ), - style='pytorch', - deep_stem=False, - avg_down=False, - frozen_stages=-1, - conv_cfg=None, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=False, - with_cp=False, - zero_init_residual=True, - init_cfg=None): + def __init__( + self, + arch, + in_channels=3, + stem_channels=32, + base_channels=32, + strides=(2, 2, 2, 2), + dilations=(1, 1, 1, 1), + out_indices=(3, ), + style='pytorch', + deep_stem=False, + avg_down=False, + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=False, + with_cp=False, + zero_init_residual=True, + init_cfg=None, + ): super(ResNet, self).__init__(init_cfg) # Generate RegNet parameters first if isinstance(arch, str): - assert arch in self.arch_settings, \ - f'"arch": "{arch}" is not one of the' \ - ' arch_settings' + assert arch in self.arch_settings, ( + f'"arch": "{arch}" is not one of the' + ' arch_settings') arch = self.arch_settings[arch] elif not isinstance(arch, dict): raise TypeError('Expect "arch" to be either a string ' @@ -180,7 +189,8 @@ def __init__(self, norm_cfg=self.norm_cfg, base_channels=self.stage_widths[i], groups=stage_groups, - width_per_group=group_width) + width_per_group=group_width, + ) _in_channels = self.stage_widths[i] layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) @@ -198,7 +208,8 @@ def _make_stem_layer(self, in_channels, base_channels): kernel_size=3, stride=2, padding=1, - bias=False) + bias=False, + ) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, base_channels, postfix=1) self.add_module(self.norm1_name, norm1) From e54cfd6951e49880b37ed919a80cf54d4a3b4ab1 Mon Sep 17 00:00:00 2001 From: Ezra-Yu <1105212286@qq.com> Date: Thu, 11 Aug 2022 15:02:25 +0800 Subject: [PATCH 06/25] [Imporve] Using `train_step` instead of `forward` in PreciseBNHook (#964) * fix precise BN hook when using MLU * fix unit tests --- mmcls/core/hook/precise_bn_hook.py | 2 +- tests/test_runtime/test_preciseBN_hook.py | 21 +++++++++++++++------ 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/mmcls/core/hook/precise_bn_hook.py b/mmcls/core/hook/precise_bn_hook.py index 384fd806725..e6d45980130 100644 --- a/mmcls/core/hook/precise_bn_hook.py +++ b/mmcls/core/hook/precise_bn_hook.py @@ -107,7 +107,7 @@ def update_bn_stats(model: nn.Module, prog_bar = mmcv.ProgressBar(num_iter) for data in itertools.islice(loader, num_iter): - model(**data) + model.train_step(data) for i, bn in enumerate(bn_layers): running_means[i] += bn.running_mean / num_iter running_vars[i] += bn.running_var / num_iter diff --git a/tests/test_runtime/test_preciseBN_hook.py b/tests/test_runtime/test_preciseBN_hook.py index d9cd71569e5..f9375f94af1 100644 --- a/tests/test_runtime/test_preciseBN_hook.py +++ b/tests/test_runtime/test_preciseBN_hook.py @@ -10,6 +10,7 @@ from torch.utils.data import DataLoader, Dataset from mmcls.core.hook import PreciseBNHook +from mmcls.models.classifiers import BaseClassifier class ExampleDataset(Dataset): @@ -41,7 +42,7 @@ def __len__(self): return 12 -class ExampleModel(nn.Module): +class ExampleModel(BaseClassifier): def __init__(self): super().__init__() @@ -52,7 +53,17 @@ def __init__(self): def forward(self, imgs, return_loss=False): return self.bn(self.conv(imgs)) - def train_step(self, data_batch, optimizer, **kwargs): + def simple_test(self, img, img_metas=None, **kwargs): + return {} + + def extract_feat(self, img, stage='neck'): + return () + + def forward_train(self, img, gt_label, **kwargs): + return {'loss': 0.5} + + def train_step(self, data_batch, optimizer=None, **kwargs): + self.forward(**data_batch) outputs = { 'loss': 0.5, 'log_vars': { @@ -234,10 +245,8 @@ def test_precise_bn(): mean = np.mean([np.mean(batch) for batch in imgs_list]) # bassel correction used in Pytorch, therefore ddof=1 var = np.mean([np.var(batch, ddof=1) for batch in imgs_list]) - assert np.equal(mean, np.array( - model.bn.running_mean)), (mean, np.array(model.bn.running_mean)) - assert np.equal(var, np.array( - model.bn.running_var)), (var, np.array(model.bn.running_var)) + assert np.equal(mean, model.bn.running_mean) + assert np.equal(var, model.bn.running_var) @pytest.mark.skipif( not torch.cuda.is_available(), reason='requires CUDA support') From 7b16bcdd9b6786bccf0f3dd15c820574b1b5919d Mon Sep 17 00:00:00 2001 From: zzc98 <40905160+zzc98@users.noreply.github.com> Date: Tue, 16 Aug 2022 11:14:17 +0800 Subject: [PATCH 07/25] [Feature] Support Stanford Cars dataset. (#893) * feat: add stanford car dataset * feat: add stanford car dataset * feat: add stanford car dataset * feat: add stanford car dataset * feat: add stanford car dataset * feat: add stanford car dataset * Update links and using cars insteam of car * place ependency scipy from runtime to optional * Fix docstring Co-authored-by: Ezra-Yu <1105212286@qq.com> Co-authored-by: mzr1996 --- .../_base_/datasets/stanford_cars_bs8_448.py | 46 ++++ configs/_base_/schedules/stanford_cars_bs8.py | 7 + configs/resnet/README.md | 6 + configs/resnet/metafile.yml | 13 ++ configs/resnet/resnet50_8xb8_cars.py | 19 ++ docs/en/api/datasets.rst | 5 + mmcls/datasets/__init__.py | 4 +- mmcls/datasets/stanford_cars.py | 210 ++++++++++++++++++ requirements/optional.txt | 1 + tests/test_data/test_datasets/test_common.py | 148 ++++++++++++ 10 files changed, 458 insertions(+), 1 deletion(-) create mode 100644 configs/_base_/datasets/stanford_cars_bs8_448.py create mode 100644 configs/_base_/schedules/stanford_cars_bs8.py create mode 100644 configs/resnet/resnet50_8xb8_cars.py create mode 100644 mmcls/datasets/stanford_cars.py diff --git a/configs/_base_/datasets/stanford_cars_bs8_448.py b/configs/_base_/datasets/stanford_cars_bs8_448.py new file mode 100644 index 00000000000..636b2e14be4 --- /dev/null +++ b/configs/_base_/datasets/stanford_cars_bs8_448.py @@ -0,0 +1,46 @@ +# dataset settings +dataset_type = 'StanfordCars' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=512), + dict(type='RandomCrop', size=448), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=512), + dict(type='CenterCrop', crop_size=448), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data_root = 'data/stanfordcars' +data = dict( + samples_per_gpu=8, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix=data_root, + test_mode=False, + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix=data_root, + test_mode=True, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_prefix=data_root, + test_mode=True, + pipeline=test_pipeline)) + +evaluation = dict( + interval=1, metric='accuracy', + save_best='auto') # save the checkpoint with highest accuracy diff --git a/configs/_base_/schedules/stanford_cars_bs8.py b/configs/_base_/schedules/stanford_cars_bs8.py new file mode 100644 index 00000000000..dee252ec767 --- /dev/null +++ b/configs/_base_/schedules/stanford_cars_bs8.py @@ -0,0 +1,7 @@ +# optimizer +optimizer = dict( + type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[40, 70, 90]) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/resnet/README.md b/configs/resnet/README.md index f1d32effde7..d32fcd64e03 100644 --- a/configs/resnet/README.md +++ b/configs/resnet/README.md @@ -72,6 +72,12 @@ The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don' | :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: | | ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | 448x448 | 23.92 | 16.48 | 88.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cub.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.log.json) | +### Stanford-Cars + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: | +| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | 448x448 | 23.92 | 16.48 | 92.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cars.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.log.json) | + ## Citation ``` diff --git a/configs/resnet/metafile.yml b/configs/resnet/metafile.yml index 29aa84df37b..4be4bf9bf48 100644 --- a/configs/resnet/metafile.yml +++ b/configs/resnet/metafile.yml @@ -350,3 +350,16 @@ Models: Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth Config: configs/resnet/resnet50_8xb8_cub.py + - Name: resnet50_8xb8_cars + Metadata: + FLOPs: 16480000000 + Parameters: 23920000 + In Collection: ResNet + Results: + - Dataset: StanfordCars + Metrics: + Top 1 Accuracy: 92.82 + Task: Image Classification + Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth + Config: configs/resnet/resnet50_8xb8_cars.py diff --git a/configs/resnet/resnet50_8xb8_cars.py b/configs/resnet/resnet50_8xb8_cars.py new file mode 100644 index 00000000000..2d2db45d08a --- /dev/null +++ b/configs/resnet/resnet50_8xb8_cars.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/stanford_cars_bs8_448.py', + '../_base_/schedules/stanford_cars_bs8.py', '../_base_/default_runtime.py' +] + +# use pre-train weight converted from https://github.com/Alibaba-MIIL/ImageNet21K # noqa +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth' # noqa + +model = dict( + type='ImageClassifier', + backbone=dict( + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint, prefix='backbone')), + head=dict(num_classes=196, )) + +log_config = dict(interval=50) +checkpoint_config = dict( + interval=1, max_keep_ckpts=3) # save last three checkpoints diff --git a/docs/en/api/datasets.rst b/docs/en/api/datasets.rst index 585f5586675..640ce1ad7d2 100644 --- a/docs/en/api/datasets.rst +++ b/docs/en/api/datasets.rst @@ -39,6 +39,11 @@ VOC .. autoclass:: VOC +StanfordCars Cars +----------------- + +.. autoclass:: StanfordCars + Base classes ------------ diff --git a/mmcls/datasets/__init__.py b/mmcls/datasets/__init__.py index c71dd50a201..095077e2321 100644 --- a/mmcls/datasets/__init__.py +++ b/mmcls/datasets/__init__.py @@ -12,6 +12,7 @@ from .mnist import MNIST, FashionMNIST from .multi_label import MultiLabelDataset from .samplers import DistributedSampler, RepeatAugSampler +from .stanford_cars import StanfordCars from .voc import VOC __all__ = [ @@ -19,5 +20,6 @@ 'VOC', 'MultiLabelDataset', 'build_dataloader', 'build_dataset', 'DistributedSampler', 'ConcatDataset', 'RepeatDataset', 'ClassBalancedDataset', 'DATASETS', 'PIPELINES', 'ImageNet21k', 'SAMPLERS', - 'build_sampler', 'RepeatAugSampler', 'KFoldDataset', 'CUB', 'CustomDataset' + 'build_sampler', 'RepeatAugSampler', 'KFoldDataset', 'CUB', + 'CustomDataset', 'StanfordCars' ] diff --git a/mmcls/datasets/stanford_cars.py b/mmcls/datasets/stanford_cars.py new file mode 100644 index 00000000000..df1f95126f6 --- /dev/null +++ b/mmcls/datasets/stanford_cars.py @@ -0,0 +1,210 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from typing import Optional + +import numpy as np + +from .base_dataset import BaseDataset +from .builder import DATASETS + + +@DATASETS.register_module() +class StanfordCars(BaseDataset): + """`Stanford Cars`_ Dataset. + + After downloading and decompression, the dataset + directory structure is as follows. + + Stanford Cars dataset directory:: + + Stanford Cars + ├── cars_train + │ ├── 00001.jpg + │ ├── 00002.jpg + │ └── ... + ├── cars_test + │ ├── 00001.jpg + │ ├── 00002.jpg + │ └── ... + └── devkit + ├── cars_meta.mat + ├── cars_train_annos.mat + ├── cars_test_annos.mat + ├── cars_test_annoswithlabels.mat + ├── eval_train.m + └── train_perfect_preds.txt + + .. _Stanford Cars: https://ai.stanford.edu/~jkrause/cars/car_dataset.html + + Args: + data_prefix (str): the prefix of data path + test_mode (bool): ``test_mode=True`` means in test phase. It determines + to use the training set or test set. + ann_file (str, optional): The annotation file. If is string, read + samples paths from the ann_file. If is None, read samples path + from cars_{train|test}_annos.mat file. Defaults to None. + """ # noqa: E501 + + CLASSES = [ + 'AM General Hummer SUV 2000', 'Acura RL Sedan 2012', + 'Acura TL Sedan 2012', 'Acura TL Type-S 2008', 'Acura TSX Sedan 2012', + 'Acura Integra Type R 2001', 'Acura ZDX Hatchback 2012', + 'Aston Martin V8 Vantage Convertible 2012', + 'Aston Martin V8 Vantage Coupe 2012', + 'Aston Martin Virage Convertible 2012', + 'Aston Martin Virage Coupe 2012', 'Audi RS 4 Convertible 2008', + 'Audi A5 Coupe 2012', 'Audi TTS Coupe 2012', 'Audi R8 Coupe 2012', + 'Audi V8 Sedan 1994', 'Audi 100 Sedan 1994', 'Audi 100 Wagon 1994', + 'Audi TT Hatchback 2011', 'Audi S6 Sedan 2011', + 'Audi S5 Convertible 2012', 'Audi S5 Coupe 2012', 'Audi S4 Sedan 2012', + 'Audi S4 Sedan 2007', 'Audi TT RS Coupe 2012', + 'BMW ActiveHybrid 5 Sedan 2012', 'BMW 1 Series Convertible 2012', + 'BMW 1 Series Coupe 2012', 'BMW 3 Series Sedan 2012', + 'BMW 3 Series Wagon 2012', 'BMW 6 Series Convertible 2007', + 'BMW X5 SUV 2007', 'BMW X6 SUV 2012', 'BMW M3 Coupe 2012', + 'BMW M5 Sedan 2010', 'BMW M6 Convertible 2010', 'BMW X3 SUV 2012', + 'BMW Z4 Convertible 2012', + 'Bentley Continental Supersports Conv. Convertible 2012', + 'Bentley Arnage Sedan 2009', 'Bentley Mulsanne Sedan 2011', + 'Bentley Continental GT Coupe 2012', + 'Bentley Continental GT Coupe 2007', + 'Bentley Continental Flying Spur Sedan 2007', + 'Bugatti Veyron 16.4 Convertible 2009', + 'Bugatti Veyron 16.4 Coupe 2009', 'Buick Regal GS 2012', + 'Buick Rainier SUV 2007', 'Buick Verano Sedan 2012', + 'Buick Enclave SUV 2012', 'Cadillac CTS-V Sedan 2012', + 'Cadillac SRX SUV 2012', 'Cadillac Escalade EXT Crew Cab 2007', + 'Chevrolet Silverado 1500 Hybrid Crew Cab 2012', + 'Chevrolet Corvette Convertible 2012', 'Chevrolet Corvette ZR1 2012', + 'Chevrolet Corvette Ron Fellows Edition Z06 2007', + 'Chevrolet Traverse SUV 2012', 'Chevrolet Camaro Convertible 2012', + 'Chevrolet HHR SS 2010', 'Chevrolet Impala Sedan 2007', + 'Chevrolet Tahoe Hybrid SUV 2012', 'Chevrolet Sonic Sedan 2012', + 'Chevrolet Express Cargo Van 2007', + 'Chevrolet Avalanche Crew Cab 2012', 'Chevrolet Cobalt SS 2010', + 'Chevrolet Malibu Hybrid Sedan 2010', 'Chevrolet TrailBlazer SS 2009', + 'Chevrolet Silverado 2500HD Regular Cab 2012', + 'Chevrolet Silverado 1500 Classic Extended Cab 2007', + 'Chevrolet Express Van 2007', 'Chevrolet Monte Carlo Coupe 2007', + 'Chevrolet Malibu Sedan 2007', + 'Chevrolet Silverado 1500 Extended Cab 2012', + 'Chevrolet Silverado 1500 Regular Cab 2012', 'Chrysler Aspen SUV 2009', + 'Chrysler Sebring Convertible 2010', + 'Chrysler Town and Country Minivan 2012', 'Chrysler 300 SRT-8 2010', + 'Chrysler Crossfire Convertible 2008', + 'Chrysler PT Cruiser Convertible 2008', 'Daewoo Nubira Wagon 2002', + 'Dodge Caliber Wagon 2012', 'Dodge Caliber Wagon 2007', + 'Dodge Caravan Minivan 1997', 'Dodge Ram Pickup 3500 Crew Cab 2010', + 'Dodge Ram Pickup 3500 Quad Cab 2009', 'Dodge Sprinter Cargo Van 2009', + 'Dodge Journey SUV 2012', 'Dodge Dakota Crew Cab 2010', + 'Dodge Dakota Club Cab 2007', 'Dodge Magnum Wagon 2008', + 'Dodge Challenger SRT8 2011', 'Dodge Durango SUV 2012', + 'Dodge Durango SUV 2007', 'Dodge Charger Sedan 2012', + 'Dodge Charger SRT-8 2009', 'Eagle Talon Hatchback 1998', + 'FIAT 500 Abarth 2012', 'FIAT 500 Convertible 2012', + 'Ferrari FF Coupe 2012', 'Ferrari California Convertible 2012', + 'Ferrari 458 Italia Convertible 2012', 'Ferrari 458 Italia Coupe 2012', + 'Fisker Karma Sedan 2012', 'Ford F-450 Super Duty Crew Cab 2012', + 'Ford Mustang Convertible 2007', 'Ford Freestar Minivan 2007', + 'Ford Expedition EL SUV 2009', 'Ford Edge SUV 2012', + 'Ford Ranger SuperCab 2011', 'Ford GT Coupe 2006', + 'Ford F-150 Regular Cab 2012', 'Ford F-150 Regular Cab 2007', + 'Ford Focus Sedan 2007', 'Ford E-Series Wagon Van 2012', + 'Ford Fiesta Sedan 2012', 'GMC Terrain SUV 2012', + 'GMC Savana Van 2012', 'GMC Yukon Hybrid SUV 2012', + 'GMC Acadia SUV 2012', 'GMC Canyon Extended Cab 2012', + 'Geo Metro Convertible 1993', 'HUMMER H3T Crew Cab 2010', + 'HUMMER H2 SUT Crew Cab 2009', 'Honda Odyssey Minivan 2012', + 'Honda Odyssey Minivan 2007', 'Honda Accord Coupe 2012', + 'Honda Accord Sedan 2012', 'Hyundai Veloster Hatchback 2012', + 'Hyundai Santa Fe SUV 2012', 'Hyundai Tucson SUV 2012', + 'Hyundai Veracruz SUV 2012', 'Hyundai Sonata Hybrid Sedan 2012', + 'Hyundai Elantra Sedan 2007', 'Hyundai Accent Sedan 2012', + 'Hyundai Genesis Sedan 2012', 'Hyundai Sonata Sedan 2012', + 'Hyundai Elantra Touring Hatchback 2012', 'Hyundai Azera Sedan 2012', + 'Infiniti G Coupe IPL 2012', 'Infiniti QX56 SUV 2011', + 'Isuzu Ascender SUV 2008', 'Jaguar XK XKR 2012', + 'Jeep Patriot SUV 2012', 'Jeep Wrangler SUV 2012', + 'Jeep Liberty SUV 2012', 'Jeep Grand Cherokee SUV 2012', + 'Jeep Compass SUV 2012', 'Lamborghini Reventon Coupe 2008', + 'Lamborghini Aventador Coupe 2012', + 'Lamborghini Gallardo LP 570-4 Superleggera 2012', + 'Lamborghini Diablo Coupe 2001', 'Land Rover Range Rover SUV 2012', + 'Land Rover LR2 SUV 2012', 'Lincoln Town Car Sedan 2011', + 'MINI Cooper Roadster Convertible 2012', + 'Maybach Landaulet Convertible 2012', 'Mazda Tribute SUV 2011', + 'McLaren MP4-12C Coupe 2012', + 'Mercedes-Benz 300-Class Convertible 1993', + 'Mercedes-Benz C-Class Sedan 2012', + 'Mercedes-Benz SL-Class Coupe 2009', + 'Mercedes-Benz E-Class Sedan 2012', 'Mercedes-Benz S-Class Sedan 2012', + 'Mercedes-Benz Sprinter Van 2012', 'Mitsubishi Lancer Sedan 2012', + 'Nissan Leaf Hatchback 2012', 'Nissan NV Passenger Van 2012', + 'Nissan Juke Hatchback 2012', 'Nissan 240SX Coupe 1998', + 'Plymouth Neon Coupe 1999', 'Porsche Panamera Sedan 2012', + 'Ram C/V Cargo Van Minivan 2012', + 'Rolls-Royce Phantom Drophead Coupe Convertible 2012', + 'Rolls-Royce Ghost Sedan 2012', 'Rolls-Royce Phantom Sedan 2012', + 'Scion xD Hatchback 2012', 'Spyker C8 Convertible 2009', + 'Spyker C8 Coupe 2009', 'Suzuki Aerio Sedan 2007', + 'Suzuki Kizashi Sedan 2012', 'Suzuki SX4 Hatchback 2012', + 'Suzuki SX4 Sedan 2012', 'Tesla Model S Sedan 2012', + 'Toyota Sequoia SUV 2012', 'Toyota Camry Sedan 2012', + 'Toyota Corolla Sedan 2012', 'Toyota 4Runner SUV 2012', + 'Volkswagen Golf Hatchback 2012', 'Volkswagen Golf Hatchback 1991', + 'Volkswagen Beetle Hatchback 2012', 'Volvo C30 Hatchback 2012', + 'Volvo 240 Sedan 1993', 'Volvo XC90 SUV 2007', + 'smart fortwo Convertible 2012' + ] + + def __init__(self, + data_prefix: str, + test_mode: bool, + ann_file: Optional[str] = None, + **kwargs): + if test_mode: + if ann_file is not None: + self.test_ann_file = ann_file + else: + self.test_ann_file = osp.join( + data_prefix, 'devkit/cars_test_annos_withlabels.mat') + data_prefix = osp.join(data_prefix, 'cars_test') + else: + if ann_file is not None: + self.train_ann_file = ann_file + else: + self.train_ann_file = osp.join(data_prefix, + 'devkit/cars_train_annos.mat') + data_prefix = osp.join(data_prefix, 'cars_train') + super(StanfordCars, self).__init__( + ann_file=ann_file, + data_prefix=data_prefix, + test_mode=test_mode, + **kwargs) + + def load_annotations(self): + try: + import scipy.io as sio + except ImportError: + raise ImportError( + 'please run `pip install scipy` to install package `scipy`.') + + data_infos = [] + if self.test_mode: + data = sio.loadmat(self.test_ann_file) + else: + data = sio.loadmat(self.train_ann_file) + for img in data['annotations'][0]: + info = {'img_prefix': self.data_prefix} + # The organization of each record is as follows, + # 0: bbox_x1 of each image + # 1: bbox_y1 of each image + # 2: bbox_x2 of each image + # 3: bbox_y2 of each image + # 4: class_id, start from 0, so + # here we need to '- 1' to let them start from 0 + # 5: file name of each image + info['img_info'] = {'filename': img[5][0]} + info['gt_label'] = np.array(img[4][0][0] - 1, dtype=np.int64) + data_infos.append(info) + return data_infos diff --git a/requirements/optional.txt b/requirements/optional.txt index 8d449aae5ee..cc0228041b1 100644 --- a/requirements/optional.txt +++ b/requirements/optional.txt @@ -2,3 +2,4 @@ albumentations>=0.3.2 --no-binary qudida,albumentations colorama requests rich +scipy diff --git a/tests/test_data/test_datasets/test_common.py b/tests/test_data/test_datasets/test_common.py index b6bfe3bd341..5ec38184763 100644 --- a/tests/test_data/test_datasets/test_common.py +++ b/tests/test_data/test_datasets/test_common.py @@ -761,3 +761,151 @@ def test_load_annotations(self): @classmethod def tearDownClass(cls): cls.tmpdir.cleanup() + + +class TestStanfordCars(TestBaseDataset): + DATASET_TYPE = 'StanfordCars' + + def test_initialize(self): + dataset_class = DATASETS.get(self.DATASET_TYPE) + + with patch.object(dataset_class, 'load_annotations'): + # Test with test_mode=False, ann_file is None + cfg = {**self.DEFAULT_ARGS, 'test_mode': False, 'ann_file': None} + dataset = dataset_class(**cfg) + self.assertEqual(dataset.CLASSES, dataset_class.CLASSES) + self.assertFalse(dataset.test_mode) + self.assertIsNone(dataset.ann_file) + self.assertIsNotNone(dataset.train_ann_file) + + # Test with test_mode=False, ann_file is not None + cfg = { + **self.DEFAULT_ARGS, 'test_mode': False, + 'ann_file': 'train_ann_file.mat' + } + dataset = dataset_class(**cfg) + self.assertEqual(dataset.CLASSES, dataset_class.CLASSES) + self.assertFalse(dataset.test_mode) + self.assertIsNotNone(dataset.ann_file) + self.assertEqual(dataset.ann_file, 'train_ann_file.mat') + self.assertIsNotNone(dataset.train_ann_file) + + # Test with test_mode=True, ann_file is None + cfg = {**self.DEFAULT_ARGS, 'test_mode': True, 'ann_file': None} + dataset = dataset_class(**cfg) + self.assertEqual(dataset.CLASSES, dataset_class.CLASSES) + self.assertTrue(dataset.test_mode) + self.assertIsNone(dataset.ann_file) + self.assertIsNotNone(dataset.test_ann_file) + + # Test with test_mode=True, ann_file is not None + cfg = { + **self.DEFAULT_ARGS, 'test_mode': True, + 'ann_file': 'test_ann_file.mat' + } + dataset = dataset_class(**cfg) + self.assertEqual(dataset.CLASSES, dataset_class.CLASSES) + self.assertTrue(dataset.test_mode) + self.assertIsNotNone(dataset.ann_file) + self.assertEqual(dataset.ann_file, 'test_ann_file.mat') + self.assertIsNotNone(dataset.test_ann_file) + + @classmethod + def setUpClass(cls) -> None: + super().setUpClass() + + tmpdir = tempfile.TemporaryDirectory() + cls.tmpdir = tmpdir + cls.data_prefix = tmpdir.name + cls.ann_file = None + devkit = osp.join(cls.data_prefix, 'devkit') + if not osp.exists(devkit): + os.mkdir(devkit) + cls.train_ann_file = osp.join(devkit, 'cars_train_annos.mat') + cls.test_ann_file = osp.join(devkit, 'cars_test_annos_withlabels.mat') + cls.DEFAULT_ARGS = dict( + data_prefix=cls.data_prefix, pipeline=[], test_mode=False) + + try: + import scipy.io as sio + except ImportError: + raise ImportError( + 'please run `pip install scipy` to install package `scipy`.') + + sio.savemat( + cls.train_ann_file, { + 'annotations': [( + (np.array([1]), np.array([10]), np.array( + [20]), np.array([50]), 15, np.array(['001.jpg'])), + (np.array([2]), np.array([15]), np.array( + [240]), np.array([250]), 15, np.array(['002.jpg'])), + (np.array([89]), np.array([150]), np.array( + [278]), np.array([388]), 150, np.array(['012.jpg'])), + )] + }) + + sio.savemat( + cls.test_ann_file, { + 'annotations': + [((np.array([89]), np.array([150]), np.array( + [278]), np.array([388]), 150, np.array(['025.jpg'])), + (np.array([155]), np.array([10]), np.array( + [200]), np.array([233]), 0, np.array(['111.jpg'])), + (np.array([25]), np.array([115]), np.array( + [240]), np.array([360]), 15, np.array(['265.jpg'])))] + }) + + def test_load_annotations(self): + dataset_class = DATASETS.get(self.DATASET_TYPE) + + # Test with test_mode=False and ann_file=None + dataset = dataset_class(**self.DEFAULT_ARGS) + self.assertEqual(len(dataset), 3) + self.assertEqual(dataset.CLASSES, dataset_class.CLASSES) + + data_info = dataset[0] + np.testing.assert_equal(data_info['img_prefix'], + osp.join(self.data_prefix, 'cars_train')) + np.testing.assert_equal(data_info['img_info'], {'filename': '001.jpg'}) + np.testing.assert_equal(data_info['gt_label'], 15 - 1) + + # Test with test_mode=True and ann_file=None + cfg = {**self.DEFAULT_ARGS, 'test_mode': True} + dataset = dataset_class(**cfg) + self.assertEqual(len(dataset), 3) + + data_info = dataset[0] + np.testing.assert_equal(data_info['img_prefix'], + osp.join(self.data_prefix, 'cars_test')) + np.testing.assert_equal(data_info['img_info'], {'filename': '025.jpg'}) + np.testing.assert_equal(data_info['gt_label'], 150 - 1) + + # Test with test_mode=False, ann_file is not None + cfg = { + **self.DEFAULT_ARGS, 'test_mode': False, + 'ann_file': self.train_ann_file + } + dataset = dataset_class(**cfg) + data_info = dataset[0] + np.testing.assert_equal(data_info['img_prefix'], + osp.join(self.data_prefix, 'cars_train')) + np.testing.assert_equal(data_info['img_info'], {'filename': '001.jpg'}) + np.testing.assert_equal(data_info['gt_label'], 15 - 1) + + # Test with test_mode=True, ann_file is not None + cfg = { + **self.DEFAULT_ARGS, 'test_mode': True, + 'ann_file': self.test_ann_file + } + dataset = dataset_class(**cfg) + self.assertEqual(len(dataset), 3) + + data_info = dataset[0] + np.testing.assert_equal(data_info['img_prefix'], + osp.join(self.data_prefix, 'cars_test')) + np.testing.assert_equal(data_info['img_info'], {'filename': '025.jpg'}) + np.testing.assert_equal(data_info['gt_label'], 150 - 1) + + @classmethod + def tearDownClass(cls): + cls.tmpdir.cleanup() From 6474ea2fc086798abc2f1a0c23507f52d56d4c7b Mon Sep 17 00:00:00 2001 From: Ezra-Yu <1105212286@qq.com> Date: Tue, 16 Aug 2022 23:38:08 +0800 Subject: [PATCH 08/25] [Feature] Support EfficientFormer. (#954) * add efficient backbone * Update Readme and metafile * Add unit tests * fix confict * fix lint * update efficientformer head unit tests * update README * fix unit test * fix Readme * fix example * fix typo * recover api modification * Update EfficiemtFormer Backbone * fix unit tests * add efficientformer to readme and model zoo --- README.md | 1 + configs/efficientformer/README.md | 47 ++ .../efficientformer-l1_8xb128_in1k.py | 24 + .../efficientformer-l3_8xb128_in1k.py | 24 + .../efficientformer-l7_8xb128_in1k.py | 24 + configs/efficientformer/metafile.yml | 67 ++ docs/en/api/models.rst | 1 + docs/en/model_zoo.md | 3 + mmcls/models/backbones/__init__.py | 3 +- mmcls/models/backbones/efficientformer.py | 637 ++++++++++++++++++ mmcls/models/heads/__init__.py | 3 +- mmcls/models/heads/efficientformer_head.py | 96 +++ model-index.yml | 1 + .../test_backbones/test_efficientformer.py | 241 +++++++ tests/test_models/test_heads.py | 59 +- 15 files changed, 1228 insertions(+), 3 deletions(-) create mode 100644 configs/efficientformer/README.md create mode 100644 configs/efficientformer/efficientformer-l1_8xb128_in1k.py create mode 100644 configs/efficientformer/efficientformer-l3_8xb128_in1k.py create mode 100644 configs/efficientformer/efficientformer-l7_8xb128_in1k.py create mode 100644 configs/efficientformer/metafile.yml create mode 100644 mmcls/models/backbones/efficientformer.py create mode 100644 mmcls/models/heads/efficientformer_head.py create mode 100644 tests/test_models/test_backbones/test_efficientformer.py diff --git a/README.md b/README.md index 09e227339c1..eec6036b944 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet) - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer) - [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit) +- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer) diff --git a/configs/efficientformer/README.md b/configs/efficientformer/README.md new file mode 100644 index 00000000000..ecd6b4927e5 --- /dev/null +++ b/configs/efficientformer/README.md @@ -0,0 +1,47 @@ +# EfficientFormer + +> [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191) + + + +## Abstract + +Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm. Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on iPhone 12 (compiled with CoreML), which runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1), and our largest model, EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :------------------------------------------------------------------------: | +| EfficientFormer-l1\* | 12.19 | 1.30 | 80.46 | 94.99 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) | +| EfficientFormer-l3\* | 31.41 | 3.93 | 82.45 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) | +| EfficientFormer-l7\* | 82.23 | 10.16 | 83.40 | 96.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) | + +*Models with * are converted from the [official repo](https://github.com/snap-research/EfficientFormer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@misc{https://doi.org/10.48550/arxiv.2206.01191, + doi = {10.48550/ARXIV.2206.01191}, + + url = {https://arxiv.org/abs/2206.01191}, + + author = {Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian}, + + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + + title = {EfficientFormer: Vision Transformers at MobileNet Speed}, + + publisher = {arXiv}, + + year = {2022}, + + copyright = {Creative Commons Attribution 4.0 International} +} +``` diff --git a/configs/efficientformer/efficientformer-l1_8xb128_in1k.py b/configs/efficientformer/efficientformer-l1_8xb128_in1k.py new file mode 100644 index 00000000000..f5db2bfc63b --- /dev/null +++ b/configs/efficientformer/efficientformer-l1_8xb128_in1k.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +model = dict( + type='ImageClassifier', + backbone=dict( + type='EfficientFormer', + arch='l1', + drop_path_rate=0, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-5) + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='EfficientFormerClsHead', in_channels=448, num_classes=1000)) diff --git a/configs/efficientformer/efficientformer-l3_8xb128_in1k.py b/configs/efficientformer/efficientformer-l3_8xb128_in1k.py new file mode 100644 index 00000000000..e920f785d84 --- /dev/null +++ b/configs/efficientformer/efficientformer-l3_8xb128_in1k.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +model = dict( + type='ImageClassifier', + backbone=dict( + type='EfficientFormer', + arch='l3', + drop_path_rate=0, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-5) + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='EfficientFormerClsHead', in_channels=512, num_classes=1000)) diff --git a/configs/efficientformer/efficientformer-l7_8xb128_in1k.py b/configs/efficientformer/efficientformer-l7_8xb128_in1k.py new file mode 100644 index 00000000000..a59e3a7ed5a --- /dev/null +++ b/configs/efficientformer/efficientformer-l7_8xb128_in1k.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +model = dict( + type='ImageClassifier', + backbone=dict( + type='EfficientFormer', + arch='l7', + drop_path_rate=0, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-5) + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='EfficientFormerClsHead', in_channels=768, num_classes=1000)) diff --git a/configs/efficientformer/metafile.yml b/configs/efficientformer/metafile.yml new file mode 100644 index 00000000000..33c47865e9f --- /dev/null +++ b/configs/efficientformer/metafile.yml @@ -0,0 +1,67 @@ +Collections: + - Name: EfficientFormer + Metadata: + Training Data: ImageNet-1k + Architecture: + - Pooling + - 1x1 Convolution + - LayerScale + - MetaFormer + Paper: + URL: https://arxiv.org/pdf/2206.01191.pdf + Title: "EfficientFormer: Vision Transformers at MobileNet Speed" + README: configs/efficientformer/README.md + Code: + Version: v0.24.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/efficientformer.py + +Models: + - Name: efficientformer-l1_3rdparty_8xb128_in1k + Metadata: + FLOPs: 1304601088 # 1.3G + Parameters: 12278696 # 12M + In Collections: EfficientFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.46 + Top 5 Accuracy: 94.99 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth + Config: configs/efficientformer/efficientformer-l1_8xb128_in1k.py + Converted From: + Weights: https://drive.google.com/file/d/11SbX-3cfqTOc247xKYubrAjBiUmr818y/view?usp=sharing + Code: https://github.com/snap-research/EfficientFormer + - Name: efficientformer-l3_3rdparty_8xb128_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 3737045760 # 3.7G + Parameters: 31406000 # 31M + In Collections: EfficientFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.45 + Top 5 Accuracy: 96.18 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth + Config: configs/efficientformer/efficientformer-l3_8xb128_in1k.py + Converted From: + Weights: https://drive.google.com/file/d/1OyyjKKxDyMj-BcfInp4GlDdwLu3hc30m/view?usp=sharing + Code: https://github.com/snap-research/EfficientFormer + - Name: efficientformer-l7_3rdparty_8xb128_in1k + Metadata: + FLOPs: 10163951616 # 10.2G + Parameters: 82229328 # 82M + In Collections: EfficientFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.40 + Top 5 Accuracy: 96.60 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth + Config: configs/efficientformer/efficientformer-l7_8xb128_in1k.py + Converted From: + Weights: https://drive.google.com/file/d/1cVw-pctJwgvGafeouynqWWCwgkcoFMM5/view?usp=sharing + Code: https://github.com/snap-research/EfficientFormer diff --git a/docs/en/api/models.rst b/docs/en/api/models.rst index 78701a41fe1..37938e34d95 100644 --- a/docs/en/api/models.rst +++ b/docs/en/api/models.rst @@ -87,6 +87,7 @@ Backbones VAN VGG VisionTransformer + EfficientFormer .. _necks: diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md index 7a9a750b86b..46b42a97e68 100644 --- a/docs/en/model_zoo.md +++ b/docs/en/model_zoo.md @@ -145,6 +145,9 @@ The ResNet family models below are trained by standard data augmentations, i.e., | MViTv2-small\* | 34.87 | 7.00 | 83.63 | 96.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) | | MViTv2-base\* | 51.47 | 10.20 | 84.34 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) | | MViTv2-large\* | 217.99 | 42.10 | 85.25 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) | +| EfficientFormer-l1\* | 12.19 | 1.30 | 80.46 | 94.99 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) | +| EfficientFormer-l3\* | 31.41 | 3.93 | 82.45 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) | +| EfficientFormer-l7\* | 82.23 | 10.16 | 83.40 | 96.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) | *Models with * are converted from other repos, others are trained by ourselves.* diff --git a/mmcls/models/backbones/__init__.py b/mmcls/models/backbones/__init__.py index f1e772dec11..ad7b8189943 100644 --- a/mmcls/models/backbones/__init__.py +++ b/mmcls/models/backbones/__init__.py @@ -6,6 +6,7 @@ from .cspnet import CSPDarkNet, CSPNet, CSPResNet, CSPResNeXt from .deit import DistilledVisionTransformer from .densenet import DenseNet +from .efficientformer import EfficientFormer from .efficientnet import EfficientNet from .hrnet import HRNet from .lenet import LeNet5 @@ -44,5 +45,5 @@ 'Res2Net', 'RepVGG', 'Conformer', 'MlpMixer', 'DistilledVisionTransformer', 'PCPVT', 'SVT', 'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c', 'ConvMixer', 'CSPDarkNet', 'CSPResNet', 'CSPResNeXt', 'CSPNet', - 'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT' + 'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT', 'EfficientFormer' ] diff --git a/mmcls/models/backbones/efficientformer.py b/mmcls/models/backbones/efficientformer.py new file mode 100644 index 00000000000..fa3b14eb6e0 --- /dev/null +++ b/mmcls/models/backbones/efficientformer.py @@ -0,0 +1,637 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import itertools +from typing import Optional, Sequence + +import torch +import torch.nn as nn +from mmcv.cnn.bricks import (ConvModule, DropPath, build_activation_layer, + build_norm_layer) +from mmcv.runner import BaseModule, ModuleList, Sequential + +from ..builder import BACKBONES +from .base_backbone import BaseBackbone +from .poolformer import Pooling + + +class AttentionWithBias(BaseModule): + """Multi-head Attention Module with attention_bias. + + Args: + embed_dims (int): The embedding dimension. + num_heads (int): Parallel attention heads. Defaults to 8. + key_dim (int): The dimension of q, k. Defaults to 32. + attn_ratio (float): The dimension of v equals to + ``key_dim * attn_ratio``. Defaults to 4. + resolution (int): The height and width of attention_bias. + Defaults to 7. + init_cfg (dict, optional): The Config for initialization. + Defaults to None. + """ + + def __init__(self, + embed_dims, + num_heads=8, + key_dim=32, + attn_ratio=4., + resolution=7, + init_cfg=None): + super().__init__(init_cfg=init_cfg) + self.num_heads = num_heads + self.scale = key_dim**-0.5 + self.attn_ratio = attn_ratio + self.key_dim = key_dim + self.nh_kd = key_dim * num_heads + self.d = int(attn_ratio * key_dim) + self.dh = int(attn_ratio * key_dim) * num_heads + h = self.dh + self.nh_kd * 2 + self.qkv = nn.Linear(embed_dims, h) + self.proj = nn.Linear(self.dh, embed_dims) + + points = list(itertools.product(range(resolution), range(resolution))) + N = len(points) + attention_offsets = {} + idxs = [] + for p1 in points: + for p2 in points: + offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1])) + if offset not in attention_offsets: + attention_offsets[offset] = len(attention_offsets) + idxs.append(attention_offsets[offset]) + self.attention_biases = nn.Parameter( + torch.zeros(num_heads, len(attention_offsets))) + self.register_buffer('attention_bias_idxs', + torch.LongTensor(idxs).view(N, N)) + + @torch.no_grad() + def train(self, mode=True): + """change the mode of model.""" + super().train(mode) + if mode and hasattr(self, 'ab'): + del self.ab + else: + self.ab = self.attention_biases[:, self.attention_bias_idxs] + + def forward(self, x): + """forward function. + + Args: + x (tensor): input features with shape of (B, N, C) + """ + B, N, _ = x.shape + qkv = self.qkv(x) + qkv = qkv.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + q, k, v = qkv.split([self.key_dim, self.key_dim, self.d], dim=-1) + + attn = ((q @ k.transpose(-2, -1)) * self.scale + + (self.attention_biases[:, self.attention_bias_idxs] + if self.training else self.ab)) + attn = attn.softmax(dim=-1) + x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh) + x = self.proj(x) + return x + + +class Flat(nn.Module): + """Flat the input from (B, C, H, W) to (B, H*W, C).""" + + def __init__(self, ): + super().__init__() + + def forward(self, x: torch.Tensor): + x = x.flatten(2).transpose(1, 2) + return x + + +class LinearMlp(BaseModule): + """Mlp implemented with linear. + + The shape of input and output tensor are (B, N, C). + + Args: + in_features (int): Dimension of input features. + hidden_features (int): Dimension of hidden features. + out_features (int): Dimension of output features. + norm_cfg (dict): Config dict for normalization layer. + Defaults to ``dict(type='BN')``. + act_cfg (dict): The config dict for activation between pointwise + convolution. Defaults to ``dict(type='GELU')``. + drop (float): Dropout rate. Defaults to 0.0. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, + in_features: int, + hidden_features: Optional[int] = None, + out_features: Optional[int] = None, + act_cfg=dict(type='GELU'), + drop=0., + init_cfg=None): + super().__init__(init_cfg=init_cfg) + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = build_activation_layer(act_cfg) + self.drop1 = nn.Dropout(drop) + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop2 = nn.Dropout(drop) + + def forward(self, x): + """ + Args: + x (torch.Tensor): input tensor with shape (B, N, C). + + Returns: + torch.Tensor: output tensor with shape (B, N, C). + """ + x = self.drop1(self.act(self.fc1(x))) + x = self.drop2(self.fc2(x)) + return x + + +class ConvMlp(BaseModule): + """Mlp implemented with 1*1 convolutions. + + Args: + in_features (int): Dimension of input features. + hidden_features (int): Dimension of hidden features. + out_features (int): Dimension of output features. + norm_cfg (dict): Config dict for normalization layer. + Defaults to ``dict(type='BN')``. + act_cfg (dict): The config dict for activation between pointwise + convolution. Defaults to ``dict(type='GELU')``. + drop (float): Dropout rate. Defaults to 0.0. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, + in_features, + hidden_features=None, + out_features=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='GELU'), + drop=0., + init_cfg=None): + super().__init__(init_cfg=init_cfg) + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Conv2d(in_features, hidden_features, 1) + self.act = build_activation_layer(act_cfg) + self.fc2 = nn.Conv2d(hidden_features, out_features, 1) + self.norm1 = build_norm_layer(norm_cfg, hidden_features)[1] + self.norm2 = build_norm_layer(norm_cfg, out_features)[1] + + self.drop = nn.Dropout(drop) + + def forward(self, x): + """ + Args: + x (torch.Tensor): input tensor with shape (B, C, H, W). + + Returns: + torch.Tensor: output tensor with shape (B, C, H, W). + """ + + x = self.act(self.norm1(self.fc1(x))) + x = self.drop(x) + x = self.norm2(self.fc2(x)) + x = self.drop(x) + return x + + +class LayerScale(nn.Module): + """LayerScale layer. + + Args: + dim (int): Dimension of input features. + inplace (bool): inplace: can optionally do the + operation in-place. Default: ``False`` + data_format (str): The input data format, can be 'channels_last' + and 'channels_first', representing (B, C, H, W) and + (B, N, C) format data respectively. + """ + + def __init__(self, + dim: int, + inplace: bool = False, + data_format: str = 'channels_last'): + super().__init__() + assert data_format in ('channels_last', 'channels_first'), \ + "'data_format' could only be channels_last or channels_first." + self.inplace = inplace + self.data_format = data_format + self.weight = nn.Parameter(torch.ones(dim) * 1e-5) + + def forward(self, x): + if self.data_format == 'channels_first': + if self.inplace: + return x.mul_(self.weight.view(-1, 1, 1)) + else: + return x * self.weight.view(-1, 1, 1) + return x.mul_(self.weight) if self.inplace else x * self.weight + + +class Meta3D(BaseModule): + """Meta Former block using 3 dimensions inputs, ``torch.Tensor`` with shape + (B, N, C).""" + + def __init__(self, + dim, + mlp_ratio=4., + norm_cfg=dict(type='LN'), + act_cfg=dict(type='GELU'), + drop=0., + drop_path=0., + use_layer_scale=True, + init_cfg=None): + super().__init__(init_cfg=init_cfg) + self.norm1 = build_norm_layer(norm_cfg, dim)[1] + self.token_mixer = AttentionWithBias(dim) + self.norm2 = build_norm_layer(norm_cfg, dim)[1] + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = LinearMlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_cfg=act_cfg, + drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. \ + else nn.Identity() + if use_layer_scale: + self.ls1 = LayerScale(dim) + self.ls2 = LayerScale(dim) + else: + self.ls1, self.ls2 = nn.Identity(), nn.Identity() + + def forward(self, x): + x = x + self.drop_path(self.ls1(self.token_mixer(self.norm1(x)))) + x = x + self.drop_path(self.ls2(self.mlp(self.norm2(x)))) + return x + + +class Meta4D(BaseModule): + """Meta Former block using 4 dimensions inputs, ``torch.Tensor`` with shape + (B, C, H, W).""" + + def __init__(self, + dim, + pool_size=3, + mlp_ratio=4., + act_cfg=dict(type='GELU'), + drop=0., + drop_path=0., + use_layer_scale=True, + init_cfg=None): + super().__init__(init_cfg=init_cfg) + + self.token_mixer = Pooling(pool_size=pool_size) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = ConvMlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_cfg=act_cfg, + drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. \ + else nn.Identity() + if use_layer_scale: + self.ls1 = LayerScale(dim, data_format='channels_first') + self.ls2 = LayerScale(dim, data_format='channels_first') + else: + self.ls1, self.ls2 = nn.Identity(), nn.Identity() + + def forward(self, x): + x = x + self.drop_path(self.ls1(self.token_mixer(x))) + x = x + self.drop_path(self.ls2(self.mlp(x))) + return x + + +def basic_blocks(in_channels, + out_channels, + index, + layers, + pool_size=3, + mlp_ratio=4., + act_cfg=dict(type='GELU'), + drop_rate=.0, + drop_path_rate=0., + use_layer_scale=True, + vit_num=1, + has_downsamper=False): + """generate EfficientFormer blocks for a stage.""" + blocks = [] + if has_downsamper: + blocks.append( + ConvModule( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=2, + padding=1, + bias=True, + norm_cfg=dict(type='BN'), + act_cfg=None)) + if index == 3 and vit_num == layers[index]: + blocks.append(Flat()) + for block_idx in range(layers[index]): + block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / ( + sum(layers) - 1) + if index == 3 and layers[index] - block_idx <= vit_num: + blocks.append( + Meta3D( + out_channels, + mlp_ratio=mlp_ratio, + act_cfg=act_cfg, + drop=drop_rate, + drop_path=block_dpr, + use_layer_scale=use_layer_scale, + )) + else: + blocks.append( + Meta4D( + out_channels, + pool_size=pool_size, + act_cfg=act_cfg, + drop=drop_rate, + drop_path=block_dpr, + use_layer_scale=use_layer_scale)) + if index == 3 and layers[index] - block_idx - 1 == vit_num: + blocks.append(Flat()) + blocks = nn.Sequential(*blocks) + return blocks + + +@BACKBONES.register_module() +class EfficientFormer(BaseBackbone): + """EfficientFormer. + + A PyTorch implementation of EfficientFormer introduced by: + `EfficientFormer: Vision Transformers at MobileNet Speed `_ + + Modified from the `official repo + `. + + Args: + arch (str | dict): The model's architecture. If string, it should be + one of architecture in ``EfficientFormer.arch_settings``. And if dict, + it should include the following 4 keys: + + - layers (list[int]): Number of blocks at each stage. + - embed_dims (list[int]): The number of channels at each stage. + - downsamples (list[int]): Has downsample or not in the four stages. + - vit_num (int): The num of vit blocks in the last stage. + + Defaults to 'l1'. + + in_channels (int): The num of input channels. Defaults to 3. + pool_size (int): The pooling size of ``Meta4D`` blocks. Defaults to 3. + mlp_ratios (int): The dimension ratio of multi-head attention mechanism + in ``Meta4D`` blocks. Defaults to 3. + reshape_last_feat (bool): Whether to reshape the feature map from + (B, N, C) to (B, C, H, W) in the last stage, when the ``vit-num`` + in ``arch`` is not 0. Defaults to False. Usually set to True + in downstream tasks. + out_indices (Sequence[int]): Output from which stages. + Defaults to -1. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. Defaults to -1. + act_cfg (dict): The config dict for activation between pointwise + convolution. Defaults to ``dict(type='GELU')``. + drop_rate (float): Dropout rate. Defaults to 0. + drop_path_rate (float): Stochastic depth rate. Defaults to 0. + use_layer_scale (bool): Whether to use use_layer_scale in MetaFormer + block. Defaults to True. + init_cfg (dict, optional): Initialization config dict. + Defaults to None. + + Example: + >>> from mmcls.models import EfficientFormer + >>> import torch + >>> inputs = torch.rand((1, 3, 224, 224)) + >>> # build EfficientFormer backbone for classification task + >>> model = EfficientFormer(arch="l1") + >>> model.eval() + >>> level_outputs = model(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 448, 49) + >>> # build EfficientFormer backbone for downstream task + >>> model = EfficientFormer( + >>> arch="l3", + >>> out_indices=(0, 1, 2, 3), + >>> reshape_last_feat=True) + >>> model.eval() + >>> level_outputs = model(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 64, 56, 56) + (1, 128, 28, 28) + (1, 320, 14, 14) + (1, 512, 7, 7) + """ # noqa: E501 + + # --layers: [x,x,x,x], numbers of layers for the four stages + # --embed_dims: [x,x,x,x], embedding dims for the four stages + # --downsamples: [x,x,x,x], has downsample or not in the four stages + # --vit_num：(int), the num of vit blocks in the last stage + arch_settings = { + 'l1': { + 'layers': [3, 2, 6, 4], + 'embed_dims': [48, 96, 224, 448], + 'downsamples': [False, True, True, True], + 'vit_num': 1, + }, + 'l3': { + 'layers': [4, 4, 12, 6], + 'embed_dims': [64, 128, 320, 512], + 'downsamples': [False, True, True, True], + 'vit_num': 4, + }, + 'l7': { + 'layers': [6, 6, 18, 8], + 'embed_dims': [96, 192, 384, 768], + 'downsamples': [False, True, True, True], + 'vit_num': 8, + }, + } + + def __init__(self, + arch='l1', + in_channels=3, + pool_size=3, + mlp_ratios=4, + reshape_last_feat=False, + out_indices=-1, + frozen_stages=-1, + act_cfg=dict(type='GELU'), + drop_rate=0., + drop_path_rate=0., + use_layer_scale=True, + init_cfg=None): + + super().__init__(init_cfg=init_cfg) + self.num_extra_tokens = 0 # no cls_token, no dist_token + + if isinstance(arch, str): + assert arch in self.arch_settings, \ + f'Unavailable arch, please choose from ' \ + f'({set(self.arch_settings)}) or pass a dict.' + arch = self.arch_settings[arch] + elif isinstance(arch, dict): + default_keys = set(self.arch_settings['l1'].keys()) + assert set(arch.keys()) == default_keys, \ + f'The arch dict must have {default_keys}, ' \ + f'but got {list(arch.keys())}.' + + self.layers = arch['layers'] + self.embed_dims = arch['embed_dims'] + self.downsamples = arch['downsamples'] + assert isinstance(self.layers, list) and isinstance( + self.embed_dims, list) and isinstance(self.downsamples, list) + assert len(self.layers) == len(self.embed_dims) == len( + self.downsamples) + + self.vit_num = arch['vit_num'] + self.reshape_last_feat = reshape_last_feat + + assert self.vit_num >= 0, "'vit_num' must be an integer " \ + 'greater than or equal to 0.' + assert self.vit_num <= self.layers[-1], ( + "'vit_num' must be an integer smaller than layer number") + + self._make_stem(in_channels, self.embed_dims[0]) + + # set the main block in network + network = [] + for i in range(len(self.layers)): + if i != 0: + in_channels = self.embed_dims[i - 1] + else: + in_channels = self.embed_dims[i] + out_channels = self.embed_dims[i] + stage = basic_blocks( + in_channels, + out_channels, + i, + self.layers, + pool_size=pool_size, + mlp_ratio=mlp_ratios, + act_cfg=act_cfg, + drop_rate=drop_rate, + drop_path_rate=drop_path_rate, + vit_num=self.vit_num, + use_layer_scale=use_layer_scale, + has_downsamper=self.downsamples[i]) + network.append(stage) + + self.network = ModuleList(network) + + if isinstance(out_indices, int): + out_indices = [out_indices] + assert isinstance(out_indices, Sequence), \ + f'"out_indices" must by a sequence or int, ' \ + f'get {type(out_indices)} instead.' + for i, index in enumerate(out_indices): + if index < 0: + out_indices[i] = 4 + index + assert out_indices[i] >= 0, f'Invalid out_indices {index}' + + self.out_indices = out_indices + for i_layer in self.out_indices: + if not self.reshape_last_feat and \ + i_layer == 3 and self.vit_num > 0: + layer = build_norm_layer( + dict(type='LN'), self.embed_dims[i_layer])[1] + else: + # use GN with 1 group as channel-first LN2D + layer = build_norm_layer( + dict(type='GN', num_groups=1), self.embed_dims[i_layer])[1] + + layer_name = f'norm{i_layer}' + self.add_module(layer_name, layer) + + self.frozen_stages = frozen_stages + self._freeze_stages() + + def _make_stem(self, in_channels: int, stem_channels: int): + """make 2-ConvBNReLu stem layer.""" + self.patch_embed = Sequential( + ConvModule( + in_channels, + stem_channels // 2, + kernel_size=3, + stride=2, + padding=1, + bias=True, + conv_cfg=None, + norm_cfg=dict(type='BN'), + inplace=True), + ConvModule( + stem_channels // 2, + stem_channels, + kernel_size=3, + stride=2, + padding=1, + bias=True, + conv_cfg=None, + norm_cfg=dict(type='BN'), + inplace=True)) + + def forward_tokens(self, x): + outs = [] + for idx, block in enumerate(self.network): + if idx == len(self.network) - 1: + N, _, H, W = x.shape + if self.downsamples[idx]: + H, W = H // 2, W // 2 + x = block(x) + if idx in self.out_indices: + norm_layer = getattr(self, f'norm{idx}') + + if idx == len(self.network) - 1 and x.dim() == 3: + # when ``vit-num`` > 0 and in the last stage, + # if `self.reshape_last_feat`` is True, reshape the + # features to `BCHW` format before the final normalization. + # if `self.reshape_last_feat`` is False, do + # normalization directly and permute the features to `BCN`. + if self.reshape_last_feat: + x = x.permute((0, 2, 1)).reshape(N, -1, H, W) + x_out = norm_layer(x) + else: + x_out = norm_layer(x).permute((0, 2, 1)) + else: + x_out = norm_layer(x) + + outs.append(x_out.contiguous()) + return tuple(outs) + + def forward(self, x): + # input embedding + x = self.patch_embed(x) + # through stages + x = self.forward_tokens(x) + return x + + def _freeze_stages(self): + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + for i in range(self.frozen_stages): + # Include both block and downsample layer. + module = self.network[i] + module.eval() + for param in module.parameters(): + param.requires_grad = False + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + norm_layer.eval() + for param in norm_layer.parameters(): + param.requires_grad = False + + def train(self, mode=True): + super(EfficientFormer, self).train(mode) + self._freeze_stages() diff --git a/mmcls/models/heads/__init__.py b/mmcls/models/heads/__init__.py index ad520ce4ca5..d7301613094 100644 --- a/mmcls/models/heads/__init__.py +++ b/mmcls/models/heads/__init__.py @@ -2,6 +2,7 @@ from .cls_head import ClsHead from .conformer_head import ConformerHead from .deit_head import DeiTClsHead +from .efficientformer_head import EfficientFormerClsHead from .linear_head import LinearClsHead from .multi_label_csra_head import CSRAClsHead from .multi_label_head import MultiLabelClsHead @@ -12,5 +13,5 @@ __all__ = [ 'ClsHead', 'LinearClsHead', 'StackedLinearClsHead', 'MultiLabelClsHead', 'MultiLabelLinearClsHead', 'VisionTransformerClsHead', 'DeiTClsHead', - 'ConformerHead', 'CSRAClsHead' + 'ConformerHead', 'EfficientFormerClsHead', 'CSRAClsHead' ] diff --git a/mmcls/models/heads/efficientformer_head.py b/mmcls/models/heads/efficientformer_head.py new file mode 100644 index 00000000000..3127f12e371 --- /dev/null +++ b/mmcls/models/heads/efficientformer_head.py @@ -0,0 +1,96 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import HEADS +from .cls_head import ClsHead + + +@HEADS.register_module() +class EfficientFormerClsHead(ClsHead): + """EfficientFormer classifier head. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + distillation (bool): Whether use a additional distilled head. + Defaults to True. + init_cfg (dict): The extra initialization configs. Defaults to + ``dict(type='Normal', layer='Linear', std=0.01)``. + """ + + def __init__(self, + num_classes, + in_channels, + distillation=True, + init_cfg=dict(type='Normal', layer='Linear', std=0.01), + *args, + **kwargs): + super(EfficientFormerClsHead, self).__init__( + init_cfg=init_cfg, *args, **kwargs) + self.in_channels = in_channels + self.num_classes = num_classes + self.dist = distillation + + if self.num_classes <= 0: + raise ValueError( + f'num_classes={num_classes} must be a positive integer') + + self.head = nn.Linear(self.in_channels, self.num_classes) + if self.dist: + self.dist_head = nn.Linear(self.in_channels, self.num_classes) + + def pre_logits(self, x): + if isinstance(x, tuple): + x = x[-1] + return x + + def simple_test(self, x, softmax=True, post_process=True): + """Inference without augmentation. + + Args: + x (tuple[tuple[tensor, tensor]]): The input features. + Multi-stage inputs are acceptable but only the last stage will + be used to classify. Every item should be a tuple which + includes patch token and cls token. The cls token will be used + to classify and the shape of it should be + ``(num_samples, in_channels)``. + softmax (bool): Whether to softmax the classification score. + post_process (bool): Whether to do post processing the + inference results. It will convert the output to a list. + + Returns: + Tensor | list: The inference results. + + - If no post processing, the output is a tensor with shape + ``(num_samples, num_classes)``. + - If post processing, the output is a multi-dimentional list of + float and the dimensions are ``(num_samples, num_classes)``. + """ + x = self.pre_logits(x) + cls_score = self.head(x) + if self.dist: + cls_score = (cls_score + self.dist_head(x)) / 2 + + if softmax: + pred = ( + F.softmax(cls_score, dim=1) if cls_score is not None else None) + else: + pred = cls_score + + if post_process: + return self.post_process(pred) + else: + return pred + + def forward_train(self, x, gt_label, **kwargs): + if self.dist: + raise NotImplementedError( + "MMClassification doesn't support to train" + ' the distilled version EfficientFormer.') + else: + x = self.pre_logits(x) + cls_score = self.head(x) + losses = self.loss(cls_score, gt_label, **kwargs) + return losses diff --git a/model-index.yml b/model-index.yml index a57802a85f0..a48ab85a4cc 100644 --- a/model-index.yml +++ b/model-index.yml @@ -30,3 +30,4 @@ Import: - configs/poolformer/metafile.yml - configs/csra/metafile.yml - configs/mvit/metafile.yml + - configs/efficientformer/metafile.yml diff --git a/tests/test_models/test_backbones/test_efficientformer.py b/tests/test_models/test_backbones/test_efficientformer.py new file mode 100644 index 00000000000..01d9daea4b8 --- /dev/null +++ b/tests/test_models/test_backbones/test_efficientformer.py @@ -0,0 +1,241 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from copy import deepcopy +from unittest import TestCase + +import torch +from mmcv.cnn import ConvModule +from torch import nn + +from mmcls.models.backbones import EfficientFormer +from mmcls.models.backbones.efficientformer import (AttentionWithBias, Flat, + LayerScale, Meta3D, Meta4D) +from mmcls.models.backbones.poolformer import Pooling + + +class TestLayerScale(TestCase): + + def test_init(self): + with self.assertRaisesRegex(AssertionError, "'data_format' could"): + cfg = dict( + dim=10, + inplace=False, + data_format='BNC', + ) + LayerScale(**cfg) + + cfg = dict(dim=10) + ls = LayerScale(**cfg) + assert torch.equal(ls.weight, + torch.ones(10, requires_grad=True) * 1e-5) + + def forward(self): + # Test channels_last + cfg = dict(dim=256, inplace=False, data_format='channels_last') + ls_channels_last = LayerScale(**cfg) + x = torch.randn((4, 49, 256)) + out = ls_channels_last(x) + self.assertEqual(tuple(out.size()), (4, 49, 256)) + assert torch.equal(x * 1e-5, out) + + # Test channels_first + cfg = dict(dim=256, inplace=False, data_format='channels_first') + ls_channels_first = LayerScale(**cfg) + x = torch.randn((4, 256, 7, 7)) + out = ls_channels_first(x) + self.assertEqual(tuple(out.size()), (4, 256, 7, 7)) + assert torch.equal(x * 1e-5, out) + + # Test inplace True + cfg = dict(dim=256, inplace=True, data_format='channels_first') + ls_channels_first = LayerScale(**cfg) + x = torch.randn((4, 256, 7, 7)) + out = ls_channels_first(x) + self.assertEqual(tuple(out.size()), (4, 256, 7, 7)) + self.assertIs(x, out) + + +class TestEfficientFormer(TestCase): + + def setUp(self): + self.cfg = dict(arch='l1', drop_path_rate=0.1) + self.arch = EfficientFormer.arch_settings['l1'] + self.custom_arch = { + 'layers': [1, 1, 1, 4], + 'embed_dims': [48, 96, 224, 448], + 'downsamples': [False, True, True, True], + 'vit_num': 2, + } + self.custom_cfg = dict(arch=self.custom_arch) + + def test_arch(self): + # Test invalid default arch + with self.assertRaisesRegex(AssertionError, 'Unavailable arch'): + cfg = deepcopy(self.cfg) + cfg['arch'] = 'unknown' + EfficientFormer(**cfg) + + # Test invalid custom arch + with self.assertRaisesRegex(AssertionError, 'must have'): + cfg = deepcopy(self.custom_cfg) + cfg['arch'].pop('layers') + EfficientFormer(**cfg) + + # Test vit_num < 0 + with self.assertRaisesRegex(AssertionError, "'vit_num' must"): + cfg = deepcopy(self.custom_cfg) + cfg['arch']['vit_num'] = -1 + EfficientFormer(**cfg) + + # Test vit_num > last stage layers + with self.assertRaisesRegex(AssertionError, "'vit_num' must"): + cfg = deepcopy(self.custom_cfg) + cfg['arch']['vit_num'] = 10 + EfficientFormer(**cfg) + + # Test out_ind + with self.assertRaisesRegex(AssertionError, '"out_indices" must'): + cfg = deepcopy(self.custom_cfg) + cfg['out_indices'] = dict + EfficientFormer(**cfg) + + # Test custom arch + cfg = deepcopy(self.custom_cfg) + model = EfficientFormer(**cfg) + self.assertEqual(len(model.patch_embed), 2) + layers = self.custom_arch['layers'] + downsamples = self.custom_arch['downsamples'] + vit_num = self.custom_arch['vit_num'] + + for i, stage in enumerate(model.network): + if downsamples[i]: + self.assertIsInstance(stage[0], ConvModule) + self.assertEqual(stage[0].conv.stride, (2, 2)) + self.assertTrue(hasattr(stage[0].conv, 'bias')) + self.assertTrue(isinstance(stage[0].bn, nn.BatchNorm2d)) + + if i < len(model.network) - 1: + self.assertIsInstance(stage[-1], Meta4D) + self.assertIsInstance(stage[-1].token_mixer, Pooling) + self.assertEqual(len(stage) - downsamples[i], layers[i]) + elif vit_num > 0: + self.assertIsInstance(stage[-1], Meta3D) + self.assertIsInstance(stage[-1].token_mixer, AttentionWithBias) + self.assertEqual(len(stage) - downsamples[i] - 1, layers[i]) + flat_layer_idx = len(stage) - vit_num - downsamples[i] + self.assertIsInstance(stage[flat_layer_idx], Flat) + count = 0 + for layer in stage: + if isinstance(layer, Meta3D): + count += 1 + self.assertEqual(count, vit_num) + + def test_init_weights(self): + # test weight init cfg + cfg = deepcopy(self.cfg) + cfg['init_cfg'] = [ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear'), + dict(type='Constant', layer=['LayerScale'], val=1e-4) + ] + model = EfficientFormer(**cfg) + ori_weight = model.patch_embed[0].conv.weight.clone().detach() + ori_ls_weight = model.network[0][-1].ls1.weight.clone().detach() + + model.init_weights() + initialized_weight = model.patch_embed[0].conv.weight + initialized_ls_weight = model.network[0][-1].ls1.weight + self.assertFalse(torch.allclose(ori_weight, initialized_weight)) + self.assertFalse(torch.allclose(ori_ls_weight, initialized_ls_weight)) + + def test_forward(self): + imgs = torch.randn(1, 3, 224, 224) + + # test last stage output + cfg = deepcopy(self.cfg) + model = EfficientFormer(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (1, 448, 49)) + assert hasattr(model, 'norm3') + assert isinstance(getattr(model, 'norm3'), nn.LayerNorm) + + # test multiple output indices + cfg = deepcopy(self.cfg) + cfg['out_indices'] = (0, 1, 2, 3) + cfg['reshape_last_feat'] = True + model = EfficientFormer(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + # Test out features shape + for dim, stride, out in zip(self.arch['embed_dims'], [1, 2, 4, 8], + outs): + self.assertEqual(out.shape, (1, dim, 56 // stride, 56 // stride)) + + # Test norm layer + for i in range(4): + assert hasattr(model, f'norm{i}') + stage_norm = getattr(model, f'norm{i}') + assert isinstance(stage_norm, nn.GroupNorm) + assert stage_norm.num_groups == 1 + + # Test vit_num == 0 + cfg = deepcopy(self.custom_cfg) + cfg['arch']['vit_num'] = 0 + cfg['out_indices'] = (0, 1, 2, 3) + model = EfficientFormer(**cfg) + for i in range(4): + assert hasattr(model, f'norm{i}') + stage_norm = getattr(model, f'norm{i}') + assert isinstance(stage_norm, nn.GroupNorm) + assert stage_norm.num_groups == 1 + + def test_structure(self): + # test drop_path_rate decay + cfg = deepcopy(self.cfg) + cfg['drop_path_rate'] = 0.2 + model = EfficientFormer(**cfg) + layers = self.arch['layers'] + for i, block in enumerate(model.network): + expect_prob = 0.2 / (sum(layers) - 1) * i + if hasattr(block, 'drop_path'): + if expect_prob == 0: + self.assertIsInstance(block.drop_path, torch.nn.Identity) + else: + self.assertAlmostEqual(block.drop_path.drop_prob, + expect_prob) + + # test with first stage frozen. + cfg = deepcopy(self.cfg) + frozen_stages = 1 + cfg['frozen_stages'] = frozen_stages + cfg['out_indices'] = (0, 1, 2, 3) + model = EfficientFormer(**cfg) + model.init_weights() + model.train() + + # the patch_embed and first stage should not require grad. + self.assertFalse(model.patch_embed.training) + for param in model.patch_embed.parameters(): + self.assertFalse(param.requires_grad) + for i in range(frozen_stages): + module = model.network[i] + for param in module.parameters(): + self.assertFalse(param.requires_grad) + for param in model.norm0.parameters(): + self.assertFalse(param.requires_grad) + + # the second stage should require grad. + for i in range(frozen_stages + 1, 4): + module = model.network[i] + for param in module.parameters(): + self.assertTrue(param.requires_grad) + if hasattr(model, f'norm{i}'): + norm = getattr(model, f'norm{i}') + for param in norm.parameters(): + self.assertTrue(param.requires_grad) diff --git a/tests/test_models/test_heads.py b/tests/test_models/test_heads.py index 4ab18c332c4..e0ecdb6b5c2 100644 --- a/tests/test_models/test_heads.py +++ b/tests/test_models/test_heads.py @@ -5,7 +5,8 @@ import torch from mmcls.models.heads import (ClsHead, ConformerHead, CSRAClsHead, - DeiTClsHead, LinearClsHead, MultiLabelClsHead, + DeiTClsHead, EfficientFormerClsHead, + LinearClsHead, MultiLabelClsHead, MultiLabelLinearClsHead, StackedLinearClsHead, VisionTransformerClsHead) @@ -319,6 +320,62 @@ def test_deit_head(): DeiTClsHead(-1, 100) +def test_efficientformer_head(): + fake_features = (torch.rand(4, 64), ) + fake_gt_label = torch.randint(0, 10, (4, )) + + # Test without distillation head + head = EfficientFormerClsHead( + num_classes=10, in_channels=64, distillation=False) + + # test EfficientFormer head forward + losses = head.forward_train(fake_features, fake_gt_label) + assert losses['loss'].item() > 0 + + # test simple_test with post_process + pred = head.simple_test(fake_features) + assert isinstance(pred, list) and len(pred) == 4 + with patch('torch.onnx.is_in_onnx_export', return_value=True): + pred = head.simple_test(fake_features) + assert pred.shape == (4, 10) + + # test simple_test without post_process + pred = head.simple_test(fake_features, post_process=False) + assert isinstance(pred, torch.Tensor) and pred.shape == (4, 10) + logits = head.simple_test(fake_features, softmax=False, post_process=False) + torch.testing.assert_allclose(pred, torch.softmax(logits, dim=1)) + + # test pre_logits + features = head.pre_logits(fake_features) + assert features is fake_features[0] + + # Test without distillation head + head = EfficientFormerClsHead(num_classes=10, in_channels=64) + assert hasattr(head, 'head') + assert hasattr(head, 'dist_head') + + # Test loss + with pytest.raises(NotImplementedError): + losses = head.forward_train(fake_features, fake_gt_label) + + # test simple_test with post_process + pred = head.simple_test(fake_features) + assert isinstance(pred, list) and len(pred) == 4 + with patch('torch.onnx.is_in_onnx_export', return_value=True): + pred = head.simple_test(fake_features) + assert pred.shape == (4, 10) + + # test simple_test without post_process + pred = head.simple_test(fake_features, post_process=False) + assert isinstance(pred, torch.Tensor) and pred.shape == (4, 10) + logits = head.simple_test(fake_features, softmax=False, post_process=False) + torch.testing.assert_allclose(pred, torch.softmax(logits, dim=1)) + + # test pre_logits + features = head.pre_logits(fake_features) + assert features is fake_features[0] + + @pytest.mark.parametrize( 'feat', [torch.rand(4, 20, 20, 30), (torch.rand(4, 20, 20, 30), )]) def test_csra_head(feat): From ec71d071d8b8474c3860edf8dd1449f3ac8490f4 Mon Sep 17 00:00:00 2001 From: Jiahao Wang <48375204+techmonsterwang@users.noreply.github.com> Date: Mon, 22 Aug 2022 10:28:33 +0800 Subject: [PATCH 09/25] [Improve] Fixed typo in `RepVGG`. (#985) * [Improve] Use `forward_dummy` to calculate FLOPS. (#953) * fixed Co-authored-by: Ming-Hsuan-Tu --- configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py | 2 +- mmcls/models/backbones/repvgg.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py b/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py index f8fc525d0c9..06e63dab136 100644 --- a/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py +++ b/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py @@ -1,5 +1,5 @@ _base_ = [ - '../_base_/models/mobilenet-v3-small_8xb16_cifar.py', + '../_base_/models/mobilenet-v3-small_cifar.py', '../_base_/datasets/cifar10_bs16.py', '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' ] diff --git a/mmcls/models/backbones/repvgg.py b/mmcls/models/backbones/repvgg.py index 0186e623965..ca8cc605006 100644 --- a/mmcls/models/backbones/repvgg.py +++ b/mmcls/models/backbones/repvgg.py @@ -230,7 +230,7 @@ def _fuse_conv_bn(self, branch): return fused_weight, fused_bias - def _norm_to_conv3x3(self, branch_nrom): + def _norm_to_conv3x3(self, branch_norm): """Convert a norm layer to a conv3x3-bn sequence. Args: @@ -242,15 +242,15 @@ def _norm_to_conv3x3(self, branch_nrom): """ input_dim = self.in_channels // self.groups conv_weight = torch.zeros((self.in_channels, input_dim, 3, 3), - dtype=branch_nrom.weight.dtype) + dtype=branch_norm.weight.dtype) for i in range(self.in_channels): conv_weight[i, i % input_dim, 1, 1] = 1 - conv_weight = conv_weight.to(branch_nrom.weight.device) + conv_weight = conv_weight.to(branch_norm.weight.device) tmp_conv3x3 = self.create_conv_bn(kernel_size=3) tmp_conv3x3.conv.weight.data = conv_weight - tmp_conv3x3.norm = branch_nrom + tmp_conv3x3.norm = branch_norm return tmp_conv3x3 From 517bd3d34b0078362f9d3f17ca8c6ff3df6f5b28 Mon Sep 17 00:00:00 2001 From: Andrey Moskalenko <55856406+a-mos@users.noreply.github.com> Date: Thu, 1 Sep 2022 13:03:49 +0300 Subject: [PATCH 10/25] [Fix] Fix device mismatch in Swin-v2. (#976) --- mmcls/models/utils/attention.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/mmcls/models/utils/attention.py b/mmcls/models/utils/attention.py index b94c4679c15..4e795ed0a10 100644 --- a/mmcls/models/utils/attention.py +++ b/mmcls/models/utils/attention.py @@ -261,7 +261,10 @@ def forward(self, x, mask=None): attn = ( F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) logit_scale = torch.clamp( - self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() + self.logit_scale, + max=torch.log( + torch.tensor(1. / 0.01, + device=self.logit_scale.device))).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp( From 6d8c91892cadfbca391aab0eed2ca41b609090c9 Mon Sep 17 00:00:00 2001 From: daquexian Date: Tue, 13 Sep 2022 15:13:20 +0800 Subject: [PATCH 11/25] [Improve] Upgrade onnxsim to v0.4.0. (#915) --- tools/deployment/pytorch2onnx.py | 22 ++++------------------ 1 file changed, 4 insertions(+), 18 deletions(-) diff --git a/tools/deployment/pytorch2onnx.py b/tools/deployment/pytorch2onnx.py index 1da95946706..85d795f1f84 100644 --- a/tools/deployment/pytorch2onnx.py +++ b/tools/deployment/pytorch2onnx.py @@ -113,26 +113,12 @@ def pytorch2onnx(model, import onnxsim from mmcv import digit_version - min_required_version = '0.3.0' - assert digit_version(mmcv.__version__) >= digit_version( + min_required_version = '0.4.0' + assert digit_version(onnxsim.__version__) >= digit_version( min_required_version - ), f'Requires to install onnx-simplify>={min_required_version}' + ), f'Requires to install onnxsim>={min_required_version}' - if dynamic_axes: - input_shape = (input_shape[0], input_shape[1], input_shape[2] * 2, - input_shape[3] * 2) - else: - input_shape = (input_shape[0], input_shape[1], input_shape[2], - input_shape[3]) - imgs = _demo_mm_inputs(input_shape, model.head.num_classes).pop('imgs') - input_dic = {'input': imgs.detach().cpu().numpy()} - input_shape_dic = {'input': list(input_shape)} - - model_opt, check_ok = onnxsim.simplify( - output_file, - input_shapes=input_shape_dic, - input_data=input_dic, - dynamic_input_shape=dynamic_export) + model_opt, check_ok = onnxsim.simplify(output_file) if check_ok: onnx.save(model_opt, output_file) print(f'Successfully simplified ONNX model: {output_file}') From 0b4a67dd31734ba19961f3b088d82e909a6d511b Mon Sep 17 00:00:00 2001 From: Kai Hu Date: Tue, 13 Sep 2022 03:24:29 -0400 Subject: [PATCH 12/25] [Refactor] Re-write get_sinusoid_encoding from third-party implementation. (#965) --- mmcls/models/backbones/t2t_vit.py | 21 ++++++------- .../test_backbones/test_t2t_vit.py | 31 +++++++++++++++++++ 2 files changed, 40 insertions(+), 12 deletions(-) diff --git a/mmcls/models/backbones/t2t_vit.py b/mmcls/models/backbones/t2t_vit.py index e3160ccd756..2edb991e61a 100644 --- a/mmcls/models/backbones/t2t_vit.py +++ b/mmcls/models/backbones/t2t_vit.py @@ -218,27 +218,24 @@ def get_sinusoid_encoding(n_position, embed_dims): Sinusoid encoding is a kind of relative position encoding method came from `Attention Is All You Need`_. - Args: n_position (int): The length of the input token. embed_dims (int): The position embedding dimension. - Returns: :obj:`torch.FloatTensor`: The sinusoid encoding table. """ - def get_position_angle_vec(position): - return [ - position / np.power(10000, 2 * (i // 2) / embed_dims) - for i in range(embed_dims) - ] + vec = torch.arange(embed_dims, dtype=torch.float64) + vec = (vec - vec % 2) / embed_dims + vec = torch.pow(10000, -vec).view(1, -1) + + sinusoid_table = torch.arange(n_position).view(-1, 1) * vec + sinusoid_table[:, 0::2].sin_() # dim 2i + sinusoid_table[:, 1::2].cos_() # dim 2i+1 - sinusoid_table = np.array( - [get_position_angle_vec(pos) for pos in range(n_position)]) - sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i - sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + sinusoid_table = sinusoid_table.to(torch.float32) - return torch.FloatTensor(sinusoid_table).unsqueeze(0) + return sinusoid_table.unsqueeze(0) @BACKBONES.register_module() diff --git a/tests/test_models/test_backbones/test_t2t_vit.py b/tests/test_models/test_backbones/test_t2t_vit.py index a7e6861cb14..f3103c65555 100644 --- a/tests/test_models/test_backbones/test_t2t_vit.py +++ b/tests/test_models/test_backbones/test_t2t_vit.py @@ -5,10 +5,12 @@ from copy import deepcopy from unittest import TestCase +import numpy as np import torch from mmcv.runner import load_checkpoint, save_checkpoint from mmcls.models.backbones import T2T_ViT +from mmcls.models.backbones.t2t_vit import get_sinusoid_encoding from .utils import timm_resize_pos_embed @@ -155,3 +157,32 @@ def test_forward(self): math.ceil(imgs.shape[3] / 16)) self.assertEqual(patch_token.shape, (1, 384, *expect_feat_shape)) self.assertEqual(cls_token.shape, (1, 384)) + + +def test_get_sinusoid_encoding(): + # original numpy based third-party implementation copied from mmcls + # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 + def get_sinusoid_encoding_numpy(n_position, d_hid): + + def get_position_angle_vec(position): + return [ + position / np.power(10000, 2 * (hid_j // 2) / d_hid) + for hid_j in range(d_hid) + ] + + sinusoid_table = np.array( + [get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.FloatTensor(sinusoid_table).unsqueeze(0) + + n_positions = [128, 256, 512, 1024] + embed_dims = [128, 256, 512, 1024] + for n_position in n_positions: + for embed_dim in embed_dims: + out_mmcls = get_sinusoid_encoding(n_position, embed_dim) + out_numpy = get_sinusoid_encoding_numpy(n_position, embed_dim) + error = (out_mmcls - out_numpy).abs().max() + assert error < 1e-9, 'Test case n_position=%d, embed_dim=%d failed' + return From 6ebb3f77ad5ac768cb06a11bd0ffc85c2be5c43c Mon Sep 17 00:00:00 2001 From: Hubert <42952108+yingfhu@users.noreply.github.com> Date: Mon, 26 Sep 2022 14:12:51 +0800 Subject: [PATCH 13/25] [Fix] Fix attenstion clamp max params (#1034) --- mmcls/models/utils/attention.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/mmcls/models/utils/attention.py b/mmcls/models/utils/attention.py index 4e795ed0a10..1aae72ae5a7 100644 --- a/mmcls/models/utils/attention.py +++ b/mmcls/models/utils/attention.py @@ -261,10 +261,7 @@ def forward(self, x, mask=None): attn = ( F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) logit_scale = torch.clamp( - self.logit_scale, - max=torch.log( - torch.tensor(1. / 0.01, - device=self.logit_scale.device))).exp() + self.logit_scale, max=np.log(1. / 0.01)).exp() attn = attn * logit_scale relative_position_bias_table = self.cpb_mlp( From 56589ee28072a882b8fed55356a7da7be6206a63 Mon Sep 17 00:00:00 2001 From: takuoko Date: Tue, 27 Sep 2022 10:44:40 +0900 Subject: [PATCH 14/25] [Enhancement] Update VAN. (#1017) * update van * fix init * b4 result * update van * keep old config * keep old config * fix metafile * update VAN configs * update example Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> --- configs/_base_/models/van/van_b0.py | 21 +++++++++ configs/_base_/models/van/van_b1.py | 21 +++++++++ configs/_base_/models/van/van_b2.py | 13 ++++++ configs/_base_/models/van/van_b3.py | 13 ++++++ configs/_base_/models/van/van_b4.py | 13 ++++++ configs/_base_/models/van/van_b5.py | 13 ++++++ configs/_base_/models/van/van_b6.py | 13 ++++++ configs/_base_/models/van/van_base.py | 14 +----- configs/_base_/models/van/van_large.py | 14 +----- configs/_base_/models/van/van_small.py | 22 +-------- configs/_base_/models/van/van_tiny.py | 22 +-------- configs/van/README.md | 25 +++++++--- configs/van/metafile.yml | 46 +++++++++++------- configs/van/van-b0_8xb128_in1k.py | 61 ++++++++++++++++++++++++ configs/van/van-b1_8xb128_in1k.py | 61 ++++++++++++++++++++++++ configs/van/van-b2_8xb128_in1k.py | 61 ++++++++++++++++++++++++ configs/van/van-b3_8xb128_in1k.py | 61 ++++++++++++++++++++++++ configs/van/van-b4_8xb128_in1k.py | 61 ++++++++++++++++++++++++ configs/van/van-base_8xb128_in1k.py | 65 ++------------------------ configs/van/van-large_8xb128_in1k.py | 65 ++------------------------ configs/van/van-small_8xb128_in1k.py | 65 ++------------------------ configs/van/van-tiny_8xb128_in1k.py | 65 ++------------------------ mmcls/models/backbones/van.py | 27 +++++++---- 23 files changed, 504 insertions(+), 338 deletions(-) create mode 100644 configs/_base_/models/van/van_b0.py create mode 100644 configs/_base_/models/van/van_b1.py create mode 100644 configs/_base_/models/van/van_b2.py create mode 100644 configs/_base_/models/van/van_b3.py create mode 100644 configs/_base_/models/van/van_b4.py create mode 100644 configs/_base_/models/van/van_b5.py create mode 100644 configs/_base_/models/van/van_b6.py create mode 100644 configs/van/van-b0_8xb128_in1k.py create mode 100644 configs/van/van-b1_8xb128_in1k.py create mode 100644 configs/van/van-b2_8xb128_in1k.py create mode 100644 configs/van/van-b3_8xb128_in1k.py create mode 100644 configs/van/van-b4_8xb128_in1k.py diff --git a/configs/_base_/models/van/van_b0.py b/configs/_base_/models/van/van_b0.py new file mode 100644 index 00000000000..5fa977e7b2f --- /dev/null +++ b/configs/_base_/models/van/van_b0.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b0', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=256, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/van/van_b1.py b/configs/_base_/models/van/van_b1.py new file mode 100644 index 00000000000..a27a50b11b8 --- /dev/null +++ b/configs/_base_/models/van/van_b1.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b1', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/van/van_b2.py b/configs/_base_/models/van/van_b2.py new file mode 100644 index 00000000000..41b0484f44f --- /dev/null +++ b/configs/_base_/models/van/van_b2.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b2', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b3.py b/configs/_base_/models/van/van_b3.py new file mode 100644 index 00000000000..d32b12cc1ee --- /dev/null +++ b/configs/_base_/models/van/van_b3.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b3', drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b4.py b/configs/_base_/models/van/van_b4.py new file mode 100644 index 00000000000..417835c9f5a --- /dev/null +++ b/configs/_base_/models/van/van_b4.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b4', drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b5.py b/configs/_base_/models/van/van_b5.py new file mode 100644 index 00000000000..fe8b9236066 --- /dev/null +++ b/configs/_base_/models/van/van_b5.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b5', drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b6.py b/configs/_base_/models/van/van_b6.py new file mode 100644 index 00000000000..a0dfb3c7c6d --- /dev/null +++ b/configs/_base_/models/van/van_b6.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b6', drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_base.py b/configs/_base_/models/van/van_base.py index 006459255f8..5c2bcf0edd7 100644 --- a/configs/_base_/models/van/van_base.py +++ b/configs/_base_/models/van/van_base.py @@ -1,13 +1 @@ -# model settings -model = dict( - type='ImageClassifier', - backbone=dict(type='VAN', arch='base', drop_path_rate=0.1), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='LinearClsHead', - num_classes=1000, - in_channels=512, - init_cfg=None, # suppress the default init_cfg of LinearClsHead. - loss=dict( - type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), - cal_acc=False)) +_base_ = ['./van-b2.py'] diff --git a/configs/_base_/models/van/van_large.py b/configs/_base_/models/van/van_large.py index 4ebafabdaaf..bc9536c6410 100644 --- a/configs/_base_/models/van/van_large.py +++ b/configs/_base_/models/van/van_large.py @@ -1,13 +1 @@ -# model settings -model = dict( - type='ImageClassifier', - backbone=dict(type='VAN', arch='large', drop_path_rate=0.2), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='LinearClsHead', - num_classes=1000, - in_channels=512, - init_cfg=None, # suppress the default init_cfg of LinearClsHead. - loss=dict( - type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), - cal_acc=False)) +_base_ = ['./van-b3.py'] diff --git a/configs/_base_/models/van/van_small.py b/configs/_base_/models/van/van_small.py index 320e90afdc8..3973c22a11f 100644 --- a/configs/_base_/models/van/van_small.py +++ b/configs/_base_/models/van/van_small.py @@ -1,21 +1 @@ -# model settings -model = dict( - type='ImageClassifier', - backbone=dict(type='VAN', arch='small', drop_path_rate=0.1), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='LinearClsHead', - num_classes=1000, - in_channels=512, - init_cfg=None, # suppress the default init_cfg of LinearClsHead. - loss=dict( - type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), - cal_acc=False), - init_cfg=[ - dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), - dict(type='Constant', layer='LayerNorm', val=1., bias=0.) - ], - train_cfg=dict(augments=[ - dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), - dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) - ])) +_base_ = ['./van-b1.py'] diff --git a/configs/_base_/models/van/van_tiny.py b/configs/_base_/models/van/van_tiny.py index 42791ac3beb..ace9ebbb172 100644 --- a/configs/_base_/models/van/van_tiny.py +++ b/configs/_base_/models/van/van_tiny.py @@ -1,21 +1 @@ -# model settings -model = dict( - type='ImageClassifier', - backbone=dict(type='VAN', arch='tiny', drop_path_rate=0.1), - neck=dict(type='GlobalAveragePooling'), - head=dict( - type='LinearClsHead', - num_classes=1000, - in_channels=256, - init_cfg=None, # suppress the default init_cfg of LinearClsHead. - loss=dict( - type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), - cal_acc=False), - init_cfg=[ - dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), - dict(type='Constant', layer='LayerNorm', val=1., bias=0.) - ], - train_cfg=dict(augments=[ - dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), - dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) - ])) +_base_ = ['./van-b0.py'] diff --git a/configs/van/README.md b/configs/van/README.md index e39dfc445a1..a84cf329932 100644 --- a/configs/van/README.md +++ b/configs/van/README.md @@ -16,15 +16,28 @@ While originally designed for natural language processing (NLP) tasks, the self- ### ImageNet-1k -| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -| :-----: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: | -| VAN-T\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | -| VAN-S\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | -| VAN-B\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | -| VAN-L\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| VAN-B0\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b0_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-B1\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B2\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b2_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-B3\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| VAN-B4\* | From scratch | 224x224 | 60.28 | 12.22 | 84.13 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b4_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in1k_20220909-f4665b92.pth) | \*Models with * are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. +### Pre-trained Models + +The pre-trained models on ImageNet-21k are used to fine-tune on the downstream tasks. + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Download | +| :------: | :----------: | :--------: | :-------: | :------: | :---------------------------------------------------------------------------------------------------------: | +| VAN-B4\* | ImageNet-21k | 224x224 | 60.28 | 12.22 | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in21k_20220909-db926b18.pth) | +| VAN-B5\* | ImageNet-21k | 224x224 | 89.97 | 17.21 | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b5_3rdparty_in21k_20220909-18e904e3.pth) | +| VAN-B6\* | ImageNet-21k | 224x224 | 283.9 | 55.28 | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b6_3rdparty_in21k_20220909-96c2cb3a.pth) | + +\*Models with * are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). + ## Citation ``` diff --git a/configs/van/metafile.yml b/configs/van/metafile.yml index 13e28c16ec8..c32df84abfd 100644 --- a/configs/van/metafile.yml +++ b/configs/van/metafile.yml @@ -7,6 +7,7 @@ Collections: - Weight Decay Architecture: - Visual Attention Network + - LKA Paper: URL: https://arxiv.org/pdf/2202.09741v2.pdf Title: "Visual Attention Network" @@ -16,10 +17,10 @@ Collections: Version: v0.23.0 Models: - - Name: van-tiny_8xb128_in1k + - Name: van-b0_3rdparty_in1k Metadata: - FLOPs: 4110000 # 4.11M - Parameters: 880000000 # 0.88G + FLOPs: 880000000 # 0.88G + Parameters: 4110000 # 4.11M In Collection: Visual-Attention-Network Results: - Dataset: ImageNet-1k @@ -28,11 +29,11 @@ Models: Top 5 Accuracy: 93.02 Task: Image Classification Weights: https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth - Config: configs/van/van-tiny_8xb128_in1k.py - - Name: van-small_8xb128_in1k + Config: configs/van/van-b0_8xb128_in1k.py + - Name: van-b1_3rdparty_in1k Metadata: - FLOPs: 13860000 # 13.86M - Parameters: 2520000000 # 2.52G + FLOPs: 2520000000 # 2.52G + Parameters: 13860000 # 13.86M In Collection: Visual-Attention-Network Results: - Dataset: ImageNet-1k @@ -41,11 +42,11 @@ Models: Top 5 Accuracy: 95.63 Task: Image Classification Weights: https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth - Config: configs/van/van-small_8xb128_in1k.py - - Name: van-base_8xb128_in1k + Config: configs/van/van-b1_8xb128_in1k.py + - Name: van-b2_3rdparty_in1k Metadata: - FLOPs: 26580000 # 26.58M - Parameters: 5030000000 # 5.03G + FLOPs: 5030000000 # 5.03G + Parameters: 26580000 # 26.58M In Collection: Visual-Attention-Network Results: - Dataset: ImageNet-1k @@ -54,11 +55,11 @@ Models: Top 5 Accuracy: 96.21 Task: Image Classification Weights: https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth - Config: configs/van/van-base_8xb128_in1k.py - - Name: van-large_8xb128_in1k + Config: configs/van/van-b2_8xb128_in1k.py + - Name: van-b3_3rdparty_in1k Metadata: - FLOPs: 44770000 # 44.77 M - Parameters: 8990000000 # 8.99G + FLOPs: 8990000000 # 8.99G + Parameters: 44770000 # 44.77M In Collection: Visual-Attention-Network Results: - Dataset: ImageNet-1k @@ -67,4 +68,17 @@ Models: Top 5 Accuracy: 96.73 Task: Image Classification Weights: https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth - Config: configs/van/van-large_8xb128_in1k.py + Config: configs/van/van-b3_8xb128_in1k.py + - Name: van-b4_3rdparty_in1k + Metadata: + FLOPs: 12220000000 # 12.22G + Parameters: 60280000 # 60.28M + In Collection: Visual-Attention-Network + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.13 + Top 5 Accuracy: 96.86 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in1k_20220909-f4665b92.pth + Config: configs/van/van-b4_8xb128_in1k.py diff --git a/configs/van/van-b0_8xb128_in1k.py b/configs/van/van-b0_8xb128_in1k.py new file mode 100644 index 00000000000..1acb7af38eb --- /dev/null +++ b/configs/van/van-b0_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b0.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b1_8xb128_in1k.py b/configs/van/van-b1_8xb128_in1k.py new file mode 100644 index 00000000000..64483db867d --- /dev/null +++ b/configs/van/van-b1_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b1.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b2_8xb128_in1k.py b/configs/van/van-b2_8xb128_in1k.py new file mode 100644 index 00000000000..88493dc2e0f --- /dev/null +++ b/configs/van/van-b2_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b2.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b3_8xb128_in1k.py b/configs/van/van-b3_8xb128_in1k.py new file mode 100644 index 00000000000..6b415f656fb --- /dev/null +++ b/configs/van/van-b3_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b3.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b4_8xb128_in1k.py b/configs/van/van-b4_8xb128_in1k.py new file mode 100644 index 00000000000..ba8914f8209 --- /dev/null +++ b/configs/van/van-b4_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b4.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-base_8xb128_in1k.py b/configs/van/van-base_8xb128_in1k.py index 704f111bf51..e331980db2d 100644 --- a/configs/van/van-base_8xb128_in1k.py +++ b/configs/van/van-base_8xb128_in1k.py @@ -1,61 +1,6 @@ -_base_ = [ - '../_base_/models/van/van_base.py', - '../_base_/datasets/imagenet_bs64_swin_224.py', - '../_base_/schedules/imagenet_bs1024_adamw_swin.py', - '../_base_/default_runtime.py' -] +_base_ = ['./van-b2_8xb128_in1k.py'] -# Note that the mean and variance used here are different from other configs -img_norm_cfg = dict( - mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='RandomResizedCrop', - size=224, - backend='pillow', - interpolation='bicubic'), - dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), - dict( - type='RandAugment', - policies={{_base_.rand_increasing_policies}}, - num_policies=2, - total_level=10, - magnitude_level=9, - magnitude_std=0.5, - hparams=dict( - pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], - interpolation='bicubic')), - dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), - dict( - type='RandomErasing', - erase_prob=0.25, - mode='rand', - min_area_ratio=0.02, - max_area_ratio=1 / 3, - fill_color=img_norm_cfg['mean'][::-1], - fill_std=img_norm_cfg['std'][::-1]), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['gt_label']), - dict(type='Collect', keys=['img', 'gt_label']) -] - -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='Resize', - size=(248, -1), - backend='pillow', - interpolation='bicubic'), - dict(type='CenterCrop', crop_size=224), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) -] - -data = dict( - samples_per_gpu=128, - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) +_deprecation_ = dict( + expected='van-b2_8xb128_in1k.p', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/van/van-large_8xb128_in1k.py b/configs/van/van-large_8xb128_in1k.py index b55aff165ef..84f8c7eddd0 100644 --- a/configs/van/van-large_8xb128_in1k.py +++ b/configs/van/van-large_8xb128_in1k.py @@ -1,61 +1,6 @@ -_base_ = [ - '../_base_/models/van/van_large.py', - '../_base_/datasets/imagenet_bs64_swin_224.py', - '../_base_/schedules/imagenet_bs1024_adamw_swin.py', - '../_base_/default_runtime.py' -] +_base_ = ['./van-b3_8xb128_in1k.py'] -# Note that the mean and variance used here are different from other configs -img_norm_cfg = dict( - mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='RandomResizedCrop', - size=224, - backend='pillow', - interpolation='bicubic'), - dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), - dict( - type='RandAugment', - policies={{_base_.rand_increasing_policies}}, - num_policies=2, - total_level=10, - magnitude_level=9, - magnitude_std=0.5, - hparams=dict( - pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], - interpolation='bicubic')), - dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), - dict( - type='RandomErasing', - erase_prob=0.25, - mode='rand', - min_area_ratio=0.02, - max_area_ratio=1 / 3, - fill_color=img_norm_cfg['mean'][::-1], - fill_std=img_norm_cfg['std'][::-1]), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['gt_label']), - dict(type='Collect', keys=['img', 'gt_label']) -] - -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='Resize', - size=(248, -1), - backend='pillow', - interpolation='bicubic'), - dict(type='CenterCrop', crop_size=224), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) -] - -data = dict( - samples_per_gpu=128, - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) +_deprecation_ = dict( + expected='van-b3_8xb128_in1k.p', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/van/van-small_8xb128_in1k.py b/configs/van/van-small_8xb128_in1k.py index 3b83e25ab8c..75d3220b47c 100644 --- a/configs/van/van-small_8xb128_in1k.py +++ b/configs/van/van-small_8xb128_in1k.py @@ -1,61 +1,6 @@ -_base_ = [ - '../_base_/models/van/van_small.py', - '../_base_/datasets/imagenet_bs64_swin_224.py', - '../_base_/schedules/imagenet_bs1024_adamw_swin.py', - '../_base_/default_runtime.py' -] +_base_ = ['./van-b1_8xb128_in1k.py'] -# Note that the mean and variance used here are different from other configs -img_norm_cfg = dict( - mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='RandomResizedCrop', - size=224, - backend='pillow', - interpolation='bicubic'), - dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), - dict( - type='RandAugment', - policies={{_base_.rand_increasing_policies}}, - num_policies=2, - total_level=10, - magnitude_level=9, - magnitude_std=0.5, - hparams=dict( - pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], - interpolation='bicubic')), - dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), - dict( - type='RandomErasing', - erase_prob=0.25, - mode='rand', - min_area_ratio=0.02, - max_area_ratio=1 / 3, - fill_color=img_norm_cfg['mean'][::-1], - fill_std=img_norm_cfg['std'][::-1]), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['gt_label']), - dict(type='Collect', keys=['img', 'gt_label']) -] - -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='Resize', - size=(248, -1), - backend='pillow', - interpolation='bicubic'), - dict(type='CenterCrop', crop_size=224), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) -] - -data = dict( - samples_per_gpu=128, - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) +_deprecation_ = dict( + expected='van-b1_8xb128_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/van/van-tiny_8xb128_in1k.py b/configs/van/van-tiny_8xb128_in1k.py index 1e001c1c329..9f83e77c6ba 100644 --- a/configs/van/van-tiny_8xb128_in1k.py +++ b/configs/van/van-tiny_8xb128_in1k.py @@ -1,61 +1,6 @@ -_base_ = [ - '../_base_/models/van/van_tiny.py', - '../_base_/datasets/imagenet_bs64_swin_224.py', - '../_base_/schedules/imagenet_bs1024_adamw_swin.py', - '../_base_/default_runtime.py' -] +_base_ = ['./van-b0_8xb128_in1k.py'] -# Note that the mean and variance used here are different from other configs -img_norm_cfg = dict( - mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='RandomResizedCrop', - size=224, - backend='pillow', - interpolation='bicubic'), - dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), - dict( - type='RandAugment', - policies={{_base_.rand_increasing_policies}}, - num_policies=2, - total_level=10, - magnitude_level=9, - magnitude_std=0.5, - hparams=dict( - pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], - interpolation='bicubic')), - dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), - dict( - type='RandomErasing', - erase_prob=0.25, - mode='rand', - min_area_ratio=0.02, - max_area_ratio=1 / 3, - fill_color=img_norm_cfg['mean'][::-1], - fill_std=img_norm_cfg['std'][::-1]), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['gt_label']), - dict(type='Collect', keys=['img', 'gt_label']) -] - -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='Resize', - size=(248, -1), - backend='pillow', - interpolation='bicubic'), - dict(type='CenterCrop', crop_size=224), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) -] - -data = dict( - samples_per_gpu=128, - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) +_deprecation_ = dict( + expected='van-b0_8xb128_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/mmcls/models/backbones/van.py b/mmcls/models/backbones/van.py index 1be52b68716..925240ed80d 100644 --- a/mmcls/models/backbones/van.py +++ b/mmcls/models/backbones/van.py @@ -264,8 +264,8 @@ class VAN(BaseBackbone): Args: arch (str | dict): Visual Attention Network architecture. - If use string, choose from 'tiny', 'small', 'base' and 'large'. - If use dict, it should have below keys: + If use string, choose from 'b0', 'b1', b2', b3' and etc., + if use dict, it should have below keys: - **embed_dims** (List[int]): The dimensions of embedding. - **depths** (List[int]): The number of blocks in each stage. @@ -295,8 +295,7 @@ class VAN(BaseBackbone): Examples: >>> from mmcls.models import VAN >>> import torch - >>> cfg = dict(arch='tiny') - >>> model = VAN(**cfg) + >>> model = VAN(arch='b0') >>> inputs = torch.rand(1, 3, 224, 224) >>> outputs = model(inputs) >>> for out in outputs: @@ -304,22 +303,34 @@ class VAN(BaseBackbone): (1, 256, 7, 7) """ arch_zoo = { - **dict.fromkeys(['t', 'tiny'], + **dict.fromkeys(['b0', 't', 'tiny'], {'embed_dims': [32, 64, 160, 256], 'depths': [3, 3, 5, 2], 'ffn_ratios': [8, 8, 4, 4]}), - **dict.fromkeys(['s', 'small'], + **dict.fromkeys(['b1', 's', 'small'], {'embed_dims': [64, 128, 320, 512], 'depths': [2, 2, 4, 2], 'ffn_ratios': [8, 8, 4, 4]}), - **dict.fromkeys(['b', 'base'], + **dict.fromkeys(['b2', 'b', 'base'], {'embed_dims': [64, 128, 320, 512], 'depths': [3, 3, 12, 3], 'ffn_ratios': [8, 8, 4, 4]}), - **dict.fromkeys(['l', 'large'], + **dict.fromkeys(['b3', 'l', 'large'], {'embed_dims': [64, 128, 320, 512], 'depths': [3, 5, 27, 3], 'ffn_ratios': [8, 8, 4, 4]}), + **dict.fromkeys(['b4'], + {'embed_dims': [64, 128, 320, 512], + 'depths': [3, 6, 40, 3], + 'ffn_ratios': [8, 8, 4, 4]}), + **dict.fromkeys(['b5'], + {'embed_dims': [96, 192, 480, 768], + 'depths': [3, 3, 24, 3], + 'ffn_ratios': [8, 8, 4, 4]}), + **dict.fromkeys(['b6'], + {'embed_dims': [96, 192, 384, 768], + 'depths': [6, 6, 90, 6], + 'ffn_ratios': [8, 8, 4, 4]}), } # yapf: disable def __init__(self, From 1047daa28e349eb9b8b785bb35603715708d8eb0 Mon Sep 17 00:00:00 2001 From: takuoko Date: Tue, 27 Sep 2022 11:37:49 +0900 Subject: [PATCH 15/25] [Feature] Support HorNet Backbone. (#1013) * add hornet * add hornet * add hornet * add hornet * add hornet * add hornet * add hornet * fix test for torch before 1.7.0 * del timm * fix readme * fix readme * Update mmcls/models/backbones/hornet.py Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> * fix docs * fix docs * s -> scale * fix dims and dpr impl * fix layer scale * refactor gnconv * add dw_cfg * add convert tools * update code * update docs * update readme * update URLs Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> --- README.md | 1 + README_zh-CN.md | 2 + .../_base_/models/hornet/hornet-base-gf.py | 21 + configs/_base_/models/hornet/hornet-base.py | 21 + .../_base_/models/hornet/hornet-large-gf.py | 21 + .../models/hornet/hornet-large-gf384.py | 17 + configs/_base_/models/hornet/hornet-large.py | 21 + .../_base_/models/hornet/hornet-small-gf.py | 21 + configs/_base_/models/hornet/hornet-small.py | 21 + .../_base_/models/hornet/hornet-tiny-gf.py | 21 + configs/_base_/models/hornet/hornet-tiny.py | 21 + configs/hornet/README.md | 51 ++ configs/hornet/hornet-base-gf_8xb64_in1k.py | 13 + configs/hornet/hornet-base_8xb64_in1k.py | 13 + configs/hornet/hornet-small-gf_8xb64_in1k.py | 13 + configs/hornet/hornet-small_8xb64_in1k.py | 13 + configs/hornet/hornet-tiny-gf_8xb128_in1k.py | 13 + configs/hornet/hornet-tiny_8xb128_in1k.py | 13 + configs/hornet/metafile.yml | 97 ++++ docs/en/api/models.rst | 1 + mmcls/models/backbones/__init__.py | 4 +- mmcls/models/backbones/efficientformer.py | 33 +- mmcls/models/backbones/hornet.py | 499 ++++++++++++++++++ mmcls/models/utils/__init__.py | 3 +- mmcls/models/utils/layer_scale.py | 35 ++ model-index.yml | 1 + .../test_backbones/test_efficientformer.py | 44 +- .../test_models/test_backbones/test_hornet.py | 174 ++++++ .../test_utils/test_layer_scale.py | 48 ++ tools/convert_models/hornet2mmcls.py | 61 +++ 30 files changed, 1240 insertions(+), 77 deletions(-) create mode 100644 configs/_base_/models/hornet/hornet-base-gf.py create mode 100644 configs/_base_/models/hornet/hornet-base.py create mode 100644 configs/_base_/models/hornet/hornet-large-gf.py create mode 100644 configs/_base_/models/hornet/hornet-large-gf384.py create mode 100644 configs/_base_/models/hornet/hornet-large.py create mode 100644 configs/_base_/models/hornet/hornet-small-gf.py create mode 100644 configs/_base_/models/hornet/hornet-small.py create mode 100644 configs/_base_/models/hornet/hornet-tiny-gf.py create mode 100644 configs/_base_/models/hornet/hornet-tiny.py create mode 100644 configs/hornet/README.md create mode 100644 configs/hornet/hornet-base-gf_8xb64_in1k.py create mode 100644 configs/hornet/hornet-base_8xb64_in1k.py create mode 100644 configs/hornet/hornet-small-gf_8xb64_in1k.py create mode 100644 configs/hornet/hornet-small_8xb64_in1k.py create mode 100644 configs/hornet/hornet-tiny-gf_8xb128_in1k.py create mode 100644 configs/hornet/hornet-tiny_8xb128_in1k.py create mode 100644 configs/hornet/metafile.yml create mode 100644 mmcls/models/backbones/hornet.py create mode 100644 mmcls/models/utils/layer_scale.py create mode 100644 tests/test_models/test_backbones/test_hornet.py create mode 100644 tests/test_models/test_utils/test_layer_scale.py create mode 100644 tools/convert_models/hornet2mmcls.py diff --git a/README.md b/README.md index eec6036b944..1eab19a399e 100644 --- a/README.md +++ b/README.md @@ -144,6 +144,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer) - [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit) - [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer) +- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet) diff --git a/README_zh-CN.md b/README_zh-CN.md index f6235a0b85c..6fee274c7ae 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -143,6 +143,8 @@ pip3 install -e . - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet) - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer) - [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit) +- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer) +- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet) diff --git a/configs/_base_/models/hornet/hornet-base-gf.py b/configs/_base_/models/hornet/hornet-base-gf.py new file mode 100644 index 00000000000..7544970fb2b --- /dev/null +++ b/configs/_base_/models/hornet/hornet-base-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='base-gf', drop_path_rate=0.5), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-base.py b/configs/_base_/models/hornet/hornet-base.py new file mode 100644 index 00000000000..82764146314 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-base.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='base', drop_path_rate=0.5), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-large-gf.py b/configs/_base_/models/hornet/hornet-large-gf.py new file mode 100644 index 00000000000..a5b551133df --- /dev/null +++ b/configs/_base_/models/hornet/hornet-large-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='large-gf', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-large-gf384.py b/configs/_base_/models/hornet/hornet-large-gf384.py new file mode 100644 index 00000000000..fbb547873ed --- /dev/null +++ b/configs/_base_/models/hornet/hornet-large-gf384.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='large-gf384', drop_path_rate=0.4), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ]) diff --git a/configs/_base_/models/hornet/hornet-large.py b/configs/_base_/models/hornet/hornet-large.py new file mode 100644 index 00000000000..26d99e1ae64 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-large.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='large', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-small-gf.py b/configs/_base_/models/hornet/hornet-small-gf.py new file mode 100644 index 00000000000..42d9d119761 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-small-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='small-gf', drop_path_rate=0.4), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-small.py b/configs/_base_/models/hornet/hornet-small.py new file mode 100644 index 00000000000..e8039765528 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-small.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='small', drop_path_rate=0.4), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-tiny-gf.py b/configs/_base_/models/hornet/hornet-tiny-gf.py new file mode 100644 index 00000000000..0e417d04b11 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-tiny-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='tiny-gf', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-tiny.py b/configs/_base_/models/hornet/hornet-tiny.py new file mode 100644 index 00000000000..068d7d6b8c9 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-tiny.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='tiny', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/hornet/README.md b/configs/hornet/README.md new file mode 100644 index 00000000000..7c1b9a9b768 --- /dev/null +++ b/configs/hornet/README.md @@ -0,0 +1,51 @@ +# HorNet + +> [HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions](https://arxiv.org/pdf/2207.14284v2.pdf) + + + +## Abstract + +Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention. In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework. We present the Recursive Gated Convolution (g nConv) that performs high-order spatial interactions with gated convolutions and recursive designs. The new operation is highly flexible and customizable, which is compatible with various variants of convolution and extends the two-order interactions in self-attention to arbitrary orders without introducing significant extra computation. g nConv can serve as a plug-and-play module to improve various vision Transformers and convolution-based models. Based on the operation, we construct a new family of generic vision backbones named HorNet. Extensive experiments on ImageNet classification, COCO object detection and ADE20K semantic segmentation show HorNet outperform Swin Transformers and ConvNeXt by a significant margin with similar overall architecture and training configurations. HorNet also shows favorable scalability to more training data and a larger model size. Apart from the effectiveness in visual encoders, we also show g nConv can be applied to task-specific decoders and consistently improve dense prediction performance with less computation. Our results demonstrate that g nConv can be a new basic module for visual modeling that effectively combines the merits of both vision Transformers and CNNs. Code is available at https://github.com/raoyongming/HorNet. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------: | :----------------------------------------------------------------: | +| HorNet-T\* | From scratch | 224x224 | 22.41 | 3.98 | 82.84 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny_3rdparty_in1k_20220915-0e8eedff.pth) | +| HorNet-T-GF\* | From scratch | 224x224 | 22.99 | 3.9 | 82.98 | 96.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-tiny-gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny-gf_3rdparty_in1k_20220915-4c35a66b.pth) | +| HorNet-S\* | From scratch | 224x224 | 49.53 | 8.83 | 83.79 | 96.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-small_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small_3rdparty_in1k_20220915-5935f60f.pth) | +| HorNet-S-GF\* | From scratch | 224x224 | 50.4 | 8.71 | 83.98 | 96.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-small-gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small-gf_3rdparty_in1k_20220915-649ca492.pth) | +| HorNet-B\* | From scratch | 224x224 | 87.26 | 15.59 | 84.24 | 96.94 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-base_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base_3rdparty_in1k_20220915-a06176bb.pth) | +| HorNet-B-GF\* | From scratch | 224x224 | 88.42 | 15.42 | 84.32 | 96.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-base-gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base-gf_3rdparty_in1k_20220915-82c06fa7.pth) | + +\*Models with * are converted from [the official repo](https://github.com/raoyongming/HorNet). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. + +### Pre-trained Models + +The pre-trained models on ImageNet-21k are used to fine-tune on the downstream tasks. + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Download | +| :--------------: | :----------: | :--------: | :-------: | :------: | :------------------------------------------------------------------------------------------------------------------------: | +| HorNet-L\* | ImageNet-21k | 224x224 | 194.54 | 34.83 | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large_3rdparty_in21k_20220909-9ccef421.pth) | +| HorNet-L-GF\* | ImageNet-21k | 224x224 | 196.29 | 34.58 | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large-gf_3rdparty_in21k_20220909-3aea3b61.pth) | +| HorNet-L-GF384\* | ImageNet-21k | 384x384 | 201.23 | 101.63 | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large-gf384_3rdparty_in21k_20220909-80894290.pth) | + +\*Models with * are converted from [the official repo](https://github.com/raoyongming/HorNet). + +## Citation + +``` +@article{rao2022hornet, + title={HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions}, + author={Rao, Yongming and Zhao, Wenliang and Tang, Yansong and Zhou, Jie and Lim, Ser-Lam and Lu, Jiwen}, + journal={arXiv preprint arXiv:2207.14284}, + year={2022} +} +``` diff --git a/configs/hornet/hornet-base-gf_8xb64_in1k.py b/configs/hornet/hornet-base-gf_8xb64_in1k.py new file mode 100644 index 00000000000..6c29de66b62 --- /dev/null +++ b/configs/hornet/hornet-base-gf_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-base-gf.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-base_8xb64_in1k.py b/configs/hornet/hornet-base_8xb64_in1k.py new file mode 100644 index 00000000000..969d8b95b6e --- /dev/null +++ b/configs/hornet/hornet-base_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-small-gf_8xb64_in1k.py b/configs/hornet/hornet-small-gf_8xb64_in1k.py new file mode 100644 index 00000000000..deb570eba0e --- /dev/null +++ b/configs/hornet/hornet-small-gf_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-small-gf.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-small_8xb64_in1k.py b/configs/hornet/hornet-small_8xb64_in1k.py new file mode 100644 index 00000000000..c07fa60dbd7 --- /dev/null +++ b/configs/hornet/hornet-small_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-small.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-tiny-gf_8xb128_in1k.py b/configs/hornet/hornet-tiny-gf_8xb128_in1k.py new file mode 100644 index 00000000000..3a1d1a7a511 --- /dev/null +++ b/configs/hornet/hornet-tiny-gf_8xb128_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-tiny-gf.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-tiny_8xb128_in1k.py b/configs/hornet/hornet-tiny_8xb128_in1k.py new file mode 100644 index 00000000000..69a7cdf07ce --- /dev/null +++ b/configs/hornet/hornet-tiny_8xb128_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-tiny.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=100.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/metafile.yml b/configs/hornet/metafile.yml new file mode 100644 index 00000000000..712077220e3 --- /dev/null +++ b/configs/hornet/metafile.yml @@ -0,0 +1,97 @@ +Collections: + - Name: HorNet + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - AdamW + - Weight Decay + Architecture: + - HorNet + - gnConv + Paper: + URL: https://arxiv.org/pdf/2207.14284v2.pdf + Title: "HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions" + README: configs/hornet/README.md + Code: + Version: v0.24.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/hornet.py + +Models: + - Name: hornet-tiny_3rdparty_in1k + Metadata: + FLOPs: 3980000000 # 3.98G + Parameters: 22410000 # 22.41M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.84 + Top 5 Accuracy: 96.24 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny_3rdparty_in1k_20220915-0e8eedff.pth + Config: configs/hornet/hornet-tiny_8xb128_in1k.py + - Name: hornet-tiny-gf_3rdparty_in1k + Metadata: + FLOPs: 3900000000 # 3.9G + Parameters: 22990000 # 22.99M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.98 + Top 5 Accuracy: 96.38 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny-gf_3rdparty_in1k_20220915-4c35a66b.pth + Config: configs/hornet/hornet-tiny-gf_8xb128_in1k.py + - Name: hornet-small_3rdparty_in1k + Metadata: + FLOPs: 8830000000 # 8.83G + Parameters: 49530000 # 49.53M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.79 + Top 5 Accuracy: 96.75 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small_3rdparty_in1k_20220915-5935f60f.pth + Config: configs/hornet/hornet-small_8xb64_in1k.py + - Name: hornet-small-gf_3rdparty_in1k + Metadata: + FLOPs: 8710000000 # 8.71G + Parameters: 50400000 # 50.4M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.98 + Top 5 Accuracy: 96.77 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small-gf_3rdparty_in1k_20220915-649ca492.pth + Config: configs/hornet/hornet-small-gf_8xb64_in1k.py + - Name: hornet-base_3rdparty_in1k + Metadata: + FLOPs: 15590000000 # 15.59G + Parameters: 87260000 # 87.26M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.24 + Top 5 Accuracy: 96.94 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base_3rdparty_in1k_20220915-a06176bb.pth + Config: configs/hornet/hornet-base_8xb64_in1k.py + - Name: hornet-base-gf_3rdparty_in1k + Metadata: + FLOPs: 15420000000 # 15.42G + Parameters: 88420000 # 88.42M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.32 + Top 5 Accuracy: 96.95 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base-gf_3rdparty_in1k_20220915-82c06fa7.pth + Config: configs/hornet/hornet-base-gf_8xb64_in1k.py diff --git a/docs/en/api/models.rst b/docs/en/api/models.rst index 37938e34d95..0c317916d37 100644 --- a/docs/en/api/models.rst +++ b/docs/en/api/models.rst @@ -88,6 +88,7 @@ Backbones VGG VisionTransformer EfficientFormer + HorNet .. _necks: diff --git a/mmcls/models/backbones/__init__.py b/mmcls/models/backbones/__init__.py index ad7b8189943..a919a42cba0 100644 --- a/mmcls/models/backbones/__init__.py +++ b/mmcls/models/backbones/__init__.py @@ -8,6 +8,7 @@ from .densenet import DenseNet from .efficientformer import EfficientFormer from .efficientnet import EfficientNet +from .hornet import HorNet from .hrnet import HRNet from .lenet import LeNet5 from .mlp_mixer import MlpMixer @@ -45,5 +46,6 @@ 'Res2Net', 'RepVGG', 'Conformer', 'MlpMixer', 'DistilledVisionTransformer', 'PCPVT', 'SVT', 'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c', 'ConvMixer', 'CSPDarkNet', 'CSPResNet', 'CSPResNeXt', 'CSPNet', - 'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT', 'EfficientFormer' + 'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT', 'EfficientFormer', + 'HorNet' ] diff --git a/mmcls/models/backbones/efficientformer.py b/mmcls/models/backbones/efficientformer.py index fa3b14eb6e0..173444ff22b 100644 --- a/mmcls/models/backbones/efficientformer.py +++ b/mmcls/models/backbones/efficientformer.py @@ -9,6 +9,7 @@ from mmcv.runner import BaseModule, ModuleList, Sequential from ..builder import BACKBONES +from ..utils import LayerScale from .base_backbone import BaseBackbone from .poolformer import Pooling @@ -201,38 +202,6 @@ def forward(self, x): return x -class LayerScale(nn.Module): - """LayerScale layer. - - Args: - dim (int): Dimension of input features. - inplace (bool): inplace: can optionally do the - operation in-place. Default: ``False`` - data_format (str): The input data format, can be 'channels_last' - and 'channels_first', representing (B, C, H, W) and - (B, N, C) format data respectively. - """ - - def __init__(self, - dim: int, - inplace: bool = False, - data_format: str = 'channels_last'): - super().__init__() - assert data_format in ('channels_last', 'channels_first'), \ - "'data_format' could only be channels_last or channels_first." - self.inplace = inplace - self.data_format = data_format - self.weight = nn.Parameter(torch.ones(dim) * 1e-5) - - def forward(self, x): - if self.data_format == 'channels_first': - if self.inplace: - return x.mul_(self.weight.view(-1, 1, 1)) - else: - return x * self.weight.view(-1, 1, 1) - return x.mul_(self.weight) if self.inplace else x * self.weight - - class Meta3D(BaseModule): """Meta Former block using 3 dimensions inputs, ``torch.Tensor`` with shape (B, N, C).""" diff --git a/mmcls/models/backbones/hornet.py b/mmcls/models/backbones/hornet.py new file mode 100644 index 00000000000..1822b7c0f13 --- /dev/null +++ b/mmcls/models/backbones/hornet.py @@ -0,0 +1,499 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# Adapted from official impl at https://github.com/raoyongming/HorNet. +try: + import torch.fft + fft = True +except ImportError: + fft = None + +import copy +from functools import partial +from typing import Sequence + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +from mmcv.cnn.bricks import DropPath + +from mmcls.models.builder import BACKBONES +from ..utils import LayerScale +from .base_backbone import BaseBackbone + + +def get_dwconv(dim, kernel_size, bias=True): + """build a pepth-wise convolution.""" + return nn.Conv2d( + dim, + dim, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + bias=bias, + groups=dim) + + +class HorNetLayerNorm(nn.Module): + """An implementation of LayerNorm of HorNet. + + The differences between HorNetLayerNorm & torch LayerNorm: + 1. Supports two data formats channels_last or channels_first. + + Args: + normalized_shape (int or list or torch.Size): input shape from an + expected input of size. + eps (float): a value added to the denominator for numerical stability. + Defaults to 1e-5. + data_format (str): The ordering of the dimensions in the inputs. + channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with + shape (batch_size, channels, height, width). + Defaults to 'channels_last'. + """ + + def __init__(self, + normalized_shape, + eps=1e-6, + data_format='channels_last'): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ['channels_last', 'channels_first']: + raise ValueError( + 'data_format must be channels_last or channels_first') + self.normalized_shape = (normalized_shape, ) + + def forward(self, x): + if self.data_format == 'channels_last': + return F.layer_norm(x, self.normalized_shape, self.weight, + self.bias, self.eps) + elif self.data_format == 'channels_first': + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +class GlobalLocalFilter(nn.Module): + """A GlobalLocalFilter of HorNet. + + Args: + dim (int): Number of input channels. + h (int): Height of complex_weight. + Defaults to 14. + w (int): Width of complex_weight. + Defaults to 8. + """ + + def __init__(self, dim, h=14, w=8): + super().__init__() + self.dw = nn.Conv2d( + dim // 2, + dim // 2, + kernel_size=3, + padding=1, + bias=False, + groups=dim // 2) + self.complex_weight = nn.Parameter( + torch.randn(dim // 2, h, w, 2, dtype=torch.float32) * 0.02) + self.pre_norm = HorNetLayerNorm( + dim, eps=1e-6, data_format='channels_first') + self.post_norm = HorNetLayerNorm( + dim, eps=1e-6, data_format='channels_first') + + def forward(self, x): + x = self.pre_norm(x) + x1, x2 = torch.chunk(x, 2, dim=1) + x1 = self.dw(x1) + + x2 = x2.to(torch.float32) + B, C, a, b = x2.shape + x2 = torch.fft.rfft2(x2, dim=(2, 3), norm='ortho') + + weight = self.complex_weight + if not weight.shape[1:3] == x2.shape[2:4]: + weight = F.interpolate( + weight.permute(3, 0, 1, 2), + size=x2.shape[2:4], + mode='bilinear', + align_corners=True).permute(1, 2, 3, 0) + + weight = torch.view_as_complex(weight.contiguous()) + + x2 = x2 * weight + x2 = torch.fft.irfft2(x2, s=(a, b), dim=(2, 3), norm='ortho') + + x = torch.cat([x1.unsqueeze(2), x2.unsqueeze(2)], + dim=2).reshape(B, 2 * C, a, b) + x = self.post_norm(x) + return x + + +class gnConv(nn.Module): + """A gnConv of HorNet. + + Args: + dim (int): Number of input channels. + order (int): Order of gnConv. + Defaults to 5. + dw_cfg (dict): The Config for dw conv. + Defaults to ``dict(type='DW', kernel_size=7)``. + scale (float): Scaling parameter of gflayer outputs. + Defaults to 1.0. + """ + + def __init__(self, + dim, + order=5, + dw_cfg=dict(type='DW', kernel_size=7), + scale=1.0): + super().__init__() + self.order = order + self.dims = [dim // 2**i for i in range(order)] + self.dims.reverse() + self.proj_in = nn.Conv2d(dim, 2 * dim, 1) + + cfg = copy.deepcopy(dw_cfg) + dw_type = cfg.pop('type') + assert dw_type in ['DW', 'GF'],\ + 'dw_type should be `DW` or `GF`' + if dw_type == 'DW': + self.dwconv = get_dwconv(sum(self.dims), **cfg) + elif dw_type == 'GF': + self.dwconv = GlobalLocalFilter(sum(self.dims), **cfg) + + self.proj_out = nn.Conv2d(dim, dim, 1) + + self.projs = nn.ModuleList([ + nn.Conv2d(self.dims[i], self.dims[i + 1], 1) + for i in range(order - 1) + ]) + + self.scale = scale + + def forward(self, x): + x = self.proj_in(x) + y, x = torch.split(x, (self.dims[0], sum(self.dims)), dim=1) + + x = self.dwconv(x) * self.scale + + dw_list = torch.split(x, self.dims, dim=1) + x = y * dw_list[0] + + for i in range(self.order - 1): + x = self.projs[i](x) * dw_list[i + 1] + + x = self.proj_out(x) + + return x + + +class HorNetBlock(nn.Module): + """A block of HorNet. + + Args: + dim (int): Number of input channels. + order (int): Order of gnConv. + Defaults to 5. + dw_cfg (dict): The Config for dw conv. + Defaults to ``dict(type='DW', kernel_size=7)``. + scale (float): Scaling parameter of gflayer outputs. + Defaults to 1.0. + drop_path_rate (float): Stochastic depth rate. Defaults to 0. + use_layer_scale (bool): Whether to use use_layer_scale in HorNet + block. Defaults to True. + """ + + def __init__(self, + dim, + order=5, + dw_cfg=dict(type='DW', kernel_size=7), + scale=1.0, + drop_path_rate=0., + use_layer_scale=True): + super().__init__() + self.out_channels = dim + + self.norm1 = HorNetLayerNorm( + dim, eps=1e-6, data_format='channels_first') + self.gnconv = gnConv(dim, order, dw_cfg, scale) + self.norm2 = HorNetLayerNorm(dim, eps=1e-6) + self.pwconv1 = nn.Linear(dim, 4 * dim) + self.act = nn.GELU() + self.pwconv2 = nn.Linear(4 * dim, dim) + + if use_layer_scale: + self.gamma1 = LayerScale(dim, data_format='channels_first') + self.gamma2 = LayerScale(dim) + else: + self.gamma1, self.gamma2 = nn.Identity(), nn.Identity() + + self.drop_path = DropPath( + drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + def forward(self, x): + x = x + self.drop_path(self.gamma1(self.gnconv(self.norm1(x)))) + + input = x + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.norm2(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + x = self.gamma2(x) + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + x = input + self.drop_path(x) + return x + + +@BACKBONES.register_module() +class HorNet(BaseBackbone): + """HorNet + A PyTorch impl of : `HorNet: Efficient High-Order Spatial Interactions + with Recursive Gated Convolutions` + + Inspiration from + https://github.com/raoyongming/HorNet + + Args: + arch (str | dict): HorNet architecture. + If use string, choose from 'tiny', 'small', 'base' and 'large'. + If use dict, it should have below keys: + - **base_dim** (int): The base dimensions of embedding. + - **depths** (List[int]): The number of blocks in each stage. + - **orders** (List[int]): The number of order of gnConv in each + stage. + - **dw_cfg** (List[dict]): The Config for dw conv. + + Defaults to 'tiny'. + in_channels (int): Number of input image channels. Defaults to 3. + drop_path_rate (float): Stochastic depth rate. Defaults to 0. + scale (float): Scaling parameter of gflayer outputs. Defaults to 1/3. + use_layer_scale (bool): Whether to use use_layer_scale in HorNet + block. Defaults to True. + out_indices (Sequence[int]): Output from which stages. + Default: ``(3, )``. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. Defaults to -1. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Defaults to False. + gap_before_final_norm (bool): Whether to globally average the feature + map before the final norm layer. In the official repo, it's only + used in classification task. Defaults to True. + init_cfg (dict, optional): The Config for initialization. + Defaults to None. + """ + arch_zoo = { + **dict.fromkeys(['t', 'tiny'], + {'base_dim': 64, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [dict(type='DW', kernel_size=7)] * 4}), + **dict.fromkeys(['t-gf', 'tiny-gf'], + {'base_dim': 64, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=14, w=8), + dict(type='GF', h=7, w=4)]}), + **dict.fromkeys(['s', 'small'], + {'base_dim': 96, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [dict(type='DW', kernel_size=7)] * 4}), + **dict.fromkeys(['s-gf', 'small-gf'], + {'base_dim': 96, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=14, w=8), + dict(type='GF', h=7, w=4)]}), + **dict.fromkeys(['b', 'base'], + {'base_dim': 128, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [dict(type='DW', kernel_size=7)] * 4}), + **dict.fromkeys(['b-gf', 'base-gf'], + {'base_dim': 128, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=14, w=8), + dict(type='GF', h=7, w=4)]}), + **dict.fromkeys(['b-gf384', 'base-gf384'], + {'base_dim': 128, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=24, w=12), + dict(type='GF', h=13, w=7)]}), + **dict.fromkeys(['l', 'large'], + {'base_dim': 192, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [dict(type='DW', kernel_size=7)] * 4}), + **dict.fromkeys(['l-gf', 'large-gf'], + {'base_dim': 192, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=14, w=8), + dict(type='GF', h=7, w=4)]}), + **dict.fromkeys(['l-gf384', 'large-gf384'], + {'base_dim': 192, + 'depths': [2, 3, 18, 2], + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=24, w=12), + dict(type='GF', h=13, w=7)]}), + } # yapf: disable + + def __init__(self, + arch='tiny', + in_channels=3, + drop_path_rate=0., + scale=1 / 3, + use_layer_scale=True, + out_indices=(3, ), + frozen_stages=-1, + with_cp=False, + gap_before_final_norm=True, + init_cfg=None): + super().__init__(init_cfg=init_cfg) + if fft is None: + raise RuntimeError( + 'Failed to import torch.fft. Please install "torch>=1.7".') + + if isinstance(arch, str): + arch = arch.lower() + assert arch in set(self.arch_zoo), \ + f'Arch {arch} is not in default archs {set(self.arch_zoo)}' + self.arch_settings = self.arch_zoo[arch] + else: + essential_keys = {'base_dim', 'depths', 'orders', 'dw_cfg'} + assert isinstance(arch, dict) and set(arch) == essential_keys, \ + f'Custom arch needs a dict with keys {essential_keys}' + self.arch_settings = arch + + self.scale = scale + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.with_cp = with_cp + self.gap_before_final_norm = gap_before_final_norm + + base_dim = self.arch_settings['base_dim'] + dims = list(map(lambda x: 2**x * base_dim, range(4))) + + self.downsample_layers = nn.ModuleList() + stem = nn.Sequential( + nn.Conv2d(in_channels, dims[0], kernel_size=4, stride=4), + HorNetLayerNorm(dims[0], eps=1e-6, data_format='channels_first')) + self.downsample_layers.append(stem) + for i in range(3): + downsample_layer = nn.Sequential( + HorNetLayerNorm( + dims[i], eps=1e-6, data_format='channels_first'), + nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2), + ) + self.downsample_layers.append(downsample_layer) + + total_depth = sum(self.arch_settings['depths']) + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, total_depth) + ] # stochastic depth decay rule + + cur_block_idx = 0 + self.stages = nn.ModuleList() + for i in range(4): + stage = nn.Sequential(*[ + HorNetBlock( + dim=dims[i], + order=self.arch_settings['orders'][i], + dw_cfg=self.arch_settings['dw_cfg'][i], + scale=self.scale, + drop_path_rate=dpr[cur_block_idx + j], + use_layer_scale=use_layer_scale) + for j in range(self.arch_settings['depths'][i]) + ]) + self.stages.append(stage) + cur_block_idx += self.arch_settings['depths'][i] + + if isinstance(out_indices, int): + out_indices = [out_indices] + assert isinstance(out_indices, Sequence), \ + f'"out_indices" must by a sequence or int, ' \ + f'get {type(out_indices)} instead.' + out_indices = list(out_indices) + for i, index in enumerate(out_indices): + if index < 0: + out_indices[i] = len(self.stages) + index + assert 0 <= out_indices[i] <= len(self.stages), \ + f'Invalid out_indices {index}.' + self.out_indices = out_indices + + norm_layer = partial( + HorNetLayerNorm, eps=1e-6, data_format='channels_first') + for i_layer in out_indices: + layer = norm_layer(dims[i_layer]) + layer_name = f'norm{i_layer}' + self.add_module(layer_name, layer) + + def train(self, mode=True): + super(HorNet, self).train(mode) + self._freeze_stages() + + def _freeze_stages(self): + for i in range(0, self.frozen_stages + 1): + # freeze patch embed + m = self.downsample_layers[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + # freeze blocks + m = self.stages[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + if i in self.out_indices: + # freeze norm + m = getattr(self, f'norm{i + 1}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def forward(self, x): + outs = [] + for i in range(4): + x = self.downsample_layers[i](x) + if self.with_cp: + x = checkpoint.checkpoint_sequential(self.stages[i], + len(self.stages[i]), x) + else: + x = self.stages[i](x) + if i in self.out_indices: + norm_layer = getattr(self, f'norm{i}') + if self.gap_before_final_norm: + gap = x.mean([-2, -1], keepdim=True) + outs.append(norm_layer(gap).flatten(1)) + else: + # The output of LayerNorm2d may be discontiguous, which + # may cause some problem in the downstream tasks + outs.append(norm_layer(x).contiguous()) + return tuple(outs) diff --git a/mmcls/models/utils/__init__.py b/mmcls/models/utils/__init__.py index 09d7273593c..05af4db9bcd 100644 --- a/mmcls/models/utils/__init__.py +++ b/mmcls/models/utils/__init__.py @@ -6,6 +6,7 @@ resize_relative_position_bias_table) from .helpers import is_tracing, to_2tuple, to_3tuple, to_4tuple, to_ntuple from .inverted_residual import InvertedResidual +from .layer_scale import LayerScale from .make_divisible import make_divisible from .position_encoding import ConditionalPositionEncoding from .se_layer import SELayer @@ -15,5 +16,5 @@ 'to_ntuple', 'to_2tuple', 'to_3tuple', 'to_4tuple', 'PatchEmbed', 'PatchMerging', 'HybridEmbed', 'Augments', 'ShiftWindowMSA', 'is_tracing', 'MultiheadAttention', 'ConditionalPositionEncoding', 'resize_pos_embed', - 'resize_relative_position_bias_table', 'WindowMSAV2' + 'resize_relative_position_bias_table', 'WindowMSAV2', 'LayerScale' ] diff --git a/mmcls/models/utils/layer_scale.py b/mmcls/models/utils/layer_scale.py new file mode 100644 index 00000000000..fbd89bc2f00 --- /dev/null +++ b/mmcls/models/utils/layer_scale.py @@ -0,0 +1,35 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn + + +class LayerScale(nn.Module): + """LayerScale layer. + + Args: + dim (int): Dimension of input features. + inplace (bool): inplace: can optionally do the + operation in-place. Default: ``False`` + data_format (str): The input data format, can be 'channels_last' + and 'channels_first', representing (B, C, H, W) and + (B, N, C) format data respectively. + """ + + def __init__(self, + dim: int, + inplace: bool = False, + data_format: str = 'channels_last'): + super().__init__() + assert data_format in ('channels_last', 'channels_first'), \ + "'data_format' could only be channels_last or channels_first." + self.inplace = inplace + self.data_format = data_format + self.weight = nn.Parameter(torch.ones(dim) * 1e-5) + + def forward(self, x): + if self.data_format == 'channels_first': + if self.inplace: + return x.mul_(self.weight.view(-1, 1, 1)) + else: + return x * self.weight.view(-1, 1, 1) + return x.mul_(self.weight) if self.inplace else x * self.weight diff --git a/model-index.yml b/model-index.yml index a48ab85a4cc..56c7dc9729a 100644 --- a/model-index.yml +++ b/model-index.yml @@ -31,3 +31,4 @@ Import: - configs/csra/metafile.yml - configs/mvit/metafile.yml - configs/efficientformer/metafile.yml + - configs/hornet/metafile.yml diff --git a/tests/test_models/test_backbones/test_efficientformer.py b/tests/test_models/test_backbones/test_efficientformer.py index 01d9daea4b8..88aad529c84 100644 --- a/tests/test_models/test_backbones/test_efficientformer.py +++ b/tests/test_models/test_backbones/test_efficientformer.py @@ -8,52 +8,10 @@ from mmcls.models.backbones import EfficientFormer from mmcls.models.backbones.efficientformer import (AttentionWithBias, Flat, - LayerScale, Meta3D, Meta4D) + Meta3D, Meta4D) from mmcls.models.backbones.poolformer import Pooling -class TestLayerScale(TestCase): - - def test_init(self): - with self.assertRaisesRegex(AssertionError, "'data_format' could"): - cfg = dict( - dim=10, - inplace=False, - data_format='BNC', - ) - LayerScale(**cfg) - - cfg = dict(dim=10) - ls = LayerScale(**cfg) - assert torch.equal(ls.weight, - torch.ones(10, requires_grad=True) * 1e-5) - - def forward(self): - # Test channels_last - cfg = dict(dim=256, inplace=False, data_format='channels_last') - ls_channels_last = LayerScale(**cfg) - x = torch.randn((4, 49, 256)) - out = ls_channels_last(x) - self.assertEqual(tuple(out.size()), (4, 49, 256)) - assert torch.equal(x * 1e-5, out) - - # Test channels_first - cfg = dict(dim=256, inplace=False, data_format='channels_first') - ls_channels_first = LayerScale(**cfg) - x = torch.randn((4, 256, 7, 7)) - out = ls_channels_first(x) - self.assertEqual(tuple(out.size()), (4, 256, 7, 7)) - assert torch.equal(x * 1e-5, out) - - # Test inplace True - cfg = dict(dim=256, inplace=True, data_format='channels_first') - ls_channels_first = LayerScale(**cfg) - x = torch.randn((4, 256, 7, 7)) - out = ls_channels_first(x) - self.assertEqual(tuple(out.size()), (4, 256, 7, 7)) - self.assertIs(x, out) - - class TestEfficientFormer(TestCase): def setUp(self): diff --git a/tests/test_models/test_backbones/test_hornet.py b/tests/test_models/test_backbones/test_hornet.py new file mode 100644 index 00000000000..5fdd84b34c3 --- /dev/null +++ b/tests/test_models/test_backbones/test_hornet.py @@ -0,0 +1,174 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math +from copy import deepcopy +from itertools import chain +from unittest import TestCase + +import pytest +import torch +from mmcv.utils import digit_version +from mmcv.utils.parrots_wrapper import _BatchNorm +from torch import nn + +from mmcls.models.backbones import HorNet + + +def check_norm_state(modules, train_state): + """Check if norm layer is in correct train state.""" + for mod in modules: + if isinstance(mod, _BatchNorm): + if mod.training != train_state: + return False + return True + + +@pytest.mark.skipif( + digit_version(torch.__version__) < digit_version('1.7.0'), + reason='torch.fft is not available before 1.7.0') +class TestHorNet(TestCase): + + def setUp(self): + self.cfg = dict( + arch='t', drop_path_rate=0.1, gap_before_final_norm=False) + + def test_arch(self): + # Test invalid default arch + with self.assertRaisesRegex(AssertionError, 'not in default archs'): + cfg = deepcopy(self.cfg) + cfg['arch'] = 'unknown' + HorNet(**cfg) + + # Test invalid custom arch + with self.assertRaisesRegex(AssertionError, 'Custom arch needs'): + cfg = deepcopy(self.cfg) + cfg['arch'] = { + 'depths': [1, 1, 1, 1], + 'orders': [1, 1, 1, 1], + } + HorNet(**cfg) + + # Test custom arch + cfg = deepcopy(self.cfg) + base_dim = 64 + depths = [2, 3, 18, 2] + embed_dims = [base_dim, base_dim * 2, base_dim * 4, base_dim * 8] + cfg['arch'] = { + 'base_dim': + base_dim, + 'depths': + depths, + 'orders': [2, 3, 4, 5], + 'dw_cfg': [ + dict(type='DW', kernel_size=7), + dict(type='DW', kernel_size=7), + dict(type='GF', h=14, w=8), + dict(type='GF', h=7, w=4) + ], + } + model = HorNet(**cfg) + + for i in range(len(depths)): + stage = model.stages[i] + self.assertEqual(stage[-1].out_channels, embed_dims[i]) + self.assertEqual(len(stage), depths[i]) + + def test_init_weights(self): + # test weight init cfg + cfg = deepcopy(self.cfg) + cfg['init_cfg'] = [ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ] + model = HorNet(**cfg) + ori_weight = model.downsample_layers[0][0].weight.clone().detach() + + model.init_weights() + initialized_weight = model.downsample_layers[0][0].weight + self.assertFalse(torch.allclose(ori_weight, initialized_weight)) + + def test_forward(self): + imgs = torch.randn(3, 3, 224, 224) + + cfg = deepcopy(self.cfg) + model = HorNet(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + self.assertEqual(feat.shape, (3, 512, 7, 7)) + + # test multiple output indices + cfg = deepcopy(self.cfg) + cfg['out_indices'] = (0, 1, 2, 3) + model = HorNet(**cfg) + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 4) + for emb_size, stride, out in zip([64, 128, 256, 512], [1, 2, 4, 8], + outs): + self.assertEqual(out.shape, + (3, emb_size, 56 // stride, 56 // stride)) + + # test with dynamic input shape + imgs1 = torch.randn(3, 3, 224, 224) + imgs2 = torch.randn(3, 3, 256, 256) + imgs3 = torch.randn(3, 3, 256, 309) + cfg = deepcopy(self.cfg) + model = HorNet(**cfg) + for imgs in [imgs1, imgs2, imgs3]: + outs = model(imgs) + self.assertIsInstance(outs, tuple) + self.assertEqual(len(outs), 1) + feat = outs[-1] + expect_feat_shape = (math.floor(imgs.shape[2] / 32), + math.floor(imgs.shape[3] / 32)) + self.assertEqual(feat.shape, (3, 512, *expect_feat_shape)) + + def test_structure(self): + # test drop_path_rate decay + cfg = deepcopy(self.cfg) + cfg['drop_path_rate'] = 0.2 + model = HorNet(**cfg) + depths = model.arch_settings['depths'] + stages = model.stages + blocks = chain(*[stage for stage in stages]) + total_depth = sum(depths) + dpr = [ + x.item() + for x in torch.linspace(0, cfg['drop_path_rate'], total_depth) + ] + for i, (block, expect_prob) in enumerate(zip(blocks, dpr)): + if expect_prob == 0: + assert isinstance(block.drop_path, nn.Identity) + else: + self.assertAlmostEqual(block.drop_path.drop_prob, expect_prob) + + # test VAN with first stage frozen. + cfg = deepcopy(self.cfg) + frozen_stages = 0 + cfg['frozen_stages'] = frozen_stages + cfg['out_indices'] = (0, 1, 2, 3) + model = HorNet(**cfg) + model.init_weights() + model.train() + + # the patch_embed and first stage should not require grad. + for i in range(frozen_stages + 1): + down = model.downsample_layers[i] + for param in down.parameters(): + self.assertFalse(param.requires_grad) + blocks = model.stages[i] + for param in blocks.parameters(): + self.assertFalse(param.requires_grad) + + # the second stage should require grad. + for i in range(frozen_stages + 1, 4): + down = model.downsample_layers[i] + for param in down.parameters(): + self.assertTrue(param.requires_grad) + blocks = model.stages[i] + for param in blocks.parameters(): + self.assertTrue(param.requires_grad) diff --git a/tests/test_models/test_utils/test_layer_scale.py b/tests/test_models/test_utils/test_layer_scale.py new file mode 100644 index 00000000000..824be998844 --- /dev/null +++ b/tests/test_models/test_utils/test_layer_scale.py @@ -0,0 +1,48 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from unittest import TestCase + +import torch + +from mmcls.models.utils import LayerScale + + +class TestLayerScale(TestCase): + + def test_init(self): + with self.assertRaisesRegex(AssertionError, "'data_format' could"): + cfg = dict( + dim=10, + inplace=False, + data_format='BNC', + ) + LayerScale(**cfg) + + cfg = dict(dim=10) + ls = LayerScale(**cfg) + assert torch.equal(ls.weight, + torch.ones(10, requires_grad=True) * 1e-5) + + def forward(self): + # Test channels_last + cfg = dict(dim=256, inplace=False, data_format='channels_last') + ls_channels_last = LayerScale(**cfg) + x = torch.randn((4, 49, 256)) + out = ls_channels_last(x) + self.assertEqual(tuple(out.size()), (4, 49, 256)) + assert torch.equal(x * 1e-5, out) + + # Test channels_first + cfg = dict(dim=256, inplace=False, data_format='channels_first') + ls_channels_first = LayerScale(**cfg) + x = torch.randn((4, 256, 7, 7)) + out = ls_channels_first(x) + self.assertEqual(tuple(out.size()), (4, 256, 7, 7)) + assert torch.equal(x * 1e-5, out) + + # Test inplace True + cfg = dict(dim=256, inplace=True, data_format='channels_first') + ls_channels_first = LayerScale(**cfg) + x = torch.randn((4, 256, 7, 7)) + out = ls_channels_first(x) + self.assertEqual(tuple(out.size()), (4, 256, 7, 7)) + self.assertIs(x, out) diff --git a/tools/convert_models/hornet2mmcls.py b/tools/convert_models/hornet2mmcls.py new file mode 100644 index 00000000000..6f39ffb2ec0 --- /dev/null +++ b/tools/convert_models/hornet2mmcls.py @@ -0,0 +1,61 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os.path as osp +from collections import OrderedDict + +import mmcv +import torch +from mmcv.runner import CheckpointLoader + + +def convert_hornet(ckpt): + + new_ckpt = OrderedDict() + + for k, v in list(ckpt.items()): + new_v = v + if k.startswith('head'): + new_k = k.replace('head.', 'head.fc.') + new_ckpt[new_k] = new_v + continue + elif k.startswith('norm'): + new_k = k.replace('norm.', 'norm3.') + elif 'gnconv.pws' in k: + new_k = k.replace('gnconv.pws', 'gnconv.projs') + elif 'gamma1' in k: + new_k = k.replace('gamma1', 'gamma1.weight') + elif 'gamma2' in k: + new_k = k.replace('gamma2', 'gamma2.weight') + else: + new_k = k + + if not new_k.startswith('head'): + new_k = 'backbone.' + new_k + new_ckpt[new_k] = new_v + return new_ckpt + + +def main(): + parser = argparse.ArgumentParser( + description='Convert keys in pretrained van models to mmcls style.') + parser.add_argument('src', help='src model path or url') + # The dst path must be a full path of the new checkpoint. + parser.add_argument('dst', help='save path') + args = parser.parse_args() + + checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu') + + if 'model' in checkpoint: + state_dict = checkpoint['model'] + else: + state_dict = checkpoint + + weight = convert_hornet(state_dict) + mmcv.mkdir_or_exist(osp.dirname(args.dst)) + torch.save(weight, args.dst) + + print('Done!!') + + +if __name__ == '__main__': + main() From 4d73607fb821da8640f93b2762d3b52c55329fe8 Mon Sep 17 00:00:00 2001 From: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> Date: Wed, 28 Sep 2022 08:17:26 +0800 Subject: [PATCH 16/25] [Fix] Fix config.device bug in toturial. (#1059) --- .../tutorials/MMClassification_python.ipynb | 1491 +++++++++-------- .../MMClassification_python_cn.ipynb | 1469 ++++++++-------- 2 files changed, 1483 insertions(+), 1477 deletions(-) diff --git a/docs/en/tutorials/MMClassification_python.ipynb b/docs/en/tutorials/MMClassification_python.ipynb index d0acfa58237..e0466665ebc 100755 --- a/docs/en/tutorials/MMClassification_python.ipynb +++ b/docs/en/tutorials/MMClassification_python.ipynb @@ -1,557 +1,183 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "MMClassification_python.ipynb", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XjQxmm04iTx4" + }, + "source": [ + "\"Open" + ] }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "UdMfIsMpiODD" + }, + "source": [ + "# MMClassification Python API tutorial on Colab\n", + "\n", + "In this tutorial, we will introduce the following content:\n", + "\n", + "* How to install MMCls\n", + "* Inference a model with Python API\n", + "* Fine-tune a model with Python API" + ] }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + { + "cell_type": "markdown", + "metadata": { + "id": "iOl0X9UEiRvE" }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" + "source": [ + "## Install MMClassification\n", + "\n", + "Before using MMClassification, we need to prepare the environment with the following steps:\n", + "\n", + "1. Install Python, CUDA, C/C++ compiler and git\n", + "2. Install PyTorch (CUDA version)\n", + "3. Install mmcv\n", + "4. 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"grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "XjQxmm04iTx4" + "id": "DMw7QwvpiiUO", + "outputId": "33fa5eb8-d083-4a1f-d094-ab0f59e2818e" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Mon_Oct_12_20:09:46_PDT_2020\n", + "Cuda compilation tools, release 11.1, V11.1.105\n", + "Build cuda_11.1.TC455_06.29190527_0\n" + ] + } + ], "source": [ - "\"Open" + "# Check nvcc version\n", + "!nvcc -V" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { - "id": "UdMfIsMpiODD" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VIBU7Fain4D", + "outputId": "ec20652d-ca24-4b82-b407-e90354d728f8" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ], "source": [ - "# MMClassification Python API tutorial on Colab\n", - "\n", - "In this tutorial, we will introduce the following content:\n", - "\n", - "* How to install MMCls\n", - "* Inference a model with Python API\n", - "* Fine-tune a model with Python API" + "# Check GCC version\n", + "!gcc --version" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { - "id": "iOl0X9UEiRvE" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24lDLCqFisZ9", + "outputId": "30ec9a1c-cdb3-436c-cdc8-f2a22afe254f" }, - "source": [ - "## Install MMClassification\n", - "\n", - "Before using MMClassification, we need to prepare the environment with the following steps:\n", - "\n", - "1. Install Python, CUDA, C/C++ compiler and git\n", - "2. Install PyTorch (CUDA version)\n", - "3. Install mmcv\n", - "4. Clone mmcls source code from GitHub and install it\n", - "\n", - "Because this tutorial is on Google Colab, and the basic environment has been completed, we can skip the first two steps." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_i7cjqS_LtoP" - }, - "source": [ - "### Check environment" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c6MbAw10iUJI", - "outputId": "dd37cdf5-7bcf-4a03-f5b5-4b17c3ca16de" - }, - "source": [ - "%cd /content" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4IyFL3MaiYRu", - "outputId": "5008efdf-0356-4d93-ba9d-e51787036213" - }, - "source": [ - "!pwd" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", - "text": [ - "/content\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DMw7QwvpiiUO", - "outputId": "33fa5eb8-d083-4a1f-d094-ab0f59e2818e" - }, - "source": [ - "# Check nvcc version\n", - "!nvcc -V" - ], - "execution_count": null, - "outputs": [ - { "output_type": "stream", - "name": "stdout", "text": [ - "nvcc: NVIDIA (R) Cuda compiler driver\n", - "Copyright (c) 2005-2020 NVIDIA Corporation\n", - "Built on Mon_Oct_12_20:09:46_PDT_2020\n", - "Cuda compilation tools, release 11.1, V11.1.105\n", - "Build cuda_11.1.TC455_06.29190527_0\n" + "1.9.0+cu111\n", + "True\n" ] } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4VIBU7Fain4D", - "outputId": "ec20652d-ca24-4b82-b407-e90354d728f8" - }, - "source": [ - "# Check GCC version\n", - "!gcc --version" ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", - "Copyright (C) 2017 Free Software Foundation, Inc.\n", - "This is free software; see the source for copying conditions. There is NO\n", - "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "24lDLCqFisZ9", - "outputId": "30ec9a1c-cdb3-436c-cdc8-f2a22afe254f" - }, "source": [ "# Check PyTorch installation\n", "import torch, torchvision\n", "print(torch.__version__)\n", "print(torch.cuda.is_available())" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1.9.0+cu111\n", - "True\n" - ] - } ] }, { @@ -573,6 +199,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -580,16 +207,10 @@ "id": "nla40LrLi7oo", "outputId": "162bf14d-0d3e-4540-e85e-a46084a786b1" }, - "source": [ - "# Install mmcv\n", - "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", - "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.9.0/index.html" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", "Collecting mmcv\n", @@ -614,6 +235,11 @@ "Successfully installed addict-2.4.0 mmcv-1.3.15 yapf-0.31.0\n" ] } + ], + "source": [ + "# Install mmcv\n", + "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.9.0/index.html" ] }, { @@ -629,6 +255,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -636,19 +263,10 @@ "id": "Bwme6tWHjl5s", "outputId": "eae20624-4695-4cd9-c3e5-9c59596d150a" }, - "source": [ - "# Clone mmcls repository\n", - "!git clone https://github.com/open-mmlab/mmclassification.git\n", - "%cd mmclassification/\n", - "\n", - "# Install MMClassification from source\n", - "!pip install -e . " - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mmclassification'...\n", "remote: Enumerating objects: 4152, done.\u001b[K\n", @@ -659,10 +277,19 @@ "Resolving deltas: 100% (2524/2524), done.\n" ] } + ], + "source": [ + "# Clone mmcls repository\n", + "!git clone https://github.com/open-mmlab/mmclassification.git\n", + "%cd mmclassification/\n", + "\n", + "# Install MMClassification from source\n", + "!pip install -e . " ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -670,20 +297,19 @@ "id": "hFg_oSG4j3zB", "outputId": "05a91f9b-d41c-4ae7-d4fe-c30a30d3f639" }, - "source": [ - "# Check MMClassification installation\n", - "import mmcls\n", - "print(mmcls.__version__)" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "0.16.0\n" ] } + ], + "source": [ + "# Check MMClassification installation\n", + "import mmcls\n", + "print(mmcls.__version__)" ] }, { @@ -707,6 +333,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -714,15 +341,10 @@ "id": "nDQchz8CkJaT", "outputId": "9805bd7d-cc2a-4269-b43d-257412f1df93" }, - "source": [ - "# Get the demo image\n", - "!wget https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0 -O demo/banana.png" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "--2021-10-21 03:52:36-- https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0\n", "Resolving www.dropbox.com (www.dropbox.com)... 162.125.3.18, 2620:100:601b:18::a27d:812\n", @@ -746,10 +368,15 @@ "\n" ] } + ], + "source": [ + "# Get the demo image\n", + "!wget https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0 -O demo/banana.png" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -758,23 +385,22 @@ "id": "o2eiitWnkQq_", "outputId": "192b3ebb-202b-4d6e-e178-561223024318" }, - "source": [ - "from PIL import Image\n", - "Image.open('demo/banana.png')" - ], - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { - "image/png": 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\n", + "image/png": 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MFFOyICVCcJhOBKKMiiYgkln3nRwNhj2uy9GG5FV0V4XoyV7TI1hLsN5d9aAcJdBohI4fUJEo8cjyQ7T7KcESrO4Zuvj3zsEODLVsjd4s9sUQ7ZD0RK4riCd5QkBKPeDgEVvsn20PjiSlRTF4h0iblQdgS4lSiiVd3SClrRSHwTHYs7Tjs3NO3NYb+213WDVRe4UY+eabXyKExF4NEdGuqIBWq9iSGJQnRCREunZSE+V0ufD4cLZyneCnnEDwikCtCdV7t8Uv0BS7SdipiogfIkJyPK73Tm92KuecraEshtWXfQd2YkxcLraBRsas3TZFUGVbCylF8pStwijVm1QDk7asI4aAvb3QarfDIliTPE/5gK9G1imOByPdMwE9fgaBnO4N3JgseIjo0f/IKaHz7LCckFOE3imt0aIHMAnMy3LAQXursFvVlLycXbwR3Honhng08EeFE0SovVP2nSlntFuJPuXMw/lrw8CxzZUGxKFq2LZ1rO0o8GavOJ7uy4A9FLrafUr+swNuatI9g4yePacDP48pEJPjrr2z6RXJsx2Yas3SGDNkC7ABKLXy8+sIQGBZFqYpH/cp5EjYE1vZkdoc/rSAFIJQHV+tQQkhkaM1/hWFZLj2vu2U3nn37on3H54QojWmu9Kb0ppt5Bytgg05MCcjKoyDR1WptbHVxqNDDejI5i3wiDXziIED1+5YFaytoTFRW/UDG1QS8zyh1Rq+PUAekK9nobU1ylb9uYI2g/xEO9agAggksYrj9dOLPed3iRDtugnBoJdgcIgkIXRQPzRQmOYJkUBrlek8M+XZ+ndxZnpDWlDttGaJWM4TMSeDN5sdfskDfQiGIKQUDb4Odi/avntVInZ4q5L0nkQ0VWKAlCPnMFt/NFhAD8GCuTWMxdZvs30bnEgzDqaRnOY3GTxdaMGIBuIxIvh7iUPuMSXL8H2/xRAJUVANSPakqXU0KAQhBEtMrbKLoA26oSv9aOPIcT2DpDPy7fFKiCV5qkhXgxa979F6h97Z6+5wl1dER2/rXuXv206I0Ei2T7q1FdI0HfA6CLtWJzp4TxKgG4yGKq+vL6Tzsjie75miw09TyqQ82EnqG8C+YK2NEOWAMMRPRxll7ujuK2grhDkflUjv/cgoxyYZmfvYZCklW+i3m512tdN6Z8qZIvbz67ozLzPRM4au40oM20/JN1mrRM8qx7MYgX2aJgZdorZGrZXr9Xo/uEI4mFIhWqAZ2fxokneHz3rvlHWltEYKgdPDA4I12NU/+3w+27X4qhjQWGuNIJ6hOcsghHAE3BQj5My+74QYWW9XNoX3X35pC0siGo1ooKqErvQQrEfUoQdjNIgKOWYIRlwTUTITt+2Vqta3STl7o8oyIBzuKL15tmx9lQFPgFBrNShyiUiEvTb2fSVOiVgTeyho71yvV87nM2m6N/p+v9c4QFXxewOosq+7B4FA2VZv5nfLsrRBM2bc2AQpeFZGp7ZCr5VdApMkuozGojXrWhdySBbMwx2y7KgnDxZo9loP7HoElYHj23LqR0CwnlQ3OHFg/r7egx8y3dlhju1ZhRBtu2mz75+SIJPdl15sjWkQrtuN3BtTnq0n1IVlnvnqq68OzH8wYoxZZNskSDDoNIxr9XWIJTzx4cyyzICtncvpRIjZiQGW/cKCKsTRcI+2fgMe/Lp6cA2U3okoSayiCctyMGmInVYLrfXj2ccoft3RkiyxIDd6BaMCGgE2J2PZAajHn+N7w8GEExGoFbodNsch7s8sJgFJR9VSq/UpoyTvZVvFBtZ8ziGhYveoNIgSPR4o2Q/FgKDxDr+9va7m6xSPTm2UA77WXl9fOJ/O9ll+sMx9ggyESFlXQJlSZrCtFEvMY57IS0Zro2k70AEVS4p7qwbqxUjtBk927HAKCtu+8Xp9JUkXz+67n07GXqqt+QkZjv7EnUEUrEkWBh9N0FZBHL8PnZwiGgOtW+9iPEztdhOW0+loZo1sedwYgFo2vv/4iV/5lV9h34stxpSQnHn59luWU2VK8WBbaL8fEjFYf2LbN1pTLueTMXK0H80o8QfUS/HKwk7SEK0RPlhd6lnkwZDqG9M0ee/CKGaMrCAlYu+G83sGUEuxDN4bgOpZ54C2DJYzVljO4aDdNe+bWHZj5fy+7zy9e0fZi7W7/cDtfv2JADFQuxJ6h2gHbCCQstEy991KyhDjEQiv2856faH2ztPTOx4fLsySkSWyroVGJ2C9qEkM52zqkFspTFNmztkxVHvOp9NC8sDZamPdC9O2Ukr5hQ4J8MZjN4qgJSGdecrUHdZ15SfffcuHp3cs59OxjohWJY5g07VRtdG7sdB6MvaTiNgB6usq+j2049zgq7H2W7dGaIwBrY2H08myYg+gloU1S448WZFgScVtL2zryod374n+LFElh4jFRLEGtdzfK/halJGCeg9hrEERIeVMqZVeO2tpxJyIGqiqRAmcl5l13djWnSTWI1BsjQ4aOaiTFcKxBzpKfrhwmpcjgZnyhOTMnCeSRAjWYLe9Zz3IFAzKQpxG6dBbBLoI0aGyEISUJnIU77VaDzCmmaBWURs19V4xjAR0HLx3XN8+f3K4tjkaEKJwu66Gu8dIK8bcHL/bWnN6rByBNcVIygEJ3gQPd+ZaFEFptK7kmAle+YPBTN0roCkZYrHvzkpylmhXX1VvvsfBeDoSTe9BqvehRFguF77wJKX37msUtnUj5MgkVs0LQrNsmxpsLYVkkHDcxp6we7W3SiJ5cqJoq0iMiOrRqFbtNE/Wf/Dl16Te+9H86ePi5plt349D4bOXY8KibzImEfamBGm2kVTZSyWnRFwWyr6TspeGAqgcVLWDAuhle/XsOqWJy/lMb40ppwPmuTg9rtXCVgrLshzZgPaxscWDMNSyU9vENE9HVtdaG2Ga1jzjCIFt3YkhkdP9sAohUJs1n3volFqZ59ngBV+0XZV5Muy3O8e8FGsGl1rZ1pV5njmdTgwNScrJMnU17UnzhTAyDCslFQnxWGDzYht3nqfjgFBVaim2gFIituAHTiZJoGfL/lWNyQSQnQEh3eCkOWWYTmytEETY9sLz9sqjPqF0gkRELDmwzDCDVmO0CAf7pamibafWxum0sLeGlpU8XSjF9Cf3eu7nv86XMz/4+mt++KMfcXu5Yedvgmjsi4fLA8vZmnRMmX3fXN8ix6EiDGamYckldIPBmmVUg5F2u61c5gUm78WM3kKwzwxq+pIafKPja+xI/GyzjnWjXhFOMZNOVhmLWI8miUMlop4Kd3vWg6EXgwfMdlA7x1pUGjHYZ+eUmXLj+fkTAKflRNXGHCIyZZbzibbCx0+fmOeZd09PR4Ay2Mx7HDG6TkoorRJximmwClZCIIVkxIxpclgaVDqBTKIanBYE7XcqKs2g3JwS3fU+A+I9EiThoA4nJrLg72UQy1tdwcFaxAI3QRAiMdgVJOmEEE1vMs/WjxGrynPXo9JI3nMZdNSUM6dlAcFYiDFbfw5jbVlSKUg3VEJRZ0p55S4mtsgOK9r6sGerYJXhQFr8d8ZeH6QCg4Q7te5GdMmZqMpyPtFr47atlFqppR19GFolJaMsN20QAlOI7NvOtq2GVJzPzDmxbSvX1yspBx4eHi0O5IgmO6SaVxVlK4jCcj6hpaLRemr02ggpMomw+uGQp+xwhfUgxpdPIjQ1HDckR2JFSE5VHSCbdmXbNvI0gQu1Suks80QYpSAOE+nbKiUeYrand+8wFs2GSOR0shPwcrmwrptlXmPRd6PCqZ1jni0Epnk+WE+j5xBDoLbOXqzzL+EerI2UYAsgiVDeLMxpmphyfpONRFKMVP8eBhPZ7+Zk13J7vfLy8on3794fGzJP2Wh/rTn9NRCSCelatyw2RhOzjYXWdbBMPt80YFTU+WRof3WmA34fomON9I6kRNZOb9U2cgoEDSwxsTw8oGrsqOfnZ7btxlJm5sWYELV6U5/mWYlhotO82Gks4nwRWxLaoJfO8+1q8OFyYlvX4/D8RV4pJR6f3vHy8sp2MTHhuhd7rsvM6bwYfu4Mp3XbySkdTDH1rL43tT5a6CRr23vGbYya9Xbj+nplXhZOMdoGb+pVZ0ebNUWrtuNUsObtvdIMTku26+kEsQqk1so8T9zWjVqtitEpWnbq7LHmiENMyTZscWjADx7rBdoznb0CVGc6PZxOBvO4gCzGaHRWsQx3niek8xnEEWJgva3c1psJIF0AZnCT9fCmPFnPIEVyiKiKJTbWibXv6mywpoGgltBEF/UZKc8q6PEsjqRTHcdPFtAtWFq7P6T4Rq8kx4HQWjMYOQTSlI/+BmAMx95Jeba9L8I0KOfVnrs2+13UhITVEyx1yrut4SEQdU0I3lju3SG/uzgY8X15kCccTcD6pYqxGa2qhKRQBmzvz+ltZSEi9NCptaAeb4aGQoISEGot7G1HW2M6zRgl2H4yKGg3mnbOE2cnSsQpOXtRWE4z8zSBCoqJVox9FpinhQCkkIwB2RqfXl9poiRr90FSW5Qp2Mly0NByQkUJtZJECJL4eH1lW2+keeHx8cHhGWPUhGQshxA6ezEsep5MPW2BzzZpc673wPfhzoA6egjjxku0Q6j3Qx19WuZDuNdb8yDtWKjfuiF0k+OBW8C3nkX0stFKveQPbS+VcnvmdDpzOp9s8cdotDM//Uutd7YW0JupQYfCeQBfXTuXhwvzshyNu2mayDEdfZge7KCVbtTTKNYDUh3NdHGWhanWU84HldJUx7DMM92DtMFTTs0zigWgaARp9juW+tp/dzkNGUGjZVg5RE7z6WjeG0XPmsPSBaEZ+613wLJdeqchJFWSZFSMLrhtm6nF952pTNxuK09Pd/bJz3x51RS9gd0dRurNnh/q2h6HLc7LfMB122bVYvR1k3NCW6NqJYdIEoHYCQ3XbwhTtqDVdTQxbQMN2irN2HptHAZOh7R7lEkxIZ6NWifH1PAxJlKsCMbvb310t0w1Li7kGwdBVSVLPKDqwSZSuolInYnW/Nmc55mWEnurTkiwbLdVO5DneTLlrXIkflGE87zc9RweuB4v1jdDcbZbRIN9L7hDJUP7MJ5TlEBwcaKqa08cZRh6hjvIhWlP5K7HCk4NuvdqDKox6DiTUCbrBJOT9Sq6r15Vu3dW2QaidG//GPSkVdGUTHPS7JDKclw6IP4cXITYlZD9kFJxkTF3jdHoMyguPDb6t8USO4wqto4ihhIoSqIZPA2fwU4jVokI58vFCT+mbWimpuR8vgBCfn3huq5GdZehLjdtTQmmF2o067dGMV1NqcwxI8tCadY/RMOxb5PH5V6rISI5U0rh4eFCB1IX8SxAqHVHxaTee6tUGqma/F9iQNShpH3nuu08Omd5NL5NJNbog5oGBxsHlOTNleDQRGumyLQMFdv0R6bm9DKE8/l0NPgAWi3sLocfi7v3Qq3FP8MWyaCqjswf7BpHXDHoZ/DRrSIq643btrEsJ0zU04xO+qZUHBDbeI9SGj/+4e/w7t07vvj6K/Ztp/ZG2SuXy5lpcgl8tABWHXsEq+KsErKDM/s9jTGiLvyRiDfz2wEzjewvSqB2u0Z1YRZYD6iPDSSjYdUNTxZTeAaNILaxREwfsO2VmCdO2Q6yXgoi04GTjo3cu0Wcrp2g4TgUt2pK1rZ3cg6cTiagK90Oitv1xsvLK4+PlyNT/FmvkBLZA9mgJI6mZNN24OiqJggbOKMx8gJrKfRSmNOD0V23QovK6WQwZcqRBxGuYVQD4sHQsshRHaC4PqPYgS0W9HI2PUWQCCi16rH2UkqkbIlIzumAOUTfUncHpCsW2ERIKRw9kNHjCDG4a4BDGKpo8P6ZCrd19UNopgBl36BXCCYYnWL05qmvfg8MyXU11luakClxva3kaWIZ1UdvdlhGjr1pZ/EbaIk7gQXFdAdeNGj0JFCC63Zci0OnihgJRo3wkFNAgt3L2A2zTxIsyfHMWSR6I9v2Tc6JpsGtKAKlWkUfvacVAv58oIXqNjIQ/dCptVp1HxKtKQRlfpNtDoErzjwcFGL8u+poY417i0ODokaFbS5Ow6BPO7DvgsSRvL6Fmu2WG11/9MgeHw0WnJL1D32loq2zq0ItTtUFiUJp3Xtxhgr12hwxMaaWBzQTT7fG3it0Yz2KAMEqxBRQWu+UVlm33U5ub0IO7UFpDdFOCFbmPT09kWcT1Rz6CcExf4MsJFuDB1cX1tYIrRGHyMiDwKDFjhvU3UrCmjVKSv4Q/X1arcZv7405JVo1Gl5MCWn3UtCqDznsOVo3LUQ7msWjGeQHkkNdT+/f8+AVw1CED0hslKCj6R0cg0w5QYzWE3C8uWw7ilJcGV5qJ3TzScqj8R2CWY40y/innDzQFLoTCkIQL78BDY5NeC9HhKLOypgmK9cB1UCnOZyFfzcjFaBKyAYBNjWBpKQI3ZgVrVVSNuWp+IJtqkR1+wO3BRHpIEaPRewzgt/vFIIlFhKNlWMniOHg+8627VzOp0NM+bNeMUamZOK6h8dHWmus/sxTjHQR2yDjUAlC78a2ad2sM0oyb7LelCUbfCYdO+TENynBoRGBNlDT7n5BgwJrf59SRD37jCESpjutkUEh90NAvboVZwVZPNFDW9G9ATqe53AQGLipqB5iv+R7YFfbF/RK26tpILI134m24bfban2sKXGeZiQ6scChQenG4HoLedRa2dcNFhOgRocVtavpmQb99Ahod7uJOxowMmUPkq4zSP79hyeYCJZpx8qw7ZBQ3QlB/L6oPw49LCuis/MEYV2NjDJlE8oO5lVInRCTZfyCOQc4bASBFHBRXUCk8nq7mrNDVmrrTMt0PM9xb1D1/pZXHaoHaxEdVFYL5hKcPBEEulfo2ghqgky5L9fPNFL3pCkgoY+8FLPisUQ7pIjEaKiNkxkanb7t7Fs59DUaTHsyLROhKbUY5dqQm8ZeICVT229lNdYYgdt6QwSW5UwPHaypn9y3BqYYicnsMlqtEBw6UgCh1B1VIeTM+XyhNbOcQO/4bfdDBjgyfWtEJwbVbKgwYZTMQ9lrN2rfh+GeZd4jyLdm/iQpJ7JYL2HdCyJwWqaDWithYOZuctWs/Eo507ftoKAOM0Lt/a5qZjCZ6iGyesuqsOZRsZ6DQ1Q5Jb755htSSiYIwhlV3bxmeu+cTjOofcfmwqwwWZ4QHQJTZ0FIMN3HYJf15sEqRqJCCkJVUzKP37+cT8QQ2PZq/lZvKp1g8ZAIB6vJgRtah9Bc05EyYL4uRbo3s4Q5W4amOhgiVqr6bXOIobPX5o3lyOTfbfydJVlCce8h86/5+YfEkamGQAfm8wJYZUbrB7tO/L2MtttJ04TUikgjqrGOijYTG3arIA1VsUagBN6sH6PHgjUdjdpqmPABE3gCoypmfTIybPewuh8w3r/onWEaKYzKNfj9e/OFFdatkGM31XIIiFuOIEZzzt7r7r0dnkpJTK8wBJPLMtFbZ5qNoqm9oMEqTzOFDKQ38NH1euX55Zlv5Bu+uXwD6uZ+IVnS4N8tffbMPLHrw+jPVLtjzVVHO1N4myWHo2+n2tm7kV1SMlM9BltR7dn0bnoJkWTBPtwx/GmaqK2608Ew+bSGvrNi/LT3prqOaxh2FTuqgdP5bCZ3yLG+7VFYPKi1gTq7yKucUdGOmGUJRqfUdpiAhuA+WbXTesUdi4x0EINX+xwoB9xhqJzzm/9uqUWHu20OFqeSBF72ld46p/PZDgNnKdHufk4jflU6dKsuhWQtBDdCbNL49OkTl/OZdAn0DpoCScXk2d0z7AP/9KZnaY2qhRTspOmifjgE8Cw2RNi21Rq7KVH2nbLvRwMxiDfpPONPwah6dqH329CcRTRPRu9rHaOF+kKOIRAmyxCtWciRdXQdknJTQ7ZeEW8ugwXS9fXV4JY8Hb2NUe4bf/x+cA2LC3XMmRCOhvDwoOq9OVVOj7+rZWfdyhG0d1X22w2dZubTcmQkVs3YtRs+bu9T/Z6IQwDDX2pdNx7OZz+ALUMam7O1xlaqPehaTE2MElMmwiGWCtkM04wLXa3k96wOx4jLeuN53zhfFub5RGvVLByaspZCnObD20oEGqZNkBgprRPj6Cnd3VZVgabsZaPWhb3sZtQ2T/y818DpBzRSigm+eq1srbCN/pA646Yb5/4tKy96/6LsGyEnpmk+nDdBPMOygNIV0OYWKHEYANuzrY15nmlqFWKKE3FAgjJgAqNTuiuZVVpYQLT3dgxa5CAQjKBjMCacnR7etVP24nRToQVlkmgnF9AlEByGlBCdBu4sNMXWlAREO7s2skZ6UKD5Ur+7la7bZvonTwZqa+z7zilGmijZ7//vxtHHP1uPL6G5uX+UkLyhKw7FWbKVjn6DVSbxSNbE+wO2rg3iCiJOXXcIW0e0EFLKpjr2w2GcC4wK6c2elhAY/kYAGuz3RSqkYQUSSFM4MvgYouuLumXv3Hub4769XaNWKXoPT5s3oMVdC6yyGvRrxLJ4OSqwNwuNO5T3lgkVVGkxcn54YNo3R2g80csjSRBK2Swj9MR6ULynmKFUggpxnhEXLKPWc5pS4t3jI8vpfFwj2knqZnxmn2vl6J0903l+fuG2vjJNiS8/fE3o4g2cfhzU23XlN//Rb/P+3RNffvX13UobjgqhNSVicFGeJmLvhy3AaNIeeL9nj4PlgB8o0T10BpVzKJNh/GwkpOyB3YUqYhTG2hpbKfSuXILZB3SHjMYXCXBULDGaSrz6aa7d6HxBzLZAe6dshZD1MPqLIfB6Wym3K7iKfCiqa61M3CGDsYDtAP1danDupevwKaplp/aZJU4YOmXOoAMSaK1Ry8YyZTQlo7vVxnlZzOy1N+ND+3cJGkiSiY5NajMLj9fbjdv1hXmeeHqY2DdxkzX8nlsTTkIwAVgzLcB8Dq6fubPBRlN33906JVgw2NfV7Y1//kuCsCwLy3JiSq9cR5DwhCY6ESKKJSOldfPLivcAeByowZqQSjfKt3ajtwbr/xzJR0heNYzGt5u+RWvo1WAeZritRkp4v8Az4GQpoyn+xWE7UykfVUQ3NwJDQrxJDhaI3rRqilPJh3jMOyeWbIQ4mmsH2WPg1yIGPRlcBBPJ4VJjv4gaE048AL1/9453T08s00SpO/un1RKYKSO9kePJIEO5wyIHDdX3hCEsxtyyPpdl7HpssaHW1oO0kvPbvexsMt8Hg020joo5xuNg0qM6GIFfxoI57uXbnpeqkRxyikcGj9jYgN7NKl6lERWrxkYC7BXsuHYjjODN63AII0Wt5xrjOMjupqcpBJpYVyISPPFQaGb9EiV8Rg0fse+uyrbKvzfbw+fTiT0It9tKDEbikd2ucZqNEvvd9z8xn69puR/kIdGCU+nEKNgRT678c/JpYSi3gyhNIY3SGb/xEu+Zey0bIQUuD49kLwd7GA/J4IWHy5mb2BfI0XyOQgj2PqOx7MFwXY27+/D4YP0OXyja3Lvcm93Da+ZtpjJKptEb0G6aBdTpon6oaTf77pgS+7ZRN6OHBomY5b99t20rzHMmpvwZS2WoYwcL63CaHeK4Wg8hXdVuKkhfcJ+en83XZ5qopRyZ1+QeRLUafjyqjre6iO6w1/gsVT2smkWwHlA0P396BQ3EaDbenY7um0EBfhDMKdrhEwJhNN7FNoCtE2OMDZiltIo05f3lzMNp5jSdqdWOeUUIQVnidLCKrDRPhAAvtyu9mmCr1srL6wsPl0dUI63tlNLJWam1U+qOzQaoDP45b4LiT3udlxMvp/l+H5sdPD/57lsupxNxMjaVdv1dDcuI6IA34ewN8FEZer/RAvFkvl5DTGdJktkxvO1PIdGqM7+2VgvP643H09lddzsegj6DUYepnoEYuH2Nd7jcoNKSpX7oCXrtJkrMbtDYB+zgFYMfEEE5RHYWUBRUSBJpA7axzINkvIbPhFN0x9lFKL0ztcanTx9BhafHR1977rfEyHLNiiKo4ftDaZyC+H50ybjI+MP38mAxec/zqBRHxSXAbu/tENLpdEbld1O/FY4nIWZmJyOr/9yxdTyLGBMaut8zeybt+FmDtmgN0YbEZP2CAY8N2NQhqEBkEA7Qu2kpYkLY6Kyn1ivGfLM4V4PZ+wz4UIKptMUFvHpHk45E5/gu3ZKbkfClFNn3zrQsnxkPBpTb9cbj5eJWPnqs1+waEOvz2Loe6xTt5JAodQfJtiyNBCTHDRzVRHDP/Jwnnh4feXx4YJ6nQ5UN1ogadM+Hy4U/8kf+CE/v3zMG4LRmlYJVf0OBaq9t3U3t7Jleyu4v0+0BjoPi7WMeGfZdj3AfMjJ6HbjNx3CSlBAozXoL4pAXYtjqaZ7Yi1E0g2+QON7zuGm2MILcPeWt12GrPud8WHMI+GClzOV8Pr7DEOwwtoPfPwl39ae6YtM+365hyvmg5dbayWnifL54Kf/5S4djqx8qAGlZyCnRaqNLpEs0TxmJaDDTulJtY3RVmliDNKfMw/nC1iu3bT3gEIMHBl48AqFtmvP5RMwTEGml8Pr6erBAkpMdLFhXtnXzwSob6/X6cw8IsAPyvJyIOXFeTtYo7x1pelSHIURKN4+rQRsWlMHcMlVvQoP1vLT1z+YAHPeyOVQXA9ftxvcvr3c81+dAxBgPv6fX9canb7/zZ/pGE9BtENJ126ilGq5tmNRxL0O04F+HHcVhGHfvXR2Dug7G1b36VWcs7XvldrNs25qs9/0jfg51+pGEWLC39UuHzQknI1krpTDNp4OOmd2OvLXGertZEheNrdXFBKPm0Wb+TbZLBr36noVbLyC4u7Me1/r2ZT/rzL141y2MhPNzurz/sv8hP2UxfQ7b3IejHdYeb3okIMYN8YBrFbzdmz7k+eP5eqUxTLvGAS2MpNWeWQjpfmDoPeaNOS+OzB39Fnv23ezLw10JP2LCMDyNEsnJ4k8Q4bQsLLNRwEPOnM6n+/Axj68jFoMcz0SQI+aWarDeNA2PLqtI0zHFLYA4z9myZtCmXkKPrENtETuLImo4MuZlWZy3bw9zv5qP0bwsXC4XUjC1dG/NKoDxUFQIIRN8ENDb13GKjrLvzQIZi+/tQJDb7cpt301IguGk58uFXipl23m4PFjDUyHOMyNEjMqAEKyp2g2DrLWaUV0Qszmv1RXaBjcJo9pX6J3lZLoKO818etxYPGLNrQGtJQZeb/CA9g5T9qV+b8C3spNiOIR0XTuo6VmqmjJYCQwHXSurrTJs3YDE7llmdby598GcEMRjVxJBQ7Ln6/e51YLMJ0KwASWtVVAnN7gNi80qSY7hC2FeOJ+sz1FaQcTZbR2iY/n7vrPVwl4rp4FJ/oxXCIHT6cTjwyPbtrqISnl4erSg5FqIXq1CGRtrrB2Oj1AznnNbkFoL6H2qYWcwWIxhdLuuxBTY5xPDUTZhsIUG6O6k+fT+/ZEEiZimJsZIija9rWsnRfE+1qgWbNaB1mrJxZThjbpXtTPl5FToASF5pRACrm50KKy9mfPQjwCXUmQthdfrjfk0MTucM0RlUQIrjb0WgswmFrzdUDWK8LatlGVhr+UQhzbvHYRoVFaD4yBPni178FQxrt1Igu+Zvficis8x+Lcv6xVgJASEOKqGn7ZO1CtkfXvafP7ew2frSPC4wytTSJTuDEknEoQQjTlYdjrlOEhiSt6Q78d3064H5DauIHjVLghRldG+yDEda1O1IjGYCnzcM1W02vPpXr2O7zVi3YjFIhBy4iTCtm9ugChHXHy4PLBtK/Hkc2TQu6NztypOusUOo1X7vBr/ffUYEWMgfcbVjSY+6b3ZhZhdKREbgzjFSO1yt5DwG15KYV6sKdubYYuSswVcP81idGGNy/zBWT210Utxap59gxQdAnKsdcBfnz141btK1KuGIJHX12ezmfZsL0jkddv4+P1H/uCv/gFrWrZC6Hd78EENa9Vk94gcn9n2jZ4SKWeen5+ptfA+fnGMBxwqbsPF7aCKbh98TjZlrLdm1h5+6h/F8vj9ENBaKet6jI7U1pEY2F5fqb0flhwGVQSU4eRowc+CiWGmrQ67lLsGwnx+zEojyBuGCYaJ1ybUspvjLsKcJ/ohprOs4uV6ZZpnLunE67qSfe7DgGJyPhHELUcEa8ppgW7jJ1UT+7by3XfK48MD23KiP3aHHX72y4SKkeg9pvP5jGY74YrajJDz6UzLlb14Y1ctkA6IEXG7h2A00bUUQoA5mlI3BVPPW4YoXB4fjEXnh0LsgSDJNr0b752mBLNl6qNh3rsa4SJGpNqzz9Nky9uhxDFiU+AQM4UQmLyZH5BjUJQBRhBVII4ZLIFOIEjj9HA5MkywYBFyoqrS1SrV02S6kN7vNiUiwsmHU6maSZyImPK/VVShXCqtFMo0k1PkspyPiY6W4EWWZXK3aKzvFWzt3Xfsz8gEftd/dhTGq4z7wfL7JhJy/8WjOhn/yQ8CCeLqeg8xR2VsYOrofdDvIs/Wqjtiu9jP9yp9wErBzVQ8mVOsvzOMIYe2xG/CgXAgrlcwi5VB3hn0asn3PhPcD9fxXQaEDSCtUZMwOSxfFTRawvv49I5TPSGoTb3zQ0u8r2SMQ6OR79uOBDMSteVmIt3WzG0jHQwZv5Dof+KOkL014jSR3ZpYtVkJExKEwLZtn52iBrF0zqcTp+WNMbSfTkNAFDxDqq2bEChEa+KhTNPMvu+WhR8P3FgSNjSo3oOpv19rjdP5ZJvM8UFbaGYwV0vh0+urqVO1kYKg2Tx/h6IZv2m12XecvXwbjbaBiQt3C4WjucSYB5w5X85HRjX6HH7zjsMIQHxxamt8/Pgd19vGF+8/8Pj0aJll60hOpMF8gbvoMNxZD1FAQ6CsPtrSKxJVZZlPEIzt9baROgJnFwtIij2TKWXPDo3MIGKKcgmYG6VY8xqBlAUT7ok3Sa35vddiVD+1DZRdiDmw7xSSqUZvt+NZ/rzXAaNNM1PK7CGhk83+Hsyg6PRT2Xa6RCMWRJ/21k1rMkRIDU96qtKjwS8hBCZvGkYJJij0BZgkQA506ezFqOBTtD5Gq83pm0Nopk5/DQdk0sWamBLNftoEnxFCtKYvdm1BoTZT1h9VqtuqaDIPqS4myMKpxeL/PkbYHu/dO/O8IG1U3G8SLXENjHuX1ZFETNNBU75cHhAxeG6eiqn6nbppzWXTiViGOw6IN5+hnzfhf9HX7+4n/EK/83MU/G/f0yAW/MTwJnlMIM3kFIcbrbP0kM/6EtEPiAEfNhTtzdaQWCDueLNZjUQjMTgNXCEEo5nv1SpJQMRcBBJmnmoH1pgrc4fMxj+PVwJKSkR/5iL3GeEpRY8194PGWBxqBxWgIdO0Y6XA/XAbQmYEUsAcX5fTyW5CLcZOSJnn11euz5/4pW++sWagG+SJCDEbR3lMXTN/nGEeZlhWjOL8f3U0JNwhGr8JoSvLMqOtU8ob2qxXCQPDK7UyTa7b8Aen7nM0mFGDJltKobnGYZpmHh4vpBzpZecmhv0+X68s82yb0QN4HA1qMT7/qFb2fSPmiXfv3lE9ExTu2HMIkXWzkZrn08k2B5Ha7DAYwjsJY/azKZnRTsoTn56f+fjpmSSBeTbcMapgjis2FrS3doxCVITkgjnEqJBaG4g6nh69f4TBQTg9Dr1bUqv5GZmIrpo9dExIV2tqHRi4UrQzkciuW5EonHQmYYs4iAmXSimmkg12bVEs87df62x1NUV8gm3bKdvGz68h8A1rMNeUffCNO7IO6ERFDvU6QWitcisruSVncClFgWYGf0G7DzNqQEUY80PkWK+1qvnuzHeNyOvrldt24/L44H0oa5AP0eBdHBfQEBkFci/lGAcro1JlVN73QwTPoC1PK9RSWZaZHs1l9dPthSlmmy+OBYScbU8Mq/iX68rTwwWi0dan8+xBwg5SVfOwMqjZKPASI7d9J4s5LqQ8kdLE86fno7vwxYcvvMeu1G772/odhgrAnWgywvE/7uuzvsI/5q9bEB3NcQ4I56ceOnL/h1EpIzjMMhiGzq4Uh9n6EBbeg676/iIYSiCqdB14vyVeY360rUKbyCkKeTItSduMzhrdXsPih73eUvMPVtmbhnaI0SxGeuJabrbncyR1E+Q2DfSyoU0d7nRrHd8/te521okxMFUVCd5DFSilEQ6RW2uUbeX1dkO9BCr7xo+//55ahrgtWgYfo1Uab3FSx0HHZuq9GY2wNzzBO/5OFW43C6opBfKUELFDZgTqGKNTffsRbEdjaPQnRkY7HCPBsxnfhDYEpSJBOJ1PxHmykxIxocmAUsBP/XvP48AOvTIa2f84GFTNFyomL9XzzGU50zsHPj7e235uKGLt59dt47bufPftt0xT5pe/+WV+6ZtvOC1nRM0tVJs9xNv1lb0U+85Oo2zO3QaO6kr8gBg7wA4vY5QljLcu48vC4S0fsIwE70GNHxARiHiW7eVwNFbXddtY992yJDoxmhf/2xgh2JCqVquJ3mq1TF9Hc7Ad9/XnvaZp4vHx0WGnxLzYcBxxexJR87/fy34cUjISkZhAhlo6I64EVjXNS8zZ8eXOtu1v1oCPq/Rgoao8v77wel3NWwlzGbjTuA12NHFeul/8myrnCKLhLhTd29ALOeSbIvu20ltjnm1ITG2V2srBfe/g68MzRbHnLSnZ6E2vGofP1Pg++HPea6F4UDiuS01UZwe/MxxrY7+tZrfje8HM5zG68dAeCEf1PijDg5Dxj//yiP2P8TpEm3r/91LKZ0K1n/5RdiiPflEYcCwcDCtjwjlINRIlGcLIIc4zZGQkyPfDx50KBBCDv8WhsRDHWl54fHgixGhjEVqj++jatEwu2vzcDPBtjGq9+z62dRaDmRaaj5XceyxroayFkcQMT7acvO+FJRI2VsE+7/r6QmhqtroRm0OACDfvI7x7euJXvvnGpsp1cxGVGA9f85CTwS6uLl3XzWAWF8sohqHlNNF7hQONV2KyByFhNFP1jVgHg1QYugIX6+h9cHgtxRlUd1weOMQpt9uVj999x77vx5S6lAzLPp9PPL5/dzf/e1POdYvyxwjU55dXwObZggWFOnyjfPPXZhTKmBOqJr65enYwHm58ExSmPPP48EDZN/7hb/yG8djnCQmBve4UF8S1WpjnE+/evSeEwMePH7m9XI9Gewzymd3zuu303lmmfASEmJOJv9RYO12VHJPNFwaHsJStNYcxbKEc1Ny9U3UInZS2V+pebAzoXvj+00c+vbxAU1JMNqkLjE8urkjulonGtxbNpVJrPVyHf5HXsizkPLPMC+ZAagnFd58+8un6SgObrRECUzjxcHokxUSrBjk2VdcB3ZXGrTWnhBqvPwabtzCo0K1Wnl9fjwpjnmfev3t36CvAFfRvglXplabGoDJqZT0q4l48MHtPKYZo8yRCOMZ5fvr4Pb/xD3+D19dXUvTRst10OkvO1jRW0ya8f/+O2SmqIua7czlfPEEJ3h8YQdT7jzh89ybYDGjjtq62pty3KqbI+fGJoW8YvcLarfJvpdn3fPMIjaF3p8D//+Ol3exs7GCyGDPcj+9xZ/ww7jfnh6Pj9TbeOHwWjEX0s3vnoL2jFtHtve/C3PEyW5J4mDmKN/LH2Teo39OUjbHnB0yMiZDMWiP5kKforLDo1xQdeh6fJmoswuVsc9RDNE3S+A45ZlPmYz5O3pRCUmA4KRxITJL7PfJEOIhjaK/rFVDyPPnsVGFOmS/efzh4+2OiTArBmjLOBrETy77wwJfVqwwNwm29sa4bY/xnzpHTaTEzqjceQTAYP/3QJQzfIVXl+Xrl0+sVcFMv7x20vRwZANjBcrutfHx5sQaxawdO02QNRBGurzf2fTv6CoMyaKeyY7giSLdsTsbAoCCHXXhvDdR6FWZbrnZYTJllyb7xyqGtGOVcFKMcL6cTv/LNN+NWAjb2c9t3wy3nmdNp4XQ6Wd+k2VxtE95x2B37ZA6WeeI0zzaop5lHvDo2GmPwGQmjMWhQ3fCuak6DtCzDDrbaGhULFHaAWIYYVDjNM3memOeFZV5Q7dz2jfW60cVmIgzRl6mg62FBUNabkx8seP+is6+TN6xv68r1ej2MIj89v/I7P/ydQ4ugauybvMxOFjDGTA4Gw5nq3sdThuizr+/zsz0/NKy+Vcq+MTxMH85nHs6n4z6OvoE4XjG8ABrukwTH/WwNMxzsg1ptVcM0JRNFKpTaIEbSPEGMlFYdn3bngGl2q/t6bGxD2NyVVsd0M99Tas1q1e6cfTlgqewzp2MwwkMX5csvP3BeFmez4cygyvV2ZV1XI6LUgrZOFFMkX33WwRBh/aKH/j/Wz7350VrrEZjH3wWHvQfDS8RiRJrMD+2z96PTvbd6XIMFIRgN7dGzG4eDP1/7cQv2io0XHfPoh3vt0Vge6IkMyZoch8M4mAZJQkRIkjidToY8HNdhl5fC/d9VMF0Ldp15yua9JIkppcNeacpjdjcESUiysbRtrz5/2n4/xXjE3/GK0YSCNmskBG7bxrauSE48PZysUSP2heJeSSnbwvPyxppm5pgpwWiQMk2Gizpe37s1t0Uy6/bKvm9cHi7HiWzPw7xNktvz1mKl0Gd+Tr27WVtzCqpRXE/ziRwbL9dX9taRm/Hj99bIIfCDr74iTZlSKmXbSJPN4W21sa6rNQclu73CGO+ovL68cAuBDx8+ICLM5/NBL2ytUWtH8htH2taORWHQg71P9gW7NrURxP77giA5oSinaeH0g5NBLiKGFQrH592tUoxi+P7piRCCGS46q8oO9Ihuxcy/ghFop3m2aqwWiIkkEyEOGMCgkaadoObf0oMQsR7CVjsxdceZxSdbWZajdGqpRqOLwvvHRwjCrs2DmS9qsQDbgEikbp1JvaF3urD4Oti21SqjXyBYSBDmZWaa5oOSGOPG5Xxi31YfqxttgwXvMyX3DWqN1gNTNH8jMza3XScYrGKld7JJas4AOS9362yrfIpZqZ8sS1/mxTJxwedDDAorR4+IbodxCNhaplszORlzKgYTAw4I7vF84csPX5iTq1pVK73TS8fM/QFtZgwZrfpQ/3zUR+H6Iff9x4+oKu+e3jEsQqKYLUMQ60tc3Yzy8XxhWU6g8PLywqKVvDzZnOUSeH55IeeJaVnQ5j5dEmw0bO82Z5oxC+JnP0uDbR3fl9/7d+PPO2XU+jfPz8/85b/8l2lt51/4F/47vH//4b4+xp+/C8P3d+UOo97V2sc+E7mvBwlEFw132+QGMbdOD5Gc3d9JcVuQ+6GiISDdYyRGQf/sIPLLGH0NUYGuRwySZmyqLpHgvYEa7u+hXTkUW2/RDzBvLc2+rzshTMxTp1SraLfvdz59/MT79+84LzPFPcGs+LLEYlRIe6vW5wSSID7IY6LuhbLtNv9AovPIrTQ+6JdvHmR3ZoT5q1gnfwh2cp78ZxrneeZ8mjlcL1VRDfRmGXaeklNWzTMJtYZPTve5C9I702xzcZMPksdVn6UU1uKnbG20GHj3/okcAttuE+zenU4ECdReUDh0B2P5yICFotsK+CIa/PBeq8+RsEx7TJs7fGfG+2APLfkDzJOpogUXFXqTSDB2lQY7rbd1Zd+N225VTKC3wpiaJxKIkxkBjqZ8KaY5eHx6JC+zZbzBlJ0pJiREeozmCuk9nIaSvNlf9p2ebDDNnKcDT04SDutyDYLWxmvfmfPEknxm7rDfcMgC7xuZl1CnxvtGH+NWb9uNnBemaTINQgw8PT1S/fv8Iq8UAl98+IAofPr4PRIij4+PPqzFFntD6GV3GxIL9mvZaV1NdS2C9MDByROD54LxFK1i9mBxOtkhcH195cfff0dvjQ9ffsnZRZRmeBdRrB9XvZqxz/CBOQ4l9GaDsFrr9NDYrzspZVP++/q/bRtBDVpbTiertFCE7qaJ4m4FlpsOBo4FsQbD8dRhkuW8ENTHjY6AiB1WCqzbzrfffsvT0yOP758Mhgree0TppRFyQCVwvV6Z0sTJB1zl2WixQrBMd0TgX+BluiE5BF9gCeH333/P7XazirPsfPnlV1wuF377t3+Lv/t3/w5/+k//B/yF/9t/wnq78q/9a/8z/ui/8q98Vsn97qrhZ36+Q9vHAeGv9qZHM6oUs2zpxtLzWDdgZGUkvp7mC4f7r9l0eP9Uh8fXXRiMV6OBQAzYLGoU4v2agio9Ru9z2jWqRXT7e8/Mqiebycoac4KUGdWNVhvn04k5JXfwFkLpR98ohEjZrmzbzuPjk40gYEwJTZkYM999/MQPf/g7/OHljzAnpUXLrlqrJHHvmN7N9kEGNdIOie72hgNO0tEgHdjdNIErPv0JHVkCaspVQnDaZ6Q437z6kJ3VJ5rNDhd1bcfJe17mY/F37UyXBQnC6+vrMUYzRRtQEnqwMkxNGBdiZJ4mdp+WFmPk6enJRgfK3dAM1aO6STGyrsYjz9mtStSsiXuzKiUEGxIiISCD7hqCCdZUaYzGIJ51KikHUrsPd+nNsPOybbYZY/DegDBNia6NPDm1VJsPgXGascMeCV8wvoirurXIqCR6Ix72Aka9ExWINuWtbJvh8qMpLDfk8mhVTMADD9RiGbAQkQDX5xfqvHO6nLHSvJn5G52cfSobhbrt3G5X1u3Gcj7/QpvbkX3SZHTUFCM1RvZSWbyJXWtxwaeYFQvW+A1esquPLu1iumCJBsds+0aowZp+KdIb9BjQLmwol4cH5tPCspijr4ZAi9wPSElEh9E0NGOPRRs7U8vdQiY52aHVesxYsQmQypwiyWeK9K5uBW8W2TlA7daQh7vVuE3D82a7GESlzeCky3K+V2mq3F6v1H3ji69/QHS34fcf3vN4udDbCHZq8+NTdOKCVZiSEiEFPxyT4/iR2qtx71FT9XubdxzCjsoD4eg3IvD9x2/5T/7sn+dv/1d/h47yt//2f8Xf+lt/k+fnZxcE7vzgm1/ii/df8Fv/4O/zW//oh/RaeP/0hLTCX/krf5H/8R/9o3eoRA6S+M95jQPlp5xpI9n1DL13q5gG6cR2k6/1o52NV2lywFCIQcJxzHOxGaa2BvFDBhyTOmoco46L9c6sT9p8rDQuWDWoydzG7lWXiJJjoCGg5oJrtuRujhgzDw+RbTcyEVGIXWh99ycmRt8/Z787xpxq2n3GdQi8f3pi3XYTjXDHqI9gnZLNS8BPVpQ8DLdq9cAO0zTTmzct/a5bECmHhfg4AbsO2T5Hcw6RI3sX/9kYgnHEnRabojlH0pQabVPfbjc72d1b6Pr6zA9/+CP+8B/5r5FTYrutlBg5Lwua7v0M4NBGqK8awyGrV0j2+dlX06gOMtkx64YndoQgrLdXyl55WM6MOfZvG18hiDVTW0dpBGx+d4yJ0xJ9mpwP+0iRvVcb+RncNRKO4U45ZtIlmVuA/16tlTicaeP9gOhik7tQWPeNHM18MKZsow+jZVQhyjFzYPKMUfZimHdv7K2wxOUYjtR7p3SHbqLRAT++PCMv8Acm61l0bwom1fvgnmtjyhO3deXl9ZWnh/eE/POtw3NMLPPM6+vV5hGnREzRx8Pi1WA+BtmUZgnHaVlQtYGQjDI7A47XE82e4vWjjZo9v3tEg6KlU3o3WqiIZXidw/NHmx24MWQfYm8CrBSgBev3ldKc8n3PV1vrdgC0wiTJIKicuMwTISSr+lR9IJg5p9bW2OrGKVjfTw4DP/cgcprs6/Vq410dwhqkDO3Wc0xviA05Z6Z5JqRkOg1vkBp0haELfechXqyPiE3Ok2aHakyRMCC9N0H489j7BoNQg5j+wd//B/yv/s0/xn/2f//LvLx8ZNuMDWmeW5ZF11r57d/+HcZsi5wS79490nplLYX/4r/46/yNv/Ff8k/+k//NN/f2bUv387pCPvu33+cleFV7twO/T8gc7rJ3e23GMeGB3nOyo1IZX9uqP3dFOCpYTxLtF4gBStWjd2j29F6RSEB9CJXAMXtE1cwFG+owo/mNISDdPfBqIsZqTfReEXW3DCqvL69M08QyT8zzTC3WQ22YuDIqhC6WBU7LzB/65W/M0iJFajV/pWUwgDzrb93oUepTjg6qWfUgr3ZTuzbPZzu9W6Y+rCx+95yJnNNh1TVGhA6V8sD8Y7Tg3wZtr43MBcA47wZvWnY4z2c+vP9wKJxbM1O/2g0CizE69DBWkx6H15imN1xZwX2VvA8wfKPw61pvG9999y2vrzdOpwvny+W4X9nhKDBl+vX1xu6KVpwuKx2D30TsIZWKSjB9g9Nnmw9qssTYDsNBb5TWzIKjqxuJDdaX0dkaA8dUmjRiEiQ5e6Iblh09N+oe/E7TzJJn5jQ4+X5/XMGdkgWkfR9wgV1vR/nw7j3v3n8w3YUObPguAmpqJITqszmuzy+8vD4zBtT8rNe0zJwfHpinbD0tr/Smyaq6gTfTlG1bqcMXy9lft+1GyhP5zVyEoPYcz6eFp3dPVFF6q2jtvLy88PLx+W4U2O7Y9vcfP/Ldd9/RBm2xd2PggdlSdJvieCsbt7L5EhsZqr3HHL1qlUQgOH5swSVaCWHfsXeu1yv7zRKGkIaIzSbfSbeq6vX1hd/6zd+klHIEtaZ6kDHmaXaigV3bEfx6P7RBGu70SFqnbHbtpRRu68p2vdHVKbHVZhbImOfg0dLElMNG5k3mHoRvv/0J/+b/8o/x5//8n6XuG7Va8tR7P9hCrTd3iU3M82xOyyLUvdKcIfYP/t7f41//n/8v+Jt/42+w7/uhq2pt3F9587+f/hqN73GIHlRT7g3wEYxr2Xl5eeHT8yfW2+2A4kcS+LYnMijHgmX5ZnL4+XWMXkJM0RnR3pOMuIbnPhWvdqsI7n4N3goX60EO88fjs936P/kzz9PsIw4ikqCJjTmdzzOzIxO92UFiCWVyUouSerASJwZzDW1lp9ad1+sVUeV0OjG7eMOU1uGYt2DNuHBYfw9KYivGzjmdzzY6MmejJzpuqv57iDgne0JSJBTLhoaflA7cSvUI9iG4h749DpoaJBYdXqjNvFbmeWJevj5QyhA9QxqNHrHGYUfuzU15owrHDPvUq5aOWRbvpXA+n83V9nYjxsDr6yderytffviC919+xbreaM7QkhCOmRbGSjJ9BqrWwFbjvNshq4cgR0QM+vHvPu5DG6V+iCZYU6N9RrFmWRbYujFiWqvsdbf7czE/q1DtHge1EaaKEntEQ6eqaSK6KNWGKDP44LU1+r7boTFYE9UbXb74DXOPPJ7PtrFwUzUZG+5+UFwWw/pv+458emY+feR8uRDC7zXd+92vGALz+Ww9plaPsZ5VMbFcCLyuVz59euZyuRCmidI7z5+eEQnkkMmTVaJbtV6LdDO0nFL2menQ6Oz7RqvWzCPa4aPBqomy76z7Tp4mTnk2UCUmOq6Qxkr+KU8sg4ygLtAjcA5CzNHHWipl2/jxtz/h6cFsu03vYx9b1e5ZyzYAp1UlpOymcGMKXGPJE199/RVLno4E621jdrBv7k3hYPRRzJAxev9Pezdrj9bIs9mWtFZpLbKVjYUTZrXSAbeS8MMNcVU++sajyddIb/zxf+d/z5/7c3/mgKQBo9MOTcMRB/VIKMf77L3T6OSYKVL5f/7ar/Gv/k//J/z3/qV/mX/un/tv8YNf+oaf/PhHvHv3jv/6f+OfOEw4Re4Hwlt19r0vqUcVHtPdgPDt//bd6f36phE9gKK37CAZ/6fcx9DdeybjsBhmh6O/gZiVecP6EHZgBWLEbHrEIM2m1foUvvc69wPoaJKrkILQuwklU1TWAfsJhG7sLxEhEWilUnrzuSDisdkOXYObVOwE6crr7ZVPHz9xu93IKfHoGP2glmlIzmwwJ9ERxJrjqgLs1YRgOWdz3vQvTQi8vr4SQuTycDFY6PmZW0q8f//eYZR+VA7Jg0q1b2+K3hih22yIEdBLbSxO/dy3nWGHMGicEoIxFILZlS/zfFc+94bhmOE4CBV1ha9x1EvZiSHy/OkjikEXVZW9FJJmHh7ecT4/cbmcjXbaG70a46T5NWvvdlh6kzGmxMuLQyY58/Hj9zw8PHLKkw1rLwXJyawscEjkTTbeUbP1xawZuq/Jqt2eF3e47+XlhTxl5pT54bffotWarylPIM3YTd0cVdu+c7lcKHVDPMNuXTktJ+LlgRTdB6kZb99mSEDXdjTotXfv4QQkm9NkG+K15GNbsw0uohTi5eIDWH724TBeIUZSCLx7/47b7WrQmEMo48DYNmuGn5bFmrnrjXmerf/la7WL+/YEOeZpqGf4DaNFv//iA63YNatYf2VUgF999ZUFW1UkZkKwYXl134x5lbPLztzArZgwdF4mgijb7vRfx7rNULJwyxtnH9kbvbMZxUbUDq5+KbtX0mJrSGyWRsyZH/zgBwfce4dJzHByzGEOMma2+LjcZv5gIdpIYIAeIgRhdq1NEJyK3dFW6c2mu10WY9KpwyIiweZa6+/Nnv/f/6+/xv/5//R/dKJAcvGeVTCD1g4+dc4dHgaFXRXkjYXELInLZeG3fuc3+eP/zr9l0GmceH35SEoT/+J/91/kX/6X/vv80//sP8Mv//Ivk9L8e5x/x34SfeOL5OswSDiC/6DYnp2KnCRRd2MU4r+jMnovv9/rDT32zYF6oHHdEjhaPZLC+3Xa79ZQCRW3Y5GjGjFfKe7VHGBDxTqBxtr3Aw8r+2YaCb+eoTXL2ckjagmmyd0CqXczYdhWa1ROeWJazLv/cFh1nvvuc1JTyuZUiH3o7x6iIyFQ9h1RY9C03pkmYzst8+xlqqtMY2SZJ/OsOXoUZn5lDW7xjNEW7pQz1+vK9dUC37vHR5scFcwrPTm7ZkwJG46xpXcCkWnyhzrwRREQW5y17HS1HoU1Ga2CWtedecqcLjYHY86Z0prpF96IkcBEfvtekTkQ3U65+IE2T5PNn9C711BwGGs8ME0BOSZlCTSzSVm3HQ3mBkuwkZk9GLbYxQJsR+2AEUWCu83mzOO7J2QIe0rjd378Y5bzmS/mieKNEztkjF1zOp8ZYxoVw5DP+UTpjXXfXeQz1rZl1YplU9nFX93FeUGE4XIpEg9NSnB6bcHuR2uVvWycpp9fSeRsxolTziAWXIZjaGvjMI2UUp3pFVmWE6dpoddmg6lasWDjGXaPSi/VMv8QmaxRZNVO2+h1N+jKGXX3DDPasCrxuQ77yrYXJuCUs01yHMyWCNknsVVVaOYia2NvG9M88wd/9Vcts3e4c54mfw7W+Iop2SHh6uJ1XRERJofQNm3Gqoq+2XEYQ++K68H/9yhuMKbYEKYuED2QNpS6bjRg6Z1pPrGx24S+00SMBoPu+048nQ8sfGDt9zbEHdL6k3/yT/KT775lnmdutytNOznPEBpl2yzeePERR7NP7/CbiPWZUgikOTPPJ2KE19ahNl6v3xOAl+dv+dP//r/Pn/5T/xf+0B/5w/yz/8w/zT//3/7n+R/8D/9H/OAHv/J5MeDXPHRW5qTbGDTXEYwHxVoQ9lIoZeO0nAjD7cHBbjkCvL5BuuTzP8c9wphFeBKNmp79rc06GJRea6E6A9J6Q3fXCPXYeRw6iCvHA92hrGmKtCZcrzupmxYnqfUgSy0m5hz9qzrQZSElsWEi8zxB70xTZp4/HHYDOZqkO+aM7oXvv/+er7/8yszphvHYgePZQfHw8ICezxZkS7HBON4YjimRvK/RVbmcz8zTbErYoQsAbq9GkYxpYk6ZHoz5kVNij2IHWcxICuS4UIrBAstyQmKgd2umj6zk3pswvcXpfEZH2efQUWud0/lEzmaZLcA8sgsRm/3qXk8phCPTEXvSBnk0m908qhDAh81YL2aKkd1pvvM8Hw30d+/eWbYi0WGBeDTyNQRODw+sbuOcMJ+da7lxPp9JYQy7ET8M+nFoqSoxDspe4pd+6ZeP7Kg1PWAqaWYt8u6DewLtKwKkaSIRbd54VdZS2fYr8zRxWmZsYpu4O2ozR2evBLtyiBWXaT6sjgcZYt93Uk+eLQbGsPefhSED7hdmM4+X5cK+F/ZtZ7vdHNqCh8sD05R8AzvUIp0evHEtgjRx5oiQVGBMDZRgfZrR70iJ19uVjx8/0bTzcLnw+PBEU4OJCGK9l5CYltlzEDkCTPBAl6Y3A2B6t/nR02SkhN4hZlIQ9nXld370Y07zzNdff8WYnVCVY4rY4RDQGtteSO8m5ni3eh8ahBFAbHiMWTWYEE0PamfMNuZ2ePvg8xD2bTMjxr0wfXhn66JVUlfW65WgENOTsaK0kxjsvM9f44n++q//Pf7if/afGoW97Kx+KFgvweEVZ6CBeyLFMV+9EodpZBcISt0a15dP5HlBdwvqS7J4tixnptxZ68o//Pt/n9/+zd/kP/sL/wm//ut/l3/9j/2v7Xd+n8r1QGzeNKsHRO1dBusd5Pv8EBzOulcS3iP4PT2R32dtjy+t4gO0+rFfAkZ0GPBPUCM6hGCHf/FkOATby+roUMd6ZN3JFDkb9VV8r/Vug+ViCPQYHa0IHLRY10cFExp1Uog8XB6MhRIsWw4hWJPMs/HTlPnw7j15aAzEGqDWi+jsnjEzhvGJMWjGyM4xQU5SIubINNuks05jTGvbt80cS92nHjAMPZg6tmrjfHngqw9fslzODjl1G7zy+kp540rbOwcmmXNmypmcrBF2UHJ9Aezryu324gdAPOT54/tv28anTx8tUPvBqA611aNhZpjqMs+HSeC6rubymYzFFKbM7PoP4e5DNZgRIyNorbF3U7fWUsyXxd+ztIpgthyWLar3Bg1uwZv3SKNR2VazbN/3naKND+8/8PD4YAEyjcVlCduYFpZiIM/Z7EEMMGSeJ/bbyrc/+hEvz5+O3lH3/sdggQDW/IxDCOVZqZMOBnXTkJ3Evlf2vVBKO0r9n/kK4bBJmabsdhI3tjeOmVNKnE8XFyaq4/o++GVsSAGiWxz0dpTwikHJtSsaIq+rNVfPlwsKPH965ttvv7Wmda3HBEAVm9EwzTPiIiuDPu1AG81Q1LJHmxVu905UiThkm7NZbYTPmTpH+BlkDhHyPPNwubDME6+3Gy8vL9bk9KbpSFzG5LmXlxf+4W/+Js/PttYHnv02qUA5hh6hyuXxwZCArujeKFvh9fXGXiu9GlW17eWz5/95/LMr/9N/6k/xO7/1W9RWeX5+sQ3aga7MebL7ge2pFCBmo1SLVgNPkzlFSxh2GsqrD0Fq2qi9MBroNnVRyHE59Bj72vhzf/bP8tf/+l/97H6O17GngzHkYkzekwuuZzERp6g5tuY8MXAwe9aWxt+tQf4xXjoOpXuSdTCkHG2IITCfZqMDDX8s/5nD9qXfe6H+tqQYWJaTOVccMTkfsUY9vtdqCE8pBvkZ4tMJUSGrEIoboqldFME8cJLPJaY10jxzfng0Ob5L2rMkQopH88X6FD60uzWa+7/Mp4VpmiFA9WldWaJn5mLlN1Y+rzfDk1OK5geEewep2nsjR5A1AZ+V5cv5Ql6839Ca002tNBs2yvds1ktvD/jnx0fO50df7LgVuv1MDIH1drOZstEy9eYmaGmMOvXriW8221Dwii+eoELE+Nba7VAdrrPPnz7y6dOLDX5Rs8Bo+8667sbcoJPmmSnbnOWcI4+Xp6PRLwSjCUYrWWstKDbDeHLZfg5meKgxuFe+ZWvi9EzVzr5txCBM88wcTQm83lY+vXwy8V4tLA8XLk/v/AC9L9DkCl7tnar9mOWbszVzB6V6BJLeLIju+86PfvRjPn38xHbbjr7A7/sSV5c61bfshV7qceCrKsVJEvTuRoa2gW7OglFLkexaQiBkm8wo9vBwHt7BQppOZ75494EvP3zJ07t3xyhSEev5vL6+mkBRu/X3nI02sBPzO7M12J01kxxC0WZeSBaYO1OM/MrXP+DD+/cmanQmYQS01KOxue8+QCslC+C9G3XVe3bFE4aczPbDKrhCAG7rar/vgeVtktNaQ2tlWRYeHi5Go/R7VXvhtl/NaypEijsl7Ptu4tjef098FOD54/f8mf/wP2TbC7eXVyNpqBCDuy+rkuNElnjcW+iE3gi92r3rK+bMagLgIMHt+zMpRKOCajvYYtqt6Z/jRIqJdTU/t7/8l/7SEUDfnhR3Ud3n/7Ng7dPmDP05etY2EXI0jO8eZ+P97nfgZ7/ulibwtrkNeHNbj+TG5sjf13r0Ht1IwAYT1H45UL0Ca70jwQ634HbgrTVC77ZGnGYtosRk7td7K2b+OQ4EO8nivSPfG1XNN0ajU/e0MeeJlDO1m7dPkGi9Br2zb4KIszaiPWB17LrD62qW0XmayX7ym5d95v17Cz51L0Zp104tZq/Qy36fInecoPYzQYR5cZW3BDuUittdRLGGu08SG02h0SQH63WklHh5eWEvO6fzQqhm043Au/fvKLtTeR0qG9XP0FlEr8DWvdCaHTaSB53QGEJ4OZj9d5IIecncfufK7WZCvIfHd0YMyJlLDDZ+kEDp9fi8HDIpR0SFrRV6q8Q0ISnQVzuYY0i0Xgz/FGN4pGRiRPPydBdZb9i21thLMWuLmFjLjehGeqo2R/np6R0xiA1s8mxuWCdYE9u22Rg5Y86a6k25fGSsOSZEK70XejO8dVtXtn0jL5Nt1t8HeRI3ogz+We8eH5nm2UdrmgbC5i6YiE4Qogb2sh+DnmwT25rS4cVFt6ytmX/X0ICclpMFihh5PJ15FSBmHi5nSil8+v57G8a0LFZBaT9GgUpwvbTa/R6MlgFbD8h9qPAlCGXdmHI+HH/xKtIcWGFy80ntnby4zUhXzucHTh22fafXRp4DtXVSwE04lYeHBx4uDyY89aFbe737QE3J5sSUUsghMM2LkUB68IBkVjshJJ6fX7nebnz5xZdMk7Gpop3gv+eZ/ad/4S/w1/7aXztcmePYw4iN163FWDtO8xRgDom9rwYpTxPVp0qCHt5GMQllv5LnmdQNRrGJjkqeM7VUUhdKgN4r63bjb/6X/x/KdiXPP0XA+TPj+bCdMZq5OgHAYkKndzmC9tHz+Gn420953X/ufsLe3wOg2wwcdTPKEMm9Hupws0eyX4gx2gROJ0WEYMPeFjDvN7XBYIOgg1cuum3s+269vgjTZLqYpMEzKhGCT1hRhKDutF8KKVmQK8UywRJtiM/eqg/7OR89CROgVhiuqb35UB+fNAdczifDq2uliQ/TiZGoQo8m7rqVK6fTidbNM/92q3StBhnlfDRokrNvgljWfl03Srnx7vGRnk2UpG2wmAx3TepMnm4znU1jIWgzltTwUAlBTFupgTDbPemtEXz+dMA4/+NEVzg8W9pgGvROWVdjh/jmCzEyzTOpNeKUSHHil3/ll/n06cUyT+1Ejey1kaZ0wHnVrbZjSuzaiN11rDHTW6FjvlUsi2WOCEnUhI40NESHqcx5dN93wuLiLUwDA8YHF2B9Xbk8PXFxWETDWLiWWWmvB6Q38Ns22tlyF/4AxJCMHRPeeNk4ZRlRkmRu25V1vZm453z6fTds7x3xTHotG9PlYve8Vl5edl5eX7lcLjYcxisFFZhPJ6aYqPvO7urz4JqgGAP4WtU3cx8mjKAxrE1ijFyWkw2ZSYbjPjw93ecptGZ23Z7EhH5notjcouB7Nx7mhqb7k2MK49qcJi3iVWtzuqJVJFspfPz4iUueCOdI18qwuam+boeYcN838hieE8Q1HCboHOr7Y7CWH/ZDHxSwLL9779FcAjrzcgKEveywK/XJoKbWqh2yvMXmYdtW/sSf+BO8Xq9cTpM3aJWgNm0bn4LHYGuFRI5GMonMSBQkKr0aYUM8NrXWCZtZ0HQ1dk4XqzBj7MbGyJ19rQiRHBNTFn7rt3+D7777CT/45hdT+d+D9oC57DAfB+cI6N3t1Ad89wueD8dr6InS0RuzyqLUSi0NDVb5BxdpWtWIrRO9Vy/Wd23GgCR6zOu0lNAulNJIMfP09EArjdu6sd9uNkM7OuGjt+P607iwUvY3o/FsHmqUQb9UujqvF29grjt7c7opHIs6T5M5QnYrC1upiDcPtTdknjilia1sB+bWeqeVguRMb1b6Tim502ED6cQkzHE5sngLgkqIM+LS9dqFKdpwpFp2swPRyrbvzC5qK/vOPE3M3gs4nCAl2aZwRtPWK0ncIbOaD7zGhM4m6ClqNro43TNGm21cSmFZFjuZPaA9X8259v379yYSErMBN6dFjKM/Lbx7F9ncYnynULaVEE8oRt0dzffq9Mha+8EkUlX2XpA8Gb7pAk+VSMomvAtiWUXzPo6ESG0KWthuK8u8GM4KhN54eveOOWd6ELRamZvFxDkovK439n3ni/fvSdn1Ba2RpjHAJxA9AVEwlk8xHUuhEnpkrzuCsG03vv8E75/esbiLLj4C09akwxhdUTcYnOeJ948PXK8rL3txA8bCdz/5lhAiX3/1JW2PxyE+BdfcxoD4OqNb03VyOaGKsNdKKRvTtJBygn2nJ7PcGGIzg/QaGiPv3z0yTjRt3YW17qkkd/pir/2ogE2E2m1tH/0KO9CmKbO33WdHGITTjiCtbK83czQe1FPjulCqjRqNeSJKMOKGm0oGMShkb/Y+OSZvYCf3WTOybu0NejAVdTKRlTbQUAh5YrjRtlrNlfdyIqVM2So52twQTWKmk9ih8+f+o/8rf/bP/EdW6XuQo5vfm5tU4FPJbBAOOw/vHwElL27HrjZ7plb3wQIOV/rW6FFBMqE3Wt3RLkiv9NZtMmKr1Ai0wE9+8hP+3t/9O/zgmz/4cw+Ft/qD1se8OV+TDoE3tUqwdVM4300Jx7Pmpx8Yvy9HwxmH3SDLUipoQDr0MCApbNSu6r2akLuwD8RnYqhDSDYxERciShJinxBpxFrJp9kq9Np4fXlB5tlgTFVCQOjVGpvj8porYVV8JrUYpbX3TkiJHowxdD5feHz3SA+BbTfsvA5GDkAwyET94IkxEtS41WPqU9B7c617id57J04TrRReXl9Zb1cre/yks6D4ZpUMHnVvxMnGh4acvQw3vC3lTJgyKSc31rLlmUMkR4dOQjTfdRFaHY0gpfXKx48f+fjyQq+dPE9ErMmuendwzJNNTRvskYHzGhNoobvHTymecYR4zDGWIG41bLbrrexenVmzdeg2sjuHhhSpVJ9F2/j+u+/40Q//EaW6tYoO/YJlskkicWCWwWChGBPreuNHP/wRP/n2J+7tZJYstcOUE3QTToYpeHaXETG88uX1yrc/+ZZ9N8V9a5112/j46ZmXlxfzRvImdSm7wX0Cwwq+lGI06VJIEnk4n63BeLtxe35hsEMO/Ypan8aUwuYObCNybVZGa43z+cwf+NU/xNPju+OgelvJCwZFmQjQXIin7KNwHYrsqqyl8PzyyRp5u+lHjG0WKD5zoTUb+am1sr6+Wn8siNmxjOCCNYKN1lqprbKXwrpu1gsZQss3wWKMyIwxscwnltN5FM4IcHq8EFPk9eWVUos9k2Bq/Y+vV9bbDbOC75zOFxNIeTUaZNhOGD/+dV0ppR+sKVWrulLKnrHer+uoxv0Z7mVn8/7i3gqvry9cr1dzHvZrXa83/u3/3b9F6+a2HFI8YDWJ9wAMEESJooh01q0A1ouopTrUOwYC2TwN/H3ynCh7t56IQK+d3nYkR85zZp6dNBKEbd/Z1sKv//qv/9SgfTSAm1Vw4/oGnPr20BCRgyE5tAmjrzOC9V3c9ns/DqCWu8Hh29ddtW0D1ZZl9mFvFvbUAzrclddv53dM82SQkrr+SDgU2NOY2zOa3GI2/DmZb5Mp77nD6R3rkOd5tkHwfTT7HD7wjZ1jNO90b0xaSRvcT8sCMdwbwWZl3XyD9AOzv5Yb1+dn8rwwT8aSatXGZ1a/WbVWNz8zJtIuwbDyEKxB4xlWdlhgK4VtW608jba5tm3jJz/+ESLwg1/6JdzswMVC7qYo7lEzHkz0FNw56l3EewlWMscQKLWaaCdneimHvxNqDA0y7KVwvV7J00QSMcdZgefnV/IUWeaE4iV3CK6OtvsQnBWU1B9aShYUvbGZpswy+XwJd6QMySaSSdt5fb3y9G4iuddZC+YDZbCCMLnupGg9qsfHhwcTP8bktLhyuNuqWCYTCRAivVRiMCXol+/fcz4ZXl96pe/F7AqA5XJhu2303KheGRIjvRreb46mBn1s6+riqcbz6yvTNDNpJ+/FqNfVcNSmhikb5VK9HwU0oYsx0HLMXC4Xz8LN9CxopLVywChVjSIoQcg6OOm2njtONvB+gIh767u7Wq2FtVQup4nsDJHrtvL8/MLX88Rt2wkE5tPZoJ3e6U7rFIk0GnXb6V14eLShMmYhb8G390rV5oNkAqtXA2FaLAj5vazA6+1K6Z33qpyceTM5Ht475GmypG40PWN406ztlFYot8Lj+QRd2faNFIO5vhLRbjBG3TZzM/VZMUkn9l5YXy15uy03G3QzTYh2kGjZSRT+4z/75/m1/8d/bs6xI8MVGxhrC9TNDbFGvkE5CXEGTwgGuwjCPGWuL/tnMVfGTJLWXZUckRRQSQS1fqJg9h5lN/fd55dP/K2/+f89KPfjdWgcRtBXt+rgbtUxLPr7EfwH3GSsJhF7njHGQ87qV/p7Kwfx5PDNNXx+SNi8jtGQD4bXWk9RlOjiN2OkNXozK5aUbRjS0fASzNEXDldp7R3NpoWLqly3zSp9bMzA5O8dREjDAGywH7oqIUciZt2w7qtNTUuJSQSqUQaaeYMz7DVSznbTx4IcJ9jIlt70LOI0mbpS7QGkaSJL4OV2RdWtlhWmlG0DTVBb8MlRbtvRGj05pVA6t9sz339fEOCXvvkG7Z0pZTv8/H1KqYRg9DVrjym1dar4A1MxHDs21m0n1uI9i8y7Lz4QJPpci3JknGPB9lZpOfuozwASbDiR08sEmMb86piozRZVCqaULWVj31aWaSGFRAv1yEYPN9Fog9JbtwNtmMnV1tjWG1VBgmkauiguW7Cpc62yzMuxCMd41cvpREcppdKb9YzmZXHrDQuggvVw5tmsTw4+O4HzcrKqqCl7t7W0nM7knFjXlSGgzDmzb4Xv1xspCA+XBx4eHu7ZvirX1xeur8+c5kzMibpXluXsrqmdWna2Uh26aT7WViCKqd33wr6WQ+MDDTS5BiOaornsaDDoTHpHYzRXYcQN0qzH8+HpHT0GMoGGTUFMWdhuG0ueWJaTKY8xiOHp3SMxJnpdKTRyrWY2KYNnr+6ya5tQwnDfdQtqnMGCkkKilI0ike169arT1OPNK6tlWfjywwf2befl+YUYEzlnHh4f+fT997z0zvsv3hvM9wavVocn9t0Os/PDgojy/PxCKRuXy8n7GsF0I63xcruRU+bdPBNjouyV5+dnY8QFofqUwTRlZ9B1eoT99ZV/+4//b6m1cDnP4FPQmveqGt0SEDHdUSAQcvSJlRaAt7WQfYAO3Wa6m9NxoNdCZCKGwM5ujMkcPTNvxroEQlRmjPGjmvj+40f+6l/9z/n48SPvP3z4LDCP14hrIy589uqWcnbBNTJ6HMwpGYX8OIBGCTgqSu5ViDCGXN1f6geT/XNj6ACCtwWIaiaIGghNLXYB27bRWrd55248aIJioxn3cej551iCY4aCIU/odaXWnc2HpFW6oRGoqV9M2OZNuz7m9Aq1t4PSeASqYJh/q5U4peNUAhf6jFOqd8KUDJd78wBCCDxcLtYg2XdXlWZq8OHwEpliOg6g3ho5RGYffHS9XkkinB4u4PhmnGfm+UytzwwrjpwSv/zL31gGrbbqzEURd+sUn45mPQ9Ddc0HKcdMTWYSVnojE5jSdDThRY2yOLlVuIqwlkopz7x7ekeINocAEdR59CLCw+lCw7LiAYNsjq+nnNhWW1HmH+/3C0HS5GZ+TrGsFWEiZMsUYgx88eWX5k+VMrX2I3FpHT5++y2gnL5ejoyn1saUzAJZVMFL0yHYaS0QFlN69l0Ooz6R0Twzv/kxq2CYxT08PrLkzFoKOWfmaSZOBqnNOUE8G9aq94EvMdqQE7aVy+WBUhtl39HajNSQTddSy+5NS6PqdhdhSbfAerk88f33v8leNp4e3zEsCrqKsbd6d4ze4MHS2t1zzLU6IsYAIZioqHL32NnKxt4bT9Nke98WvA0hmhZa7zw8XFhv27HmLfFyFl3rEKyfum83kmSmefLJgu4u6pm2sYjgdD5zfb2y3Vbm0dtTywhtIpn778TItt748Xff8/LyzIcvv/jsYLhDJsZeLLWg3SbtNU8yYrLhVmPGs6oFnXdPT8f1DGFZAOZlIsd4n97WbdTtbd14dzrzH/+5/4C/9mu/xmnJ4IaBQ1QLw7LGnk3EGEwW39rhs2aCNfHGvFHdr9tKUJjmha6Nve4kH6gkarFqr4V5TjZxMARj/W1WzYU88xv/8Lf5R7/1W58dEp9Haz84RobngV613yGmt3ASeDJzh+P8hPpMcDz6MOrY5++28RBL+y2x1OB73VaGWavY/rNjsB8V85QjLUaDNbvVaeLIB920X+rPtJZC9GFVyX8nT4mCMnvPsrklFyGQpPfDIhinTqUQwSer5Wm2silEpx4qkgNLXuzOqR5BELewHvBIiDaXuoHNLhhwTTDbX0nJSv94dznNbrqHKHXfYdDlYiA4j7+IsFweTO3qJniXhwvn8+nNw1D2ZiKq0u3PEBO0RlUbvTg20WAEdO1ID7QsFuB7o+3GCoopmSpWLYMP3eCaeV7MjGtgzm51HPw9r9crj48PnjnZnDYDoK3hLNrNJoLA5fEBuuHrs5hOY8eYCibvjagzF2pSTjGitbHkmZoyIY5JVXpkExJtTkcSoWhnUsvmsltalF4JvgAHH7zWFWLgHCK9NnQoOr1Fani0LTS8fxdj4hQikgOFZtVMC0gSQhM6jS6Bc5pQJoOzmmsa3J44icGd274T1xsP5wfqvpuqW8xQ71ars3h8WhgdSULfK6qVaYrkuHC7vTobbqE7nGnuxOJsKusd7dU0Ja8vr3z95VfkxSCV5vYWtvTs8A0tkM+RKZmHUgqRl/VKa8pjXjxZige2W9Ura99f3ZOM15cr33/8SAiRd49PvHv/CNqdbTdxzEVQYZ4S0zRTazdvpmAisRgjrUVKXVmW09FTu5xOvH96ZDmdrCoSMRuX7ti1R7GHy8NRTQL84Ksvue43xkCcuhdzW5iMbRMlsN5WalZOpwsPT0+k7C60rR3Dg4onB6/Pn/h3/w//LnvrTPPwR7JkDe2uY0pAJ4sQp2hxJ7SjOd5rR5MN44nuYKqtMe2JJoYISIhoPwhTdiBkiBqp7gSr3ouKwWj5qQU+PX/H3/s7f5t/4p/6p+4nwpuAbbdF7v/yBjkKmCur/dV9SqVddKETCQoFo6Uba9a9tJxl9nt71vrm/4dEy3zdenWmUbckvmNz6guKVH+/OEGr5lzRfB5Fd7seMfis++jjkSiMpC8lE+k1lxtMy0zd92O8dJIYD36tiDClREjZOuIIOdpwDVqjhQDBKV5iAjorlT1Ta9XsHELkdDkjXdAo1K2YO+s0HU3eqpatTWIWwARrqrRW7ca/PTXVYJAYEl+8f09pDe2NKoodQeGzEz0ka7iWfaMPtXdKRrENxqBoqneXTFcoLnm2TU23sjGYiVfvHXGNhXRztuxNaHsjzta8eni80N1iwzzg5RC2hJBs5gAG4RQRG/Ua4qFPaXXM6bZAvZdKqRvpvGBeQe7jL8GGQV1fWBazmS6ho7VDcwsIwbIx7LD64v0Hamvs24pOk1Vm2dStpTTmJL7ZbeM9nC6oez+9bCu1FM6XCzEIu1abpidejYupcweGWnsxyA6s39PUs3QTnhXUPIswewBVtVK5G5W6bIVr3kgxUVImTDOhNRisLKddj9nPAKUYa01EeLycaArrT35iB3+sxsLLJ9zNwXRBBJZ8ooWdUirremOrOxMLYD2L4AcgYZTvwYWjd2vo7LMg7LRMiCp5Wszk0cwTrOTo9eDwp8nMCaMGn9FgPSJwNkw3HN16FNZwzUc/QbzBamt3ni1Z0642E8C9r2zegf1sUIs+Yz0OVKWLsG6bHQJzZvaKevQIGP0/oEnn0+uVxwchxAdLVEo3eMkFlXutxJyYp8Rf/It/gb/0a3/FPODEFPdNjWHVqlVzASFqJy2ZOYhrSrq5sbsHV8cMPGcRQq/0bhVJCDOgtGL9w2nOoJXWC7oZlbbv1Qz4wOHfiJZK6ZG6N7777rvPAvTbsD1mSL/966MZ7XHJfxCV4NBQc4puo6oQg1NZo40Vtl6u7YND9X98gB5V2kjWRr9j2PIPxGa4Lku/Izdl23GzBP8mPsws2rx1/MCUQdVGQaweETHyzZQNAk290qLYdwpKOmheTrPLeabTkdqQnK3doaDR80hV+pusaFBoxxfFiyORaHvDVbY2HOZudpfEsoiODVzHH8C6Fcfto/nJeLDV3qnBdBHRP9c8+7s3y4Tg79/b3eYD9YUvBhHdbps1cJ2yqtpN2Cdy9CkgMHpJMQZ67UeDWUZAz5FTWDxwG9yhMZmhYbWpdahNB+vNZ0GESJZAEqUar8yzECGmzKhVA3DbV9ay83iazdabjrTGEMW1qpStOjtGyCFTtJJ0eM4Ht/61DYMY+cCMFLtv0YF1KkmE6myzHqE3C7wpRArGSW+9mwjOg08pY0CMJQzGYirsarBASGn0Kg+Yi2Z4eHeIramJLxN272KMtLJR6mSQSDO6bsqTsaIc6w3VmrghBoPZpslswvNEL8V0Eq6uTzmzrRu1VS4Xs+rovbuNxsTlDPC1ky/MK2eIAlUg+MQ909uMasq2Wp4nNLpyNRmRIziUWbXRyw5ETvPEFCM5RU6L6Yp2n1YnwQ6bYRw4fIIsTtv+GhV4iNaCNK8j85kq1Vhus4Y3exJn95meI3BvrA6Qo1YbaJWTrdGUkmeqbiTI+KKdEBKPDxdyNqit90II2arGZlz+XI0Ce1s3/sS/9+9Rt41pnp2t5b27EY4jNhp1csZSDEbRdDfm6hB4YkJ7Z7ttQLTEyDUjvVmCMk2JHCJr7UhoxDmh1cgzrSkhiU1jc4hXgkHpv/Wbv8Hv9/ppA4q6NoJa9aCix3exHLofyXZv6nqhxnTsF3V/L6tm0I5qcHiNt+fTm4twBbiadsl6HhFD9tqxDwcMWIsNHBp9jlorpevheD3I0uM5qN4dBUQsBmVV9t2NQt2FIR1D6HMiB+shBIQwB/O58WZa2YqV9U3tdFacjunq0a6moJ5MKWqD6Y03khxOYp498BpfX2uDnJFWWbeNacpMTlGdckaiKa9VjCoWYnRjQX9Ao3EsYk6U46SN4Ritaqwpa8JogOj2FZISsVbWokxTcOO3fnwX8UZ+DImQXRQm0D0z6QPjdighBDUVdBhmWXqwaYoztVKSOz4dbAFoaZ49WrktEUpT4pRZpnhg8CEkWrDvPc0Tz5+eUW1oMOPkFCMUe/+mTp/TxkhyUwjUaDKnVqvNnhiDXKqSfFyC0ZE7Hz9+IkWbJ12nmXZHzI0dVSuvtyu9m3HY5XQmdGvAju0jVTFXtuHPb5tkShMh2oQ602U0xA//7fXK66dPpF9JzNNCj90D+ljQFuxKs94ODVN/98bqtOFt22z+eDDzs967W3/XI3k4+gVeIZwfTuSQvDyXw1xRBPdKSoYStkbIBnGoqt+RjoqJyErdWUu1jDgEylbQtvFwWkzE6BYOyhhoZeLG1ryZ6wNzBLONdsDErF1G3vOGWCNiJIQQjdpsTfvo0OPRe3V24B1LR6y/eD6dmCYjdgw34hgi1efPz856VLG58CFOTke2fd92c+8dbsdNO3/rb/4Nfu3Xfo3Jx7xar9O+QyvFrq13klimaySJe78jpuDQWCBJIObZlPNdaBWWbHRemul2emswZxYSezVb/3Sa0FbYuw9Hs4DhqvBASJnf+e3foLdiMPRnh4KtMzPvvOtaRhxv2iyh7N0SpFpAC+qjgI3qbpqbrp2g7o4b7oOM7k14ORycj2fjf4q7BzeUmIwUI9qPymXAVqHZDAyaz4Px9Y0ovRR6Chb/xgHRmrH9PEFtzhZlECwc1gRoXUhjzsMUTLVoD8upfwRKNwpeAq6rqZQf8+VwmARvBMc7vh96ZKc6Fcx4vnmyLKXVSppny2iSeB9ioqbG7bZxvpwP3nAiIDHy/Hqlaz+Uvcm9j45HqsrryzMg5quTM6/7lfXlhaf37y2oqwni5tMJsCwjL0bBLbWSgeD+/01NJxIHwwHDUyVGbrdX4xs791x7p+wGWVwuZ9MyeDYYxGZMv3pTd8k2XB5x1oyf8ohtni4JTeZgK9G8lpqadTVR6N2Gg8zLQjsVktgB0dvod1jWmYIxHPbQaXVH5ox0e0ZBsT4DplSfspW219UcOU+urj6d5mNjXJbzPTB1uxnVK4daKznbzPC1FE6LZaAWsHysZ/B0eTS/p2zN/1Jt4HpvhGZZ0KfnTzx//MQyzzw8PTpUpz6H3CxipDu+rInWO6VWugo5ZvrUjgpiiIymaeJJBpx2d+d1/hODrWUB4E31KmJ+VD5zOMdEFdvURbsN4HJAXIKpl2upHhAhdrO+mJL3BzxxsMBvtEwcRrB72e8iTLVBUtaAFLJ441L1YP4oIE6FNJsUOzgGtIncsfU8ZYcgrHnbWyfF2asSPyCcVSjRIR3vV+KiT0tDK7VaD8oyVqWF7kG10UrhN//BP2Rdb1hFHg3WEIN5tTcbbiWBnANLtvnz2q2fEJ3Wnrz3koIN/Oqt0gPkDPMye+JaUW3croX9trKcTgRNbOtGPNveCTlQ9nr06sAprK3x7Xffsm+vLOf398NhrHNcPKdDV2L9HPUeVa9KKaYL02YHYaQdMxii2AC2vTV0VpvdgiMR6mLabH3OQTUPITp71GBtHWk/ckCEJn4fVF1FmjoDMZCiG2e6xk2bHl566mrw4d81UIDRgxLJtPZqAjxXcYuXaykM+ww1f/rg/ic41TkEIYp56VSHmmK0DHdQQKt2Zz/IMVY0xsg8m/6it34Yni3TxLbv5Kx3m+zHR/NN+vSJ4nOLDUqwZu28TIew5Ri649jdsH6Y58VOVl8MKUXDXG83W8zTREY9kxOb3ZoynF18M7jPx1jGelQtdCXPCylFPtXGbX0lTTMpm2dVjPNhId6bzS/o/s/dFZgxBG7bSsyZxbnrTW2u8YjAP/zR7/DyeuXLX/qKx4cLY2pYQcnuWjssgc+XB7RDLQ2cuqle+c0p0VDzssnZ6Oi4kVozoWIi0lz2L2LeSYLQc0a0M2fLAm+7Cd2iz+Vey4Zq5zRNnBebMdFqIyyR+vrCqxYezxdKaXYNx+q2g6W8bpzPwbI3TWyt0rbt2ETn04mcs8++fuHp6R2mbu7gLgAR88MaGd8QOkFA55nLw4Xb9XaskcEwCsPQD6sOdjWlf3B/foBGIWIzH6T3IZuxvkhMpBgpvdFLYfdwH+eZIFCqjQG2eca2DqNz/msDCf1wLiBwaIpqrWa54K6+wfs7rRaDfiWwzDO12vcN4hVSa3TuBAwQsvtQIRzVNP7sTT07/JLsGo997AfECEwhWF+p1Wqwb57Q1ijenD6fTzYGl8Diyu/WO+u6cXl6Yp5PbNvq38n6DU2hbxUJmZjtoJdgxpB3tpvSa3M9UiJdJkK1+DJFg1qeX66IdGd0Fbp0+g7xIXFOma2udmChlHV3G/1IKwa0aDOHhhiG07R6D8QOCXXqaorJtV5ewYoca6h7vJRuwVRQp3eKU2bd4hvxyZnRk1PbCylNFvhHPwNoPnwpeoU56K/R78uoQPHzulTrnY0qXb1S2vbdRMN4pew7ZUCZBv9HhhvzqOKMDWi6q1aqV0RKsIrGFzRuYRDkyDZ6geq8f1E4pWzZcLQb0VszRWptRDtLaU57bb0ZgwLDjm2QvLs1lkKpBjOtpbBtO+tW2Url5XY7LB6025zt5XSyyVm+2IeFyPjfu8d3PD4+kUS43W5cX69MKR12xyZqcmVpb5TebJ6AT5BTEWdYWZN8ydma9c5Wqhge/v7pHZenJ0IUWlNeXq4299sDkvNSjmxl6FDCARcYVlmaCfqiepMqwHVbkWiLc99tbjWtc7u+ot3gvSlm799A7ZWi0MT+pyjff/yel+srL8/PlG4iuObXArDfbqyvr0ZfDWI2F6qc5zPVg0B3iKv2Sq2FGDFRU1B627ldX2lYSVpLZV9vxCCkKZnHDKCeBYpYhl3UWGVr3VjLjgZFo7Fu4nyyqi4E8rLw+PDA7IZ9NinQg2cwlo01NMWnoNms7pyNLx8cIhg+OgOffbm+uuWJ/ffaO8EnctGU4GNJc5gOqNE0CT55LpjTpsiwfims20prxXpFHYLTtsU1ppaR2xM3lNLfL0SEUaWaEWEgWoAEkgiq1Te/bdjWPCghXgXYAKqmoyE8Aq2tMcGoySJwt4nweSq9UMqV6/WF1stB3rBRwP0IJKpmxEgyLYnZvRcLMDn5XOlqPbNS2beddV351V/9Vf7gH/7DltTRUalG/e6NKJFTDqQczLCumx113cxCw9apMuVE6Mp+XYk5mnnhfCIK9H1HFfZitiqZ6I7jnWlKPJ4v9j33apC368okcFRyQY3qXF1FjmCTB4Ng4r2OSCeINdNVu0FMDftfb8a6Cjb+OE3ZHRO8CsVYReKuEKWaY4WxzMJR0fdej/sNHOt1DHnzO2hrttnPQ2Mv5jpRGY7Wgd7d8DRYcilqDXafeOOVqNkCSbCe3l7svcq+0ponjdHsdF7X1YaMjbnEVlbjY+1cXt47pVXCrkjOTMGaz80zQzGy0zGAZzUA14KEl/q9VuKUiSGhwXBeg3wgtmb+R1gz6MMXX5hferXJU8MrvfbREBr2cUCwLFpEjqHvc7JmaX195dOnj3z99dc+bSlSt411bZzOJ4MWVKh+8pa2k7Phsss02bhUcf92MZx2LRsajfY4HvaUI9N8pvfG7eWFd198AO+PjMNhGKelPBm7CscYY0TVFOw5JOI08wf+4K+gHbbb5rbfmRohYV5a0i0bk62hXv93kXumgWH61MrThw/kmI1hpBbY4v+PsT/9tW5d0/ug39ONMWazmrfZ/alTVedUle2yXa4q23EIRHwJihAgIJAPCBQQ4l9CQiD4iISIAhKBhCDAkYhDEgsFiBxX1fFp6jS7dvu+q5lzjjGejg/XPebaFRxXLfmots9Ze601x3ia+77uq3GOMo08vH/Ap8TNfsdcK2P0pCEyFNmQjwa7ZawSt/rFBc84TXrv3rPY83/96p7WKsdxR5t2MqErhcXyreVNFLicHlmXzP3dSAiJmmXf4IO+Z2Obla2qtPnCNI4qMkIw8ZK/GtV5NKPprbMuEp2dn0+qaI8HbbhaWC4zfehmN4Ha6RhJPcg+O70w7za40zmpumVXD5d1EeTZGvvjXgd273TLjvUxmL7AUgW3rsHpOdYiq/VrtW9fyQ6GVlGYz34vh4Om0y2YpxlO/le1VUGx1qH20lTMOH+txpd1JoVoHa51mU2QZIqBWhuff/EFr+/vePXqtaptw82v1vfuJVsDm4vIl8z/Oey7A610XFnx3nG7v+N7n32PP/nH/xjoBBfpvbGWheNOyYpjDDinuVKv2yW3XWiqpXItlLLSCgyj6LrLvNKcwsBk/V7YeEjzecY7z+EwqUDMM2OKLLbOeqs2c/K0Xnj/8I68Xq7vQd3AhvG4K6R3HRLb2Pe7TEp1srrAW6uinTqxKEvJnM8zN8cbfWfshB5tnTjt4YbR/P2V/OGtO47BXA/allOnr5Irl8sFj7chvjkW55WSq5hwXV1RtYF5a82cazUvK2bsF3ykd+vcQgQk1HMpsZ8mFQUgWbtPYgq1JhuB4ALNK5d5IyHHYbRB7KyqL0RqFATg1sLz0zPOdQ7HvS6DIeEtznJZZpLBSFvO7LXy3qpwrwFXjIkNRqB31svFksi2oXEjn2cuZgERYmS331HQjbzbj9R6xzQqaGOthfOy0HJhSAP7aWIpDUrGOfntl5ztRofoA4tRKnPYhkD6T+mVp8dHai0cP/qYwzSx5oy7k+DIN1jJNtR3Zgqm6nCtitMc4iSzLjzOWwg9CjYpOdPgGnDjjNHhu2Pd7MeDJ7mIi86U26owg4MPP/sUR2cIEh+K6x/ovrPaAP3+9StSDKyl8fTwRLy/Jw6BMY5c1ouookG4eS+Vy5o5jJoFjMYEqwiHnQbx+muT06b3wv5Fp96U7dpeT4/PdghnetW68shTZpsXXDfsNjQ2GCPGdA0xor0oVVvwYENw5/bQHKfTmcfTE69evaZWVZPTfmee+5XeI3Fzeu2NYOv6u7+b9rLxBc0YPNzFU3fdbDys8t0cCFbv8KXy/nwmOs/+sGe/m6CLwkx3PD4+sdvtiPElh7o7peGpE9047P56CJdaroPljYVF73bhmH1CDNapSFNSfOHG9tx3Owofo4XL5OsBowMKhrjljIhsoBFUv1pObxAvVZeStFEFhycE7dvWOsOkQlAHqqfmzBSjoOOccU4iWe89/QVxVbezygK7mIdVqQVXIETpblxpXC5nUQa2i5jGkHTxOO+ZhonLZTazSa6FZje4MtfM5XRmXcr1WfaNJnqNShB822vFB09F+dDeB3OJjriouVzrVSu9uyuUDLY/msSxvus88M0CuTA9Do7OBp8bCcL0F9XcWNUBVzsXRc7pXVCUyBmVXBprXhgZkTVqo+byncvNBMK58PR8xgdJGXIu9NKpvVCzkTzEzLgSDFRA4OQx04WNXRaZqB12e1rwLOcLl1x49eoe34OGdIbXxWGAYWCsTUEVa7aMbMUUgpnFGbNmC8UoGxSE4aJNxmLOO1VGFXoIDOYPVDYTQu/JNivwXpYMCkdqtCADsJvDntLNv8fJmK6FqAqWzuXyzDwvTNPEuNmD22YcYrqKg7ouesEmXjYUr1+/wofIGCPnZWEIgTGNYpE0Ybg+eLk3upcoT7xiPnvt5JpNGS7GUXMa3E67iTTKSt07NLD0QHVGw9Ql6VOn1xfmhS43R2riRbemxeeivFkUNt+IiDFC7SQvVkS16igEx2Sbm+3wpticyRTIa2FtRlu1eYqCXsKVPeO9ujrbl9bqe95+8KH5bjVK25wp3TUwZRMN5Vo5HA9XIz7AoBMjRoRguP92gVdimGygB3ev7jkcdozjwLLCbrdjGIZryM52YC5lIbDZdmyRr8Jwl23IWDWfi2hWtxblqayl0uo2yNel4oLHlcLj6cy3X39Fd/A6v2I/fWS/NxJ9o5jxZKsqPHoXLDHGSDwcrkLM7e/srVGdp1pHMIRgM5ktvyDyXY79spgZp81hQ1CRt3XmtVRCSnzw9g23t7dsVhAhKHwp4OldthMtZ5qXojd4MdF607pIScVLa5YlQqfnRlkzN4eDXU6m5F9XPnj7mlYzoKjT6AXvdJwGta2zrMWelcSzci/211lA73rO3jlaroI5cTqDepeIzrRNJTe803NMwdGIVxdZajOTQLucbI/bZFgzjSZIVR5K0NqiQxpT76G5zyZIfWHOGfUfzIiz46vjSox1G0FIsKDr5qJta925TnfVYOtqqXum4LY5g/Nac0uW2LS3SnTQg2jlzinzQned5pk5yzRR4mlnEOaMQ5fW8+OjzljXeX5+UgIpjajq7Dtybu9ppfD8rEO0vu5M+z0eR+6ddckQPd51g386gxPTcT9N7KaRp9MT8+XCfpquopzgA6WrM9nUstFU3r1W3KD0KLV7zXz4lfHQ7WLYIlCdU3Tn/ZtXGgrjqfZSkg+GBysvoZTCcj7x9HzmzQdvdbA0o1N24YpUtXi1a6MUTDjlPSFGTs/PtF64OR5prjOmkefLmW+++ophGnnz5g0AtRh1LAVVVU4vAmPBeKf/62isqyCuGBKX+Swuu4+4BjG4K1OlUnE9WDeSWMmcns+ku3vDT0WblOjJUvgc5gEErRfWi/yKhpAI3ZG7VK0peO6Odwovcg6fIkPrVy8nMYNGcEEsN2TkN19m0jCSqzlKovyC71bjrYsvDhIfug67K0uqm/keJK/NvSGnGqRvUI3EhZtqODqJgZwTRBSHUUy3asIhByVEduOeJQSck7Cr5E6t6/Wi2dhNAaUffverlM06Xv9/WccXvn58pJaVYRx4fffaDhQItt560N/p08D9feRw3FPWIoioVnufBZ8iB7NSr6ZXqTQioqT29iLU2yCpUo2w4LylIyqCstYXl9v5MrMN8+WZBWGImqflzHmeub29FU21ypl4GtO1y2utKgkSz+n0zLJUpmmkGsXZC9CnWyoabGpr/e0daLnhBvm6/d7v/wH/+//dv07vnprP3Bz3pAkuT5oxyIdNJUSzn+u8FOa1d7Px11oR7GeMNP9CMoijyBUhRnwUvFN6IRZRjceUmOcV8AxjNJGs/u28wrzMslPfKhmaLgkrXloz/YOTNsd5oQzbTLBXrWPnvPzmTLui4bWKHQPldJHjSTjNgZrOxU0Hg7kKgyjYOEd1jrVkG+xrTujoFmm8UrIK9asXlDHgauv0milNgtNqxXstK2kIUPtV9zOv0iKRK8u6MFrUQ0qD5lG9E4Ph2tncBsuysOTC/rDn7evXrFWV8Xg4cHM8UHKlGAzkzNzr3bsHWkP2Ew72e2s1XcehX/R0ema/30MIkPM1FQswVpJar+hkTd6CNuGWi9yqgldSSuR1ZV31QUL484EbuRaSk0vm5mnzfl7orWr46wLNdW5ubuBGYTXzaSElsSdciCyrLMC3YHjvAxETGHVnL8bYNlZhyx9/Cx/akrdkM9DtMo1JB6LvXpdC8HJD9QfmeYbueXh+AuCDN2+u3lY+cR0u9t71HD0vlsFNF5AYLI2A8j/WUvF4iJFesyrnoECX6Bx0WVpAt2pJh846z6x5tYGwnFHX3AlBGQ4xCvdf50rf7ZgsVL50FCRjVQohWKaFKuBas/QeLePdYJREa6NttrJBIhhEGWIiJqxzGex/NyV7lHCo2sVYWiO1xOsPXvP09CwiQq+k5MmLdbAbddq566XRO5b528wXp8siRNMpSi1yX52mK/MuxeHKcFEms55f7gXXRLuVKebGT9fMItvcxTt1Jj36K5tqO6CuHUTfOjwzA+yN/X6P9/0aQrVRRrsTMWKjhh+OB1KIvH//nufLhcNuj3Oe2dLdNgdmDM4zxEWzxXlRXswyE2Jg2u/pvV2JEC2qW3RGUGk0idecp5bKPJ/5O3/4h/zdP/y7/Af//r/HOAZevbrhcnmmlcK0S6TJ8hGMVeScVfN0yrwhBhgkYw6vwZPsAO2I5eVTpOXMPIsIklejhveA9wlaNpeTRgjC/lezI1HBIOr3dbotFot1YFHF5wa9dAn8erc9Z0mDQYcX1VhK9G1YrqCyUhvV5p7OOzyeteRrEaqLQfNG6FeIK9u7XOvmBNsN1lLKZa3mcjGrQIgxcJmXK0llG3udTid67Qxj4HRalIgZPZdloRc92z/7sy94fH7it37wm6QUda7WylILsdLJl4uqhZSEURUJNeI4MrmdbKDt4AlJQ+7n84nn06NuKzz7/Q2udeZ15nhzIOAUcGK45pRGkk+sZVHlHi3O08uoK6+L+MytsptGulMgzWZCVb9Dsw0xsT/sCCmqBd8KAZQRUGgsl4ustWPk7QcfiMeeld7muvV7xvN1UV0DhnOHDtW9mM/t97vrpm214GPk7uaGw+EovLY5LWR7K61WRUK2RgyDHEdtwQCULr+lih3kXjhpKYX9/nDlzK+tiqaYG/N64XyZmaaBaTqo6KERcBSnxDGCBlKhO8uGkEq+LCvzfDHPn4zb650mywzItUEveO9Yyso3777l9PzMNO346OOPGFMkuPhii+5h3I2qoJyEVkutrJcZ5xrDMBJSoBuFLvQkG5bWOSRhoC7oUqlmR91sVhXsACVEQpg2WR6eQIoDPgZSivTujemxuao6Ts8nqoPDzYHaK/N8YV0bbtVFVEqz9D0J8ATPJPNBWtmS2Dy6pGptVBSi9Pbta6v+TSroOs6SHIXdyhY/uECjsiyqasO0s/cuxlWZV/wQcYMxnDoyseud87zQa2W07IB5XvBebsK1VZY1M5ieQfCQZVH0wJCM7+6D/MScY15XHh+fmKaJ+7tb8roqQ9ps2h3KMi6li05ZteZ3uwPTtCfnFecjKdlQ2SYmrncCCe80CC9Zrrtp2KvIKCuH/ZF/9b/73+Mf/kf/HnevJtLoeD6ruvVJe6Hb7Evdlqjoyo0XZLSfJkpr8hECxhA4HA7MeWFdMsMxCV4KDmaxKXtrLKXgvZTYl+du3cYofN+LTNPo+NBZL7NdsuDaJhJ0VrObiby3C31tNK8OuTuHdxL0tQ3DZ5vBYJqRynktDMHjgqNSaWHAuSYDQ+dwJbOpnreTbF1X0eV9EIm0dQpwmS/W2cPju/f03jgeDvQOOS88Pa9WtGhGuFmNPzw+0mrjcHtHKyu5rvhsXX/Vur27u2VIgwWZFRtDd5JLxOgDficDuGrii/00cbmcqZbu1hFVEwe5NcaoKsw7xzQdiCmR4oALgXEaxXxwEsBgopgwJHsI4K0yckAMidA7Lgqe6aXKk9+q+GizjOgVVN9NxZ2SqIo5L5RSrvz0arf8vCzsdztarczLwu3hQDNxWuzKM/bOUXtlHOXg6exS8MOAL4WlZA04zXBsq9J6a6xdJnFZfaeKENNWBLsonHNXmEn/3NnZ4bTaPObpfGJIw9V2YvDxmidelpV5XYhJbq273cQ0jGohiny1aq0y9moN4qCKw8y7Nl55jZGb2xt6r8zzbPRhiXJKETyxhnAdgsY4cHf3Sg6dNrzC7AR6FUwYx5Hgo6qk68xjYw850UuD/K8ckHyE2mjOicrcO8WsUpxX1ZKGgS0fuBnU0Eu3oby/doYxqtuMfrwOCINz9L0+d84r5/OF3SjzwK/eP7KcT4zjyDiOJO+t+3A4Jw799qUuplOs8nBscwO1875VOpVWzXLEKswe5KabzbIZL/v86DxLNRZX64yHHdEcVefWWOYz0e3xw0CrhafzmVuHwRM2bPaiXp6eT9Rp4Pb2BqJjY2/qDxWe3ukWDKV39/aDDxgN3tr0E9shdrnMDEMimP4jhC3gqRDCwPF4IzZj1TDcO3WurUJ3Ta5tzuHjILcAwzpLUa7J7/7eX+cPf//3+NUXf4JzK9F1Ztp3Ln5Pc9vhasWbMeGm3cB+t+Ph6b2e4zBwfzgYldy6LbOyXucZ5zXjCElwpFTrzpTikVbl3NC9lwDOZgCrMZ+cLH3VkfamYbxTl12KZlGxaShf6bguHUIvVsjx8lVqZb5cOJ1nWmm445HYnc5jN2vdGUzYbbbasThW73h6euLd+3d88OFHnJ9P9CYGqgvqFnIufPn1l7RccZ99SkR+bsu84r0kHxFwo2Zq3inThmZD+qaLjdZ5//W37G727Hc79vudyEfesd/vqFV/Z/z63bfspj2H6YAPxsGOif3tDRiuJjK7vZBlxrmRED23r1/jWuNig6KOPGg27/zNmmLogerEY+61Xi08NhO5ajjsGBOrc0qhilJ0phB1kDgZfT0/P+Nw3L+6JwDZNsMW8dGz/t2bmxuJx5q50aZIcJnT6STX0XXl7v4Vu0kZC83gJQfX8J/j7kDrL7hfLoXFDNGmcVS4un1vCIGaknQh9nO2wYI3Op0PYrCUKgFVjFvSW7cA+UhuRRYjzjHsdvikDmMTHC5lZV3lXFp7JGDzixDw1S5hE+5ofuOZXCBEx5JXbg+DHRI25ymCyGop1snseP36Xi125Wot0HO/HjxaX6pA9N8Jz57Gidoy6yqGVgqBaTTuuPM071jywsO370nTyKvbOzac4Rp12jeGiSCrIZpaOGw5A9IHbDOj7au3TS/guFyER59zJqXIcTcyescwjex2k6pmgxx65+q5s4nN1lp5//jANI2C41YBRnmr+mon3EjV/vD4RO2Nu9sbnPf88osvub+/5+54kLlk18DRHHu+YwynKvZs1eFYG/v9QYmKWfh0ii9mfBseVKtyNJTD4Jh2extAvmQU6J81cB38CyMwpURr/voevWVf9y7WW7Bn2ns3JqhgLRUH4KLZR1QVSd7JpHGzdxF12Jl62PP5z/+U9+f3pEHi1DR5ppyoudBDArOQ0d0SlFTXwbVO2gVqL8Q0EpJnP07EcaQXaRcqMvCMyTIjWiFOIwnZqYcYqa1wuDkKPkbJls1JTyB368wvfvwj/vrf/AMup2xhYNWep7GNamctmVb79RJUfoTWvwSNFde6YCHnrmtxSok+2rPByAo6bJT+59XR1c33bFlkINmUs3F6euKbL79kGCdOp2fmnPmN3/hNQnAiFMVCXlZ15OsCpdBTJJ/P+N0EK+ReSFOScr1VFcc2h/POcbi7FYW+yz32NF8IeMGRSMcSay22OcSCWBHzJDh/zZdwxrVuRsdaLzM+RSVYOceUErXD5aIg++PhcGWMRKD4ZjxvDUd9aVYBCquNGPSAFrfzXpGWMZLbNtD0tJo5Pz8RU+J8lhXEtNsxxGj88wGM3iesVgswoIFTHIxpMc+6mXsjm1FhN4z+/dOZdV24ub2h+aBqwTbExj0GsRFazvLAR3j4Zl4YwovJFnBVzepzbP/clZsREw9PT7gpKTPDDj6fBiKOkJLmN11V8VZ1OEzJGRJP5xN5Wbh7fUdKAzF4vv3mW25vbhnSaN3AFofoNGAM8uh3oDClqorId1lRrE1ZxR5Hrvrvu3cMXnYea6lMgw5AsdlGrZ9s7XbOPDw+sv/0Uwt9KjgPZS5m2QCP7ondpIuwVzGn4qBBZggWAxk7Qxrorm8hW4QQRHKw9eOcgwBj2yjQE6XcUkpmXQJ3d/e43rksC7V20TeRvmct1dh9OgCCD7T1xOX5mZv9nuYh15UhJMFaBkVhqu8pJdYiMWnzjbvjkcMmDMRxns8s68px3BGHgY5mC6UpzfHu5oZkxnrOOdGz3eW6H3tvCldynVev7xiGxBdff0XLjbv7OzYBneqR/sKs+Q78oZhcVdcpJlOfe3Xarb9oeszaxdsQWQeh8PFKp1dV20NMMl8MkSFGWi8GR9l8q4u48Wdf/Bnn0wNjMlg5JMLNwPO3J9Ye5ILsIPhtJqbDFa/zqLvGfj+KbpxEc14vM0sVNF0btCLtzFoqzAsMHecTSz4LYrZ2K1pHVGumEhicEhZ//NN/DHWWzf+50hOsreGbqLBCGPpLyiAyFqVZXGqRMLc2zR6bIQetdXMV6KyrzCW970QiW6KGi+bKgDrx0jqjXbA3xyPn84mf/vRn3N7c8ObNGz7/+kvu399xf3cv9CNkyjxfC1n9fGhjpJZVc1mD0nNVp19rtWJBtOgxRouO1tp5ff9K55idMblX4oeffIKrXIfDU4qczmdyExTkLYWuGbyylMJaKqlXhjCBU6CHM1ZIW1fafqRmBAmZXcUyX4hpYD9M9GQNpxMvXNWvuw7PWmtistiwNsYgUU3vvH77AfO88O7bb0QZnUZ8StR1peSVTbw2xkE2EFmMFIm6HCM74ptxK6WFPXcdvCkFDjcH0hIZhomOXW6q44QN13q9ADZ6ogvh6s0fh0FdQpeJOg610V04Zu+QPMRxJ1lLrQYLFeiJwQfCOBFCYCkrzqmzm/MKzjEYRhyiMWRaYz9O9GFgTCM+imP99PTIsq58+OGHBBfF0KC/YMGIa1+xYHoL4nFR+GutmgFtZofeOU7zhcU5pjgopIadeePogq61ksYkbyXLsZA4ruh3+0hKkbcffYTvjvPlTKWS0GfelKW6ZQqtZcqiVrpXKe/31lHRNVCWJYfNkmJk5z25eM7hjAvB6L1ip8QYWLMgwdYblM12RlBNLiu1CmL95JNPbfgMa9Ql7TtyNu4YBNWZ9jtGdtQq5ezd/b1Wi3WwwXlG03jkvIqBErTGQ4xs8Zm5ZoITSyj4eB3O5izzPlF/VUCNaSDskx18m1XLy0WxHQbdzNjSlULaMBPwK5wrF+R4pS875wnjYPtQRoPi6HcoC805ptvxOtfbBvmtVcuVb+A783kWicJJLb5lqAQGqiu8Pz2pOwjRHB7cy9yuQ62ZFMz1gEa/FBMu6mAuy0LpsLqgYKsgq8UOrJcTtWsGGWJSVIHvZGCujdAaIcIUHX/8n/7HvH/3Fbv9h6xrpWdRX3M2LG/zUGoKM+o46oawGNyoVEkNwLNd/sE5StXPOZ9P6rhSxJGvHZuYdI1lXfn5L37JlBIfffqx8rVbI4bI/fGWGAOv7u7xMRBdZF4Wvd8izdblcubtm9d6F73y9PRI753bm1ua88znM+uyMB32xnbaVOa61K8mrYYMjOP4otHwEGMVheDh4R3fPrzjs+99T4dnzpQOu2FkXmdyzhz3e4bdRHKiI5a+8TYgxcA0HNkfJgu82WAPZRivaza78EBrtmANWuidq/pQ5leFOA4GI2nhPj098/T4wKeffsZhr3jN3W6v8BgaaRhkER4kzHo6nTjuj6Yi1CYpxSL9cmU2Cip2EW3Ve4iqJpOXoV7DjLGcupJkvksOxbxuA+Qt57qWQskradhTc+H8fBEOHgcmJxfcGnTzz7OGZuM0EjGdh4gm1FrNQG6j4YlxgI/W7tow3Hn2h4NmJ87skYVv8Pj8wAcffEhIjrUUHh8e2B+PjCFQuiqLUosSxuwCFBFEQ9HQzPHXDARLXpnnhfD6NdNur1nPcMPr12I3NXNndV0Fxm6KNCfak87VRnSeaCpRH5zmMT6YYaTWTQhRM48wmN5kpbQRvtNB4Phz3dp28Plgvjtx4Hi4oayFx1aEq6eR0AAqLet9BbMGaV1mlLX360AfGvNSNOjtUlwPVR3q4AVFnC8nns6zwYGyTC+5XA0tow/C3qsu5GRkgh5MqVwLPo7Q45UF5Rz03Khe1m3O+ZfOqcHN3b3WURbG3NrGZhGdMsQIpZBbFqTRG84n60L7lZ1TSzXRp+A6kWuUKw7dTBQ1szrs98T9npJ1UTjnWeaLiSn1M2qp0BzjAPO68ObDt+x3R5blRO2wlIWeV6YxEpcGNGorBBKmqrxi+yVX8qpsGeVEdeKgeRUEMl0iyXEgjpHduGNeF+ZlUQ3sG34I4DwrneUyqyBJg4rfvpLGxuef/5Q/+pM/4W//4cfMdaHmhZLbFVoe0kCISQmVziJmjcCyoQNrziwl060LL6Wx1EpeFhqQSyYx0C16tIDRXSVYXNeFaT8S8FzOF8XeCoTjg08/wtXGSiMO6uB8g/WieSXBc7w50KNnMUiToEiCFmWBJD1SlP1M3GJzRTGupjV7oYa/RDPI/j4SK43kI2uprJeF80kDvjhOanW7FuHj83vWvPLB69daoF18aaWaiefcXCP4cK24XNOiL1U3Xuud2+ON+R6pyl6KGAnDkMilsswz005BO5fLhbiboHU+//xXvH//ns8++x5DHBinSVbQxubZZOvBR07Pj/ziF7/ihz/8oS4aE6bUvOLGCRcDg2GXOJmidXRBherlD9Nefp4PXf5U9hCTzUtat7qsyfK4lWreOAi3b5VlPkNtDHfjtcupNjhrrTGNo+iW5oK6WSBsecglZ8Y0koZoeCd0+7w+BJ6fn3h89y1vP/6Yw04VQGmV+/tX1NYVntQr3kNKkVayvF0QDFfRMLq5ojbZwl5wcM6ZUOShNcbE7d0NN4eba+U5X2Zh3UFWzn2biThlFW+V6Kao7r0IF66VZkNohcw4QndXxbViVQWBxeKvFybdXS/Hl0Pxz3/13ulV7qvLODLudvjzM7VaFW+FSV0zPkUSjsuawejDDnBBFglrLmIoVW1Yj5L8qI2lSFDobavHLc+6KtY2pUir6iacDzSnofJLQp2RIdxmUOjYfLS2joC2UYK1ZkqW1bkfBXGmlGT73jYhIAaZialXzfL5kjOTfX/vzqjpL88LpAaey0qphZ3h6nGQa7H3gTjIBj8MkwqtLu+p8zozxB0hqYP03iktDkeMicPuQMmaqw/TyHQYmcYjX3/9DeslgxME5HvHtShWpSnH6Vz3NV7FZbPK/rg78r4/k2vhbpjASw1d1kK1LsfFwLrMXBYzzjNWV61n7t7uUcsC61nfU1Z5e+VaWRehEjiLAEUzh1KrYC9zdk5DopTMfDkzTXtq8BTLHXfOSYQYIsGhyF0DcsqyEsxx2fvA7VGixjlnJgfguCyLnktMtLzy5Zdf8fT0yCs7g+fLzIcfvpUJZamseSWNA4ebG8qyyqliHJQXb5/Be09ZV3KuV8SGrvXukEVOMSFuySLuxIAGV6/fvOLu9S1lXnj3+MgHr9/o4Mwr027HYb0BXpwaN4+abNnYEW/trV0Q5gUVXeD2cOQ0TSyXE+d1ZfBBIpEUKetqN5k0BDGaEhO5NPrumNeF4+HI8XB8qbaagk4qUhe2Kpy9mqX4Jx9+aD8XGxZnqY8DeB+JBHLPdpFxzVjoKRB5oXs212XrHYJt8iSL6zgIpgsB7zuBjo+R0DupNmvpPLv9jVmOY+GlwgKdd0SDEdx3FLGbG20KgtiKJbHdpVt8CAopr6oAdRYEnk4X7uYZpsGouHB7d0d3ZmFSJUKc9ntdMsHjS6MHx2AY9VKKrMy9swuqc3p8ppSV+7t74jhYrK0EZ+tartXz5XJhiMr7nkzLUErVOsAU8q1Q7GAVxz8zhChoxb7nOoy2A7u1Sh/Ebuttg43+/y+G735tiuzdbkdeMyfvzWHWCbqx95p2IyXLkn3c7QRtlczGn+9d8OJA1GXRMs55yjLzeHom4Li7vWGaJoa9VNIlZ81MkrqCDTLoTu6vvXQYNphVVNrN/n7jvKcQ5E6K4Be5E3xnHlgKrjd2ux3F8o+vGo+NhdctwtZ7llxoOZNxpDRQjGkVY5RvWt/iVR3DMFDOmcvlQqmFo7thHEdubm6MVmlr16ChkjPrspKHytgmhmEyZ1Vx+JdFkBM94nwlRVEzh9Hz/U8+4umy8P7hiVrgzasPcR2++Oorcm82R3HXQb/qAROrOQ8RBW2tK+fziWk3yYV3TNRZsxTXX1h3KXq5FtD4te/d8WufHkjec8maJS7rzGXR0HbNhW/ff6vZQJVNxqZez7VwPl+4OrfWTDdvMEUmJM5Pj+RSubm5Ba855OPpwp/80Z/w0Scf8+btGzIdv5FqvKeUDF2+qUvOLItQhqDIHZrT+9rtd6QUSHHkeLgRVNcrzSOTQbR3a5PxKkFw0y8//5y74y0ffPwhea1XvYf3SeaNRXbuu2FHWTLn0xPLmtl5TzyvC7thED+9y1o69G74qQ07neP2Tgulobb7+Xzm7v5WEaFVfF3XnQWzdIi6IYNhwXevX3E+XcjrwjmLAXN7c0MaBwYva21nFVHpEHGMgw69OCQ+++zTKw7sujNrc93KQwwsrW1gO8M0Mk47epMR1uF4wMdBeGfjym12BOWUOyXlrWsmXwrTzjGmQbd4VyUhKllgXfI1Q8HHQLDhr+EYuNZZqtpVMaccD+/ekZeZm7tb5hAZfcC5QBh31M02IEpclC3bexgUa8lSeT6dWXPhzf0dazXn3VYZQmI/jtzdHolDAmcCO6My0l/a995V5fXgzMJY39ucuP1Yd7N5+tdamKYB7ycO+734/7VxmhcLUpfXU22CkHozVTGdtarriigzgw69VPKycsFxGEcGAu+fnggh8Ob1a4ZBMw2ZJ24mah3n9hJ4LQvruly1K//0G4IrayciaNM7zzROLNa+y5p7oeVOzSuPj8+ydYkvl5UsWTQj0HB6oGStSzdMTGtRGtooC/1gl0qIQZGxxqXHyaWTpiSzmF7+btGGZW2xERNak0uAd86Eottl2e2daIZQi6wjTqdnWm3c3d4ypkRzmxW04NwQHB2Pd3pPy3JhnhfGaeJqF21QbPeeXivfvHtHa0rzG4fRLGu4msy1ZkNtOrvdJMX1ONgz2CKBIYSRcdxZ3Wz1s5Oj77tvvyClHbvpyN3NnlY9p/MDx8M9h+NO4q/S6DXQvMV1mg6yG7Oo1BdhW62KQ01eF+pumpiXhc3KYxhsBlkrx5uJN68OlLLi48j93Ye8evuWh3fveX56JIXBfJ483ifmbB12N5vLUmit2MVs5pE5K7nTO+q68ng+kVxAXDBpPnLOxo6wbrWLrRn6d8KmnEgCa1Hx6lMgGKmh1CIK9zSQUqBVWf+oyNbRl7ziEdZ5xgHTYUdeVkJIvLl/Jbp6FquyY3OuJnsYFwO+O1n816JsoSDlevzm22/59ONP8F2+Q+Mw8PrtW06nE8HLq8g5R+j+equXvLKcnnGv7tnv92Zi5yiukZfMfJkZx4HDdAAfWNfCbrB8gsvMu3ffykvpsGcIgyiQveNiFA+9VlbqVX2bYqC7cDUbK3xHUdglSJOISX4sUR6+gLjFAaeHjuVPOAQRNQ1qO47oHSuy8UhOgsEetnkAlhGh7OsUBpZ1kUHZKDuR0qt1JdqoUxKcVUtR/q7zXOaFYWiQBp4fH7m9vVUamlOV2YPCWLzvtNIIAXbHPQ9Pz6yPT9zc3KIQ9s5XX/0ZzgU+/uQjDrdHnHeq6lqlZ0EOYQgKGNKatzAcwTelV3wT1LZ5OTXXoVdCUTez+TiV1qBLjLYuCzEmxmmCgMUjNtoq8ZLvsJqWAsQ5b6UT8RymSdnoTgfrkCzJz/DPYJ2ShrFq0c/nZ9IQaW1PXjOt1P/8S8I6CbrsMvbTjt1+x7IunM/n6yXQDTaY1wKtUivE2NkcYM/LhfPDMz4GXpm3UW0Ku8o4bu9u1VU1MaOaQUbNLjaa5zSfuJxXbm+OkAZCEyc/l5X9fk+jczqf8V7v4DjtiD4q46I2xiiYdpszNBuUrksmJl1+YxpZ6/ISqdn7n6Nv9q51272chEtVBemjGFolV/bjZO9IuSQfvX17hUCmabyypNjmHeaE4HH4NJISxDSpWzJ4WaZ7WNdnlT/FDE/VXq7zSl2fxOn3MvY4P3/LOO5I8ZbH9w+0nuVk4Bwb3H41TgX2447HeoLuGH0gp4F5XdntBi7zQl6KdSCVshZuj4FPPh6gn+k9knvnh3/1r7A/3PN0nundMa+Z1l+Chi6XmeAdqUu/9Pj0KNX4NOHM2VrK/aT3HwN3r9/gihlE1sJclW/+m7/1A8Y4WNFZDI70xCimZkyRec08Pjzw+vUbRbqWZjPKwtdff83p9MTHn3zC4XCg2HqKQ7zuK1cq56dnmnPsDztBv7FzvLkhDtGU4Zt3U6WbWaarigZY5wsxBG5ubkgx0Gon3h4PJPMKSk5slOgcw5Be8nLbJklXC3t3vGV32OvKqF3sHNfxLVDrhWAh7GurdngkCTOc8Ma7myPDNLEbd9TciGMywzRZdV+WxQbQVXTWwx5lYW9eNfIC8s5RUABMT9HYB1aFdrnTjoa/z1nhOZv7ZzB6K51rW5vnla+//pL9bgfB4YqM5YrzKBwu4oKThP5yoSHL7t47rkLz4LoyhbGNe9gduL25oVLMWmRgmVd+8tOf8v1f+zXevnmDy4Ypd7GC3DApstQJfnnz9jV0R2uFNUu3cjgcqVlGbjEE1lXzlhAia17pzpE6EO1/c4lvv/mG3W7icDwwBrGjPDJTXHJRXkJQoA3owupd3VH3qmZvDjfEEChUZSw4qKtM27zXYDGaIrg2DdDjdlHbbKJmXfOH3c6GoCo+NtaF3rFMztZ1oWVZLSt8KJPG4T/3ktBJossijYPgoGG4FhzK19b62I0jPSWWsgB2WffOftzBXrOylJK0Netq4jL5kLnWTJAnP/9KNXdTWZCfzxf+9Cd/yoeffMynH3+s9+FFGV+WCykOHKaJ6pUAWaq8tsKQ8L2Tu7Qz3myfSxUuuttNpr6Wo0Cahqs1Se+VUlYrBpRMJ1h42xNwcyNlbS5FFxxmPdNVme6PR4lMrfDrXmr+vGaRBZKe/TZLeQkBM6aMl9Nucp3H04l1KUYC0twDm6ltWfEQWJv0J7118rkrn3xK5PNsfm7SUPT6kuS4xf/u1sSyXpjrFgGg/TwMieflzLIs4DI3u5HPPjqw26m4cz5yPBz4w7/zLzJnKc6HYGaXBhf7oDnTuqzkvl7PFm+00egkaJSNl2Yn3c4dZxGrtRacMy+4rv3U0FwlhYgLgqN8UHcZTMPw/v07Xr95TfMB1wq5FKZpJEZZ+WS7ZDZtUbdirBe54PaSKbkRx0hZq1niWJnvHbU03r174DCN7A97swASM2uDwGqVNigOhz1z71Yp6wDqDoY0ErZKMGvYeRVueUdI0RS7RVd78ISAPGKQ6vVyfpbSLwC+XQU9u/2BiOP5fOJPf/5zfuuHP6SUrEr7zWstfvNY8rbrtyoTRFmsyCPId0dPUYyUvikiG9HJJ6fUxhgCDlnh0qJYGzanCC7oFm6dw+2R7hU6vz5lpt0kBKujdryLLuvxHHc7mvkngXQeyotQZXQ13grehDMaYNIgDgPf/7XvGV3y5fLEAl+C33xtOkvOjFGCv9wKuayUktnvD0zTPTmvgmKWWaK7u1sO005WAa2qIg+RdV14enwghMChQo+dvKzMWX5Ow5CUS2CDaydWLKE70YG72CS6yyq+d8jVWEYegg6U2mx4b3OQYdxgO+WUny7zVcxzO9xwPp9YUuS4O8j2uFWzB5AR3eFwwEV59pfeWEtm6i/Cvn/a8Hr7Cj4wDhO7acd+2imTu8gmLS8ry7JyvL3h/DBTeuFm2l0rq3A8UoyIMI6jGC6WMNZ7uzrnehdwvhOap8UItVJ6Z5oGPvv+r3F/cyN4rwrSDONAbRWouOgZvWxGclHaWUQzoeg9a620JpfcGCK5FIYYqMvK+8cz0Wke8a4V9uNohAj9nCkpnOrajZjjaQyj1MkBSlGQjXcRfGddMy4L1sB1inewsXgsz6M7ZSD34PFOkEjJyzUmsyFNRGpwPp+Vw+LAIVKG9C6daqwJaTHMQdZ5ei88nx6YgjRDOa/4FgldbgDdWHFtm6VMnnpuPD+f2R33jGO8aiyiT3h3Ztp5Pvt4x+7oSG6HiGKFDz76Nb73vd9iyZm6rlw67AYRZeZ1UVaDFR1ior1QtF2M4IUyV6Bm05j4zroWLpez5j7jcKVD+wDzfGFeFqb9hLMLtzbNTXop1Fa5fXWnZDjA+0bpldN8IqWR+zevwQxCe6sstTJMwzUnuwfHYb8nm91PB6NHS7PhUiCgwuS4MyFxEStUtuH56ie1lMzTwwMxpaSXhlrn2uThnnuhowq7py0RTLe4/ORVYXqvwG9avw5hXe34FBnB3E3BNc0mxAAqOBt4Pr5/x3K5sL+5Aeeuh0sz7C4OA+uyME6jYbdcrRh0eAd8N/FNEW7om0JpSn2JEE1emH0xmmmM8oAqpRCLGB8pJV7d3vHw9HjNF25FwkLRc4X1rlV03lg9LXTLqW14l3CtU3o1/URCHqky/VvLSu/iIb95+wGXy0W4v5kENpocPy2HoWECxyIO9vFwkDNnLvgoKuYyLyzzzBATo2HDLnhalq+Mm+SzNE0j8fu/frUMby3w7vGR0/Mzb968YTfcsyB32GVZrwd0c5CzqpYNU//m3TtijNxamMrVI8o89ZectW56x0dRYjfM/vZwVB56SEr3MujoMp9JaeT5fGI3jNze3l7Vv8s8sywr+327mhxqKf4zhthdcMdhv+P59KQNP6u4GX3j/Cw/rEPdy+wR5Eb8HUrppiWQQArh+1teBmLxCPJ01FaYUiI7QYy5dMZxZLfbs2kwipOjKTZzaAZDrFmCRoeYJeuyasCP8PYwKh50SJHoPIfDgf3hwHmeaesqu27rFCoSu6UYZVWPfrZPnmUVK2d01+HctejbqtJo2o3aG6fHJw7TRAvOLpxmAUkqznzvlv5WGL1/Cb/yDocXo7Hl62xA6nPzPbrucDb+EtvrrK2x9kr3sk/pRRddDOqs8EFOzl4OtCkk5mXleHNLT53S81XMNu0iv/7JKw5HObWmYSSmQs2B3/iNv8Jm7dN7ozRHv8wslwuXZWYaR6ZpMkW5EhZDsMTO1ihZHYFvTQU2UJtnzYuF+ARFDLfGEJMZkQp5AZFlnIeWlRNRirlqh4Afx6t+R35aO5wLtJI5nc48PT3hnePu/pYtF53er9Cjp5iXnDrdGD0+ymGh5MJuGDidz8znC59973uMZv64uSLXWlnnWcxCZ5BLKWpnvZcIBOcoRdzgIYTNHBFwYnq0Fy8d1/o1UH7TOjgf2E/y9pEDb8d5qbTLsuBS4v7VPb/zV/8qu6MMAcdRg95oLI2t/XJemDl+a+81DoOtyneYhSIOR3UNV2DJstCIDi4m2ooxsK6Zec6M44BD4SYeU0kFudg6L5HY5TJzvDlKhdi7krmaDqsSGwNJGgqvYJ/ajS7VGvhKpiMDZivNqVcGzRgjvTSIau02ZTd01lYVYuQjz0/PdExTEBQ845qje9gf9gzjKGzbKz40WSewVdutVAiO415t5Voqp8cHzUuSsVzoJJ/wwXMu56s6u5dOdmJU+SAqZzMc/7uZ0a6JZhud53YaFYWKuMAuBMqSmeII48aokXp3t9txOV+4rCu7+0nzEWCeZ6toHWVdeXh84Hg4crnM7A9Z/9s/44J4uSg8wzAwDiOXeDG/K7i7u+N4OFw36FLk3np1VbUuJcZAyVaUBO2JXDLTOJrRnirLVhskrd0epBua55Xn5+erK3IrhTgkepftx+3xBjws8yIIy6vjxeZmW/pbQ1Vs7JVusbkpJXZpYPEqKBSHClNMeB/MzLAb7GmHfMmc1xPe38mjzHuLCNYDi8MgqKko6yRgTqj1xZU1V+sc6dSiXJab49HWsXZlzx3Gxi9+9RNazXivTrK3rkOkdUvz4+qZ9N0X55yjZEEk0HUueLMTD4KqbPdTvfZFybOyz32iukpMjnrJ/NrbNxyOnsllYpgQgqSY3O9/7weUhgVzeVJXWJPrcLPb42w2Wc3AsPWXM7K1xlpmUgrkYB2F6/RcSD4y3WjgX0ohtM7SV2J3JB/oSfkyhQyrYPySRWLoXh15y4V5LVftjaosifhiDOx3k9biWgmpgkFkw27C9cZcMvPjheNRswjvHctcrmyvS2uM48Bxf8D3zsU84hzu6rw9jkprjL1YO1JFBwx4lrLKJbE2Wog077d3qwAc3NW0Tni1BsoNz7pqnpAsCyAGtc0vkZCOOI3mWhq5v3+lA83gmtmGjM57ed4PI94GpJj/vnPbpeWvQx1vlUzFLDQaTJsvU93gK21U+e2r0osh8vj8bNDIZC6Z4nxXXwQ5tcbl+VlQUoxXgzxvNF/XG8l7XG90rxrJD4kxBNbt8mqazYAoj9E7yw5Xp7Fh210fk2ApbylEnpwnBmGwa+2UvDLtRlwH5wM7q/ALwrE3Y8RLnnn88lsOxxuSD5Roaty8UkuXOHKaZGHdJCyja+jcvfyoSi+40tmN8piqc+bmeGSLm1TQzxZ802S7Qsf3ROpVhm3NQSxU70gOenO4nq36TdcM89watWVud7fXCy6avfZ8uXCeF0bLNvlnfrk//88hBOVBV4W3lCUrYAcHSV45ocq7v/dgyYkN6BJ1lcIwDsznmTVXjse98r1tSOxDYBhGI9BIUxNC5PY40BuW/+5smusIYeD56Vtcc9zc3Oi95kwK4t3vDju8E+V6S6nzZiUdjGW0rplcqhke6v1viY4bQ4wOwzApTrc1duOgtEiv2cymWPeucD6fOez3HI5HxBiMV9O+rWPrHWiF7rZ8eWOD1ca6nCVuTYkUd9A6v/r8T+lbt9IB59lS2LQxFTjUcXgnuGTj46nslManO0caItGwf5nTIcppB1zXBd5XQhK9nd54/Spx+2okuVlMruBwQddu8iPHm3t6tt9Tu0EzAz4WSq8kJ3FoBzlJGCxPDLgsWm0IiV4zzXm8GefJtj+QsyChlEaisdQAlmVWEVq3mV0wLzd1irU1TqcT67xw/8ErelURM7kIoROHSBpveXp44OtvvuI237DfH0V46BLIRi9/uWDIzjqvrHklBBW/YRg4HI54JHZ2TgW2smHkHjukAUbwyTtCB98dQxfbZ10Xvv3mW+Kg7IDtYmhoYKQqxDQIRrcM1op770lp4HQ68/Nf/YrT5azbsFouq3ekmNgMLDe13/bVDILwztSz4YWu6ZyCTjZHy9EO6+g8EVmTt5xhsx6wQ3dtlSVnqWHNkM97zzLPbGE2JVdayZRcaEUuoEMabXivtLP7V/emQm1sl7v+IxiqWNUm6q/S+2J0NrDy+B6sahFnv+SVtWSWUsm906KjJ2cJYRr0llLZ73fsDwcIXm1jCLSshXN+fmbtlbXpc7m45TF05suFd+8fzHpkg2n0uw+HHbubG0IMnJ5OXM4nWlNHGYbB/sZsnZraZEfji2++5KtvvrbNbCIyPPO88NU33zCfzsSQoFVOpwvPjye6F7//3btvuVxmUf28KshiVUpvjbou3Bzv1eE593JReE8ch2syYd+gz7/EVwiBaZqIY+JwUOZ1Mf8sc2BSxvs4yIbDOrmNVVSNKVRzlcmgDcAvy8KaV0qpXM4Xm+WYi2ip5Cx2XorWmXhlB/Su4uKDN294Oj3zy1/+AlAi4DCN1k3od+Ra2fKyRWHWz6pNF4FgrWy0U3+1BgHBRltMcBoSu3EwavhITIFxSqLxRs9+P9G7rUHhFuqOg8WDOulUvIM0DCgMSgc+HX784x/z888/N38iQV3z+Ykvv/wVV7sQMOO7zVNquyBs31sHtbkXtK7ZYu+Qc2aw9RC8IzppqBQG1Kh9ZYPPHI1WCnf7kddv9oSYGZLs4GN0poDvlHa5nldsQjIc1MrT6UQuhZYi1cgwS141j4rSTDjUfdRW9HeXqnAys+npwLIsfPXVN6Z5MAKCQWcOdWTeqXL3QRdy6Q3XZH+/Ox4wXjeTRfSWtTCfFsqykuLAzc0N07THe6E667LSqgrSw/GIogAy3zy8Z11ErR+tC8YYd8TAEAfovMD8ZslTS8NjzIcUog3XLHazGVsFDV2DU6Xs2tYSapCNHZK1Sn08TgPJe959+w1/9Md/xHJZmIZRSXenExruuKvz5vbzug1Z9tOeV3d3vL6/Y5oUSSkoyQ5bL4OtWhXm0ZroiRfzbnKbD01wVrgJI356eC93wzRaG+VJQ6LXzrjfs9/v5AwZPEtexAaInjgMDHGUpXmIhJg4P5/56osvOD2fiM7jog7S1sRy8sbk0OBToUEhGB1Qn1YukePu2pU4dNn5bvYWpRlOKrbUGJMZWouVU8qqfOooCX4Isih3rV31JMM08cnHH3M87Ek+Xu027u/uOOz37IwZczjsmHYHnOPq3ltrUYCLZXXPubCshR//6Ed88Wd/RpqiHYgrzRWca7pwLifWvHC6zNdKFWC5XPj6m6+5zDO1FAsE6tdWv7VGGvfcHo94OyQ3GmLvctKtVfTYsi5XiOQv/LJvC8YUc85xPB5w0VkuxQZfQkxihsk+WsXBNAw6zBzs93t2+4Oog11D4xQjX33zDb/8/FemwsUM85zBXYHzuvLtw3tRfwdBCuM0kkvhi2++VuedInhvwVrRDns5bF3DiL6zT1JKTNPIOCZC9GB6G9FfuTL9oEk1a+aW67oKOrXZjnce5yMffPgJx93uquDeYKjeCi446XC8u+ZxdLS3LqdnPv/Vr5hPZ77+4kvOJ1GWv/7mKx7ef3tlWLmmPe/pV2GkLoQGvV3JDRtTZyOEtF6Z85nTsuoMCh7noiEKWltrzqQhst9NJO8YfOd4GEg0Rqc/IKUoVrwz40garjdad+afZR1K9OpaRgkUaxNdfDft1Qmar5LzdoH0Dm5LhHP05hXMljVXur+/w3kV3vreLuv/4AlBGq+8LKyXC3leCU3PK4ZA8p7QdK7muu2XTjd3gzFF7g5HzTBs3qHZMNJWlCwBqY8cdhO7/WTnkC7l0iqtFmourDVf4bRrJobaNWKzgbPUuZW+Fg6TvJFAt/jT+cSrmxtCdy+COWdrt1vkZ1VlE7oj7RJv3n4gkd3dDdVwsJDSFaLa6LXNcZ0nbLRPnIZYrpq5ljOrAic/KN/gNF/49umJcTcx7UYeHh6YJj2IrQItJmrrvXO4vWEak7m3wjRKd9FMxVtK4XI6c3t7y2RBMcF5YvImTINsthDeq6LRAaasi5WF1golJHVmPjAMgmO2cHpQR1Ttpt+S5a45vlgQSVX1FvA0bNaBgkpabrgIPiV2u0nOpM4T7YB3zQ6TVuk5SxG9ZIIxQzYOvS5oXWghJnaWOdB9J+fGbrcXgcAO0RjEivirv/u7SkdDN1u1Id5+1HD86Xzhcp7xwZGCPKFarqQ08fbNW/bGqBCUVa8OpD5EUhqvAUSiO1f60rlcLux3By6XWWZl86qZgIt/Hlr6p305bICsC1ltvWe9ZJ6fnyilcn9/B8BSCksrXE4Lx90kn6og9lGMA8syW77HxBBVQNUurvu6FjGLfLPfU2mhioKcV06XM6/fvFaKGZq33d7dcXt3J68kr0gf5xyPD098+fWX3N3c0o4HphgZUpRWwlll3xspCb6qpUi/Gq3bcKjjv+q6+/XSi1FuAt4H1nIWNTwOdPSut86q1sblcpKnlQ/sdzvbU/1aiQbvuH/7ls9+/ftMKZE2tkxb+eM//k+Y1zPJ7L/1KoKJ/awwdCZQ68rr2L6vO2h+O8wEyTyfnwmD55B2aDdUXNtEnJpbtFIJvhOnTkgQxZMHuin5X6xRYnRc8sK4zrTazfNJM4l5vnCb7mh2MEcvzRRdhRuGLmCfxdl7CwFOp2em3WhCyE4ajDLcxX5yG90+q4BKlrPzzbt37KcdH759Qwg6G5zvV1+56IJEdF0WQM4IFL0qr3yLCu7o3WOwrsYC0jxpSF959/DAGBN3d3cU7429KoRmo/1qvejQirUpbWmKiZ4G1l4Zpom9V2U598bnv/qc/W9Lxu5w+NbxAbPB0CE/JeU+VLO+3e8nPv7sM8BzWQvH45HT+Qyt04IhjnYTuy6Nwab+XGbdutNuInZYuhZ6bOpCFjPHW/LK/mZP8on9fqfkugbdbxW7qq5tIDcb22O9nBl25t2ytZxGvyx09naA0RvPl5mvvvyCDz/+hJ2xq/aHA9U2CzhKXfHBMXaL5HRYFSaMtbmKa6qMSy2srTEEDYmWVnj//j3H3Y6bdKs/O5gdg3kvLaVeLUIqUtxOwyDoyaoC192L2lp1Em4cCHjyuhCmyeiVqjR8F4vkdDnTWmOMUc/BKKwphOtlA5DMm+rt2zckr6TAbeDVe2Ow0KJpetEwVFT5VN+UNsiLcylgbLQK5qNVc6EE8D4Rgt75fL7w9bffcLw5cn//GuhkU3T/pUCn/gI5AZyfn9kfjxyPR8EABl1W6/KambvVBo+nM847Xt/d47sa8ZgGgofSO2WWAvztq9dXuuE2D9q6lJY1w7m5OWje16qKgdbY7SZBGFWXS++NnLsU7c7x8PDA+Xzmg48/YuegtI7f4K9SyFnqZ++cugmV9+bxJHirNekh6KKubmp5F0UbD96S12onmANswFHKxRxoHfN8obXO7d0tOAkdS1lpNjT+4W/8Jmzzo1J5fnrPj3/2JzjkqCqkQRYiG7S4TR8cZllic8n+nffWq/lauaAogtOZ/aQ91wyGqzkzDgOjWbbfHnbsdhHvKj55gjO9A918tjzRBWprnB/f8/rtbzK39aqfOteCj5obtvZCnNkICmB7Oyi07HyZKWXV3GvYE2MxEkG/6hKuVjre0XNTrK1ZosjJAV7f3coWpW0mpcUuNJ1P0cv0sFuR0Eq7zhiSQ/HTZjjYrmgPuvCLrJOclyXR7WEvE80olli284Og7kO27VKS0xpxHIfrph2imB8NMLIEh/2e7/3691VJOlVADn3YpVccGhiXXK+zC3HcNSTObWHzoLmcnqn3d3hdy3ZQNKpD/kj2AR8eHhgNP11quybddd94Op25nC847/ngww/YDYNofSaa6qUZE8rjIxzSDgdXnHFKicu64GJgTEo4o4mNcntzZEjiwQs2kP3B4/OJu2VmPww8XS6sy8J+v2dKwrDX3NUmukClkYzSupiycllmqXSd7A+G4KzqdPRSWJeFZzq3pu517jsMgxCJsYny5zbOs7DC5uRfpfQ+0ZFL12LxKTAlr4F5mK60xtaMJhfUtZxOzzw9POJD4NXd7RUDpneqIWS+y0VWmhPH8zzTTCHtvSPGSQeEHYANCZJC8MoesGo2BW3wEIINxh2tB8OrGz3aQb11t71zPB4Yd7KUtrtcStNc/mJRnZajbRgNiX/zhz9kuVzorXH/5hXrZaGb+nyDB7a51evXrwALq9k6SCdTv1oUa7rMi2DRWq86HrkG61B2NnOLIQmPb9LaqAMr1NDYjeZs26DXyv6w53BzFNbdjZK+FpZ5UbjTNLKsEu8djkcNbU3IV6tYTX4TVVlX27dDw1tXXhrOBVJytheNyw/XNXhzPJLGJJzayQ5fTYBZS6AiwqG54XldSSHw1Vd/xi9/8VOCh9bd9UU0MBNIC0dig34MZjJITbRjwHfzQ9IvWHPm6enC7e0Rp3wDQXrNCfmo6mK8S+pgrGtQM2GMKJu1uNaYl5PWYCm4kAjec9zvtfdbtQ5LiAmoqF2NhaQpt7rM2orNJU1J3TtTGOkoqCyGYIQUdcWi1u5YswS24ziRUr0WzrW95FJsVmWdztqKhIDBU4w+PQwDuaibH5L2Y86CqJ2hQz68oCG9Vqbd/uphZkANNa+M0+6F9o32n/QW5tVja1SzBjs4s1XFh+MOZ9CCq7LLzr3x9PDM5XwipM0Gwz5Q6bLa7l1eR7Uy7SbuXr2y6MZOMOzr3eMDp9Ozmd8pX/rm9obj8UYHuJOnSUDxf+tlMThKN2xzzvzoRQVUKKxUopvXC85M34YBnyJ3d/eM4yimS9tsNyJjUqdQereM6UwcB374g99kvz9wySvPT096ia2xNg3CZUGh9jI4RaKW7dX2plsaz9rLFTpzW2Fil1UM6ZqTG71nvlx4eHg0g7+XoWjvml2sNfPw8KAOo0hfAdjAz+JP12JKVkz9qecYY+CSM+8eHkhp5O71K4Ol1usFKIWoiAWjQWPrskDVQPzrr77i6fFJ9iqtczaP+0aD2q4DfEIgBInBnFcGxFJWa3H1WbrBjht/QaE55gbqnLQ2wfP89MxlvnBeZnJZ/+ILYjuBEAyz2+3Y71RFOR+Yn2aenp7JRbbOyzLjer8qtHe7HaNZqINRP528qUovrKsyvZ2zwavXHEqzKOWUpzTgzcRQVfG2bhvLKkdZumDb0ptgKrPLF9QnPFrzHR122DscRtEgl6Uw5yzmVAwKMQpblG+//hwtt3AlXLSmuUOtVQe6czw/PfHtu29tRjSYbf4L60a0Z+lNRAtV4dhKwbVGDI4f/fSPOT+/o7t6jQ3oDcEFG9GDl7/p+qq2ar1ZB9u4Zqi7AM0FER9axXmn310b0UeGNOCjWJW9y4Zbz7xf5zRak3oHgcb59CySwbLSqlwD0jDiLR6393btpJ0TYUJzMmduEJrZHW6OjJOsekIITONIq4KUuhktbmhFTCOlNp6en+XcarY5zaFZQ1fOekpJlOPywrILLhi8qOc6DqOQRa+C77KummVuZ8WVXNOus7e86YzsMHn3/j3v3z/QQ7gWNM45UkgMKXE87IjF1NTedpMLwqKcHVjNSYE8xkitdgbT2YVI2Y9sfjEpRKoNW7GBY8krtVUO00EP83C8JtYVGrF5VeSTcPWO6KzjOJkPOngnCKl5qCVzuD0qL9k2rdSpQeKybXgjYTOPD4+kGLk5HoW3xaiDywbMySfzgXLXaig0+f4XWwDgGNMolol33N3dkoZELY2yzDCMjObkuSlwN5fWlAZqlNe9FKka/Ne+8fGVXXD3+jU4caCdczyfL5wvC7shUWrn4fGJ3W5itxvJeWVplXHccTG7iJubG6ZhssVhG9FwaIBsA6ltFlHMYXUYBNNtc5PewcfIfkj27qXsbLOq6ePxILaDd9zc3rA/HGitK0Zyv1fFm6uGhM7Tg5gVy2VmSAPDqHzuvnRWMvs04kI0mqgostJfNJlNNmhlVfVdKg8PD+wPR9K4M3uN71hk/wVf2wB+C9qpVW8+DcmMCy+8f//INA68efMG75WOOC+LVXujhsvVjCudJ07y629NNM3gA+csiGqMA9VcBrpzNC8my1YbDM7jh0gLXtBNiKaJaIC48d0orhss0VrX3+BUALXWaCVTe2OeF6iVu7t71pohZ6pzrOvMYdorsAjH49MTp9OFV6/uSUnwZ4heiY7ryq9+9Suen574/q//uuJTU7IeQLROnKP1SuyWFd2No2Sq8NYrP/7RH9mh666sps2w0YxCrgeXI2CpZ9b5dDYB2TAMhJS4XM7ARpOF07xyc9ix5JnWOtNNohll2weFLG0MIsCKuJfhVUPU7K/ffYkPMO0mFWMOfKv41q7wU7ULopR8dYzQpeGuA2sFealzSUkixof3z9zeHYlJlvebZUjwfqvOdd6Vld5EWAghvZCFQuCSizGjRptBqSC7dgDfYfl99dWXfPXV1/zOb/0Wm0tvTIHejH1qa88mqdd/Ly/K4DgeBL/XDbLszf6OSMytk5ym/FsGawpiDDWEyQUfdJN1CFEVxaVmpmGCzakSy4R1XVnLwXF7d49zJgoyQYprmwmfePXxmvImqXjv6mKc4YgBWGomuWg6A0E1W9B8LVkb1EnaTzFxSm/88ud/SgyR3/jBD8AyDIYhslS1zWOKpBqY14UUlSnhfCB4rFJRHOA8X+jecdwd6MMkSZyDNE2iCG9qc1so377/ltvjEZcST4+PTGngJh7kV1MlCgINsJMXc2S1rOn5cuF8uTAMA3EcKa0ymC9VzcJBh92e3TDw4ccfc356xnW5yFa6qOXdXdv63nUZ9lbJ9n7PlwvewTBOguaaLv84CBLJrRO9hphPT098+/U7Pv3e93h1d8dzPrPbHbQ5zF56CFEpa869VE3bkdA3VEAGYq7BsBsM0pIKv/tOlxoT3zPRJZ7OohMeLAq3bApWB6FrVnE6na5q6f/cr+8MtlNKTLuJy3wi18Lt/YHHx87lcsJ7z/F2z3HcWzeo4qeWwuPTe3qH73/2Gc1pXa7bHK5kwal0lnVlmsarArabLsM56UNch14y3TqBECY7aJEzQFcaYHEWBIOnuUBHHPq8riIL5EJeF11WKWleIhsBQZBVgr/zfKaUxjCOirWsjR7AJXBRClsfPMHbZZECbz78AG/uBt4H+lrpUb5hW+nfW6G5QLF/X5i94K2H91/zy89/gkveugfBOV2sCjb9CR19vt7gz02XtG5bDfyr/4N/jf/wP/j3+eMf/ZGYUUGBSmteWcrEPGd8aOz3E2U5E6k033FhxEdBaNqXxoRy2wxEDq/P777hvDyxZqeAo65gquYSPqgMb6XocI8R33Q4bxHE/XrmQadyOOxZ18zlsjKM8ntzTrM5to4/ryL4mHHqMO6oveh7nNhOAKVWUZTTwVLyJKLcum1n847WIXq4u7khdHnNiWorS/htVqVZ6dauG+3Xe+5evaK/f8/D4yMxJXyMFsMqk9DuHVFzCKVyzRaoPpiVhrcKvRb5iDTnOK+rfJnWrArWOQqS49OU4dpNS+GabJ03O18fvJS7XtP6Upq8jbratxgl5lspDN7TC2ZZu1UCgpHwEs3lmkU3602xnXhWa8e9CxxvbtkfDhx2O+YsKKXnRHRBnjI+KAM2RhqN5NRyBe+E+84Lj88X+va37ZElcO9MPgH6/ubt81m34UPkNM8cY6SXQnHqckqtplpXXGAKjrw2CHq5LVdV7EFRot0qlnEciT6wWISpc9Cq/Hr2dnHWKk/5rYUPwRnurcHm0iquFmpS4L3mGHLYdBFLH+qi0nlVW7477u9fczzest/vddiloMFf1TuIzuFMqHalzlUdGNsG3R1211wQGuzCXl43A9duYBgGQSOtsy4z5/OZjqDHcdrhHRwOB804TJzZTDfxl/3a7/asx5V5PoODb7/9ml99/ks+/egT3HS4YrhlyfJMigOln3h4fOawGylNF8fp+cw0TKLudg1E1zVzvlxk45ACtWRaLeCCbJjNdVPL9+Xm2gqrVpSeF0c5Lvetk0YzO4BhmtShwZUFGM1+oxYlvK3rylpXHt4/qMu8v2XcjcqecHDYHyzkXhfROApao+qy2o0jw+tX7HcHzTh8N8ioXXOyvbFrNKgWS6e2iqudP/qTf8zp9CTlOZ5cV1ozJTkbg+mFdqx74aXrdc6zrit/8Id/j3/5v/JfI+fOj3/8U0pbrPtu3N+9usYK7w5HHcS9sebMmPx3nq0Yilvi3Ua7Dch++93DF7x//zWH3QdyPG3mTuAtfMthtGnBWr11TvPMfjdhrFlaF114Xdarrfp+P+qANbzHd/mWOe8133Jynd50Os6FK+SvbleOv6WqS4tB3UdZF8GX9qxKLZQ1wzhyc7hhvxNMXG0P+aDBNDb/EcgjWxXNNaX9kdRAf2tA4Uo1Z8I4atDfbSOHEJRI1S26EoVfXPKKj579NFFKYV0XaheF83I+UcfNU0kARwCi041f8kprRd4yzkwpumYWzcuuOYVAiJ51bTTf1AKvNmCNjroWsTDsAea2Df+A1shVwSdTTBBVfVXE1vnhD36wTYJ0EI0T2YZuvcmkbXSeKQZK1Rjt4eG9YLEhyofeqojkgikkvZhJzrOu1fBTPeAedHC+ub+X4aHz7D76+CqF3xZBip5WOisyHUzO4B27TO9ubmRZfbngolK+Nitq1xUnyTgxhShZfjDlaTXGH2KqVBtAdsRqSYOw5WGaxLfvLwZ53dy39XjFXrvkzJQSfhjEz6bRc+PUMvc3N+RWuayrZjmbxxHqNGPyV2fc7ct3r4F5l0bEOylNW2vsUtIsZV1xKXLY7UQ+cB7fO3FIYpN1y302CuBmt/yX+jLYwHsVEcuy4vAM00CvMrakgCdfqde+O17d33O8PWiW5Dx3d69smHyRL5IdKtvh3zQtZtxNYo/lhbA/QCvkrHS+jUreqywVUhRhldzxFuzkB4MfujGYvFPOghP8F+wi/smP/wkPD0/8zb/510lTZG2eVzd34Bz7myNlkf9Y6yYYa52yzReawSAhMa8XHp+fGJNZjqCI1jU/k6KG98syM04TrsvPSJCgisxO5Y//8f+H4Ix42zplheU848LAOCY65TqXaLZOu9Gaetf5MAwT/43/9r/K2hx/5w//Hn////b3+fzLn+Cc5+ZwYLCcit4cw86U/s5zWRdubw5ofmdr20xAPZ3ujf7tddmcz8+cHr6lrhGI7KZJ7q6hyhnae+sYPHlVCNBuGvFerDEQc6k2U2U3MakcgUCnGWU++UChXAOZei6sIVDmmWXN7PcT43gQ5N2aHIGDvzK/lMjrCUneXr2p4/QuEEaDlEumOUfCKYLVBuY4j4+OVuXO23WEaS0jKM57uWrrBWBQVaKumbUWonNOISI2dG1Oxl25dT7/4guezs/cv3rN8MGHBOdYloXuHWkY1HY1VSQ1N9Z1YV5mhmHgzZAYQuR8mald8aS1NJ4eH5WwZC8a1Oo1GzCVIjiqV4W2B+evg2Wc4/nxkdP5zPGwZ9jvGUjyU7KKYRzTFQNMUQlhog468GLq5KZoxm1+07oO7myYo/InOsHDeDhIWAR4l4T122GgW7lbtxTMy78bbtuZ84UpJcYxsi4y83LIs35TD4/jiKvd8iz0DEue7FQTk+xyOXOqlZ3x0LVIRJOjiYW02TD7EOm+GjNG0MxSCkNQjGLtwidd19+yeW6FDZcJjujhkguPj49wc2S/2xNj4N3DA8+nM9TK3qC2bHTO0oWZDwSiS4InG7gYtIm8iRcbphDWs8w5az4SRQkOUT5XYbe7JvWV1inzigsR7wsuZzm43vg/Byf9xXeE1m0ad/QOn376Ka9f37Ou2TjnTdbnKUIBeuH+/o6bm4MOBWe1n2tczhdKWUhDpFQnBtYQr0ro8/lMip5lWXk+X/DeX/Uz2DN3dKIPsnTwnlIWw5MDD09PYpslZ9TQLg2M99fnkktlCJ5ht+PNMMinJ0aGlIylomqx14YfVYG21liXzFfffMNxv+fwyV7r28sAcFlWs/ew3JhlpbvGMGjQuazrdd3q8vKcLmeca3z7/it++We/kkmlc9YVrjw9L9zcJqBeh9POhKNazy/vaD6t/PP/hf8y3/v0+yxz5u2Hn/LX/sbv8ct/58c415jnmf1+R3cwHfakJGjw+fyeaxRr2PygKluSXQVct4jZLg8o+srXX3/O7/zOr7Gsjtw9h5iIXmgIbFDgir8WVd5QG3+dQ8aoIfNWLNZWNtNdeuuEoAJHhIRGGAfzcEp4m3fRJW4DiYAv64rrmnHUIh+raMPs7oIMWc0huHbBT0OQFVJLiSlFnJEeApEYZUffe2PyiUqlVVmEdEN8FBMshpsLiZYt+bBuVsKtafjkPfOyMj+fiMPA/fiG27s78yOXb1EYErtpIudMhat46PT0yOkyA53DdGC3G3n38N6MpPY039jt9zgv24mRJAfUrJjHbPht8KDO3Fmr261VVyzfOI5Mw0QKGiSm8B0KaKs8nc88P8um/PXtLSElYpIHe68dzL6gOcfiHVNKlFo4PT9by+Z4Pp1YzjO73Y6b4+Ga7dCd+bPoUbI5lTa7LEGtdwgOnJw885J5Pj3z6niDC4HH9++IIfDm9SvOpzPfnk7cv3pFjJG74+2Vlz0NE5XObPTdu5tbDcjsQs22uSLgXOByPuEC7IbxyhzKtZCXhfEwEY2hcZ5n0YZ3O3bDYAPDJjFj1+bOy8w0TYxJc5H1vHI+z9ze3Oj92QxmCBJIebfhnPLr2p5LtfnRZmDX4fp8cq08n56JITFN5uRb5SIazCQyek+vwsPLknHDhEMJbsXUw/9Ms7/vXhLOsZt2zJcL2ZnjqQtAxsfAclppIXA5nYhxYkhBXmA+0bvnclnwvtmh3q+W2t5tGHFgrY3cdElcLifu337Aq9c7YnAMPqJQHllRj8moveb/tHXxeM+dzbR6NyotgeyV9e2AvGRaK0w3B379s++pM7DLYxwnnBNltDtHTPJhKlX6iSlGPnzzmv3haPtOlv9DSnz49q3EWeb9NMYESRfsui5cloXzfOH2cAs4QkikkKg98+Mf/4h1fSb6Tm+evBTm00wwiHNTgG9MQIzhtHWzuXTGac8//1/8F3l6PpHSnurhb/7+3+Lf/fv/JrLHyHz1zbdM08h8+hYXHa1nQvccdsP1YHZhszHpW4VgX4LrtnCkh4eveX1/y9MFLmslRBUvtpxRRocgtd5FDy816yLtOnPoGN0ZY15uhaRYXBvzrWbpQ8YQLDdC52iMQZYbWfuiNulrYpKNy1qrdY06F7wRekrOuJioy4Xz6cy026lgNiSotEovjUahORmRjjYvbM26j1oZjMF3Ol+Id4lGJwUYD3v6umyiRIu7bBWGIFXmEPnszSszoqrm8eG5uzmqK2kNhydFMZJiDLx9+xHj+ZmcC8OoW/vmeGRMw/Wm2k/Tla+fgrBBF70ONKR4XJqw+RENsHOr5JoZQmC/P5ikPbCZjOUm2mEIheA8JWeWy5myrqI9biEaq4m3QpDFiFXgpVcW0yVUTJNQpY8PKdJKwzlhw0uVcCyMQa/foDQsSa9WWQN7PCkE8lrJZSXhrurPm+MNtRXW0vDDwAGuoqoYwwuf23yl7t+80VBrSNReRe0tCgShNYKfCFGH1eU8G+WycZh21CrRliJGobTC8/PpOtjaDVrszVTveI0n9scjY5RCvtZKDvDhB2+JMVFzZhNDhRhx1nJvMzHLnwFMwBN0QftW8TGyrhfyCsNu4nBz0Pow1tkGHeUifUBxzfyC7NIBfOvyTaqCb148l/6iW0JspmEYOJ2UCCdRkbFHYtasaKvYguP0+EyM2hObS2brleY6eblwPOyvYrTaDHJ1nvv7+2tBNIaBJS+UOot84brcBBC0RoP5cuLx+ZHduGecGmlM10hYFWiNEOQY0L3DD57gRmpV5bpcLjjXuTkeBQm3RvDmPmyMlY2tF2JgF7RP1rwIbqidwdTYgnKasrpjFwPHe4Zx5KM3byzr2ssOI3l2u1eUcuaXP/8ZCahe3eLlcmEthWG3N7eFSjfPp9bMZtvIFc6pc/nbf+fv8Vf/yl/nssyE6Gm58MlHn3J3/5r372Vh0mrn6f2TBv0tMgazsUgOnwTjaR1tDnHSW2z8KtfVFEYc777+3JThR2qCdTnz7pt3vPnwDcMw2lxP69J7uFwuPD+fRNgYhiuZxnunwvEK4Yr11oQ8svnNtbqyNE9swTqQ8Oco1l33CWnTcIGKCJ/MiqbjnNhvm5fV5bLwxz/+Md4Hbm9v+ODNG27v74ku0F25GvatlwtumrRfcyYX5c4PN5P2sX+hObdSuSwzq6x5glXtgk7EvnFM40Q1SKR1Yfzi2SqHNQ6JGoziZ0whN8BYR2IMnOaVcajcHA5snjGimZo/VKlUr+FQbzLNC3RqCCZV3wZZsMwza87Eww3hKv6xXAkn5eXWXpcm59Zx/Eh25eNolf6WPLXx1BXr6IMW7Ogj4XjUINY5mwuIBrbSeXj3Ducch+NRcwKzsOi1XiGojY89BE9zcoM9XZ5Z15WbwxHXHbkWnHfsp70xsrzw6K4WUlVWlVrZNus4qlpb8krJVXRfOxRrqSwhSzQ3TWJ2lUKpmf3uQO2FnDP7aXeluL559ZrzfCYOiZKNQ+5Fzyv2GdI4sPHZAVIIxBBYLitrqxqYhwClkMaR9flE72I/b8wwEB5qVBZ7pYqS7L2z3+/ZHyZ6aZzn2XB2/Z6NKbWY/uL+/h7vxaBay0o5N16bwv8vCh/67lfwQVWY5UsfDnucm8l5JaVELpn9bsfj6USeL5ReGfyoz+IctWa5v9bV5jCOaPYSm6CtJyu0HLx/er5SMnNvTJYC+e3De9Z15XuffXY10dzt93jfWctq7CZh3jbrpRZtCNcjmFbgfDmT0sA4Sa1bTY1uYxHpCGJgni9mcifPrWWZeT6fOe52MlvsneAF6bZSKKXRl67oXRB8KdyXyY+stTHQcSniQ+IXP/lT3n3zZwqUKpmlQ6nQe5AWw9uH0DjA7PVl8+Po10PvD/7w75FCYvV2WLXGhx9+xF/57b/B/+Mf/N+1viqUIrbTmBLRO2KC5MXSc71b0VlfoDq3nRsW7WqwzsO7P+PrX/2Uj77/ewzdcVk7p8uZu3LPMIoNFKxbxkgk0tlI9e4M3to6DdmMyFam1MyQJqO+qvhLSXqKb56eGHzk9u5WnagVvq2pCNS5Wwk9kuIgmvPlQkoyRw3GVFrWFefg1atX5DXz+PhoWTA7COn6Pn2MjPs9y/lMiIHn52d+/JOf4XH8rd//A6EgVxp/JDcp81PsRN1w4Yq5ldYIFo5SinDPNEQ2WMo5803vjegctQHBGZ6GZgItMV/OhLBTq9K78DrvqVkZuz5tm1oMmd47pXcdsK1DEDQif5OB/WEPQFllODZE2U2cl5Xjbsc0KCd2bt1S4CI+bUpTswBwztapI6SXcI3aGrk1BtN3gFxKZaWh77u5vcH7yH4cbMBcyLYZFdf6YlbXPaoUvXz/y5ptgURc9/jeLDPDrDtM26CWvGluALTSWVplP2r+s8wLT09PvHr9mhg90Y20qJjZuRSSl8iplEwtcpndUqZ6l4VG6pFxHBjGAed0QZ3nWRUajnfvHpjnZ968/ZDDfi8KXHCsS2ZZdAANMXHJmVQL4zTSWyempEPTFqXsThy+SezorSPxfuDueNRnDAHfZbvy/v07wHG8u+PN/b0Wq/fCctuWgZ3o1dS+Dr744gti9Nze/gU02P/M13bwCV/WAdCaiAJbZGlKiVaKqIDGaJNJm9ZRjAMxDoSgmcw2a3AxsV7OYhJNE4fSOK8LN6bQ792CZYaBUqsVYmLAHKaJy2XGO62F1pX7HjaRXlfYUcsrMShxMHgJtp5PM++e3hM++54w+25xw8EbDMr1c3rnOJ/P1HXFH4+mStaho6rWDqCaGVGmQO39alq3BgjVk5vD1QZ95h/94/+YNc/C+n3Ee6t48UTfSden7/X/2hUIQvkRjQ8//JSPPv6Iy7qwP+xY5pnTvPD6/p6/9jf/Fv/hP/wH5JqhCZIcUlSYVG+MRsV16ALqVjyG79Br3dbqoouE7ljnhS+//pIPvreyroKyv//970tpXl86kY2Ft7nrOud4enrmy6+/4uOPProqlZ3zLGtBXk3eZoeZb799R0qJm5s9h8NOYt7SZNudPMEgqmr6F8Bo2BvU1a55E5SCN3q8a4I9f+2zz1iXBedgGidCEIS+0VhrqeScSUNiHCeWWZfL/avX8qhqjpDGa+Ez+AEixFiJcuB8sUb23pnyz+7dKm+dzRzNw9VR1PaFnEsdSqpyOlR6V5BG8xID+SC/n+4kgNtyCHTIBnnoVChebXtwKlRb76QUDK4SPzxNI6Vk1mqJYUZD894zpmhOltJY+BDsg+sleKPLbY6SFG2KdV10aH5XeBM2uqmUs8k2bquFVrt52ZuNhc1EMPFUcjJqwzkOt7dM5uWjSlYc89bVJmNt5lwydSm4rtxvOgw+8XyeBVEYC6HSiOgyUnY0zOczD8ssu5Fx4u721v4m5Xcs6yqbZR+puRlFWQMyjOEwmmlg71Lbbgyoec0s6yKIcBiIYyLWzvPlTIhJ77s5LAWD7jRsdl5+U32bNYWArw1CYDlfeHp+5v5eKvxx2iuLxGCvZiy2YEPYL774gsPhyEcff8LmMPn4+EjvldvbW25ubv7SHcU0Tdzd3fP09EQ1P6/eHYebA73Iv2YcR9KQKDkzny86dDbhnhNcEcwCobSVKYki3Iw9FWMU/jwmUWKrGF+bsOzNq9fc3hV6bczzSoqBmJKIA0F6BxVgwYaf8fr3Nzo4wcDRmEjlcuZyOrHMZ6YxsZnkqpvoykXOmd4dLiY9L5tj9KZ3IscFVLA4x3EnuAzXTWAm+ErOzZ407CBUfvlnP+VHf/L/xcWGa8VcAKRid8Ex7gdeetItAlmHL7aHaq383b/zX+LTTz7Fh0irma++/oalFe7vb/jtH/wAeqDmmegih90eLDfe+w5BAk5FgUYN1X2kkxVsZJDWy+rQvkk0fvJP/hFvP/4B54vj9uaW+/ubqy2OcwHnPMPgzeabF/qug9Pzmfq2Xde4814wOlvxIWuOd+/eiwiz/zVcUeZnDJHcM3Tl32j+5cEH1pIZotb++fmZ87xwc3OUAj1qHSUfpE/zTt2HcyzLevWla1V0x2h6FO+cupLauLm94W/93t+ELolDafKDA4V8uTHhmqzyo0MDnsqLkq915Q8E59ntFErTwNo/gWa9NkpR4JCevAZBKoo7h8OR0+mZby4XxhC5ub01Fa6M4RovHj3eKTCnG2a/zBem3V4pUvZ9vWeoCopPXqZVzsnBdM2VkHRgVpQF0eiwFtIgZtRWFfj+skBKyZTeGFMihIPhis4WlKdQTQUtl1WCBqtrNY8eBMPMzwtff/UFH3z4kdmOeDDm0+V8odXK/tUrXXQ2hC+lkbcXFwKFznK+cD6fcNEzlYHd/kjujZ//7Ge8e//A3/i93+Xu9SvBQN0YW03RqBscELrjfDrJcG8a6OZr4xAFeBiSWFHouS8bPddgnMNux3G/w6fEWlagM89nxnF3ZXfQKqW8LELnPN5LJNW0oFA6cDdCkCqlDTYJzrGfJsRDl8Pqq/tXytfdBuF2IG8uscqa3mOgP/tp4u2rV3jT+VzL0s53T4J/6lfwgf1+z+VyYctHCWa3XJzsLRwSE6Y08tye6R2GYbjagKck2KYDCuqVBXQpin29rBeWvOKiRbvWTm/VMtU9rXuiH+ihX3+/NEpJa75Ucqm44jXktM8XY7TcaSOb2Ezx/uaGMQYOhxvZrwRBkq1VfEw2R3BXmNI7hzP32b6x95oGq2GI9CbXAZxjuYj+Oe32YgsBnYpHyvNf/uInnOcHXUi2Bi6XmbVkbo57UowE1ylGexXldRvsyvJ/OtzyB3/r93n/8MhlXnj79g1xiIL6KvwH/89/yOl0wkcVH4JGCniJYKkQJrvIW6EUCWcV3qS/2fqM6/53qPN+fPya0+kdh8Nn9N755a8+Z7ffsT8eoFY5LYsuwDKL5OCcI42Jjz95C07nRGud3XSQjqdkRdRa5/b2g3uGQe7Bl/MZb0FEJWd6jIS+hUd5vOsKQfIqOP0wsLP3Xey8bq0KOkQwofeOh4f3/Pif/IgPP/mETz/6WBYtQYP4lAZi0hC9LCvDODAMI3kRXByqGSE6rq4AzknPFi/rwuFwoKx1Wz/GP1DVGqLZRaOwb/np6BBtbhukwLo2/DZUqWqNdrsd65otPF7fd1UN2i9zTcHha1OO8jiODNPEsMV0OjQ4xhGGwFCTOhE72JyTbfhQFayillk4/GmVlP7Dt2+uBnfO1JJLln4jxmB+Q2rvSleIfLfWdckLvSwaMjWxdLz3luBX+Pmf/unVaiRGx26YWI3KdjzcMO32OPN6clGVdCMLsquV01y5i/qZ02Gvy43Gbn+Qk2iB+zdvGaeBcRSPe11mwn6vZVsLIUR2+x2xDjyfTrTWeDqfeT2NxCiJ/bLM0ntMYuqEKo8nbNE5PDk38npht99DyWKCeacNvxSGMXJzc6eLqVUOuz3RWZttkEWlmbpWgSbrurI/HuS/5KAHPechDsRBcbXSLvirc6oWqNZHzqKnfvTxxxwOB7rnWiQsy8JxumHLT/7LWnRscM/usDcYSxd6QYVDDFIgbwPV+1evqetqB4Hw5twbORd200g1d+IYoy45pIpOccAH5bO4IA1PphJdwDnLfMZxWVb24yD2HTD4wKbqra2p0iyiWoqyLD3RUitPz8/cHQ+klMRtb9UuMB3CtTa8K+A8Q3oBfbYBpcvF7PJNOQ3fQRY0E5vXVZ2mrRXFFGuPLeuFP/nRn6jbwMu0MmdqXdntI+M+ErxIIK6qc/C9k6l0A4PyuvL7v/fP8eu/9Ts8Pp15fHqi1spHH3zA+/cP/C/+V/9L/t3/y/8ZF5VFPwyiIffWab1yGPakAXXbesHkkhmiN7KI2fB3o1s46F0ISnSe5fLI/PzEb/7gIy7PFx6f39O7BKzd6RkUE9eVVhlNdR8c7HY7rV8XwYl96XH0YEy2ZsSaXC25t5MGXTprrZSS7UIXRX2DmjYkZwtXW6rmCqvN6MZJyZTBB+XqWKb6MO04n89Mu4lWGss6M447Njqvx8gmqHuJSQxM1y1/B0deF9ZamKbJCmTnucwXhnGUcKYWMxurlMtMnib246iX2TprLvioHN/BR0qWUrTWQveecr5ILNQksNnvdRDQYcnGIGqI3+xkFpZCoKyqMKJzpFFsE3pnydXMzTxDTfRe6I3rDKW1wDgMIrb1blSxIG5zEAtgLiuTbZAqVFd85ThYZkNmsLmL/NZ0+SU8YZq4zAvffvMN0zTx6v6eijqvYUiMceDnv/gZbz/8kP1ogitEVxtSYBcmzvMiKwzzaq/Wujs88+VsGorAEBLh9hbfISXBcjV0PvroLbnccTqdrwrZWi0TN2gAH0OCGLj1/ooti23T8a4RU2CeZ9JFxl0tOiIOnyItZ4Y48PzV13z7zTf85m/9gDiO0sT0zqvXb1iWmRgGPPLnyTRZgbh2rfaJUfnGG2PFtBl5WZH9d6SXSsuZtN9rhtmaZZm0a8hQrfVKbXXO8e279xyPB86nZ5zzvHr1mtYLz88L034US2ddr3bgf9GXDMyi/mMMLrkLdFwUFh08tCzDtOQDLQScOYB673j3/hv+9Gc/57d/57f1e6tol+C4PD1xOZ8Jd4HYZTsSGjjfIFdqhBQTy3nG5qH0qHUb8aIm2iGe4mDQh1x/Q1UKZMnqpKfdqH3XG4PlXbfacJ1rKFJt3To9U7jbM9jgUvoL+2br8h3mUtugeoleey90cyatIRBc5/Nf/IIv/+ynBO9Nndtktz9NtAli1EyK7qyDUMxxqwqqakQcgd/563+DNa+Mw8D3P/0ev/zlr/j7/8n/lf/w//UP+Sd/8ke4pA7u5nBDb1k72XV6y4SgIbYEvZ7SHEMP2us2Q6IJ+oxGqHBmjdMJ9Lbw9dc/Ibl/gfd15e7uXl2AreveZckevaj2Sik0CMsLOqqt4Kx/jingm5h9mjdF4v2dGHwdY3dVxhSIlh4nIeb2jlTEOOOn5FI4nWdizHgnlXyKKgh8CJR14dvHR24OB37zN35TxVqurK0yDhPeCW1wYBoRwdvrcoEYxWIckpFAZJXk7KKCTizrQjJubTVFtfx3AtP+cPUCcU6DomwK1x7UBcQkV8lhN12rHHHutficebJXo4hKBJQhDEyDqifvnFTP0eOMM9xzxyWHj45dsPxgOktrBP8d//wOiUTv2Wh1/TrE7r3y6vbmOrU3I2J6d+zHyZhN2M/WA/euyTLCVXzSQVJT5Hg8cjgexFH2Tu2tc7z68APCENkf9vJwaaLINhvGtyrBUx9Hcqtm0NVk4+08d5ZxXBuWFaIqvNnBuWUc9955fHpknydev35NK+JCpehxDVYqyXmG/cRoFWfpOsBpkJIVAeuCj06Ct9ZwVYw2oqyIC5U5Fw6DubkGMV9SGmTnoI8uYZ9p2Zz5efWqgXXOmehEAfZpIAxB0GHTZiO8RF/Ket5TesE3f8V8Ny8saRsUAPTu8Qu6udl+9NGHpCERDFMvJdPbqJ//l/gKMV7dZWOM19lSa41+jVfWenE2a7qsi9EeR8Zxx9sPPiD5QLAutRkUteYVgs25RrOgoajCpClz3WujllKY9sqh9t1RzP5iiEm0bN8Zg2YVZc2U1jU8tn0aQ1Qec/Uq3gx3b122DZspd2toLhVkkaHMgMbmtSWbhqIMCss/cED3jsnCo2S+J1g5xMQwen7xqx+zrGdiaNCg9YLrMEyRXKt1+0YY8Yjh16sC4qKwjY8//T5/9bd/l2Wt7KfIr371K/5n//P/KT/72U/Z7Q68fnNPXVc++ewTnt99y9NpsdteTMCcM7tph7taK6Muo2lTdWxmesUhX2ik3TW87/z0p/+Ed+++4u74Ac3Ler33zmleGEIyt+OqopjOYD9LiKvWf+uF2I2ZFzBhZTP4U1OZnIsMPp3DG+W9t2aW4oglucGtvZHXYnRmxZN6p0wTcNYNBv7088/55osv+P3f/31e3d9RW+OLr75mWWY+fPMWP430K9Qm/dRlvvDzzz/nMI68fv1aBJeuteUNqtRcOeN7VGC2YIvAbpgYQmI/Thx2E0PQ7VfNG184n6OshaVUllx4Op2kU8AxDKKIbVF7kl/Ua8UEjlwyyzLTqnQNtVbOq16874KXqlN7tKlMCdCT0VetWlcp5AneM/hExxNdZCmVECLTqGHiXAq/+vwLfvXLX/HtN+9ppXHJK5d5vf5OA3c5r6J4LiVzXlYb1jv2hx0e2SUEJ35yqxpi372647jb00oj2+DVd8/D4zOlNC45E23g9/T0yLv3Dyy5mMmbQR2lcF4XOXhiB2q352GLJjjP+4dHcJrflNpZcyV3Deadc5S18nSetQF6u34uHz2H2yO73Y5eu8E4ygDxHc7zyqtXr/nhb/4W+91Ey+XqVhlHYdfNLi8xAmR30O1Q3qzFjctA65ai5mVS4BoKcmpSHStESf9uCI4hJFzvLKbf2CiFKSVub+/Z7Xa8ff2aaZx4fHpkWReKfYZq3vrbaPQv9eUwhpCgvi3oSp2N2cdHT3BRlhsxWuANPD0/M6TEp598ImuSebG1rZnNtN8RXKCslfPzRXM7r2c1WO5J6Y04JPb7SWw8x5WSHW1GMQ4jydghrVTRS61i3mizGzxXStYzRYXG+XLi/fv3LHllNbx8+3yXy0Jel6s6vNpMovV+/e+DMXmGmBiPB8LmdKyXg6PzvDzys5/9ER3LdkBQXLPnm8yzCqSJkOW3IB4/Kp+mlMwf/sHf5u0HH4oJ1Br/m3/9f8vPf/mn3N3eMh4GzqcLb159yP/4v/8/YrfbmZDVnFC9+cH9ZwZRKtLqdUVsF0W1/SRzPmhdRIP59MCXn/+EOJqSGh3AsuAxhbvZsScvMkEumafTs0w4TTfhDSrMubHMmefnZzub3bUy972zLAuNFwtv+qYREjOrNiEorYvNF0JgnCbFyBrrSQmDjle3N7z96COL31W++nEvKHhZZkvBe3Hi1RyoctjvRLtOiWWeLWyrbZ6fRnToxJtpQk2XM6w2ST1rtEO6GDJbjGU33H4+X64D6t4bpUNErqbdNOmtd3xU4HwwBWeMAT/twTC5EGQgFn3U4NlEegHj7G6iOdQyxrj9/H79Pb0LMwzW9STD+Lde+uHhPe++/ZaQEq+jrClKrngvqKptFLO6+eV34cY4a8PFZsnIFrgWie/208QYB0AsBocWUKmd6I0QYHYPa5AW43hzyzKfVcm6QM4L337zjsfnJ3bTjrdvP1A3xsZ6EhOrucar1/fc1rZFHlOrxF+t6XOvtdGcjonSmsrJLpijO29DZAdNw8h1XTnsD8pqeHwC4KMP3qooAHYpEq0r8t6xrqsiFJtZqPTGEIbrJVFKwdlcqZd6rV16t03cZAMSYqA6J+GVk7tlCoGlrHjg+fmZx8cn3r59w83NrfjaZmt+YzkjGzyyBaO01swd+C97R7wMrGXXkQhV8NiyKCcC39GcuNF7Zbfb83w+k2smRQ35nPPUkslOFWsA9uNEsPWyUceTlyj0OnPpYqv58J1Iz9aI48SWXNdx1pV3i73V3DB4R3ONtmYLtOc6TK2lUGOkrIV3jw8spbDbTby6vbselusy45jwQcrvKQ2Kgw2By1worXJ7OIBT+BW2Btdl0bs1y/Jf/uwnfPH1L7mOgly9/rOz88Q72AJspLl2eq4GBw3DxO/+jT/UfCAE/p1/+//Ef/Tv/wOm/V7v9jJzOV34r/83/1v88Ld/h9dvPuDrr7/Y5BaaiZVMaYVhkvVH64XQnHQl4YW5aQ/+ei647axyncF1fvaT/5SPP/shz0tjvz8yjklBYq3ab1NXpFmBSBdTai8XlrlDFLPT36jUzlLqajVBpK8M3f6Obd6rg0xnr3cihhgbsodmKYLe/KD0Hmvv9Jz54O0HfPD2Azbb/s3y5+7ujnlZeffNN7x++4EubGu2xiHx+vaOEAPrfLmem3kVzRkjMfkQ8MEWGZ3rsJarOrCZG6Igh2g3aG2dOE3spz3Re/b7A0PwFrbSjX2jDxG9l4Npq/ynP/oRP/7pj8XXH0SXpIqJNAw2kEYQFvbQneFjvovSGpE6lNqhaMFvYmHlS+hvz7Wy1EwIkTdv3vIbP/gBv/7973P76hXjOHBzuOF4UI6z92ag16xydtHM6xZOlwvrWmm1M8aIi4KJDLiyxaKDVAFI2iJLFf+9dNlrJBeN/TAyjHu73JRdUR0MKYDn+tk351xVf7JZH4aRlJKxq7BFJ2ryMl9YlgvRbywJYY9byIwMAoU5+ihoIpkjJV3P7vT8zGVdyMZEWmvjcrqI1tstqN7cbl3jSqPVASc31Gw0QZ/in+syNq1KuQbC6B0pdyOK9QI4H0jTSBoGQtz8huqV4x+GiPfyAZO3vwgAl8vMZb68sJz+4lvCfMFkW3FjvkdaDzIerBuzLelZbQfBftxdleHjMOBM5LeJANVCqagZxwHfxbrzdsg4q9Q2qKfZhb6Wyrpm+/scOa+seSZGb4edKZSt+3NeAVcOsQxrFzxZgfF44Obujv0wKLb2SgF37PZ7clbnNMZBB7/97dM4sBsVZpOzPMZqLszzzFx0KW3C1T/6R/8JoM7asc0Iq8089J/GS3HZvvNynIPS4Dd/43f43q/9Opd54Sc/+RH/h3/r3wQXqLXzdDozTUf+pX/5v8o/97f/Lrl2fuM3fqhLxzI9tr1SWiNci0bILSsczF72i+vsS8dxzbjo4GLj55//mH/j3/hfc3p+Yr+byGumA5fLmT/76kvm5UJKkRS1px4fHyxvIhhVWL87WpG6zdhah+fTiVqL1leXqNWxxY46JvPC680cH7rwuY3c4Id07QitJREbrcn9dllXcyGocj8wIkWMge4c6zLrPRhhIsZEGAeenk/88Y9+zLvHB0vIS/Ln6p1oM8FICBLEqDRlzrK/AGt/eqf371g/e8/Om6y/miMhLxhy95C6Y62dx4dHwt09u/3E4CPrMjNE+bjk1R5YisKZLbwjhGBRkWLLeDCGhzoGb6sgRCG8KUrsU7ra7YA2Tu1bqlRnPwzsp4Gcq1VoQC/03MneQVfk41oLQ4iWK21VYIxSwIbAgOi3+I4PE42tg1IGtARgidwKy/nEWgo756nOX6uqbkNpB/RWScHz5u4V9f7VdSNtg6xqrKFW9JniFgZkdg2tFrzfcdgnyjbsR74+IQR8lKOkNArYBd4ISWljBP3dtVS8MUJ6t8MzONbTidP5xD2vGVNg7UoOdHL9oDtHbnqPQrUCy3wSnz5Fub56rmrm4DytydAxjsmMBlVRhxBk3Jdn9uOOu1d3Sjekc35+AiqvX7/CdSfvLK9ktFwaoRQG5OfU9u2ahvYXffkom+QtwKgUkTZy8JTV4bwd+lV0h4y6Itc7D0/P3OxlXb4fZTuzlkwxn6nmJNh03eOoVzWtB8tz39hbK8tZzLOjMdaqHQJxTLo81oXog3kidLbcFxAlVv9LMyjQU7vo3rvDjoCXcGw7Ir1cdcMwXUVhmifV62WeUpRjQGu6xAdROYeqXGbXG+/efcnPfvFPcE7eAM45ejHEwTXolv1hRZDgDgc2kKcBxfFX/tpfIzjPN4+P/B//7X+Lr7/8hmEYcb3yL/wL/yL/yr/y3+HDDz/gsqyMrvP6ww8IPtJY1N6rtGC9LJRpEFUdzSSUCLl1AC+zLh3cWidbVe6dZ7mc+X//o3/AF+8W/rX/4f+E/XggpUAaBtypyVTyqHvm+fmJP/nRj9jtdvyV3/4tahBeuBWctXXzexNFu9VGM5PE0lVQ4UQV762DExpSLX44RQUozWvGu84QAvNlZjGx5xYfjEMOxnZZhOA5Hm919jnwK2Bz4H3Y0xtWLKp7vzke+OjDt9ze3FyLyRgjRHmmOQexGX3Lt866VqI3O4QsClkuOmhjDKy9apO44frAQ4qikzZhfqF3fErk+Yl3337D8ebA0AdSjPzu7/4N/n+U/WevZlmanoldy233uuMiIiNNZflmu2E3m5whNCMBkjDzQRhpBEEC9Aso6Pfpm0aABkM2yTZT7ck2VZVZJjP8iWNes/deTh+eZ7+RBQjo6kOwG50m8pz37L3WY+77un3rcUViTcWqbjA6AjKlaoxf+VDdU8/Lr1KKeiBgSgVjKkGt59VaJbUUgvXYYEhZbuL9OOEwtH0raIG64BzE53CYJCMjl4JbbTAUWi8ZCdM4SvKcKkgCllOM4l4MgZIjM4bONlotV4K37LY7OTCQZWTJGWMKbW1w2mnNKVGotH1Lg5Gc5CoKK+M/pHRZb8+ft3eOw3gCYxlW6/NLYn2g6j4jzhNTFQnqNE08ffIEFxwuOEpaZscC/ksxyjx1GLjRQyJX0Zys+g4fGhpnmJNouhvvBfWuVa1ZvlfNkBhWazFexkxxmdZ9MOUZZwlOOtTGBKwpgiJGqmrnHL3rxRiUK9EUXCm0vfwuxnFSTpM7K6BC2wqOJWWa5p+wk0AURo0PZ+lq4zzZeZHYNoILSXkk5UKwwu7ph4GCYWfk/17CgQwie8XqOKAuiANhLZELphTyr1wQUV5wa/CNl0LImfOIylsvij/Q5ssq8wnI5Yzfh0w1DqsOOlEaStdTNDzqcDoxNA22aQT01rVSiOn3IfBK6faEBCvdkSlGx3sz6MHrGs+XX/xn9vs7fBAOGtrxogfN8i3rOF6/quSUYKjVstlueP7sY06nI7UWHh725AyN7/m//d//r/y3/+1/R4qR0zhx2D+yB9b9mtA2xNNJ1GIVjIO5qKhW8+OX3Zj9lfnjh04DRd8vC2WMI1RYhZ7/+G//Hb/9W/+c/+P/6X/g8f6Rvmvpnj+Xn1/HSU0IfPzRRzRtI1DAKhch2kE5nbrIPsGy2WwoOUv+RhaGWcrp3FEa9UnYoiO6UqkFOk1OzLlinCMY6VJLqvpMWmKVSUOvWBvnRIlqjaPvVyLDV9P0uUiWu4MmtHzyyaeUXCSdDynGD48jx/HIqhvw45zoGgne6axYyLM6qxftelSZa9V5t8zM5cWXw95gnMzcspFfS9MEdhc7oSXmQi5F3NDIGCPVTKuz7loKSk/XrGYZbVErNRed29XzKCc4T66ZWi2nNHM6HNmsVrL1r5VY4tno5ZxnPN5TK/SduInlKFeFlBH2yuDl5e8aiWE1wdDVDmsdznuBraXMqUTGeSYoLM2HBof54CgvlWIE8LfMzIMR41KqH1Di1VSSkcW3z+rCrpmaZCRTjQTZ+BAwrqgCRUiTj8cDcZz4+LPPyLEA0mVUKzJeHxqsNcQ5crHbYa0jTkn2Q7nIqMgHusFxPBxk5mwlQGmaJlDpZSnqGzANbePpwyA6+CzZEXmK6uVQ6ByGTllZIUgHmIs45k/TxDxGttsdXePIFVHKmQ9O5qWSLbUwNINgMYrsOOZ5JseZ5CxZO7vQted5finSjf5KmM0/9mUQ9ZY154NN5rkygpryiHEWq3+2KNAEpU4jiA2W3ycQS5EcYu0GD4eR3XqHBcY0qb8GfY7BF4NpAm3TSP4IIg4wRnESVQ6LKUXSnFmvexlrAFMVHIc3jsM8sgQ32SrqMW8EoV1Txuhi0hkUv4KqwCSi9ZtST9khLReciExsXjwFXjv/yI+/+DG2ihMbvRSlAFmuh+WGWE7lDwcTRXZm3/vBd/n2t76Lt56r7YUe7oZ/8//4N/zr/9W/ZhxHplkWr6v1mlIqNzdPGfqB9+MjVjljC2q85ATB62L9w/jLucCCyVi6ivNYsFb9XxK2Y72l6Vq+/PKnTLMwtCR6oDKnhEnSafXDwKeffnp20C/LfoPEPS8iO/G7LMvixMJ5K7WqKk/ou9UI8rvWqt4OhSBqXnwpmXmWnZCzjmJlKb8YaUF+t7UWUlTXfhays6lQcpRzDIkodcaQUyQ7h8dRqkBSrTGM4yxGypTpugbbtg3OepbnfYqL0kh+kTln4aM4K+Yza7+h5JAfJuXKFJPozq1V5QxcXd9wOp0kpF0vF5BDo3Ee1DB0nGZev3rFNMXz7mMJVAHBUMy5UqJ8LAVxe3tjmMeRn/zkp9w/PlCr6MljTKQaEbRuZbfZcX15eaYtFsoZJZxzwRkdgcVZuiJrsBXGSV6+NWNLKQABAABJREFUoW3xPohSy8hCqlfDn1M65LL8l73csjRfgkukuvDGnh8OU4Q3I39u5TDP1GqwQfTy+/2B4/GId/K9+GpIk1Abby5vePLkmQTUO4+znuk4nf0KQ9/p/FE6j1/+/Gf8/Oc/4/HuTi7+NmC9LK+2lztcaHj5+jUvX71iAaHlFDHeUnJmihMvXr1iP44450hG1DXGafVpFzhZ4XA8iotbVS3jPMrFHoIYF7PEqJpa2e8fmOMEZQFF6iGvL8uCsBYnslcPQyFnGXUtKWalyss2j9PZjPTrfrVNy9D1eqZ9mPeel+6IKicX6eqyVpIxy4I36/cOsF6tCF78L0Mr2OY0Tzwe9uI/0J/bV4OvhhgTp5MYl+I4iXomy8GQdGQktNmZGCcWwKF0GZpeqCbCEIL8mRoRWxVt7zTtcbvZEryMXJclf9EdRoXzDuY8S9eLwmlim4hYBDnx5vY1t29+ifcZZ2S5egau6hu2XBKmLmeFkJStKu0qjj/4g3/Nbrej1soUZ776xVf8H/77/57/5r/5r8kxYqg0vhXys3U0IXB584TVZisXlPvGRQTM08yvCF1rFQNkFnuwHLb1fGfVb3y3y1+LMeGd4eXrV8zjUcavzpOSKJIq5ZwbEXVhnpWcMM2zjLSl1WOaTpyOB1k2q/zbGqvBRJyVdTjtIqyoJsFwOh5k56EjzFILQ78ieBlB1pyJ88zpdBRhyaKM0vOylErSqYoc07oLKxWjXXjOmWkaOZ0O7B/2vHv3lnmeWQ89282GJ88/knAwi9xw1sq81VvoQit5CGfppTk/cCAVruSuSoh4ofL4uJcZmTPc391z9/4Waw0X2y2rvsdUWcylORHHqAeBVDM5Zd68fcv+uD8/oGfFR60K2rMYLw5SsrTUp3lm1Q/88IffY71ZkZPkOpxOJ5ogWQm1JooR443xTjqQJDGU6AvWhIZUDc5KUEdO4h04no7M8ywLyVqZ00TrPOt+EDdtaLDOM+XKlGRUNEU5IL23SE6SPBxGPz/vHRkJV4o6n21DI6HzRtzE3tvzbDBnlTsuDllVAg1tiykyoqsxs398ZDwccUVekyYENqsVxgdizbx6+YKf/uQLkeQBNUul75yj61quLi/46PlzhvUKUw2zzs83w0DfdnjfUKlMSWTDS063MeJlMcYITqQL4gvJkXGceP3qBafphCmIgsYICXdOidZLUl5KEVMKZBmNWDw1y14mhEaCe04TcY6Uamn6lhBaDocTUY10i6Jjke392l9G5vpt29J3PcNqOOvEvXfSJSU5FIwXs5pU5bKYTimpxFuzrMOiqS+s+xUELyNcUYmoWkhVgrnoQl5AmtXKpWCUKpqRIqpfDcJaMvrf04PZWysdiNNISmslT74WGZ/GBNWIqirJ95c0UyUo9jw4rwIAznN74LwXbPRAG8eZ4zThPfzDj/+a/ekeYyGZKmrBms6XwyJyQA/f5ddhDBjnqMlyfXHFD3/4m8xzIubCj/7sz/A28H/5H/7PzKcjc0yyJ0wzx8NeLqsqDudnT59L91a0+kLcw+MUhalml78ue7apStLg2edRyocFsNFIZGScPU4TVHj9+mtevXnD4XhgHKezRNpgmVRltkAesZa0LOcVwyEzJ4NVy0CtyleC88WMkS5lPAolwSDdjDGV0zTKs2Ila7oJLT5IIZaNBBfJriPLOaCEjJIzh4c9r16/ZP+wl7PHKPbFoDtJWSr+7Be/4KuvvmaaIqvNmtVK+WdBnt/xcBIhkSQsyaFF1dCTKi1u37bU+oGfI4A6WcRap2qVkulCYHhyI0vG46RhNQFbJR0upcSc03nz7pqGVWh0XCW8oI8/+YS+Uy+Cum+XEYsQJaUKzwXarqVtmzPrp2l2xJgEyFdhGNbYari9f6ALDav1IIHzKVNUFVMS8jM7mY9672mcoKJjLXhnubm8IQSn7aOhDWrqy3B7+w7fNvTrAes9IJVYRsRgRZoKjtMIxrBaCRr88XDk/u6WeRrp+jVPrq4+APBQvId1XGy38lkXkdBihLiZDSIz1hGH8yICkCCmhoKAv4wJ4mSvhe9/9/tc764pKuPMOopj2fOUxOk00g29zKeRnzFnWWR7LE+uLynOSoXnHDlmghduzuHxIAXD0EPJPOwP7A8HVsPA0PWUkskkcetbR/CBmEe6phEJss5JFx16NcIe6sJKFUAZMx2ZponQ9HjvefPmHSVlNt/7nJTS2Qg3nmRvsShL/rGvRUUmi34nXWiRttognVhOSbwxwGGeOB6PXF1dSEWcsvgT9PdWkIN8aUVaH4itLFC9bSiaXphypgkenOZfW3WtKxKjqK695oJxRrAwQDSVHCOtb3ChIedIsJ6cE1mEaqSSdVdi9FKOGBckSMY0HE8n3v3yPf2q58nu4tyxex8o6UOqo9H5t7eO3XZLionXb1/wt3//l1gP2ZYzirvUqgt38yufrzHq9dG+zFrDTOJ7P/hNLi+vOR4m5sMj/+Hf/zH/z3/zb7i8vJTYXr1lfvGLr3h8eOAP/sXvi7jBN3zy6af82Z8gKgrNxzVWR44p4nzg7AtIlXiaaFeNXrxZ9xQZg/yMsriWzrSmRDaGtulY9S1v3ryh8Q1t1xK8ZY4z43ii2V2ecTyNFnHtMDCNIzkXGi+GSEGcCIallkqxRrxhRi4FUyXLZZzkuS3zTHU9V1c3SoC155FVSXK5WCzWWdrtVooiw68If9q24f7+Tvw9N9d6Gcr32oaWQmU8Ttzf3mKdnDXGbOi7lmmeOO33pCxJmAbwRR8o2TOIounheOC0P+K2VoJWdLaezy+UFXmWBUrlFCO9s5hSuX3zks3ukpsnT+QDz1niTkODa4RtElpZ3pZcOJxOhMbz5PqaOScxmakscAntqOoyddbimwaj38fyEghSWWeq1uKt4Rc//wVv3r7hanfB59/+rqhYKOcqzTnHmAQaGAKCVfCO4A02i6rKBlGkLCqXYsCUql1MZN4/8q3VIAd1lIETOXKaIoPz8uB+Y/aIsbx985oXL14C8K3PV3RDLy9mks5qUTdVNdF4JyyslNS0OCeG9cBmvdYW1soc1kmU45KtUVNiSolRUcub7QaMoemCjNiwYm4qMgbb7S6kPZ+msxvXOU+ME6cY2fgNwbfKApK3X+JrLY+Pe37+85/x7c+/zWa7pVa4vroitA0LPjn4RruCiZO28V3XLsJXnLEMQ38e86SUyIwSu2mR/JJcyGmkVnEA/+Ll1+wuL3jy5IpxPOKslSzmNEiwxa/xZYyRsVBK7FU54maRwHrrKN7hkjwHU8o467i4uEAQ41VS6oxo5quBlObz3gvtLMbxyP4wMvQt6MVf9F2rhnPrHxTUVtQI8+71G+4e7littnz09AmN685+lJRlrDSeJlGrOSd52dZiqqENve7e8nn+fAIlh6q4YRxpQ8vQ98Sc8AR8K2j7mCWLYZ5HVpsNuRhinvjbv/0RD/evwEzUkrEusOQ9LxfE+eBFcp6FXlzOC31H4Pd/719KsWIqLx/e8/t/8Ht89/vf4/bhTkCKWulvdhtxvxswGPKceXL9Ea4N5CgBW8tOggpzKnRVoH65ysUt+8KMN5J1bbQjWL5lU8EZRyWB87S28vHTGy4vLrG25fb2jlQrq9WK8XDg9u6e1WZLjTPWaBiYjrIrUlzNiyncyIQm5cTheCAET9v3eCv0itCv6I2Y56hIrHKpOK9eiXkE70hZ9sXeiez7m2M2W/WCM6J6a7uWSwWKllKYponX796Q58RHH32EdZ71dsO//C//SznT54miuSU5S2fYhUDX9qSc8DEnQmipGkxhgqVxgdQGTjmy7RplFotXIWuLZpaqwULnBdMbWs84RV7/9KfsLraYogYga/HeqSbYMo8zKHpgPfSMMZ61/w5ZJi5u7UWWaqxqz61I9jCqE89ZmPlVTC/FGWqSuL+LiwtSLrx7f8fHHz0jlQQZkpFfaAiemGR8lWqVitwvUl9RPcy5EqyQZeM84kOLx3Fzc8PxeODh4UGS9XTWH2o4V8UmGFZ9L0EhOatGfcVuc0nbBZ5dP2U8zjjANY5cxPFojSHlzMPxwKrr6bqGisd5L4e0UxdukTwCq4ocSqGo5NRh6UJzjvbs+06ot0rPrWSMERlsRWbzeY7U2opRzMkF3HQtx/3+3CobY0mzjLdWa7nkLq8vORwPQMU3jk27kapYncpxihyzGHa8ehPWq0GNZPoiUc/Lfvm/xfVekO7Wgo6ZGkwxXF5uyeVjxvFETFFiNJuGXAp3Dw9c2C1N84/HmhpjaJpGO4pC07YMtQiG3KiXITjKVDAaeO+DV7WaJThRAhojJsuUPpiuZGwFPrS0reRBON0VVFU5WW/Z3z/wxZc/4/PPv8WVVn6mwHqzI6XC/vDAu3eWi+srSkq0fQex8HA6QimEdqVjAQfVcDzJDrANUglaK0jqFCcILcMwcLFeC9bfO8Zx4u7+gVolN8VrwNjDfs/D3R2hHzieIq9f/Yw/+5s/pZQZF2SEUkumFMOylDgDXlk8Cl6FEfLPx2TYXV5zc3XD8SSjy+9863O+9dknvL1/R+MaQtOQovy9J9dPuLm6kWhPD6YaPvv4E4Z+xX3eiwpWP2/vgJT1ktY43rO66ay1Ekd9NSiyWv+aJdfKZjXw6efP+eIXP+Wv//ov+d3f+X2MkfCr4D3NxY5B4Zun45Hj6YR3WzC6sLYGQyDOk7ynzkkUr4GhE8NizUlyqudZ3qfgabzXsKiiJjnthBrxCn391S95/fYdv/vbvyXcN4fuE0baJtB2PQZ3ljsPqxU5J46Hgzzbc2J/PPLzr74ipcj1xSUfPf8IXyrZWFKWdMPxdOTy8hIfgmZyJ3zOlZrn84Mxp5kaDJ0d5KCzTsYd1ipQyoKVyooCU4znyM1qLR9/9hnxpz9hHiNNIyEZRpeRFhmTnKYZ33Y8nPY0TcAZIBd8CExWgturHuJOzWtt05BSOcu4UpLvpebMnLOoeqxlHmVRur285GloyYgqYxxnqhEH+NJen9HlWMgSquIQ7XgqEjEZgizrnClY24vGO8mhZqzhl199RYqRZx89Y2d3GKzA4XQbdooTVauNUgq73ZaLyyse7u64u7+j6VqcsXRWzDLTNAqVtxSJocyZOfes+47NZo3f6tgtRTHXVENTZa7tvQgQcgTfyFIs5aQBT5ZTlIdQEOJ8WNKWSrFFsRGeEjPWyMWPkfyFZXzjncAFT6cD291OzYeW73z726SSFD0czkC1koSAWpMYEptVi/MyfonzeM7ktVUQ0LVUvAv0vQTdLP6FYbWi1cTCUgouO55e3VCtdKSn08R2k0TiV+v5Iv11vmoVoNpuu5U9VBJY4H6/5/B4oNZC1zbgPG9v38m4s5EdTUF2R8sObWGdOS/y7lrEyVraRkaiup+ySfwPnelYr9caDevPIyacFF2ffPKcx/2WN2/fkN68pe9aSoyyu6hFfzfy3yxUKJXQOnKcycZKYVESaYqaVSGeilFHlMse5eryUh3IorhzTnwbXSeYne1QeZEOHB7f4xSPY9AsmlKISR3VVVDXYpkUdM5CTTPWUVLhN37zt1ntLimpkHLGB0l8u7q44Az0tpYSI0ZHj+cdJZV+u6EdVtgHMaAKAl38JTHls/N+uSBSLviS8VaqcFMzxVhcReX1UpRUZ4hlptbEeuj48z/7E37vn/8LhSV2OOexTiStj/ePTJNIc4fVir7vzzuCKSVMELPcdDpB24oqTjuO43GkayvjONH33XlcVYsUgIV6Hp8uz+cYE/3QYbwwp0iyUDfG8NVXL/j4k+e0Tbc80Nze3TEejtzcXBF8YLfbEdSwl7P4LKSoSRivmT/WsLu4+CCucQ7nLL4LQaIAFb2RZgnssRXG/YncDXLZ6hI7IyoPjFH0tvxac87UaeJis6H5/g/lMK2FoNgDgz783rJdrbDG8OMf/4RpHvn+D3/I0HlSEf4TTsYl1fhzUp7EjmpgeFocpvILifMs88Ym8P7uXqrRlPDXVxK40jVS8dR6Tspbgudz1qU8Fdv1jKcTtULftsSaCEl02FhD0BXXIY0cjycuLi/49JNPgSI4E9kjnl3kc5aFfq0F3+6Yc5RZX9fTdh2Pj49sm61I2krCVUvX9dIitxZjduQUdRSIjJDUn1CyjHEc4ryuSJqdc1Y8G9YyTzOn6YBZrZFfYdZvUKo9bzhTPZe5ekmZu/d34kq/2BGso+laOfRLxngh2z558ozQir/AWEueZ9AZ/tItzKP4SfqhxzYBV+X3FlzDFGce7x9Yr9ZsNhtiynh1v9fqzyMX77WLrIsJsgKieErVUWf5uZrGcjwcaNtOOohcF8XjP/plvvHfcTree/fuHeNxYhz3IlENXg58NTSiuRql6DinVr1IBWpnjaj8fPDk/AFV45BxU8wSEZoaYQRdXd1IAmGpPB7FOZ9SJDethBbFxCnNOGPx3hJcIGngrUlSkUqhEAh4Xrx5x267Y7PbCEzPSW7xYTyyCh2hkWJojvGcxmeM5EDEGNkMgyoMlXrgIn/7d3+GsRGvShqrVfhUixaCqixaVJzfWFgvv7ph2PJf/av/NevhgsPxJL9j50hJqm2qkcCknOUZqJCizOatE5GN84GL3SVv3vxSPm/95xDvr8h1raQ8Lgq1nDPVLaNzJBnP6/drjFwMbUPbGPb7Wy43F/z93/4NX//i56wubnC5kvJMqJ5RY0JrKaxWa3Gs66K/loItBecDzlmmOjJNI8OwUrFOVluBoF2ck0yIWgEnSHOD4P3THGnUBPzJs6e0/aBLcPE0LNnvP/7JP3D38MBv/OD7dL1Qt4e2ZdV1soNUU64x8PHzT+Qy0zGwMUYYT1bIwouy0KsjHGOxNoh1WxhDGV+h94HOOa6vLvFWliRGW2fHB28DQBOW7X09B+q0rTo5s2iLHx/3nEax9JtSNWUuc/P0BqwlKq/ealVmMcxzIlexod8/7nn//j2pFknqclY1vzJr9c6x22xI08T93R1vXr8CJFKzbzsWg5hDyZsL4K4WwfUGGeXUXPBBKkRDlTmljv+Kvkwow+jh/p5SKrvLHRe7nQTXG5kjplpFUVULgypxFhqrzKAnulXH9fWFVMokQtfQb3uaxvN4PHA8HfDe0K17+qEnlUysVSSMLEYqg/cGnMDqUplJaZZQFgq4ShPas2Kl69qzUg1gnhOHxz3H0yi7JrQDsvL5mlIZo7Bg5BriGxGtAhOUP2c+q3v6phHsQFUKqRUJpgQtJc06F+nwZreh6RtO80iuiVIXV7gYOEUkUrAWpbyqP0NhfsE5qikcDgfAM8aZ0+l07j5+nQsCPdTapqVTKWxW/tR63XNzc8PV1TU5Feacuby4YL0WOjJFdSp6uTn3wR+wuN9TTPL9mnp+wRcj5rAWPlExlb4VJPjhcKIsZk3f4JDl97e/9S0++/hbXO8EJtmGRokERj8zicoVIUJm6DtCq/DIxWHrAxbJ2nbOgVXsd5YC4HQaGU9HVl13RsJInoHjZz//Ma9f/hK/pFHq/8wxS1zteUYut4NRR/559IanZHj28Wd8/MnnTFPGOWgaf1b9GSMeB+eUaZWEwzTnyGk8UvKy/LZcXl5jkkJAjaJsinzuOUWcMfqeiGR++b3IFEG9Ilm5bKYK1ToYrq8vmE8nGiCdTvzhH/1bVkOjcnAZt59OB6qBq8srdtudFptJD98qaqcsxXbTdQzDmqYRUUxWFE3TBJquw3/jz7WqTCs10zTtByGJtbi2JSVFb1A/5JI7y0fPnn9IRNSANescTd/p52PYrjc8e/pMTzaoVuTSy9ienDmejjJOVbFRqvKzeFM1c0DNNWlO+CDmOnJmGidsGzDWMsVMTBNt24nCq6ISK/MBiaFtjLTA4q40XlAXtUh2QM6ZX774mhwj3/78c7a73XkEVGrl9du3xBh5+vQJ3si/l3NijLNIPxE3onGWzWotCF3V6l9fXbJarej6XrwYephl9WRURLIZY6ZdlDajmNe6rsMaw32cmUuSF7cILjdjCCox3K5WNKGhaeTDlMWdLow1cMiZgLVyIzeNuMJd8PhSzgdgLnA4PDCNJzKVz771CdXA3f0tfdefjYgSgpNxzogBLUPjghqtdF9kJWAEIJRA0zTISDScw4McXqSFWQ7hFCNvXr/mydOnDLZnnEbiPHF9c6WKJFFUWHXtFuDh8YFaK8MwENqGeZ65v31PBbYXO2rjVQdu2PaiToo5CxCywjSO+KdCG45O2+k8Y5oG487rxA9yzep+5dmw+nIYBLznnGO1Gsh5plYJ3pmmkRAcbe3O/+4/9mWdpW0blcM2bNdrEVW4pVOT8ZDOJgiN5+FwoG3FsX04nCTFcWFlIfG78zxzPBxlPq7PqHMirV3+mima8mcMp9OeYbXW7GYZ8B9OI10TqBbGNNPYRl3i+l4Zg6kC43NG8OFusyHqpex09OaMk0MzS9jNIkYRb0QQZzmN7GJUTegsnOYj/8uf/RGpTpp7oBiQKpklRlaE+vcAHBIWkKnGS8dhKqlY/ovf+n2c7Znm0/k9MLaSouA9pAMR5tXSGcRpIqeKbzSYzHkuthfgG6jTuQgyRkZMoqIURY9RW3rBClK9Lqq+RMnCF/tgtyhcbje8e7fn4XFPt9nwox/9Kf/7/91/R9tv1JtjWK/XXGy2xDkSYyKejvSapBmL7ENTiszTSGhF3p5SZh5HWaI7w2F/1DPDsNps6EIglypVvxOydZpncir41hOniXmONEEyfNq+o1pH27b8s9/+TfI807SdGJ+RcycYR1J+VdJur8wzPsgO8xzAlCuH45HtZiPZ21l+BpQObGupKsszpJgEU6tV336aGReEt7UC5XOC2GidZ86FedYsWGe/8c8FXVZLhb7qW1n2qRyuWtFL3z084NpGM6xlsfT+/p75dKLvOq16DcNqhTGG8bBnPBwIBihZQoGCZAHknNlttjy7ecJus8FZx6x00DlLvkE1MOcMWTKDi+qGS0mMGl5fa8H4QLDh/Es01khuQa26uHRs1oMspKK4pH21eGFCCwpZL8+l63JOKmpzDqYXN7i1ENPEfv/AdJpwxnFzfcP24kIuxv1RxkhWJLIBJ//Pe2xw0m10coBIDvISMaoxqVNciiiqhbZvdO4tFfCTp09Zr9ekLOMi1zSMx/Gcg933HUEvW6nWZF4Zo3QP43FkPJ047B+Zx5l0DnMRiBlqimwbCQdyznIcT8xVEtpKyTI/No6YMlETwGqR/Ib9/kEOP52bmuWCzfLcees0DEbkfyklUVCdTmfvxD/6pWfbstfpuo6m6yhlloUpmheRMvM08erVS+5vb7m6vKTvV5rE6BV0Z/WjqudnBycmVECQMxi9IAw5xvO3Idyd9dmgab3nMI7c3r1nXrDu1oKTl9t4x/54xFrH4XDg9at3HA4jsSR517wSFHLSTPgoRiwrO7dYqnZv0nm1Tct63alIxGM0x+InP/lrXrz4CT5UjPH6nIsKJmdRClktIxYkuJjXnEpZ5TJa9Wu+891/Rp5HClHMukopmONIySOlZqZ5ppRM18gOIYQgh+g0EZVE/PHzj8WrpDsMIbDK8nlx+hvF35RqzmpJlNYsV1iGmvQRkA7It4Wb6zV3h0fiHLl9+4o//fM/wmQ5TE3RPD29bJpGgp1STqRcmI5H5mkSNWfbaGEqoohhLaOpNrTsdltqSdw9PBBHuTCjdu0WMQG7EPBe1GxB/TYVlO8kIykKBBfo+xUPD498+eUXvPjqa+ZpYsqJ0+kksnVdQqf0AZ2SshQRKQvRuut78d1YzcR2cs544x2tc9RiEBaSOfNcToc9m8327JHouh7fZOocGVPieDxKJdR6grFUJyyfEpPST2XUcTpOYKpor61UKJ998glPnzzBesfD4x5nBUtwf3fLxeU115eX7I8n2uAZ2lYWLaUwn0ahwBqIJksspLEEa3UxU3k8HgXO1XiV2k6McWS1Gmgaz5wyjZWAI5AOwoWgudmw7gcqkaQgvTGKYartWkiVU5w57A9stAIIyIdu4JyAV2olW6g6W616+LWhkTZYO+B+WLHZrilFZZ41a6CPk4O8k0O2cTLCM04ugJKzPjhSLZhiBFseAgUoWbqtWDKuql2oVMZRXsAlKyB0w9mh6YLDG08p+TxKAjHoWJxmO2y1lZYLousazM0N3jr6rgNjmOJMY2V0gFkcwpbry0ve39/z93//D/zO7/wWE4b9/QPDMOC959WLV6w2a7rmRqoZ43gc90yTCANKlSVpUmNY08iFezpONL3ItMZxFECdjr/+SV+lqvfGa+HSUlPkcNjjjWOz2ZBrZbfbKaMf4iQ54F0nh2vRkcOyTO27DlsNp1kko7PmU0sHIfyoolLkAvRtJxeSkdlwN3QyJgyeHCPWONnNlYS3HksVr0TwbNZCd7WjjDmgyvzQySFqJIiFBSMRglyuZ6yEA4yX8UsByDw+3vGjH/0h1qr60EouCqCGOZWnCYVI/zxVeNUPfy0n+OzTz9iuVoxxogmBaNJ5Z9A1PXNZls5SRC2fY9O0mGCZS8TOE/Oc6NarM8blw6xLZbZ5UVuJpNwVQ8mQbSU5YR65KmpCWwNGDahYQYJf7lrevoW729fUavnRf/hj/uB3/yWbiyfMkwh1cs26l82yszFyBqzWK4lGsEJ7rUhhJd2tx1oR+bRNw263pes6QtexYOHjPOOtjGetd8IKKwVnPW7tefP6NW3Xy+h1nqlNQ1KBi3eWy6sr2Qt6T9Yu0luL8w0ln6RDQDq0h4cH3rx+Q62VzWbN1fUV/bBizhJ2ZICmaVkYGxiFlUlQiRiqutWKtm149eYNr9+8wSAYhP00UQqsdxsuLi/k8KqV02HkNE48Pj5yOByx6keIJfH29pbjNBJz4u72juPpJK1YCOdftms8Hz3/hIuNOP/aVtOwjMG7wKpd0ba9PAhGeEegjkljKCmxPx548/IVpxQxOB5PRxonS0dj0Jvc65xUUtT2pxPzPOtuRC7L/XEkzRFjDI8PD9w/PMgLjASPD30vYwNdj+VlsWrU8ZoTOc76vSqosAoSZHkpoSjWG5quAWS2PMWs5FTHqusEkFcEEuiMElOtjCGiVqJN20jWwSxmofqNF22MSYMVZbafU9bvC0pZxk/mDHUrSQ9LY4k5cnf/QNL/TghBHZuQla0UnMU30tlUnUejy/U4z/LMHA4Ka4RPP37Oql9hqwDzhtUKi8ylp+PIPMez/ny7uaAdOqFcTiN3t3dnFLL4JipNH/BWLrdlIQdStf9Tvrqhp21bQUdYK8hs73j3/j37gzjV53ni8vKS68sdSbk6ORdK+kY3ihjqBMFgddxRCKGlC80HBLyGONVUSFEMgVHn6DI6kYPHKX+iKKkYqiiDSiK0vXLVDKGVEahB/BpimtRuDVGvZcR0VapAEWedeYMgKUaVQQppOPK3//l/4f37XxBsxlb1yCCu8ZyiXEJGzKPZLDN+qzL5hUCbIcFv//Pfx3cdlYoLFq9xrTllppzkUjFIJEGVQscaPaKsCDe6bpALe9gxDHIWfDBKoOcFiuJYLo6iSp56joaFD7G5S/iViHAK1sPz59d4G0nzyM+//BlffvljgoVcE6dJOVxO3uvFwS2ZMlafxUI8e9DqeXmfa1E1pjwPUKkp4ayhbTv69QrrHftxZK8jKWMcs6Ldry4vWa16SVVcZLM6qWiahouLC3a7C1wTxOVvrY4xj4xTFJ+ZtVIYGhmdb9drYsxMc/rgZTGG0LRkwJaczyRCCfBIjMcjY8oMfU8qhf3jo6ggVIvdaRtUaz0z9B9HGTk4Z9lPI/ePD0yK7G7awHq1whjLOI68evOa93f31CoyTzmQxOiy6nucC1ANwQaME7NME5oPjHRFHzRNoHENVfpw9uPMPCX61ZrgLPM8UbJgDLqh18Q8eZAf9nseHh85zTOxJLKpJJMV8TCT4nwGYe0uLlmvVtRSJKAl6CWBmHecEUv80h6kmPnlL37OF198STWyYLXWYry05TUVKIJWMAYd8Ul34Y2og8BSi7TJy2F3rpxqZZwj4zjKIaIqhVzEz5FigWzwXt3MVZzuNWWGfmC7XuOdpTGOrFC1xnmIMsLLSaqIFCMlyiIwJpEml5J1dCLmt5oqb96+4+H9HfNplDjPEMg58bh/5OtXr/jiyy/kInOe7cWO588/wWFZrdYYZzkejwTvefrkGRfbjXQEaWY8HLHOsFmtMdZye3/H2/s7ccfq/qskicsc55HT/oitleN+Ly/jPJ/n1b/OlzGG1Wqlju1AN/Q0TcNqtQIrs//QtBLUkgv7xwdAEuy8l66mfGMmPc2TjHFTFlyKE1fzeDypgcuqS7+QEIREmqMsg7WiDs5gvBzoMU4aIyqjqMVj4pw9fw/GyOxdsj9k/yWHJyym2GqrzKpr/YBVN1CMHF45F7xN3N1+xd//7Z/RhoxDolBrVeFElShVVNYukMT/f4h2GZF0qwt+43u/LRL2GtWcKOOl129est8/EHyLdwLprKUSS2bOBVN1D6XeEuc8u6srqahzETwHHy6FopduUUluYVklSZpkpghWxsrIaemwloPcuspuHfj+957ThcLx9MDf/qe/YL+/Fdx9zhjjqNZhnRej4unIfv8oXpjls9ALHWPo2p7gA8F5jC6np/HEm9u7M5oFFNOPZd33eC/j09PxwOk4UnPWEVSDVeNlzRl0rO9DoCBYcG8daZ55eHg4nxtN8Dgr0nJTDeu+5+ajZ3z2+Wd88unHWGsYxxPOO/rVIFk0qeBrrfJgmohvW4b1GrAEJ6MSZyzf+fzbgizIAn3rbMPLN2/YPzzw8fOPNd/Z0W22eO+52IlqJ8coaWmlslmv9QB1fP7553q7aqpUTByrOAVDgThnWY5bCRJx3jOpNrxVCWIu4uS2VQ/OUulCy6qXIKFTipBnVquOOCW6tsE5y8P+yOPDAzHO7HY7ALqu0apZbvScK5cXF5LylQp9E1j3nQSB6F5iydbwVhrm4zjivcNT8cGzWu9Ym8r97R39ek3XtQQn4TroAV0qxDmqPl0e0mmeCV4IozLmk3HNmR1fxWXdeI/TkcKcBNNwc3nFOE461pMD4WKz5TSOkhmhSABrDA7p/pZuT1OnyaUw6AK/1Ir3QYQFzpJKFpCc9SxxwoeYBMvSDXogLyMeQxNagh+5vnkijm/nIAkwEN8oCsVpmp58AKHrtRjxJF/0Mkhn4OPldiuOewo1RVwrXU0XGnKBd2/ecfPsCaZCXPwzv6b7Wg58f5YAtm1DSi2b7VZNoUbyw1NWDtVOLoQ8U4G+H5jHkTHNvLt9z+PjA08/ekbrW6r/4Az2Xrpn7ILiNpgGStIZ+uL1aAP74xHvRYgAooZBmU7OyXLzOM9YKzgG3zQYDQtaLgaj3iAJPbKMxxPWe1xj5PLTd9FaoLE0OKyv/OVf/nvG6Y3gt7OoiGpNYGfyrFng35CQFYq8j7Wo5Ff+XkyJH/zg+1xfPpGfJ3jmOTIdT9ih5/LygrZZ6U4tM0eZu6dZsDsxCD9OjJTw+PDAnGe2u0u+/uoLLVqW1TPUksi1pVW1kGj9Kq7ac1gUKsuXDi3iaiMvYDFYm6m2cnHp+d53n/Hjn7zk5Yuf8vb2Jd/65AccTknGjaIhZ5wi7+/v6dvA5cWFcNqo1CqUAFOlg3DGaqErAVYuBJ5eX9F1nRrx3Hlf5YM/izSaEAidGCPjPIsIxsp0pXovHT0iHLDGiVKwVJphOOfzrAY5F8dp4vb2Hc5Y1sMK1whqKM6z/vuyH6wV7u/vsdYJqt4YyUimZE28qqRUBROr5p9ixMlrDJINnQtd3zOnSKPqH6sStFXXERGYX60ikzXG8P7uTrDcm7XiLeQgXO82eCOwsXmOsoT1cNyPZCNz/pxkNJNKPi9RjTomRTL34aEsOeNNJVY58DP1jFhOUTC4u4sLLrZbstrR4QOWOai5RYxG0gVklZUtCOWiFapFkNcS+7eSihx4+vSGcZp58/YNbUqQxUdhjFEaZyQmcZq3NkhozSiV7zzvefnqFRbDxcUlT66uMNZy3B85TicutztyzRynE11pJPdXcQ2ZzKrrxYCTk8ianWRhxFToG6eeAFGNNVpdihROXIB1kb7py9dYeQ6WzykVmZcuKII5ZmJOrMOgYz2DCZ6m9TwbxPV8nEZSToJcUA9EKZVhWIlfIInKxixEXWN1nAjjOOFc4PriUnTl1mpVm4BIzAbvO9J04tXrNxRXuLy+5EN1+et/LW17SlEWstZgvWe7XmN9UPaPxIQKlNGSy4eRrWsCvbdc7C5krJTLmRs0zzPGwno1kKnnUWFRj4D1glGw1X0YYaRCKhNN42lDI91xkeCoJdVuOk0yrvABZyU7e472LGvt+xZjHPcPd6y2m/N7IkvdJeGwCr3YgPeZv/mbP+WLX/5njM+CsLZgykyuBrJUzxbOxUitGiBl1LNwjgk1kDy//Zu/I9WvC+Q8c39/x+FwZJ0zN1fXOMVcz3NknCeJFcgZ7wKNFUMgRnxaNjg2qy3Pnj3nP/+NyM3t4qBGMt9dSeQStDsQRVrNhZiMoHeMGCGh6o4waTEhMQJYgWc+ebpivf0OrTd8+Xd/wueffpvtekWU2xyLEUNcCHRdj7XSkWeWzwHmkilxhqbBVWViVWGUNU17HlWVHH/lPUNJA6UU4jhyiLLsX/c9IfRibrYWvJBehZAcBSSpju+26wSCCbgiP1/fDEBlP0oUr7VShPd9x2GcKfsDKUV+/JOf8tknn+GrYm5zreSaiVSNOoQGQVbUWinB6gcZGDpBOOAdRuNC2zbomEQenHGcOR2PrNdr+rbjNI2c5omVzn1NEmVDtHI4TOPMXCKh8bTB8+72ltA2tK4VMJwRjru4GnVxVpDLoQoSAVPPagJbqn4/FYIXPDgCKhtWA63e5mdTXRILe86FoM7IJ9fX8hjVD2HlxjpSnLFGTFEgu5zdZg0K44qqkgltEKWVmngEKSyH+fF04Hg4AZDblpweZT7f97x6dS/5032vHBtRaCVTaLuOwzTRNA7I3O8ftYrUXcg883g4sFqvhBBbxS8Sp5nbN69Zbbc8vb4B1c5b585uT2NFxfP4+MjD/T2+abi4vKDtu3NE53KweX1hi0GyNqqEHC1L/KgLWpwlThO3b95ydX1DE1ox7iF+h1KdVqCikZlzwhuDbyxWUeB3d3e8e/eeJzdPWK/X8nL6gG90hGdEnuvbwMXlTp+TpBd/Oev9f52vZY9grVwADkfftkwYYpwE5a1zd5H1G4gWW2VMI2eVZ1j1hPAM69SRrRQBUeJUyIXgxCxqvRxWtX7wEC2z4e12c4YGGiMEz3QaqbN8BmBYbzcE5wSdn4QKOo0SV2m/QaXoB/GB+EbIrxV5Z1KGnB3OJfre8urVz/jzv/hDDFHGUAjvSNIVHTHLSEeenQ/eEBScpwJgvYgq3bDl88+/T1rEBBX60OK2kgFdS1UyajnvrYqzrFZbnBXcRTESJGSArmlo+pary2uhJqCYHv0dZyMJb6lKlsvywZeaVfgAJng5gPUcKZ7zWEq2SuiSubJeS07Hm9c/5x/+/s/ZXX4XfEvTS/Jf1/bYQS7JUqL+9wyxZvb3j3hvWa83+jHJ+Gwcj5QiIyA9WuQus4aisanzPNEPKwzo7ws6a/FtKyh/uyhKRVVXHJgiOdq4D6wsa2VEdjqdWA0DFxc7fPC8f/+OGBPt0ND3KzKFUApTSey2O37/n/9zEZUsElZ5eASgJ2aOhnGa6d0HVICpQlltgiAM3ty+Y7NagzHUFKkh6Nxf5X96aZSSabwjuMBpnLlIsoStVlQ7UxIuepoiNniaRirtthtEUmssTbP8uU4fRDmQa5EWLkWRSdpvjGSCl/l6sIY4zbhWuDk+i6bcKvG2lEwxwq0JVhzEyyH3QYMtVZGQQRtqBeeMtuCi9kp5pg2BU5zo+wHjJE/Y6PdUdf+TkQoiZ0l7897x4uULqPDd73+Xi8tL1pvNB+lfrZQoiolgHfvTid1mIKXI3fuXZ9lmrgLzmuZJF1qVfmiZ0kzTBq6ePhXzjr43SyVavtElmVI5jSNv373l6vICZ6/ln0sR74Qjk2sh6iXjvePjT57T+OYD5MzI796oI99guLy8Zr0aSElcyqlKdrJvGhm9mSxwRr1oZewWZOn9uGeaJq4ur0RqGBPbXYP1TkN1PGma6IcetzG8f7jncDgyDLIHs5hz9Os/+mVgaHviHJn9LMojI4wjgdSas4TZFDRIKpGMZLnXnDnsHzmPWmIme/lcmkaSDJ0LlDJLNol2zzln2qWStYbq1FmrVaao5BD0eNNIEmAVmaTRZe+SrWEQ1Ma5yldlkxBFhUoq1atUkalWySaxifuHt/zpn/1P5LQneEtMVqxnVqTPNltsrlKcGauxuBr+o5JTjEQL1ypS288/+ZSm78Wbgbi7/drSKNctppmu7ajF4IwjLCw4dXxnmyFNGCVH1+rIqfLsyXPx15RJL6lvvKtVLmJpjqy+dUo3TknySSyy90OBshmcrxhb1NtRoFqMnTF4knnkF7/8Tzz/1g/IdEwp4Zxj8O4cPgQKNTAG5sxq6AhNd15gU+SyGkeJ9MUoDLFq9rQJlCBcpryoyKwhzUlzsCukdPZSSNNmyDXhjSM5R5onQtuRa8YihUe1lhZh1JmSOT7suXv/nmG1oXWN5qIIwHK3u6TqiJdq8IsjmVwoWGl/jLheZQ4qC8xa5UWzOsmLcZa5uHd45ziOEynNdL05o5f7tczExpO0yJdXF/JDOiNSS0Sz3ZkWVpXcFVrvsMZysdlgnWMaRWG0BKVYZ88L6FKFJ+WtP1c8Dn6lavStsG5e3b3n6c0TWu95mEaMsfR9J+EdJUslFhq5vEqhcYIJEYKpl6Qm/WMF3ZApRuRspzGddcUi2ZPQnzxlpWomxv2ebrWSSzdJ/uzQ96TQUnLi4+fPWVyzFqG/1lo4HkdckMstx4prPDs1ejlj+fiTTzFFKnxb4emTJxxOI+/evcb4QLd6JvNT5xjWgxxCy8isSGLgIj6YUqRtGp4/e8b1zQ137+84Ho80fSN+mFY8MK5WCWg6zUD5cMhXUdlYhMrrTGBOE94H+n7gcf/IP/zDP9C0ge98+zty4XqHMQ50SW/lLcdh1Ci34tNPP+Xh8aBjGUun0Zs1F2zwpFl+dmsdxla6TkZc8xwJ4UMR9Ot+hbahmRtOpxPOLsFR4gROKZKMoc5RwpOoWCumNqxVCqyha4KkuJUikZUY9X7IkrOkTDcM5wIszTM5ShJYzZU4zjgvsbRoVrtvRA1jTMU4L8ttBOnw+PjIPM1YZ7h9/56Pnj/XKExLniMmePGj1IopaqQq4rrughgvx+k9f/zH/yMPDy/wbdXUQyNZ1XqYVnWZGyu/54IV4ch5SFIxRoN3jGcaI9/9/m/QtmtiypLEaD0Vi/Myn08pk7ImGB4nnBcywpyiXhqBXBOpFJHaa2F3cXVN2wwi4ND/ulOJPcg9YTUGUL11OINcvFrRg+D3TSy4GiXVD1E4yuhJjJvWZgyO23cveHj/ko8//R1evL3n/v6etBqkU5ApFcc4nfE+m80W76264mWXWpCRo1OUOKVgvMMZyzSN6k8a6Dq5UETdxjngq6pYpVLFk9FIbGlMiZwS+9OJIWd8CFgf1B+ik4IUef/wgLWG/ekoo6m8FrVoVUOkOsMdkLyX3HADsvzyMpaRvOQq0j01nyx+fIMeLrlgQzjfUqtOnHoLumIhaErlbTkVYQtZlU8aY865qo3ztE1zXqqKkiPikX1DjLKIaZtlxihKBaP6/tPhxBhHLnabc+BHQQx2KCX26upKsgtS0sWhKAMyguuYYiJXmYUvKbBCk6w4HNW789w1pZn96XRevM5xVI8DzKUw9CKVndNI0MNljBHGk0a4FkxWX0XXcBor6/UgnVFKTGrOylEqVZcF9kauZ88KCIws2EZeosWPUSveW66un+gBbGj7/tzN5JzEDFQyv/jlV4Sm4eNnTzHWEZwnGsBbrCk4JzGeaY7nSqgm4T/NoybGNSLdLVUW6DhLQLDuphbSlFipfyKlrC29oxZYb9ciC64iI7TWUoyhpkRxUrF671mvt7RtzxIRuUijSxFcvDUW30gnNI0nVqsNjT4rzjl+3VHTN78WT4oPQcaZOXHYH8ilsFoN7GMizqMuBjPWdEzHE8fjSHAW1zWqBpODupTCOE6UmuibltrJ0nrBdzgX2D8+cjzu2V1eSsCRrewf7nncP/LRs4/w9gNxtuas0lZz3qO0bUfMkX5YKdxOE+6soNdDK8iGmRFfHCH0lBzJ/gTM/Nlf/H+5vfsFxiRMSRrVKh4DwcLLSJoqzmYZxxmq03vsPGoy6vNwNKHjN37w2zgXiGkSZViURTsq7Rb3tyiX7t+/5/LmCmrm/uFA1zRstjsa37Lf34OeOTZbGt8QfM+xVF1TFlIBaz+olWQyndULseDxHTkvgo1lNyE8qpoLxRqV3ipzDkGXOOcgn/j7v/sjNrunkA2nw4GcZ5q2kRGsgRcvXvDu/T3rjTDqttudNBbzTFIkS1WKgjEGp4DGBern24Y5Z5wBZ73InlWuO08Tt/d33Fxdi8eiaZQanc77xKFrtdi1VPz5z856bj8eDrTB8/VXXxG+/W2cdrPi8hdfR+M9NaphtRiHpaippzJOMytlsH8zk9AUzZk24mBsu47W6M2dhE+zKENQL0Ct4n6c9YcPrpPlnJzyorYp0qJ6vS2XvF1jw7k4aZog5hTnWO73HDN3D/fUXGh6OUCO48x6vZLvtxbe3b6j7XrA4I2hGOmGViEQ4yxUTqrmAci4YKHbhuAYp8T93S1N07C9vCAn4QjVUvDe4JyMHtarlXgPSsJYWRKD4LmtNfgQuLm5JuuCl2yEhKpjgj40UA2zpv0ZzeswJpNjZMyZru/IFL5++RJq5ebqCowk1y2p7zEXXKiSdBdkYV/KoskWtUSMkePxII70eeL93XuMMTx/9lT+fpEHI8fMerfFGCOuY6eIa7k9Ca0n4Gm9kCWNsTSIgdBoClaak4wMrGXOkc1uw2/91m+KUqXtZD9TslzETjrUxlvmKql0y3MjAVTyIpeUSN5jjUg4M5G27bFWLo7NZiNquEkc4W3bUobhn3xBOOfP7lxjJR50tb0ANGin7umHQU1u6ezoXa0HzoRU1eifk9AMdEEu7Gmazjst3wRsEtTHaZyAKgTlms+X3TxNso+YZ0LbCE6/FlwIBBeUT2QIBEFaYMlxVlm2YZ4nvE4GYpyp1jDHREoH3HTiiy/+nHfvvsCbJMo647FWMA6mipGVIhJYjFEOlSiJ5Mue1U+1SvVNTewubwhtz+F4FJVVlcKtCR2JojJcKUBiipJDnzImOPq+JbQdxsguZBg2+udnxjiL6bFr4MF8Q3WoYU2xUJqM94acLM7ojB6rO0jhNZVF7KI7S2dAmT/UaqWjqMIMMylifebduy948cu/5Ae/9b/h+sk1h/FImtJ50rJZr3h43NPaQL9ZMyfJEX/z9i39smeM4ldyXoKnQPajXp37cn5K3r2phaD+mmmeCc6rEkliE5ZLDW/PLmlKPSsSF/Nmzpm+a+nbG0rN/M7v/i69Ls6Nc9hqyCUxnUa9fDRN8+7+Du89V7stcZxYd5L8ha1kST+Ul6aYs0HLnwmqWW+4zBQzHV4NPxLBJweE0YPdyq/BOKGBKtep5Ir10uZMKZ5bnrNKxohuGSdLVVOk1U05Mk0Tw2qgazxdtxXXZCnEHBnHSEmFzTDw1Ve/5OtXr/n+975H1/cMwyDy2eAkoa4WbJAPdzxE8jTStI1W+zKCa60nu0zU9hRtexckiUcWp7bCXMt5nzGrIa918gDNaabvGvFFUInzzHGKDG2gcZ796Ujbd/KzGIl/XWRsP//yS169fs16s+Xm6prVasM8z+cLeomxLDlTbCWnRakkM81vylNzLnz2+bdlSVoqyYgBC8RwOGXxGQzqbEX9FEbLs9Y34ESx462lmspxnCglM6xF5VWco2kb+e+qgWwIDcnIzDW0A9VIYltFxAM1G8nGAJ23Ow6HPeM04b2lbXvWxuDbRhVO8j3UKTKsOhEpZCjTCPSKnMjo+fhrXxLeOc0BkQ7ZOC/omCzvxMXFTp9HSNZIVGg34JyRqEvEiRuaRggEplCOkYfjgfVqi3Eek5MY3GJimmeGYaBf9eeFcynSOT598hRwGjvaysWTMlYvz2qqUoFlSR5jZDweaBqv76uHVvhO1UKJkDAEFzkdX/Hi5d+wP3zNqrXEaKklUFMllkIxRXYh1Z7n/tVWdV3L6KmqgKSeU9gM2Mw0Zy4ub4g18+7uHR89fSKL7hB4fLzHtAK/rJqPYIHVEh9bDaEVKbdgWVSiWz21Ot69fUPXWjZ9z9uzsVtHcShmXvekxkCqBluK4DWcyKNdlkKPqjiRCqYGpDjWPYuRjh8vXbipAZ8n/u7v/5BhteLjT39PYJZz4mGOrDrPdrdhaHtCP2AxTFlwNMEHyah2Dkqk7QLzLKTgQhGHtOa/OC2mS4ySMKdG1/Vqhd9udREvF5itjoR0nbnC9cWljNC1y55rxnhP1wSJtzUGVzJtyowx4udJRvhWOpdu6JnGCR8CNSX8q7evuby44Gq3xXpHAKZRsk2tlQUY1sg3WxFaqhWUN7rMtc7hvbyMIQRCNfIL1Qr3Yr1lHOUAyikxTiNdEMzt4vY0VhrkZZySFWxnbOX+URKdhrYT45leVE9vrsHoDqQqJ6cKKbWmyEeffEwXAlfXT3j7/j0xJdJ+L2Yp56DxzHmSGWUq4GHdd5ywEhoSDDeXFzRKxVy+rNXMAGT+WWPGBqGmqtlYOD5GgIkoibJQaXzAhYZCpIyRuUZhqjiHDdB3/bkCSLnQ6mEVj0emGLm4vOTj5x/LYTDPys8pYMuZIumdE9if+iJkUS0jnaZpNNPYMk0zh/09Xd8TlL8zIwefbwLGWXXEO0qSdnXBgPONSn+KgkeJujgVsq18XraKv8MhLk/jPK3+jsV45Iglyk7MVGYVR4DBIYvrmGaaIPC+tmlwTaMV/geonPcywnq8u6dZ9Wx3lwxDRyoCqez7Xx/2B0LVXa3WWCtKlFqLeG5iIufI4ZCY54kQPI1viEXFCdryj5PE+C4Kk5zFLWycxXmLq4YpFEVPyJhz+fcBSjHsD3uC/v6Njs1qNRrmlbQ6VGWMjoFTUj6RkWfRusKcR0rSLPcscZvWFx4efsGL13/NNL3EuSxBP8ZjnVFEfcIikmeQLtKqRj8b8YopohFrKtktY6gPaq2Pnj3n8fHA27fv6ZuO3W5NrYW5JMoJgnOCuymSi120my/qki6632zQACkjo+Z+6FmvGtabteAvSpHsm7pc9Jx3NjKLB6ohW9lJhCwyfyEmi6x+eVYBcok436qs12Cs+A+SjoHMfM9f/eX/hxQj6/X3aNs1tu1wpkLKkkGdIkW9B8EFPv74uYxXqeAHYs7Mc+L+/hXPnj6j6VYgKmRyFWaYa1phuJ1OwghrW/nhtAg1VvIlapIED4o8n1SYszjDf/bllzw83PPsyVOeP39+dpR0XUNTpCtZfs5MwRsnaJuceTid8LvdjovNVu5OA5TMcTxx++6Wy6sLrre7c6xoyontZi0uV2DRblkrwTzVGKwNGL2F0yxOX9uJ0SeXzIsXL8AaPnn2kS5/5S7J9YN8FSSEBGd58/IlL16+oGkDH3/yKbv1Wg6jUuTCqvWch13UguqtIXnP6XSA0tEEzw+//0NWg2iLg/cC+CraQVDOhjLrHW1bNFS90K9X4oqtBW8Xy1nVZauGzOgLbFTKaEvlcDyJtl3DhkQ5JL+cHKOM76yh9w3b1Vqq4VLAVGqSzqltGlytnA4HrIHvf/+HGAOrvpcxUEq0xjHlifv7Bx7u7ri6ueFyt8MWQ8oV46ssghfAoB5mLjiO+0d+/vOf8dGzj7harWn6ARNnkhr5rLUcDgfG44lu6Omb7mzAy7r/aFwgWeH9b7fbs9chadJhrrI3mOIk3H19g01BJMs4TMk4r05iI2O4WBLH44F3796zXq+4vNgyzzNd0+Cd1T0GjPOB42Hio4+e47yTLPNpFMduTMDIfJpo2iDO6V/zy1gZJTrniDEzjqPEqxqZKe/3j7x8+ZKUEp9/9m2Rqhoxplpn6fteKkArL7QzIjowrpMqMKM7uEKkCoOMivXaDcbENB7xw0pm61HGOqHtwFYaK3LWRd5blDMG4pOYphM1J6xvKaXwcHdHPI6EztENcP/+K75++Vfk/BbnKiQrahiTkXqo4Axkk7F1uYksigGjVtl7e4ygq41mRmPxVvYRzlS+9fm3+fTTz7i5eaYRApbgW64uW8aYVTkniqwlltWHcFYoVcRg6ryXs0bpBUE7565bUZx4tLTmZ8mFLrnKUsLJtEMW7x5TDKlGBiMXtnGy0Ae9bFz+lYOzmIpFGoBcZhIenx11euDH/+nf8tFne773G/81put5+/YdcRQvk1HRg7MNsWTSLHaD4B3VGLxxOGcIXce797dsdlvaoJdhlWw9W0WOPUfZQXnv5QLS5yGrtynnLOrBrJGyaT67uJ/c3NA3LRe7C5xzTNMkeRRBY4p9oDonyqdaSVZl87YQ5hn//OlTUFxGyYnVRvWyTcOqHajWQwZvCxVPKcIsMdYS44lMofGCS05RDSEOvTwMOI/Vpc3Xr17w9u0bthdbCRiydnHzSVvjREoWc8a5ShxHvn79klwyT3ZPWXUDGAdG3K/WCJJ4qcCWByuVSue9RD/mkb5t2OreIjin2uiiqyorubFGxgRNK85LFyyNVmcgSzXdF0suhZV5c62C1xC1lYyPihFFGMaw9o1IyYw8gKdxpvfCOOqbwDRFUhwZVj3zNDHGSKuwQest+3FkPp3oVisGlbm+v78XtIa1zDHyeDhQamWz3dJ1LbMyqDrnMU6jSAu/IuktpdB1A9/61udcX16SqdScJJwmfki6i1Pk8eEBa51A7KpyeeBceZSUsaaKwcga+n6QoqEuuAFD41pAHvjxNPLw+MhqkIAgH4T2mUoiTYmhFR8LdimaZNnZDx04o3p9wViMJ5mv1ypT5e1up/scOJ2OzPPMxU7yfv8pfonlIDRuyeIuenl4cspstjve373n9us7Xr9+zWazkcu9illyVJ+Cc5JzkVLBWQ3WwZx3MMUYutAwGemelzFuTDPXV9d6+Bf1fRgaIxXwN13ktYpxNSWZi4cQaPtBZbIGbwKmFt7evmA1wHH/yJvbn2LZY50hFtlmGJtEhVMEyS0BDUWyC7AYEzBkkQll6TKqNXgNGxKxiuyOquaADL34pGTxG5i1AzJVhCPFWl3A68Wkz6YxIpowSfZhRke88umB1UmEUxk0i11jMaMg04lcJCMmpXx+9o3zMBtGwHUej0cs5YgKqGp2Ss06brKyDCdD0E63ihm1lDtevvpTQuP5zvf/NS2Rd4+3bNdr2tDjrEQRx3EkpcRqvWachYqAgfVmS7m/583dHY+Pj9xcX7Pd7bSiV/OgqgCtDQpuFLNqzvk80QmNGDa9QSJ9YxKFnbWst1sudjuRxaNdN4rdp5JLIk5SxBlrISVs2zLtD4zjiJeNfpYqjMwYJy52O66vnkCx2JIkyrLxdCqFElx0FvlajYC8HLUWzZEwxJQx3uByEU18zpRcePLRM549uaFRRc7ZieotznghyeYCxlIKdE3Lk08+4cn1zRlLXnJlfzxI0FBKNK08MHGeMNYxaFJUaMz5YFwOe2qVqsEYaf1EuiGVs9PkPWtoTFAPRTk/mFVlrmi+AMvFYQ3OekwuRMR05b10EcLxySwqspwyj6cDrWvxXeAwnpjGmbZraLtO3L456YNpZYEJZ6OiiheJc8QPvVzSbSuOYGuZ00yJCR+a84VYMVRTqFaWYaYY0hQJbeCqv8I6DxViFsR0sF5w06UwrAdWG0EmOHR+W4R5n5K4VHOuFFNEemolqlQYQfL5LmE8cZrYjxL7WEth8g7rLF3Xyfw9i4tX6QxMOUuLjuFw3GONuPxFkmglRrSVkU4ILWmamKYTw3Z1Dvax1kmUqOKil2yFf+zLWTHRlZJlGe4sx+MJZ72EBHUD3/n293n29DkOS4pJwodqIWVhc2UtHJwT4vBxmqQbVdWZPBPSSrsqo5KcE29ev+F0OvKdb3/rLApwrsE3gtSXQ1SeyrvbW7ph0EsbMb1pV26sqntq5snTC9r9gbu7L5iOr3FmouChiEow+ErNjpRnStTRQwWQotDiKE4YZaUUvDFEIwmWWHOGSi4j31IBK5X6YoZONVNjJHQdj48HTtPE0HX0q7WSHpzE6Krs1GBxfkHgf1BETvOknaLM3Jd3UV9wqtxWMiIPomScpvn8u/VGDs95jvhTixnkdxCqtHi1QHWZZRnvnPLR6LEuUUhS5QPFFnK545df/0fGw9fsnv6Azz7+Dn3fy2Wp/Kyf/eIXvHv7hn/5B/8S5yGmgndBeV6O7Wp1TuqbpomUIn5YYXWB3S4k7JQwQf1KpZ4X/yVLt+uc5XQaWW822BCI0ySFkZd8kuos1omMHQRk6q2n7z6MY40W633X0TYNPiv3xQWrfoMKVhQbHpEkOi+Gnpzzue3LQqMjA+MsQKmiaODFneytl66ggm89n33rU31JPCXJgT+nqJXxhsYtiqbKaTpSc+Fbn3+mXJ5CyhFjCs7Dbr3Sk7ucHbdjjHSNldkkktAVdH8gjlWDC0GqBaNwLKO5D2ZZyMucNdclwVg0+/r8YZPoIYw+iFC5v7ujOsduJQHpxsKUxOyEc1itpjHQtR1v797DYBhWA9e7HWMbP7wE2p46DfxoQ3tuQVOJnE4Tm9VKFDzjSKmV3XpNLpK/kGLGB0cbAgUJV4k5ESeJeG2C7GOyrZSUSRRs5yQvOyWslyVYUVWEMUDRRaoGlxgnVNKu6844kKAJhaXUs9Gucy3FKzn44ZHjSUiWz5/c0HQ9j/sH9scRZ73QV4OnwZGzAOgkwElUSyE0lGI4HUcwVUJevPyerXOUIsDD/X4PxtA3kkf++PjI+/d3bNZrejeI5vzXaSYMuOBpS8PhJGHy4zhK3rGzzFEgeiEEvPGyV0lF6LRzBAetlaW1wdC0Alsc44TXjvdwPGIrdF0vWeXGMZEpaabvOwEDhhYfHCZLwTJNwjPy6t1p246uE3HAsjfzxpFNBhsgVqwfOY1f8/jwY0p+j0Pc+zUVwKFrU0pJCqXT55WK9wZTBcNujYD3qoEaIEQx6omMV1VKsm6VfHQKTdNRYsbZwPF4YB5PtE1H2/eCs4kZezwSulZ2e87iQyNFfc3CEVKxiuTBSMbKnGae3FypmqngXCPjpQrVy+gtmUKoWT8b7QXnSgiQRhiPkf3xyHrVsBkstuccRSvvLnhbNVYArHpThIMlWfbWVazxeDPyuP+C+8eXbC9+xmef/QGbi8/kHFD8/v3dHY/7e549+4gpzqQcKVOibTr6YcWlIvjfvX2L854YhZ799Pr67FmzznI8Hnjx+hUfXd/Q9r1cHlUEQ02VHfE8T/TGiPq0FKiymzFV9k0pifemcUG4dprTro++xAEY8CHgg/eCtK0SrH4aZ+L0yHq9kaCXou2flfZNHgEhP1aKsEqQmdgyQ7TWY6y4iedUGLqA14O41EqJUTT1iFuz6zuNUM2inzbastZCMIGYCs5qpOiS7IXMvduV8IJyjCyZrblI5a5N8Ln6LXC+mLyVF22OE94LPnfZsXhjyEYYLiq5liWrXijzPGkVYFTvbb/xS5ADrC1CTy1qwislKz6j5ebykliEVOlDYLWRvUWuYqxZLr1Fny8+i0lmxabqoVjUQ2qI2q5P04xxVhHIuvyrHzwILnh5wb3DOyMk0FS1TbU8xoRLi5Pa4xr57xiqVjRV9wmCrLbBYUqlbVvmcSLWSGibX/EmmCJVf9/3bNZrGie0ypwWblFknE6afR20IrZ4a6CRljx4R9PISPN4PFKKeGCOhwOgDH9TcM5KxsfQnXOEjTHsdltiKjQ58U+ROS3uatlLRMk4Dq3sY6xkV9RUqFaWq6J9F0e1tZKVLc88kqkCeOulKzRAzhzmmaZpZNQH5BQ5nEaurq7PlwFwliBPpyPBiYz2dBjJeeZ0qPQrubAXbEiaK20DU3pgPH3Fw92Pmed7jE3UMpPTCIjSUOsAjMk4l9UVXCXkpho1oUmGgkA15e3K1pALhGTJpWJl+Iu3llikqNgfDtxUQxpPBGexXUcxwh8aNOf9eDwxzbMm+wWcXQyfUp7NRWbzJRUxIpbEajXQ961+36L+q1XwHiZLp2uW94hK4x1jglIt02y4u33keJgx5sDlRUfsHeNgWG09bc30ncPZluANvnF0rYw/LTJGpaQPHY8x2FKV+JyYDl/ws5/dcrn/Lk+u/xmuecIPv/cZH390zXqzI6VKnmdOpxHvAl2/FlkrhlM60TRBfUGVRs+6Rb4eXGAyE8fDieki0VYBiuaU2a3XYvcuhdNxVFe2wyrBOwMmziq9lrNjGIYzx22RuFc9J1OM4AreWItH5sn748j7N28Yx4nNDyTTIZYKy8hgmekacQ6mKDRMlgq8yMXQNK083LWQamGK8/lQXrqMWgt905FdISIVGc5hqhBAqQnaTn7FZmnNrCir4JzoddwfMM7ShoZe5WMLZgLdAVSReAsqpBSCdec2TZyQszDddRQhH6EurdCllco/Y0okZA7sVU203mwU9JfOHYt17kycXMZd1jhp++LEPIkLvTecv59ai3RkxojHIcv3lEuCXCWBrkoFuT+eWPe9oCl0ib9eLUEsehlOER8kp8E6Sa+Ti1ks/IsWu1ap3LvQyMWiL5kcTg40Ba4itEjjxFNCNWRNohMeCmrUg2oFM2CMBC1JtyFdTEGq59D1PB4fefvmHeFxz831tYScqHpj+fxkZCimq77vqVkWi/vjSNvJBeuMoet66f4qOvJr6TcDq2HF7fu3NM1T+n44vyC/zpezXtz43nF1ec08J8bxJKoc33CMR4wVqSlO2E8BS8oyEnTekeeZ42kvXakLtK3neNwTc2E19BRgikn3WvDx849EuGGt/twqrLAW34jb/HQ4cn/3nsPpiDOWjz75lL4fBGdeAZexdWI6/ITH+y+pZY9zwsvKVdLPlJ2gBsdK4ww1WJG4xmVAKnibag0ui2S9lEp1VY1eBePkwgNRPpkacLFSy8zbd2/5wQ88pXTM88hqWJ89MNVYumFF6Fru7x85jBMb70nVKbVBxjW1ZlLK+NDQNC37ecYYS6mG0zhRsKS0jKkqOKPGWPV15Iq1gmWJc2Y+PQoJoIGbqwtam3Fupuk6Qm807bGh9R7nDE3Qs8ss0QgVbzsq8q4UI8oib+WadM7i0x23L/6Ex7t/4MmT32K9+yFXuytq6JjnwnHK5Jxou4aUZ2KSqYS1RoywTorU7XbDNJ1ISTrIpOfudz7/FjkX7h4fuLm6hsZSShXpQGjJTWa/PxKCF/y3E/9MaFvqNDFPkdVqUOVXwiwTolpFx6bFg8ZBGIqxuFpY9x3dx88JTmzeiwzWOVksShA9UGUfgdE5pB7CcnjLB5aSHFDOWF3c6tK0iuxPOC/yZ2aQb1LT5yyOaZwJTSA0AVsMqsWTx1pHHqYU5pRobNCDROeyqrMXMF6ibRpa60Q1oYdkVoVU32plmLNWQR+q4JzFzl5VpuusxI8G7SAK5Vy9Rb0cFlMgVVpzoTJKu55qoSmVOdezMqumjGmDtOnOCN66CvCuWhn3vL29xXvH2q9ovLB24jzDeiVVu/6Cl4hMqsy6Wy8vRi3aYlbJwwDIXhZjycoobjpJHq+xViV7YsiRWFRZAEoWsub1GiThy1pyFtOasGnUzDRnatvo2AEhlJoPL69GEZDmrKM+L1kNehjKsyKX9TxH9QxA21lsE6hU1puVyKT12ZqjVOuhCcS50LYC1hvHo+YkfIgK/XW/jBUMTNcNzPPMNMUzF8t7ia70riGWWUJ2jGFOQgCVyFVRMXXdIBGnTnZdLjSsVhWvY5jXr15wcXVJaDt6258VcfM4yuiv6/WzEQmlNY7t5QW+7QhWDjXvnX5OhcYm3rz+Mfd3PyHnRyBTM6J+0XcVA6ZkYpHK2BjJ/DBmyVyQ0emCqMBZUaJlR0kSXeqNSEfVQgLGCLSwgmss7+/ectg/ME3CVku1QJbxECxdrGMYOu4fH5jncC5GhmElXhTvcYD3hrAeiONInOVSff/+Vrptb3FVCkq5rSvGy7QjIf/sPE4QoelaLnc915cXXFz2eDtibaYLnm4TaIIEaQUfMKbQegsm45tA4xwO6W6rHqhZkRfG2DM9OBhH4w25PPLm9V9xeHjL9c33GdafEcyazdAzzYLfSFHk7s4Z5nnZ8Yr4wVnLPEVyrWzWEoiVS6YLUkx9+bOf0frA5cUlp/nE2/2e7UrOBe/kApDO1pwvGmslc2SeZxrdgbTILsLUyvF4kn2qE1O1r8ZIFWDBhkCj8rM0J8lZxVG92OyNc6SYmcaZJjTYoBI85EVIZGKt1Jhou8BCPnVWE8swHI4zxhtZXKsaY6n6UZJlTIlq5KUOiG671iqHg+ZWLxfPdrX5AKczcDodsEYw1bmK5rdrVPGjB53xgTrOPBwOxDhztbvAd6IuKlW8HinLzG75UHNKmlxnCQoa2x8kktA7h3GStwEoU78ihE0ZK6364XyYDV1LrY12MpJCZxQ1kdWtXQRCj7FCw2ydJVeYonRCT66vyTkzpxEwqjyq55HfIgcGWWZ673GlnruOWsWteTqd2Pj1GYDnnDvnXtQkMDYJlakU0UQKJhxVQJlGfFbL8p/K/njk9t07bp5cs95dyoJt4evp3HOeJ3COm5trLi8uiEUQ7csFURUnblR3H5wl5kycZzoFM0pgu+R627rskeQzTKeTZKPbTAgdu8sV1nqm6SRxmL+myskYQ+Mb6VIqWDvycP9Ammeur67o+xVxOhFHMUy5xil+fslujhJU1cl/sxrD4TiJF8W3zDnS9B1Pnn98/l1lNdFxXrQb5nHm+HggNA3d0BO6lnbo2G53ElQVHFMUdPqcHrl9/ffcv/spmEmc01lx3kaBcgt+xlj1hoOWeVgkgyJrF2109ydAP6/5HwZnOMcDmGJw1ZCQZ7+EQtNavvrqp7x4/QLvez66eSrgxHlm6Cs2tNKBF5nLP7kSU+XpdFBAoRQcjZPl+ThKVv14GrHec3f3jtt3L3BIVgRGdgTGVkSrK+OyaY6c4gljK+2m42IzUG1ku4N1O4tQwltCMLQBjDUqfZ3pvKda8Rf13st75C2Nk+LZKUW5FI1iLUHy4quhFIc3DusqOf6SN6/esTm94eL6n+H9BRAw6P40zljb0DYNNnhRugF4Txc80/Go61rDbrvVFYDls08+JtXKFGesc8Rp5M008uzmCd57Hh8fGYZBvFYpcX9/z263wxnHy7dvqaUwDAOXTnIjSs74BSKpRbyfTieO0wzOsN6sZL6dZEndeLFY1VrO45fGebIXsJtDYkFzEdt4cJZaJoIX41MpCVMdsQj1cNW1MlsfJbRjtV7zZBjEhyCDZuFBhcBF2zLHqIeB+DI635O8IbgPwKusfP1YxHySCnSNbPu994RGDhTfNGeFUcnCnJ+nibdv37IZVvL9Vnkpqr4YwvyfWHWDjtKS5A07UV416psQjX/hNM2YWglOZrrjrLuXkimlFQWU+cBf8t7jFcCVkFGN8cqsrOrFwLDVZLYpjvAN8UDVM7dpNI51uYxLwTpLjFl4LKqkqaae5YXoUss5x2maWQ+DaNVrkhQ6HfHYLHJnrFHpoSfPiXmccaHgW3eWH5ZScMiBjjHEKDRX3zS4JFhxkLCU2/dvSKXwyccfAbKPEJyLfHY5qefF67jOQAjSfU2jQM2Wy8bYFlCzZ/DY6rAhSIFShTJaUqc6/JlQG37NO4KF4STvq9B2Ly8vOZ1O4heyBtO0+JSpVNI80/e9FDo6nk0xQZW9XrUGwUmb88jPOi9jtCou9zwlnBOlmEiRhfC67KKMSjULhporycPj/Z7OGVI98vrV33N6+CnGHhTAt3QOFedUvZORixWpvuXSdFjdrxQcrkqCm6mLdFqKPmMqNlSyLbiMsJBMIZdKMChFudAPjrv7lxQz8+3PfsBpTuzv96xXA9Y5cpwxXoCWVnd61cBqtZFnoRQsMoufoviLqm+4efqU4D1/9hf/ltN4T9M5DdFS34ApYAq1OCoZUxOboYeVo2lbuibQOEPXVlwjcnfvRd1kqyZJesPQNLhGitRVI3tT5zx9I05w5xzOOrwLlJr0nCzE3AgOJyZKhmDF5Vztkf39XxHjgY8++Rc07bVczsWQSyRH2Q2F4IhGcTS1sn888P7hnqc3lqHvz3uDnBIXux1zSqJ2DJ7VsDp7IZqm4WK308tFDvyFDxW84/mTJ4xxpg0i+797fJQd3nolZUMVNab33tOmpAA7SONMTJnVZiWzZaMLDxbssBh+zsY3fYGX3N6m70QbPp2IMdLoQmY99Fjvub685Hg48u74hmkcud/v2a3XcjDkLEog82F5EudJNMBoGl0p58OmJuG4OOcpU6btAoPtWXygjfNUVdq4ZYRh5BDuupbWCxitaUR5YpxkGMjy2nI8jjIH1zmwD0KGHe8f6fqOduhVcTJKzGXXapqbKAWGtieOM4+HE9Y2bNdeZvfLclrNVsZabK0K/tP9TIxyOFpRKInCyDGlRIozvc73KQsiQQ7pcRyFTmpgaL/hMnayZJbMYZiS5G9vNhudzguyYToKKMw3nhwzY5KHtelaxvFEF1qp2DQZbUoTjRNjU85VQqKGFZ8+97x885a7u/c8f/4RbddRYsQhMLLteo2xIjioRnKcZaRlWFhOi7Pd+4acI6UWmhBY4h6tEVf73fs7MIahX9FbS7GJaoXzJEvyyjieZK7tLyiNCiT+sS9dtzRNQ9d1zCnKfojCdj0wjhNxnqlZcqJzSZSiyH0rQEaH4TBJUVVEE6yftVS5XegQRA2MMXF/ewcG2n4Q1HoB4wxd2xGcl4x15yWeskSsl8u89QHrJu5f/h3z45c4M0vvXuPZAyCTP08hkas8X6YsyiRLxeBMI5kpNVGKlVYBA4oXt8VQjSXVGRBEB1k0gKEUYKYkOWxlwX3kj/7o/81HH32Myx0X6zW+9WcZfa3L7zmfvQcVkWFXI6gKH1qCsZRgSMapuezAn/4v/w6DIju0vPN4ZMsJxqHMN08j4w7aJtP6Sjs42jbQdB5nKt4J38l6g/UQvIXg1MslBrxSINhyHvs5L6iWWq2OdAqGwmACuUjIV0pV2GdGJbVtosy/5Osvb7m8/i/YXv4edrXCx0rOEzYF5mkm1pHgGhlh5cTpeCTGC2qPnmnyDFcd1QK8efOGUgrPP/lEYk8PB1brtcb9QgAuLy9lRGwtXd/TDytSjKQc6ZtGCmTrcNYzxZG721u8C4FglHVYpVL0VEqUoA9rBSFrjGAVKCKbqhbSJBfKsB5ETlkFrW2KVP7B2g/bfyNoD73U2F1dsx0GCuhoR9VTpYjGWQofYsq0rT3LWFNKzCmx6XuKEdBgTgmcsPXrPAvSQ2mzWV3docrIqFqLDQGf4JAFKOZ8gFrOWdGAkG37FkPH/jjy9s1rrm5u1K1dhVm0GJpKYZwnGg0yolZBHDiHb1vW1tIPElGYa6Zve1JSB60NHE5HTuPIsF7RhSAVlI4mlhyKcZ4Zuk72CFVUJLmKqTDpy56zeEYa4DRNpBx5fDiyWa1omkDMgmzw+jM2vYxAjsejXObOE4JjOs3UbFkovk0IeCuo8qnO9KuBdQjc3d1z/3DPzfU1q9WKYiqHxwd26y2h63DWcpgmjsejyDx1SZ9TkkxeZ8/jylI5L93jLHutJXN3GAagMJ9GjM5b52nGWEPfdcQSaXxH27UYZym5UMZEVXmgDUGVc1kUa7/OBQFnT5CMnCSf2FpZkJtaiXEvIgDfYMlELKGU88jQGElCfHh8IITAercl58JqGGQvh2RZlyIjC28N3aonuCDdwymRS8QbLzuwkmRZPE4UhdsFbeOm9I6XL/4z+/svMOaEMQlDpSzvlhZeNi93n470StYwnyLGWYUo+eyoNpFkHy/+CGMpiwiwCKW2VtmROFOxJpCqIdhMTuAr9KuGr1/9lB/96A/5Zz/4V1ztrkXabA1WuUpL1+4VTbKMoCyG+/0jQxfZrNdUPAGHt5Uf/dV/5OXXPyW0gvE42+xqxRl3HsEZwAWjf1/4T4uAsukCfadGOld0n1g0YtQJyBGR/lIL3gaaRp7b4Ay+adWwl4UaK/+07gYLNlQam0nWMiaoVXwmwRtyPvH6zV8wz0eub/4Fq/aS0+Q45ANpilDANAkTLG3b8uzpE/VCnHRfBU1jmKdZxqdUrq+uZDycEn//d3/Li69f8Du//dt8/MnHZzOhO5sXRRJc4yzIj1Lp+07OCGvOarqmafDj4YDRzf0Zx9C2nPYnrDO0vaht9OfGOJGxLhb4BSHtgscXwWCD5NHGMp5HK0LBFHXEOJ4YxwmQRLeUPmQYG2M4xQmfZD/h3ZLtjKKLJZmpWAtqxns8nZinEYdnTJGLzUY6HK04F7ctRtDEJSXQWafVsYqMBaJw3ZElz7pbkSiM7295/UZctc+ur5nPGcIyOnIaAlPLh0DzWCqNN6xWA3MWOmye9ZI0Ukl6J59tBcH+hoCx/szvMVXCULCWUA11CVbS0Vg1BusUA00gKS/fekfrOqaTAPdscDwcjjS63KzGYIuhpExB2mvn/JnDH0JDHxr240ScTmAqQ9iw3q5ISbTcx+OR169e0PaD7jMMaY6iAvGizGiGFj8HnLfn56JqlnI1ApBDd1XByghtGk8cT9qZDRs2q5VozSnYoNwpvVCaxoOzXF9e432Qtr+IFFb2DjKmwRiCD7Q60okx0ra/XidhdMu75IxbY2nbhjjNokBC/573IohUM1bOMn7COcmBsFUySVBFTpEoV3GVS473nCTXZJpGYpwwRn4XgOx1DHgjCI9qPaEKFOPN+6+4ff23lPwKx4g10m2nHEGRMyJlFtrpmdBcs2QF4FTw4VX0JYiZmr0SETRetRisxJ/JqEspqVlHmNaAKUrsNYVTNfgKjTvx0y//kt/73f9KDZaOxhnSNFO1UPBGMjlqtdqxFKwz7DaSzJdLJmbDMHTUfOBv/+aPgRMuFEFyqKS44PDGarJBlYW2KuFdgNY7QmtprCFYQW1YLwIMiaw2NF5gn9Kpyu/HB09opYuRblwuFCoi7bVW6cBifgxOxnzFWHKWAK1SCzULaNFbi3WJx7v/RJxO3Dz7V8S6YRwzzhnyOIkZs4jC8eJixxc/+wL76Hn+7AnDsKJWwzw9ijS2a2XHC5jg2W52dN8buLq60hhgUXxaY/ChpcmFlBIPj49UY875Pe9evaKbI6tBIpE311fYqNkNx2mmtR6HlYSjWnh8kG8gTTN1TtRUOD4eZeZdjbyIzlBiVv3yB3dtqoVZl8xgKEXa65wrh9NEqvIiLRTRRatLleAd5xwmBLqhF9eq/jMuBFGNZPmzU0r0ytI/jidWbacSW1HG2CUTAKil8Hg6Mc6T+i00Fzpn2cG0Ld5Y3r6/5XA4UijEaWI9rPjWZ5+zWa/OwR6w0GqLzIfVDWl1zt+3rQLYhFxbUqJvW/phkBclK7pYu4ZGaaFxFoeksSIBLVbyNKaaVKoomOnbN285PO45nibevbtjCSwyzp6d7P2q5+mzp3jfsFmvxKyILMVtEJ6/tbLALHpRtj7IXLNtuVivxINQ4Hg8sCCHHZaUhPoKAmlLJfHzn/2cmCvBCeZaFDdSBbe6YB6Pe/ld10LNUVRmum8Y48xxmqgVfNOeu0pnBeexqDWWbPTTaVZTk/0g2bVO5/6ZBZWSoihGLJb94XCOYP1Hw4jMh//tgnz/S8a2846ubWkUNigWFtHpL18xZk6Hg/zem4bD6Qg5M7Tio6gpc//+jtN+D0AXGozzCmAUA2HMWQ84QWMkBS0Gayhl5vHuK25f/wNpfIVnwlrJSMlZqlqDgaohTsj6qOhoOCCBT0bHhxYJ7RJPkz2/I9J5iYPbOKRosQYnGw8aJ2MYawveL/+e8qpcwTeG169+ys9/8de0avQ0RkY2i6tbxlsybZ/GI3NOqprr2e62xBi5e/eemma+/OKv+frlj/FNwlEIFhoDjXN0HsHwW4cJ4L09QxUbVzXAyxBaRz+0hEaS6DAF4+SCQFWCNngalUA3TdBCRtR+zqBpfxCcIThJraQIuw01njpjaawUo+Jcj+LCrhZbM513TNNPefX6P9CYB1atgwTVWbyTBbZwpyo3lzc8u7lhtdrq783StKJ4IgtWfp4mnLF89tknfP7pJ4QmMJ6O8t5r8Zlz4c37W+YY6duWVd/LDnGeMFRRQuZEUVGTbfqOxnv6rj1LPYXHk2g7eVGDSqPQwJpxnNGUEXIsMt/O4okwVSqDaRolbs/yQX5lLev1wMcfPWOzXrPqO8VhiC4663JccLZLwIn4GbCG0+nIeDqdVQUhiKwSXSKth4HgPeeYRqymeaFqGehCoPMCGptSYtIUJmHMCwdlvd6w22xErhkCm82W5x8/p+tkTPSBgS9jH2H0WNpeMOuy4qsi9ZNCjEZ/qa5a8jhyPBy4e/9eNP8YcsoE58/7DDDneeR+PLLfHzjOI/uTHMztIDPxi/WKj589xTmDr1KdHR5FtSWIBiPKCJXIOpX4lpgEqVAq8xjPwL7TKPCvEqPKP+Uw7Pte5LrGkGrm3e1bhq7ls299qpUKPPvoGdvtmlIq+/2e9+/ec7Hd0g0dc854Y2hWgtUYx4nDaWQ/noRjn+VQD8Gz3e5Eqqt+D8FrWJZbUv45mSl7BeIdH/fknAmNRF9aYE4SJTpNHzramnXMAr+2wgmEPZarpN4FjRkVVzqqplF9kHaXInSYRbEFzHPCGc84i9u1X0YtWYB8pWTmPOO9o+la3cPICGTOkZqr8oOMqgkrlYlX737CNP0C3EiskYx8jrVK9yUrNv1ruYoKJ+czTkMuYAHzLfHEkl6XsbXgdV4frFwewTmpnJ3Hug88quX/YzKQsN7StBbrDd56ugb++E//J8b5UTAbVTI7TCnEOONUpFEKPO4PzOPI6XTg8fGe6TByOkaa0FDKzJ/86N9R66i+ETF9yjxJ3hnnKsaJZNo7j7MQXMUEi3dOZP2t5LU4b/FGfu7z8h6L9Q6vSFbn7Pm5y0WKRGNFvWhLJc0TKc5S9RvOAhljqvCfLBgjplRzBpIted6Vxjrq/DVvXv0hef6azabj4uoG6wyv374hxoy3jvVuQ7fqUR0JxgjBoWlbjBF8yZIZHnxD2w3sH/Y8Hg7ie9Cpxd37W+5vbwm6a0wx8vLFC756+YpplHOfiuyHa8XnWcw08jSJLR6dT3kjUDexxYvpzvkkXHVjOcWJGIWJsjhic83MaVZNvSamqeY7xcRRK6vr7QbXBInzWypUdVhmlszcqrJD2W2c5ol5nDEGMUUZXfoqvncYVlpBylLMeckcrtZyHGceHu55cn2NC55SC/M0yz7CyiE1zpGha8/spwWeNX+jczDL7AuYxoh1hvenB0LTsttssc7KknSKDMNC6iy40FJS5v3jI4f9AWMN680G7zzzJOym0zQKb6mIx8I5jeNsOi53Ow7jxIuXX/HZJ5/RdB0Xq7V8/0aUXHOV1LgUJ3IOmMYyzkdevn3Nk+sbhmHFnOWFlMO+UFOm7VtlMAnyuyDVt7OWxnlO8wTzsjOQqNSL3QWrzYDFnZVvGw0pylW8KduLCyxwOhxofUu/WuEV0+G9PXeEqSbSKdP4BrdgVVSSczweWPUrhd5FrHdSAQ0r2taKhNpaHWcqzgMJ7ZFdgCc0HfMc2e8fuby+BiR+1TunYfL/+GURQmDoeo4j1DpK4I91xPkoB4y15CjhW8YYptPIfn9kM/TY4Al9S0mCSElzwntZqm53O3mW5glRLRcZ/QXPmzfv2G0G2kYYUqY4Gek1DbUeefPq7zjsf0HTjBijF4AWQ+d8h7Iwy8r5MzUIWFDuHCsRrXBe1GOko6hGxkkeSADBYbIMrbwzUIP+41IwFopyliw1Q7aVzhtKseTgub39ii++/E/83u/8bzllidSlylzcGCmsrPNsLy9JcWbWC7WpSmVwji+//Cu++OIv8EFwIcVUmfp4yE6J0gYMGWc8wcqo3ARz3ikZUwhWPBC16H7OOxm3lSKLbhdwFdzyodSskm8R7NQKlEy2RWRiCtCU6CKDqXoJVNkLlSh8BHLFkERRTBVeqZFdXM5veHyf5D1xz6lNw+XugmGQGGiTRQyQYyGmKCq/IBGoD/sHHu734Czf+da3RFQyjqSSWa9WklsyRYz3bC4u2O523L59S9cLOfbxcMQY+OjZUzbrrYowFuqu90sPKUu0KOOntulogmzX5WCUm94ZuY3ByIvfLfp/ySd4/+5Wuw9/vtlr4ewtGOPM4zRig5JTdUdwdmKfX0uLrZrpqovzFBNv377m9dvbs/qpqPu271fEFMl6uPpGeCmLIa4Lnt1mQwiB4+FErZXddsuq78RN2jX0fXvOkV1u3cW7UGtVBVbiYf+hajXGcDpNPNw/MMWJApxOIzGncxA9RivR/CGDYDUM9I3QZ/u+p5C5v7/jNIlqZH86EkumaTpWqxXWB1xwrPqNSNaU0nn3cM9Pf/IT3t7eYvX3M6xW9H1LrvLzb9drXHDc39/z+tVbgZ05wYY772mcPy9Al8u+V2EAaqiJJZGqPBu1VrbbDX0jC+Wk6WQWyDnKARwCTROYovCaDqeDtMVFWvBhWLNZrwnDIO553eeI1nyBLhqOp5HTPDNNE3fv3wPo4jdJzO04k1IkNIKHefvqFe/fvQUkw7zrBkoukvONYTwemeckAUu/fiMh8tempQ1C25xS0rGEyBtl5PQBsX73cMft+3cCFyxZUuKUaXQ6HZjnyKjZ8bXKMrwLgVF/Hmslcte5ICOmc6cViNMDX/7sR7x7+58I9gg1UmvUBasUJbJwl0O+5kotwtVapMWCm5GRkLciLDFWTbFy5slfR7INJHjMgStnoJ+3XnZoxlGNkwG/qWJEswZbC7ZmGpNxGBqf+Ku/+I/s9/eyZ8iZcTxyOI2ULHnruWZ847GhMqwGtrsdITQ0IfDw8IL/+X/+f2FMOit7MGL4xBmct+A8wTq8la7HhoDxTg3CBusMTfCar+21whaUkDMG471cVqIb1bNArsBlpzOnwjRHIfPWKjna3+hKq+ZTGCPInJIKXrtGoTcgiika2V/VQimCJi/5BV/97N/z/t1PwMBueyGij5yxVXDq+4cH0jQRrCEEhw+GN69f89d//VdM40nG1EVoEL2SXUtSLHstxGmEWjmo8Giz3rDb7NhuNlxcXOK9dJcpRVV6GkPbeLI35xjSEALzOHE6TlxfXqq5RBeP3mOyIeaEtZ6maWVpkgvvXr/BmMrFxmtLqSHizpIB6xy7i504nnWZJMoWdekao8lsa1rviHlmHCXTdzX0fPT0GX3osI0jeMmIrcbgmkDNhbfvbrHWcnN1JTGcCBisoPr+aZYXTTX2y6K85EiehZEkfBQ5HFkQG1UqMzFDWULTystkJIzk+UfPdLkpuJBh6IX/pJpycSknQttwudtyHEeGrgULJUfmGJnTzOV2J1jqCrvdluAC8zyxoJWD8zx99gSs5fF4UAKq4f3dHW/e3/LR8495+uwJQz8IHsSJgCAEAZPFeWI19HRth8ewzxN1LoIf6Fd4ldA2XUPjWmKSpa/cFe6D9NRa5iTfc9+vxHBVKliHr5ZZq9f9wz3ONex2VxjtJk9RfwehoWa51Gzb6WWcaF1DiQuXCpF4lsJmsxEHvu6rHh4eWa9XghEpBe8sKRdJ2soCIDRGDnFjIcbIaRoZVgNTnGly+CeNm0BhZ1G8LZ3GbK7XK6Z5EhgfVivbwvX1Uy53F5RSGU8z9w/v2e52tG1D0/U450k60061YK2jaRyd6uuxlRAstRriZKlVlt5TfGA+fc10/Io2JMmil5NJD6yqnR1ahFU52nSZLleDqBlVekJVCbWIQz4ooRZvhEUPZAQPjpXOYvEUZY11E+aY/FnGykI3IobWpgJ0vHz5M37y5d/wwx/+HjFB3/e0TSaWRGPNOfHOYs5jTHIh5RN/8qf/I+9uf05rGyIVazOlyE5Fpn1W9iJGCtTgJbTMW8lcd14uMBsqwVYab0lFphiLknCRVVv9EAR9Loe4NYLtpmRKtmRTcVb8IrbInqIiBFxTzbmTEGMwZ8uiCBUqzhrBHumAGlMJFmJ+w8Ptn7MedrTNjRzsdcbTAFJcOxdkZ+I8+MDNxSWX/+oP+PTTb519EX03SHGndAkDZ2qCMfDx06dnjtOTpzdQBYw4x8w0nZjntAhCggZsiNwNLw5jkXRxXoLWIiOI0AROGrrtvVjXc5xx1vL05gmmsVqTVuSKr0wpSYD8MOiMzgoDyFhZYhXFT+t4o6ZENd80MDUSoD6NvHn/jovdjrKp52hVi7i9d5udqHeskUhFhbx5J5b5eZ7YGyPdwzKj0wsq1oS1gZik+h66XqS5Vh42r/kGuVRaU4kqPQOdP2q0qzGGMk2MpeK8oWkC/z/K/qtZlu7M88N+y6Qps92xrwEawNtodM8MzUgUNSGGvoBC+ni60YUidCWGbhTBUFBBiaKoGVE0M+Rw2gzQaJhGA689fpuqyszldPF/svbBMMSGdkc3Gufss3dVVuZa6/nb46TpYZkmYuzYjiO0iidKedNFcyDrZF+aNSTb4te8RQ57a8kCbp7cSGnhHD/6yY/5+quvWKaJ48MD/ZMnqtkskhPOs6oxh2dPaQbrQWOZNAE173j5/DnjMOKC4/hwoHZZuVxA36kdq9lUh1VKLtMCm4b38qP41mixU9ZPa4zjjnk+cZwObPrxcRVqUQ+1N77JNPhdHMRX2Ka93e7Yby7Ybrd477jodtrsUuL9+3dM04knT27oItweHgjBc7Xfs7u8YJknam30Fx1pmfnZz35Ozpl/8k/+53z++eecjhP73d42kj/8y+MIXVQHRBU/lJJerzcRx/sPH8i1cLHbs5wmhqHj+vqGlBO1Frv/KiEM5FzMtdzItbIdtuSQOM4T0/GEknvlx+lD4d2brzg9/ALvjlAXW8SQctDuxWb68ZoU8tiqiGxXdTJbVWzODJI27OpLQc44ZzJ0586bge4/bxAwEhXUx83I2cax9rpUDzRPYaHzjuwKJd3z3Zc/5x/+5N/mkGYtYEArhfHJE1bzYYyDxacrtfUv/vK/5hd/89/SD0W1wm01739k9qXgDBXxztONg7KMvEeSYC34uEIIowQj1nuykszV5OTOYmAwuqk5qeukhlUeVAsqtnJVXoXmsZ9R1SJnajtlnxb7t+umro3b2SYi019HoGPnYC5veX/7L3j5/N/D1SeQHS1o89/sdAj0pXDKBRc9Lz79hOCUui0PTWWelJj8uEapTjp4pXv3Y29d8IXgHD5Eg/kWFjMO55Tw5AQli7SqxqTXSt8PPLm5puTCq9evuL3/wLHMHOYZ7x1DP0huVZVjXmvWDpWtL9Z55tPE+9tb7m7vON0/UJPG6OilQfa0xw5Zu7HG7faMI8oTkKle2fYPpxPLdGJ/sdMu33RrF+u2uLjYPeKr8PvyV+Dy6oKhi7rDg6P4RovqM4ircSsEOh9wwQnjx4H3Z0il5MS3X33Hq9dvBR3FyMNp5uF4OquznMMK7D3zlM5hcOI2pArbbHZyq+dMTlIvnOaJXC1Jt60PvzeRQDuby3LRw7AG+0Yf+P73fsAXX3xhhTeqn61YVEVDapLQkaaJaTkBjuuLSy6vLtkOI6HrqK7x+tUb/uLP/4rb+3sFN+qOxjU4HI6SCWf1CXRDpxRU16xnwTGniePDUVjyOOgkYidCB8J6q4rlvfPUJo9E33eE4JQLtcwcjgfmrBtYEJfC1IKDro88f/ZcNa2zEezes93siJ1au1JOeC+pYSuV5XTk6eWlHUp0orSn9Q/eIJpNj0PX69o4eXxyzjgj0JWZo6jm1R/UDz2b/ZYQOhbrGk+LpLIhSFcvtLWIJ/Kw2YzUWvjt777iNB2o7cjD/W+o01eEdqLzae3gsa5jwRmtymDalizYo+mzc/weIiIJJl7SYnc2zetAiwkvwOSeeqYe/1yGQu8qzhecUx4UFBHgTsR29I7ozaAWEjE4hiHz1dd/w/F4L9w8JU6HB2VwIUisC4Py20pirid+89Vf89/8N/83PBlXNPk46UtNdqrT+ApW55oIodF5J+WSTUDijixHbAjq9fD+nPVWLXNKir9iMt/HEMyaZKasTRBrS/q9jkpOM3VR6vNaopSK7ltvn2/OmVqKCtfcmmVn918uZtTULTmGxnL7N7z65p+TylsUNy6+rzXbjJzn4XhiOkw457i/v5UxrlZOxwP393cWs6PDpP5TGwUlncVEq1+ltDXA1en+MxVhbNaq1uCjXBZ9CLU1ur7j5vqScehZSmVJ5dxz7H2jtkKMTuFtJYEXPNBK5ZQT3gcud4PSJxf1DlAr9w/3jL2gldB19vsq1YqFXKumRGuUvNC6wNXFnos/+ROGYWS2aHJnHMKKxa44ceh0Qmi5agoAwjBCM2IcT8kza6GI9x7foI+R0HU68W+hVsWCqB0rkOeF48MdtTVuY+Dm8poherJlItVSmHNlcJVxGM6yMx+ieJsp0XXQBun9D0uyTdIz9j2eZmOpbqJSLdvJQR9kPtTRTtdLp3EVJs3zTG4NlnKO7YZGP45UCuTKvFgQoUWnXF9dk2omRnFKt7e3bHcbxQ+Xwt39PQHY73bWwCf1znGeCZbZ5Gqlecfh9EBJlaGPhC5QUqIfheGLEAzqRzZSrYt7puWkRX0cSWlhM2zwPrDf79mNG51ozWNzrgL1ke1upw4K+7w3Nm2suTuhi/gQOM0zm82Gf/zv/c9EAm4G7h7uTMHRfn/l/AO+hqGXgc4i6/u1A3xdRGPk5skTekswHsZRC3DNDENHK8K455Jo88R2v4dWyTVpMcrJPCWNftzy/NlzYOZ0eMPx7m/x7ZYuihR1zpvUVcqbZtyIy80UTYAV08jV3Utl06DZIaThbNH5eCK2/cJpIm/NqU+hGpTlBa2spsFg/TB1nTDQs+uo4JV8UMtM7wdaV3h3+zXv37/i+ac/Zr/JnJwKpLBmPkXAVPoAp+mef/b//r+Q8yu830D2NBZaUwpso6iS13a50gqeQjf2+KD1yVlmlWJoBCF1yK9w1ou5x2nKE+R0p+GjiGpXBRe5ZvdhLSTnqckTqln5fKO1fA5QbF6+ilVZV2qlLdpEcqecstIaLTUanlYz2aDt1gobNzAdv+Zw/3dcXz8nMxCcJ81Ktg4NLnZ7qtPmmiuUeSZ4z2azZdxshFgET+x7HUiXBR+Hc+J2wym7z+D1GqMdyEyoEiOxeY1T3715RYyRi8srhYoFjzf3qO86WuhI6SiOe3V5FcjLQnDaJNaTK03l6ftx1AkhhPMNmPMCOMZeeHqtRRlQXgFux3k2V2PAO9jv9+Sm78GjrKNWKfOMD4EpLQScGsuqTki5KoYgukjpzOV7zgVSrHTLhfm4MHSVuNlAg1/+6peclpl/8Kd/qsROIgrt1kIcaBznWSOb1W3VajElaX1woe8VkufNLBeco+RKIFhdZGJ6mLnY7dkFMzKFaMUqmqiaZcHc3r3ncNCp9PNPBzovGKCURPMOCmx3O5ZlJpVM30eWJWmzC4Fi8eU5a4HbbgX7lJIMyy3knMhLYhx7/vSPv8B3+sxKy8Im54XtdmRvMSTOgY+wzAuduZwP9/d8+913UOGTTz9l4wI+muBgmWWaqo+9HJrkm9znXTnHl8cQJVsMEe8xElfw2P3dPR/u7wgE9psNFyZTbjRiUGTK6uReF8oPt2/xL16yGbacTkdTSmpiyfOC7/moiOh/fMNY40JAME6thdB3bPc7+WoatJJwVRDlaqjLRSmeIQSaeWeC78gdIt9L4nSaFALpoVE5HSamknjy7JLl/hX3H35DaB9wQcbU0HQYc82UgEVKPLdC3A1c9Ra/4cUrtFWu6sGtJUFSVIlW8OeTrTjhJs9Jq8SqOYLWCNVTQ7XsqSKPiFd0hRW5a7NygoC8h+h7Sqf+krYsfPfd3/L97/8ZuRvoWiO3zKnA6aDK2W7YcLUb+PnP/gXvvvor6AK1KERPLvWsza7Y5L2S6rUSTK5LrYQmTq15bT4BZx0aDShnyHntjdGCfrZ+imuKmrKdWGYd/FzE10atnfjGEChBbmZx+BLeNNuM1nSGOatOITLj+hFMVGG7E5VK5wLOdbZ2TBzf/Y6by39A8UG+kpqt28fRhYElnch4+sHUpF7hi60+8k7rhFBKIc0z0zQzjj05CW3pgzxo8zSRc2UcTIpfK7E1VdotS6akyu4is0wz+0EGi4gML+phiCrR6aTJbw66vjfiR73OtVrhuxE/zjtqyThvuuPWOC0zwziYHM2fcb7YHBsL1dIpz9iN1lCLA8QqnbPCzgp938tjYael4Bx5WSgOwjBQloWvb2+ZThN4ePL0CfvtlpQTm01PH1TxF4Ln5fMXLDkzbrbKISqKT/ZO00Yuhe3Q8/n3JDEbuk7qhaZ8I6okgNvQCUpxVafs2OFc4eJSTvBcEqd5Zp5OMh7FjtNRyZelSuM+DMqrOZ0Sb1+/ogIvXz6n9yogag02vaI+umiYqNO1EtSlALlujCzLco7sxhRX83GCDXg0wRTr03Dbrb6vKsLi+skNsVZiCJymE33f6yQWAn4z6oCAZ7ff8cPh+2RTJaWSCAaJ+a6n894cwOKZpmU+E7ieQj9smdPMw+mB7bADl8lZo34/qLR1GAaexWekeTpDiGusR6Naubx1h9j7HTe78+Jf7dBzeXkJwHGZYZlVsBUCZwLxf+RrlXP3fc+79x8EKwVPjJ7jMZOmmX6Q4bM6nRJ97AmtSlmVMv3QEaOjJc9xViTKfjPqVO8cx/sjr779luvnV5Tynjfvf4krD1QPsQm/butKbgpBnIk0ilY78ZGN2jyuLjjXn6GT9VZY0epaq8ld5SDWQuzwLuPNebx+d6uV4hS8J8GjKlvxOknXVR5lr8dVbRY0LdzBaeP55svfcPy3DjQX6fueJc189+23XO4vaK4jhJ5v337JP//v/hm+6818qumo1Aal2ucu70GhyKJBJXYday+2W/PRUE2oBAG9DhLVSop0MYUsG5GtsEm99pKTTt7e4GQaNep6hCp/VzAyaKmZXdwo6cBiS7xTW+BSiw5fpTItR3ZowgjR0VqwDupKKSKXa4PoA1N5zZtX/4rd1b+F6y4Jw0DOC2k6yjMRA8mSEELQVFeaOOTNfs9Pf/ZTGvCP/uzP7FDg2O+3lFLposd1SitItdi1Ezzlu45WCpHgiAS+9+mn0t6al2E37ojrrWFW9Ijjdl7YhkDt9WaxXyoYItDIOtEbCcQ6AiN4JKyYv3PQFHIXvflBna6od4FlmugGJa7GKt6hmupIY3BlmRPdKI5jrSKtTkRWNELqeDpw+/4tmDdgPp642O7ZbJR6WluloxG7nqdPu/ODM+cZamPoB5yvZ7xut7vAeU8XA0vKkl/6DpkeM69uP5DmxJOrK7a7HZVGmk50fU8MjpSFQ+63G5XNg+Xr6OZ8f3fHsiQ+efEc30e+/9mn7C92ysK3RarzKpRPWV3Yxciw4Dwu6gRTasEwAVusC3mWIz4EwTmlVHwUjLZWt7pWHxdZr8lMYY7wcDri54knT54pl6tqYS5FksSuH+nd2hCmbpFpWdj0I9kDQW5t5zyXl1d4M4qdTkeeXPdsw8ARPVg5WQ6Y3T8pzcJKtwO9iStA0EEI0XgcSGmh5UTc7ZXt1G/wQd3fXeioJZOXBBupb0rj/HD9/V8rNWsb0DhwPDywiRvoBurYrGayUlJm7Admt0DJRB9Z+h5X8nnBrS3r0GW6/OPpiPeO/WbH3x6PpK/fsVycqMtXxNAgN5rLj4GOrF3r2jBKrZAbc8rkkogEWk3glIYK4VwxG/yKr8iJLm6offRO1QsRXT1f62oQeq2K45Y6bE2Ibfacr5uXPsdQHTRP8Ir+l2N/4d39W9J8xIWRFgVJpmmibrdc7p8RY+Wf/rP/F9PpNd4I7LVlsZaVfFdont4bxsI1+n6rJAOLGHdOCkvxE3ZP+w5QxfAqXliPB+v7qdW6H+0vvJk5fdA1dK1SvTaSXCurCmCxA1dKhaF77Dvpukgrle1my7sP70kln3tlal2E1VVPLQstKDSxFs/QVR7ufk5rHZfP/22y30Hs6EdwMeCrx4+S/KclqQuoyBTXclaDYqu8u71lPh558eIZoRtI6cDhNLO8f8/zZy9wbs3NqqScJZrBEVWDCa4LxOi4f3/SxNkqBWfyM43VS1q4vb/j9v6eT14+pxsGjdReVu85zYoW8KgVzaRoPgS5qYsWrtFC+eq6sRhm52xcrjlzPJ1I9/c8ub62roR6rumkQDTMORvm32yCqQ02WxHYqVSeXj3h+vqGkhNLKYz9yFpSL520U0Cbs6A852gpUVJSbo5DxfZF6qvqRZwtx/mcWBmCLJD3Dwd+/rO/xvvA5h/9IwYqhEBNC7/98kueXF9zc30Dvic6T3GFbL6DYRBvcmPmqtIKLI7QB/abHWWz1UYYOwKOTd8xzzNjF6gOajbuY3UWZ4UQrjd9XrKFoVXGXlI6fKGkAr7ag9JoRSax4lSIFEKgtsycEuN2q1iH9eFsjXme1ToY1iTPJue4C+QWrEq12gIlAUC1RbPUTPSe3bgjZz2Y0UdKs4BIi2YuJRuk5FiWSf4ZSwM9n6sNV+1DpOApqdAiqr10hc78DffHE9++fsUff/Fjhs2G/W6DD+tx6O/nJ7z3bLc7Ukrs9nvu7+7IOfP27VtNKF73Yq6JbEU+Dcc0LZqAnKN4R4iBmALNR7a9qZxcI6XK0AVePh24vf2GMt3TBx28WjQewDdDdeoj8VkEPeVSqcsiItmtC7mgpOC0yYnaccbjGd+jd3d+n6UZHFWxyBbFcmhAEQflkHAFW1xqa2uikRJ9GxSnDujgHc4lWpuJvefu9jV3x/dc7F5Qlokudvzoix/RSqPrPV/+7uf87jd/iW+Z1oI4lGL8Q7PXXg0ywhsX2gixETsz5tp7cLbpuSp+s1jXjSLQV5jJ2X3abBTV1LQu/NVVUgx03kPVXaehSWF4lGZTujxRu90W12DJ1jPiFPnhx5FDS2x3e2qT8rDvpYJyBp225khpIkSHa5qEo19YTr/Et5fE8EOWEvAxknKi9508Mh5SOqqwab/He1XO/tlPfiJBQBfZb7fELpKmCe8C20HpzKXIfe6dQWNOfq/NZkNspUIXOT488Obde/a7LTdXV4/yUC9lTAkQh4HvvfyEd7cfZCZbF6VslZHTwsW4VXjdAC47ycWcooOXRdHGLkbrg7Yz1aoicp4aHS7DzdU1uehBCyFQi7PxrZFdJfhIa5l3795yeHggLZk//cmfKDG0VE4lM8+TzE/BE2PPMAQeTgd+9tOf0TnP02fPeHpzozylZo5q5ygWR4EhEL5hngg1q83HA6dZZUWlFt0IRqg+ffqETz//Ps9vbvR3aea3v/str9+8J6ekCsEQyVGBen4tLEqzKGunYIDTMbPf9dA8LkTSMjEMAyH0imluIkKVXeQoTYmpqSgjqnrFIayk3rYbcDHQWqbiqTWpXCgI3/VebWPeq22wZY3GSh/V5jwMggGSwV190NieXSM2dQhgCqDqK4fpgTQvXF1dQlP+0N3tiZoL11eaJKrh0ctplucmdnQelsWIXOMB1mKpmqEVtc7N80wcOih24qvVzFSSb8/Hhf3FRtxXKtQxEjtNHYfjgZwThyPsL+J6Bv6DvhQJMzCnhefPX/Du3Vv1dgRHK5BqOXcjeDzON+4PD5ymiTiO1FnywjInWuyopmzxfccQHMEtXO4L8/EdUKkl4NpMc0ZGNxGyZ71RKbSaJcrIi/2pP0NHq1+kGlkdvHiV0hSP3axRby17chZr0qqiQJzBWhIAI2iqWqO1QSTOzJnaRAyasnu5tELwDe8sFs855tOJu/v3PHnyGTELy6/e0fUdISz81Z//U1o60Qim2MI2NXClQZP4pBnnYMUY9HFgiJt1YTlv7OvEFbteP6tUXIhmQGxyQjeoJLyL5j8Rx4j105MhRQjNERGk1koxaEkRJM45SirUiqaYppiP5pqFWzY8QcKaVGzoKoAMkK1p8W/VUXMgRHFGIQZO8y23t7/m5eYpKQ1UO0CVkInNW0SNqnCHnKguy1wcFdSYUqLre1pz+BjJs/rVnz57oc+xKf3CRUm8v331mt999SXRBU8rhd12y5xEhjaPiEYHZUmkVujpKRTi0HP9VHpmSmPJC/Np4vLiksubazPdCLPWTizCpAHdZrSUTJF4xVJI+17qptIasSK/hoPQ9WysnjJ6JVBmhPPj/ZlYmqfZSM5MybrwWJSGC57gVsNQow+Ry90O7xU50ltXhW6pNQbEsRlGey88xgH7yDIdefvuvVQg11dnviIEz24z8g/+7B+y3Ww4HI9kUyXd3x2IznN/e8f87CX99UhJmXmegcZo1ZRLEadTqcTomOYT83xid3F5zgMqZA6HA6EL9LHjsJzEhXRRN16uNKeUzcc8LE1AvQ+kXCk5iZQ+CU/fjltyTbjg5KC1SIHOezME6posS2I7BFJKTMeZq8srur6no9oU5pGaUKe6cRwYu44VPY5V0s7VxDgvEyH0dKEnIXI+RP3boe/FM9TCUiv3H+65vrxit9vgXOB4f+D1u/e8ePHsHB2T0nKW/AXv8WMPPlpEgvo0+q7js08/5fLySmmrpbDb7VQg///HV6mJZZoInQx++/1eTnPvGGJkKrq3U1KufxgHrnZblkUb8nKacVVZWrkmclpoQREr9x9+y5vvfob3EwL+PYJGipoiqYKQWtUkiJ6pWqodaB7lvWuz3+9VkbcGeJNhWk1xM2anNbyJq5t1TKxk/wq9qFrXDBUG8rgGzUxlgpjFSeRacUgR1HnH4kcyes0f3r2i+9G/y0whpUIXPMO2529/9Rf83Ze/JLRGLs42o2ZreVWkhXtUaNHsmuDpglJzPZZW7daNMUjZZROEthirmtWWCVnQt16fvlcLvcF7tdrmYS57c6vXGuhco2aJWNbJwsXHe0qTjYS1xYkfaWuUh0UJVZvaokeRIxmqT4ZWdIx9x+2H33C5/4xx88ccl2o5TkVoD9okTvNJDaMeqfBMsYTzpLRwOp1k9Iw61JUsGXkpieA6WirU5rjYbImIl1ZUd9dxc3lpGnzwdhN6U924iknuMsHJcbt2Orvthtk2ixg7xmEgxIjrZGT6cHvPdrvh6vKKZQFK0jhznLg/3DOOG26uL+miwVI+aAOaJuh6XNINepwmbi6vqE6GN+cDP/j+9zk+ecKyZHb7HTlJAeFCYLDXp7iHRmiS4/3xF1/Qx96kgZbG2tQbnGoxNZQauYwS5eHhge1mw5JV0CEorbHmEFcac0q8evMO5+DpzVO6oaeVxg/+6Ac8nCZVh9oiVlPm3YcPUCrPnsn4I5VB4NbSd68ur+w0iiRqOHV2DwOx78jzTJ4XiQJ6EXE+BCiZFkxil6Q+m08TcbuV9jlGet9zbEfA+q9rPmPM6sKNpmRqlFTY9hvuy0HqLufZX+5wUTddjJ0KZErFh54lnUQix0DfDcxVhPVxPjF0yiEqVa+j1ExKVQ+3cRFLKSpAsUa0WhtvXr9jO+zYboLh5Y3L60s2u52MTSg1NOBxwZHIBlVVI/EtrsWJw1L1a2UbOafKutXh+/d8KaVAoYepKNYgzwvZ0oXv7x5ItTCOA1999SXvX7/hydOnfP7JJ+RoMEwq9BGal9fj4XBgu9mSple8ffMzPEc7JVfktsYW5WpRQfWc5ip4URvqml7gmk0yJg/X19luxxpm51ZBkltJVovS0UFZsk4eOQYfFBUuh4UmJXWb6yhQKOL6fLVTfxUh3yog9EGzzonb96/UGojEDi4EyBN//uf/NSXNtJJtu9Lz2Wh22HPne8PBWVVYWqEbOlKRl2HsejngwZRWjw17LSkiKJVFajp7DohOIikPgQJVm0sji1tpgeKbnX0r1UWi3r4a/pIqCOa8QBjpY29ubhSg6mEcBpa0KPfJ2jwkIMhnWMzkaRZEFWlVU1/vEne3f8vn+0+Z/EhzgY7A4hPg2e33OGTi7fqOVHUYramy3WyIfc98mkjlse7AeXUAich24g594ObpM65bVSxHjOowqN6Tk7D7oYqEq97hWzybyZzdROtGEaPw5ylPdF3HYGX2zSyRx9PMNAsqca0xdsF08jMxRk6HI7/5zW94+fwFP/mTP7GTT8XFwMBAnheC9WCP3YolipBKlmZ5SonT8cDT9sQav7SwRJtmXBNG2oLaRmLsFATnRFwrw0j6Yx+0CNHABX14vQvsd3uad/St8r3PPmO32zLPC4stDGtXwDD0nI6TJgwCpSWePnvK/jRJNeIay6zcoM9evjQHtCI8ujBQciKZ6bDrOlx8bP1bb6bYqS51s93RDxuO00mfjXe4oIiBlGbwXr4UnE4wtXJaJoZhpOt7dmbbTynLeBbXPoZgTl7HdJw4HA70LwZ245al6PUly5iqtRGj1+TXR/o+4t1AbtnqZC2W2kcpN1oC51QMEwK1FD48fOBuuaWUzMuXn7IZ5FOhVYqZ1T77/DMKlV/+6pc05/njH/6IfuhoDVIr1Fx4enOtCbUJW25NCrWh65mnmY057QXLaFN8bzHKz58//4M2iPWr6zr1Vs9JcSCzWupi9Pzyb3/JzeUNm+fP2PUbhk8/ZYiRN2/estsrFmRZ7vFhg2viUK6uLqint3z53V9CvVWwWhN3tp5k0dKhE34ttKKU0SWVc8ieKIbwyFWARUyYiMSEHevCy+qXaOsmIs6hNvEm+halO7NuQM59JK985ATXoLr1hdZSsHOWLfLYdK4mxuP0jjTP5NBpXsmZL1//ji+//DWORd9vJH1pVSkCFqG3QkHmGdd/903IgR4GaMWUWZyfHR8Et5Vaycuk64M6RlxTtLnK1RwZiCZx1yVrphqrZh7uiE6KtFrEUygos0LKLH6hjz3Ry/fQqqbCED2xRXIFlyveF+ZZNQOlNGoBF7T5tSKoKwS3br8cHn7Hw+lvGTZ/yuHkmN2Mt3qH6ALDbjxvqjUXxe6M/Rl2DF3kdDwx9J1ZFpoiZ7powiCsObCqo8LlSrXxPFm2/3raaGDKFdP8em1uzRlkZBc+JTUpbfaST9ZsJK+D7X7LxXZrrmUzyxRpp72PfPGjH7PdbmnOka3hyaOwrcN8ZLvZnnHk7Thqojgd6PtRuoZa2G4G9psNzmmsw0Eqi5FyXpI9rw8SgzqcU3OevKP2e/vuXK+55IXqnBm64P7hgRgCw2jmqWp1p0mYcEo6/e6fPKNd6wMqVJMQerAohvXhPTwcSa1wvd/LvdsqD8d7DvcP55+f5pka5DQe+0ER6mHVdSt4sRZNPnHojKQVmZyXrCjgTWQpiT4GKpmSMn7jzkUxp5Mil/fDjgIs+WgjsGpYN7sNu3HUqbFhm1dvPcB2+nb+rCxa+7aj0/tOOUlb7iR9FeEstVGZZ7bDSD/K8b3f7BiGjiVlQjAc/+HA9mLPxW7P3d0dv/jF3xBCxw+//0eUvBZSPSYFhCDgW9JLZ7lVlWHs5Yx2Tu2FtdIHdQK3ZsVN3R82Sawtiav6KtfC6XhSwU0IfP7p52y3G6bTzMXFJZ7GNB1JLpNmndhc14msd4Wh61nm93z17V/i0ytCZ6oxy/5q64KO1Gfra2iUR4Wc/b2FOMtDY2ogb4ejc9QGkItTOVHzHy2kfLSJaE5oDlwVaX2GX50zaNZa71b2F6xjwYP3SNapRUgyVG9cpMqw0nSyQ4VJtmn8xV/8c5bpHa5K5uwJ5Nq0QbSVZDm/WqpTt01tjc1mo3bIVumd8QleacPBN1PvOEptpGXhdFAkeWuJECPjMJKLueA7lYJlCi4LPtc2ocw6mj/nYbXWaFFwkssZ13XiZnJhyTNd63CusVbiOefp4kAtsBQLN0XcXy2KCIoYmW5erFIKWNhq5xPv3/+al5sfMvZPcRRSnkgpU/vIfEoMQ0dxuvfD0EuMUgqlZU6WmtGPA2lZNMEXJRR4GjEO5FYUfNoFou+s+9jyTgIwbEZcjHIYOmc3mnb/6m0MjZ5QlMzqu57eO2pS3klbSaVc1L9qIW3aQLJuqibxmnOVTz55yVqtV2rm4XAkBgvoCsoJCjFySguvXr3i9avX/PhHP2K731NKYdNvGcZe8sa8WHe0Nj0/DAoL80Gub+Mq+q7nNE10fU/wkj92PpBM1nmaFr759is+/+z77LZbSmscDw/s2CrddJ7Z7XbEEHg4HNjttoSuJye17EkT22jRMMeauT8ecc5zdXGJ846HDweCj9xcXZKWibsPHwgh8vTJDRKeB2qd6E3Jg3P0IfDq9bfcXCvEcEqLOqbR51VrZQwdebQFzx6Ww/FI7Ht2F3udiIpqGjvTlKf6GBewunmxFNpkxr7qPNF1ZBJ4yTbPJqF54XhUo5x33qpoBeE1X5mWie040neaKMjq4Jhr4XK3O59CT4vKr97fvmM6nLi6vrGOjcRut+Hz7/0R281ICJ7j6WhGvw1LzhyPD3TdcOZhtlvhw85bbH2uhFDluB4Grq6vdRr9vYiOv5/Adk4TY2uFUhV1UF2lDz3zpPtvnhf67QDHiWk+MW4Vd348TWzGDTEEjsd7NuOIK/d89bu/IJ2+IoZsMKcOah4ZECn59zaDmis5FVqREsw5KPZwVqtcXRf82gzCbfX87oJN3Gree+QjdDrnUSrrjHRtZ+7arsHj9RC8pU1LCp9qU0ex/7WFriZqDTQnscXp4cDt3R3j/oYYA9PhPV9++QtcnSktA5qYW5XZrTZ3hox0lhXfgdN1GdbekZJ1KLGIntVf0UATEY772zvevP6KZVYsyW7TcxoOpDJzffOMod+wGXsLBlQ8TWkNnxyuK8a7CEZaN4rOvq+WhKPTAp8ktMEFgrd5ppnpzntCjJBUZpWzXre3tF7fK76olkoLq4Km0xw1TTy8/5b9zROmXFnmrCDL2cQxsZfs1z6L3Ap4z/H+SKuF7YVyy7pejXbTaTknVF9fj6RJSRD9MBCjCxCEQ8euU8xsa8rjQaSz4AggBEJpTHmWuilEM3w4S7W0yOgumFStcZomhs1gSjKdTh0yxDgHNS3MFhxYqu7XYTOwCb0ljxalLTbonOSgux+Z5Rydd5b5xOFwz36vfobizFiFyKbqIJhhaM1S8cHz5Tff8OTJE148e6ZduBbaWZrr+PzT77HZDKSU2G5H+hDYbAZNBlGLhe8iF1eXRCN8p5JwYaT3UcYyoBr2qEiHyrIspFLouiiFkhO0tNtd0o+qMK1FpTHBR+M91Bb29t17ptPMvE/0fWQ7bCRBrkmJuCiXiqpRleboQqccJTvFH49HYoxsx825Ba3kzN2tNqmb6yd8/c3XnOaJH//oCyMroS4Lk2vMJ/OwDHKdtgYx9lxeXHJ3d8tmt2Xt4R26Xgs0jbuHA9ttYewHKbwsAqZaoOE0L5I0+8rQbeiuB0YrQCm10JrjT378x+x2u3OjYKMR+sAubplPJ3Iu/Oznf0PXd/wH/8H/Um7qpvvAecESXTfSHJYRlXk6SILadX+4wsk5rxReHKfDd0yHE2Pf8atf/xpa4/t/9D1wMNVC6AeV8GxHQRcNTrPBufWOb7/8Genh7+h7KY1KFaxALTKF1kZxKy/QTLX0eJLXNRTuDjqBnnk2vyaaVttgPN4162V2FjCHWZQsAs/+u1ulrk2Tg8gLO1GvoX7rbnEmd1fprL2+1mgIig0+kGzzcM4zTemcW0ZL/Oxv/pL7u7fUsuCj4q4bjtoyrZpzujWUG2WuiBbtmqz5WZbKt+L8aLpptLORlKoq2ZRsKrN1JOdCyoWcCkMPqZjZt0Cx0NCCej3WDhO58FcfiXEJVUrMnDO+q9RU8KFCq/jY2VpoG+ljlonu0SrIuLZKzYoJqqVqfXZRidotQk4cH77l8uaPGNyO1uvQUptQkwa00ui78eOblrYZuX84nDknnEj4GANd3FjAaeXD+w/cHR74kz/+gphq4dV33+Gd4+WLl/IezLMsQyHQLHslg2SOSLFTcyVVEd4d7qyiyDmTJn3IVOi7jpISzTW60GvCwFPJLBbBgVebXK2VeTrxu6++5Mmz53z64qVOgdXJJOXhydMb5ln5J75BWRZ+9+WXAHzxQ3VgJCotaYRsDjpvGfEhUMwP4JwzA6F0+85Ony4G5uORGCLjRjBL7xv3DwdKbVzGkYKiJZzTdIWdglsTkVct8G3oNW5LRZHpQk8XhJPP84nWKkMnrbNzgd3F1jJYMsE7qv2e03yS/NV5QuctLC4QXc8hiXzeDBtKnuU7sekhdr3CyMyXgE0aQ+yMqNQYm9LCzf6K29s7SqlSeoBOns6iBXCqji2VUy3cvf/Aspm5vL6ijzIT3t0fpLAqle+++Y5PPnnJ0KsUpQ89cbP6JtoZ41WMM0xz4v7unu24IYy9xuUqcjaEaD4McxVbEqx3jqurPV3UCXLsBw6He1pe2F5fUvJCtdNozY3NOJCmmX3f0W23uFrYjQOhVVxL2Er7hwwTmry9xBdffvMVv/vyt5ymJ9Aqn33+maYfu9OX00QMF/RxZD4uLGUmLyd6X3j13c94eP83+F4RETJtavEK2g3Itmg3W9GEFxdyrmeuIeChOUXw29daO6oFbC3OahYPbUkKrpiwVV/F5M7KZgv6XTJBUIs6ttv5hQDO+DJn7ERbgWrwLQhCxnEOP7XpgqpNaRg7govUOvGrX/4lcz7QSmYM27Ofyf4lq8kN0195Ps6lcmy6Ad+SNj1khgtGiAtSjlSv+2/odwyx57QsNAepwNgP7MYd262CIqNFX7QGZAX5OW+hmanSArTcoNehwbtGdZLQUwsxdJScpPpqkURhNEsBeKtNrqY8cnS9kAiHDss5FwUkBn/+3PHWuUJinr/hePwG+h9C6Am+0hucXmtlKRlv/M3x/gBOJtmcFpPOBpZ5OUPIEpsoIsd5uLrYax25u7vnV7/8FT/4wQ/sxjEME33owQWqL7z65huOpxMvXzzn8vKarpO6yVUnvqEJa80lMww9Qz+QS9VpzeCYte4x1wRFkJVOoVEu4Zx5++GOf/4v/iU//MEPuNhs2O4uJEOTf5RlTgqp6jtKzfTDwOeffWYkb2TJi51eYOh7wx9nVfv11siWEq1W9hcXkpXmwrxM0FSOdLm/wFvybKoJ7wL92HE6nCi2sDmg1VUV486EnmvCO7/65lu+973P2G22pJSN2HsMFHt6fc2cpcBprZCqppdSsk1bXs14XUfXDbTcSDEzdB1j6HDmVu4sQMytTmcHDaXolpTwXTh7Po4PM6GrRB+lpR50Q/V9z5wWrm6uKCnhauPFJy+V6WRR8c4ZPegdF5ud/t3YSxVSCrU2NrsNwSnz6ZNPXuCDU0qqLTzj0J+jIxIZH4Kl72qifP/uHfX6ks+urwh9h0uJeV5kkOp7pLzKHB4emPPC2A1GcCtttesDzV3wP/33/31qLhwfDiwl8/BwpO96njy5ZowdC81CDW/ovNzpqTRG94cCTvZNVZ/78+fPLRfK8fbtO8ZxQ8mF+4cHnHM8nCbGYaPX6ixIM2Ru3/yK+/e/IXTFpgCnw4H1n0vFJTnmqtBbewHKOZZCRs/mO7yLCspEe12tmePxnmHYK27hrPRZyepVVqp7c+UmvJPQo9qm7IrIctcU3OlcNAhKp3Jn95zpj7BVWr+r+o/EVWndsoBMKTPzfGDYXvP6m7/lw4cvGUMgVU8taqwIxRz45qbGyFttOv68YXV9pIuSCrdWaC7YZis43UfZ/By6P1Oa6MeOVNSZEoIW6YvLHSFatazTyRweBQT+I6o8FS3YbnG4UGlNPezBiUfxpbC0RoyN4ORst/1O78N5QEZR7+36n9eIiPfq2Yi+U3hiq7gaqU73S60nptM3tNM1zQ9cXl3YdGninVzOKrg5J+bTxPX1Fc9fPIfmOB4PgAJWU84GramnZ1oS1xeXeOeJIQT+3X/8j7m5vGSphcPhSOeUbpnnGb/Z0HBM88zth/e8fP6COVkMcnMcpoMynZxj223UMRGCScI04vXjQN8P5Jw45oW8VEJQufzcFLm76Ud8CHz28gX/m//1/4rtZkNeMsfpyH6zowvWnNcqXd9zPB2YTjOXV5dyulqSbXaR4KQG8D7gXSU5mJeJfpAnwvtA7DT2+TV80G2Y54k3H94znU7s9ns1g9UgOV0/sht3ZyzWO5nHWhMUJlORI449Iavf+HB/YL/d4r2SMp3jTEp/uLtlWSbGLhIHdWp43zEMOnkrO0YGlzVXqOZM1w26TWuVc9d8JI5G7EzZ00Qcu75y9+GW0PVc7nZs9zsR19ExOnEJrmlOwDui6wj9OkoXpU02jcbbcSsCsjW6YSQOvXqygcU2gT7o1O6bp+vH9WkgenNbl8p0mnT9rVReiKZj3O750Q9/qHgMBy1nlmXh9va98QtbdtstNAjOcbnf0/cjzmHmvo6aRVzHvseNgYfTkfu7e6pzjJue6FVyX4D3b97Rgte0WCqn45GhG8/prX/vV9Prvr6+xrnGzc0193e3vHzxDO8jv/n133J/UBvfxXbH/cMD3jm2l3tcPvDNd7/l7etf4OJkPN559bAfvy5Q9ZwXReN8Ci95OauURBI71vxgW57tQOS4u/uKT158Cl0vaM+t0IizzUQTY7EQv1y16Xu/egR0P6pbIn70Otvvbaqr6XRdyotxjsXIjFWu66qmGZeTKoBb4Ve/+CnLwwdKnfHmdaAIFlunEN3l623jz6+fBpuuQ8r51fxX6JwDisGM62uTRD5sPJf9Fdvd9hzJMow7bTT2/blCCG3F3s4T9QpPAQpBJcvcWyvEoE2ywpKqpc5qksDCHaMdwtfeDe9sAgwBijbnVpM4BSdpbGueXCA68LHQkNx7OnzDkxf/ANftgHZOaj4eTzw8HHj29Bm5wdX+En/1RMGDzpNM+RVMMeqrJA3VOabTxLMnTxmGUTUMu+2Glivvbj/gHIz9SPTq662tMM8qZ//0k095/uwZ+/1e0igaS5p5/+Ytwzjy5OqpYA4gUSlJ2vvOd2TncKXKu0Bg2zm8i2SvReY4nazZTHn8T2+eanztC/OykHKii51usiry/NWbt7x/95aLt3t++KMf0ve9OICgboGSCw/Tkdh3xNjTdwMxqNt6zXEPXdRI13V0vVRz6f07DocHbm6udaprjwsCVSd1EZWNpSyklNn1I7NJ6IZhJAw9P/nxn1IopCVbrIFusNXwtx03UDIhdhrHqzLpx36gOauL1B1GcJ5jnigpsQu9IgNMiaAgPU0DpTojn3pSy3RBRecABZnNaq1s/VYbOJxhEUOUyaXQe28bkqTHYV0YnEWs2GmWNe+oVKVkut7GZyPGo8Zk75VhP08T0/HI6TRxc33D1ZNrtbnVSqCSs/islTe5v7+T+7RU7u8V0nhzdc2w3Shd2Dlc1cNS0kIqle0wkIFhjJoervX92+2OWtYwvMp+tzWVVaa0iavLS01ErZ7DAf9Hv2xxCj4Qox1gKrjYscwT2/2OruuYpiPb7ZbSsgLu2sSrVz/l62//nCgREM0lsNjqWiqsDtwzxAKuQG6ZlqqZLjmbwmT48utHdP43rcHV9TWXJlbQgvTRW3ArWmVxOE1R2DTwRkaXqmmiVVMIOnEYvjUIhq3bNiEXuNUIgPw6rRIMqjoX7uibCd3I1cUVp+M9f/ebn9OSVDf6KZVS1XnRjFdcD2iCtm2Rdo2lZeLY6y43HtDjaP7ROe68O/dqeKcsKR875RtVdxZbYFlM58j0ymPkh3NaJOxA6oFqyj5tqoFWReC5oNftnKO28Ej6t0BbvSUOYt9pE81F6dKxoywzS8l09PS9OEOQjH3JC51r4OXtmI/vmI9fs7m8YkoF77tzxfSqQgtRqQ59iJTaWJaZOSfSsjA4BGVHQU/pdMJ7zzSdOB0euHp6TYyx47gcFVdbdGJeT3NjtxVm7xpj15FCYDJfgKsikj//5DNhnK3pprCdrzRFY4sMl4IitqZ4Cxdw3tG7jkJREU3UYpGWTOjU2hSDV2Bas0RW72m+MXQ9T5/eYIymSE17WGqF6oVHppLoWiB45TxJ+u8pwasycpXraWomho7PX37K06sb+qFnNm7GOVTrGQJxGASZZXXX7ocNoQt89c03dF3PJy92LCkRQ1R945Ik6ewCHnjz9j3XVxc4HK9fv7XFK7KkhdBp0e1jh050E52ZGvs+0DqFIIb2GDPQqtybrkqxFUNkSjN3Hz7Q9wMXF5dnOKwPHcVZYUqR07tVI7DsRjwuBx5ubxl3Wy43e+6nI9V5wYqtUb3k0nUlJZtOm3lZYNgotAyPi8ZlFYWotdoYYk9/2VHKOz0QIZwjXRbzZ6irRJ/7brsDF+i7aN3ZheYqnVORUD2fnQUBNt9oXuGQOIeLgfHiQn4GU8FQCiEO3B9ucUPHjd03zSCr1SPzB2FO6zpSJcHNJXO4vyfGyPXNFfd39+x2W2iV64sLak28e/U3fPl3f4Fzk5Jim7iV5hq1qMLV2UGmtGJTriO3RE2ZVvLKEdOcdRuALYLWJmmL5bl6N/acz75NK98aEujWxY5mHQru/OaaEbO1tPOiXFtBoflZwXs8chDOILKVmPXVwgNpFKfCHV8V/6LF3ONd4be/+yXv3/1Wah/sRP4RrNPcSjD7/8EHU1vAo9y5dYNwzrwh9p6dFz8avMc3dZoQejEbrWJJ9Kyd4PBoQFx9HqoutnDDoJih6lSnIGmqF8Hs5GYIZn51tqmU4s5hfqvSEzADoQQG2XLwCI6yeL783Vd8+snnXFxutaHXimemlZ7WIpWM943j4UuGyy+gBJbpJEXS0PPk+RPqonUzEljSDA2tT86zubwkuECjqTXRNfrBEAbfcTpmdWM3Glf7Pc+fPmW7GXk4HsnLQsvNsltMCx/U1hacSrWzBebN1kVQSjV3axNDb9fBVVPorFI2pMJoVZknFUVeO6e+gWIL2NrctOKk1W6c6DtarQyh58nVDS8+/YRx2JxlekoB1ch2c3XNxf5Smfg0UlYCZ3CSkjonKWCthXmerLtA+TGtWYuaDwQ8Q9A1WF8/iL85TEfSlHj57AWfPH+Op/Hh7gNff/stD/cHCF5tVUmqif3Fls1mw4e7O8VvdB0uenbbLdtB0kilbjaW00yqSm7tTHIavBI9a5EvgCZYLVdNAVKjFWIvddicFg6nI8Fr4Y0hMvievh9Y8sRxnmQecg7nPfPJTjE+sjTxS30fcaVZiGCjukZdTJJZjbPwillRrtLA0A36mdhTa5DlvKgEaDNuzvBDroXTfKJW8FYTm3MhN+itnEXTQrLOAsfqjQDs85aLu9VC39nn1kW6oHtHvga4u7+n1cLFxQUPt3e8fveaLojfUm3jfP65f8iXc7DdjipAonGxv8A56KIUXKXNeCc+5uH2a/7u1/8Nvk16XyHhbFHR/VRYoZ1aRKTLGFrNb7A2SEvB5Fchjy2dzXwVwavI3iMTl5zZjwvTusmsX8UgJXELeg2r+W39+R/PVuuifa7Xbka+0lhP85JMC77ARULrZPgqC7UmcXuhcTq95le/+AtKPtoG9/ga2zr92G9//B3r9KL8t+jXbK8MHwFuH8UVGixl3iIspsRyn4JTQ50KyPSjXXv8166imAyDgalqVqSJqMZqRVuDWiDb358zs6q54bMOEq2JcNcxRwGJ+v1CFXAytX319Zf865/+S968+8DKv0jRZQcZBO1Pp3eUfEfXD9ROfoi8JFxBKbE+nGP6mxdy0+yAJZlKJTdLmmiVh4cHWms8ffZEm/1pnqitEfGkXHj75jX3x4P+ueXvr/+jsC7hXt3Y0290qo6xJwRPqdowXFNonIptDEpZG7Scw/tIC4HqPBQ5z0tT1kkIkSXNItAtbtyxdmwv0vSnROgC+6sLxs2G1DKnycw33ukNx8jQd7ZpNXofNR5X4bwpJZxr+CBzTU7KL7m7v+NXv/41r96+MfJKP696z9CpCMc3yWu/+c2X/Mv/9r/nN7/9HeNuy2mauDs+8PVXX/Htt1/y9Xff4iqM2y0herq+YzfuoDl2+x1//MWPdfNa/ar38pKSdQocd1tCkdHL2x24mqlwKLiw00nFY3K61hjHnr4f2W63dLbI4pzSdY8PzGlimSeTfUZFjDtHrombmxueXD8hek/vAh2KsfBdwA9RMAEe30eTT0o+Nw49azvaunmBFGXRrZr9xocPtwrD845lmslFk2prjn4YdKqZ1N28HQaa18nH99FKe0TIYjEbAU8ulgZUNFeEQcVULatnJC8zX375W5k+O88pL5Tg+fDhjtPDbCZPmSqpv+9w/nu/nCPGgXHsubq+Ydhu5Jh1jtDJ90PXePvqV/z65/+MUj8Q+4L3xaA4ncpzSZxPVm0lNbUmtpLwqzrJy9jmvDv7mqTstTid5gUZlbVKdF3418V8/Tlr6unjoizeYV1/BdpgaarrCV6yWb82WpBto2+2SJa2Rt1IOks1KA55HVYIrTTHZnfBh7df89Xv/gZPpJkSx+yD8j21dt441qqqddPQBKb34JsR5jZBrXbAdQJqYAeJdJb9Kr1WiQRrzJJzuqeqs95rrKCo5d/bKB2C53wTzFho1uq55mi1M+xKg+aUPrDkRGqZ3ETMr/5ao5wARxcDm9Dxk5/8Q374o58QYiAtawSKakzPhVDV09ID08NXOGAYB6pZBpwXFPb+/TvevH6vV10r83zUvVZUgFRzYoiRVDU5rJEzFXh4OODv3t/x3evXCvHrRfq22gR5OAWUTbYAq8tAxiuNjFXkcFCch3eeOS3c3t0CjjEOrPWWKgoSTleK7N5N8il5Kkqh9x0Pp4m37z9w9/AgOKFYwmTwVjRUOS2z/rxxPhkEvMV+9AQXWIoSEFvjnHU0dD2hD6aEksFKqQRNdZi9TCjb3Y6L7cY6npUgOh8O3N7fM88zLnqWmuk2A5998im73Q5nUk3vPU+un3B1cSU1VlpoVZ3hlcZpOpJqprd4cix+ozZrqHKOlBOtWpSFt+KRebbo78cFTOCCokWqPV6lqq6x7yNd19HHjv1G1Z4pZW4/3PLd19/y9sM7lmmmC4K2apXqyXtP6BTRfZ4kMQgoKM48lyxna/Bn78XQdXQhcrHdPqqtanuU4vnGfr/j6ZMbab9rphQVpbRc2W83QON4nEizgvoayiQaB3k8bm5u2PQDacn2YDWbtEzhpT5VliVxf3ggZ3EQ79694+H+HhccT29udM96+KMffJ+r6ytqzlpk66Ms+A/9klTUM/YjXd8JbhoCtc4UKmO34fbt3/Hzn/9nzOkVQxS4487wTxEmDpZg8NGnmys1azmuzmATi8SR416nxI8TXNNa7OTD772+latYQ/HWTCs+WkilGLAVi8fYjWbqNng8yTuH3WvYEdr8Fu7R7NbMBV9qppTFWgQN18cxbrZ88813PNzd4uz3+fpxxhK0j6n48+RSzrxia83EFo9d9nKAN+Mj6jnvyRnL7ew9rwHppckfQS3nTc43bWxrRer6Sor9Odh1/WicU/T4CpM1iuNcSsZH3E1akm0i64T3CHeGEPT5Bs/FxZ5+UFKD0mk7PNE2wHr+PAMwffgaX+/xWX0avouqY3CNkvWe5lnr5rMnT9VClxdyUV5U805FZIAzA/Dh/mBwdgwmYazE2PPJ55/SucCSE8O4wbmiKOKmInAshwSELRZU4hG6SEozNWX6OLDf7sg5MacFTJ0R/Ih3TnlBOTP0AJGH0wFX1IIFlWEcGbYbMNNbBdtMRLpEby1OKeFiIBdNDsEigYNzDJvt2dSzlhQ93N8zbrQJdFFyzOW4MA4947ghl8LNk2tubq5JKanb2nuGoefhdKBNmj66rmfoOn74/R9YzovC/TbjQGsDlz/am+t2Jna9gsMaNIPk+tDhOid+Z30CmqSRqdRzX6//6GEfhoE1PTWlxLIs7Pd75rRYkJ/ynnwXeVhb7kpRAUnTaaaUzDCOxC6wGbe4qHrTCpxOUpHFrqPm5ayXF4giw1CzyWGZJrA/K63gWrRRXJpyb1zMh9sPxD4yjhuiKdM2mw1LWqw+ceQ4HRk76dNfv33D3/z8b/jjL77g2XbDNE0W1Nhz7iFzympa5iwTYq/qW2diiyVUegIXuwsIkObFugq+IKXE6TBxc3OtA0IzWNMJipxSph8KblXX2Ofy9/ET3qkjfTpNuNbYbbZM85EBz2H6jp//6/+Uurxh14kTciZ7hIJvHk8je2Harq4+BL3jQFUsRWvWuwIyNpuSqWkDd61YQoImLW/qsVot/n0VX7AGWhYeAw1X9B9wj0SxeitWSYMW1doaXprYM+zakGx25XMa+qybyU1Lk9lPgase53pantkMI69ff6tp3zah5rVRVNaRhrPZT8Op04bJCixV+XT8Y55UddX+PpgIwe4dL3K5ukaogs0qQQdjv84JVuW6Lvyu0ao/b67rd0mRdgbn9P3OYMAof0hnOUYNxWuou5rzZqUOFV3B9Vo6F2yz1baU5kw/al10HlyVOs3ZulY8uNCR8x1leUVqkRgHXKdY8tev39ANG/VIRPVLUCplSeL1nJMVIYsPaVU9NH1QEsO4G4nPnzyVKc57SmhqObLdcfU1dLGna45mZo/B93inD32IvULqLIlCKpvCMi/Ms4WqOZEmIn8cvlS8Nbv50pht0RlK5mKzp1LZ73csOZ+dzEqWbBQL5QrOnzNYSsmEjcPRCULpO9swJAsMMTCnZG5E4eveJLXQ6LtBN0kp+vchmAwNanTEoePp9TXns02tpmkW9NK8dnZxo42SqlWadsTgoMnT0Pc9+508BktKdD6eb7beK1BsKYkh9oILTJ673ljembEHqZmcg84URjUE8GoE3HaDDGfTTO2UBtmqsm28GQZxTlJmq5/1wJQWttbNu8r0prTIlFcbrSjvyI8jrRRCHzXmYwuEc+QKKSdO84nY9wxxUBKmd9Qkp6z3nqEfTRGi+y3nwv3tPaANMefMdrtlWRIpa8o9Ho/MKXG5u6Ab1CFNU+KliFqT28Yo70CFVAqb/ZauH0X6BiNB7QQ3l8LpNHG5V5LtvCS6cWPKFP8HEdhadD2XF5cMseP+/gO//cWv6OKBX/7iP6dMrywpt4Ev4OSmp6xGtWqfq51+m+4hhcLpLpafzQhnh3B+I7qjCyQeuQa9nnxe2B5NaO7x9zhNsmfRbMMIWN3DrqmzYl0a3TodGKSEf/x9IlXtd9g9qo0NnaBbpaQKLVFrpDQpCl0pvPr6d+fv04ylhALXHi+8q5wVVs5pgtJkpINMsGiY9bNaPSfnO9NL8NGo5Ib1069O8wr0rFWjAW/rjSL3hbTp965LulRDouRrqzZNwDmevKFdZOVTgQ6FSvrQnRs9WzPYr0Fr4SwGWSfaXAqvXr/j5vopMSZcFwlOycner02SiPPNiWV5z+7yh6Sqz7w66GKHq4VlmgkXW6aHkzazqMSAaZlIcwKEHimBQfzkMAxM00RsYlrIAaaHA3f3dzx//oztsOWUrMLTFqt5XpjzwuV2J/y3qLgmmTmui5HovZTItbCLW2LsWJaZ06Kso1ZtM8FxbCI6CZ5nV1fqavWZzul0FlGLVjNs1QHd2Ouhcg6XC32IHBbFWbve0UpjsmkjxoAzEjOOHaf7B07HB+Yp0A8Dfd+x26nB6Xg6KlUTjfXR3rM2vEx1SljMFqeRShHU4jwuOkrR7661Ms2TEVEeX3rlyFgyZymCR7I5xpeiEX6InZrgTBJ6PB3pfafoC7sxSy1k26CaBX+dlUumnHDO0XcdtTVSUXSFy4pm8DHQDzKlFSpd18nxHDzd/opSCqd5pjXFLHscqwa4FCPsqjJgvIG4tWgTnpaZoRtErNdGjB3bYcD5jsPDHSHopru7v2O/2+P7QF4Wtdh1kZwXnj9/zqeffmJ9CBrrD6cj7VjouoHdZsNgUBrOMwxW/WgLxcO7t8xz4vvf+xzA2t5UEuRRkGW04L9mfMAYrRqTRptnam3sdzvp3f/AL30OgsX66Hnz5ju++ubnHD/8DOobxijHb42F1uTuF++o56RWO7nWZoevjxzVVtZTG5aPZKX2Z7I5nIGhxir0eDyxrhEdH+92K/JRDK4Rvr7+ZzkvhutJObh1inO4KgWjgfqsJs7zAmr/jtpIdbGfp8MaxRzcxdGHgTfvXnM8nnDBFtYqXtJlcR11HXDcOsd4k26velK9a0f8SM6r9xKcOBuap1UvgYBzOG9dYs5O5AT9fhf4iDbXb2gG89mmZCQl1ApB6qaP4d/z9T1fP2XYea9GxuCk9PJenMcjRKZIeI+6PVJVOGkXRn72V9/xwx90PH32Ix3e7ZDQqKiH/JH3SPlIN0ZOd5Pu51bZ7jbk2khzYlPU3hmc4+7unngZ8Gaq2262DH3PUjJxECpTa5EgYz5J2lmXbJk+HYHAssxoz8Y6hEUKX2129gBm7ZY25hOU95SaMoOqA4JTM9tXX7PMs/BurwjinBOjj+w3GzZ9x2meqHaKDz5SctGNXDTQHY5H5tOMRx6IvCScg3HcsO16dsOI956+6xhiR2pZTWEelirC+9PPPmW3v+DrV9/x/vVbLrYXeu+u0lmz2Ot3b3j77h3DqOpSbXCTIjH6qM5r53SiDjIerWPuUhcajWiO8y52CMYXftlwzMvCcZ4tF6spXbI2ka8fpWqO/UA/aKE+nI4mDe4YO3Eb8zyd4YRlXoy0D6SiUA3vHEPs6TolQAIc5yNLK5Yu6um8OIZSKjkv5FbIy8Iyzfzql7/m1es3bDdb1Ye2evZtdF0nG39T0m2z09OKfXd9pPOBh4cHPty9Vbx4lgLOhci8zLb5PJoBFQ2w+i6KyqMMgqOqVSuVzPZiZ6fVR1WQR8TlZtzimwp01HAXrB5W4I1zsBRVuk7TkePxHhxMpxPzNPPV11/y9u1rS8htf88UsZ7OITiR6j6IqPUcCHxJa98Re5NO2klVB2stsq5Gw5RFkHonOXctMzknal0sfwvBSusivEY52JcCGR8d0+vXuRfCreqgdn5fHy9t50WtfrxInoX9NJeNu1sjO+xjavl8ml9RAr0eSHnWfVPFk5Ui93AyL0oqhbevvqIy6cwfLVUYKGGdAtZX5k0UYZLWJvx/3bz68exlRk0XkdYCFmmN803wm4uE9njCd7Wd130oeBp4eX5q1XrmKZyTEkzU4E02v45SzaDk9bMomHLTYl6UFiv7m3OSNmsYUy3r6nw3PELoifPEfmC/f8IvfvkN96eFNE3oiVtJ+fVwoMnn/u6Omo5kZE/oUdwGpdrBqjGOWidTSjw8POCiY7/fkVvh4XRkOp5UmOS9uoScw885M+UFHwPX19c8ubnRKa8JPvFjT/Vwf3/Uybm3+j+vTKRWskx0VZ9JzgmCx/vAsihC/Prqipubm/N5x4G1wilTaTtu6HzUZtMaS8ksNfNwOvLm3TvmZebVm9f8za9/Qc4L/TAyjiMxCjsehoGu789NVCF4i7UWj+Ga47TM9JuR62dP+JMvfsyn3/uM0EcFytXCZhx1cjWCWouXegMudztNI86fVSUi4TXup6oioiH24BwBlaX7zogx9elRqExWptJo8m84b8UkUhEp0z4oqqQLBB/Yb3fnmHaCGrU6Ly5nnmZ8F8g5s2SL6bDo5BW+wGG9EerTFjIkY9S68HR9j/eOzW7L0A9cXOx5+uyJHhg0/YSgNrxGY1oWmR+dIpN184EzhcdSFgiO7bjhcrdnv98ToqfvI2/evKG1xnazFfmclnMJSkqZJS1421gvdluePLmRo/n+gYe7ey1c2YrqVyVLbVxc7Pnkk5eCEp0IzXEcmU8nKZvQiF1a5ac//Wv+9V/9a3JSP8bd7XvmNNOFyDQdmC1C+f/31yNmjpf3wnvPb3/75/yX//Q/5HD7d/RRURaCTwx+cYEmMTy1KdwuNzloS6kUE2Ws5Gxr1SSqGPxRz/j1WqjTVoOoroxh/M7MbdWWzsdF5fHLYJQVllr/un3ks8CZBPSR6F5/n2/ipM6LVpOcuZSFnBdKWjR5lsdColYV7V1S4u70YAS+Ve0ak7D+rmYLzfp++Ojkvl4DaGpT9GsFgK7tmkd1nrG0kp9fq65H4BFEaudyoHPSrHM0iyApduJfyfLzcGYQMx7qeWuzWKNV7mq8jp4Pqc/EPTR7nd72HBHGhSpZry988acvITTevTmSi57FVqRMOl8LsUCkhwdOx/fytK0HiYatszrYK1bFc3VzzThs6LwCRvOSGLqey8tLcRQOGQsBP44bcMKzu+1IN/R4HwhDh7PCGuccxVWmllnyTAuObtwQei2kQ9fhu8iyLLx585acdHOWkvAx8PTJE/rQSXHkLJTLCLr15sY1Yh+1AdVCnrVQbKya8urqiqdPnhuOrZ1brkjTK9cM3jBe1yTR7Qe22x37/Y6WK9M007nI1dUlu82WPC+M40AMyi5xVRCEM++E+IeG7yKhjyT7M+fl6o5dYNOPBCdCpnno+44P9/d8+buvzPAFm9ijwqfAfrcj2iQlM5W8AdFrEe6GnjUriSp1VAj6Pc3D/Ydb3rx9B8Fxsd3T9VIwhRBopeg5CqadMfWUVGnhnKobvIqVvDFk66EouEA0o97TZ0/pQtRp3klBczwcNT3kbPBcUT2inUyOk4qVikW2U/M5GrxWxSEHFxiGUe73Wjmdjtze33F8UI5MjEHTWvTUpFN0F9dKSvkNOhfpzJzomk0SXpp5HzvWqHPX3JmgXifSGEXAX15fEGJknhZCcMzTwnbcMu42KNTtDAr9vV/mjebVt7/gv/ov/kPu3v+M6LItdloCnC+6R1vRRtoy6zTinHKAcpqln19x7XWaqRlatW5nBy7gzssgH20Q6+xvjxTWr9zWDeCR+1ifG0li7b5uHy08DYNoxAdYLy2VcuYLJBN1tCK/TGmCJFNeyEmwZc3W7lYdLaGSpSIBitYwr8Mchic7j6+PUI0zaGuFxdx5ZlgnI5H7CqNcN0PT+rlCI4uIr9nu89XPpedcyq01Wt0+j4YZ3+w6ulUZpQ1Evoe6pnfo7+0ZkrdjVXPG9YUaz/soB3atPUpx7QNafSC+OSyglicvdzjv+Kt/9bcssyc4+U0kDiici7lapvmZ0+kt+MZ0mjgtOqj5rmPcqeJg7V0ZYmSz3VCd4PEQvB28A0PszbgpKDmWmoQ7+Xh23q7tb61Wpjmx3W55+uQpp+kkLsJKfDZBLHpuCsDqezHipSQCI32viHDamlvfCE2kaTWoggY1qcd1GBQ3Xqx3tjNFT+cDvTXegeCROc10LRLCcMYhIyIac63cvn7Nd7Xw5PopT26ecHl5ac7KxuHhwDSf2I1brsbRFFviQT57+ZJ5loww18rxeGCzGZVvU6G4ZmUtumlC8IRm8QG5Qic/xb3BUB6F3JUmh3P0jvfv3wrCM8KvoWBE54DsCPHRWeqcVC+hj9Q5M+X0WCcbPYol1sN0Ohy5Pzxwsdur6NzG6VIK+MZm2JxzgJRnv56csD+vZBrv37233uadZU/pIQ0WERC7jqvLS4JNbYfTrEXeO8a+N+NflOdlURTIZitieNMPfPrpJyxLUsXi0LPcT8TNhs1mY4cHqWOwE41rjS5EPnn+gmh67uBkiErTpOiQreK/p2li6EeGToVHKwQi+KYn50oIkR9/8QUpZYIXpBU7ncBbLgIMykJrHash6t/8elxsRRh889Vf8p/8x/873nz3U7ZRCT/NC4tvbvVEPG47azyIr0ZcZsFiOSdVfraie+px2RaR7KI5b53NB+vUYHr8x61Dz8V5E5Ge/zGyo5xhKIkn1nTRav+2anH/H0BY8Vz7+wj2Nds4Kjk70jTTWmBVT5Vqks8cqMWRl8I8N2KPIKTOUU3TL4VTUDGZmcbaR4qjcCakHxdvqbeqndBty/Zym9sCxDoFCGpLlCYPUKVQq/wZ3iI5is0CuRWCZZzpM2t8LAirzgnKsirjNe1Wk5bBg+eJCNbwveD4CM7ydsSohiXq0LE6uodYePnimp//9BXffXNgt3+G9w23TpoALli+1QzLA6OvuJsb0nKkpqxpYtufE33PPSLe8d03r6itsBm3bDZbutgxrwfYos6S2I8bvaAixhz0UKbTxCkt1FLoY6DreqIx/K0Wpmlic9GfR1Vqo+96Xrx8oRuabFknZjoxvX3Dkiydh1TxweH6Tqdg1+i84idKaaj0ZlCkRtXJWQfsyjhsFYZnRJvypAqhKaOk1sbthztFaN9c4Zrn7v0dwTtubm54dvPE4kJgzsK3u3Hgk08/ZVoW5uNRpP1mhNbEgxRBNsMwnBU4KvPQgpVrhuy4vLzkYn+BDzAtmoicQ9EVMTCOavDzzRP6jrIsTNOJ3Xan4K36GLveXCU3R52VhfX0+kabUxegyBWusMHEV999R1om9l/8mC4EDg8Hsoe8TAybkXG3V7FJQVJD4U4aRZtqQpdlYU4L3/vsM4t7F9yR54nOdxxOJ/b73fnE25zndDpxOJ34/NNPGYaBJQtm9DaCex+EqbtHiCR44boKb4xnNVWtleAEZZaqovdUBRUdjyfm08Tz589pQc7yN2/fcHVzTVd6nE00MnxVFUBFg8nGwXpCNEnM84LDMdWFy3FguxnxXcR1nuPdgUNc2G53/+bWwO9vGI1aFn73m/+O//Q//t9z+/av2Zm02pHVasa60D1GL5zff4WlLbRaZFzNggOLQTmP8A+sRjs9b4H60eJjAMlHi6da0j6eg1bfnHjXlc9YJwuT1rp25m4d4cxdNCBo7BcEzSPFqy/jLpojt0nx+M7MeFRaVRlpBZbcmLPMj330xBDxzX/0WhqrIaJRbJNffRr8HscCldh5PV+t0VqiuR6cElmrrQePEfOWoWYTXqWJG/tIo9DMhd0MalsPSDaUnwnuNVTTrZs3nhY+Zsn0Vn4v28pCE/Hho43AwhWL+TlaffQ91YqPnp/82R/x7atb/tWf/5ztLvDZ59c41wOZWmQIji5Q3ImH02ueuUbfefpup3UxZeOHKn0IMkCi5/+zl5/oIN53SmeuheU0qa8neOpS8M4Mca1WWl5DyBzTNDM4z/XVJV2nOOvgPF0UNt+ZGxrD8ADzJIAzk02z3QqLxShV4XaO1VshO3zNheM0aXddYYmgsvk17wTn6PoocsuHc0+t3er6QHLjOJ+E/S8TpRTGcWS33TNPJ24/vOfh7oFWqmKtYwfB0/tOvggjgPsYuX2443Q6cbm/YCkZgtJsnZ1Igkk3XZMDu9ZHt3FzqiWcpokh9udMIp0wTLKG53B8wNVC7AKX11f44FVIlDUm5pTJczY+wKvkqVYR2bOyVlKeeTgdoDVefvaSH37xBeN+qyiGWvGl0o8bQvBMy8ycFnLLZyPeOuXFpoTKkhI319cEa98rRfWauZUzx7AuTt55qIWL3Y6XL1/IpVkrVNhudwzjKBjq4V5R7I4z9ONRR0iaZ5Ux0YRd2+J3SjOpZpXj4Ohj5M27d9wdHwhRWfz3x4Ni1Uu1KJmse7OTBLobde3HqC6OVhtULZSKxFcHxXpW70Nknk6kmkwt9kgOf7SMnBffkmd+8fP/kv/4//y/5f7dLxmDI3hBOWtQoP+9f6EYGH8+3QoGqVmRIikrVsQJ4/s93H39EjTSzoRxOyfHfQTDWMpqNcL4fJI1qMHZ7z/vMeavaAU+8juf8XyPFk3F0Nd/wwFtT2BdzZMO5RF4WhN5q8geSFWHmeMs1VlnRsDqtAeu/iCRufr/vU3pAUmzeeSdtcytPMU6GTVJ1zEy+HwFV5PeqiYrBRkAvV1Xzv+55mH5j7gJTSxKCnD+o4Qrp1RVhU1isJH9bKpt6o2cV+OcNQca1K7cr0orhVIUzZ9TopRkOVmwvwz8O//O92n5yF/8xV+Zv8LRml3n6ikIOkvLgVIe7HCuBAG8J8+JUm36carGLVjKRZRJuTalaow7FbpFLx9SbD6wtKLcpJJsu7RvGEZiWNNX67mprrVmUkoVD1EbyzzT7XZSDwcPJeObJ3bKWsJ5si84JG1bjWql6SJthw00VKHpzfELZGuMS7WwGQZqeVygq6XRtlLlBXCNUD2bYcMPvv8DSs48efKU6OD64oJtL9nrOGyMaBOBFGLFV/j622949slLogtc7i544EG/I9nGNvRs9iJbT8us5EyDglz1dAb71KTFKjnUc+3UFZtzZk6TPmDT8Qsey3Su45iTFkXfUxb5PSpSYfgQzieVTdio7zrJme1qoVTHfrMjt6rd3yteuwG+l8M5Zy34D8cTtSpDaR2nmwdfKk+ePFMvdCm2OXZEHDvrkAhRZp9YNTbHEJmmBLYYOZzh/o5cK9thpPa9MqlalSACqMHOv2F9wBzZVX22zpHnhe0wKnsGR04LT2+uuXpyQxcjuWQ225Gr8Uqf/9oNWTOHwz2XV5dgZqVSEpGOLnhO00npuF1P6NRJ7Y3ET5YbRoWb645W7XlY4yKMvKwOSjrx13/x/+Sf/if/B1L6RkVOzRZxg2Jyy/hmTurmDIPPKnWymJmaqn6nxa6vjt11EdSp2J2lj5DthOrACb6gmnqLVTFjTXXWhWLCWYp7hEBqyx9xGuvmsM4Nes5FE6x8RLVF76OwP1fPLuja1o2i4lynU/06zTRPXZr1y2RKgaFbNxrLS2q2UZxhVv1hdQ3fdP3FT2CThqOtpkFfaeqhtB+ic7LMHOXMp6yek+YFlzUfV9yQM3qIFTvaGWr90mamZ9mvReLYZuYev/Ws/KIRcsEPAdWuVqje0psrBUcwxGDltNS+KGFDrVVV0QjSffb8gn/n3/sxQ4w4rzorWjMpr65aqR3NzeTlgcAVxXO+Z7NtACABTc2ZUrUhxnE8J9Y2KtEmHYfDuUKUY9Z2PadIiM4FQQpOhFRuld4udM4V3wUpm6icHh4oJWs00e1CaJ7D4chhXvjk+QsFv9EUh+ElZzzNE+OgF9cNgzJovIwvpqngcDzw6s1rttuRT569ZHWMOi/YK4RIXWZ1YHeR0BzbzUhrMo4NnchrT2McRi73O6lbXKMUyU5jCOQs/fDLT15qiomRmyfXZJsG1qiDdCpcbHeSq9p7SqVwOfTUjrM5L9hmQeqYjic24wAxsCwLx+OBy4sLigMfHMuy8OrNdzx99gLvRB611sgtCd93wUrtZecPQacZ6dALMXhK0WRwmheRT33k9HCgi4oh9sDhdGQ6Hnn54iUX2/15ynHeU6ynIm/0EOQs4cF+uxO5PE+qIQ1Rm17TQ5OrZXTVTDbb/5p2O8+LSkz6jrYkpfgGmebk7teEmKZZU5IZ4rCUzYvdXgt0qeQizmq/2xG8Y0mK8+iGXuqq6Ei1EXyTIXFRz7SzRbfWhlOwMfcfbmnAZ59+xrzM+JzAZIGv3r4npYUf/uiPqUVmphATjzIWLcRl+sCf/7f/V/4///n/iVbuFW3iJKhw1YLb3EoMr4uyAtQmOzmWknX4qMqmKm3ta14XrRUQUFZTKe0MXan6V3CVuIp1gXfmgHdnNVVrBcwUaMgNpWZzMK9sh5EX66aByFMAF8IjjGWr4NmlUD96vbWR2iyCuOmaNduBqk0tuWTmuUjE0Q96TQE5moPpfOo6qeiUvzpmqvER6ytphox1H3EmZ+hbn5J4kjNMZa9TGh1ya/S2+LtWoK2Njqa3MgpDU8X6QvPjLrAu0FU/U8iJ3nVr4kOKa7iczeynnxuczWgGNcnHoU2rlqrJxiYgyaUztTn6biRwVGICkZKT+BCbDhrgvVKXl/menO4h9gzDALURhg5K1ZrhPMV5SsvK1Jv1DK6Jkc7WsWzIR8SBd4EwRFMiNJNA6UZT1zLgHW9fv+Gr777m08+/z7Oba5GfwVNK4+nFFad5FtU2jDTv+earr+hiZLfZU2s+55LgnEht55hzpneOGjxp1ilpu9nhg2O3VUTu6ThBFOq6piiGEJjSTOwCfacLUbwcyMuycDqduLq8oNv01AzLcsIxMG5Gg3sqbHtahthF+i7CxZ5WGvcPDyxpUUdFSnz19df867/6S26ePuV/8U/+icxxQ6cCn1JIWTryPnhykWJhXiZqhePxQAiei2HDOKrrONcq6MhB10Wurq5kHvSBSqCUxDzNnO6PPH/5Eo/ju3ev+fD+A59/9imhC8qO6jrWyJNUCl3whOhxBtlV13BBm1OMge1eHgNJUXtKKUSnZrm7w526MILgv9Iax/lIWbKKdZogDm95TK3Kte6jPBPpcMQP7iOpZVNzHh1/+3e/IcbIH//oR3TO0fqOlFQolZdE6CLbQeUnc1voi8WPTJOguqE36EYenOYsirk0itemrGbEQhy02ZwOR8ZhOPMBLSd83wsCK1kQGzI3nSa5+A+HA1vLnjodH3ChEOIlMZhCqGXu777mX/yX/xF//a/+c7p2T4j1rCSzSgfI1jdwhnnWDctiwAuUXIUBZ2HFkUDxynHS87auFAje+Gj9c659xEF8BBvZAuTlvKOhlNKIQjQbdvJvH62C9u/OTXP2VTFj3roYtqbNBs1VxbT56z8pJNZa1daKTrpVJ+5SMrmiyuOkbp3gw1k9hVNR1eq1UO/H40YllGk1QD7KYD1A7AhufT165euFkifk98utVml1sI1DEmUbZez7vGu0tpYkqYmPlVdtDaotyrZ9aYIMnDOdnD9360jwYteyFmq1kNDzpqZ/JZS2KSUXTYZpKeL+sid2G969+8C33x24eXqBC47BeaXQ1ipe1Dl8SzwcPuDCDcOuIzjH0aTdrQGpkLuGM1VZcBb9D+q0cI7s5eMoKYteiEElF61BFzqduJpjmWe7YJ6+k1los98R3/UspxPu5vpMrI3DSMnCz7quo1qpzp/9g39INIVGprFMM66LGnFiZze4Ln4XO6iL6XMtqC46vve970kSuyRddCp5Sez3l3x4944Q4NnNE1xwdDGetb3b7ZbTcSalt6ztcZtxVPZQEZnchw6l3arzYYgDofOkeeLtm7f0m9E0xAu7zY6n109YlsRxOvH05oaK4jFSWnDA0neWFRTNiS5T4Zpw29HjcOSS+HB3z1e/+x0/+MEf8fLlC05LMmgCxm7Dh7fvuD8ceP7yBall0pLY7bbqetaRjtoUgR36nvv7Oy4vr/BVrXfv7+40Edgc/dlnL9ludmd9teIw3OP/mjprzfqhNi7GLX4fCDhSFqHlvbwGq0piLaEZdhuN9V4Q4TwvbLcb+lFqpu24oR9Hpmmm1cwyT4xdj+sHEWsx6zWVylyUy/TlN1/jveOLL76QcTFX5tOJcbTNzM6MKWVySnLZB09yjnmeyVWBj47KlGdiq4pG6fdSfAVHh1RmIQa+9/ln+BCYpiNdABi1OdRMLplXX/2Mf/af/R/57qt/yeiQvNitwoVqRGg6l0x9fAJ3DXIpzDXRUiKlSk2qlgSU6XQ2w3jWxNMmLAnv1Pq3/tCVL9FippoecV6Cl3TAdzi85UWte4L+zLYT22Z+PwZj3X70esQxNltI1yCPNSm3mehFZV5p3V5Q+quj1UBeJOhYFnETu602CEFIFgYprOnRUOoe01/9yqD5x/fgvXgp+Q00CYk7seW7NVroznutO28UgXMBWC1Kea2rF0N+r9LkR6m1EGMkhFX9Z8/N2TPyER6F+AmpDm0T81J4tbVQCmdlTjaEUY270J4dQ8+0TJqbmg5vrdq04xrPXz7h7j5yf39gdzFS3UAMjyS5J5LcTM0nrq83uF6qzD52dp85ZfL5Ad95XOspKQsCq4Ioa62QG9MyMU0TN0+fEEspUiPUBtFRlsS8KKp77AZccByPJz7c33N5fcFPfvxjwSFFXIEiDxwER/DS6+ecGIeBofcsJXO6P9KPPa0W0nRizoXL3Y7mHdOScW7Gh8i4MVK0idDzBIbYkZuXCzB6ysPM4XRiGEaeXd+Qa2aaF+H+Fx0djhqtgrNlbj88cLm/kE64i3K0Tgv0Pb4LpJoZxw3JiSwupZIruKjFkRj4ky9+zL/9D/8RtVbujgdKyXx4f8vlzRUepxBDIJ9mM8GtfgCdeD/cfeBit5dPwSla/f7+nl/+8teUUnny5AneTmwBx8PDA947Xj57Sq7qtf7s0xecZiWLtqITTEqFUHRKubm6ZrvZMKVEt92wr5mHhwM5ZTbjwGYYoRZOS2LoBlxzgsy8Z9Or+4DWxAEFJ0NOUtHNh/t7LnZ7HCK/N5tRXgJDkJuD+4cHhtgxxMDD4YEKlNxDgSfXN8btJPoYqNWBKbxSKaTpiN9spYaLeh2xC3zvs8/OCcRYLIi4BCmfasoUmxQ678klkZMeqnHc0nWKUzgeT1zur2hu5WYyuRb1a/eNzmsqrLGD0M6Bb9XgnWV6y89/+l/wX/2z/4jp7hvGwZ0POCuk0pypVRDvIMOc6f9Bv7dm8iJuqOUseNIjGaxdR00Pq5FL/72atHNlDkTur4SrDGRr46LHtPfVYi2cLUQGWapBpknibguz9qF6JjmFRdu/RfdsaFrIq3Tb5/WxUCktQWuU5Ggt6PltiuDIVc/TnBPT1IgRnYScelK8l1+n0SzuY0V01BXdzsY6SfI+HoC898Q145uKgi3QL3DFpr/HIEOnUcTI50eXdrMpeQ3Xo+kzWtKRcdjg3EYqp3UzCIrgWTkb52QqXIl1cUmPoYofcz/UTDbCfRUENIOoXJCoIWUdFGpdi5QKKU1cX++I0XP37lbRI5cBv6ZJOyEsvlVynoQkNEwhGGitKLbeq7itLIlpmnjz9i0vX7wg9lF1BU5KzY0VvrnWiK4qpG5pCZZJH4xDjSJOb8LFANGMSd7jalFb1UcXuo89yzwzpVnqkugpWSTI/vKCkhM1OKJzVLcQu47b+3upOwxjO+bEZjOCh+molNBV9+6DNUo1ja/H45HL6yvCIjI05yzoYmOkUq08uXpC/7yzMDz1T6t6FOWth0YsOom1AOTCsmT6PqoE3CSVhyKVRmrKMgk+8Pb2HdftWgm6ueg1dk4mLxesrU/kcVoWdUJ4z4cPH3DOcXF5wT/+n/xjLq8uqUVPRuc9x3niyy+/lKIsOOLxQHdxIax/Fi4dhoHjdCTGyHYz8O7DB6ITjBVjpI+R8clTrq9vyLkwDgNdCNze34tPALIZqHIpIt2jPCq3H24ppfDixUuVEJmzutrCsB23eBdwVMsW8nodwyioKUYuL68IIXA8HhVY2HVn6al3jnd3d4xdz7gZ2fiRZa1YLYUY1+YwXcdhGEjzQvKePka6XgqvMlsHMHKLl1rofK/Jykk51VpgHHs2mx0tOOb5xN39PS+ePsVVR8oL8+TZXu5sk4F2KqRxxp8GdpvEh+Ov+Vf//P/OT//q/4HnxGbTYai2JM+mUPH+3wzL0+dfmr6nlar2r1Qe2+WcN27AGYKv4DitleuSZFWdRkA6myDBup8fJUHasNH0qD9+hEbWqgqq1yLvNblIQt4Ehdg7+5i/cObxEF1S8ZYAfZbA1nz+7BQqGHBVRPdSM3V2tOqZF0gzbMZAINCtP9/UZnwMJdl7becNQ1HzKze0/pk8VOAMxrMBW74U14xDeAz/a8HgUJO9eudE9K8bolPrYyo6AMQwUDKUIBo3eOuyWCFBO9i1skJRj0geDYV72kEKo0cSjWi7XPXrXuvsk24MfWQp4viiEwGuCpBGC4F37++4P9zxxW7PcSn0faVYKGdxmYrndPog74v33N/dcXWxx8VAm5XoPZfMPM3s9nuWtPBX//qvuLl5xg9/8H2GfqC5dk6RTikTq6uELlKXia/fvOXp5TXvPrzn2dW1dmrvcV3Hy2cv5KwtmeI8RHSSK5mhH6CJX7h9955C5erqiutLKU8ELVXKvND3UbBPc2wHkcubcSTVTF4WHu4z42YgpcS4dge4RDksbHcbvbGSVKZDg+jpXORk46AKcjwPhyMXFxdsN6Me3aqu1xDlngahl7GXkqcHUmmE4NhsVI6e5pn3tx+00I6VEKPOK13g0xefsOqvt2Y4U3GQZ1lmciq0oeGLZ39xYc+xorJTrey6nqdPnihvCs/cEktJbIaBL/7kj4neM51OHJeJ97/7HTh4+fwFsZf7eNsp1iQlTRql5PNCLOerTspD3xO9Vw+GYb4hBnWPV5UV5dZIVVPD05sbLdbekQoMMTJeXnGaF2Vrdb1FfuiaOgeuiIz87u0bPnn2HGIkpcRsNa50krJ4L4JSS5mKnloUIbkuhIoXyYwmu42GmcZudU4r+qE1wTAxdnz9zTdMy8zTp8948+Y185Lou8g4jtzcXGuUz5mH48lCGVUtGUIkRihzZqmFrlS8C2z9nofpNe+/++/5+U//M969+RXb2IHvBS/Z3fOYlpypVRCJ/h/BGg3lFNVcxZ8kpRBQHxf+ihZr98ixnjcaj5M0uHEO4JNi57zu2eBROa/kWk7OcNO62dRV/+TWCA/D7Bv8m018HizNQH+u9TmwJh2vf36GvOBcJ6BfGwTRpUStHSl7jie9/th7OoM4m1NYjfedNhfjFVw1NdCZiFmJFE026xS3dlKv+L91ztGM4wDEkfAoa3UuEL2TFNmvZkGZ5yqauIIVEYFk+7UVfNGftTMf1IzTKLhVqGJXu9kmXlqw92lchqtyngddc2d/bxeTWhtdjIx9x7QUaoBQKrPda745psPCcoQPDye6YWDZZvnCnGAwaqCxiD904oBP80LLRcVhwVOWxLCV/+v68hJwXF9eakI3BRtZh/HD4UCsteEo6lgYR5ZlZn+x10Jq8bz3t/ekmrm6upK+2AsTXGVSPgTmnLjYXRC948PdHXd392z6kb7vNco1p1iOSWa05lZMXCNly3pAnf3sqxuRpf1mlAu2GX4ZPBe7K3Kt3N8fiFFGqaurK90ozhODU/phqSwps91s5U5u0KrC81YNd0pJBHLfkXIhuo5pWnAOPtzd8pvf/JbLqz3D8OLczZBzYU4ijlfCr5mt/nA6qS8irieLtcYQliXh/VqB6s5dEUvJ1JxxtsENXY8Lnutxw3A88PbNG7756hu248izZy84TifBC5ad5L0nuIHNMFhfR2FJC7f3d6R54WK7Y7fbE30hLct5DG45U6Pys07zzHbcEHl0cCvtMhC916bhYFomaJ1OwTwWsfigzZ8Q1NGA5+n1FalKtbGugK1ULveXwj69PztpfZPD3TV4uLvF39zQOW+hk5GUs/4zJRG9Q8/t+/fsLy/IWflLy+lEiJHvPX3OOA4KP1xmTvPEbrdnvxkVS9AETYDMm6lm3rx/z6bfcNEH3PE1X33zr3jz6hc03nIxaNHymJLETs/VFknJXiGXCuvJuwlqqTmz5AWSIxelzPJRBLdfDaofkcZr0U49cwa20LfHxNaPw/xWZdPHX+2j/7t+VVeVeKAfTvMf/301N4I7g//NFtW1n8GbePPjxbe1onKbFYIyx3wzbiu3xnTKLCfYbKAL4ELFR5sKTKno3DrHPG5YWkD9710bWH0L4m88SqmtGH/isWtaTaa6OuVk2KvrdbecNJyzJjp33oucdwSD2gKdNh4yj/0b9hM/bg9sBu+xmvYAb+vj2YuhEa2pQYrVD7KGu9IqqeqZHrqOaZlpsaNvj5/vzdOXfPf6VzzcLjx7vmZ8Ddq4DP6SfyyRyYzjCEBCSdQheG7rBHNmcY6LqyuuDQ7uwgoRFgiecdxonaJUCYNNax62I/v9Ba5W6qQuge1u5P50IufEh9s7LrY7thc7XGsMndrdnPVbb7c7QlCZjYsBbymnc1mAQrFav846s5ciBj/GQOyUtd6aHt7jdKJmuTcvLy+JIZJyYbvZsCwLxQcKmZIyh3rENdhtt3TjgK+eeZrYDaPKRmi46uh6i/0l4F2xm9+pPCl68tI43N0z7LbUXOg3A7vtXlHerbIsKvzp+55lWeQdMF1xa41vvvmO3W7L9eWV+hqcl8nMyFjn1MmwGQac85xOR07HI/srdSM753i4vwPnuLy+otuMvHj6nBfPXnB5fY0L9mAlmW5c1517IkDXcz6dmIquS6tVJ+9VEdQPdrpRl8KA5IDQCN7xcDhwOp24vrzWg+48uSlW/KzeCNLiNx9tdNck8OLZc1aDkO+k9MpLxo0KJFQFra5fCHoQ1q4QwbWSyV5cXdNyIQU5k+eUePXqFcPY8+z6KaEXrHScTvR9z/WTpzyLgRg7nojDVFgkTfdQLVqsDUdZSsK5qIehSiZ8c3ONL5l3b37J3Zu/5HD6DV0cGcKO4ArN6f5VzaYRrK0ZxAK0tZc9ID+Bgu1qzfjiOOZF6hE4R4U0I6u1BTWQeh7WvoPWzozrquZcw+LOtb6tnmGOx2W0nRelsxmsOZ2uDeapreKr12K/ThIecAHn6nlDEqmtja36urJQrJlKtWrCXDONSpZCUftlpCWYjhKddEM4K3uUuOrsNcpHtW5Bzek6rDHk5/eyEvqrOaMZtNQ8zjec61ldBx6oFl+zrrFSaT2qiUqrdL43L0YDU7utVbDydzVWVdfHIYGrXHldvlutEFYfioFMDdQTsfJIxlGdpxq9idUAX01NitOkpYQA8L2nNY9rnjnNfP3VOza7S7xT3a9b1WxBYS3USs4zqc68f/+BpzdP8DimNLOLOy42WwpN6iUfmJeFruv47rtXtNa4vrpmGDX9bzYb4qYfrZIz6EZyOk20Cr7r5IkII88GufDGrYxo02ni9ds3PL2+Zr+/pEZHzY1KttJ5mb0O80K/GTmdJjZDj4/SXS/LgvOevu9YzHMghl83zjwtuOYZR0laCZHUKl99/RXXN9c8vblhTonRWsTmaWYuCSbHdd8ZoaQ0VtBrI2Y8/TmHKeUkojl4SoPQO0qZ2O62+BjZP7nm86Y00WQPYwiezaiqymWRLyGVwvv372lNSaQAx+OR0AX64FnzYHzniCmYv6BRmjrAt9utTrQ58bAc+fXv/o7OR35o8tXffvUln3/+OdE53r//wNt376Q6INJK4TjNXOx3rD3S4ziy855lGAmxJ3hHqlKzdF2QFrsUhhBYSmHsBwKR02nidDqdYZRoBSSl6QDRuyjCDi89vBXa96FnhV1a5XwwyGRCHyTttc0qxgBE+RicFCApJY7HI4fTUVPk/lIn06JDRd8F/ujz70nS6zyuiuv67LPPaKUyTxO+deTamJokh11UvP1mM/JwOJBrZqSTiidX4gg1JVKZCL7h8sK7b3/B3e2v8PWOy3GjiG+STp8tyGRFMX17ZpVbevNs2fpLRX6iUhMlFUquOHv4tczaybKtMNBaLuXB23SCF7/A2kXtbfN5RGE+7nFYvxyaMhQSqJ3FE6jOnrF1oUJkOqzmOG8lV9VygNYsJ+z1riS9tVSuG0t10Kri1WvV55BFYqcF5mNjPjW6jVNIZZ2Vf+TDmbxdX/kKKa2Dw+MGocMVDZvK7bpHO3ysvd2Ns+LSh2DKIC9TJCJkS230vkGrRO9t8tMz0Vh322bXW5vf+nPld1kJ50cPjOeRGqr2mcT1Qyr1vMnoP0So6/PVxnH+DPQX0KTcWkM5BQtDbZmLvdbkr373nh/80TO6fsR3ntD1dE1k91IXHg4H9tfPef7kKSlnfvbzn7PdDPzoiy/oh47OOQ5FcTDjoOij43Ti7vaOZy+eUam8efWKi4sLYquVLj725OZl4cPdLZvthiEKNglendGrXiN6z9SaFCT9+FE6KszTQm9dCl99+w3TNPHFD3/E1cWFKRMq0zTx/vaOzWbDTbhiiIFSVR4O4DvPaTqo48EFutgRnefu7pbD/T3guLm4onPC9ErK8ksMvbmxVaRjeX5454idp5bCnBL9MMj16rx4BgehVhJQFym2Dqcj0+HEs+dPSSlzOGlSiTHSDQMPt3fc3d/x7NkL6ehz4TjP7Lcbdrs9y7LQ+UDXB0qxG7I2WvR05xuxMfQDMTideEshOMf15RVd1ymqpDV+/KMv2Ox3LCaX3G5HNsNAHzpyTvhO1w8nn0vsO4vyFpxTajPvg2SXu3Hk/uEeHyMXu50y/nOh73uiu3w0EIGgxax7I7pIagkCBCJLW+icV39IySrq8ZG+QUoLx6NSY7fbHeMwkpeJt/dvBTECF1WS1FUddn1xRXXqBXa1Kv6dImG9V35VCJEW5G6/v7vn9bu3PBwOPL254fmTJ1oYgk0/DrZjR0ueutyxzB+oNXNCJ/plmQgxUOotKX1HOr1l8MaduIhzTWfL30tHrUTnyA5aW53L/iN+QMq/XBeWeaHmel5QzapkC9+6/Bqc4s5bx3nh11ptKqoiLbymlxVMstPqeQIRpr5+eqv6DKcp2jnrZ7YXe053WtU/6+9eV2kHrikOP7iOVvPvbVC5QSuzvrVWWlWUSUXx8SkVDkfVYm63vcxfUfDl4+bwuIiu3Hp0TjETPH6dX9IZVZPQw/sIZMB/tEFYzSpa3H0QJyoPRiaFIPexd49eFtsg3EcbjcNbwdc6FWja1qLuWROz6jmzCdYEZ4eHZgjFSsSbms2tv2jdjJoZ7Jx501yleS/+0YEvDZoOZpvdwGefv+TXf/0tX/72G548+yOGUFi3Tuc9rnj6TtC8c57f/PpXvHvzhosvfqApzvwe6ppvuKhp4vriihcvntOFjlQSh2kipYXoTLZXUBCbozJNJ7WvpZmBwn6rYDiPp/eRUrT7aBHXAjKlif0w2klR1vebqyumzQ7n4HQ8MOeF68sr+r7n+dMndJ11054DAO2kUB37/YUgK2SDTynxcDxydXPDzfU1Pj4Gwq0uRfCE2HN3e08umefPnj7igA7VbYaAqxoY11O1b4VTUigdHvpxoLTCvHTnrJNtP5DmRHGwTIkYOrb7C1JKlOy4uLzkyuJCWq1K1o2eZUnE2FTFGqNurqabcogdqRYe7o4MfU/oBeN8/7PvUaicDke6YWA7bpimEw64vrzk5uoKUPNajJHYdKDztqC8+/CBZZnpQieXsqW3huDpY2CaZR7bDMNHp0rFrrg+CiC1WsXYoAVn4WSN0Pd0IZBrYr/fMc8L1DVCRGa3uemB887he23cqlxUCUrK6mr46puvcc7xky/+hDlYm58PLGWRy3jJhBB1GveCB0IAykJugW++/YbTMrEZBq6uLvBdoCXBnr7O3N2/ZXp4y+n4nrI8ME8PTKeTOiTKzMXFyNMXN0SfwGX6aFCesxOt5W0p7Keas9UmpnWxtngM7yTLzUtiyUfSnMllgdWU5aCaKuzjFc+Lfz9DRFqhsp3mHw+Xq7SysZLPTuMLa2R4MRmuAxdtET6LPM9uYjCdvoE3lYInqOKzVSOT19gHg8TcI5ovHD0L3sgLqUjuCupPLk2Cp5IDpylxmKEfYOiCuAiB9bZISt5abcFe845YobXWOMd3u2D3arVpq5hL2HGOqPAKgnTF+AyPamzPRjhB652PhKADgHNeB0a7TtVSZF2112ib6xoJIsJa6b1uVSdVR4sKRPRNzyF+nfoMKrKd21uwZa1FELHD7rcVXuy0gFticCkN74JqV2NP31c2Q89ksT6+OVLLSsuyylpv6rshDBzLxA//6I/48Y+/YDtuhPI0WMw4B035ad3A4hWXDzpUvnz2gn4ciGsy5brLxS7y/MULqYesR1XNZZXemthOp6MW2dCR60IcekpamHF0w8D9wz3D3LPb7wlh5nQ8SRobohaBrIKeGIJFJmOR2RDiCpoK81XGU6RVlQttt1tijIr16HphnV1HWhI0ePPmNcuS2I6jiJouWmiaFC3ROfABbzwIqHrUFT2t2+2eeZmY54XNuCXVQimZmhJ3D3fEvlM0RucZ6KitMueFQGAYlQB6mmeGfmBZZn79m98QfGDcDHzy4iXj2JFq43gUtLIbN3pALKKjD5HkMsEFmRRL1Um7YdEVGCGrbu/tZmv9HJUPD/eqI3We3WZLsJ6PmgUrKMxO2Oxm3FCd1BvROULfc5pO9F2nk3vWA5z9CgtpgfAOckq8/fCBnBMvnz0nlQy+w/vIskzkKbPb79iGvYIYaczzTAyBi91eMS443rx7y/t37/nTP/1TXJaEtzmoJlSIQRNuMTmnD3pvPsrAGDvH1nc8e/KEYRxxtZDqxPH9G27ffMvp+IFyvGOZPuBqZj594DidCF3j5tk1z57s6eNJ5zmvxVcJnxYAaJNtrWqO08UrlJpskW/6NzmRqSxTYskLpRSWMoscdmsGDo8TicEUsJ7m3Rn/12KxrlnrKVc8x6M01FnK6wrT2nSyZgOtmLjBGK1hDoJ6nngefzesU+3acLZ+tbNuNpxPqqubugKlqlLXGAb9eYWUHXOqPBz08ze7gRaUy+VaxbtVpbR2Vj9uhuqQKP9fsv7sx5YsS/PDfmvvbWZn8PEOcWPKGHKsLKq70Wo1oRZfBAh6k/TfSfoTBAigIFEPpACKhChoeBHZBAtks6u6snLOjIyIO7j7OWa2h6WHtbYdj6J3Z+XNe939nGO2bQ3f+r5vse2TwJKwBEWfjU5aaxAHQjDDSBUImszWJ7RLVwEXRXXv4oAYJ7QaEdyuq3c0rVNsm2NIuhVSmy6IsP2v7r2EXFKy3SpzozAtmUHOtubWZi2hZ36HBduFo3zpUsT4Wt04tbj0YL+z9QhrtlWyXUdiI5RIwzr5XFbefvcWgPuXd7YMrgXWkinLgiTbE38+zexeH7i/vbPP447e4zDZcwg2dA7RXmjJlZDMYGpZVqZpQFVIAaPF1Wr21MEq9DTuKGqrNWOM1DXz4cMD4zjw29//ge/fvuXLL7/ko1cvELHhyXw6E66MilhcAVxrYxp3foGUc6189/1faEX55OOPScPA9fXVtjO7NWWujd1uzzLPPD4+cnV9zbpkijZu7+4YhoH1PBOPB8Jk/PrcGpPZvzvTxB6y3W6HVrNpjkF4enhAh8iyLAzRbIc/vHvP9f0N0+hU19QgK1SlBfM6iRXyvG6zi/sXd8zzwou7O46HAxoCQ7FMPU4DaRgYFpstDCGQW2F5OnM8XiExMjieHwb35i+Zp/lsPkkxMU12qIydYPDgYb+3davB/JHmcqavSCwl+6C+oaUP1IwFNK8r427PmCJZHYuvRkWswYzYQgg8PJ34r//1v+Y8n/kP/tW/4u7uzjtRowEPw0j1Z7LUSq2++jRGWrbuQFX56MUrDvsDZc2U1nj68IHj1ZWraQsFJQUYUyJXw7xzNgdRQuDl3QtqVFIcWB4/8PT2z3z79jfUhyfWxwfK+khbzyzzA7SVU/6eF28+5uuvv2B3HGghI1JMQCT2lKu72ErH+9XsyiVE3/lQTKUr5l1FyaxNaWVlWVZytSQhagFCPJGYSZ85iHb6pDGILGBaoFQ3ffSLpzwbINvfC1YQ9O5ZPHFB9C5AvHK9YPcAEtQZNJ6ONtprNeNIT2Di32LzkM44Kp4g2nZN2vb8+J7xqhSEmgOlwMPjSllgtw/shkQKikh1XzN//jy5BQl9MSodhJHtw/mMojXEZ109tUVxzYdVTm6AaYXfNtuWvg7UflcUg6OsMGjU5pRZog25xWdGzuz07GDdjYg32eL7azwDi7te92Qi0afXBZGE7biwFLhRd9Wup9ix2L4sWermqtAV2nad7Jxe315zfZUISX0hGHQFvY/lUVUeHh4pzbz5ogTKvPA0z8ynk8WPEHn58jW7w8G2Yvo9qK1QxT+4QCq+vnS/M+/8mOvWLaxr5vD6NRXl4emBw+GK68OBZZlppRG9vsq5kILtnGgoH7/+iH/3D3/Hd99+4OOPPuLGubhVbXVh9xwqa6a1xjfffktei1WE08T+cGBKiVoaMZllR87ZrBha5f3DB5bFXEF38QAtcnNzwzhNfP755wzjYGso14XbqyNdTp82JTae+QObykgCBau4Y0zc3d3ycD7z9PDI4bAn15XPPv0YSYNVi6jNGWIgx8A0jgwxseSVNCT3qIL7mzvkBvbXV/a6tTHnlSBm0SsixDEyxEhI0f2zLGBQG1mtio9etUgIJAnEw5H9ONr59SfbLEKyDyyFNa8+kygMXn0HsWpTqlXqTRtzziDC9fHImAya63zw7h4ZNGwsjRiFn//VLzg9PTLudrbCsthgNueFwcV75/PZBH4hAAlqNWFma5xPJ47HIzc3N2Z18vTIU145+gd6PC88Przj5uaWu7tbQDnnjLTGOCRCUPZXO87rifnxW9796Te8/+a3MD9QS6XmCmSeHr8nrx84XF3x4x//kjefvSENStXFnzyH6LWawZE4pKE9wFZHRmxfiQUQSyaiSm6NvK7kbIujWu3D6D5kxSiPHevekoO9zmaNoaAantW7/cFXf3TDZVAtgGaeL0Kw39gwBzt9FuCf4fgunLMEUB2osnW3W9cgEROqmX+R/Ybi8BYGUClosWG1BXDbnqE5Uorw9JQ5nxoS4PqwIw1ClGJupVIRGbz38IG8P4Z2602YSAs+tvdNlP5nu2o255DYldJmNYIz/FSscNOmxL6K0xOEOcYq2goaIuKvgkNqkT4fsQSuChrMKVi2QO1IlCeoJu5YgT+sdGAwEMTZfoAEq07Vk3UnDOAQ2/PVwtCH4G7WiBESQoL7+z2ffn5v++iXlfGw8ybTeq+iQisrSOb2+obdYceQIqXYDnuJiZAMqQlRKDlDU4Zp9FlTcz2RNwK/+vWv+fLLLxAJ5GWxQCqBOo5cH67ozpz7aU/OC2UZWD24H3YJWiUvM3G3N6xOAsNxz6effs4Xn0WmwxFaQdXocXFIDNNokIKbxF0dDoSbxDgYLVQ9GO73O/KaeXh8IMaIJNvDPA0D+3Fid9ijpTL6/mUwymSnYQ6DJa7etIeQPAb0R9GfHgGRSBKlRpC1ME57xla5f/UCLRUpzgOPgcBgw7WmpDRyjIP/Ntuit7ueaLnyxz//Ga2FF68+Qkv1G+1GilHoezaGaIkniVHO4mAeUMs88937t5R15eM3H3M4mJXFuD9wOOygKadl2eYtWowdXWsXeglJAkzGlQ4VXwtaCJ3SqsLV8WgNtPvVSGcRqRIjSBMf5p8ZgjEpfvTp5zw8PbqhorFLJBcez2fkfOL6+oaUIqKBvNhhJiUO04SGQBjGrYZW10wcdpPNLjQwxsh5WXn6w++ZBrMI0JKNJFFWxt1AXh54+svvePvd7zh//x2sK+W8Ms8nCySc2R3hq5/9nFcf3cEYED0DwQLGNuy8QAq4C+m2T6Ea0KzN3I0bxWzNqzkm53UhrwulNFQH+kY5CzO2oay5pUT0eVWvOC20X/5kEIsPoJ9BIGaa5wZ4W6kd2fAXP3u21tR/LHZ4yxwFbObgr9h8t3JoCMk/vTp8ZjsILOboD3fyCGgVaBhbsInn0wBro7bAkitPs80mbo6BNDVCcBMXh1mEjMiAOPEEhdIfxT738QQqPeh6p6DRWU6tMQymq+ioBgTv1ALEhtZCIF2gG7dqrTV7oRocR7UEGQkXerInn36rOrmgu+r2NRb93wxWSiBGaG5EUooXiMrps922pO/iNmJNeHYSnpMHLiCk/VMgJeXqOvHFjz/i13/3Kx6eTty8vKJvw7MGVKkKb958zOl0pubC6q9wOBwIKRLjHcX3+KzzzM3NDafHJ3I1cS5DMGX2eSYhwjCMNsQy4BlE2Y87Ss62O1mUthYeHh4Y7xPJ6Z/VK8v9/kgcjA+cxDjqt9c3hBBdS2DeIYIwzzOlVm6vrreW6u7FPVTlPC/GCa72uq/uX/DN99+aAvzVS1P8TonD1dGdHDue6SxzhVoqU9xZXdUCuaykmHh6OCER7raht98ANdV0wLjspRQ3Gyz0hSJVK7urAyUXTucnvv/+LTf3d7YD2o6/DcWxqqPlwlorjw8P7I8H23WAHQYJYm6wC7ab2g/i6fGRh8cHPv/0M0YnAJRx4P76htm3rdkAVIyNpgYZjcNggEAutAGGOmyHV0RdhOb1YJDLWsY4QbOBMLXweDpBDAzj6PO+wmmeOe5ciIgyxsBSClMQZp+JkBLT4CwJEdb5TC5Gqx0OBzMYG0dKa9ZZxOhKfnv6Ht6/Z9rvuNrvqbWQmwsnY+SzN584j185n94xpAnNmSXPPD4+8Pjhjzz9+bc8ff+O9XymnGcLiqExXUVefvyG169vGA/JB6ArcRBi7PRFd+70oNhdP2nVsXGMhw+gtiO5VaGU7CaGM3mtbiUfaG31CtGqcEEvMIT9smcJwjqHDv10Rsym5O1xwoN/r20vgrPID1eqWReh7hjbfP+EOJDTSqGpaRhMTW0UUWLxX3BhGIlrJUCJNJoYQCkN59dbsyK+YCc3pbaBvAqncyYvyjDA/mrHMChCQaRtUBAkmiuuG4rtorYOMzjsF1ov7fzjhQ6fOccyFFfO969IuEhMNpGmhWu7lr7sgMaIqtGCTafiw2TYPNSCGFMr8D/86jCUQfQ+cwreE1rrgUS8Q4MQ+gIrh8I89tj99RZKt3y0zYpauySKS0JqSAp8/PKaXfqaq6ujzVufJzM1gaGIINHWwi7LYq7VITLqYHC0mKnn9+/e8/7hgfvbO8ZhMDIQBlMFEdLXX3zhLYhtT7OdrzA4yyfnzLfffsc33/7ZlsDc3zMMibfv3vH4cOLTj99sSr0+QCq5gMA4RJYlsy4zwzggEfJiBn3HaW9/h2xLfUS6IEm3taD3N3e0G1PkdmFQzRViI8fGbtwhwGmeGaMJ+yqrZfTdSK0TuzFxOj3x+PTIMIxcXV3Zg6S+HxcbyJeymoTezbxKK2gpLGVl1IRE8zi6vb3ZNs01FVu/KcLpfKaUwvH6mn2LfPHll4w+HAohoCGQ15mUbDE5tbEsmTgM7Hbm2FhUqcvZJPohMt3dcaPK+4cHBg+4Wqqxr5YVWiONA03cb380umgQIau5OY4tcTgcKPNMTImb6xueTo8svn9i1cZ5Xbi+vqLRyIt54O/3E1NKmwNmqcV2MHuA3497m5UI0AolF+7uX7DmlWmcHMe2c56CcHN1ZfW1499vP7zj7//27/j8ix/x2SefGfOs6wmKucW2ekZz5f27P1PKSssrpcwspwee3n3L04d36Hlhnh8p+cTxas8nP/qcjz95zf4q0iQTBhvCJzFx5wUewNW2ug2VK926sF7mBm6e121B5vNMa6ZeL81ghdaw8bL65gPFgrlEEr45zIeSfQgtcgkUqoEgzZOLoKwWOLVjzfZlJ7N3EdETT7PPhLu5diUwSt/FrlQnEHmwEnznOxv2j7odu7ZNjdwkbL5IZiqZoYmJYqsaM6gk1po4PVXyIsRROR4SuyEgwYfxEp09iXfuwbH6/rrYkpxm57iksC3lajS3DdceRSEFugJaCTasF4ih+mA/IHEiOTRocyzzm0tBqD5T6DBb14JYQHeHYQku7JNL8qEn825vgqfuy0KnzjrbpIcVCPYeu6Or/aSbCqp/k3c7Nlfy2UcTaJVIomCD7ygwHSP3suN4vQeH70Sqa2qEJRfmZaa0xjgmphQpsrLkwocPH7i7vTV/tMF2Bz09GV09DCYfKKqkGNgddyS3NLdqKiVatgMRp8mtHWz48frFa17ev4Ag5HnleH1FW0yMYbbNxURUYO6qy8qiC6fzieU8c7+/47g7MA0TN9e2qhP1QKzG395Nxr5ptREwXUNKiWEY+Mt33/L0dOKrL74wtgBCK42Zhf00kUKgqA3VT/OJ47QjpZF1zVwd4HhjTrDLeeF0euLu7o7dbk9tkNeZeV4ZBluXavbstm8AEfMRKtmgEW1MoyXPECODD7EfTo+cn54IKXHEdhwk78w6xtgH7q1kW5aDDZTEh3KvXr0iRTMHrNpY54VdjLQY2E+TezI1JAaePjyx209E3/yniCmnVRmDrWUdNHKulXenJ6veo89QWiXFgb88fc8f//xHPv74U+5ubrYg2FrlkAznXKtvQiuVYbejewhINIX8PJ8dAhxZfS5xe2P2LX3bVZeUWtXl0E4KhBSIk+3W7RWZKeNnnh6+pS4n5uUd6Mp6ntG6ksuZOi98982fyOeCaOF0+iPHfeDLn/2ITz77hGE3kEKDYF5cwY0lJUQb2HvnELCg1AOw2ceY3Ulr2dAXOpzQyOvCmhutrazuw9S675IHfwsmzR/+tAUg8QRwqQgtKDUXW1nC6DXmJSFsugo1nyD1AI8noWqFN7EPv6Vj++pzBJuv9MmGwTX2fDnaaAGyl7FO9ezRuA9SmwitVbRmWrU9yU0rtQbWGljmxjJn0rAj6cLNYY9EH6L2qrYXxb2r0MvuBav0TbRq+akP+H0OtF0Te3tRhBQTEuqW6XoQVnUG0ejwXMQU2ZgFh7ovlXpXpQ7TiQbni3XFt3WBF5HdRhh/VvXbawZ/c+bnlnx+ZG4RDfNpatp1Gb2T0Ge/BZulbE3DxerHdvtgq56bcavGYWCVBRBDI5oldVGcYm4kiHVeiG7YV1B248h+TGgIlGWhrIW7mxuO+wMPDw+c5hOvX77i7MLW69tbEtVWkTYErStzXs0UrzVSjNzd3fHRq9dEx9BPjyfenh4Yx4nj9ZFSqu1OwAYsXWz3sC6UcuLF/R3hpWXm3FxyHsUpr6bNKCWT19U8lZpXms1siDW7gC1Gbq6utoMiQYgaWc6L8e9jsgWGrZFiIo07FGyukTNRbH3lH/70R/7h17/mF7/4BT/9+S8AZS6Ntham45UzNcwtMShMcWDOZztctbAs5mo6xGADoOArWHNmN+2Q0LURyRblxADeSZQ1k9JAnldqjEwu/ltKQVR59+EDL5yVxbry9sMHXtwHhpb4/t1bcsmMw8TVfs+4n7agYDYZgDbO64rEQFTDvw+HA4+PD/z2d7/jzUev0dlENNP+wJ//9Ge++ebPxnDY7RCFwzRSovk5hRBYztmgPd95/fD4gd204zAMtFw5z2emGBnG0WYQYuJGW/conJ5sp/i42xk8J5b8Y4gMYeTNR28YB+Xp4RtSEk6Pb3l6ek9bZwuC60rNK09Pj6xrRtfK6fED3377Zw77yO3twJc//ZrXb+62HeTSg7Wz2CLRr5GJ9PrD3AfBl10D9nCKVoKqM8CaUVrXE+taaJIsqdVsnlXYAqbovHi1YQCthwKxdxTU/IEsGXYKpT34NiYTh6rE5gI2md38vyyIDDxf9AM/AJyM8dRprGoqiOYdgTisYbi7O8Vug/PLkNtimDGaLDk55VbsOhgZUZDme5JzYJ0Dp4eFNB4IQRnTzjeGLvbZDd2yzyEOE/ey3CK1XTvas3tgMKxoePb58C6rMoTBB+EuBAweJ591aOLJMW7rSj3Qu++TbmG/twgds6n0vRR9XvFc3wQdjrPXi6Ezo/zH6R1FnzMNFx1Y8P+j5lRrL2lkAdR811Bx0Z79HlvyZOmq+wHGEJmcCmu2JvZvVS703zQE4mLMx3meSdFIOxITWoyRmirktTCMievbG8qaWZaV79695Y+//wP//r/8l6SizVYG1sKyLIxjYhwn3wAWCME4t60lW0c5JHZpZBgnc1zdmfmalgohkO88HY4AAQAASURBVLUSBXbDxKnN3lYGNK82qB7s4HfxXjcC27mT6trsANviG2uPW2scr68NimrN4A7HAMf9ZPueWyXXamZ0MRnm7fBXzoWnpzPDlPjLt99yfX3jQjjD1Xf7PeO0I4RIyys1L8yPJ07LmWncMdBY5rOtjYzGJLc5DpS1UFvju/fvub26MtHcmri5umIcBx5PZ3LO3N3csD8eaaWwqG50Uo2BXRg3SKeBG2wZdyzngoymoQi+qnStlevdnu++/x5K5e7lC9u1sWZ2u2nDFdeijMPAmzcf8+e//IVlWZnzzG6YeDrPfPrpJ/x7v/wl0zj6PutAbZWcM8Nodu/TNFGrhbxWMof9gRTcaE+Em6Ntyaq1oJjtvL3Hwnk++c+qQXVDZF0Wx80D6/yBpGcmGXh6/wdaOVHagtbMvMzk00wpwrKunPIT5/fvmdeVQ6z85Gef8OqjK+5fjOzGZP5MtUG0+5JScAzcAvHmjhsgaLXK2Isa66ar213r9p/SDF4ri+0XyFWROm8dlymurSptzfZPmGLW4m4TvHLUC93TA7hiYigJ3ZW/r7Xs6cVgoUqHQwBM0Cfa5wgXLBqtHnjiNsA06lZnu/XAFz3QWmBhC1LPBqz9Bb06hcZaM7Vk+91NqBpY5sK8KPPZ5o43V3e8P/2J6XggsNCt4XGRbg/Q0rtRf4+C00B754YtY5LoC4X8PXXZoISApBFiNP1EU0+2fdbUnGQUCG5EKMEMNWMI5sbg9FnVZ5LDhk+FXETpicMugXefktwWvidVXwwUrE8RHOoKHUqUS+ILl2VS4veviSDNvreJxYVetfTZknqiKH4mjJNi4ljzYvTeQ3FbJbvGMQ2MByWIUnNmKYVWGvv9jqVW2mKeTZJsl4Rtizzz8u4Fn3/8Gfd3L1jmmbSfps3LaBztgSsuvkpiC0f++M03XB0PfPz6DZqVq+OVZ39/UJpRG0spJlIbJhjhJiWzRtbBlu0MNrQ8n2dEII7W3iWHqUpuBr+MgVZMtfxweuDh+0de3r2wZRnYwR3EfjYilFoZUjLLXNc+qEAcBupqe6zvXt5S18LPf/YL7l7espv21FYJwQbOrRTClNCYyK70HSRCrUQ3R1vWmdPjEy/uXjDEyJINKnv/8I7f/vo3yJdf8OL2zlekBnJtfPjwjt/99ne8ev2aVy8MThp3EzvfcdG0H4hASgNlXdlNk8N4pp/YT9fmcTUvPii2VZ/7cUL21m6uuZKXlaurIxoseYVge4UbcHt3y24aOeQ97z98IGng5cuXHHdmXY7brlQ1QaXzFwAxlkZTpv3BLFnWlTyvTNPOHummrncxd8+CVZ1DmkixOm02uIiuUOZCPj8S5USuD8yPGW0ry/pEXs0O5f37tzw+PSIhkeKIxJXjceGrH13x5vUt08759o7nJ995EiRuPjqXGtQ6G1sj2jwguQmcWJBptbp2wP5ctNJyIS/ZOhhVtFVKLcbVl7gFFHtAO24/PJc5bHDEha3i+0s8EPec0PlVW3XrAarbRnQmk6hX9+6e2v/ueU/RyIj2RTsJlWfWIeoWHpbSnr1Xh1XEo2XXI2ijNqUumVyMZaXAuhTKCuvcOM/Kx28+53R+x+3VnuCDag3NNRBtA2r6PKtvkNyCs5qmWzFr7S3P+eeXcLmG2hrTOGxFo6qaHbtfgih495MhBqvYfWgcggX5TlTtX+r0/OcCCyMeqNnbBzNJBGibPbj6bhW5PC8BarAlWPQuzt9Pc8PAoPZebPZjHlHVoBMbmgfrrGrtZoQNEbMWseG8JZk0jP5WTefR5ySCsRFDs1lubZVxHAg6MD+eUYX9NFKTGrTvsXy/m6yDEXvf97d3lFZIxXOiamNdVj48vOP66oZpZ9BPCpG7u1uG4F7jzVaLRh96NSDGxLosvP/wQBoCU0oXVkeprPVsgrvDARFhmAa02qKgIIE4mB3F6gN0nVc+PD7y5vVrnt4/sRtHG7plGyIZn9c8dHKzbkGC2F5jX2pvF1ZRCrVVUrpmN06M086qxFK83ROKCYA36mgcI6mZClcCnOcT33//PcO049adWHMt0CppTLy4u+Nf/ot/YTi7swvWXHh8eGCeFw6HI6enJ/affUoxUxu0BuJux/npxIcPD7x4+YJaC2/fv+PV/Qt+//vf8c2fv+FHP/6a128+4ni8ZllWrq6uSXHg/HhiHEYU2yZ3Op1I48i8mv1FLZVhSL5PW9iNZlqYc0YExt2Om9tb5vNsNttrtt0hADFS15WQgjvGKrkYNfLhPDOlya5zisZqakKKkRArmgtJlWm/ByLffvcXjseJMRUoJ85P73h6ek+thVqeKHlBGzy9f+Dh6R3z+cz59MESIjMv7m749PMrXtxeM4zGtxcqra0MrgoldN9+w6qrV4iIeWFZNRacFOFOmTTz9S/Z+Oihodlc+lortFyZzzNrydSaDd+n49YWwOoGhQxARjQ9q8ZlgwaaFro6t/YZgci2wzk0oZI9kHtVL6OrcG2wnnTY5h1NbJGROGzWgpnQOd7lyWu1hCSXYGwfwOCa/ufmdtv04SmeNNQMOwLQajaIjUBrgSUreV1Zy8DpVLm7/5QQKiIzw5hsS5t0x1bTHjSvpO0Cq8MuXk8HZW3VFNP09aBOgxXBZSYE31cdgWgyYLfGCM44EhfrBbe96B/H9ObJM5I0Ou7T88E2h+msNKRDXoa0xDhYEg2WCUS6geh28TA2VSBKMnZt6w63yew6sI7biW5my6GerCX6rhJxZlq/v/Z5pJmw1PaPWIK7DNItUaAJZaFpJYWRlAIjibdv31OGRApmrKmtoTESB4u9rVQjB4TAsDNBM61SSrG5sM0EjA0UMPXqEE0rYWq9xtX+QMNsn4varCKoUKibfT4KV4c9N8cjj+dHIoFht7eZxTRCsQckNqMFllKtBRxcsdkqa8lILRymPeM4cppnPv74I6IY7p+rtdmjDra+sjVTeodIq9YV2Ga2wYdOSoo7dtNIKZUwDb6y04JazbaTV6IJcTrem1KgRjt4TcwuYskrN3e31mo2Zc2rMZC0IUNiP5gWoaoxuKyjbhADX3/1lXPdbZCa1UzqInB+euLbP/2Jl/d3TPs9H08TIQSurm54Os+8uL2z2co4cXNzy/XxyMPTCYC76QYVePf9d/zmd7/jRz/6wjbEiTLuze4jOjsLoNXK3c0NpWTq6vCBQyx7T579EKpTay3P2+B2Gmwrm0SjXzZRlLyJXrXazoxxjH4tK7uxMcgD6+mBxw/vWM8nK0jymfN85v27tyzLyuPDI7lkQntidwWff/YRH338muMukpKSQnPIaCCJgpiyu1dSKr2zsJbdiZ+u/A6XAaBXSq0aTl+yD2P9+jQXx9VS7OFptneE5msge62/dYD4n5MHV/pju403pcNJ0nF52eYhFl+6ZK4nt2h4u4ohRs2oorHz4N0epQc32WChPj/oPUZ/B5e5g2wUWvv34NdHgBaqbZVzfUUEcl1pZSU7xXapmXmOzGvk8bEy7g+8eHHDt9/+iqurkSBWhFSHfSQkTI3RK2rsOhpd0d6FW9drNWW6Uh1XDx7iPen2QJ4c5WhGbyU4BCXFr0E16Fh8A6Wfh4TNCIMroKUniKZe0fd+yuFVae7masIyDZHuJRUirrOA7l9nrgK2ItcHKHZt5eLzFELaCgS7/34u+nwMddLDpo7YTP+6hsZmKgrexSR3sbbO1d4fQVjLSqswjW7jsWb+8u2fuL9/yfX9rWu38OVLdsatGIiEKLRqaEbKy8qf//xndvs99zc33F3tqT5waVUZNTJ3Zk+E8/nsxnw+KYpq9jXryvH62h1hhbAz+4y1ZO5uXqBnCzQlNB4eH5iXhevDkZAig9tUDzEyjiNxTNSHyvl85mq3p2hBVBiTWVLXWm2vggaiKqVkszsHwC2em5lj6eADqmbmcCpqjpUhEGLfkVDoDI+SV8PbFa6uD6wO27x6/coqUuzQ7aaJ7NveRJVWjPFDLQzTDtXG9e0t07p3zN8GUsM4UfTMu/fvoTZe3N1xfTyirdJWw+5LLlzd3XD38t4gpWa23V3FPA19JSvMpxN/+e47U1OrcjjuOZ1molqFv5TMw+Mj11fXhpsuCw+Pj8zzwuuP3zDnTMuZ/W5nFDtvW1stPDzYrvNhiEzTnloK5+XMbrcnSgRfNlNQq5pqBm3EENF6oixn8vlbHt6+pZUHlmVmnVfOpxNPeeH8OLM8PRBT5fZKuHtxxcuXb7g77onDRBSzU69tMa8dFcZgrBNJmOo/DhvshGPS1nK3bYBp+5Et8Da1jkG1kGuhlWweOLWa1qRW1jwTNDgturi54UDXURh+fLHUb+JMqQ7niwvYVDdmD1jAAit4wBS11t3Yz/TAbYHOo4p2eni4UIrRTfQl2/+xv3eCLc+hlJ4ounlez2Y2C/BuQZz2SvA5YKVqpq1WnGmdKFqpc2BdhIdZaEH46Rdf8d13v+FwCKTUFd0O7mjYlPumvQieEB2X6dCWmr1EwRlcAkGjCeD9Eyjq0GBjin4v62y/twUfXJteOoQ+a+jwka8tFYeiJCBaEUmgjeYD/oj9m9ME7DP4HAOCr17WLXn1S6x0exsYaE5eMFJA6IUEdk5kQ7Q2CSObsWG/T8/ORz9PBqH3zsrWqHY9Ta3OpgsNKV0MCue8Mj8tIPDh8ZH721u+/OprSslosRlebdX86MQStbjrQ8mQy8L+eLCZxKsXL2wLkirz6USapm3mUMWrjWgVym43ma4hKiSjZDVR4jjYqj9xFtNS+PD4BMBNrix5JUpgPwyk2ztU1SEbJU2JemqMw0QcE3leubm6ppXG+Wx22VfHg9MpFQ3YTCIqa60kMVuM4IuMmpoVhDr/X7Xx4uULW2Nrp8huQLVkEIdkokFVSDsOO2ENM+/enVBsVqNVmdeVFK0yyKXyOC/sp5ExJasbO9tCFK2NKOahH1Ni75bk2pS6Nn73298RQ+TqeGScBta1OMapyBDROdNEWGph1Ma6zuYsu2G60KhMh4mPPnpNSoH91RX67PnrPk131zespTCEkaUUPvnoI1OgFls4U1R98VDYbMLn2YSDa165PlxxmPZoDJznE9TK8XhN3y4XFabRkkSuJ5bTmafHd6xPj5yX75kXE7vN55l5WVEt7HcH7m8a15+95tWLG6a9wceD8VJpuqASSAIpTF6BRcZkQTYJXgB4q68XP6I+EO1mffbIWFegzYVFtZDzyrqeadWq15qLKacbtI1zfgngPbhv/HzpPYPb4nkA0OdK6F4hioNceqFEd1jqeZw3eMHer320iEg1ooff+I6NPx+sGrvmQprV7Rc2Wt+GqBuq4lDTZSZhgetC2W1YgiitUrKQtVHOcJ4b8yKsc+Svf/ELqpwIKbM/RhoFqcEH673+985GLOBu9rY8x3jsuQwBqgqhCk0KQtqCvHjlra0R3FbCICYXGor4Ui9/POQSdPuL6EYKgIuWBLpPVKvF4p5DQSoX3pclUnFrDV+NIB4zFLPlBkzm4wmiw4HN/JrMXuTy+bdbFKA1Ox9puz1WaHQRLZ6E6PeXPqS36xK8m29N7MzEwQW3preKyR0ThhEVX03QGh8+fGBeFqZxYEwjr169Zr878OHpgfcPD7b+mCBcX19zO9yznE68ff+OqzszvKu1EhrEwXYilGq0yDhEFldjm9MhxHFkXhezgaiNTOb2+prRl9Qf93tnM6njeZE6zzSUfThwdbgixcjTwxOn+czN8ZqQhPPT2dxe3UrYhpMGedR1garMbfVB7khKowdrgMoUE9PofvJaCGqwwBiUpdrimCFYZVJboWplHGxOMYxmLAfw4fTANE2s2dxMh5R49eKOdTXBey0FbYGUzEr96emJdV25urmhqlqljw1Yd8PEz3/yU0ottuu5NBu8BqGuhZyfzFAO5TzPrOMAIowOHr97+5Z3D+/44vOvGKaRq6sj+/2O/X4011wRNApDGjjPhfk8I2pEALCB+sPDA8MwcHU4OpwjZqKnlvivj0d247SZ7a2tbruo4zigFMYAi/pATwqlvOXD29/z29/8PYM0Sj5TlncsekaCcHd7w+t45HDYcTzsGXeJ2lajKFa3nmAEKdCye+0MxGSWEva/ldhcKY96QHZ6qw0E6JYW3Qq5VksO67pQazamUp5tf7qz52ot1LXTIr0LpYA8GzLKpcoT7YphH4I6DHmhp0qPxRYIvOrr0MTzlZwiNrjtIcD0JdEBH3zRjcFUEhxCY6tnrfgRS0A9cGwzhgCi1bqdnsSwZ6MTE9qWeCzbtFppqz0LpVSaRsoiPC2Z9Sw8PVW+/NGPub274de/+luubszmpGpEY9uug4VBc2rt0EoICu4DBm542NTIWVzU5nZhvKrboD27r7uDxZLmBnkSAlobLVaSpK1zuXxYB5fCZbhvFNzLXMegyWZRPrpdiKTt+5XOmAI3k6FVYzuiBlXa6mT1Arv2itTOIn1vCBaLWtgSfMRtPjzFt3aBKYt3XR2qs67WKMnWnfnra0Ewl+iyNmIcGaY9t8NAK5Xj8YqHhwfm8xPjtNtYi2kYOL/9noe3C6d5pmrjx1/8hCEO7IaBOA4kaXa4Wi6Ic/dPHx5M1TeOSDO3xT6jSyHw7uGBP/7hj8iU+MnXXxPd5nqIiTDAfjiwzitLWQlJPHAHtCm5VYaQCBEbvGqlrManXvPK8XgkJcMbRyZiTBxvjkYHVdtUth8nclnJ85kwTMQopCBMUdH1wSqAWAilUWRgqYE6G2n+fH6itcLd3a0bz8GjVp97KEM0Zs9aGrViiak+cRhAtFF9l8FuPPrI3wZkQ7QKVwENYm6urXCXEjVnkiRiMspvLaY9GMJAGo2u27cen2fj418drxmGkRQ71m2HI8ZImkaO5ciQIi0vUJsNxdZsjqtBkGLrYzWbBuNwOJDEVpZKEK6PV2Y8JkIthdNskN3hcEWuhevDYdOA1AB1yaQgXB2v2U+JWmw3eEIo+sTbb3/Hd9/+Lae3f+Lt93/k5f0LpiFy9+KKw3jPOI0cp50NJMuKhoJq8Ic6QzS7jJTM3yYNBvFsFFBv1XsSUFHqmp1cYJVdjKbSzcWdW7Fuap1XgyPVKsNasg/GQWsjl0KnGWnzgCJig0BXH6uID6+jB2QrOIIoVe1sB+3F/aXqswTjaLGqV3oW/HqV2Fr1rXuupBbrRpTu7rr9KksOW37RHj89GLYtmFj2uHhSiXsRbZ3GVtAa9LIV9f3vVWjZbPRzFZa1Mc9wnuH2/jU//vEX/P2v/jWH64EhGv05iBUYhEBQS5riy70ua5nM36g3xHiiktC6NMOQHHXHVXXFNQFtgRCFfXIIz2O+eqWeQtyKK+3sLbEdGylATB0IY/u8iFNTnZZM6Dve7f7gq3+RiLbnwRwuW+bYzsVm0+GdY+z+WZ78babJ1lV0p6f+lrTfR1MAYqr0wA822D07V32YrqJENbbpMBy4Pt7aYEh122Ex7twAVIRxMPLQx68/4uZ4xR+/+TPy/j2H3YFcFqZpJL14SZRAKqpuUx1Za+Z0ng0Tv742WbiYK+Diy1rSNNlym9Cccgp1nVnnhevra7uZMRD3tuNBGpSyUoN5iMRgQxmtxhfuZ8e2PkWESK6FdV0Yh4FpN9BapiLUBuOQOC2P/On3v2aZH4gjHMaB+5sdspi/fa0LefmWWs+IFJzBbs+mUxX/QZuxmaK4/N0UisO097M8UIwPSmAgpIlaBWJiGBKs16CJZWnsdjfs9rfMudLWjErgOA3cXB9tqVoXSqkNnGyIqhtNDQnk+cS8zEyHI1e3A4TAQKRWmLNRMFuBndhWvrvrW2ptrKsd1C6+SdGCfkqJXDNhCFxNx21Gs5v2VoVHk+3PZTY19mTOqsNgrpu52LwlxoCulZgCaGFiRddHtD1R2kJsmfXpL7z/y98y6szVRwM/+fEvzewMHyC27AF0tWIhWBCK0YL7MO5obUVir7ZBfC1t6v5GGFRngUx9s1g0QZvae0YNn61loYWAamFZ7D+t9K7B7Mtbrra/AnsoWxPvHMJmZWHuou6142Kq6sErSbSVtzi80LoSoD/oTnVV9fdvhA383wKyVf6tzyGam8oZoI5WRaVsgTRgVeOlG2mXgPGDyP8MxHJ4S11MZ8wqz4EXUJ3+Lmun/lYoLVFWmFfl6VQ5n4XddOCf/JO/5nd//BvS9MQ0JIOifXKaQiJ6BSzRupXo799ou564PIk2v5/q7ZF0Zhp9i8Xlvw37LzYHpZIkmumf2+sELMYY9t+TawWpIAMhRE9kDQmmJ9CuH1HQeNFtCc6U8+sjoXmCCM+uv4NqYv2YMUDNtLRZ9e37s83BQfscwa1/cEFx38khmF1IQ0gOJQYCdZs/eeKVy61uIrhm1Ii0pSFxYhr3ZDUpwLpms1f65hvefPKx2fiUvoZW2e8PfP2jL1jfrKRh4HQ+2QbBYWCZF1JEaCH68okTh3Hi6uULg4O0OVbXfP+tHaXjzTWfy2c8LAtUG+49zWf2hz27dAARd2NtzOvMoJGSjZp52Cc7uNoYUuDd0xMxBMZxxxiEpdhKyb3sSNEraDLrklnWhXSMPL79A6F+w3FcGcJMLAvnt8oSQB17jqogmRDt1ARnGWiDtbq9sNrAPSbb5dwazMsTp/MJRvOnEYmsa0VUSHFktx85HA48rYWmgmjivDsyHg5IGAlxT66BaXeg1QkNiVwaOTfef3jk1cefcDiYpYkMEWnGxkgpcAxHYpyoayHExmN+RDOElBjG0fb5NmXJC6hVyblW40Mf9nbIanN7buH0/oGYBuIYL5EjOmwXIFZhfnhgmkamIaFRaflMCjBE07+sT2do1n3VthJ1oeoTpT7R6hlpmUEKn7ycUJ1Iw4iGgNRsojm1TkuiVduSzIo9BBuRSwhIVEKNWwIwJZqJObcWovX5gz2QNIcafGAdsPdbykopDcRsM9bVPJZaNbvrmmfj+9sTZg6tGNRlfVGPXz2cBePtqzhEoAS16i5gAerZd/o7VMzxs88mov1rY/u75gNK6xAj2vc7+P6OoGwBymOTBwiDLy77n+VZbrhUm5e5h+ec4LYaYjODFgTpgrHeRTShZBMO1gzLrORVOT8p8xxQTfz8n/wT3n74I7W+5ebqQMUoxIREbMWGtmCOw2piv8vA3Iq1oJfVRiJ90VEXneGVeKBbl/T5RimZcbCdJTEJqrY0qMPLjeZut3JJkIgL4FxkJnGr4s236ZJIY+gGiTbnaLVtO1m0mX7DYCt7vRAdBlOHQqMQXeAYPXF38VyHP3sC6kkSBS0FYrQk0sw/i9A7KIPo+0ziOXwJJtbcrFuaFTtLqZdEGyNlfeL9h/e8fvPGmZ+WpHM2N9wg0FJCmi0zS+PI6fGJabI11GlZV2vZo92Y3WFPGGx3cm/PRYRxGjnIftNK7I57D4yBw/HIbrdjOZ2JMRJTIkZhSAE0EmIgqXmt1LKiWhliRFSZH99z3B3YHfas+UyKkSlCGqG1D5TyaF1MfaC1Jx6+XUj6xN1+ATKpjmgz7rxgSloSruOIxMRW5TUM5kzyTKSSsIGbC1iWsvJweseYD6S0pwHraeb8eCYOAgTefPyG08MHntYzQ5wYhkDcJYZhR/DuahwmSIkhWAIiRnPS/eY3yN1LalN2+UBrgVoh7Q6EsEfLBCT2ceD9t98QJfLqzRuqrkQa87JQFtNe7NOAtmQ7rnNBtNG5GVIy98fjxgiJg4Au5PURamY3DdAeifV72rmS0oQWpdQMrCxPTzQ9U8qMOE4trWH20o2xm6OGvOGvIkKrC0FlU8CGIaDVzMcIbIN/87gRNpGbn/6m6u6cF1s7BbqnvvZgIgqtblbTuazUtVCKsV5KwVwE1mJ7S4CmK7X0AJ641NuDQd+OBXcowSpIs4G3vcNm4leJ9mB5uEf988gzr51nD3L/jY3u0dQt2cUShLYNQjIvpq4RYAuQPZA1zeAUz54E+pdqhyOevx9h83Fysz5b2du2jsk6KSXXSCmNdW2sC5yXxnIWyiLUEvjrv/4ryvKO09Nvub/dI8GEploHqigtGDVdtGz30yrkS0KrCk0ykLDVrmFLIIAHYjP+s/ffu51G05VhGlwjoRg1tZ8BmxlFmlfeAaSap5V37TF2eu8luXa6sKq6dYtpF6z5EesStoRbfI5gthzmtSUmAHYvOpvAC0raPF8vBAi/hxgEpo6gSBxsNuPJ3E6+Letq4vDZdj8vicKYUrbwLKhTjFHG8chSlEZmDIMtX1tXT3Y2g8tl5XyebaeMJ4gxRoIvj1vn2eQCIqRaK60JY0rsp50l1lJJRFYKms1lNCCOe5l1RsdcW2tEiWa2tdsTQyKqMIhyWt7z4d1fePXinnV9ZNUGWgz6CBax7w+VaSpIfiDWijSl5hOFTGuPaDvT6oJoYSf28Hb3yFYbDfP4gUrJK7XCsDcWUBoiKY4m5BPDk1WVSV0EhnGB1QdJpZnvy1W5orTKboIlr4y7QM6Ndal8+913DOPI4WqHLI9UPbM8Fuo7Kyd2QyK3zLibOB6vKHFgECHEEZMjRh6+/T2qMIfRNmM1SHszJAxyRGSgDBOpZpCRx3cfQMxtllKZ3z+gy46bmxt7vhaBWmirsrZsKvFhoA2BUgoxBub5hLaVnE/QGmuI5HwmSiPKyvqhGBWwc6WbPegpJpoWhpC2ktaSrhKjDZBt53MhhtG0GQHEo16Qhiab90TBKr0e6B3+sLjm6x2xM6bY7w7dlKeLjnBIwgo6Xxe6mnXMapBa1Wr8/lzNi6bWDdI0DNlEXt26WbnA+mat0Dyo4sEgOlSIwz09pGGBwu0wGvYZwCxAgphXTg/eBlcZTbW2vkynuS2DWhXbXKEsvYp+hlc3+9MGE/WhqMMPImxC0kui4PL5+rUGlORzwkBlpdRCXgo1K0uGeYnMCywZFg386KuvONwM/OnPf8v9dSIOeeuuJEWS2sIl684SkUr1wGUFmGxMIe3Op6hf30vHY199GdDlq6hZxh8PV3Yh1cSRVXRjI1kydCsQUTSY1iSo2vnqupF+UXxHRe+8NnkJ4VLp+01XbOZlX5eipiccU0kHQlBXMLNBh323ix1C7y207+wQL1rw58KmFKKBUlbWdWV3sJjQyRkdcgJLqrHTsBxu+vTLHzFOOx4eV0RsvfNnn3xKjNFWK2Pjipuba58fKRrN5LB5TDfCSmM9nUjDEDGMNKBDoC6FWVdCGpiGgYyZ3ZUAujxzdDTCsbX4LTANA2ES/sv/8v/H+++/4Z/90y/I859Zz9/x3WIDGEsogJpP0Bh8Dejs4hFnL6BWjTQyg5h7qWHugRT6Qwg1JKtimyK1cVrOaFV2JMYwMcQdKjAxogHfEmVW6CFalteY7P00YWwQSIhMaMm0ujIcBggjx6uJd+8f+Ptf/Zrf/v73/E/++T/lej9Q1sL7x7cs60qMwvF6T6uTV36BJS+kcaS12SqaGhjCaHqIMlOWTBoG4rxSaKZsdvM74oiESG7Wcq79AS8L8wlaSS6EMQgpBNlsEM7ZhEa5lUs1VCr9Uc1iJ8WWZTVMV2ND7RD6Q+eJQYENGw+EQRhS9cU9EdU9rRUkCsmV+eZ8agHepEuV4MuZgvTZUWGIgycnHx57ADAMd4uMG5vJBu32/bkV1vVsbqxNacWsIpay2l7v6s/OJjbz/1J/iOlmez2Axq3aUx3YwooPSi4wAVzSRC8+DPoSMRuLp6cZVeH6eHDk3WilYftf/WHXPnLwSL+BB3YpNpsIj23atiDkY/gtuPbuuLNhejJurW4zCQ2J7kPkvRu1QF4aa1bWuVGysiyF8yIsa+Crr37GR5/c8sff/w0vbiam8URgpISBwW06KokQqttG+G4I/zxo2vQC3RKl3wxtwYIkzQV4Dp/1Ibh5VaC1IAHGKaKaCTLhiL37O9nZ7HK4oIYgIFjyxQQtusFLwdcE2JVovojJ4G3BnFsvNFmcGt4p6NrnYAjNhYG2XGlwV4u4nREJHeLqDl2yHSNLFNFV6n5PHEbdjTtUFz68fcv+eM1uN20U6v6frbsmUNScE2LcoW7AOK8roAZVa6MsK1NKjCltCaKJUJdl604lCNM0UVolroFk1D6HY3Ijq4nYUspM0wvOj0/ksnJ1vMLd5h0msMbbTm5Da+PXv/l3/J//L/8HfvrVKz559Y7d8MRhBMpASBdnQ8QYU6ILqSnGchGvkOzgEy6shNDPgNTtwgQxTDWECJLIeWGnB8YpMcaB5FuroppIxAKFrycVg8Gwt05uxlMKAXbJW7ppR1QhjYm1VvTpiZf3e/75P/+5K31NtzAOA+Nk7WGKNm8ZBrvAVRq2GXAlxsiUJoZxJLqRv7hl8bKshJBY5jNP84mbqzsIYgkxTNCMPYIHtiE5h78uVlGlgASzQhHXLajDCpPU7SCFiPvvdGl/g2aqTdQ6CYOXjc8epZia2Q3+1K97IFoyEXvwQ7LZzBbTXfW12RsgpGDYp+kUZub5RBz3mBlbY55PjGM09TyN4ApYBypQrWixIsUWua9mvldmAEpTWqmsy2qd7lbqWxGkz2EG2g80A7ZX3dgkfU+DuEXCVjB6QK+9IKyXSlLp0JLijGnKupi54fF6C+5bIeTsLHuTnZYZPLhc4IjmCcRYUP19hR5frfreqmOeVcI9KTWbcfShBsnjls2HCI1aGuWcOZ8LtQTmVTnPyrIEchY+fvMZP/ryc37/2/+Kl7cT+12FdkAEJqBFdTgxkEMktmq2IY7ZaxOHzgY668qeTIDsSUwci2/uFG2iO7sSLoAsld1BnNzi1z6oJ4pAV41XVkMICF5sgqSLfsL8uwAXpnVYsAONG6TjswVVhaAkCT8I/L1TsKZFLnBR7C9UL53Cdj6gGzzZa5g+RyTa/AaHBB1i1AC73R5iZH46ISnYTMHJz7bSwA6DhgYVahHu7l+SxoErhXlebG2pFvZpx83tDQFlWReD5ONAW1eyF/BDtLnnuq4IgTgmklkVGMQkKZJa5fpwZBzNOnpMthBnjGGzJ0BsK5oC42hWDX/83e/4T//v/zG3e+FnP/mUiPkshTAwjBOtnVENXrE5qwAX64nZX/ygvVO3vlX1Nit4HrabFekPm92EcRgYx8SQhq0hF+kJwoabQthUpTgu3gQfqroJWYIkA1EDw2Co+CDK3d01ISZubg8W6E5nq4TTyDAkxvFAiIFcKsMQacUsDHaTXXSjr3bedSUAcRD2u2vmJZNzYRxHxmliHPfENFqb6h3Uw8MD59nEMNM0MqRAiMqQ1CmMTpYn9kerR68t6BIC4lzypFCKAitBElWawxXilGdxb5kIsfpB79YibmGtimpBa7AKstlClBh834de9hEEr/aWfOLx6YHddCQNAfFzkNczw7A3aKdX8GVlrYbf52wLlqqqs2+q+S65iKjk7CtEK4rRa/vGro7/22fomJHj8z3K26njEgEcLiNulgfS+jW+VPXQjS63nIFq4+rmhtaUqsU2PTTZxJTPkIotMG0wE8546mIwH5L36083jvadCULbAmTTRl/3ac9ZM3ZUe24o3u9jpS7KeV1Z5kYtypKFeYVlhXltfPTmc77+6Vd886d/w37fOO49yYW0YfKhqf36KoRklXrKSvGgZVWxP7lBqK4viZgHlrbL815tnO9UhLZBPrVlisKLw2Q0cu/YtqSjBgvajbkE8gCbP5ZGgVpprZgHFOLx6CJuvNyRfoP9em7HokN7hoyIdF9a6DOjrXMWIUTfSKc/JBdUh3N6R2PFV39N/0xiYscAFhOG0fUTljTDsx3UFsdsW6ISGccDuZk3nnGPDFYvpZhRaFkotbILe7ueJRLIjNFtZ1JE1oAkWz2cUnAP86pItaA87Saezid2w8Q4jjRxDNVhpoiwAilETqczf/t3f8t/9B/+HxniE//yX/yMw5jJa2NAWEO2ziEq0avPVivB2Smo0lpXx7qIKHYut/n6N4VENOaANdV02hu+o0IFktgWuFYK4lbepoXyByeYaKnjxiHYMpq1WgVbqSQSMY3UmulDrRDSBsMc9oPtmUjmABljJMSBtdqi9+v9RK4NjZVr2RF9xWlrdbOmzqUYHcL9UvbjxBgC6eqKlKJZnktkbdW7kJEhKn/87k98/+53fP3VP+XTT954BV+5UC2b7fUNjuk71m2YqFH9jtPOx4K2IzhXa5OjRPucnnhjH9CpVXoiQm2pl6+0ZtRoqIRmwSs6xKR0mIEuCcA2262cn84c9lfu/Nv9aOD2+gWNCjUjQPZKXJqyrgulmc1xH7qVbDYaXVFb1DykJA6gCfPxYaPN+nO8daUXb5yOF3eoow9S7aiZ6Vr0DsTxEjHLbrYAZxBT/7MFnn7djXbbUKQF79ac8ujn8rK+tCcbX1BE2+6rvX/DvJsGxCtQ+7N/HtmQby4Db58zKmhbLKJqo+TGaS7MWSizUCqcT4WlCGsO3L94xdc//Smn0x/Z7x447BPRq+8+SLXoHAgt0GI1Jk60wJuqQcKlyda12eXqA+q+M9x7RQlIrbaYx699daibYmuR9+OVqeql/fD6+ef2Hss7Cx9lkRllRLPPUBAvIiZzc8XNAKNV8pZEXMS4xZne9V/uC1s/04VulvyLXnY+WOdvLgbNEZuq7nTdk2FTLw1sXN1aMNhKTGsiHXbECAilU4W7YFhBxTrbvBY0HGxCe154//BAKZnD4YoAnM5PzHPg6Xzi4ekDX3/9E5s/DxF0JOfCn/7yF25ubri9vbUNmKGQqprvTkjRl9UUIoEpDbalqTVbwJOM57/mbBDPEPnv/93f8Z//5/8Z//a//W95cbvnl7/8JdNeeXx4b5n95kjWTBr2ZgMeTUQ2JReIGRpto0L1DxxsxtBqsUVHPjNoahk0NcPM+jBMetXrnPGgENJgN1kt8UmwTNpx+x4xTIgSrUuKghTjLCc10ZqIEjXSogcKpy+GGNjvzdDPIVdSEGI0ZXQKgagmIBw6tl4vgWgaBpPR0wjjYN1RdUuEGIiDdTqxVEoTQqzc3x7Z/eynPD6+YRgGxsEChqCGnWpgqStEzGdeetEbNqgKVSTYWDBivWqK0SG73nqbdw8qziG/2AeIKDEFSq7Usl5a9CRQbVDYjdtsw13fMWGsESFwvL5yqOMyhO73ViuspVjn15SaV3JtrCWzLJmiJojTKuSSATb1sTZnKzUPrgJo9UAw0Ae76kyeXjyGoM8Urt3yoH8yhQZ9iKrNHtTqga9tAV1csd87Fdn0OKgljrBBqboFt96J2J8vyaAPpJ+rsu362+83swW2gGmJ2Dqj5l1za4szs+xsGHSTDF5dzixLYT5b17CUQM0w10opgau7l3z54y85nf5E0Lcc9okpKUOcbL6hQkMoliOsU612nTLJq2QjgyQJfkssscQObbfm9GNTNzc/a7gCG7Vntlb7fGkEGQLashV+3eyQfm2NPWYXqv9uv5OKEyfsWQkALVuHpo6JFN0C/qU9uMyNnmVf67g77GdTaKO2umvs1l3S4XBLAerki43OHHqU7zAoqKgz1K266jOIDl0F70ZVtwec3lKV0gjR9nd7O8Svfv0PfP2jL7m/vUVCcCEvHI/XpBDs/SmEYOsW3tzfc1pWymIQlNZqViHRE4QoLKVBqIy7ifW8ElOgJauEhxRYlpmQIv/Zf/pf8H/9j/9jRJX9uEcb/O4PvyPoS06Pb3n16prDzjDPJCu5zARgt5soQ8e07WKkoZk3Uq4s5xNXV0dAGVJgmqzZrprtwRgiosIkZoVby2pCnBgMUulDeG2UavQ40bjdoFadU+3GaV0zgNoCkyTGzGjaKE1tgFxBsW1qrZkDqjgdW70jGdyRsW3sigihkLvPscv3rVLWTXcSpBJDoknxaqQR3ZiuxsYY7ICnIXAVR25uPrIKGxucmcDKPe1jskBSKiENBIk+s/2hFYQdrcHjkScacUow+IPcq+PgdL3Kui5stD0RRJI9FM0qw6rVLB18X6/ZNndVcbHfHRwrB1QLVYVlOVFzNe+v2qzSK2YymGuh1EbLDS0WFAzCMr+s0PcJi9LtOdSluyFEQrswgQy2jITQX//CqBJxi2a/QD0+N/oYoesMDO5pXFTV7dm1tZ9XhzUtmHdQQnVravlB7fvM7uPyqv78d3Ol7f8+h8Q6PNG2GYz6VrVuF6Fat58pasLXZa6cz7Bk4TQbjl2bsma4ur7l57/4CSW/JcVvOe4aSew5iGKFU3McvorrS9wKgwZJI4r0vXZbYsbtzI2V5gr21umhDjW3Bm41UbVSfbZUG9werhjiCGG1eVFol6E+6oPxPsVyhlFThjiYI7Oz9ULXQXgQt1GSa3JoRBnQqrRgXYDYQ7YF3a3IhG2mgeD7Lixwh5C297XdKssE9pw6nKl6SUzav88L36b958OlaOlFRdRnn/UirGwqjNNEjCO5wuGw56uvv+J4OJBpDMnslSqNNA28ffuW+TwTgePxComRw/U1u13lvNhCttaUtCwLV4crBEsAT49PSIq8vLvftAPiexVMfLHjN7/9B/6T/+T/ZqOwIVFa4dv3Z3KBp6cnHr9/z1/9AkJT8vKOlheubw2DHsbI9XFHCNXmCjSWFc7nlSBKGtTWdCbzQTo/NiSF7eYcDkeiQiMz7XfkuqICO9nZwQtCKWaWp636ovpCLoUUol3jwRJikGRCKQyrtAK3otGWHokaftj8xjcRoiQ7HP5+KnXDuWvzfbwd//YVZep6ANuvm9BW0KD2TNBMcBY6NGPdW/dgt+pBUayCMsgPb/nNtCJt8wOjKrekzg7q9gRemxpw7UEJhziwbqFZMg7+gNtcxx1Pu8CqWQKLIXl1Zt5XS10Zxx1RlKaVvtWrqW0B24KxCDQzyst5RV1Z323jTQW90pr5K/XlV0ikqdGH+0OiPiHeHFhb9SQwbMlr0z3oM8z6wi3aHuQuUrsk07oF/UbDfPrZINAojUq0YKz/mIZq0ILh6w0YXMBmvyDgjgNbgDMIQ/2N/mN8fPvyZGBBo0McDjF5caSaPegJGnwlpoAwknMjr4XzUlkWpa5CrhGqsDYhZ7i6fskvfvYzKCeSPHJzDKRYCG3AnAsgDFZglRYYml2D4o4CbWModkjE9CRSgxVBGzwil+7Mnhw6BTS4lWGrzsbyy3Q87k1A54QZ91ft4dq6AfURnl/rrJlEcFtx2brc7rjqT60XB87eC82Ebc1ouEEGk0KGrtl4Xgx4V4GjBU7S2ESUimlSgvQ20GAmjxOKOCnhefEQtp+tqBeMeulQneCzdTj9eKhwXgtff/WlxcVTJjR4cfeCIOI6CUhp4u56RxN4ev+eZZlBAtO+UnLm3fv37PcHc5l22DS11sg5A7bh7e7+3k3qbC/zsiwMu4lxmHjKJ+Z55r/57/4N5/PMfrcHFQ67xG4aOEwWdcKU+P1v/kw7P3J9vePPf3mg/v6BF3d78vrEl198xO3NjrU8UGqhLDv+9t98w2dfHPn6q48dPw20tbBUq4xiCIQEbXXRXGvEx4mUAo1A2Stxsm1yFsSssrVQWc25dhwNB812SGosSIMhJESi0TINPbZqVoLR+tzkrSkg9dlw0A6dGdB1AZH6rW7ejscNY1UfhHV/GWmNJkpr2RKX97o9GSUxyMGsH4yHnbySVwkUt57omKi5TtZnD09zdau6B5JAUGydjLOVrGe3T93pcNVsrg0dyeRi+5wj3p5itD/bOqikNFgH4JCJBPUQuVKyvZf59ESulXEw40jAZwuZkm1ulbP5KRUX/GgpGJ9f0SrU3iE0sz0wtCf4alCxACX4A5/o1FRzgMWrSOsILnV5ATFTyM5dN6Vz9fGD6V5NWPos6Wjx4Cyuh2j9J726FFrvbN0XyKCf7CHN93F0jLtnbS6dWid5XAKTFR0b5K1ODyVAqKC27dAwaj+PQVnyTF5gWZT5ZIy7VY0cd66RNTdubu/5+uuvafLAJN9wfVhJY0IYoVUkeKJswe22rertFhIlWgEQqFRRh/Gs8kkiBE2YFVrduj5r2WzW1C1GtJmSurXiVilKSoEw+NKlDt1IwcouYxbaMxfogj1jNlkCaBpN1U8PrF4UqAdlsY4v+pUtuSHxksREIsOYtvvQdRhbKeUwkME2wU0LnSkYjOzQNxlW71abNu84ZCuq7F76nyVsHW1nTtVe2DU8Vtj5arVn04FhuOE0LwSUt+8+8DQ/8vLVa8Y42MwCJQ62BGl/2LPf72y0oIrkyuBQ/ePjgz3b40iahsncW9PAtN+jVEoxhWoU2wIndjY5Tnuujld88vEbYvItURLY7fYcpsbNIbKbAh/dTaS0cLMbSFPgs8Nr5tOJIUTG4cDpfCZGRYbEEHdMNyM//+VnHA87clGWljm32RlKyhAjJQjkzFoDwcuywsnQdW0Mw8R+dyAOI4f9niB9vXjxC2jTfRnsb6NEYgykFFhTs6FibYQ0mK0D4him+hDbHsaihRgGorO9grfG4gPEhPrNtVPzA1dL/PD6/9MoxuWOsm07s1mCm351908/rSLNPbt0c47sG9PsW+xBq1oderNjHEU2eEY7I5ILLt+cItRQHx6amKf6bAHM719S5+f7zEjMa6Y2s8AIEqktb224quHmtRZyybS6cD6fCBLdJh5UzZU2oLRqD0+t6w8Hzih9uaVi17tfS1PI9u+0AKN9yTyCm3/Qdx43t5cW6d/fA65BfAo0t6WWaiWeeFXXVdHNWUXixUNt3YLjeYfiAWh7Z8EKD+1rYfQffS9e5eKQngcpMUFY9xfq9e8PXWmbETgwJfklICt1UZZzYc7KOgtLhlygZFirGVne3L7k66+/IMYHJvnA9UEYx0SSwQbjsUM0gRpNXDhgTLOiDq2pUFOgNCEG68ptfmw8plat3u+Rb1u65J+lXwk7c9W7bIOaro97ptGeBcWgtK5vES+oLuw+cUfawC7uiUMkBXtdpbkP1AW+Ubl0gR02FIk+UPYZZhifzbGePc2qG6Jgf34+XwLxlQQWzht94G8FV+x3km01T+jD9j6Ct+Ktn9GAwYJRxFcbiLP7lFYbS25cH++QZt17jAO//c1vSGngzes3JJ8FB7HNmo+PTwSgTJP5O80rN/e3CMJh2pslfwqkRiOmyDQMtmykVMKQbNGGvyFtylxnQohEGn/9V3/FT378Bb/6h99ymEZ2U2JMhaDmJx8m8zDPumLsgMTN9REJybUSlaU0g40SUB4Yph25ZdaTTdRDqyQCQwiUYHuaRUDr2ZfNuIo3mj5giZWnk7Xbu+lo1XqrNKkMMTGMA6ozKRlbISVTIo7JdlDEOJlXfDHR25CCLVpvahv6gjCkiOC7qatjktEYQU0rilCaWQ2nyznaHoBe8VuvYgd9o2b2gCCBWuxgmfDQVKVhswK1rqJ5qxnxLVfa/D3oZc+Bny5tgWRNPdVfu6mSvErHh4k08+gP/p5ETVWtJBNNroZRlpI3503tYjAMessuZAsx2kwhZ5s1oG4H7t9bGqVY8FaFXDsF0h+M/jBvWL93Cxt237YEuQ3oN256dGPB55i/bAEgSId3fFmL3wClJ0w6gEDVur3Olvh75Q8+RLT71llzBgn9owTgj7lZdKvpT7a5gusmemR9/uUmcPb/3efJK+Bu99BBG+tkCoWIaqIVZZ4L57WxzoG5BPLi84cCuQSub17w45/8CJGZQ3zLcd+YpkZUe8bsPZmNvRoG4q8UUD0bhh9t+CkloKmiLZGoVE/CKhBa82vibJ2GD8F75rWz0Wqj6yrxsxx3yvl84nC191dOW79u99eKB3TLE4zB9zHQlwnZGeozC/G5gL1K27rvwTumVdVnagbnPr+H/mp+buxFbUZn7+eyPhk6/VRSf39iy7qCQvMO1u+ciRyjeWoByrNNiGKISfDf2XeRiKMH61rQlhh3R4PNgzCMA7f39w5H9wRs72sYBvb7A601xmEk9N0YwOn0REqJ49WV6SSGcWAIg23pcnVGdIhEBdZibKbg8vbWKq9fv+Z/9b/+3/C/+9/+78lroebVqmEN5FqQHNz9NFCzsaSXoRKqm1z5ja1VkWKl7dIWu3lSqWL860GMSdTx1yCDsY2oaM6kOLCsvkSclagrj+cTbfkWGaetxY8xMQ0TKQnDaGyDGISr44FhSsSYGAchxoUhKJIaQ4zEaWdDMsXt0BcbtgfzoxKMDaaOcoSonJfMbgzkPhxtjRRtn0V1pWvo8wmcgaW2UrGqt+Ot0Re/B8WTCduD0DsUVaMvF0frEQMjamv+iD471mLdit1XEwdBM38W/1nVZvMQoGhx7Ywtk1qWgpZCqSt5qTT3sQkhPkuCAVqzpNCMfVRKIeezBYoQyUv2ZGAOrqlZe1spSKtU6Z1Bc+qtvzl6sO9x1f35UfoSIPueHtCTP0yGjfTr2WGlTXnmpXvTywzDXr0jCsmTZriYAdK/vxCITmHty4JsGGuBvcMqOBx16Y7EX3N7788SxEX4t8Wgy/t6HlgBxZfz2Kmw6rIIrRbKYsK4NZuTa8lC1WSsFZTb+5d8/fVnjPEDu+mJ6x0MoRCHSMDOLD7AvVwqoakQtaHDhK4LhORFnC0A6wWM/UwgK2YF7jBJT6oxBFrL9AU7TdWGpX4ga1OmEW6vrsyDLdpMJiBW0Gk/t2ydlZEG/JyrkKJstF8VNlLL5q/V2hbcjSRoeE5SixuNZsaCcTL4yH6IPh/oRZYiHtztjMZo6w42kgcY1OTJJIgjAc2hJzCnbH/WS59z9SIkXDr/vpOnT9RVYF5skdcwjHz79i2H444UIj/+8mtSiiylbM94f99XV0d3L2jUdaWumf2LPafzmW+++5747i1fffkFSUsjx8IYbceDPSeGp601U2phlzbHPASl5srPfvJTvv7qK/7+7/+Op5MQ0uQBXYllBgZjIKgNMDUH1DxnKWIVsLRKbND95kUbNWFeNthuaanVsq5EkjXsfiMSZSvWHPcuK6dz5fSYKeXMstgDNqTBeNCqkALTmExMshuZdnuGIAxj4LgfGafG4TAw7iK7oVinEhq73Z4QIImiuiIxElMwZpJe7I3F3+EQjKIKnWvP9sQrwSrUDATzZGrNxGFdxakqVFd9mqvwpZNQb/XFjIFsQK9i98hZRP2q1O7jE+xwlRiMC24ltzni4px+F3/ZoBlKLtTziaVk6lrJLdOqsvrsQFUZwgRBbI5AYyKSm8EfGiJaCkszG/hajXek1QgGaKWQ6Ru+1J8a7QZ8/jtkezovlxHwYeKz4LmdBO+ivKp8PuTVTpH1s9N8YBlCQjFX2KomULyI2AwaMmJDRLR4ZyIU9TG/Cz7BX89hAJELzdWG2njndrmXl27pOe310iX9MCkAeGEiDrdJsdkMybUkyrI0zudMXiJLsQRRVFi0QRPu7l/xyWdvCPGJ68OJq+uZpIkkk+1z0eYBypJEX4CkNKSq3Zem5DQiapqoLMkCcgi+C8ICbIjGZFKviMVnRJ3k0IsTIypwmaMp7HeBaT/ZjEAxVwNMIyFut2ELjPomODEKfbRd9fY8uQ2Jn4kQuhjUnKlLyVaIIf47oyU1TNNwmWVdiCCKbcfsi4ysg7lAWb3L65v0VHuClK3v63vSQwhmjNhseliCEUk0GGTVi6NOH7aY0OdgwpBG1vUtV3evOBwPpMeFv/zlj3z65nOmcSLnQlmWTe8lMVCLWagH/M+l2YKhIfH6/p7jfs/7Dx94fHy07e3G1glUMShhXVZolg0j5jA6zzM5Z27vb6gls0sDX3/9Jf/9v/3vqG1ifTgzysA4wqyVsUUG7QfLNZfaRUNdJWiy9OBzBYBQPcPSBZuCUTzDhVbnwbM/NuYhL6gkxp0Q08jDaTZKpgbW2tDVhqN1hocm1AIxLBAeTX8RTMF4fTNxc5/Y70bge3bDyDAEro5HpjExjQpRDKtLo22UcwZCDDD4Fr86JmcHYHsTnlk1o+ZFZcNrYalG45XYaXjikIhZaxDU1MGa6XYFTSEMccODQdGSNyxf3GtGgIIJklSbCw4bazW4KM/zFkQbjqE2m8M8nh5N69CM2bYui/vQjE6ZPEF7YppuWOsKCrM0Hya2Z9CQ2WbYw+MVc09ezUSLIs76kGSBr3WVt/HFpf8Z6xLsdHTKq1VU21pRNUXulnwuMRakPYvJ9jOtW35Qzerl8tTb92o3CLSVsp2nLv7e1Bf69BlB58hb0qimr1fbVeHLNNgEaZs4SrZksNGGdUGY/K16d+HDcJwybcEnUVtA20qtkXlR5nNjXZVzaZADuUWyJrRWXr56xcefveJqeOR4OHF3OxFDJGpwA0bH7pMFKxjNK83PeBNPIOLJ3+dcQ1JqtU5DU6TWYsHcz6c5z1pSFMywj2qGctUTt2Kdbh8x3Ny8wBQQntw98W7urY7tB1fXW1uREY0MaTDLe6rNRTahnlXiMQ6UljFGDG5Nb++hoYxpsAQRIsF1L0K/z9adi/azAqbQHvDD4h1ARwGcZq2mPFcJFFWfbTYrJD3wiT+/zymuW6HhgsDts2K6nQ+PZ15/cktrym4/Ucqtn6/GEAM6mlZoSNEISTlTFosnwzCgKTCEiZILORdCSLx4+RoRJcXBhrAEJXhH/JdvvyXGyKeffEbNtoFsrXXbvGa3q/HtN98SgxAlkmvm6WFmvBt9v7RAbag7J1aHq+TyccHHsuI3jc55p224nIToN0e3i2MPRsdumz+ENmwdUiANAqNwmCqtGZ3N9hsvhn0XnO1R3TcmMq/KvGQeTivfvzPLjETEdIPC8frA9SGwmzAfq2BagBB3jLuRIdo2tRhhGIVdMvvfhi0hmVLweUBw73m77n0+ELX5YMndT4cBowYazEOwoWxoisTkxojLDwJowOBCrc2ZIGLH3UVJpSljitRSWbPtz52X01bJ2pnqi2eyrfssVrVWmsn+FULw1pVAcwdWrdZBWaC3IGoskK6LKA51dduEbovhIjAJQNoqMOkV2rOgeWGPVU8AlyDRpzyWLII/XM8gHoWmGWHwhy/SyO451cwGw2cPAds2t5Uq3fYEw8zprX4Qn11cbLsvWpTwLOh3y8M+ixDvsMtl/4H22Y4xs0RxYdVKhzDs9d2PLFhkbgS0BGpdKLmxZtsgt7i9hlZlaYnWbPnTy5ef8tmn9xz2H7i+OnOVMtGdnCUqQazjatWea6It+xK/DupVvMbqgmuv1PvzHoLZuhQooaMJAQk2M5NmqMG2L1ztz61eCgow9G2fxLt16z66KM04VR1i3PBH6wjVIFdzIr7srQBziMADvaIOB7n5gTsg9FmRiK086FRynuUCsKrecBH7ezuaz4wVlUvH4Of3ea3SsAKjadvYWXbuLUupRH7wA96t1ho3SFXEvNjWtVBL4tWrz5mzCZZvro7M88JZlOP+YEmn9iJLGWOE0RYSaQiM42AFTjX25m9/91tqUz799BOS7V4eqVUJKfHw/j2/+c1v+MmPf8IQI+u6Ehrc39+ZCZ9asogh2pIaX8kX1JTRVZVd7H427jkqEHxfhWgzpkjwTVs+5LOM3G+80Bko/THsVgd4Jqe3b37Tkfh8hS1IQo+jsTOqt6EEIFJqYZkry5xpKK1FcoZhFpoWo+8V5ZTNeTIGeJrf87gX9jth/3BGxCohRRhDZHc4EAfl+rhjGBIpmWVHCnbgZBBSHAiJDaOMKVk1Gwz/FMlewCpjWOgLVVrLJNdXxGSc9eYDsK0iBe80vANTO1gND9wWwXiisZbqCumA5mwoe6cgViWXYvMTFdb1CdVGDDuKKjh7BTGWWSOx5MUT/+BQkQ9XQ/fvt/tZxeBK38u26RMMcnm2lc2PQN998AO4ReFCbb0UVBcVM1sy2eYXTWmaYbN1wWmd0TUpz1hJcilhjCprSvDOUgvRzeOCeFfQf85CQtNK0PjD4KCGL9vebEsXvatW30qG6zjEQVUzqPPP3ky01hNlwXa90AxCXUumrJV5KSwr5CLkHCnFvINqs2Ty5uPP+eijI9eHR67vzhwH2xGSJBKDnSt12/PgA9wApAi1BWLtJGTD9lvwDW8t2b72GDZUMEVhjMLczENKmrq+xK5l2NZ9ZnALDzyw4g3W9e2e0Y06Vc1zzATCyWFE2e7xNmCParBps87UNG52rWMIqBjRw0qXDM0K2BhG1IkTRMxm3OHKvny17/+wLrkngA0Y9LPQLT28i+jhSDqNtb/hjqg8Lyx8eNwvgrUgF5gSh/Ojs9ObuQWYd9qRYbhiOa8M08S8zNAqcRzJeTXPt3FEXEd2Op2RGBnTSAp2pmtpnNaZ43TgzauPKO5SkaTBMi8s68Ldi3sLBCjDkPjw+Mjp8ZHr62tGbCDXsGUuREGzXfxAYBy9YmyFGG35eQhm+Zxicgz2ktdVzVHT91gBZkInvij9krWddxAcRmC7dtupsmrMueVN0eDujx1pTArt8pAljbYI/GjL3mtdgESQHbVklIzISCmFVmzHRYhKiBDSuGGhRHMsKg0en05MQ0SrMqZAY6FRaFUZgpDGyTbMpYGUPBBFb4lT2qAU+sELkSCNmAIhJkI7bf8eo8nrN3aM9Vgbnt5U0Zahr9SUXs3Z9Wm14/dhM7+zu97tMQq1Vntw2wDa7FyoPQi1GU+9dwNC8mGl3ZVON90c3jYU1x076cG80emPG2G3BZuLVSUF5/3X3kkBOCatDj9FHGLr1Z79potAKm1QTV+OtHknbf2pUYRr7xroA29jg6DPtAktEEOjNTvTIhC0bkZ13jf302mf7xnkpeqbx/qcBDeLwz5bp8lCo2als1cuiTI43GbXcM2w5syyNEqJzKVSc3AVtVBrIqTE64/f8OnHB17eP3G7F2I4ksITNGF09X/1udCw8ffx+Yyvx42jiRbx8iNc2GOJZF1BLNQqhNKQKEw6sBSHFINvpWsYzLcuLGtlzaZXUi4d1ZDg+vqKMIjPB5y1pwFnvnh08BjQC8xaQRZSOvjWNivyCL5vIjSCryEWgQElVoyl5RYdEoo5IjxjFaovTb+cVp8lNcCfZ54VNM/4T17I2VsOjqxou+zXEBmcFGHFbOjq7V7P6LOfDxddhbgl+/lckDBxfXMPYgPz3TSh42izDjdPPS0Lu92Ob7//nn/4h3/gxcuX/Pjrn6K1cppnRIKtsV5Xpt3IJJGcV1KIwYRzEljXlZuba37+s5+y5sw//M3fsCwrn3zxGT/94mtyK3zzl2+JUXj9+hX/4t//l/z617+itEyqymEX2B9HUrBd1CG5CR+R4APd1gOa2GCrSQ8blmTi9jg+/4qYCOkSBPoHl66A7g/yBkcl3x7W23xxvFsxH/VGGM3x1ayzXayjCRV7CKOM9LWSm0dM8N3NMm68+dIuzDClkZuCmBnekk/kAIMWYhlZWDZKXoyRlIZLoBCrzEx4ExwPjEhyT6ogHjB6u6nOwzZFa8fxuz+9aIXm1U/n08uAkH3YbnqMwGVLm31StZmBU2tFxu3aouvGxqDzpTY2Sxf2QSc/XAa6rkv1Z0dDfEbhBBisOpIMGGRhD40HFOXSeYpQXEiGGxra93YxW19AkzaIwoLrYp3NM3ZLEHMVrVq9Yu/299atRjH2Vu9YrLBXzBRMbA91Z9bApRpuFZuhZlR7EWDuqU2blcrBvs/wDrt+qmz7nvta1W6psjG3mt3PtSp5LSyz0YnnEmhZaESyKhQlToE3H33EZ58O3L/4lttjYQhXDNgOCKuwbbFUiub1pMHcklVdRNjsM4YopGCfuRBJAqrROtGYCNVW+iYR6yqq3fsUjJGY/Tnvhoi1OASlvdO3oDw35f46cLia3EIlW3nhbtG9HjFUsXnHVbfuOcUDQxisg/DCNBJowfRFRhHvsKbTsJvB1VFXE9FGdcaIbBrNTltHm+/rvhyMTk3u1PBNOS+waXA25p13Iaap3lClS3ccLn9Hz4ed6WdzzUigOrFgnhdOs/CHP/yBT778KSLmdxYl8Mdv/sz9/T3jMPL7P/yezz7+mJxXo+lWs+jfTzumYSRX724Rci7I4OXThw8P3Nxcc7w/cl5mqMrd3QuW05lPP/0EQuDq6poWlJZhnmfzX1pW/sf/7J9xddjzH/2f/kPy+oFpl2y3wWCfWJ4tsY/OGmgY1Q2Hl8KWcS3gPF9luTmJigXG0IG+H8AShnHbQ6/eTjpWK/2mYhvSUEpz22zpRwcUc8oUOtpgQbdi+L+lGqexbeyGYq6yJNK2n7aiAZrTLVMc2B9eILq46AET7WmwikmVJectIdiXPRTqlU8S/PXtv42h4JWpWtoVX4G5EQSDD1opiAwWSGVwb6ZsD1ZnXJHo6gRLVnVLKPaZHYsXs+lA+qYva1Et8RoI0QP78y95DuVgjqhmfO+22A43FR8ad7pl1eaa3LjZhmzeBU176vcnqasakj+A2c3tOpVUvNMM2yrb6Pegblmrv0dzKbbH12Ag2Z7Wdnlw/X/2jgza5cHuNFW13sJ0AWL3Fa+ErYLxjqUnw+CdDK48fh4gLvYmrTRKE9bVWEy5BtYF1mI+xhVF18b+sOPLH33ER28CH706k2IlIQyyOvNO0bhDKwQx5ksTNsNNex7sfbfWE69pY4zO7kJF3/SoMlA68y1CCo1CMjfTaGe1enGS85lcKqVWN0a0TrX6GOn6/pooCrJazycCwWjN4s9uLwENmgQNgpZmz1rwJ1bYXF3d+6C3rdgQ1vy9GqDREAebo6i5G4AP6BN9MxwSfbC84Rxb8fZDhXz/d6Oc1nbRSHkP5N/zQyZd35t+mWkF0EiTZskbizHNi8LT05lxekWLAWphXhulrI5amAuFbNvmlN048aMffcHN1RW9bovR5AuXpK2E1ohDIj0+PKC1sf9khzRYa2EII+Nhz0eHPURrP+fTiRgjP/nqK9biC2rays9+8mP+1b/6D/j//L//H1QWb5sGWvOBLIJKcTjCKWn90nqiUCl0G+1GH9g0ItEHipZATEbTE0QPau6Z7vQw9QczdijBHSFrYzOZc1CEbn1gTyOoPO9jenC7eB+JH82NRdOqLZSnY4cB5HKQiOrCmh3i3ZoZ6lm2ts+mLkgLsHHtuweMhxUJW8DTVraAYopn0JqJkuyYSQEdLXBrX/giNB829/3R3akpCpi1h3UzRo91BgXQB+NWfnlSoGBVcXRhmH2rdYc9h18Shurluvd/EzE4qy9QCT1D+5OzdRIi/mBcuiTV1R037eGsmjHPJq8qnUor/T4DaHWmTtkW+th7y35d2MgSP4TFHC7zD3CRAPQZAj537hHF2Uf+gCeJFKl9orpdKPVZjaqgBd+I7LswvJIMOnjCjjQiTcXYh/NKLiO1qDm3rhNGYDMigNbI8Zj4q59/ypefLezGM+PO5z9FvPu2PJXnhVIK+/2RGJIv/3LZm89T0AsOnpxC3CSgTag+8A8uJkzBiqCkKzUJFCXraq4FAORNoW+UartUQU3Hk5tytQscr44UWQhaiWGy4k9kUyVbUdlvRvLZwEpLMA4RpBK9E9wKrGZQpYYON9rPt1ChQWi9a/SVoHYCvJvK5lbbCyX6AFn6t9EH2GYmmC7FbsdK1JT6pb+uxK373eZnHfbTTp/2XeCoH1If2auiTalFOc2F/8X/8n/OJz/6CY9LISWb9O7Gkd2LV0iyLud6nNCcmaYdoRbCMFByZm2V83y29+jsNqWhVWgxkF6/eoliLcswpI6SWLsRjeeuQXmaT9Raef3iFSkE5rWwNqUsM19+9QW//93n/OUvvzKDvLqQpqO5lDqWp1oJoRmU4sMx/O1c+Et1qyT7Q2rXs1cL6qnBGBgRw+sQS0gbJ115hvF59dUqq7dZKfpDTfQqVNm4Ch6og7fAFiPU/YH0maxftt9/aQUtEfYb3lCSqrEqtjPnKSr4YFKKv+b2Yc3rx/fz2gBY/HvXbYAFrur0hKHq7o4ybVV160wNIDQF6RqK3uU5aUDCs/c9WrekXTNuXYRo8N4iIHRjxEsL3b8uOLtXqtv765eg0iv+XpmD31vt1xGfQQSklS0UVG1QV7/2VnUbjDg8f1b9D9XhEhPANQ94faD+3Ka7i+G2axXY3EmfQ0ndUsO6EraOsLXF3sPl9pp3T1PwJTmbE2GzpKkOjfRFQsVtFfEwJRq96IlAoupAK5l1qZwX3z9cC63A0ipVzayvhcKruxt+/OMbPvtk5erQSMPkkFylhW5u2Qhh5DG/Iy9ndscrq7odmuxGk6E7C6t1X05isqo2WrA0exorZFIcKK1YUI9KbZezb0JRoWqhls7ysnKlcOGn3b16ZRUwzWabySHr7XDo9mcVu9ad7ZScnq7VyTW6sjkW475lbry5nT2cYSW9ELmUklHE+QmeOB3pUF+8JYFLMlB1ttclNnS1/HY4xV+5FUu0/A+/1OeKF6+pRmcLioiZN/oCrnnJjOOBaXc0A84g5DmzrAv7aeK7d29ptfLm44/ZX115oSaMaWRKkeS/MwWzX7HePboyW0gSSWYta4Z+OWfO68y0M252AIZhYq2FMQ6cls7NVk7zyVS8uXJ//4r/6b/6n/Ff/D/fseYHapsNt3YBHmpSHNFAbcX2P/jJiVw6DHtNX0SidksNVnB9hVjQ6R6K7VngtWrebm4XvICJwqo69imB1mBtPnQN9dnhcu77psQVUzlqMzpk7G1gMxENgeYul5evTs8DqGiFmsx+5AJndGy5Wdu64eVtC5DNVaM9YTXMvE/U/JcMgy02jNuCr78n2ga3BfdraoorsK3CNlPKuL3nqpdtBxDs57V3R5fGOLSeNJ3G6Pe2JxpV3GffrxkWWAOg0gVWHUKxBND/3Fv3fj/MM6oZq0uVznDpGhuVBq3ZA4zjPhhHvQ+kO321bXCOXRfxB0C9DRA8oPdmoHkR4/MefTY70daH38JlgX1CW0axeVVtCq3avWhgHUNBWnKI4HlouBQIBtuJX4+EubiqqdZrZV2VeSnkGmnZNAmrJtpq4syQlC8/f8HPf/yCm+PMcV8Zx5EhRExXEZHgVEddESp3N6/cKiderNLVu/0+mI6XDk86i6j1rtC2CCoFQiREdxMoYv9RbNgvtt9ctdJyMEv56qIxvxJFld0E93c7hmAaAhvEqjnA+veJM1dULIjXgAXdFhgFovuHgSW94FmuiTGc7MRYJS4h2hI0h3LrpnC2vdxGDgn+HNiZTw6TN3GyjBcXiPBDUaWRRVpjU1/TNqRxO5Tamj3j6vYx0m1DLt14X3oliCWsZr8r18a4OzDs9jQJlHVmrcZeujpecXtzY+y3UhjHiVoKcRhoKE9PZzdzLfz5L3/i+vqO+/t7d9cwCL+WlaRq1gnB3Un3afKz6S6N2PB5SCM3N9FELwpXuyPzPBPHARDG6cg//R/9+/zt3/3X1PrBhD0lE4e4VbvQoJhXO9HUjC14Z4AFrl5NtI7SiRDVqrHWGQ3qFfsG3EAXq/TOo7kqYdtiFZJBQa3vDRBTGmpFYqKKpacgl5ArLTgFM18OGlA6rRWHbdQCgUhfot69YOzANWf5dC8m63Qs6aFWuTr40Y/E5r/UsJlIh9cuATVZtSoda73AIxWvdMWgCmnWwdHqxevepvQOKzwbCAJNkgVOxz8N2hHz2HfRXX+75mzb5xKNKsnfeANpVhGLIs3KgibBF0SZQhjt0JJsD1fbhs1Qfam0irXAHfmPQItig1/pHWGh+/AYPnx5j9a9dOdWZ6nUrsuRjaDgjaO18ly60ediN22dAdV/d0+wjdACoXXxYPNziJ+FvD30z9lsttNCXDnMs4ICSq3kAusamNfKWht1beQGtSYoEZWV673wy19+zmc/itwcVgZV9kOy62be+s5GLuBEVqXawNXPsimjLYkZ4cSLt9ahPbvmIaitLW3d7M8+Q6MSg9ntqLPEUn8mfclUy3ZOTEjqCcbbzNbg1euRaTJUwJyNo7MjfSWLw4/9qtoyIetwUrIurG+XC8GAZ+3zQKAXUoAroa3TJEYagRTbs+442LPZE4Q6fVt6UWdFkg0nLz/zg/PS0Wzx8+JTiW70F/zh77MNFMRjEv6uu+IfjV7I9jOkzMvC/Ysvub69I+dKGq3jHFJCUYZxJMbE49ODwUwhbIPrNA4MQ0JXmHbmiyVuZChx4Ltv/0IaBtNJPD2euLu9YZomlmj8/9oKqSYKZhexzCem4xEtjSWvjHEgTYnqi8R2uxvuXn7CX0+RX/27vyHP31H1bFkIC1gBSMNAa4X5dGJ3PDB4C9svkPZW8lnL1kSQDlPpPwqn3sJt8EGHBPCDjR0qCcGqxWAe+K0URK2rkFaJpdLGwR4WbdQQ/bAVO+g1IZh9dOtCMd1eCVC3yOZSthpAafS5ptTgoa/JJpp5/u2XL0seLVjgEw2b3CZhvqYbjOMp9mLn0d9RhG4lLdFmN7ivjjZvi11I1ju2/vOexPqbMhivJ++O8ftnF4C2/S57+83vSbBKqXd30vFsv17+7U0EW7bij7EfCEuN9XKfO8wkdePbb1DZBtVc2vyLU2jY5lVbPeqdQ6Vswd9+1jD2ignsuptu+0ffc6miK8/JF9XtSS5wpNmMIJck2CmMm0gL0+fg3XCtpi/JDfIaKFWZ10zN5ss0N2hF0AqjZD79JPFX/95nfPp6h8T37MMOYQfB9BRSbG6UvApofo98nZfd1zAYZh5sx3n1wBYwmKziFHV/39Whib66Vjfo0BJekOg2MYkUEkvOzMsTOQfyWqn9UfbrWoFhhPtXtwwpuwVKcghyIErvwNQLIzuNVbvQko16b/tZhOCEEtyhwdKYnfXLMiybX0hwWG1jtsmWpHqhERFnH/oZ6HTm7lLpXUGIBm11hTUY9Vwun6CzyV1WYC7LVS8nIjYjpNSmJJ/DbANvvdQReVU++fRzKmL74NeV0/lsu23EVj3EGJnSaDhhjGbNHwO7EJhPZwT47M0nl73YEmjryt3NDSEF0uPTCRkikiIZUz5u4q5oTIa1ZgqNXWvG7R8nu1E1UYOVKCaMmvjiy18yHQ78m//m/2VWECxWb3vAsLuZmI7CvMzoNDAF35K2BSKvsrFq1/zeI0G79N7EYdbiPbNZcLhgi7eqz/7ODrwKRI3b4p6RyFpnilbSaj9WA0AGlOoc8ibFFw5l0EB3fjWKnnc11Qbp/b3gLaNWu/kesmwJEX3uYoyP6NVHn4VZ8WBsKfGtWl3P0NWqQen9llUYoVtgg6p/GPLl+vTKB9tLjC9z7wycTjVuzkoLTgaw+8H29dymWiSgLfn7vkBn9KFnV9zy7Evxh713JM9sKvy61D4f0maiqO5hw8U7By8M/Lsvn5NGUKuiO4IiasIp9blMUx9Ob5mxJ6K+d1kNg99e6/kgW+36NoMftYnb0qs5C+BKCellqNMjtX94TyDNNwd2RzIZqNUKr7IWznllLbCWSKuR2ho5K7kFIo2PXkZ+8bPP+Oyzif1uJsYnhrgjxUBrq/lUScfJn0FGaq7G4ronFGSzULcCwwga4o1vwK2T/BEeSBRWQIMtxgktIq261YRQi71Y9p0O63qmZvV1pIapbx0E0Ap89mZgN7pYLbiBpoCp9eUHzxBAle4o1RXQCnVF6TPPXnBuNDSCOiTrFVXVTCmZYZi4wJbG3JJ40TJY0LelZUFtwA4VSWHzYPLg5HGhFy4CIV58quTi2xWAXiH3RUreOKNOGAle6ILTucWhK08w61r46PWPTMslwBAZY+L6cCSGQM5u6Bej7eZZZkKM5FzIsTGmxHldmR8+sJsmpjRyPp3JxQb1N7sr0uvXr8ziwV0Aw9ANqyzbRmCJgcREt0KorSApEcfEOp9YTmeWZeVw2FMKfPrxj6Eu/Nu/+f96sFipcbhcGBFEE0GyB4hL9FDt1022RKE07+ZGtkssXTXpD2NrFqM2jLzfABPjbIWoBzbBB1KO3VMbpVU7lE7BNzaHPcIijUImub8SQ7dikN60eLXlME5bSTJsn0ykURFqs+pDgu0hcLsYMnb44hYB+3l1CrHqJSH4lexe9X1uIB3ieB6R9TKABX92ekW1QT3NFa++fKU70nagxKuwbWT/7PdvCF+H3foh9zTYA1WH/eDyPTz7rDz7o/a/bzaYDqE7qRpDpLkIT0hu6vZ8FuT/29+X9DZV+3u1pNUN0noMaf7+qvYKsHvmpo3Saq9pMKZJUJrHH+NeVbWBv82UvEPzZ8Yq024h7rOwjQqdzBuJxppXSlbWtXJaYS1uV56DE3oit9fw9dcf8ctf3HN9yCQphLRHBPMaohHDQCCy1OwB9jLs7Tu9L1YklgF8+meyjW4dLeIzi4BW8YG23e9B7CRpsH7UzFYrqDkDlEWgFNa6kNdGLgrNljH5Km47r03YjcqrV6/N8sWve+yzOwJseiq/r0GJ3tVcQnn1LsEttVswF4YexNVhMDGYTIDQKoPT6EX7LA8LyD4La94NJ6kIiaIusk39GrXtfkq0mYIbzrhWpjqVuBv5Pfsc4u/PId2ofYM5lwKn+e9sRoSxDticX+/vXxDjyMPTA6LCOIwcb2+oTi9elsVM/1JinEaWOnI6PXE87BnH0dxha7G11GLR8Nd/+ANDFG52e3a7yfTtpTUbXNfCFIcLfqa2ACQNA2M0Lm3L9VkCCeRc+fD4gZurWw77HU/zmaEmPv/0r8inmX/41b9G2wcblInh0MGZM+O4B+kDJNku2IbPbV+DBZ1NJ2FXtxG2tkFCNAKJ4A9f8+/zxOOMqs3Rk2BVAUqsgUzZhrnqFSxaqYLTAu2BzrVZtc3IkKC1BCEYS0DVLK+xi62Ok4cYLXAVCxBdrCYWBz0IKkUXajCzNUJzCKbTf40IaXqMjkt2nNO565ZqjMLag9Oz4NnPI914MeiGxds3GERVO0TY4SAuXcwWbLuKGTZYxzoIx7KpPxAtaRO6AM2xLJx+cPnvZ/MCGxBDX/OI6rMKWB2yqx7ULgM+h4FBi8/VjK77gy/BxYTqSbhY5+FMmw4tGXXXAkrH8Wv/PKL28Hr1aXHIgor9uVssKEr8gf1Gp7+22udqAa0wr5l1bcwVaoF1gVIxjUJTDvvAz378ip//8pr72yOqD7YXxc9nw5CPqJcZzxAmEk4Yac7l993UwWds/YBUxTDpaO/LjOjsrIVoyV49YDaHeMSv97YrIgpRQVJgWQpLWVjmEzmvNB+0tpqpz57xlpWXb64Y93tg9iVZOET3bF+0F2DSLXbEf4v25Ci+88ZWEFt3WonitufIdvzEE6CE2GmSFqR7PHE4KnR/Lp+buefEBjFte67p999+l/rr2HzQ4pTJj2y+Jhr6qMfp13ZW7HSly6/zJKRqA/alqFmmqLKuyo++/AUSIn/685/Y7Y68uL9jP0yMY+S8LqxrZr9TdoeJNWfyupqz622Emnj3/p0llqtrUrS48PThPe/fvuWvfvlXNucobssQUmIKgVwyKSZzDpRgE/AxEQd3N7R0w5QG1po5Hvfsdp+SMAOp28M1j+cTj+vKi9df0qTx61/9DaW+M9OtTjMj2YPkOH/wgGbRz6rS5sOhENg6jj5EsoqXH1AwbQ+yNcsds+98eQvyphFo/lqBQGll25KGNBPCVbcs9p/sLBhjqxTD2ktGJBGTiU6qBx2JfV4w+GEz9kDYXB0tYdVWHDILF58Y9xaqqDGJYtsCu/hB6nXGNtj306e4SM9NzYJe7CfMw8b75HbB70Ov8gElYZb/Nv+xwbPae6peTYkJ3GzPgw/9elWkFyime91v9tzbNwW6GVwIl+7Giv3LsNY+Vk/0/rCq/XxV9bNjxneikUq5fD94clDQ7jTUB9qBrmZ9PoewV7dVqfZvP2QzGZvaLUsUupdOcKiwv18zq7P7222h2e45/p6aU2eDm05G1rJa+5/NlK9lsYF9M9rC/ih8+cVrfvlXL3n5UhjDI7W+Q2QkSAIxSDGFRAidAWaFXOqBTHGmjZ1P4/y7zoV+3WxgqpqRmKxYEYUQXGPkYjF/BonBIUnbq6HNXIZLVOqqrMtCXU6sufjZ9sVC6GZDXhV2O3jx8pYQ7Lnpj7QVSb1D9WfRP4fNBMCcfNXFgWHTHNk79dmDqLehBg2JU0+bVtsO6We+US8Lt8DOqHApkHpUCCOB6q61W/96ed9iPKiiysWJuMtwoa+97acjONW9hf4wCNI1KP59MSitwOm0sJvse4omPvroc463t0wfHvnw/ntKXvnszaekceAPf/gD//APf8/9q4/4p3/914SUiDFyc7zisDsQUuTqeOS4P/gZbYQw8M/+yT/l93/4A/v9ntoqqZVCSsk9090qoYkv37CHeZwmW2JS1XDH0phFeHx4oNbKbrdDS6M0o9Z9//Z7slYiyv3LH4Emfv3r/4qcH0mxqzrdCFz6LbEuQbxKYess+gNm/4a3mDj3pFMmO2bvj6IPmNrGLgCrEI0LbTuZqxZKsS7KhGnd18Udpfy1SoMQfDGIVwlpGBBxvxqquaAKjBK9nSn+/pzC6e+huMhNm+9eCxaw+mGLYg9AAwe2bSAuYmpUdwqwJIpRTlVx7Yn/o4t+1OEvC4yBTvF93gXYxbHgXZqva/TdkVXxas0q+bB5C+k2XrrYVffrrxvWbHOL8qwjgI6Fbcvi/e73fcb9L8z10zsIu3vOerJ7v9lyB1ta88O0Wbjsh4aqhUCiqDnUihrcaW17FyeyPSgbmwQz4mte0W09qNr3XYwU7aHd3DTEXYc9uGnrOw8sEYHYbu9qHlzrWphX6yp1DRRtpEG5u4t8/fUnfP2ze26uK/uh2WKmlpCQiaEYpbVekQJIzDQiyTtgUDfWdKhSBdW82Zj0e1hrv+Z2ncVtYWJyby+cMeYXKQbbw95UKSoW9ELvHKFV+0xrObPWszkQF8gZ9NnzCJbHP/nsntubHXhQtDxkrEYrFMW7v/6m7RwEhRA90bVGGIT+KcxC50KysEZP/XMbPI3vuIi+2AyNbI7UsDGy+jnsBWzJZwvukhCHGk1T0EWS3arFz7QY2aU/N52BiNjWum3+omHrsLr+y1hml/eTQrLYVSvTcMvtyzeU1nj1+pVZtSyzBfYifP/ue5vltcbbd++4u79jmkbGlKhaGcPAt99+x9v0ns8+/dT8pGpj2u/57NNPTQAZI2ldV1JKBiMFIaRoqkmwChKDnDoTQUJkiomn85l3372loRw+mlhKtr3H0aikHVv/cCrcvviUz/PMv/m7/wrVhSEUNAaXryWzefBb2xfcm3i2U8n82QsdArBnuM/C+xN+2aDVLoyp58NOEVOxXpoPUhBTqoZI8m1SQ/de0Q4PKFs9HCKDt+FVOn6qWyKoQV2xrCCD/2u/8XWDioY0oN1cT43eJpI2OKeLDdXb3eY4sNn5uouuWBLrq+Cls0+0z2vsffs5pcsGedbqXp5W4/93fQO9s9OAundOp7tad1M2rN1sQkyQaHqU7urL9rtaz9YidE3FRahW+cdf/aFWqls+N7e2ELYu3K0JNmEVlYv+wyCunghVLgZqaNiWBfVqri+/6VYHHWtu2E7t0L/Hh4uidp6037/N+M6Egh1zD8GSlAVHe92mQlmFNRfW1azrizlhM0yNj+4jv/irr/n8Rzuur0B1pdWZwJ4Q9jTxPRPilXzIXlFbQmitE6XtAbFKFKN/u/MBQEyFWqziVoDkynitKBlI23a2IFal+1Nj97ZkaAsXowkjCrSysuaVVjJ5zWi12UorUNoFxqwK+wO8fnNHTNahb9V2jIbqeOdycUrFdSuXYqEnOGk9wfRq3ouYZ2ddtzNoxZ0ttLKu35AJmy8IW5O9dRMAtWYrNqsSh+A26mquER2zCLK52l7eu0FMVfu59sIxiHX9fYrRCzn/tD03NbVdEDoF1pw5L5W7+3t2w4G3Tyvz6cxhv+f29pZpHHn7/h23t/cEAte3N9ze3XGaF4v1pfLw9MSyW3j7/Xd89/33oI0f/egL1jVD7p20TYDTcyOq5mV79iAkQcwufFkY04AMkVaVVk0kdHd37zin3wSB3GzD0fLwSKmNFAM5jdx/9DUffzjxl2//DuoToVVKCpfpvRbvKl2h2gehDjlsvGPtraJV/UZOCUbvU/VEcnkYjOlgr5Fc0BWx4Ktu9TyMiShhc36UGP0GKZDMiLA1YmukcbILjbnFqh8uJHoF1g+NvbfqmG7vbgaBpslfp7dKharFDin2e8TXn/ZOAXpBE4wxhDqdr1g152WhBf8tPWyUP/VlQAJOv70IuwwCuvwZsK5BsAdRFZFiv6f1rif4KzSkmk+W3ae+50HNv14uHaFBR8Ht5S87dX/wJbDRmS0ibGI2Baclj57DTMOi0mchFqCrDx+rr1C1OjhtkaLz5Jt3TKh3rz5Ebo6yS4W+PtIq8+BwZZ8zePetiyd4deFU9WMbqa2a5XKJVMxMc8kmjKsZShNocH8V+fjjI1//7BVvXk0cDgGtMyDYbnjbCRAotueEvjbWlLnqqspE7EQsiyy9mPJEHqKYi7NHMAlsorkYBtM7qHkVdYX16Peipf6zK2IFMDIMUBd3VmjkcmYtZ8rayGVl9eF7WX3u3gOvHXs+/uqGYchQoos8LdkFBI3VthhGhaL+GayTDT6zM0TsojHpNaPBThZsrSOpmOGfFTubM3DXUIj0UssghGAQpCm9TaEfevfa1fTiHbG9IH3uYYnAnv+gQhafK6lLg6WCJnPB1guTrxdzfY79wzLOYfKQiUGY18DrV5+jLfD27Vvevv0Ln7z5hOv9EUR4/+4905gYXr9kHEdEAmNy/zYRlvXM/rDn/vUrlnXh6enJknpe0RTR4MujWiPtjgdUYcmZlJK5PIq5GpZqVtoh2OEoi5naPT0+0RSO04GFwuPjI4pymp9IaWAaJuTGLMhTjCzrwvn/T9afdklyLOt62GPuHhGZWVMPQDeAPZ5L8opcpChS+iLp//8CcZFci+K94h3PsIG9ge6uyowIdzd9eM2jaku9zj4AuqurMiN9MHvtHbzz9P4Hmjeef/0nbvuv5F4pi9xPY5mCF+RrH+1h+nsYYwR95OPwlX6ionjT4+Bxi0P3TXfx5rknoJvJdiDnI3Mh54mDmx8HluXMlDPeKlaCWodhNsf3iqQ512a1pENqNzFKBvpoUTWMxLo4lyAOXysWFW2OwewrNKRZSxxUsWjkEAqi3WpxpmGXEXXN+L2B+Q+ICPY3r2FcmDC0B93qqxkemuDEU3sDzahNbrHrzZqgrq4q/GBEDQ4B4dgbbJKURlfwegkcl+IRcBMXI9vxHob53DH0DqaTNq4GyrsbbwfWh91yXHAtOpsxXG6j+wrMSLOG9toFthB/9h7UXW1abxvepziqN8XFBmmo4bCJq99bo7XGbYfbTedQmeDTxzP/6s9/4E9/vOPhXaakG71vep55Yi7D7iP0BzmHGHGsB6ICB+tVA+Y+zG6GINLAw0nZoNgrEaKm12dkSQmNQ3F8jFTG592lNnbv3NpKCsepXia87fQmN1oVo41anb2re3AXrNXHv+/w+GR8+PgUOibTXu/D/AUFUeUuUWvSwZxMYVEAOZv0T3SRGQZ5zlTs6aR3hl/acXs2wVUa4elwF/MsKf8FPw55DuhI5njK5IY8KbVO1vuVXGY6Cm8aTYJ7CDJ9dLC6vIQWBNFlFGWWw/I+4kwZBdSAc9/8d5eH2ruP31Nr5en+nnf3d8zLHEaYzk8//aT9lDRb7vAqCsT58P4jl9OJ9Xbm97//I+8eH+k9qMSW+fL8la/fvnA+XSiZxG/fvvBv/+2/4bvvvuOHHz6pYqv1SDLqAbd8fX7WhdE7Xju/9S/0rbG3XUE9e2Xdd9Z9Z86F3p3bduXbyzVWWubp4UdSXvj1l8TL+gtzX7mcZ2QlHnMKiIO6knuJiluHyTAuEEYqNo4w+qbs7FDa5lcnNkb8ZI8PBsRXzgEppRjYpdgUyYuSv6KiTIQ4yMbQF95896MVTSmhtLOEpah2jtfAcaABUbH1OMB1eLdeZdsc4fDmryIlc6iWsDQu1CS4KoEfVto6YLVYB90y7DDi8D2sF44DZhzQPeYFYfQXGyQhPrqNriGGbB6eMq+RoVGltaETCdaRSv74mgEWgbuMHS3Yba+v1RkD3+5voCQXt72P1h0dBpjJ6p4hYQv2TRex4PWN9njvMUz3uDh6ZJ2F6K0NLHlcIGQFNMUKHeygGuIovZ4N4rm0BjS9Glk8VNpubLvLyK/A73848bvffeb3Pz3w/fcz5wXc16OLIxVdPN6D+RZFxvDIVlt6qI/NwIIurMt6VpaCvWHjhIZFoTlO6hvkidzSAckNSEAU0PH/YlaXE7mP0ByZflaPCFIXvXXfd1pTh0Q7U/cbtEqr/N0swh3yBD/99D3FGnmZRKJImhkpc6HKQ6j3A5oxNABO0S5Y0uzEx2Xi41CMgXEM8V7dVMfbfE2FGIy64bjspk7iuJRTCsqwclbkhpuYswoDGwzvsNixMDq06GZr6gH/af4x0inxV2uiodVxxmf9plgaRZXrQstWuPXG48NH3r//nr/89VfAeHp6pHVn71UX12CC4vKqayGDzcaSC9++fOXLtvH+/Qeu1xvPL1/JU2GvskBaloV1U55L2dvOvq5cLnfc3z/gTdDKHgO859uVvjfuTid6LIYpZVrufP3yFW+VeZlpnrCSqdtG6p2v12emnNhCWqmcZWOtTs4XHj78jvYL7Nef6UuhxbA3xy2eUCaC+MEhCBkHamx68xZcb46DtIcbqMWq7McZpcU8BC4HZzzgqGEnPLJms4kmm1zYv2EH86O7H2KcAY81BpVV9snVOXDteHUHg1eL0l4rGybZARzwS6RqJXDs6GYHW6l1Iyf5+zcXE8oQzHH8tOShb0A26b28qbg0wByvw4NtgQ1KLfHe1AuoBX59/RrepujoeuDy8dkMsk/AM3pH6ThQD2uVMbcgdDBxCKcQHZmp+nYb1NHBSuoHRsuwVHBotsUMQviK2YwgTCUmdu/0OmAXCzZlpkcXYT70Fy6aYrT83uWGaVZUkfqwth7VYpQSrktAy90Zc4neYC7O54+Zn378kR9//8Tn7x+ZJ0Gs9D1otfPxXGJBiipuUXx0eS9FWUpKuuBIoQpOCXd5rok+LKqwrv1EQyrbRCdl7ZNc5DJbew3WWHSJeQoXZWLIHbqigYUgOry5s/nGuq1s68667uzVWGOftzaxbjdqDdjHoAaV8/OPd0yzc739jcvdZ0jDyiZz2Kx4UJJHNY3y3zswlUJJS8yCNDMi67I37IASj7lC/LPHvDJ5RLZaYuSPaI6h8+GQ1/grxbpaeI2ZfNtyoACakR8DUgYZR9npDbMpKLF+rPkWDLiE5oPKuUFzlVFMWXxP72Kxof2zNecPf/6Dzpna+NvffsYyPD488vyystXKaT6Rwu+qVhOU1GR0+Xh3z3xaxJ5LiZSU9+21c7mcVBRZ57uP39PdKX2XCdjHjx/JZkoocljrhoz8bodQ9+V2o1iG4txuN16+fGNaMvM841XCjVZ3cpHR17rpIKoRWqI2WArMki98ePodf6vO1y+/8PCghCwMHImRRhaLBT/679LbGDdtZ4TwiEHWoDXhkce8RRUwwSTS0LHF3xnsKNUWyQdtTpRZwbHTEfhDCHXi9A0MviJlsyrrPoDRENVkUnCN9OGbtVB+zxyMo6hyPWUxpnpVBVdytKeqmr3FeKwbPWWsNbzYsRmGD5b3Rkeq0eM51WjraxO99oCmVFF57/TkjByQEbg1Npku5Kj44+c0T1K62jho1S2lYT0RXk3dCr3X4wIZVdJ41bIuiWqYTu/7AYWBHzMaHR6jG4shecBAIyZZ85AddRzy25E4MCi7Hr78wVahJzHUxjwUHfApgXQU+uytBfOsc9Bho0kV/N91KKXiPNwb33/4jo/f3fP5+wsfP9xTSqb7htTw8drjAtzjRk82cgs6lqYoXlpw48E9kXLnsE736VjjKalKHfCOEJwOlGBzVRI6sKYsSM3jMldg2shdUMFzDFnjsNVBr8/HXNWptx18p3aFK/VueG1sN7htjb0lvLXDRqL3xrzApx/ek/PKaXnHELe+ms/oB+6tHbqUNAhJOKXk8CAbBz6IXpuPzjbF/h8XxmDfmRs1ReyvZUq2A3pyU/dgZkeHP2i0675GwFQc4K1BuDsfiZgGA44ddt/q+ARnkaLQej3GAAubdOXcY/LHy1FgHZdcH4WHyDIf33+Pp8L95Z62r7w8v/B4/8hyPsN1xUzBQefpRG/ypkrN2faVbV359be/8XD/yMvLC1MucD5TcuaXX/5GKYmnp3fUKpp+aTGE8ercthuVRkYq49ort+erHtJNWRGeOnuvbOuN+XIiAdu2cV1v3LadKRnZFaazb9uBgZvDbd8wZOeLQ5pPPH74kedfM7eXv5EuHfIWUnpXpWed7jvFRWlL6PCJOv+4IICgSIbIjv6K8WFgYTQYGyq/uWR6fJVw8lcnHg+Ts2RjViC4x+PTO7IfxqEXFfmYlzST4liMiYgjCc+mFFbeHZmI6cCcKGbsISQjqt2/w4ZHBxMMDIZ6nFcoZ8RKpuahARseMv1Qy4ZCIyY1NgK+sOaMOE1pJtJxEL+CvqMzULVWXcwNvU4NReXKUPVZGWGXEAeAg0UYTVzNUqLS45LrOpwDMzagtpjvmI4x7yJB9xah9z0d70UD+eHJVAg9OwTv/qCn4micqv9O/qbifHMBuEtt7G+6CMGDcXgavHuAn354x+Wh8N277/nup09cTp1cwCxR951+0zMoc8GRDX+PA3cyp0ZXnEzDakGjNT7zIHkYwqQhbFiaum4GC1BVIT1EqgGdGYmcZprXIzBHC1+HrnsJOxatg2E+N+BV3Z8N76Ke9964bcqJkKW79kfzztYa+96oW1ez1PWsWjzT3/3pHU+PZywHgcM69MxIhFSuiUNqKkqR0aNo6ENVbcd617p7te4WbDnckOMQV2U1ViBmWUzMCFqK4zqKweick/aJ4XiNcyKMUA3TzCTmt+N19L7D6PhwkhWhCrHuX4kfdlgVtbiBu0tzYqa0v3TAtVkeVcC+VlK6cPfwHvdEWSa++/QDlo3WnSkV8jnTWmW93cgnnTy1qQu6O58hZ+7PF3KW/QpZ+eUpJdbbC//hL//Cf//f/Z8PoWT59vIifm9v7HWnm7ZNb43ujWmWAntvFaqzbjeyZWrv9K2y9oZlDXXmoipla1UOq7zy/3vvfP3tCx3n/cO9GEJupLzw7uMf2Nf3fH35F3J/Zp4Dqw6eyrau+JSYy4nexbRIcLBeDgjBB/74yo6xOAtC5BqHzJvFOBaR9+gq9Hc9huWiPrZxNgrKoOFpeL4Pv5uJPMQ63sIYbBJdkkjVA5KH6ZhrhNR9xZlIOVOQb00mK37RoRJK5NFVJdPhf1T5FplMGnYlDxOGblhWGpbFieDsssnIhHHdoDx2fRbDttkRTNMTiueyI8Rey1/4vSItg2XTdwhOtj5vi3lJlzK57QGXDKZaQ1oIsTzcd4a6QytXLrdiZ+lzrTgpDsBBVW10evWDodN6FYMt2FaiGedXNkoPGvKxGBrVBZMJNnp9tG0gmV5lw9GFpV9O8O7pex7f3XF//475BJ8/3fPhLlFKD9w6jP663lVJE/2UMTbmJCPJ3bMYWEYATnqWw1A0GVQfCuq4nEih9wgagQsXT7GORYXVTK277K97CDdJRuZE79eYhxGJa9KDYGL96xKMgyyeRUfq825ObztrXWMQPdPbFesL3hJtq/QVtqrLorpsO6rcOnj3vvD580dyjss5Dm0SJJ910BIdvI2s7RZ6H331PE2CrqIY0E7NkLJgZl7xfnPBoRqEC6IW1XXSGRNwkh9IRdbFEdXCsHGJlzhOM8jzcQFzzLBGhGrA5PH1oh5znIejqxipiAfDiRBuZqNE1PIwOQTCVt1IqbCvnbV9BYPTcsIxvn37yu228eHdO3LKPD4+Mqx12q755TIrhbPc3fPzX3/hbjmTe2cqYuf98P1nllToBMzeu1xg3ZKoerWyj57bna1tcThm+rbpmLlt/PLtG9++fKHhvP/wgZnpEGINnxtLJsvk1nSQ9MA7d4nxSgnGSJnBE8tlIk8Lv/ztP0F95jwHZc0b82k+fEz+zn3VEhxBIWPgOSq90dOl44AfoTNqB5uYCLyZqBHf483fHcK15MrY1WzAXnUALhtuSwPeiIS8Lv/6lPyAMMYy8YDUMHnYTAP/POYrKYzTJlod1D1dEJhjk2y/1YWMw7sf/GsPF1H3qHR9kAHFEsnuYIWeJ6j7MaytMajNNoa2jVT14lrASDUU91bDsKwN+Oc1+0Jv5rWTa0c++fh89L17b/r83nRDakBGJRbPehgHdqe9yWMYlOhOE5Tmk6rhY/CuL229H/qbo1voweJq0hDkFDBLViFoCdIEpwmW0z2X5YlUCvd393z88MDjw5llViVfzlUtfWu0pmeckkRmRuD6KZO6kfoMofKdejmKk1q3WN+va8XMGD69uuECVj0gQel83I9lLsbeYJalTKNJgJXkJwZyF039FToxg5Iz1Rvdx5AUzKu+XxQq1Z3adnrb6FV7pdXGvlWum3Pds0KR2k6rTt3bcbniUGb4w59+R5kDck1ZhQKESr4fIrIcCWmW5NQ6XFRLnkW9jY596IcCSwZULJm/brqDPRVV+QAfxh72qPT/zgXguCiVBT1YYm5OEioUX29xbjRZfFiLbkHfe3hyha776MxydBYN6bRSvBBzdfOEVuLV6mZsK+Pz737PtJzpaY46xzGF7pGKyB+3b9+YzyeWeaabs24ryRLLMi41OM+LxIhw+MClkpnOC23fIHKvi+N8eX5mqxslvWLDv/32N67boJplylRIZry8vPD15RupJC65MGdxq+u+YSkzl0AWzfGswI8MMZiWWG9KYqTs2watUJaF1hM2Xfjw3R/Zb3+h9i+YqdpMAT2JAmfHzd2D5aIK8XV3je5BqNXrhw6qp9Pxe8Ks3waUB5aERqCvVtFDpCTtTtQUI4NgZB/7YJNo8aslFqYMxx7X9zcgG6dyVrB8i2AhfaXgk9zllxUMEg9a7EjdS0H7s+q06GKIKj2+jaoWlzvsAA7UcYBVo7tmIMRdFvaD8b5SPIVdz9MTqWe818DkDe8jI0Hc70HVGwwS+ewMm5TR5Y1f4WNkiyo6Xk/2t7jtwI+7t4A1BninFh23qNBG8p7mPEaPChGKQS6iRU8LpDJxf35kLmemaWJeFh4uJ07nE95X0jmTceZFVWvOUwxrFSaUbZNPUFb/OVlmD/aVMnq0VvM0SXjZghqdnMFCs5xYAvRLaaFWBSulsK3ukVuRXLDkwNVTdJz9uJy1t+Tf1eJyMmHg8bwL0NOwvHBaZGjTpFbGlEhmHtOzN/kaTTmz7Ps1LDYQ06cp36D3zG27cV0727axrZXbGzvw3vS/P/zpkffvZ3LR55IGFbrrnKguSx1L6myyuQqYgF7MiqI5XRfaUEdbSurYfAQrRdcYl4aCw4hnBfguiCg2nMSCY43m6HKJIsdprtx3E3aI5Uk6n0Mr0VVdgBhl8YwPqC8KTQlKRwiSirJBLhjEDMl5LDoLwXfqZhNt32kt8XT3kZwLde9K4jNj23X5318mSjHOdxe8NWrvTDnx+PSEu7PtO8sycds2Lnd3MesYGKqML5f5xLfrC2XbmeeZsq4rv337G3/9yy98+vSJaZlZX6789W9/02R7Klwu99yuV+Z5YSoTD/ePEMHadGf3nbXu1Ns37h/vyFl0sLa+4CmR5oUZ43w5s6RMngt13yMQQwdYyrqxU5q43H+i1gv7/lfq/ky2FVkajHs4YAELsVy3v+skJLsPq2uhJa8flo4msLchL2LQHK0k4oI7Hi2qPnwfALu9aWithS5Ah6D1hPuGXHc83FvHqw61cYrZQBs8/fGqYliF0fMIXeqH0dooewaX/7jcTBefM2Yy/bWDOBqvFkQJ8cFb+BoNH3tc3PghMtL0J0LRTdBPig6McfSYIhRH2lyiBUlB78mCRZXReng91HbGdYhNYWBndCvxWQz20ui2+ut7ssJhXuCqPHPWtkpFrJdpSkz5wjJPnM8X8rRwupw5LQvzbOqU2LmcZ3IpCtUKG5hSXLBYDtFfQGHFdtkwHJ2k+OQlZR2iaSIVQQaK6TVI6kQL4WTrhBW0Tg1LRrJBdR1alxwmc3rGcvbV5SzmXSHhJA/K9qAQWBIZI0X5SsK8UiwHJt8pMUzto0ghitRjL3XcGiPvAlOnsDVBzG3vBPCuOYRzUKJ77/TNue4uB1K5z1BdncSHj4kffvyApRuWE6kvJGs0M4hZT0qFYm/gZEMEki6RbEmdxCz4rAuOJrpeZ6AeCcZB70CqYS4Zg2vAbA7xJyHqHGs+lmUUnDlLMLm1FZLmGjmNLv0VzRAdekzEtG93I1oVXQx9APzeYcxeAt/0gGCThz+YpdBUIbjO1RnVXkk+M5U7fvvrV369vvD5+w9M0wWjc3d3Zr/deH5+xrszTRPnktnrEA4IiVm7prZVyk9yOGxI5GpBo21svXGaZ0qrlZwLHz5/R+2d29evakVOF5o7JVR6OHjrB2eaiCncu8J7TtOsTR6tn1NpyVimwOot8XB/r5VZd2kayhTYsPQNz88v3Lb1GICWpI1d8o26fQWux6GullOwxKAcwuCMpzjs41COm92DlfPG6Z2hpNSFF9Wzj0N/DP2MkYMsx8ZxgHn4ycRGi2vMKJol9DCVS8SsQZRDb2OBRVd0CG50QSRL4pWnNig2sWmDZBBlv0eoEGYRWKLWdDwP9x6VmBhGsgqIA8rFJKruB247VGopcG9Vsc7A+t508LSo0HE7IDnDY+FHpkIdn4HjvZGz6aBzC+sSdRkKuJmwVqkesKVHhkBvGvYj2mBOcDffczrdkXLDUud8ucdm593DIzkZS84sJZOmzDTJGmU+RTJhbEb3otTE+DwtaCfJRjvn9CxIw9tJGyyhw4KKuWFZ+e8Jk/jQtH5SVMOe1TXQ/FXl3+Twq+cVpAVPVGRZnyKTgYA7LFlc4qEjH064puGyxTrIqcSf62A9XE5t+P8Q8EcnRxfIG1adFrwfRAXlLDda0Etb20NtPbK3NfvY286+V3pL7N6gvYrmAoGjzPCv/uHPnC9JB70VXI6NFM+i59IoZToOaIarKx2yk11UUnzQULUlXhmMY3CfjgOcQ2Py6jbgr1+OTjCjxd8ZB/nYg3tTp7t7hS5tiMecZxR1PQ5c769Q81hThyTJ39qGj3Mn/tDUBVnUnx509uRIaNd0cvW20VpjOt9xOt2x7sb1+sL1+cT9hztKyry8POPu/M//y//E3f0j/8U//JcY8PztG+TM+XLRuqyDIj2SOO2A5L7dbnKOPV9YppnaVVwI6/PMdbspM7VkTucL15crt/VGyVJip1axHp6aZvSkVlDry5nnCLhv4iDfne8Y2a5OV1fhCYpcZPfWDhO9jlNRMtLeG8USu2XOlyfuHk68fPtnntd/YqGSqfq5Il+q9je1TNaDEXAshjeCNnr8DYsLYUAqJbDNqLbxWFLp/+fvc2CdesUhLgub4c7AD2Vb0q2Ea7lojZ5fOZYHNyNa4kxQKtEQS5+d4BzRX6OK84Yf7qaAl6NVVpW40/t8HOgWNMfuEUnag6kVdMfh4Ck7c/1cT10lIKGBGP3yuA3Hf3mToLHFu4nXpz9Pol2GwXmj0msSPGTauAO4bb0ilb2YUefLA3eXRy6XO7xWis0SVCbnfJ5ZlsJyKpznGXKjlIylRk4TS5nAb9EVlOAvNKyMrA5dyhaXui7pznC4TWHtUdNwJJImQGltFeJSSKHitSh5Mx7sO+JzDL1PXAaDHl2SSZwYgrHhBGGGiqlmWOuqVlG+89gfObqYYR6nkJ/Ovt24Oz9EvKZejxqZgbEL+lBWhkoZdSuJXgIm6U5tldcLw+hVh2atY0gcQ2Wg+0yvnVo39mrszWm3nXVXF9FDM2IG/8Wf7rncd1LtpKUIKgzjSEfve/gnWZbjgIwxO9lrwEK6nI+4YPOjWPMRUWzj2bzhbqNhk3eZH+riifAyNJMiBv9as64LyhK5d/beD6KE4+RgfHVHhJkWlFf8KGZAc7DUMnFCHtD1gK9fKy79vBZq897DvQGx2Wo2tSVtp7fG+w9/5PLwHaU2/nw+sddGj4Lt+fnKh/fvuZzv+V//l/+VKWf+m3/9X2tPTTNLTmJSIfbYIB6DhRWRR5fUaHuF5cwyFcq6r/Rd6XPNVXVsVYvUcmKxSQcdHsrDuFVsfECj5XRqbYe97gAjk+VQ0boGaC66G+ZMcRBfby8kjI8fPlAfGt++Rh5ryUxTpqWMzR/Jm9Nsxfsz2W6YbSTCosMBK68eLlFh1K4KLJnu/lcDLTs20zGmdqmYNYA3VfJwuDwmjub+77oMece0qCDCL8gmUfA6UZkRcNhrRIoUpKPKG0qKMYGMA0gh4HhXJsFR97menRuiO3oY1HkS0ygyO2RhHWykPjqp40cwKsMhMnCEk+K8igb1hhn3AD6M1XKUP40K6jqsh5V4hz7H0ztR94l1vakazTBNmcvFuJweyHGC56yQmIf7hZQaywR353fklLhVAdzn08RcMmVKTLORpgkzxYamVME601SYyiSLjOzAFDDRwNn1cxTiguzhsUO8qCKiH/TbXCSqNCuk7OCJHkaOltMxBB+CqTE3SwFLjdnZq7+QiAmpm+BMG9dvQA1mgiRMQ+rcGy2p2s3I3t6i4yrdmM932ofdwTJ5aGMY9hRR+R8UUMOtYEHBpodNRRyEdd/BMq1vbK3R2ip4l1eLEK83rrcXbldn2xNtN/bW2W5O1QiLkVn9/Y+fmLNjWTO6lGK+ZyCWwGui4jB/TPHvzbKAWxsMry4Yr0WuDQMZC88kGys7ipxhnJkTuOi6NbLOZX+xSe2dA4pK0XMkOHJE9CMZyuoxixhkjXHYq+g7rhuOHeXRPCZigP72jIqWo8fBnexg6tmYvRnBaDvx/Xc/qqDImdqcL7/9wvmsDJqP79/jDn/+wx95/vJVoujemKdFc54W84+kHuo4x47n27lcLtyd71jXVeFEdxdKXTVP0CGoYG0N9YMKZom+7eSc8CQu7sCW4xkcdrYe0E/AcHF59MBnxGMvlkL70OmWyGac5jlUopnzaWJaZkZA+O7Ovq+0dObu3R+xvtG2r9T1V7p/pdiL2D8J3IeOIuYJ9ANG0EYcmKQGZm46G8c0QtzkwI69knsOe47YGG8cVV6ZOiHoYbSS8r5JxykfLJJgYuh1RKuKqlipr6Xe1qGiCyjXIZwLKCleB/6qXMYTjRTsE4s7vB2bZwzftC5HVxewg6lr8dArDIvxpL43PrNhlTLs08Pm2HVoGzp8TOcTy+mMMVPmmWwz1+s3MoXujUcW5tMTy7IwLwuX04W672xrY1t3cilMBaaky2CanNNJOoEyJRSrq9SHnBFnvzqpaD4wMPrXLA/ikiTgm1cmh7uRcyGc+GINjAIgkUsMRQM/7sMcDoXUpFbxoyC1UdzGZapLowVMm2Po/mo7k6KZjY6xe8AvcUlbwKQxtHRLb6jHShNURGaOz0gak6PijrU+WGRmOYRlb8sDfe4jeGiY3m11EzEhqLted/W5oag1T7Ra2RrszVhrYl2N7bZx22Gv+h8d7i7w5z99YpkFZeQUAxAbe7AdORCxq4DQKplFUmOWk26rDIYeXbOBFmvbjvc8CrExNA4kISBkC1ppa1VdcFtDlSzvLyySGpPRI0Gyth4miJM0EqaO1oKG1i0g1oMZMi7lkbXBUUzr5cclcTTlpvXT7fBdC6AjEunkjuuemMsdeb6wrht5mrndrjzfVva9jhwx3OD8cMf/8H/7v9J6Z123A57beo0LUDfe6JQ9oHOAl5cXci6knDjZzO22Umq0o+5ObaqGW1NjVmvjdruy98ayFO4u9/TaXqsdH+rpcnQUBLbVe1M851T0Yecso7c4MI0c+gZlMzTPeBWG3tlpDdbaKLlgDktSAoX3mXn+wHJ65PbyC9vtF+Z8o6eNbBL7eRtPOT64sK8YD3FY8Ro6RMYmUyuYtVFTonslDWaQx4JwsUj60GjEZmxYXBxx6BP02fgonAFt/f0Fiw2DOrQBpZbCaXSX8+rAOMdB37FDKOTjwCYftEggZih24NadjnvR4eVO94F1G8cO0wSUmpJYFwMopWExAExvTAdLgoe7zLKceP/4I+WyMOeF2ja2KmZWsQ/kbEzTBN5oe6PHxVt32cuv+29cr7/x7t1nTndnzlPicpkoE6Tsyls3aLWq2q8rfc+kqUCLDsSI9jkuxuzYgIwM6TVSoZiYRsViHaaCEZDGAKwT5KGyB7C3sGilMGmAHkwlPSHZ6udxAXQ/ig+IosKGgXsw1UYHHB2ulOpGTyG8dFOhFoeh/m/ARyUuABU2KQqPg7vP6GATYuu8WqhX90PIOAqHve7qILpMPcV+itXqw98K9q3Re6XtTt3k9bNtcNuddu20ncMh9h/+8InLZSEXe4WlR3GT4tIJ6wnjdYCsGcKrIE0wQFKxx8EdgTyKUMDD+Te2/euYRVohFbINeo2heEb+Yzk83KL4zWMg73EJgfAqCyKG4yk40v01FsBiL2YfBeUIoDpOGv1q/Zij6HwIEaCpWJEzhEUxU9V5d1Hl3336kefnjZf1Nz5++Mi63jjNhXXd2HMVQ2yvPD4+MF8Kf/3r3/DeWU7nyAPqTMuE9cZtr5znmdZEjy1lwl3ixdHJTNOM204Z2oE9PJtaG7hlSF1SLJA9UriycL1RpeWICZSDI2z7ymQTlrXwZvewnvXjAx4CJ70gaSm8tuMDEPxpnCe9udY6KU/cnp+5tp37uxOnvLDcfaYsF+rtN7x+o6cryVZIHbdNwrU0uEVHL6r/CmhE2dODSpuDJSToSXkRcUHQSJ6PTaN1pWHXkXrm4EQbf1QK6pB0Ecvea7wKXazRRtMk349NMl5yD5zZj99VWdBj0CyyXAqlpwEFKXClm+iaxMWlNgbbVSIsF1NKFhBhJ+JGagQLCj2PPNLZYJkL58t33J0v3J2M909nznMhzYlGhr3jwyqCsTmjo8KoCbZNMFvJji2Jd49n3r87syxnLsvEnAxnJ7ljTMdFLOM36U88uQSKOVES6grigh40Ya031f+jWLKUKHk6KicdVmL/qGpVda7LwcBl2CiHdkEVI273tQN9hSDHMYfpkCm82oi4yztHNg5xL+HkZNSRR2FgvYvSPD7vbNCChx8GipZVreajI3nVEoyD6S0D53Xdd/lR6SVSWxcbaa9s7ab33jwEeGHZHR3MtqnxqjtsW2fbKvuaWW/Kj9id8CyDHz6feffhkSl5MIi0gDJaZ6/hV6OyPp4cSonrIhjQ9HoDqsWCDIAswD3qQdU6duS8W54ORKFEp6FnWeKcuh3arVSmgGvDC83FcGvt1d9shLKp04sXmsYP59iZ/bDcL9F1j52gwjEOGn0fR3O4gBYPpOOo8HVW0sWOm6Y77u8/UJZC8875fObh8oDHnGloiJo3zAtfvvzKdV35/Okzl9MD1+sXxcvmQkmJL1++cj6dj/WhDqWwhEC4emffd0o7Lgd55UgEpIN1nibuzhf2tgMxczDXYh0Qhg+83ckpk9MkFkfKnJYFJ+Tn3Uk5awgU1XGKG3NJhX2CtmmC76ZBoadgfJTMvu+8XFfKpJ/RcIXvcGK5nLB2z+3lV3q/4XaNSyl4yEOxHPOCFp8vqYSx2RhnBxsnBHKJEbAzPmnD0mAxvLkcomPRrCAzDAYP+umBWx6f/LGqhl+MDwm+a2wuJs6kuNSUj042hbOLsg6FsY6CS7YKGiZL06EDQb+f4sUo3/iouD3EPv2V1dRMOoUcGyGXifuHe949vufh4YHTvHCaO96eOU1wWgiqZ4vZdRdmPBVKliWAVzG1bNI6cSCXkw4KX7T2sowLJ5MRXM7OW+ddwQCd+XzHlEUqSGV8QFVYPBadlWCidMyKnGGQqANWHZ2sEYSVpZTCO8njx5kGqq2J2gyUNNTUAyaJCtY9zOdi8wc41WJG8cqYIzrRqnXurzOxsZbk7uLhKuxxj+l7d9MhlcZliS4BVe9xIcUF1poOTLfRtYQK2KGyyYm4KwZgb/pvj4AlGc8Vuct6w72A72zN6A32NbGucKsbrfXImdGjf/gAnz5/pOSNUk7yMYislRSq6AGp6vfkrZWS+p3uYZOSMo6KROU6DMPIcfAGePfmGR4Qb8zeHAvyyDi8DXKmvsgfS5oUpxQNys1jKG2ie/veYES5+nEf4L0eP+u4NA6q43BGHhfC+A+L/R6dnMXF43ExGoLliA4/GazQ2CjLR5a79/z27SsXu7BM0mp8fX7hfLowl8Tl/l7rp8qW/qeffs/15YUyaT76eHdP2yvbfmOZF6ZJOROn00THucsXXtaVdVea5PPzN07LTOlxUFxfXsg5Mc8n9q50Bit2eH50F8vjsFCOittM1XrfGjmosa2uEX5iWGukUsKWWRtFjolvPulsTGnSAq77kc2A77pzHUpJPD49kIrwsuSNjUozV+1cHpnOC95Xtu03OaV2mZ6V6BYIbDJHpKaPgzognrfLz4Ix0IFkri7CasAO0U2R43DW7CMxMbygXlfU+EE9cNFR1UTF5zIKS52oCMIQL2S/fUhwB9RkE0Hkwm1icF9eX30JeKLQ2hDV6UAbswflOwSUNNTM0uYCMzV1Wp/Ibnx8fOJ0nznPzrIkzBpmN4yOFP49vGfkB51I9FCjWnJIYg15RMJaN0oRppxzxi2FMCl0HlZUhfV6YMDKcGhMedYGy8oSUPiQK540BrspjQH0a89mXdV6CU3OmE8d7r0mMgMWWhgLY+ex583prcnvKxd0jde4aELxflTroU3hGEBJUeuurqXr0N9uN07zBbPw6kLwHKbX07vTkzLZfazdJl+1ASklolNyzZh6rBES9B4214wGWsWgt05jk4Fk7dASdR8IQhR/ZM35WgjpXNky+97xltl3Y9+d2jL7tnOtlW036u48XODPv/sd53OinMobdfTY8prEZR9eXPrdjB9HQrJRhO2vl4XDYEQpMiHIEa61q0FxCtimMMSAan3GpQxTLqy3Z5E7bOTI2LE3e0CwAu9gZ6eEQWLvUoArgTgdotwee7kTs64xDyG2WB9dnUoH0ri8wpeqh5B2DA+IwB9LNF/Y2jfuP3zkw3e/43mr0TWqK57neK954uvXryzLQsmFf/nnf+H+4Z7L/YW6Na7XK08PD/y2buRknJaJeVrYopsqmJTy28bd46PM/RD5oLTArk/ziSNdbNyEcdCNi8HiLHWv0daLPZG6HmprwvbztKgSchc8ZX7I0G3cpm5yVcQPoVGxxF4m5uysu/IsPAuzLMlEMWuNvu/YlFmmk6wQHJobzTI2XUhmTDyw377R+8rar+Rc9XOTBpDJjdZXjHbQ0tyJKq4clZ+GTgbWwxo4M1hSw+8lW8jzD7NADnaDYKkR1DOGaW+6k3gurzYSTVCS66AZUY1HjKtr+cr86zDBOD4vXap+HGCjmpEBQFwwIU6T4FRVWu8LqSysNbHdZrab6qFlAdLGXDItgaeGpaZ8gZyHTOMwELQogFIAYWO3SDVrcpaNQ9mTNjdesaCWYlndaikMvUcpQ22sAyMNWCiqs7C747Wq07PBCopqrSo8opoWqy0w7oM1UyHWuNT143N3EoWcBhH14INBk2ZIKls/sHt91OrKsk4EugmuTC618eVyL6eC1onAgWO9EYentDL67171zAdRwuJStLggQK/7gDIOuFP/6E057Ldtjffbua1fwRex+nzwuoYSO5LQGBdEo/bCvkq1e/xva/Sts2+dkuH3f/yR5aEwn2bZ2durcE9nC2IaDWjszfo95hVwoBljX77dO4fwz4Oi7lGtx7zP4mc5pqFz71jOeJKg8/byjVpXcl4op7vYz6F/6CPxMQqKcZCLMRDwrRAEQc/Rt3ggAqSYAwpnyJHR0i295pmMWYyj72Ue8K6rUIh1XfcaHfAd//iff+X77595fHp/MLF++ctfOC0X7h4utN74T//4n/n47gNPT094NvZ9Z9oLuRROKbHXxmmeWNcVetCaa6NGxvdcCtPTk5w3eufu7oE5J8WXtt5Js/KVvXdKybTWaLWRpyLxV7TQ+hwnbeCmW82yUaaZuu/kksixySxJzVtrpddKKpnzfOaw1DA7WvFf//oLuRTePT6psxACcHz0ZkDb2NcbnqCUkyxE0qB/yg63e4dypu47O84y31Hbyq2uFBrZdtx2YMetQmRWKLzE3izckWOgV/CaVDWq0GAjjQM9ZhMpBaT2plt4HSa+Yo3aCEFDA14nD/rHqCAPLDb+TDx5vebxszm+c+Otz8shp0VnZu/j/QUN110XIhe2NvP1m/PtW+fn335lygvn0jmdEg9P08Hm0BC5YyZr5tFJjTJQRnBR1THIHDqMZd+cSFkiuSGG7MOmPUGiiklyUDfr0ZIL0lQ2MS060niurXZKES1XTpranCk2rtSk+wFh/t0vRzoMH1YiAy7wEVdByo51redhBW9jbUJQCj38r/Qh5xhuSnAliG1w6XtXnnYKOCgFpNFr/bsMdgtvqoQIJRaQkqjXrwcpKeG9BoQb5ULsM29NKZNbpe6rXmcH7yXWjN5763on3uWvJgxeGojaJ/a10Vqh1s5tX7mtO3tztgbbDb7/PHO660y2M5WJ2hplnhhEDelFBkusHXvj7a9ByRwme8deiULjwOltFJuibh6QnavwJL6H3l7iNYxo5vT4jpfffuZy945cigqELjshdWiFut9oTf5uDaA3JkqcCq8Q43j+hjoCFT8RntXfbEd/cwl6wIDk0C3ByGd3hzqILZE1cnn4ifXXwq+//o337z/SWqXkiXeP73GDeZl5/vaNn374UXTx9caP33+i9UqtYn72Lvjo/v6RSynsXbB+Krq8koVBqInBmnIm1cZeK6W5x2AoPjD7+4OsbvVog4eQrAeMKwvgnZSXMJ+Ddd+Y8xJ0vMS+Xdm2ldY7c58o0/zaVnXntq2cTice3j3qIWXDg2WVDltt8aqnaSKVjMdHZSZVq1S+Pfjqem0tZ6bTmeadl+tO6ifKaaK2DesrnRuqCRdhyQYyC2yYryisJtSPyCjtsAGJrkEKZx0ig4ky2Baj03jF1eJ49iQskrAQD0jLo6PTQgmIISql4+/HsBMb7Xf8HC84K05VReMyTfTegl4XGCc60Ef9rQCWhS/Xws9/7VzXgvvMdV259Y18n0nTmZQGnIY6t6LNKhFcp5lRLIelgIVWQH44aueRcZ7FLMqTGE4W7Jmg/hrRhkTbruU4WC/657bvTNNEpSFBui5lKYktoLQY7Ae0N2YELcSbpBo0y7ikvEWXqAG/GFFG7shN1Ry6XDLNTWSILhGiBTRwWLw0qeK3vpOd6J4yqcyC5tTHUvv+Wm2mV6aT7F/6URb0FoPf1A5zy1QHdqP52dvOdLDuEi4Yq+/ctg1vG60Z3gutNzpNRYO3sHswmlfleHStr96GxXzB3ajNuO6Jda283GTqt95g22G5wPv3T8wz5EVzuWl6a3Wjf+qwb7rIU0QbWX89VCzcB0ZvENoB3HnZnqlrZV4uTNPMaxoKoRsinu/rGSZNjFG7TBinCS7LmeXDT+ScxSByU2had4rN7L3FHGSwpVQQthiEp+jqx0UR7wyziAkda+gwFXy97EZH5EeBM+x09N8WBJMeM0K3xufPf+b/9F/9A80SrQvWrW2Hogv9+fkZsyR3DG9ctxvcjMuygMN6XXVZuHO7Xum9kuaJ83xiP14/7OvKdDpxwMa9cdt3yUuzGXtt7F5ZlrO6iSkgl7g86lZpdOblRN9j6p8S2VUVtqZDPPcUNh4ZijBAFphSxsInZ/x6WZ/ZrisYXC539CbbZxiCJLEialUFVXvDXGKpkd0gVxLHvOigDdqjpQbJ+fbyzLqt3N3d0VKme6FQqHUCu9dB2XeSVyztWKoYRf47g7veq95nDDs9zPucuFMDHx3cduCNStkZFuYWsIRecUQoju7kuJw1WFZv0d/YCFgcqlElxWGs76Ot0TCIjucQ6SE9w0CdbEAxUf/ue+I//Icv/PJ8ipyFFU8y2/v2Uvnb31744dOCm37Ky/rCwzSRs5LfUhxuNarYPgCX+P0UzI1uHMK8g43jFoK1RA5IyXsc2kmJf6MjGKFQGpJWHOUop1TEcvIehngJzxmvG8yhcUD0xnRYy0eV7ftR4Q/bFUF64/B5FTU5Ch3qpckbKREMIICJsOALmLTR26rFkQv5PKuCtlH9B16epeodJItOV2dc/fVy/7tfHhd/0QGlOite33i9QVH1xBbV8F6fNcvv4tsLQ0+Y76QuFp+MElNU+abwoOjeWtupW1Z+9fWZ221n35y2wrrB0xP88fd/4unpjOU9BGGD1fT6GR7vIvbC4XcUzr0cRUHwxRxKVvzu1laev/6McuUTre0s8/nNOo/5TEcuuP46VDZ36JXb9Zkpf9CBmsMzIQR55rIWqSVR6yryRRCbc1ooGVH6Uwy0fRgrOmPJDCgQz7rzDjubHkVPnAmmy731UQhZoBP2d89or3A6v+P9h98zzU+KBg733uqNfV8p80w3ojMNNlfJx3mSsvHt+SqYNmX+P//mf+fXX3/lT3/6PZ9/+glLhb1WQU3LCdxpe6N6ZZ5ncsoUA3oSZ6bXhk8S/Hh/hYO6Gc+3K8ukampgg8RANFuConZ4rTslSxHrOL1MzDYdf2+vO3Mq8ksKvcW+rfTTCbdK9sEld10azXl5vnI6n5jyxO57nDiqtOUwK4xy3XamaJf7gCtq5e7unmU50VvF8ol9v+Gej2FjsxPdd1rb8LoyJcD28LwXNbKnhvU9WtywOD5aSA+MNTMS84aHvQe7pkfokRMvPwbUo9c4AlfGKjHiMFeV5dGZSfyUB9R/wEwamkXwTrTjmhoWUWu9jpoLZwKUY/DbC/zLrztrK8yzIMLalVte985//sff+Ic/fuLpITNnJ9mJ3FTJCKdXl2WDOXQ0LcYetFK38MU1AnbSn6ehW6Ch+MqsxLymCn5oatxHxoGex23v0Cu9uWjSOVHbrgtkclJ12V73F/I86fPxRoQYS1mcpVVokRIoTZ22afMt3kOi106J19wdPKzTh3NuM4XhWFcWNNsmwWnv9JRIfWeeG6lktr5zzBxAnU5QXVtrB6QHyOzyuKTks6WISyOlSHrrwwMr1pk3tm1j3TcSxrZttLrFYH06qtpEZnclT1ac1Odw9fVDpSv+0Zl1h/UK61657p3rtnGrK/vu7A3u7uAf/vhH7h5m8iThVk75jXlmivmA1uprEJD2rDyOGhgULwxG6YDSPEnwl1rmfP6e3l7Yt2f2DWqHZQlfLYzsKTrGiHd1jq4sp4mH+w/xezW6tOi03eU91VemYDi11LHqbwgWexzeCYJNJnq9sY+ayyq4YHsL7dFrJPH49zgvvB8ec8nGY8oKvMPJTdENy+UnyukdmXxY+mPQ15VlnvEuMalYiIWXr1/ZauX+dIkLxXi6f2DvnX/56y/8u//4H3BvfNo+c32+cXd3x75vZINUskgVTefJZIVG10zCvTNPM+SJ7oQjaxUensU8KWbMi1qRktS+mZnw5XFmt8ZpOsVtKr1F6nIKHW6vEeiKJTifzky5UErRbdpgbRut7pRpprXObV3lwRN52b020qLLofrOtq7Kqs2JaZoB4+uXX9nayt35wuX+QXGdBEcbaS4GWOh4VDsTrVb2zejzgHBEwzTrzMnIycG3WBz70QU4TTGhYzH0Gu6qWlAex/hgMKRQUBKOnz4qwgHVvEGYRp96+NR4BKTEpTIM/xqR5jVeQ1TbxoRygEcXNF6jnCb7gL/SGH5mTsuJbXVBFdeVn3++8d139/S2UnKnTYXmlVYrc6kD+dCizwatU22PziL2hBXBZfm18tOZPaqteI6A7D7iovv/67gkytx3Y1s36toEQ6ZMT5263njZvnB/d0daFqztQbAIJT3Cny24+8dZxoAVYn36awfSkgf5LWl28QY+SICNgKU6NCcOXmh1p5tgLnUGAQW53ncyFV09dBcjh2AUaCBSBkP5H/5drXVRM5UiQq+Nfb+x7+thL37dN7wNBL2jUCd1PL02etsCiivUfoNYk94ztI1kJ27bjX03amu0ZtStUbfGtjXqDucFfv/nz5zvS+wTWaErNz6YaRZ08vGcjQgQgurp6BaNxOZ7ONjqIsliRGDJKEVWK3s9M5+DZtxl9TPmFGKBZSy8lnDkSRYMHm9NDhjDhSEOfXfZqxQv1IgHLTZTy4Z4V4NFpSXZu2iyyiaJhdk7mgPF+fK6qjR7GFv6tQ4gpUFT1koaOdfa547NZz5+/CO3286UXp+v187Pv/zMD59/ZPeON2fHmS1z//gO6zuDBbbXyrLMlJ759vUbpRSW0yNPD0+URZnoy7KE3brTc+bXL3+TOPjdO7ZtpzhoQJ0TacqBqXa2Ve3y6XzBEBzkMRuobafk6RiIFJPwrtJZor27Xa+UUignVZ5Hi5lkV9AQm2VeFqzqNVRvbOsGydjjcrAhoApZ/jSVKIg0/W+14sXJNfKcW2eaC30XG6RXpeRN84TnArsqQ6ZJHYfXoFganjP5dBGdMPZkc4e2s7qGtSWfsNJIaWIK/NxM7Bzxm+uBTdph6aFK+rWSED3UmnoB8xS4skqSPrBWT3R2humcll0Dz6QY/Hkcowc04WNoKbigs8azlw8t/VXJanaikkl5mAQO0Znsg6c0kSfjn/7yjT/9+YnLJdFThZ6prZPsxtYykxlTnjSf8hKQX2QmuIfaeY7XA/RQ9puYZW7Ss7Rg9aQ3ueUD8hBRQIPPYVHSeuOXv/w7Uip8ePcTp/NF0bq3F+oyM81z2H0L721jroLR2xvPpAEFjizkeEJpuO42BeRIpk3M4/zY6E5X8IuD2cyEhujXdmMUYfTGyNUeGRd1q+SS/66DUHcZsFscMa3vEZ+pA2HMEzRwdfZeud2+yEAuARR4UzDUrteWUqXuyg8finwPOLS1SrITta3szeh9Z9uMuu/UJqbMXmWP0XYoBX7/hx9597RQcidN4UJrYdwURUkKcoVgFT/S1XSgv5kF4cprP9T80VkfxYvOgWnKYgzF94zmiD32UTKLpMMQ7/Z+dC3qGDuD7m5Z++o1sjSEeiHMTD7BYCHx9wXOYBv2o6Ufr+m1GIgfEovYBvzCYdHDq6NsolPdDrRhd7h7+syH7z7x9aXx81/+hYfzPff3D7g5Hz9+z6Bq55I5uSQGbhpWT5Ps/Zcsfck1CogfPn/iu++/5/7piaUU1ipb8H3dOJ3OZHdOpwu39UayzN2SKMs0UZIwLIV3azHPy+nVRTIexHCglK9+vGECXtg7U5ownL3tisuzxAmj5TFiUk2tMiIskcM0rO47e6+03jhNZ3pv9N6ZUmZvFavCKW3KWG3sWQ97OZ0B2PaNHOEl58uZk+uWbK2Rcigat07rMiFM7rxsK9O0yOwNKJH05A41dbXBreM5gtC3RAnlZcrO17ZhDufLmZQ0D0l9wmkkgyURw2mPAZ0WQItqqluNZxiCxIC/zJouY29kO7HHKnVGOEo/2nJFHAqSATGY8LHoR9wRh2mfgotEF17XE//5n35l60ZmofeVlKCG5iVlRTv+9qXxTz/fWE5Gund832nuym7o0JIu0mxZQ2HXUD65uhUjsdVdRIWvv7HtV54eP1FO5zf4GnEpqErTUSnILo0jx0PJmjqgkKvbvmJ95Xp5xq0zTQvLfI+3Rqs7Kc14k7U8uwu+GB48FgctrxTQOHPGi9EB3jPXfePUGj5PR+7y62Ub3QAp8qKj47E7UuKgCo+f0PvrpZRq0GeTLOUlqI4Pj8YQeO57xXunthazI3H/iQ7Seg9arHzHXnU/r4fsVtsrQjAKDxcttDn0touWWxutSz+x7ZXr7lxfNtoO+94oBX76/WfevbtQZlVTKWZtKWUJKePiS2MoTw8sPthHlklubL3hvTLlMWBO5PCNc+/hO/lq5Idz2LkPGrA6ihyfAXIP2Ne4oDqelRd+hDiNDAmXG66lIl1JiYrfRqcoSHwQ249C981+E318kAcCihyt9fErHfOWHqQc/Z7mqmOWmREZxLtYesvyjm9fbqTTA5fTHIN2mHImlyIniiQpQqHhObFtG9Wky8km1XTfVywn/vjHP4NJVzNnzZyXMrHv8GVbWc4XenfuL3eKeMiJr1+/UswgT+XIJq477L0yDevezsG1iO6fVHQ7GsZeV5blxNokgktpIafC5WmmDCpYkjpaH2gNZauGNs1FL80l07b9VXQVt2T3RilCZwW5xGHYOr3uCjAhxwC1QylBYQx/JVNWqyoibahM57pe2Vdht2VZmBNsbnoOJl+iTYoVaLpA2hxaEa9s3nh59phXRA5t39WDWmFJiWvRIM1SI5UUNMkuY9e4OCxV2QDY0DS0sNOWcMnThMbzQ8MbYqfXQjEugmhX3/4BftgUwARuwYaa+ctfd/75X37l22YkJrzv4I3WnPN8UndkjqXE2jv//v/4hXf3HyjWOd/FBkoZyx38hhTOMfjtjptTikgM1/WZbX3htDxQW+Xx6TuWWZe4WyhKwi9KB8ugoLaDliy4wsg5OjFbMDPePf5AmSbuH95hvZKnWZdVHuuNCA3zoJqm4wAYg+74YbFWRgfzKqYz4JwLW6/sX688PD7GRtdhklggaLdK+spxOYwsh/g6V8e9rytzvP+97ZRUqK1KrGevOpfOJouFnvAuHVKrnda72Dq9QtieK0+7BfRijE9diW0a9mtwHZsY2bO4v/L6ncpWVUjVG2y7s+7Odruyr411l8L9h58+8OHdmTxLI2R5oru0LzklconYXR/Da4+DWBRxi5lnB6aUaF4010KDVosuGghhra5LZR6EyLT3Y60PPVcK9pw1dQwj+GnAvzHlZxBJ8IbN82FQmnPGkg7PdbuKzRjmexYuqbrgB7kERkSvxJivHcZh0Ni1btWxviW3BC386CJFjkgps7edeX7k0w9/5tonzpb5/rvvmcpCbepA9lbZa6OkRN03/t2/+z/485/+xLTM5N5kmULn+fmF87ywLAvn5cTz8ze+XF/48P4DROdce+Xx4VFnbE5s1xt5Vud/mmfKFPqG2kXtNDNKnslTfq1UW8AvZlQaJWwu9F3VJSyn81HhDq+dysB4tTC9iveb3MLHPmirSQ97H3bVbRwWgrM8wRgAtT2gmCT4ciaz7hu//vorvQli+vBBpnJmRkGupSkX5sDuy5TY1ivTPFHpzAlSKUxRdXhvNHfmVmg4m+2kOZO7qjSzQqmZ5aLLAwyvTmVi2Cl/252+aoZSilHM8VSxroWQAbIzpYmUpRUoYRN8WDoHPqkwp6hajwugj3MtFmM9Zhnddfl6D/hiQCQUqid+/Vr5N//+K2V+wg2a76TemUqOqqmT84Szs7bGPBV++etX/vnnM/ePM35dOXvg/MnxqMLUZWm50zutah5wmi/cnR/I04l0WBYYhHaiW6bVXdVU0qD9GMf0Hn5HGgju+6tgrxTju+8/RbRoCUils3dnKjkU27JzFpMKik1iJjV1fuMy0nMVfp1ssK70XmQaXJgBz862VSwPYgKkUt/AKhbOpiHUi27c3ILxwnFBgA6Tve/Uusd8IkXWg2Zu4/Oum6r83jo7TSEP2iVCvsdsKWjatRv0piTBpgtgdBB9ZEOERTju0aEIZtq3yt7GTPCFl7WyVe3HP/zxdzzen8jLpgsi1L9YJacJy2HLPiWoQ5QmqngGBpNHZpkcs6Faq2zBk0Ukiwq0txYp6saGRXiYWsZesHH5uLhjvQ/jlEw3OS+o+1CyXM4ZDwKNCh4DK+Qsto/EnLEGBznlzTDaTcSH8WciHpToNqKzPDoFwfSETmR0GQe8G11I745HN/f+4488PH1ibjNOp9WN3jf2vVKKupdeKzVnSknch6DOm7FtlZf1mYfTPefzgnfpYmRdkylZOjgLGHa7bdzfS/2/Xq98uV35dD4zRNSlTIW9Vb7++pXz3T2nZdFBP/BjnGYNa51t28NITV0EtcvzZBxaCHccF0VCrey23TBzCWuasbdKieGVt06tqkjmoml9G/m6HubcfbCJO75XvKNZhmW22rh+u/Ly7RkrCb913j09kfMS+LVeSykFt8pSZpo3SpmZJrEwrBQ6RuuJeU7suywhMEgtLrXuWDFyi2GjOcuki7F6Jc0J6qgstKgTiVpkGFd9zDg6fe1Hu2rDayfp2WXUups5Uypx0I1DJzjj6ELpjTDz0gE3Lg8GZu5hFBeLFjKbFf762294Uqf3fH1myQuUHA7C2gA5T9AzN7+xLGe+rQv//I/PfPc58XgPftNw0muhtW88XCaYOyU1TEHE8RyKoIdstLbTLRTqoWylE9W/qI41wgimPNFaVednEjw11ybK+RJ00cJUGiNlTYNmP+AIZYIY7oJyzBTZGKNcUkNeOSmGnTSyT9gwoUO01t7FW+upMJ+yBqB1Z22iPmcrTDoBxYCK88RxpoDPGpXcMq3vB6wbvGD2trJvz4LGupHSBEZoFozu+wH1um44QBK7PGAlV9XWoxvEobU4jAjr726YbQe3n3BK2GtHNi4aTq/rxm3b6Tv0W6WucFngu+8/8HA/USa5E6uolN13Noc04l4V4+HDZqQXvFeGcaiq8BmzG+6b0IYxN+qNyZaYQcXMTqdQvGQVlzYcYMewt2vKiamnvXbR5nEoNqubKJOKkJhqlZxj7RiyEdeQXtDma464htXKY7GYP4yPDyImOZV4X5oHDtgaPJCBmIeYoWzveG+muUY3IQXWK4l7Pv70r7k1x/sqV/7uXK/feHl55sOHj2zbevhNtQqff/wd19uVl28vrNvKeV5UHKcSzL8d9wIp83j/EH5ZWpu/ff2Vy+UixGaa+C6KmGRGKkUOdiUXHh/fMS+TqHcBuSR3anDZm8G0ZOrW2OvKPM2CgsL6QIreRkmF1qvsv3tjXa/0Ks1Cc2cuk8yyogV3kx2HR6xeH4Z33o/Mg2H5Qd01lCkxp/COR3ubizqAy+nMVGa8Be/aVU+0fcOa02YpzKd5lgcUHl8jSXw32TdMSZGSZp3ZNOgU1XwD7GDbkCbSZqRiLBNstcWFKb3AhEfsY2gVqmE5k4KVMSwVmuvSrURmdO8R1lohafhIDJS9h6G562AM26E41KLltRQXRw96qrNqmg75QspXNq+k1MhZdMLam1xFwypiVMC5FKZc+PrtGz//cyaz4Eui3Taev/6C9Y0//vEsGt1UqM0pOR8zGO/12JwWtN/W5e7Zk8lemlFROXWt1CzhodhcmVqbYCwSvd008DNofSi4d1WYY6NWJ5Xx1FN4/uvgal6hN3rWbKo2yG9yQwjRFNnozaBVquXQV4QkrixYX3m53ZhTYe2JuWQZ5uEMwdgYIvcul1XpSTK1y0126F1Em11xL7Rd+S4lGbWt1OpRnB2r6PBp6oG1i523SmYajB28H2KxFAVca1mwKKLs7lVK7N4zrRq1Od4XvO5cbzvbDqcz/OFPf+TurAKxJM2axnwHIjNjKOPjdQ4SgA3UIQ5UN7AmnUBvLoEYQcYY3UIa1vej8n4LC8IQmroZuTkkG7lZtKB8Z6aArJ1MVreJHUWEXrc+qXEBN++vcx0z4RCHpXns+eguhsGnuodxQeg5q3t9M+jGjplIT2G/EV/vQQFsVLrD04cfKdM7zRe7s9WNKRVyTjw9PjHPM/u2sdfKVCZ+/fYb73MiWVbg1iQ92r7vTDmHn5nF+pTIsl2vkIzTvPDTDz9xu636mnmWuLhLt/Nwd0exJux4WR5odad2p8UU37qTgx3w/PyFqUxMc6GkU/jgjOGLIKOcghOf1F1s2426VqZlmKJx0PuG3fJRXQSGaF3OKYN63NBgqq0bO2rr11rJDnkS573Mmcd3T/jeON+dqV7pTV1ObV2YdE6HOM4w+UTtGmimqRwL1xymZHhOh1eTlUQzZQNXlvDOEeTRHfop5jfuzCWG3XEBJsBT4VZ3+r4rGL7BbMaU5O3iJotlzWrSwVkv5lE9a7ituYvJhr1V9ibhVfXQCIQamQMKUaXb4uLqDXoSFTInHZbJCp6NaUps1w1MkMv1ttJ8p0yaPyVz9r1z/dZ4uXP6dmPdfqWtld/9+IlWdRGv3chToTVBPsnjMo+qprkGrznLnrntnVxg3+MQ68O+3OPABsKWwnPWBeZOKjsj9TCnRi5O6o1STmJxGUx1HKTQk7qrCSdbkCDcBKu6nmU2Gfd1yzK8a0Fr9rCt7sqNMMu0sLe4v5x5qRu0yi0Ln9elsYsKHAaHe+9s12d5CNlG3XdRtpPsp3M6cduv+H5T95hgi0PPTOwiHbrlgM881kkK358OKoT6Nv5iHL4pKuGhHZjZ20pvgs7aBm5G3bsyZPbK9bqx7XC+gx9+9wMPdyfMVURYyTokTZfDAbN5lEeuOaYdYNDA62N24E6Nyrq7iCWDMq6j10neGCmHJDsotAP37yj1Um0LeM9k60qi9GG4KDpyHpeZe8ykXmFbnc+vlb5DGGhGEdadoeMeXQIx8bFRVBAQ67jGDzscFSh0iU1feXMx6+QAnvAmM47dTnz6/X/JXB64Nc0FT9MMZqzfVu7u5PR6vruoiCuFd3kUzX68HjG1VPCWPGNBnTYT1DpMXL9dNbP4y8//zOXujvvzPWkW3fivf/sbl/OZcrm7p1Z5u+Q8QUrkpjChlpxU9SG3TYEUaS70KSh98aZ1E/YYTAW84J2pnOCkDzWbxq5Cj5Q+1cOmPBcNt+id6gqXabVSe1WgvYuRsARYu++VXKBvjS/XZ6Yy83BZsEum75UaKWcQmg/vB/zjwVhpZpRcQrbhEs5MM953rntl9pliRkVVSMoZvNH6Ltx0bAwcusvpM2dlDpjT103wTXgdLQ67ZVKrI2yZNE143TGDEm67loIDnhLURjERBTy6tdo7zRLzPFO2ja04tieKwbUa5MRUUox7XB1KKdp4xWJiYpxyYm9S2yYS1+tVvProk08l6f1YYt1X9n6jGMzzHddvz7zwhcu58+7dEznDdf1KTidSqSzN8amwbS/CqU1VTCq61CxZuExKQZ3rxLY1WlXGefcgCvRBNQ1qbBK7JpckfjwbkLg+P1PryuXugceHTMnikyunuov+bKoo29Y0X0oGFowg1Dn1XmkRvkJ3muWDYLE3HTi97zKoa5217ZFVkOl1pdfEUpw6OW3fxawLHci+ftOaacj63TvrbSWXIvM9DWBivuAR/Bf6CefQw6Qk3RFh/DjuUflZJbrvsoZ3aQbcY8DtHOriTqPvK7XPtJbZvdG2Susr21pZr3Dd4OEB/vCHPzMvTs67DmsyKZ3w6JQGrJajpDsMMO2VjeaoKu+tsdPI6YS3SvWADC2R8kTqjW0XBf4oQlMUWqhYwEfBOQwewYb+xxKJdlhuD2bdNGWGf9oYQI+utYeVT++aKxCdBaYCdHDEigma8QCw8eG4MI55AkLOxwzCEFFnj458MKNSwK1vxXVGp7tx9/QTeXrPbd1xl3cSGNO0YGU6Lq9WxSDbbusrzTdJoGseIs+RX94qnl5p0Tkn5j6JUZcncpk4PzxwWs5Mp4V93WhuTKVEcBhwPi3sNXFdr8JmXYynIwi8di4Pj6y3F3INKwLnUDht+5ViRUlhx22qy3KKN9ZaVSuXjMkHP9vZ1g3fjdM8haGXxD7rtvHy/AXu7jnN8/F9rRtTytDguq5Y7eQJdQrux8S/ZA06W1dl38zwYf3gIT2vnaUkmDIjeNzyRCmNlI1OJo3BaWwF5SiLClvDaI1s+N6P7ih3Vb2WNBBzy0y5YPt2aAEsFlielPRdc2SAvxl4qfNRhZhdbWddG6eL5jGVhN+uumyKMZ8WKkbyTu1O6pUupE+q4XGx5cL5ZKwvV7atMSM7FDPou/BeK68LeUqZjZ1lKdTauN6+8uEpcbl7R5md3l/o5rxcb+Rk7NON6TTjbcf8BaOTk2mGY5Na/KaOspSF3hu1Orf1xu3b33AS295YpjusnCOPoEZanGIr8zTHpnS+/vrCz//8C8v5r/zuxz+Kqpuc0ymTcqUsF0Zu2Z4TZYM0zUw5sa1fgUqZdD9OroFw9YT17aiSPTayJcguppPXzg7kHBkpyVm3jb1uunBaCA0xwSNxadtxWLl0PmMu6hZFjQbprQvOHXqYYb4mhl5EilboyaFvdN8ZrLPaO70qJaiF/5MlmdDV1oN6KqZUq53bvkFtXK+KH/34ET798FOoxRUbUMiozNDPzkQHPobRAbMJWcoRsav3klPnr7/+Rdz7S4pZE5gJerU47Et+zfMLoPCojEUb1T9dqeqMXwO2S2Ui1R1LEdQzz3FZaIA+ui8nqOxJ30vwtKDCoaEBMHl9i4QT1iWMzzT6t8N36XAUFkOrtQiZitnEyCwR6STx2ke0KA/OPL37Hc/XypyeuVzOmr1ieMq8e3igA3/9689c1xvfffqBdd3C2FJF0JRkrOjeIGYfHajbps7VwoxyFkPMEDvo3d0Dp5OG1TYL3nzImdu2UgY/eyqFfSvUvGtAXUzurmSqO62urwHzLtGLPHpgmU+s242pGylnetuPRQ1So9JdQrdUQtzTYnjSaXtg2O743mg0pnnmcXpHGsZ0lkgNhtqkO8zLiXmemXJm5GJMy8yw8W2tsa/y/U8YqWWmJdTdLg430XEMSqNmMYZX01DYCDzSGSxX9wrT6djsyWDtG83F0885BxTSA/bKYJ29w94q59M52t52dDdzOlHCEiOlrDmOAy6nztaa8rinzHk6s+67lOjBKsNiKFs3ruuOuVGmE8sceQZm7HtT1Yozn43TVviWG3sb1hTyx7pfThTL7F3rIGWnVefx6Z75lHmcHrm7M2q/aUHmSUSH6nhO1DZRa41tvmE4yzTT+guZRw2Y2xX8FQ7oFGiJrWfqdqM65NKZ+kbPBZjYHaxpnlBr1Ja9cr3tVJthNb7eNp5vO2WBS5up/sJSbpyXs+izCUqGsu2000Ki03yn9bDHKEmeTJbxJuPAlBJzyhCMnNZ2egsXLTfqHtofnNr3IDoLXlM05pgnhD1KrD8dNbJ50d+PQax7wHIBtw5mX29433Av9GzQROxIPVHDrC8l2OoNQm09RH/NRYeutdLaSl0rTmLfnH2X4HTbYW/w8AQ//vgHypLJJTHNsvTIvHqmvRpRBhQ4qvo+XHIjDtikum7uXE4PwTwKyEUnKiPLGyyyq4fyvsc1kQ/40Y3ozvIbOBKcGIzHuGGapJ8qB6Vej3YkA46nn5IG9o6EiQnNIxwOg8NEwmKu0+PM6+GiOhxth2W1u+Ht1eVWPzsfXcTxow/oqQE7vRsfPv+RT5//FXsV+lCmzO22UaYioaTJCWOtO+smgWWtOymdmMpExvjrX/8ZDJ4eP+p5NEH0c86s2yqabynUddV8jASnEySZcV7XjbbvPD4+kVLmbIny8PBA23f2vjOfZ8puzEVOrXvd8e7Mp5k77pms0JPz7aXStpX5dNEgNcJUNF9QRWpdG8yQBiL6QeoQnoAGyFMhV7X75s7eOyMuMpusnruP+1AHqgI5nLmUgKXk1zM41e6NnCbqXqlVqlZV46+LOc3lja0GxwJQJyUWUQ7edY/FmAKPnWwGk6Wwx/BwmedgBsm/SLGZWQwqAanMp4X5tKhqcqdS1FqH+hUIYaPcq6zMarld0M8Sg8g0ZXKwLnJKVBcjKHXjZQXfO6lkpqxLevJQjns9LsLTaeHLt6qKa8AS1lnmiWU5qRpuOw+P72nrxmmeOJ0SuWzMJ3CrTAY5zSifWLhtDWO4vek9kjJzNtZeBS0kWYETqX9itAgE82xcLk/46Z4UYh91VnJQtdCPdMt465hnumc8T1zuxMhYbx7mbU2XSbuyL5XanLvTmVQEV9S+BZwDUKm6Z5lr0AORhkDW5JVbrlgOONUT0siGk8BxWDnEjEn0TeUICOfWfKCP+UYcVlqvw5VWs4MeF4Qui/56OMX3F37+OvDde6X7jqEui6Z5hYq1SahMStTa2fcaLgSzusKt0ppTb7B2uH+AP/zxTyznJUSAr1Atnl6Fw12ECovCAIt/Z1T/aQAzwcTr5GkiNVXdWD4OTLGURP/W/ok9Gkh/7/txsL4aK4uU4VaPLmTAhlYmXg3KX3/GiGqGJBpofA7ePFiJOcw+idlhDQqz3nM3kyWMD9Cpx+uIQjY83vsQ2tnQfMQtlTzmhIPaK/cA7x0rj3z+6b9hmu+YZvW9+155fn5mWRZeXm48PD6QU+H5+Znn52de3r3ndL5jKiVIG427uye2/cY//uf/wKfPPwaUr2F9bTutN52bLs3KMs0xl+nUrsF1L+WgyLbW1ElcY7B4nhdqKooz7E0Og4Q1QYeWu1rOvTFNJ05lonpnvV1prTGfTliXGZghmqqiUcMjJ6bmg6HQe2OEbvfWyWh2sVnQ3eKDHvqLNIZaaIC77YpdVbU/qfETq5JWK4VEz4kpFVK2mAWEbN6g1ybzyRTW0gV6c+ZcqC4nXMFVHhe//kzVhoGpws9JA/zhhCAKHWoBzWB4BKWQ3XsIu9701W3b8DkzZdGEU1m02UxLsdfOPBVy0IRLES7ek5G6RIsdDUjd1J5KaBLVi3fIE2Wyo8Nzd7xWLHjXOWWWMuEd1m3nfFqgJJ6/vPDddyfSfKOYQ540HM062La2k22SV5WPVj3jLu76bsq9Sy6YsBQn2Rm3ld4h57+ncab82tabKbVLmqQYhwbTZG/Otke0ZgpsvxteZtb1hWwT57M0MyWLWV8YYjpYB5MPOzj8t+DnZzMcg8i1RnEl5BzEC5PBIJbIHlBdHBqGic7qe7iJShfjXcFGNY7SHOIqDFlye0SF1sGkEYd/xGr6yJYIt9ZRkUsnI1Fi76K0Zss4RYFc+4YHBOnVWXcpt2tFCXMb5Am+f4LvP/0Dy6mEENAwm0Q8CNGjLEM2MeHc5GAQy7ibuoL8ZkKR3KndqW0LHazIFFIqDx1VFG9B9XlVNesytLCMySnL4DOetx3lHYJkDEG8Xboox45OigEjJeXGtPiZtcmiRGfTdjDNdAETcAx4Bqr22+h6XqNHR3dlIRrm2LcQxWWsssNpOP7UOrQOj+9/x/3lR26tU1In2UzKxtPjO9w7f/nLz5gZ9/f3/Pj9j6QfEr0568sVlonz+cLenZSgNa2B1jpWdFZ2y5znEyMq9jSfNGvGZew6mKTWKEl56VjjH//5nyjZkpLeDLa+qUq1TF0jfSxlmjeWedZAmc7l4Z6UFfSSu4YqeepMZWavGqQAXK9SNZs5p8uF7boOjzIwo3pjSuW4j1soRluVTmHK6WA5HY6Y4drYum7gkgLnc10iHrd3AnwuLBQdkPH3hwLyMBabhm8S7NebcPvTQto188imTqi3/uosGx+yWQ4M1Q4nS3Mw7698bpy1Naas9IGOVKkeL6a7htRpWUju3F5u7HXn/v4+RDNRsViimXDOhElpmSBtwdRJBt1Ypoz1xGmeYkEMB1U4TyWq3zBP8x1Dles0FbI5tVe2qwadl8uFl+evnBbj6d2JaXnGZlFqk1vQZDVLetkb2WRLLsV0J6WJ5At9X9lJmE/M00a3G5kTySZKSUJGAs8eUCJHHRo5FfE0E07rxm270Wtm3TprBa+jUtoou8lHqpxIG5yWTIqMhLU1HerpDXyQkI9WCNGmDM0S1hJ76lif8NTouyAeC3M+C3rmLsUCNiCHGIrjiYYqPAW6aPZlmkqH7089DgsfJn8uGvjISHASjf2Y43TC46iPzlhfV7vHoaqLdN9volj7xN6c9aqB/O5SU9fWaTuc7+F3P/6J+U6fh2DQ15Q9MfhD1uFS0+cQZB0pbJEfYm+ciHsL+5i+H4NdZUpLfT0q+2SZkQA31uq4HIbuYjwnPUMgiMZ6dRZznOgmVF0cUFayRAtbn947k2nGuIe6fIjaetdrlzdWeH51oRoWCIYotKIY/70eQrqRAEy0D2INy0jTsTYiGIJuHWQE0iO/+91/zW1zvrw8czlNvNQbvVaWi6Dp77/7njxNkX1TNYyfMvu6crveuFwurNcbv375hTLNJDO2fQ+o28PmyJlyplZB1eu6sa0rH57ecVtXvjx/5eX5yvfff09JmTIvPH/7Rll3td3LsgS7xo6q0hNMqfDl+TdKzlwuZ8ntTYtxa5sGeOacp4XeNVs4TTP/8svPvHz7RgceHx/CKFAfaraEZ5j6GMo5DMGaIaYDYQLokcbVJTobQyKQZUQ/HkBjTjMBHdMTtE1taglMlWGz4ao2WlS9rXVu28rLl29MJfO0zGJ8tJ2ekiogs1gIBOQlaxGJwBrrLqFhIYtZ4FFZGizjUgjPKPDozrTZciyq56/PrPtKa519lfhnQAseOQMpJb3HFrCbG+45YL7GspzI03zYbJuB+R5jV2cE3YNxmQt/NUErJecwMAuaLbCtLzS/8en7O6azErDdOiBXUxsVMAmr4oGlLAPHlDrmGoj+y798gZQwP/GHP8ykvpJIVEKo2UMkGPOfw5cfXcZDFGauMei+NbYN6q7hZW/GbW1cry9czjOlFFlm337m0/t3lJypxSjVadahRIrEgBK60UwaDgn4SrDABN1gEvQRa6WURPNRper7yHJbZaN1D/ZfMF3i0IQWxUMsJ5flw1jruSfE3tlFW/XGMB2sPjKHw68osHI5JSSSeWgOblg/4dZoTTYbNGerLk1E1axuC0PCdx9PPD2+53QulDIR7AbMpmOuONIjj/vIDGtBde2dZnJYHuZ0GRPM5sLbqwcbCVM3FUrnYX9uaeyrHp5N0T2M58Tr3rNR5Xo4NjB8zMYQ+bgzo+vUf+iyVHm4OeEeOwKOoLqrwxlQkkmEiTXcMkFcInULR94Blw0G0SD563v0eCEjmMq983K98vik0LRBD647fPfjP3B//x1/u16preJdFIFfvv0KX36llMI8n3g6n7FkfPv2jdu6cnd/z3R3YiFp1uQ6/N8/vufD03fquEqOXHNpJiBFl6mL88tvX7m/f6TkwtwraxbElJJMhH748UfKNE08PDxyOs2Bz3WaN2pWrm2aE6f9DMlYbxsd3UZfv33l6/NXEpm7u4tomrv8Z/Iy8e7pifPlTsZd4fxapkk3cq2IbaYHlQL6CBgWH5tphJkHhJR95KmJuZDTWLxaaAOG6ggfjSOP3qt8XZzoILIONHesObfrym29Mk2Zh7t76JLtl+kivFBFYQwrx6xCLdHWesxAioaFOTaDyXSs0bmt4q2fllMsZ3nStEGjA7YWfI08kadEWQT7iPaY6L1A2+VHgy5w20WbLZbY66bvHc/XAt4iZVEiITafWF+dzsO7B37Iif3WeP525bKc2au+R/XG3m58+HDicpay1nD0IhM5yUKi7xokugmnF0Cyx+cgvPd8mdh3qd6tp6CWyt6ELuZQQ3Cjsg8CWHTwNir2HJVyYm8Z3xu9KS5077DVinnhdt243GWm6YzXF269UraNlBULNERpwu7jIvA0YHJG1vCadLAlk+dWaw23RjZjrT0qVkFhuQs9Dx7aa8YDIXTUTzl0AAPqG780p3uTwWCwt43exxpOoR0RQ8ubLMw7KXBwZ6sr7mvQRSvrehXO3yu3rbHtTqsWDghOzvDw/p7vPn7ifJkVWYwuSktFc1g3qd0Hw89rWLunY40cw/zDQUx9fI+DtlU7aMSCmF69Ad5y+geMOLqLRiMzMtQFP6qYFETXfUOakeEjJ/ianGNuIbgwHW4DA8LTs9/bhrvRkyIIkosWH8OH+NIOFLx1PFL0Rvzs6//GmTXmESDzwvgsSxSDKfPweB8dj4ty7TAt9/z+d/+a57Wxbd94evqenDKXZWK5v/Af/9N/5MvzC3eeuGw7OUsTNgpbEMtyToXT+cxyOukC83AbaDu/fv3C6XThsixi07lwlbvzhW/LC+t643I6U5YTH6aZZT7Reuf5+RuneaFoiCHGCyZOflt33KFME96d03LiZV1pUam2feN0d2K5P2l85DpASc6+iyL4cP/AKVgg+77DVrFpUiVaJkprbwLcLSojiU40DN4FW4Tieiwc0U4zp3nCm/Oy3xTwfT6FuR9QxcHORUrd9WXn/u6sTU0OpXimxqDs7nzmNC3YNAZZxtYrJRHW5q4oSoxhhNRbV+hO27FcmEthq3qNUy7Se/R6DORTmQ7OO2MmEV733RWiVu4vGkBFoyqFpjZQSQlOZ3rXwmjIGsDH8kwoR8gkpQfThb9v+vlJQ1Rdk2HFneDp4Y5vvLDejLrttC4X31w6T0+FuwuYVSYvh/sklqiulC4P0RM+PicDZtV0Dqk0nh5PpJQpZnTfoM3UnChWOVoGz1TPquZNkAZdnU/vMjNTm76w3m7cbhvNNSD3NpM949Yw9N4vp3taD9EXmeoTvTWy73iSIK012UhgTnajNOVrWBO0R67KWCEIGJ6p49QnYZFmR5mQpbuGnSPKclS6A+PN6Q27xmVBkQNm6b3zGkFlx4XQrUPbXwfW7kf31lqldQ2oE3uIMCfZLTRnq01mgNVo1WldA9nTHXz88ANP7++ZT4scDzzJ5LFMTGnShW9iJlavR+TuYdcz9Dwo6CeFQlrGfYKwu7cgcLy9DF7zyt96Mx2U0zjMS8w39LfrwTDE33wdw7/MwhspY17Ipie5AWaZra2yBUHxyLiJYWmJ2mpg9hYEhERLhh3Z0wdopPcQxQGoKLLOYaioyiCG+AMCj6Jj+M9JMKx1XXfj8+d/xd3j93x92Xm8e0fJhXXfucZc5PvvPvO+13Crzuy3m7RCOTq/uHAgmFp1P9AZD2bY3emePEkTRk6UgAytJJ4e73QimLNeb5zP8oDqvbG3yul0ovxv/9v/xnfffcfnz5/E60XCMHcxZzw7p3lhrZXH+ZFUjFY02KpVLa3XymYVq0rfqvvG19++Us4zpyzIxOaJkowvv31jngQJ0I2eLAYoo5NVG9d2KaUTIf9H1Lm6N3J2YKa63BDdnW3bFI4xT2ytUcbitMTDWUMaeYJ31ucr6f6OnIqGklkc97YLi08506vUjN50ebkFPa9WRm27d6dMs9SqrqpeKuwmR8+UICVOZToiNxkLzqVHEYdbw6ZhH52jB87mIQwKK2ED0qT2ta7HkVKjEhMdUsImSykwWGHVk2WS6/MVtbKTcyJnuFwK7gtfn6UIfrgUypyYZl3iyUtUXZnZHLyBBSMrvRE1WcJH9xeEBLfhscOR5zBYS8202ROvWH/Do/DQhV57o7cUuR+6HluzqFu1SXqXuChRyEWuwK3BvNyTTBX5um6QOkspRyvtWGgMqp7VrPlWM1NQvGcKPeCXMWps0uAMLyHveK3SN5gjV1aJqIaNew0gYrCazbpS0XzDPSPJsGy/CdO83ozWtjgKVdHiYcLXgkrrOqiVJqdUNHdNQveaNHNoUKu6rWzw/nHm/fvP3D3cs5x0Wcrob5g0FvI0xwUdf+aqjLu9ZoSYZw2uzYEiJmKWUeO+rdQWbs+MX4MYLFbaKw011g39gJN4Y4JH2PQwcH8joD/ReYfuyeOmkV9bCiPRHsSGyM7oAd0OWj2v5BgRSnQh5K5M8d7jUkgjg2XcBK9TUjm7+zGPklmp1kVKmUHuTcRfC5isVWe+fODzD/8a95nqK0s5cd1uZBJbxK2ezmculwfavtK2ynw6UVtl3zb+9usvlLIotyfXAwmw7HhK1Lqz3W6cLhfFSOOsvfHv/+N/ZDktfPr+s8hKgzDgndvthbvznULceta5+unHH/nw9Mi6r2zbyul8Zp4WNr19piSV9d4am8sf53Qq1CqbgJQyvSDRWRKFstYGU+I0z+Q8077elH+dtQDzsgTTx8muQyFnMVW2fWU6z0zzFIFCk1St0TGeTueBAurDtYTlaO7D/wg4sqStGJ6NtjbMdjGowuGxtSobA4QXW/hsmBnn80nU1CzPHqudXju1dwbvoSR5wQhTyFTEkhgDR0O4575v8unx0IMkCdUsKJTJTFm+wcTYEePAUo9q4bWSUX3ZsJKZS+F2u9HrLpIBxi9/+xvn84m7u4ugqHkhRYsJQvCky8jSOFhinibsQQNeUmXvCdhISXi3KlOD3SVqKyVMyoYT6Rj57Mq+ji1oxuur9sThOpp6EA8ylFGrDf67IBELi+lWDfPBVGkiTzTBQvu2g52Dvih2WfIcSm0JljqJVuVinFOiJQn1Dsfdbsegtm0wlyQH4Z5oqZN7qHUt6K5JporZRw8nuKc3J+UYvPewb7Dg4sUA17rr+5g6iyOXPSCcoVh2brTeaD3gDc9hzqdigr6zh/lhSTPeEjTZlW/7Tm9bPAMdjHWH6QSfP3/m4fGOMs8spdA97LxNaELOE2UKUatxDMbNMiNB0HrHkX0PVmIviulDd+p6ZW/74YhKMOlGlFayt8RUAi0YVizHFPjvRaWRLU3YXQiyMWyQB+xVX0GwpRKZitPbpvkhY5Add08otqXDCiabSTDXU4eeNBc6XmlcWv1NR5GyEjnj/Wl8laAkrGo2YdmCLBObpKvDamR+9+O/xtOF674zmRhhGbG05nlmnnN0xlXoQOrQRXzZuzRYaers25Xl/EClKTgu6MS1bvzll7+wfLvw8eN7Ssr85//0n/if/qf/F//tf/vfiXzQ26GfOd3dHdKFHNCs90Z59/SIm7EF1DClIh+c8MlJ0djte+W2r9w/3JFLYV1XAEoptGq03EjeOZ1OLPPM+bKQLUVY/TvoGuA9Pj3S9hqoTdzKyaiWWGxjmYsuW+NQLWeTTD9rVSnBygwz57ycj49wKonaGnOW/bY8X5y+dkrQX1OHvJTXIJpspGbKGh4XS1Ps37pJQXyaFjxLmDKXGLa/oe+NBTRHBaYDSut92ytt1wC7JCPZRG07W6vMucTT1dpOobBOrbFmWG8r3hvL6S42HOFKaWTLyqYlUeMSbQg+okxKgYwB+qiEeteyL2RIBds628sL7VRYpoU5m5TGbaP3YLr3iu+GLVnGhNHdjAt65HSPN2ExuPVghRAUVmHBgS51w5Jo10qkkz12jzOlMeliQx1UbY1933FP7BUdhF2iyOva8d2BLMbdutO75gglQy6JvRmlO3nJEgxuVSmFyFm3mMkULjkv3ZnNRQ4wY29NDqcBx8rGXfoYM1Xn3lt0kEZnPyAvQQ+CFHWJ7Ud0qeI1R3CN6K0qrsUE0sX2QkpzHJoqyFrbaU0FkVfYg/8nq291DpvGU7pkHOY7+OHzj3z87jsdh6ZuJpvgNTkQ25FtYDEcVMJbQEIKZWHvmssQQJBhWMq0Vqn7xhrirQ6Ya3ifLDQE9lqHixkUHYDLUwkP8Vp0FsmMwVbS+48CLIY8Zq+RvSm68tTDctsIGqeswV/DuSJ9ztStaptYHP76xpp8ylrdyOFwoi6qe3s9+LvexyDeWEBNGbk7gIoNqYDVYbS+03vn8vADn7//Iz5f5IKdQ+SLyx6nQp5nfv75n3l+eeHu7onHu3vBxt4hJ15uN15uNz5/+oGyzAKYYjNmjJxncsq8vHyjlMx5WVjOM//3/8f/k6fHR7YttEIup4psjXme1GF5sO9wSh9qUVPSWGudrW3K7UUspLp3np4emfaZZVFle1pmbuuGmVHmEipV3fqbc4heDNl+NO9SCAYFy1ImF3nqzJZpc8NPw8Ncc4m6V/q6Q0YHlENK6WjxUi6UKUfW7Cuim1OwPYjQjykGhRbioIqUdWaB0yF1qFd21yG/7iuEKI8x0DMtxOE/pM2gnyq6nA66vUVSlgmPLueZIZ6KFc88JPp/55tv5GRUg8UBJua8iC2TLGY4KbBS/Z7NiclVCc45wXSBrkSv5h54OqGZiEWexDz58vyNtjfup0KaDdLE5MoxSEDusvAA2WhIyOYU66QSfHNEKJC4cGLkNOuw6ZBEEBokAlVtHJRP69JatOgs1XW0sLP2gFK6fH5cQ96eFHAkXH6ntXDzRd+z953bLvM+gs5IGhDcjnmlZKUeVjrdOvO8HJWvKNbEjMLFZkHQWcJZvIf1eFL2hVvAd3qP+ijH92o6fJugm57sWKuEAaMcpHsI6RK0nd6HpXcNYZM0R/SodiNYq7YNwqyyhrHkyOOZJnj38T3n04XHpwvzLBM9QTIRuOTyBiqTCgvvob5vHFh177r49raq8ja1GR5FkrqVlXW/SYOTSkCGGjw3LLQCQRO1OJjFdX0zsPagl74Kboli5O9+OQHT6ZLHoZtEuDuNKaxb5L5MrBVh9ClFR6iNGJe59p57pLkhCI2c8KiI3PXlZgkLXZLHi4nVQTLNS3m71pO6njElgIJb4f7hD3TuOM0Ltb4A6rRr3cO/rPPy9Vdq09/f1hfq5UzbdvZtI+fMj59/ZF1XFdbu+vfTSZdt75zmme8//UDrO3Oeqb3xtLyn5KJirItNlvPM//5v/2e2vfJ/+e//h3haoUszU35QNuNuuue23fjHn/+F7p0fvvuEpUxddyqNy3LhfDmz143rdsVyZp5KWBVrCKf2pzA5pGXRrdoimaobXiwse59UMUSl0012v1bkvLrGIWilsCEaWwpsW4lnajVySO57bWKOuGPLLMm8N3nLFKPvG70lrKiStXng5Q5bVHOzQS9MSDi01Y2cZk6nhSF4SslItdKjgspTwZoGWa354dJKb7Qku/E+TceS8cDPyzQfNUszj2F1VJsKncAsMeeTXDZRdS5UV/yulGR93FrH5hXfRa1LDj2Fi+b4qLU3ZUvRZSFgXUy0h3cPnO4u4JpRtJ6YbKfv7Rg+dwTN1VzJLcUowENFrIso2SQs2NVRAhLpWQ9IBVIKo0HQ5KEnPKsCsyY6bCaqsz7OA8E1mcLWh0VFZruqm/j2vGFIOVr3jZIEHxacthtta2A7ZZblyNwnLEmn0FKoXa0ylQWbhfvvJgbOXuVjJYNJY2Rt7J6YuuMBL1l0SB5wl7u6LadSLCn3o62ilYd9zEgnG8201myPWYD+vMdBl1KhV/kyaTC8aUBdI+zGQjUd8+1k8O4DvHv/Oy7ne06XCyVD9xpd3n5QTXv3qHrzoXQ2d7bQwoyVKXqq7FYWs8NZoNGp25Vt31G19WYKYZAoQZlXMfpqok/ASMP6Iip7G9iMDk2ZBb6+jgH6pxQkg3j25pGt0juV6CYAUsZcmopkPZr+GCr7cDsQnJbcDpp6e3NLpdBPjOmCLvgYpPOqEH/LoNIHEfLKLmZU9xX3xHL+ntPpM799eWE6Px5oxHBHTkn03+vzjbv7e757/x1rrSQ3vr08Y5a4O19Y143T6URiorWdum2kJDuel9uVlDJ35zPuC3trlG7a403d+ZQSe1UezZe//cbX24so4MOhNor+cnu5cXd3Yd93/t//+7/hb1/+yrKc+PHTj0yW8akz51kayu6kbuzbzr7vnC9nuV1a53S64O6vs4p51oI2vaBsFtGkwqQTgmK+ff2N55crXiufvvtEWmZSFe7daseWRSFAJq+mum5H9OX4VS2RWpMKFLkc7nujJ7WOVmZOy4yZYh/rXpkndUTtJD1E2/QzPSXSlPlwes/tdsWSuP8WWDk5Y9W4Xa+cwyXVs5GRPTkgzD4ZlERxjt83YkDeG82MnFJgo+omshk5TdysKT86FSm743JxUfyP5brXjZQSOc/0fWXddlGNS2aeg7XCKx+jeecWANfl7o67x3uSFVGJuzbw2pzFzlQa++40r2R/zf/QaahBq1dVxilNsci3Y8CbzI4cEUuJxjgYjWIWg30pX3XSilLcwiOLFBvdkdnS3vEmGwos0Vriel1ZXza6mbKhvZEnmIt44LXusd6MqWXyLJsT3ZX1GKqaZcEoO0qr8wpFmpckBdmh4cE7rW/0bCxx2I0pVR/4dAr6drjKFjMdLp6QBkJ/S/TVDYbbaK+QTrRueNOFauEQqyVkErC1jtnKvodFk2kojsva+/vvfhSt/TKFeG34KekiECVVt1o+JkLixqtLhcleze50oRglaW0L1lHnsm8bW6uCoA4dkB2HvKegp5oJmouPW+d9DuipQS7k14EBPRTiGmQrz0b6LfAmuDIRF4PnqOZTOCBonemCceWNBFSkg3y8Rt2oHaKrG+zDYbIY3eeYbkeRMswGBan5m709TqR4311nkfarVPGkO95/+DNlOvPLty+kKXN3eoTk/MvPf+Hbly98+vQjd3d3fPfxe1LKXLddM0Q6p2mhZJ0V57s7vn37yvP1mVIm8iwou5SZuUxsdee23WRhk/MxNkhJIunaXTAu8D/8j/8j6y4obNuugvFzplenXB4ueBPdKZP49OE77u8f6b1x61X4PtFOuzb25XRiLfqAU8603iNEQ4fDrceGNmkkMoA7v375yv3lHKIOOJeMc8++b3xbr+x9ZzFlzpYysxTjtlW8X0klMy8Ly3KSonnbsaLkttz7GCuBGc/fvgHOlATzLKczU8QkNib2tKmCxSheaCmxupNKUtpeKpRlxuLW1UwErBn7beN2u6qNrpW9O5f7O2xO7FuF8Kg5UquyQSmB4esltqrNVSwxnwLOqYIlXp6/sm6V83eiw5aoesh2bHAxoWCKLm3bZZa4e2f79kytlcu0cHm6V5KfSbex7fLcMmC+LAqI8rhGYuHnluheeLGGe4Wu4aJZI1miFKW4GZoZpUHnbAT+OzaEKzshDjfH5X2EuggJtl/x8eSJbjV6NF6x4nHBmWG5qPusnXnO/Pb1W/AUnEqN59ugiB69brLWnuYi3wk4YL0xM0hxSbTaoSvekTRgER3/qUY+R44OF6fgbJ5Fbw1YT7AklIjKrL5TKtSgPTYTzONDsdsqta54q1ie49DZGOmGIj7AuupqNxfkWOPWX1eIbcbdPXz48MSH9z8xn0vYvAfMYXZciDU+x+M0ToTlx2DwBP02oXwVezNzYpJdOhvbemOv6sSylej2MxV1djbsNQIJyPG5yuYtFDUtaL9m9FY1KI2zgsDErUtFU/KkYKMWBnxV0E8PmMe7HRoAkgRkI9hrvNUjw4bhAQcew3FC6yO4dLzudBBmxhYZ9u09POZ6DJL1fXU5DsqUo2fYUHFTKXz66b/i/ccf+OuXjYTx9fmFaT5zKUpi/MvPf+Hu7o7pNGHd6F7ZX67ky1lJlUXPa2+ab8meaD882A5Dw6JLd12vgHE3yaLF0XpKSXED5/MJd13+8zzTXNTw3sXSM3NKTorrXJaZ/+a//a+xKTNZloHUtytrypwvC1ORj1OtO2tVzuq+q4I6bCuacMplPnHbb4fqd0qZ9XYlp0Qp5WDiuHfK/T3TNHFaFs7zDEmxoH2vpGXmcTmxzhN7bSxlIpVE33bakoLuGB8Y9hpyf7mQy2PgdDeWacJKplWNHHM+y8fFYbdG3Svny93BzHKD7JlpmbhtK96kq7j5jYbyvM93d/z222+hhpxV5Vsh53xkVDSUQDVse91hW698+fobrXYe7u95uH/CcPnHd6ecGqezFK1b3bm/uxxzj2xAmVgWzQi6y7mRvjLnRYSAc+P55Ur1zjJNB1y3e+M0zay3mz6j85nkRl1ftJm6M80zUzkzJefraeavvzT2jbAeCRphMLcaSQphMzggpRRiP5EDW9cMRwlj8vCCJMYnUrhqHi8X1OTDan50S6+7M1mnZ1Vnvct7v6TEMsO+VlI2coKpLJQykXDqfgOUm93bmGPYm6pPM6kyMGt3ufBSlJIY9ho9ckxyiyzkhFry7mKUkKSJsaG0F7TiFNpgdKHOpRnBKjJaW1Wpu6xHrIu/P54xwL7vtC7xpePUGrBSsJbmE3z+4QOffvgd06Q9MUDA2nYs1uQg8BbT0L21JgM/H0ywOJiNOOBFM03hm9QH/OdO2zdq3cALyaaYv+h55DEUNjST6ZG0GK+g9gGxbRSTjiUf2omYITVdEN7BFcDBbbuRppm2b5QsJGFYqg9DxDEPk61POQgD470rE0avqbuIAz6oOZbkf+WDchw3y7EedZb1sNIJb6FB3oqfkSGNac+YTRFdDKT0yN3jH2i+MC+Zu7uJtlW8d+q28cff/4GPHz7qPbVObZ1lltHiertyQ4Lc68szrXYulzvcG+d54XK+UOsa1inwfH3mn//pn/jw3Ueent5pzhMXmlti3SvTNB+FmlcxJpM75/NJWTutszsUuvPzb3/j03ffiXba5Ez45ctXelf4zO264heTWtU1GD5wVY8ByKi2o+o92Ylaawyh5C744bsPtE2HIUVsmdI7DyVzNy8HXubZ+Ou3b+y98v7dB2ZmpsmP27qcomtBStu+N5a7M7ge9um0qFr3zrKcmE960C/Pje6V0/mkRLdemdLM5TTL3hvYm/BzdwmQ7uYT3WCaJ+Z1gfumOM+pcDqdomNViExG4poW1gZEALwnC8y9s15VAb1/98D57p4MTNNMtszaO5/u78A6a1zApxioJhdsU1s7Bk/rvtK2lRUjl4mnj+95nz7y8vUbp7s7Skp4cyiJfa2clpny8SP7tnF3vuDERXQ5s7XGMi+iPBepo+c88fNf/olt/xqRqtCrXH81fPVQ0AasxxgFGi08s5wUJHHpF2QjIiMKM2HScqpsh5WBHxtThmzETIggWbgbtVcopo12mSgmyuABSYj4Lhv3Vd5kudwxZ+j7zjKNC0OkhOHbo6wLaTJKMll/p8I0CQYrCIYSdCHzRzddDNkm8Epz4fCi+ppOdGS6N6C/kWlCYO6tOkP5rSFupvZv7JsOyx15cFWNU5gKfP7J+PTDf8n9wxNTiW7NJEilQwr6NIyquovu/crr1D5KkfmCDhjHaHU/LgVBMLrI9u3K7XYLlMBe6bsm/yQPKFF9SRA7xpe48iLqLmi25vA1ir2sBEZZ9McrC8hRQVS+7mzbhi3Tsf6mMmmeE8r93kVnLSm0KX6c4rG2Ks1cMa2jo4qT3qPjpecYVkdXYZr39bhIx6Xy9peIKbH+OypoQjtB39jazHXrvP/thT/+7gdOJ+nS20nM0q/Pz1ya3KRrE/3dA2YrJ2XAf31+VmfYXUmL3jmfRAHfe2ffKpuv3J8fud2uEr9itG2nhtP0NE081519X+ldGovburK2ynK6yIYlKcaZLG1VuW03bt++8e10Zp7mqDAS9+c7rrcry2lm9xoVe2NY7u7bzrZXltMcA0wGoSOqp0gS2zTQvq03Hh4eybNwVUtSdG5NQ7Rc5HOecqfkzPeff2DbVGnN0wxZAfSCJwyvlWmaSPvGBtydT5EvrQHYvt5Y5sL5fGZvlTJlTpczvVXmaabmyvaysV833j09SLvgzjIFBc47ZE33UwSYzHPkeo+BeVc0qFnCWmY+TbS9MU8TW18peVjuGl6ciQIP93z//j3uMhCc54X5dGZ2yLcby2mWZTe6fPq+082ZpoltXVHSWg7ox9imidP5rO81CRq8O99RzHi5veAY9w8P3D2+Axp2p85m+DrJRmXmkuRVb+bMFG7JeXr6xPl0zz/987+l7r/httFNaYKCHSr0oHtSVTzEjGUwUlpr1BbJZaVEdVkpNumiQENx/V1dBkEYDJhT394sYSYAfioZToJsauvc5bOqu9bxHL443gAd9rlooE6vtN3AdmYmPHmES4nnnlCyITgpOTtSxqbSlcGeNOBsvQ9EOrhjDRPlQYexZLiCU+qGaqKzuj9aWOirE6x9j0zqGjCIoIneV7ZNkEjTS8ddncO7j/DjD/8F758elJIXHdmobhNGTzACewQXxmAdFXODRECXTkGFSEfsDn/F53F6CAOvL1f2/SbYL4RylsQM3OOQHBMOS+kV+oeDLu4uptm+79D2IGaom0xxcR8VOIB3aSiI7Bk3Xm5XWq+6iM5nEQLoocNR59C7BJHHPC+J/1+bB4VXL94teHFqaWV5giQJntQd+ZtbVTOKV1sVhQjZMDBWkcjQX6jLaT1xubzn63PnP/z7f+SH7/9Azsavz184nx+kifDC9fbCfbkP+KrQXIzRfZdV0ulOCMh8WrifTwxPKEHfGy/7yu3lG3fnJ56e3vPhw3f8+vU3/vLLX3i8f6DRIcM8TTz/f7n6ryZJsiRLE/wuFKDIgIOAiSszq7u6pvdl52Hm7w8t0dLMLtHSbIPp6gIJIgM4MDNFInLRPjCLWvR6UVREupubqYqKXGY+fMDphaVbuE6z5LlU+K//+T/xi1/9irEb8N6Qc6K2hvch8ItvfyH0OkXjnk8vxC5yGA/YZogmShei1gjRC6up06AgY9ARdxUYiejI4bGdJ+WZ4PXGshEfZDQstUgyXSuMuw1dCMKHxtDKTJ0LbvAM4yjdVmnkPGNqJfQd1TjJyYoBH8ONothaZTlfiUNPiB1GLRmCc8TtBjB0Rd5PcB7fD5haSLkoxi4Plh09plZsCKR54TKd2W1HveENh91exIRVTAhzylzbhW7TEYm3nri1SiviB7Xf7aFCSYlhHHHOEGNHao04aibCLPFg/TiwGMmODtZjetiYHms8WMs8zWw7cemtBfKSMF3HZuiU5dARuit938lhuLax2qXlWulCL3sTDDklZIHpGboOZw13hzu244a//OW/MM0faE09omqlaqa5WroJaF/l4atVDn0DOJOpRthQq8ixtCxaHNDgdaMLUst6bIsOYy1BVhadAZwarzk3yi4B2fFcyoSj4XyH80Hsukum1iS+Ya7pYQypZYLVnG05txQqk5FJ/Llk0sCsB4LRQ0hZXKj7vGLwpTWFjUTf0+pCqaJCLnmRg46jfrsO8aaPqkoPVCZ9P5I3XcTVRaaaCF982fP23S+5249CQW4VbKWpZqSZdTIRwz1YJZiraZ4ulBUOc1gRgK2YiTFKsjC35W0ujZor83xlmS9ybe3PEukMNFuxRcq65F8YTJPiUWnCqiqeVBexNzOGsR9JtVLyTClSDCOCUkjnXrGtkFu7Mf5qLTKbWI+tTiam60Q3jHIAqpWJNHBZyBi1ghH2YakyCTSrvk6aj1GbPM/yuajmSKTst8luhSobKoRVF4SKNEBW6pmQZars2xwzrRmsP/Dum39g++A5vhzBwuly4acffuL+sbAdd6ScxFoIJAPHeoX+GtBw0d32JT72kmlt1SVXH+vDdksfO16en7mcz2x3e7bDyGEcwTqenj7z8vwT+8OOzXZPH3teTi88X868eXjk5fjCp48f2f/qN6Q0M80L3jt859ebtOhoJ8Ek1li8ZgV4zSYIzpPSwpIEw8wpEbxgf1MSEY33Fsmql8phrSGaDms9aZEFi7MW5z0Oy3a3Feta6zBO7D6u5zPLdabR6Lpe2EjKThK8UcRkRrt6HwIxBqECek+wjm4YheXSJKP5Mi+4zhO8J5ckJoT+XjQIRnUALr966WSJbVzSQgwW53q8E3FPa40Qg/xsa8CK+tcEw7B7Q54TSy4472Samhd83wnObQzeG6zrcGokGLsgrq5ZqLt+3FBqJTqHd4HWRaX9CuRWcmGaEz7IdPPnP/2Jv/7tr/Rdx+9/9zspOjUzjB3WNZ6uJ+62O7x3ontYC8WykNXcbLVOD7FXZllWrynDeHjkl+6P/Okv/8T5+D3BJh3O7Ss9mKBzwerH9EoaRB/CXCvWrWwX7Tarp5lCA2qz4hJKw9gg8ZZrkdXD22AxTjn1ruKsZUmJ1hL7cUQCeeW+C1aAj5SF8WSMKoydkcMVEcG12rShls/eGXN7X/A/VFbpwpscDa2tdOh1h6LThWZKpzLTyqyFT03/mhTopkvBZjytXmSCyE58lkqjJokxGUZ4827Hl198xf5uD3XCOLW3N5VKvIngWl0X8+sBLi6kRW0u1j9bu3e5TrL0XRtEY+SAs84zXa6yw2qVZUlCwHDr3kiZXbcCKtoBb/xN4FZEIYExllJFEJnmSQKx4oBtFocnM2MbLEvGdz3BVFoxZIrkVVi3Cp2xVpIyq5X7NueKS/nmGtiafH5tZYrc7P1/9hnqf1bzCm1SRAW9bnQ0zZQVhlr1O1V1TQZoTuOQb3eIiEjXD6OYRq6Wt+9+xd3mPftDz9dfQskSiDXudtKdKGzmnYiU//Vf/oXHt4/EYUO1nvPlyMPhkeN0ouTMZrNhtcBpN8KQONvObebl5YXSGunzB4buG4iyc7TW8Oc//Rtv3r7hm2++xRi4P9zJTpXG73//RxVkLuIEbeX6+VpFuFRSEgqbNYxdx+ly5Xy5kFPmdHqh63q2hz3eWFl42UZBLKhTqbx8/sRmt8Pa7qaGRgtF0/Gumkrv5c8bQq+bp4WC2E+YG/PBEKJn7LZs+o5Fx8h5usiImBaa94QYxNWyLJhipSgYS8qautRJtOeSF2IXCEHM2lqzBOtwIVByIVMlFtT0lFaJXaCWytOnzzw9PWHu7tju93jvuF4vEmITvGz/nbBOUlEW0bTIuBkEZ5cOxDF2I7WvlCUxXSdiFCOv+fmZ+4d7un7gUoTPvR1lX2CKsCNO5zOlNg6HA7UuJBfEFkNhmBA6fvXLX7O/3wlpYLpSciZsd3hTufd7NpsNpRhdkwjbqZYih3lrzFMiDgEfIqs9dckV7z01VbrtPb/77f/EX/9tZL5+jzFHfeASVEex7vZQyRIYhE0my2vbECdePUCabeRssC5jK3grcAvWKr88S4dn1u+3YIzFhSrdqrfQkuhOfCPsenGjlSUQ1kvB8sYSgtMimCW7YAW0yqKccA8lY31WEear31TTKavWhsmVEkToKYxIgankeJCvl84ZTC20cmXJmqtBpjSLpReRXEX9nRZak2S8MhdSkelks4X370fefvEt+81OkxqhujVzTZX3slJVqEUWv+iOrDXkOhSdkLRLXp/NhrCaVHd8O0RpMM0XrpezNpBZYmytaoaautZae+t0MSqG1OmCZhTE05RFZ7DJ6bOK0Fi9gGO1SCJjCBK6Za1EnopduuSAr4FBNgRsLqqxqRgNFzIY2b8pqeBmALge3uu0cMOxQIq17BrWol1auxW5W662ft1ts2GMhHrps3OjLVKFuGAE8s7FMAx3GHsgZ8fzywdqbew2W2I/8O3jG5ZlEYjfWD5//sTQj8RRDDFX0sBm3DLPM8eXZ3IpYCzjdgAj6IVz4q57ma70w8h2Owvb1BiWvNBpMRuGDV9++SUhRILzzFmeqY+fP7MZBjabEe+jZqIblmUR6KkiI5kNTtgGGIiOdj5TkQNz0/aUUnl5OmJ1Qbbb7/AhSPD7MjNut3RdR86FWrIEXzilM5bCNF0ZhlGWWZIywzJPOOfoY69mWDBdr/T9SNd3ot6eM97JjbkZBvHCd0byrbuIq/DThx9JS+bNw6N0+ap8dcGTloUuRPbbHamJWjEODme9HCpNqL0OKXwRh8ezsGCN5eH+ga6LgmMax3a7xTnLMs3UXOi6QWEJOF4uXKcr+/t7NruRz58+8fGHj7jYs93vGGJPcYG0FGLsJSehE8w51crYdbIcDdKtWQ/RduIxTyMET1WBU4jxZsj429/9lkrler0qzznT9wOd+r/AqgQVqwVyodVK7Dpcka42dp2oYg3qbeUxRrN+KbRiCX7k17//Rz7+OPLxx3+hMGHrBWtmIMrfkcdInUpVyKR5DaVVUi0EK9YnrDi1a2rHsOCap9WMC0Gmilt2sDyEoFbiVoq9s020DVZUujXPGDfIZErBaaiS0aNE3NMDkLitK418TwCjDBg5TasegkI7bU3S2AhR9gAIpGHXZqhqcUGU0LlI7ohY0jhaXWjWU7M0Q7I85LaQdhZ2O/j6q0feffmeYXA31hZVpmJnPM0sUgA0c6GtNOW1AwZaFXv+18lFoChrDbUIrVSIqWv5kJS56XohpUROQte1dCKGk9IpPbUR3YvRQooxN23M2iawtkdWmqmG5H/3YUNJVRf1GW+j2Fo7h++kCGWltFvrcbrnS7VRcxIGX614F2lGF+RW9B/NqrYCndKa+A0IQmJvC2p0ly4iPyRbZt1dtJ/PjRZWWxJeNUdO4bQ1rU+XJzScTCc3mKrni6//A6beSWPTDN//8DdO/YZxN3I+Hcm5sN8fMEFC2na7PX/3m9+RcpJWpMJ1nvAx8M1X3yI256L1WmHCeVmgVYZxpMyJ/XYrTLAlcbqeSSmx2+2x1vLlV1/Rx47T9UzOlc3Y83B/p02D4eXlmXGzEeJEyvzw/D2+KuRhgZfzmaHvsNbKwjcvBB8Yuw21irldbUWWYkYgiuAd1UeBQVQ0Zq1AGrfKe1ucNZblyvHlyMPjIzF2GnRTVfiETBTOqIBOdhDO9QRj5UaKnk0Yb7uATOH+8Y3QXEHjIpWWm5PUvCjvyRsJOylFoJS8JEyVwKVSRFRirWfOM/O8cP/wQGmZUirH84XL+cSX33xBNIEwSjKX73ryPHGaroL1HXY3Ku7L8wt/++FHLvPC6XziD3/4O2IIvHn7BoMVG14vDqvzMmGcw9kVl1UqYYUWxMHxWhLBKV3TWqrkz2r2rqHre0quHA5WbaMFLmpY6cSMHDjFW8GQVe0dosP7wPPnJ67XiceHO3BidbHkIgUpJ4ppeBt4+/63dP2Wv/3tnygzVCt25FWXn9JprQ+jxSjLSezgG6WuiYdgasWpa25xjWazUGPnWfj8bqXers2a/R+Wo7VWjBdFNjRc6BTake6sIl47quaT2FX92pXDj09U24nB4ApFVMF8TctSLOSmotSFtrSb4Z8zSnkuQsstmschsENgldpVvb8rV9IsKEgW2Juug3eP8OXXX3J/f48PlmA6gjevBABd+PMz2Ko1capdhXGixFdM34KjKnVVp6K27g2lwMrUJVY1uVSW68Q0X3UvIY2UFEpHaTITmGbAqj6GJhh8kymimoojYMxqzte0EM0YA9EFsnWcps9QCpuxx/oOZxrWBpyyrGhCx5Tmy0GAYAMpe5aUydNV2X4AYkkhojtJ/TNW4CCx69cdi2k0NUlsTSnMWZqHorsIyTIWo0JxHF71ELKTaFaICeLttAKIr79WSndtjVQq79//Ow67X1GM2ARZu+Nwd8/5dOLy05Wh79gd9gLblcq7d+8oNI4vRz4+f6Iftux2e+Zlxhjxt6tNiSPekuaFGCLBO7LatKdaWJbMxnmMM2w3W9U7yM7vehUihXMe7+T9pZSJMXB6OdIp/TXnhcfHB/Jhhzcr9IOwRk7TTDBWunQsNnrZ2GcEF2+BrJ1BqrMsF73VzsZQWsMFOUCPn585nc/cPdyzGXeioswQQtBRWJgUpVSxRTDyZ2jXaJvBRVnCVifYcwyBp89HnJcQj9Yq5/OFsNtjgpfORbuapRiC95wvZ5bLjA+BcRzwum/52+kTXQhsu47T6cJ2M5Jr5sNPH3j69Jk//Ls/QlnZQNKtlVSZXZKQjiyv/bLM2tVDnhd8DHjj2G/2WPcDY98TfVDqsCi0c0qcjif2dwe8D2yDXJ9S0u2mK1ngoP1mK4lqmjlQFV/MOYmjbpOHUg1D6Yee6Xyh1kYIUaX2Itbz1rJkSduqOTEMHbUW0jKz3W/wQeJk0SITghAO1q8JRh6QbvvIF18Hnj9/x/npe2w7I6E0ehhVKQ4Y7f+r7BVyq3gDSRe+Ailk3Q+8smuk2aiixbCVVoTNZK3w1KUByaK8bZIAtyKc6GK2OovN0IrACKZKvsmSZeKKvifGiLEeT5ZDTndSBXA13ezNpTsX2msxUFS4W2oVlXGDWj25itdRawu1itmgKZIrXNQCIRWwDg4HePP2gfuHO/Z3A30Xoc1y6DqnRUZezdop3/INrNCMVwmXtGcyyZQmXXcIo7B1lExwcxtqSGeN3F8lCYEkJ/H1WLObMXpIrvsAJKO5rjkYK9ykzCAxuKuvQr6m7Jsiz6ONATvDck38+OORX/26Y9s7OXhV7Q2IMZ2+TuEze3LNQpbxET8Ixp5zledC76/a2g2qyrWqBbbAYUYFfw1hH7VSqOvKTD+/tsJ16hArJn+6lTKa3a0sHadFsjRZXtimQVlqsjhs3/Pm/W9YlizXKUoDfLff07QQ3N0deHh4I9ByEy0FVZhM+3EvP7cWDvs7TK0sJd8Oe289NmqT1Azn0xOTDez2B5nAgFxE0CqKc2ELbjYbsZKvlVQSXewYhoHz8YXrMrHZ7QjOc76cAMna8RgZH6d5omGY55nsHCEEcoOQ1/jEBkU6FbEJyHqRVaDRmlpIQKuNH77/gel6pgCb6wjDSFkkLGO3P8gjnhK0SogdYJjmKzEKq6aVSnOWsiTsRmCqUiov52e+//FH9psth90OY0UsNFPxJXMtlegd3nkRshlkHEMOPIO4XjproTQSmes80drqqWTph4Gvvt3irSeHgi+eh/t70m4rOd3Wkm0kk6E0fBc5BE8uhXEcSSUTY+Tw+MBvf/2b132OFSM+X6Uoj7stwXfUUilWKYTGU2pW6EImZB8iJheWOmFomvDWGIdR6W9KDV4dWIE49rRSWJaFru+kK2sKw1ExXSAbbmIvYxohdITQ3ZaVxsiNKweqFO9UFkxtLDkzbh/Z7e75mx94+fBnaCdol3UtQW0O05zi3nLAtmopDow+9DgPJmBJap/dWI0YS00058UWpCmOXqvuuqpOq073AVb7uxWJNgpjGEyxOFMwine3VjRpLtPsyI2ZRZMdC6JDyWsuiCp15X1ZWZ7mypIztIptC61aalmVvMICzBVVclfNK4AuwFffGB4fH9gf9nSdw/pI9AYx2+uwXui+WX2FzOo5pAmDa2NnVYQovyfNQ64FauN0fKYfELwadSZtqgk2Tps8SZ9McxYDztrIJckOxahxpma1Ow2yMm1d7coCX3gfP9tnGDUt1GU9ukBvVXcqHnw3ktuRaZkZiiy914heWbbDkiXMy5ZKVhTCWmHPGZD34j22FCpVXY/ldZXaxMq8Ftl9qC0HxaippCrkdfkrueECaddibmJHEJPMFU6uCNvIIjvXleyBPj+1CRQW/B3b7S+4XhvengndSK2B2hZi1/F4d8/hsGe32bGkRWj5wyivqzSCj5ioQseUWeosQkAU9DKr5YjkwVglB7ycjqLnCh3NOmx0Yjx/vdIwhL5jul5Y0kLXdXRRzoU+ROZ+4N12R9f1tzPlp+cfmHYHvNEC8f2PP7DZbtluN7i2KguLPJBWUtZKzmyGgdwyc1pE24Byx3O+3TCtNu4Oe3i4I/ooCXEqDrNqACb4sMHFXqI8i0T85bSQjdVpo2lnKqdlDJ7zMdF3HaEP6qme2e12yoARa/C2dta2EVzg3Zu3shCl0YVA7z3LvPD27VuaaeS0EPZb/TmVw92BPnSUUuhcFAFSg5wL41YiCFNK9DGSXBVjOe/Z9Z0uuIR9FW1gWiaWknjz5gEXAqE6dXaEzot3P8ZgrMAfzVahyLZEM5LnkddQmyRLUV8rXq1Las14hO2SlJl2Oh45HPZUY0hZvFqac9Qli6pSw9hlOrK0FgTnb/Ia1mtfShWCfhUb9S5I7nFplW3X6cRgePvwLWM38vGnv3E9/zPOzWAytURsG4SJYaVDs2qPIkmGoo5tVpTP8hEJE8cZ6f5eXVhXvx4JyKxVmTmsHatoG0wzr2tYZUNZb2+u3ILx74md9I24KktmGngNrDdZm2OntUiCj1ouoFPLkgo1S2dfaGr0CKkmyJAqt9jb6GB/D+/fPLA/9Nzf3eF8IdcJ5wPRecGsbeTVPVbLXSsqwtMFvu4VaAghpKKw0drtS2HvNztpHIxydoxRO3M54VNK1JrFVbQkHY3kNYgth9p9S4ngpn2xYkdBE/zeaid+223czOGEnWXXotQ0xY/C4bDnj3/oyelA4L1KAAEAAElEQVRKyYsqwmU0a3oQtyLU5uxWtX4VUoHuArD1JmS8QZEKp+WmU+VNyOkQDeEKv2k90xcqFFj5PqVm7UdeGWBtNXg06vZsV6ZYYzUbbC1ByeTa8/jwNdDz9HTkcPeATYWUE10MOGuwIRCNp5Yk+esUTJFz6zyf2ZgtlEaeZ3AOZ4XCLzCuZSUdoFu1agwP94+M44aUEpfLhXEUoO/D8ZmX5ydaLuzvDwzjVhpoG3DO3tYI20G891bSyn674+OHD5zPZ3ypst1/8+6dyMPTgvNBy3IRipkNpLRIIRg6RCshSVwpZVqVTN1aG30/YoPFZ6HLdv2W0+mZZmTZXEvGNFkUdf2A1dHLB88wjCxLkkLSkLwE5zlfr3S6K3l8uGfoIj7IxFERGMpZCROyaq1Rahb6qIE5ZS6XI28f3nDYH4jOc7EX2VUEz6dPH1gXAK01scZt2n0hFy7nhPdWfKeMpxhJtAvNEPwoSXutkVoj5QVXFppp3N3v+fjTRxlJTSNTyUUYXaVWoomKSTbmPGmUZCP6jlQWvUmrhAVhZNmZGpMx7DdbTDFMNTF0PWaZicEzjiMWYWC1KkFHpVbJzS4rvi5dJsaogDKy+ho1MVaSwtVkinStYoLF1ib89yrYaAWa94y7t4Rxz4e/WI4vf6O2F6wpFBKmeVEAtzVnwOuiVxaoNatxoTVkp26drAIzxPitNbCO4iSMR/DfAKv/EwiP3oiwqVWw2QldVh/09VCjNbxCUyYbEpMUx9xhnEzWaLe+VMHkLZYyL/J6m0IZCPSSk6OQJPlPi1FwsL2HN4/3PDzs2G0sofM474BFQqPY4VzEGEnAk0N9PckNq6q3rrOAUehH+f3qB6yHhkyf1shriT7q4ZzFMgRDqY2aRYeT8pUQerV1kaUrbZ0YlParFgqSQGdlz3OT6Okv+/q/pcMX1XWtBtsKxnmxN0G1Gs4SjcW7nhQCP7cMX0/81sQLLlfJQa+rHbgWKoFmrMYuo42H6CjWIlNr1d2oketjkWwHKlRzW8Y3LRC0SimGtpIB1qKHTGkYp1G3OqkafcdtNQUszNkQ4yO7zZeE4Q3DsCMjaW+mNZZsJI8G2Y/UUrBO9FkyRcKm3wpyMy8sNRGMIWUxNa2mQcnM84INns5GvXRVp16HCZa5ziKEs56XpydMgzD0xG6UOAgrgWVrBol+jPIa0wLG0Pcjdw+PfP70USiwwQdyWrimRQVtTaJGj0f6vseVLPnAXsLa145EVjcVrCWqPNw4XUrWyvHpKAexMTi/HkJyC5YlU0NjWiZSWvBBYk5XOMt6Wd5hDMG+3pYuRi4/fWDYSXKSsdIVrZ70xlhilNHYWnGFfPr8CWg4p7bdTSadECNd3/GLr75mmieu1wun05lpmkg5UbLAEtO6J9CZ1hhYaiYWQYStXam7FlNEkHQ8nTi9nPjqy6/Y7feClVYJPHHO4aq/yfzRcbYsieuyEPtOrptyrksVIWDnItkkfAz0QVPEbIFJpoU5JYJ3bDcb/TseOzrFI8GrcVt28gBUqiiMV11AE7jHW8vL5SKsLhdwzjBdZ+r5SghRDypVQhSBDS+zuAk/vv8dLuz48PlP1PSEMQXLgmlBBE/I8k3cb0GWyEVUxdXgsVSHjvwF4yy39q+IvTwNjFWLiZYQ9581Ca2qpYL+tfaKowvOLIdqBTyG1BIlizdSouFXSqhZMw5EQGdboVZPWmYamSynETXDItIOug4OO3h8HHjz8CX7w0gzV3woWJMlydB4MB5nI16fiXXyXe2txRqhQbMKZ0jlkSnKkJrkNcuCVqaLFZo01hBClPdqDDVZcjHiXFvENSGlRRa6Zrppo9biYwygqXIrjt3WQs16PdcDVPtZIzsO52QHYdYPwLzafa9abHGGNqqX4hbfW/WQN1aX8GalmEpToHJN0U+sr0X+Iimrxkvff2uoDYnAXM01bDHaXNhX+5fbUkLtU5oq0nXv19A3V5tSa3Vi1eOo6dRqG6RsMWbDt7/8B7r+DQWnRavgQhDBqPoxyQ5G4N/j+cyHn37il7/8tb4eDWmyls4J67MYZV9NiXO64l3At0Cz6mhcwRthcjkrNHfjLTH2/OqXv8KotqXWQs6Z4IWIIEiCiGxrLqJjsjLV5ZTY7Taifbucrmy2o9j9IgzyZZ7oushmt4NSmCehuNaSeXl+wQbP0MlhEbxEmdbS5IA3kEthM44M/QC1YX1kyvMNCgixl6zWJVFLoe9H8S+ywqNelpndfk+larcr239rLDktjJuR7bBh9ZUJQSyRU1oIXuy3xUpD7KKHcZSFEo2WEzVETYKSjjiGwMP+kZoOalooPv2X84Xnl2ec88zXK6frGazsDC7TlXmeGAdhfpErxgs1dWiVz09PzMuCC44R0T3kJXOZL5zPF/qhZz/umVJiHAZqXaiI/cbQiSeUYKHCjGim4oJjZ3c0shwQxgr0N09sh4GH/YFUiu6XwHhHOp2xunxepzRvhf2SahNKaghQKnMSm3dTKyFEfPCUZaGu3lSxk8OlZEqTBWdNoiodYsd8nZjmSr99w1fdhs+f/8r55UcwC80srEmExjhoTpbpuhwU7N9Ss8WULG6XKvLLRuAulaoRVmFeTdrlVQoL1TScqmslG13tVVRjAVa1CUJ5zcgB4NbDu0K1aFd6veEYNQu81WpmWcTqAy+Hg4+w3cDD4453b/bsd5GuQxoSM+OseH0ZAsF1VKrQPu3a1Kz4h1N8qmK9x63TlikY7ZpX1e86Oa0oU2HtoHUKspaaEzUn/vzdD9Scubs7CGxprd5blpIqa5yqkAZ+1vFLtZLvCeoWbF7hpZUmpKe17ETEdsSZqAr/ogvyNUVCCrC5pcCJ64E0e5qVkYSd5Jqw5WqWF7CybWXGadSShWKsTUdVyx6nqjpTX0Vua2SvQHg/O/z1G5dqlJTgbhd1NcoTTdG6a623A3fdPaFMporj/Ve/Y//wFdiBZZ74fHqSfUdt7IZRpqIqTYCz4oVlrSVNC3nJOGc5n0+6GxUhcVmBxlzBWebjzPNyYrvZEd2Vzf5Aa+3GUJ3TwnWZ6M2g+ScGyrojE+KEaU1C1YyT6GJd9B9PJ/b7nUyfGfoQKUPBu04UksaKpN5jiL1EJuacWJbEuBm1kzOMnahaq2mcTleul5PgXF46Nmf0w23IuGyrZCe4IIdpLUzXk6bKRWLXiyusE6vf4+lI33W3ERLg9HLkOk1stxt2uz2bw6vtszBvJOi+ixFrHafTiRiDhLobx912R6MRjWTDOsRGwznL6eVIaZlvv/yaLvYMgzCASmu8eXzkenlD34tZ4fPLC0/nI+fThd244fl4whnDkpN2VoJZGuvY7LZsdhs8ntVrci4TP3z/E5txYDfsRG1thTU2T4a+6+hiLwycZcavedHNcrleiSHQxZ6c1V8qF07HI9fjhbTd0fcDbRE/llLUb2ee2NqNBKH7pvbdaj5mKnWpYBvPpzMUocK5riMiS7RqPOfTkdPlwuPdHfM0YywKiwnjomURZC4lQW0sBaztCN0jM1e8yZhyxZFBISGjjoFlPQSRwiHdvsHp8GYxNx2EGPA1FvF8wJiGbwKLWsSxtOihVI1AY4JlC55cjOQdGJzufKRrEoaINAfNCPOsymiD6Ii4nVDOQtfDuJHisB3vuLs70A8dMTZiXA9M8cQJcYM1DWOCHrr1Z5h21YNf/n/wnopV23SFAYgCJWiRk6plpUtH8j6o5raDaa1RU2JaFoqSOJIRxbQxkGoWyAZEiFblmjYlNrTmoKnxIvZmL22sxLCuB6w1BmPDbUIzRjr1defE7V0hqY9Kfmmtqq5D4Y7SBOrQvUnWDX9t4n7brFyPzGtAUylCS89tPdgr1ojNeNWdUakynTRjqTVjcbK0Xr/JSnEoqzbC3ppYmQ5k+lmbDWkyMhZh2dWWKRpHW5ph2H3Fw7vfkIvB1MLxeOJ4PPL+zVswDmO9+J4tmXOa5JwABt/z29/8HVXfnzhUSB7PXLKGXsnZPAwjT63yp3/9F4lS6Ef+7u9+R86FTT+SW+F4fGG73ZHzQilB9UowzVfmaWYYR0mwNI5puuDsRoxbc2Wz6TFA9FE+dyPmrn4pmXlJjEPP5Xwh2ULsA7VAyoVlmnHBM8aelfssQhq5MbwPsnDU9qauN5Kusiownc84Z3EucJ0Xnp6f2W83tHZmyZKWdbi/o+sHWUpHEeVJESxczmf+83/5r7x795Z//w//IA+E2gA4uWtZU52gscyJRlMrXUiT8IzpBtI0yQc7VPJVvNbHfkOulaBj5JIyx+ORzWbDsBnFcsMY+r7nwTlGfX1pyUKBDOIFn3K60SG98Temk7OOVDK9j7x/907Cf/LC6BzWOXGyrVk46IBxRsbJeis9UgCNWFI3Xf5f56v8/BhUHZ/ItTL0HSmLUGrTD0QvDrMuiL/+8XpiM26xxjLsRlqDoevAikI9WMOcZA/jjEAc0Yr5XWsSa1qotyVXs4b5fMF6RzPClnHes+TGNFUe374hAOfnT6R0xLoF1yrOqqmyWReF5RWXNkInxCKwW1sjaRvGlhvUkfVuW21kVi+lCiQ9+CRfWWCrQkPC6DMNcUttVfKnCxIKTxWOvXTmsi+JAfrRsBlh3ARCrGw3d4zjnqEXexYbRDchVGSPi1YLlOYttHU3UhR2Ktq8e/29DppQOdcEiMZqoyF0S6MYeFN6cZOnWWFgSd+b5yz3Zsn4LhKaNn2tYlfdhF2zP+Qwl+Wr0725lyLaRENgjGc1IEQhinWKMbrTsk7dQ6tOR8YJ6WGFAZv8XGE8yUG/TJMI5wx4HwG5RmLNKF9nalXbcZkqihpMNoUshWEk6Yur96z8uHprzmR7I07KkkBnWcvYLfRK/uatEDWFxBRpul0n1XbLZ1EqpXn67i3D8CV/++HIm/sOZ4TGerff43zHssxYZzkdT+Scud/fM82TNM1BzgCn0KKNURYETZqgeUkseeZ6uvLNV1/z5uEd/o+By/XK5TKR80II/c05t1WxDjfeU4vA5ev0Z50TZEi1FiktXCfxk7PWU9JCaulW3Kd5ZrPd4D/+8CPDMDIMHeNmFBFUrcLjvtuTNr2OpWsdsFznq0wOSLzi8WXm8c1bMe1r8nBbb5iniWVa+G///N9xzvHNt9/w+PDIOMom/XQ6cjmfWeaZEAPjsGWz3bJK5GuF48sTqVZ+/8c/iJJQi4PYO3NjU1nrsNqZ3T/eQ6vM8yTMERuw3km3O8/U2jjs94ybDWt9i2pW2CrYIMpqgOkyYZ1lXmZOp7Pg+rqPiV3PkhNlumKMUIFzzUJhtKJgba2RcuJ8PrHd7cRwMC30vtNCKh3PnBLOy4fjnaeYona/vbxG22FaZZqu4karN3jwgc5Hlpz4fHqiH0cO+x3BFWWSNdKShDpnHXlJBOtZFsnS9d5RdeQX4zfDnIsY46UEwdHHjuDk+gVlqqzsjpIz1FWHIFOkD5J61YeeL774ilIgt8bm4UuW88Dl8pFWknbIRa3oRERXdIKUe83IstGIUEusSCw1S7eu4IgcAsZScYLX+zURoGLLOpEKQ01eu+gbGlWEfSguKycGwYlhbQjQ9w5rGmM/sNt2hEHgzt4jzgBdh7FJ/LhurqGSm2KMZkysLZOR92KtJhfq4jRYqFoobHOkVoX5osr7FXZriCnmytWvRayxS8vkZQFrmM5nyfy2sjyW+0uKgm0V3Gpfve7CmgZacbN6WXcR0DT7vdyaP0k4E4bPrcBoPghtTb3TZDfrMEU8cltbbUN0/2FkylvSBWM9vfC/WZlaTYt0bnLGOGNkB6UNAKsBo+6QDFYN8QQ6Wj211nVWa0iUboPW8u1nAIKk/IzGa3RvabSBsVY+B3fb1RQtEAbY8Pjuj3TdA8/nSfQHZmG72QnkbeQwXr+/c4JjGW8FdjPww/d/4+7hnrEfb0W0ITulGCM//vADp9OR/WHH4fDAm7fvAREcixZKinuuhcPhgVwrsYq9Ua1iwd/Fnr4znK4XrIoK05xxbqaukLMWfdmVSrPWhw5/9+aRPkay4tHeOM7TJJa0TtgBzluicRQrnUAIkVIzT8dnptMVaNzfP0qVNla81PWi+h62fc9fv/uOw/7A4/0Da2LVdiMwyGUW9lJFaYWtqYdQxhnH2zfvZCfhLTmroEQrZVUnrpwype9xRrCB9UGapit9J6IpC+zGkaaGY8u80EWhex4vFzabDeNmpORC10W892JpDCzTzDRNeCuYr3OOsZOlfK2Vy/V0k8r3saM6Qy4L1jqWZRFfFZ2UipPgJavxna0lnDEE66mtYKsRjNIGzVpA+OC6cCtVqJzeyP7CeEdMSd6jsxo56eXhQmi01QYV/FlalCnFOe0cnSMtAiMZY8jLzDzPYqqoHBopAuKblBU3Xn1uchHB0BraIwvDRs6L4s9VLcYNvt/TE5muJ1p9ERzaJYyG8nhltBSHwkjcYIBqGs5BrkYOKtOgZpyRYKGlGCCCHhIlV5zRuE/k9Um3KEmMIJx6qlh0DB30Efb7Du9k1Rq6SLSVzX5gswt0IdJ3PcY2ggfnsuLb6oNlKpZeH3b1B7WyxPVObKa9NbKQJ0AVzy9ZpuebQthYpfIqoUCW6dyW8gI5Sf9bU2W6zhjjOJ1esLan62RCLEZyLPSEV4qvuR1axjvWeNLaVg2AlF9ZJMv06azWlXXBvN47iu1rObpBxIVV84I0VcbIdAYsVXZ+Ta14WkuU4qFZcpmlKJp1mjL6/aQwrM8ATTQO67K7mdeFel3TLNZtvrqnNl53E5I/8Qrx3AaKNbvavWpSBNtfobhGLYu8ntLz5svf8f7tr2m2Z3sQoSbViSi2SvG3QSbJvpPkt1bBI3tByehIHI9HduNOjBAncYrwQdye7x9EVxFsYD6fCUMv6ZAKt+dcCMGTU+F0euZwuL/tboLSq8UdwLDpe7x1MlkpJySXwjydbwFqVGGKGiO+fH7sekIzLDrCnS4nhZgCtIqjkRtkhQKMhbHrWWqCLJztOWU+P33k4eENqSyy4PFO7Jqb45tf/5pJF0s5SWnPZcG5QOgCPZUQRDksKXPS3YJh3O/FNtcVgXRSJsQ1l3ml9SWOxxPBB/EvyUKztSFQSFJ1lwW6gDeWXmmyzmXaJAVhHEf6rsMA3333Hd47vvnmG7lvlI9traUbBplwTi/86a9/wXjPRg35QMKJmpOxOdpANZVh6Hk0jzSV+ltvRQCn2HRrhr4ftOOw5CZLamc8L+cTVPFlMUXwUWctJS9A5ZIWmJt0CqFndb0Eeb2mGqblytPLCxjY3x8wSbrS6D25FZbTlSktoq5ujawivBAi1VbWDJElZ1rKXNJMmia8C5ggOR+2yRVoGWrOlFq4LLMK8RyuwlyKBst46ba7e3KamOazFAibKU7orbYKZFeNTJXoAy7FSm0qjByW2RqselxIZJb6E7UKJUmHrjs3o/486PkhmeOwGeHtm4EuNIa+o5UzPo6Mw5btxtH1huANLliCFvhXuGj1l6o4Myg7ydFMViRDFuNGFePowrwZZXnVwmpBvh50zmpn1wzVFO2YJXo0L6qURvQaQjiQqNjd7q24yIqgQOBPLf5i1aHmf0Y0Mc5YmZ61OFSdtl5/iaCvrDynJvtL9a2AJswxazV7nAJNoz352cDRJMpYWKiyu2pV9hMpSYjRGoG8BkY1vY7rZw8IbVpXEVX5xkbT8WozqjfQug23oihWKUp7rU4niaaT0ypclS6/WYE2+dk1oSE055vPk2fYv+Pd17+meElxa03T/rRwNmNY8izL+WCxRmx3aOg1bJRcePf1F3gXWDSv5+PzJ8ZxJFRP53t2caA5y7/+8z/z+fjMv/+Hf8Sr9mzsB6zzsjLoBuZ55nq90kchQEj0gRB4KmDWSAVjsTaIs3WF48sL/TgKdRrIeeHjxyfSMuOTFoToPFNacBX2uy0+eByG1GSrj7MaxSmwAq0RukBumWgMMYh673o+g2l03UAIQZZnLvDHv/vDLbp0ul6oORNDT0qJ6LwmTKmfjpOxU0z4dPmlLpGgGdGqBKfBn//tL/zth+/4xbe/5Hd/9wfZY1wn9ntRELrg+fzpI7vdjr6TzIbgA8F7McHzKydCHtD3795JYclif94ceOc47LZ4H3k+vvDjjz/x4fNHbAiMmwHvvHCZW2O5XPExkkri+emJ+/s3EnSkvOQlL7IA04kqGCm+3kXFmhvBydcvaYZS6aK8j950XK4nhmGDMUUyklvDNnnwvIkCxTi5UZc601rFBo+3lvNJDmQbpMMoS2LOC52PtFpZatJJqpeDplau80wrwo7IufDxwwfKkrl7eKBb/V9UILgmmuUmgr+GwVZDKkU7vyo4MzKJeN+rn9YiAT25YoxkkM8li5cVkvOhulgaRvk8cgjYqkWhCDGiNUOqTT13GmQoRuCm0EHfGbrgcM4xDB0xWrZjRx8b3mV8MET/hhg9IcpC2vtGCEZYZVZeA1WOauuEmIE6d1qnmRgmCNxkRcFdmtAn10zs9Z4rLSuvX7vYZtRfjBX4R/JcRA1+XWZAMstbWQ86QQhocrCuy+liFj0oBVZbrSnW7n4VwlWN2JXKKYZ5WHezmDZVQn0E82+3BkeCbEWdjlm7dXN7NlvVzYDRHWXVZb0zYK0WAYGImnWaVFdfGWcgeSm87kHk9cvXW4XUGgIDV/1f6xqkKTVW1Pr5tu8RbzNefwYVr+eMaG30e7SfCwYbqWVqtfhwz7B9x/la2PUKYWl3r3MfrRY524y/QTjGwpRmaipY7+i7nlTEhDKnBWM8+8Oe4KN8P9so1jFPE+f5yjBucE6Kf4wdz8cTP/30I5+fPvOP/+EfxbW5VnKrhFZZqqTdnU4vpLxwf/egz11gnsVHb7vb8vj27bqawTnH89ORoe8Zxx6fa+W6zORc6KLIwX1weGUCgSiNReDVKEviPBV88Gy3O3bbPc47tautDONG8qwV+1yFHqY2Um1EFyWjIXYYJ1a2QxwwViCdRiPagVZhyTMhyMI25UQIQTQGwQp+3GBZ5OJ+84tv2e53nE8vdMMgi0RtGafrlU+fnzDOsttupPM0aAyo4XQ64WNg8EJL80GzL4CXlyMxBunKnYgKrbUc7vb8JvzmtkRstWGCZTmfZRJojcv5yH/6z/+JP/zx79nu9uzGHcv1SjAO1wUqhpYWjucj93dvyEjYiEBcYgG8G3ecpzPTPDFacZwNMbIsky4VLbELeONZ0sxsZpxz+CZdYmnSPXQlM00L3luF6pTxYuVaZisH1enlTOw7TBGanBzgsnuY55l0najXhO09wVtaqSzXhXm+4EOgWDk8c0pyYOoDWEF0Dk0evr7rRFQoSyDJcggDtMScLrQsE2DACP23IctWI8KjJu2xLpll0qnIErUViZHNNYuBoEMmgQC7bWC77bCmEpxh6IIk+kVHtGJZHwPiXRYswamjTxHacWfFGsSYJp5P3osXWBBK9Y2rZOXec048rCQOayXxriCK7N1KNVgvjJlcGssi+c9C/5aDeikSd7nMF67Xs5YXIwFUNzM9OUEtjWQyrTlRkGhz15pQiVeRnHTh6CtWaEc/v6KUUwzkmpQFpHhRsdQ2Y0yUTtuKEK81dBcgRVv2fQKXZiqpNJo1SsCQn5tLIdpOvNvc6s/1qpoXvYIWpyJNR2sNiryXjKjdRZsl+wu7khduTCZtAFu40fxlDpPCY5oKRDUtE7NOR6sDrP59nUJ92LG9/4bjc8b4I3HcYxp448gV8jzLpsw7XIOXy4laKm8eHpmXWTRn0WMRHVc1Ej0aQsRgGAfZh6Y8MZdKtJbYd/zx7/+e4DUATncOP/30I3/723cALKUwhsBcZXfph1E+w1q53x84Xa/M86xwoyXGjq6bxJ5cG/qmHcd33/+NPvb89te/wXd9x49//Ru5JN5/8SXRCZc3mcrqB9MM0lU3CTI33im/3uG8YGGlFKwF7z1uu5WqXkVI4lvgeHrm3/7p3/jjH/6eoR9F1DPP7Dc7jLPUKtv04L14QTkEXmnCbvBeAonsDU4RfNwFxx//4R8IwfPp40cu14nd/kDnoyaFNeZ5ll1CFU/6Tz994Mfv/sYf//jv6MKGw/09XexlyTxnak6kKl0rtWBNL15VOWGcZdxsBAf0kdP1wvF4JNeKU8/7bex4vl4wxvG73/+BYRhwWJZ5Itcsr2XVAJSCs57PTx/AOu4OB4INMtYb0R+0BufpwpwX+tjLlEGmM7KjsU1TBYzYBjtXoROGTmsSTm+8x8f6KuAD0rLgnaMLkdaKYJM5M5qeuQhkJMpsy+nlyvV8whnHZr8TaKCJxXlqFRuiHNZVrDaqYrm0JmZry8y8JLFyMYa6LExKnwzO4YwjU/G2o3eRVBM1z+SSVah4xrdKK2J5XZR7T5Wpoi1J4UqBu4KDobd0nUwL202Hc0f6PmBt1rAqS7QOGwzRy+HlHXT9iHEV79TIsomLKU0MLL1qHPrQieml0UO2QXOrQ6r0pzcIyeprK7JsN7p4zU1KhgW9ZhlvPIVViSvHd06yzE438ePaNSMFa82QkNlGKLM3N9Z1d7DuO3TPoYeoWX/PyGdTmkyDt31SEUoqeN0PSTftnEr3FfOuN+dFI/bpTTUGzlA106I1jbRthWaaJDP2PaiWuxplzDVuLKbGK6mgtfz6Xhs3mDeVqhEEa9io2OHXJtbvzjhsa0oBDkiYRdNdkARR2XXBbtZFtlyjqs+LfFY9b97+jm7zBZgL42bP5XSi6zqcF1jHWff6GRURqJVcEDJTxLtALiKILKj3nZdIYufFpXpJC89PLyzLxBdfvpfm2Aam6UptQjTBQd93HO7u+fKL9+w3G9GLXU54HwkhEtdOwDk2m5ElJdnjtMbYdfTde1prr8XLRXwX+e0vf8XxcmVaZnyplYc3jwQfZNJU6pcsIeXieCf5vtZIwpfcig5jnWK/r3oFa8UBNqsVxm63pxlhgnjvOV0vdJ2EKFi1hWi1kdLMdrvT418dYHXf5tXFsNYipl7GEHXhHEKUQ+x0ZJkn7h/eKqOh0UrmMk3UnME5klJKHx4fyHPCa0qeZR23IxipyJtxECjDi90zxjAtC5vNBt8Fur4jlUI5nSi1KtNGYDesJTrPEuHtsKWLkdpkkSsRowGrBcJYobX+65//zDdffn2jwq0CotQKzhq2g9ijr4C6s47j+cjT8wuH+wND19GsIfrA9TpJOpcxHI8nzucT0+VMqpnoAg/39/RDJ51/zkzTFeeCLN5rY1r0kMBxmSaJTnWGYbtVGqcEFqVWZfJyXjjd2hgYg1g5FIEo1kjM2EVaKaS8kFO5YcrFOlmaKy/fWItxlmA71TVUWo6kchWc/2beJrBNLUlYPxliB9udZTN2bDcbYmwE3+iCw7g9xspritbhvGO+PBPqhmIicYgEb7FejP5ylTS7PohDa6liE2JdRLZ0Bduk+7wRv2vB+NW91d+sS0rKGr6kWSFrTnp53ZfUKlh3qom8zKQkxpRYR8tyCDvr8E5YTQ6jjCRdQuvytiqeLwuAcjvw5JBdmywLrt1WWLL30YWy3s2SAd2oeZLdECuXjJvLqzGrz5ShUZSSKzkaQuqQiT8XZfopAuBwOBcJncE5rwyzRjHqqFrXycTeXmNbw6L09a20z6qTpFh8N5niqr1ZbOgCglylsbDrXqBajFk/Ob0qKw3dGFpJt3uyNUuqgTePv+Rw+BbrRnbfvuM8XTldzgybDbk1iTSwlrwsLCWJVZAxbIZR91eGksqtUQvWMi0L12litxeY0FrH5enC5XLifLlgjOXtu3cUK9OsN5YYIq0Z3r//irv7B2LwHI9HttutEhEan5+fGMcN3jvKSkxyjilnPWdlvs2t0cWO0/lESleMMbgQ2Q5CwvHPn5/ZbAZ6Z3l6fqYbevogYyRJunXjvS77ze3GkqFZDkfnXoVvEmuaqVWCddYVunOe3/zm97dpY5X9v8YtuluxabXdgtwtVj1OpPhISK6kBxj9PrXKctj7t0TfK/W06nRjOU5yaEZnKcvCsN2wGTb4GFlKlo5Lza4M4K0VqqoxGsYjHOdSC7YTy5H1wH759Intfi/pflVMzHJaqKVyGLYsRW0FnNolbFeXS1G/2i6yqVt+9+vfMG42wiRS5tHp+kJ0UQUx4oXTnIUKU55pxnJ3f7gdMjSh20mXBtTKy9MzH378EeMdwzhIBKazXNUaWiylI9N0pZXKMPZynZsUy5YryecbRjqdzzw/v9CPA6F5lpJUYFSkWBqYl8z1MlHzQs4wxEDXeTCGtFJZrWDwzlg0RIsVBDZVikRl7bI9Ju4JbQOtyHXIEylNtOWId3C/C8Te0AdD3wuN1fkLfdfhg9A4UeJEK5XSOWyp4HtC5+iGKHGwnR60ii8H38kuzFocARcs3lqsEztmGkofVtgCq8VLXEH1oaC2SiCKkhehKzYauVRaSbRFDuhaDWmZbrYQTgeACupEa2+d7Wp1cbPHAazxr7YRdlUorxqCV28rh0wxeiTqpV8BMf1urenkhuZ1vEI3WiqkEbjtReSJzU3wqtIyxq7sGoWOdAfSWiOGiPVOss+V2vvzgltZF87otWz6nuV6Ytadh3stgqZqlkeTqQOx+0mKbwVrb4WuqYofs15BZQwV+RkrUQICuRjuH7/h21/9A8ZsqavHWuw4aJZOsAjLr1ROk0A7d7udNDVePr9WGz99/sBuu8WYxny68nw88vHTR969e8+3335LA8Zx4Hq93na7rWW8D6rUXs9OOJ9fWOaE3R94evqMtY7NMPLpdOUyXdhsttK050zX9Xhr2fQDx4uEEY3Dhq4TUWTXDVQt5tYahmEUrY1pErqDLoq9tXx+epaOJTjmaQZv2W42N7XiDSXU5Vcpa7ciH74PYkNQVdGIjuq1CO10t99pQyyW47KkMmL+Z6QLq6kSho4lZWFhOHPbe+SqKtpbk1Al+jB0TMtMTpntOOCcp6VEDJGHwx19H3k+nRiHgVRmWZp7KWTeBa6nMx8/f6bWyrgdJe+iypO6TFdi8Dc5fSkF7x1v37y9WWFcLxdqLVymiRiDGAtez/RdxzRlyZ6+zoQ+6tTVJNuXTBcHWXCxakSKiqgMqSTm6xXjA9t+JCNsohClGz0dr4y9fobBMzhPzYK6Hh7uuC5XBtdxuN/jg0CFZF3xGRHdBeeZa9KbROBFKkLVLE1/Xw62cSf6kst0kcnTScdsmzwP8+XC+XQk2gjWMaUF4yH6ICwa71B5l+YINFquN4vyvu+waBA8QFNOutHOr8rn3cWA2+wJrjBsGn1odH4RfjoBF63c09ptp7aoKlughdwSfRcJ/ShKau8p6Sqwpvf0IdANgaZJfn7NK7HrwVpuxAqzQng1Q17Df9YDVa7xdTnjZA1PybNAAy3rQtjT9Nqjh62xq1WH5BcLyl9vS2fp9hXWQWCddeHKWhjWhk5xetE7CQffFScTtLXCsDIWh2QNNC0guRk5aBGWjFFPJZXFybPalDnUCq2IfkdsPMCUjHNBYONbcTOgzYKparutB6iEmgmcZNrqZ3UD1tT7SXcFbV1YK6NJr/Vt4jBeIU+5kYwSHlYB7usvKZLOyNmWdVqXzxVy89wd3vHNL/8B4/ekJOQK+R6W0/WKt5643UGDOSW6GMlpoVmDj56s97fzju1mZIgjx8uJp+cX5nmSIl2L2sg0moV3799rvsyah+H06yrNWt2PdGy2O5z1fPPNtyJCdpbHx0fu60GbZVhKpm/Is5pmXp6fOR6PPDzc8e7dlwqry5/fKNG1yn7s8eFBJO6IeV5plu12x8cPP7Hb78kl0YdR9nCoEZzS4gxQS1ZhkOF6ucg45ANNFbnOW9JcSHlWQzNhz4niTw7qruvwXv1sWD1fCrWIUVkzTbAxHdHXf1uFxnKRjsyYRloW5fgKg8HGwMOdhH37EHnse2qtYlzoLKkUrstELplPnz7d9AOnlzO7w56cEp2zjLsdQxN4o6R8k7sbL0yQdL2SlklyBib50KlNIl7V6vvD50+cj0d+8dXX0o1WeY19t5HkN8U/VzFZ3/VyMLbGy3xkFwbpskqV4KJFJqaWE0uRg8U5Q86ZeZnwzhOs5e3DI8YJ/llzwlQpPLlk0VNUyFU6trUvlV/tpsw0qgexxhGcTC4x9KRlBi12K3w0bDaiIs+VzovFRDW6YL4dbg3rZBdijCwTrQu3G1UWsHr4tvVcqVhmOnclMDP0hhAtQ/Q4l7G2EP2IbVqAjVib5zKJR34RxXjoIiVfiXFgCAPBRiyVl+MTMcC43RG9x/sOSrvRUS2e1ZjQNkPOVayejWRtl2yYpyvbsWdOM8uSyeUqHbbaWhhrZaLE0NIsh77uLcS0b+XlywKxqdCrGZlcDWI7oRQrVh8lp910vemDC9UUbdCsapj0UAeZyJw0QbKLVtuNKnsfgXgk1rfSWO2pW1swaDpbkZ5cmEeZkiSPYo0BNdaQQSEsFb8Z6dmNCbKk1mKSdcpoWrCaLvWluKz7FCkCFtF0iHi83SinBUnqa82KXkV3da0ZvE5gBtEmVRVjrnYkFi1IxtLaGvxlyRn6zYEvfvUfCOEgGqFa5OsU2tv0o5x5pbHkhc8fP3I43LPd7vFWRKifP31gmq6M/YZvf/FLjDHszIHnT8+klPnNL3/NuNvqHkatMYpmsmsSoDxfqmzXQ1zuRdmFWHV3LUVQipLELsdaiSWY00ylcrlcGceRcTMSQ4QbtGdoOeOCkIuoSIDa56dnoa8qdOODIzpPHDeEEDjs9wTnWUrBr9XYigAl58r5fKLvhQKKUjp/tlKTZV9d7Raq4NJNllN919N1UT7gkplTJriA9063+AVnLZfrmevlym5/YByHG/S0sg9CkMOl1sbY91jvabrraA2s98LUmhfCZvwfDuLj6UgumesyM242PBwO5EWyHOZJMrhTSgxDT8mZl+ORWirBe+ZpElGLgX4z0m1Gnj4/4Vumj57z+cLj4yPevzrcbjYbSZPLks9hnd68TrMTWLvEphnFlWkVL3nH6mBKrqJHaI0wdLRcWeoibBdEZ1Bb4poyZcl0fcf5fMZ6yYu4nGUcnlNiN24kMa0K5TTlwjRNgmEqnXI1M1tKEYPAJsEkhYrR5DGQRXarld0w8nI6k0uh6yPRieVEU0+iprnltYnwynpD7z3NWLIuKV2pYCrOGDxnWjvRd5W7jcd7QxesMs8alqj/yHKztCyHwXIlq2tqBrxpjHEkbB7ovVjXL8uVvnN0QaYPbz3W9NAsaZm0wZA9RWuyS0rNMl9nvB9ZcuZynTg9n3DG8zRkSjkzpZ/ou55h2GBtoOs3gFCJb5naRmI39YOXQ0+dUG9GekCrGfuzdDWD2oU30S1UUH2B0oRvAUXKKGqNqhYrthVyBePkgG0gLrfKCKo6qa/+rdIulNsS19gs+QxNMyaKQKRF1ezWCoU3FXDGU0zC2CD5EhpGJJkdPysQrOwsha+shP8Y417dFVa6bDNgg8qpdRIzhbWfWMubMet1WJsvbr/q7bUIRCjBU7qo1vOr5kjo93z5i98zdg8sWeBUF4Io3avQmEsr0rg1hzee+zdv8FbcsI2BzgV2hx0pLXz8/Im7+wc2my0heB7ePnJ4vOduv+d0OdFwmhMjBdg6y/V6wbjA2PcqE5D39PT5M3/5y1/4/R9+T4yR6TLThoHj8YntbseyZKarPMdv3rzBWse8TKKwjpGh78mlMmcJf7PGcy0F7xH4LES597//8BN/+M3fiRpSm5BaC7thw7xMDENkDTxZ5iISfmWoWGPpugGrwTjB+xVcvt2gtVZC8FQnys7VFdEapBprB1GWTFpmsknc39+pla08IOO4kcW3UvlKzpzPZ4ZhoOs7wdCc1wfC4QyULPbCFOHYWyMq6efnZ2IMbPqBVgrb7QhYNuNGMr1Dh/citgtrNoXuD+YlSZZxzoqlCzX29PLMeZ7oQmSeZ8au53B/zzBuXnF2BDnwqmS0zoGzpLQwnc5stlvZZ+TEkjJdjEAjzVeOzyfRNaTM0mbJS7ZWFrxVjPjkhlWeebXgxKo4ukDtpLh8/PCB2HXcHQ70XSAGT305MSV5YDGVkiTWUKZDMe0zILnOrSl7RrDiJSfZX5hC7DqxEymLqte3HLYbSq10IerBLw+laWIXLS2HoyxJokODUHNNC1gz49wFby4MPXRdZnCGrndEJw+2d06OFmtxTUSyBpgW6dBNq2QSsdsQXKTkiRB6TAha/ADf6H2HDRZrPd6ui1hHyXC9LORsMDVjgmcpCxRDLZ75nDmfvuPpLOZpOc94ByEY7u8PxC7Qd2+kETLSqYmCV5YwraljKXogCrC/OojoZyCTgOxxBPJptt5UwAJJqT+SqtZFyKV6BO2U1+S1tcD4NQa2iahPNByacGhWKFmNs5sojFdmnM2N5uTAKK0J6UCbl9oyt55w3YioHqOxitu47SVKE9Fj06LX2qp9kZ1H1WW0vptb+6ljr1y91oTF9LNfZi2krLNxu51N1RhsFVi3KeKw6hvEkyzTSke1W7766t9h7Ibnp2eGYZQ8GIxmasvuNBjHdZ4x1jCEkWCFUHM6HUXTorvW/eFwa/zO1zPTRXYOu2FDmhPTdaGUzOH+wPUygRFU4+X5KPY7Icr+Vwvqw8M9MUjDFoOwpkqt9L00JTFaQvBczhPzIhbjq//b+fMR9/hWzBWtw2q++G4csFiCdQQnKIl/d/8GHyJ5mZU9IRUyt3o7742R7nMujc47plwobSGGQIyeWrSbUSzvdRmmH0QDU7UzRlxPhYZXmeeZEDuatcTQSdExwrM2mkplrRV+v+ZDYCSrNajc/ebyqRS43Cw1LwQnnSkIiiqsPMPT0yfeHO7wURb0nz5/Zr92+DQ67wlOPN9xhmgDKWectQzbDefzmZIrPrz6QaVlpguRN28e5bBfkjAKnKMpx/7Dpw9YDF98/aUsuKrwklMWkZr38t/n04mrFd8WWiOOA846TscXcqmkPDMMI10I6gEvD8GSE8wiPPIh3GwJJOu38PjmDa01pmnGqEDQ6PIPY6ipkMrCmh5YikBrLnjmumCbFNucE1gRBpnCLU4Va1UgFAXy6SKuCCYquKocwAWNskRgkGaNWGj4ijMzff/C0Bk6NzN0maF3dHpX+bXZqIs2IZnaLN4Izt0MdNHh3UipjXGzF+aedVjzIDkVNROsw7hGwOOik3hWGlW7z7QkaHA8JngxpKVynl6YLhnTPMfrhKWwXC+Y2IjBECLsNo67h3fsdxt8FH9/eV3mZj1tzc8Uvmvfa1eWjky+suNINBzOSvN1E4ZhwGmH3RpGbUWagbqK5qphjRHFgFVrCrFcX9XSorxeF8q1Wo1MfQUcC2IaKeeyCOdwQXcCUg2WJG60Rrv1qrwZozs1UGKL3iPSiFaqkelm3Ss2YzFVJlfRQ1RuFt/G3pTP64RlGhRlcFWF8lZTQrSorEXI8boj0gwjKk0twhF4C0OuhlI83o7sH74hNcnljlYoymXJnKcrwzBqXkchdhKL+/nDJ+pDYztuAJELnI8nhn7AOEPXd3zxxZf03cDL6SyWF1SuSQrJZjfy/Q8/0k89n56eSEuilsTbt+8YlPkphV6fhRB48+4L0TAtieNRYg3u7+4wxjLlRPCBh4eBlDLzMmuzLA3v5Xphv9trXreYZaYsDEgXg8QrWIPf7TeUZZKDTBdML8cz+/0O43pMKzfhSfROuk4ras6Xl2daLgy73frpSXHQpfK6gW+t4aLj9PRMrY3D4YAa1tP1g8BcxtOCpKnlWsUi18pHfTmf6fueqLYZN2YUMp5flivReQn3cJ5WMsHJ4tNZc7PpaM4TnMMOG7ktSsGGwGazwThH8AFqIbfGnDOxRboucj6d+fjpE199+SUhBux14jiduZzOUAu7uzvs5UyMUWikypJoTX+Glcrc9wPzMkOFlGdyqXhnxYq8VShFMH7t4lLKqgw3zNcrc06UlCEX6DK4QLpMhL6jLImXF8E3O+/ZHQ6qhM5QK8s8sxSBIiTYRWCTaqDmqgw1ZWAgTBvxnoeWMr45Ub42XVlW6EJHRmA+XL0JkIZeNCdNHUFFA1BpTmIkreLDAg9UxgiECednNnHmbl/Y9AOmFVo1AkG6Ri0J65ow3LzDG0cjaw5xxOsLbk1CU5o1hCheXcKBz7cpzjm1jjYNjxe6ZIO2FC4l8fSysKTMy/OR8zFxvi5CKW2CDfsAwWf2d5F3b/eMfSQEwzD29FEXxK1hnBzKxjhqkxW20Y56pZFiucGjtWmcaltNK60G89jVSV3f48+6ZiM4uy7CBFZSCrRRIz/RYBRlYenBLBFv8rOaUbioaTGTA8k0lFCi+4SfeUnV1VajJPHHsmqVovukalaUWfTxTYknNOleSyuQoVpVOzRYWUUCWuvktdJU9SypVQ34jJSxdWn1ypSU3y0tI9Yk6N5Cl1useRjKM0cKScpQqyWZgW9++Q/0/VucjYTouZyv2OXK0I3S5LjAMl9v1zl6sdj/65/+wldffSXuCEPHWyduC6llTIVwM/as3O231CI7glwSD3cPPBzuFBJslGUWhmFDQ4HkDPNBdhSlVPJ8Zk4LH376kc/PzzzeP7Db7QjeiseYaWq1s3A6vuBDx3635+7wwOl8JuUZG4f14ssuuFqWnEilsjUDfpkXistY63CmcZlmXj59VMdV+ZSnqyihbzo2A61qRoRxpCVJ+FASo6lammSoGFkaWX0AxnEjN77XTAPbxGEW9cVv0AivC7Zab2rvG1kvBFnRaXdzPL7w3Xff0/WRb7/5lu24kY7EW1gWUMrYyqDyMfBwd6Dre93kW3GGzRnXRfGoMSLC8l0Uo7pSebi/x4fVvruRa2YuhWxkwhk3G9FAOGV/GLDOCnXUSLrc/eEg+oIqOQzT+YJzTmlqQvGb5wXrrBacgrUiBsxVxFvXRRLZqjBeZUmv1zjEiLWevovkKod2WTK5Zi7LTKdxrkbbzlqQ91d/xtUvhZqKsCFCJ9TeXDgej3Rjh7NibSwWBIInOyOHmG16gCuNsTUJijLhZxbX1ilskCFngr2yGxbGMLHZLIydHNrer/ss8btxztKUhluRbsg6A83hithEGOuYpxMlVXzsCcHrQae9rTVI9p0TJjWGVgpLqJhcZEpLlePxyH/7l4+cL7N0qwl8gO22Z7tx9P2BzRAYd5ZxNGw20gTUVui8ve1wrNfXZbQ0GN2/GIezijnfsPKmbqMyFVjraQhTRSCaldUuy0t5tn7WlNWkS1jZJVjjQLt15UNLgajcchUkRpjbruHG+LGGlgpLzTdiQQNdosu+YJ2CslqKrxCS/IfTOAH5xzovk0YVceAahCN/X+N0eYXebrBUVRq0kYwLWJfnRfUTFW6FQWN2McKUKu0GNlWruwezUn1/vjWVmFwhy0Ry7fnlr/8D797/Fqosf41pDJuB2gyXJFGiIUbdr0oRLLnw/vEtfYhU03j6+JEv37/HBM/peqZTYo6IZ51a8zv1kLP0vYSs5VoYhoF+s+F4PiFaFIEel7Rg84KxI8YH0jLz3/7p/+J6vmBjR2fNbSdojFz7tMjeMPjIw8MbWpEdX85FpnIn90nRMzX4AAj5KHh5jv3TyzP3hwexD6gwdD2Hu3su05XDdsecNCfBGmLsmOcJmy3jODCBxF9agQ1CFMdI49ADTrom4wytyKbcYG4HfE4qLzdWmE/6Bm300v2mhPeefnMnIq9J/M1pDZwTw72+Z7/b0HW9jEcpYwz4YDGdvGGa0GebmnO1JtDX+XzGOUff94DkSFhnCX1Ep3TOpyubvqcfB5brwlIytTS2/YY0JZoPPJ9O8jBYizVwuSyghcmqFXerFetes3iH2N9yGTBwvpzwvhOr9FLIpSmdUQ5bkAS+1DdcC3Rdj1YiUT0XycUmiqVAyQlKEwwdI6FDFloRi2awpJZ0QQbTsgCZlmX5nEtmMWJkN+eFJScGM8hkVpouOsE5cZ6lrAcKWNskdTCBjV5gFAO2ZkwN1HomxplumzkMhrFbiG4iOjnoVd4oTrmrmR6oWV3BEZCYR8mv8HozWwxx2MMgQrO1EzbGKb2xiqCyCnRRMswl8/zDT8xzYTs+kuaF55fP1GVmDJYQDOPg2O63bEYhcnSdw9hCjA5soXeeSsa7Tg5jvQ61SeimBOLIzkHqs1RqszJzmi4hjMO5cDuAq/GseXSlVayVMC9ZTEuHaXQaux3SiM2Hc9BsUChKSetaL6zmULebsM3fvK0oC0tZu0rdIxk5jKvCZQ61+TBOSAO2UmpC8ih0g2kcTYVo1IYhUEwFIzBr0QWsMwWav0WDSsGxt8bQNDnqMYaS5bpJoUAmAW1aa5M9Q7MGW1dPXp0atP6VtsJo9pbdsuqs5mppDHzxzR95/+XfqQWIQFIGybYvFY6XM/Oy8BC8UH010TNRCc5yd3egFogxyJSuxTfGjg8fPxL7yGbc0IeeXAt9F2imv+XKlCzX9fH+kfPxzPPTk0Cx1pEp/Pd//mfevXnD4X7Pd9/9wHKZ8X1PcI4YI0PXCZSPRKPWVuQ5Caq+N9zOAe8FNpzSQh8i2QjJyLsmAVgV5mXGPxweADm4smmYZtnv9zrKyWXqooxcqVQ6H4RCqMsY2wWWeaE1uRDHlydCiIQYxR8oz0TTCd2yOZwXdkNVF0jvBbes2lmklAjBczqf+fGnn/jVL35Bnpfb0nvFXleW9jAMfPHV1+IFNM/S/SiuWnOm70dMLwyqFf5JtVAvF75/euGrr7/k3dtvMNawTBMguPf1eiGESNdHHI7L6cI0zeofZTG+Z1xGpkmCw4UJ5ClLIiWJYHXOs3ErrdGuEx3WWJ4+P+P7IDbtOTFfEyVUYuxfzdZq43qekWwAgfw2/agWCdobGsPx42e+++F7Hh8fuLu7R/+QJS9ykCi3XjzYjLz/vLxSCFsTiGN1US2Fp09PbPdbhn6Qxe5e3l8tTf1tdA1t5X1ZKwfEkq/qFxTFjqFWaIbWEtYuRHukd1f2YyGMFecNnQVvt3qgyn232khYa7Dq6fP//6sq4VOoqVI8xBGhktP0qgXQhe+cGufzkfmaOZ6vOG8oS+JyyTQLT58uNNM43I18uQvsNnd0Q8fQR6KXxsO7gAuOVg3GiqUK1hBsh7dGfaTE7rwaaQBWy2zhn9sbrXelDDvrdRmpmL6ym9AigLPYW9O8Ct7kXlqHe4F5xKpf45sUNqo061iNXVtpJIT6XFumVKe9u6jLc0tyuKr1BVi1L9c6JgivQNBNc6R18YwR2MiuHRaGZtYIU5liSi2317wu6+u6uKb9zDZGM8arTFDFaIhQXaeBV6PCdb9jndcJRq6O0+LyeoHc6wW72StO5OppJfLF17/j4ctfUBBvt1qUTKHmhsZLzszx5chlEit9byy5ZHrnaaVyvJ5x1uKCuLR2IdJvetY8+M53gs7UmTWzXAqgwMib7YBtIlD+4ot3Aj9aQ0qzxBRvNxLH3OD9+7fsDztIYlKz3R3YbrcYrE4oEicgO57G9XwixE7txRe6OFBbFdPPvaGL/S31z1qJoe18xLtgmOfEECVXOddExN8CP5x3EkzTKtP5wuHuQMmFeRb3UdcFzUO2/PTjB77/4Tt+/atf4X1PNtDZKNXJq6eNsiEkNF1GNeclt8JYDW5BCkheFj59/sz7L94LHKaRkmY1LKuyMHdObmTvIy0LxGS9UFezRhdai04fwnvou4749pFhkDxa44T9lEphmmZejkc2o2CG2+2O5TQzzZLS18XIdZpIOTNNM51zzFlw0+fjC/N0ZakFXwS/74cBkK7DOkcuC1Oa8CXRUuJyuXKdrmzcRvsfeXBzqXozihuu+L3AsizUtJBrE2uRGPjy7RvwQejGRal6xlFqVtWocMtTTmIgaIQ7vgjmpN2FwTZhN1Ua87QQQhARXLM6lUhhCd4j7JOMN+Z2Ixojn2UzMs3UXAkeentkt7nSdRc2sSNYA/aMMR3eRmgVa8EFg4YGyFShHlGmVfG4aSosYl3mVqblSkozIfa0XGk1iwjJ9ZhSWOYJS+R8vXK+HMmpMS0SKtQFw8PbnuhHSrnQD4G3b99hzEKlEKwnBLCuYR1UFoKLsBYKY8BVmq0kORcFIqAIDGe8quHVBt8YcMLi8ioaq9bc9kGrbuDG5LGvnTSonK7eCKPc0vxWaAW1fGhGOlkdmaweis5VTPUKW4mO4Hq98MMPf+HLL7/B6HRWm2D9bd1DmZWRtZ67jtUbyqqqutZ623/ciqEyrGorOrXqZ6uit6YHeGnrchyBtJBxwFirUJBagSA7J7QAsk4E6O6nQTXuRjFu+nfWXab8W65ua43cPLkF3n35B968/y3YHqdxodY5sgpn+9gp1Ax9DLJ/SRmUlov3lFz463d/5f7ugbv7A7kW6jLjXMHj2T8ciE4ElDkX0ZSkRDf2/O37H3m4f2Cz3bFOo12/4atvvsEbx/PTZ3746Uec83R9kAneWmIcWFWswspcVKcmk19wXsW/FR8DQdEcmXqlYX14eLiRVYzub0ppvFw+c7c94IsqXJsu0oJ2vg3DfJkotTB2HbUhVUvtrr13TClhK3R94Icf/sZ//+d/oQueGHthYijjQGio0l3c9hSA8yvvuWkKl04n1uJ85O2bN8KIEq4ey5Kwg1M/n7WoaMejS2jfvHT6OuI1DKflQi2NN/f3hBjpvIxm3gmLaV3c+ehl7wB8+c3XTKcL83Vi7CXjOwYvqlxd2gXnpYCaxvX8QquN0/kiXlUhyhSDLo70hq0pkUpht91SssQcYgQOW+Ev5zy5ZFJKkh9treRTN0OZJ/76pz/zdDpS04Lxkf0wsr/bM/hITVK4YnBc1Bog9r0kzVUVODqvh02FmrFNwkVKEfPBPvZ0vSzlbDNcpiudj9jgKCVL8FNrXKYzl+vCZjMwdELZlVE3Q5twnOlj4rD1DP2ZEGQycGYBk3Et4IyXDGuEZGBrAbd6H0kTYa0XQkKT3ZlBsH5nI5frC9fTE7kulGUmpRlspIsDZb4wnS9cr8LZLwV2+44uDhhv6YMY+fVRulBjR6xNWHfGWkdve/08jFK2G9F4UPNLTMWp51kzbfWLk4MfwbvXZbJ1EkBUm6jljR6mtoltjcRq+ltrXNeGyqAQy5rxLEpvFEaTB1tgq6qmlHXtplf7aiNMtGoquYpdiKtRFtW6Wzwc3mn9N1T8jTlVb5zT10lOhxzqekYbqxbo7jYdyU5F3kOr4szbWrlNQPLZ2hsMBiu8haSe6hVc3WLl65xOlMJCo2rYkbE6rQpBwCgbaj30DEYzw7MULVbzS8NcB96/+xX3b78BE/F4oQYbjwEC4vBL46ZnGsYNOSdSq6LMsQZnDH/67q/87bsfeP/+C5ZFHAvwEoi2tIXgA4nCNC00Pbhrq/jS+Ot3f2O6zvz+91uErHNhWSSdMCMC0MeHR0rO5NTwDqbrRCmVu/2ep8sTT59/oLTCL3/xC8bYk10j58zpdJIJxaw09ar73sTQ9bojqhQqpRZ91hrn05lNv8GvJmnCrpBqXdQyAgPLIoeat5bBjkrVtDg8uYne4ccff+Cf/umfaLWyvTtImhry4Zgm4pMVJ2oKpMpwfUNp5ebQO8JYy+n4zMvxxDfffHPrwLou3rqkZuDzp4/cv3nDfJWs54N1EpRTZS+BNXjU9rxVjqcTIXji4wPTNDHNE+/fvaO3HbUWlmTYjCPTPAs/OHhqidRa6MdRokuNxVY5rJouZ8uyCAc5GN69f6cW3ZWkUam5iS23tYZpmVeXAMGYs6XrO2Inwq7pelEuNXz+9Jldv8F1AsPpKcG43WK943Q6Ya0Vk7EfJg6HPdvNwOfPLxwOW1G8topLoqtAYa8VsluK5GpXK+vclQJZW2W7EbviOS0sOnlEE+TBCZ55Fovz1hwlV2zUvtAuGGaCv7IJE5ux4P0VcRDJ4gHUCsEGvO2wNKqXZa0noKvH1wPJGsXbm+4gAKuTbklM1xfmdOV6bXh/oh96+tix5Bd5CLYbdneSeobp8d7gfAEKwQrrTthnEecdtSzg5N73qnZ1RvL5xNZCpt1GxXhDM/nWUWPAFBCH11e7Dsn2kINPwojk8FyFcBTJa7BeDuVX5s7r8Sx8icYtkGfFWuB2GBuzisbEh0wiG+Rzp1bKuuAnktaTnooPgd1+K4ywFQprXotRuR3gTSFQrMJi67MIQrnVj80Y0U2hsHDO9WdvxmGakDdWyqwRIYd8fW3ChKtaDJDFf9W0Qafl4waTrshDW511hawgUKSyvJq6CdQVRnMk1eO8f/d3vPn6V+QE13lm1zuu1yvGe3bbPSj80qw8G25VPXtPnheuReirMtBZfv3bXxFCZMkzrcEu9tCcuBZYQ1qSxCrHKA2T9VjreLh/4K9//TPffPstm3EArAhaZ4MLgbHv6PvIsljm6SoNXQhczkeu1xOX84Wai+S0Y2nOYUrm+ekT58sV1I681srj4xuMsTgXeDmf2Q6jpkxmMZU0EEPPV198SSoZj3blwXg5NJwcwCUpiwJLFwJDjKyOLdY4TueT2CaPI9ZY3r39gs1u5LA76FnWboZkrYj5XQhSpVupN6vpG+8bQ8oJU2BUUd64Hdlsxtcxfe2WdDQKoWO+TrRa2e92OLsKTQxGsfPcMtvdjrQkvv/+R/qxJ6XMy/mJ3//yN8LptxIQ7pzFB8cmbJANP8RdVJtyR62N+TxxmSdhMinV8HQ+0wy8XC4yzhlhjyzTzNI0p8PAMs/8+OFH4SZrrKAcfp6cZubrleu80Awcdju+nydiCIxhg5sbSxV67f6wZ7ff8nB3J1L6ljlfxaDPGNgeNhoVKTijOKbW28K06ti9CqCkdXJYCsd5lolSBZOuc3SxW9s5Geeb2BJ0YSCXorbQltImvF3owsIwTnQh470aACI278ZVXHMYKx15s4j1AJVWhBHWKGCFeaaorXSOrdFqkqTCVjm+fGJOZ84XiRIFcDZiXGQb7mGUXYm1YpdurVG2kTzQzlR1+XXClrIOe/OuWf2LnAAZRoRJAqmgnk3lf4Bg5HrKvSr+UcLZr/UmD8BY4f2LQl2pq9ZgmiPr5yKQjT4XTdqplcVTS9XFcb0RQKwVuLWaKhRba2801loLLYtnbc4VH9Qap1RWHUNpYkIpewBuRaFW0bKszrU0qGYW0z8TWJfbDfPqz6qJdCs8lm/LaCOK7tq0AKAitlcdU61G8CYtyevesSqjTVhqVqcDtNi+/iytVqzWJPVWRwOQqUYMS0sx1Lrliy9+y/2XvwQ8KV8pS2LxDjRZ0rSimgvRnfTWU60ajjZDdIHT9STUZheIPjIOPcuciF1P1p0gTZhrxhqsa+x2O5yPLNOke8XKt998rfCRCOJijNzdH9RaHr3PhDJfaianJFooa6T59Y43777CNHh++szx+MLhcMAFy3a7IS3yd5YkdvOrlqOGQFKafvQdFisU9VpZrY38bZTSE9uqG+tpWQjWiz4hBnCGDz/8yLzM3N3dcT6e2N3f0Vrl7v6Bh4c3Mp4q64Iq8NAqeFtv6Ka45NoI5ayZAp2j60ahPgLv37+XsV2Lwg0z82Jf4Yxhu9uIpTS6a2jywCw58fl04rDdytdrAQh9xFjD//b//H8wxMAvv/yKWps4oDbYeg+I0M9bQ1WP+VQEv0wp8enTJ6ZlkaXUplcLEcd//af/i//zv/wX+nHgV9/+gl9++wsWKjUXqhV8erZGFkvWcJ2u4rUSIlBJufDh6Ykv378XdXfOXKcr0+VKsI44dLScb4Kh1TuqLGLAGKz4y9fScEY6l6IHiDDBzU3wRq0UA8F7UhV6sqnyHvvQyaTXdNnN688yzSi1VnQVm91ITYXaIOUX6vJMv7f4OOM44/GYFhCx8M/8eqxw37ErJCNou+iq5LAyuhhfoRTTNL42Z4Iu4H03MFoHd09YE7EmEuMW7+WwsIDR0CBnpdOX6EbZUVW1CDFG6ZVtZQnpjsXctLoC4+gC2hhdOlspeHKuGlAhaiDKNbQGX29eivJ6xJCINTva/hw+0YNvhZPkCnCDCa21UBu5iu1z1dhTg3T9QiSAWgpZn51aCzVnfT+Cm2u5YjXdy1U0GYpBvu77WIvNyrQz0Dopjk0mE4lB1eS1qp1/bmrYJzy1lYTUlLYtsJJMFLRCqVb1F3IfrEW3rsUE2UmBBFjZZuQ+blJA5GxQL6Wq8JLcUbqHEB+r0hqlOGDL+69+x/7uC87nK/M0sSyZYRgxITJo03i6XLDWiVW+aVTnb1NcrQ3nA4ftnZBygmd/OGCc0cwIy7wEJON+5unTZ0IMbMYNwXlccNB6pmlmmTL92PN3v/vtTZGdi7A+hYghKE/shAzkW9A1iOewPyButBGQomZ94PnzJ2rNbHcHnLV0oWPJRVMTXynMXdexpEQpCWMkXbGCOhgYhhDx4gWiSx1pXJjnhWg9PvibqApE6DaMooge93tRZs9yA8ZOKazKsKhVKZvTjPWONat2Hf8kGU1sEqzcETQgJ1lUSbcHXVwD7LkBU84Klhk0exdEHezWdq0WxnGgG0dazqRloRsG7vZ7/vy3v/KH3/2OX3z7LYfDHZ+fPjPPM28e3+B18y83QZXcWu9u5oa1iAzfOcu4PXCdrtTWmOaZP/35Lzhj2W+3bHd7cspEJ0HmrTQuLbHMCzHKIr+Wos6q4pPjnOH94yO5FCiZy/EksAGVl+uZh16S7MhiHpA0sEc44WINrn0WNJizdNzWWahF8xoM1EJBEryaFhxyolY14PvZSlQlTq9Lc2UcqcECxhrmWvnTv/wLD1vPw1sZyX2aqC5TslBkZRupSYUUUkOiIjEU226W202DcqxxSu2U08KUFT4QfUGq8hpWZ4bN8KiqYIv1FmOrsoJeO05r18hT2SfhjHohKTvKmleLbdqtSKGMOpSOLEeWZBIYNd1zulw2DeKqdJeVBWtOs9XXsargjT5sN5+gNSf6Z1AbKKvGSOYCyHtabdnlgarMZblBO/Wa1co+KH6vzZnxr/s7RDQm/YIWi7pOAUL6FMxfpyljaW21z9awn9aoCh8XJDu8aute7fr6LVUFjGXNSdHlsqikV3hMafTN0YySTtv/WCDkfJKkNyEsyN+tRuNO9f6q1dKsfE4r06k1gWFqM3h74O3739KND5yuC9N0YZoXnJUzLPpIUqv/55cjfddjNxtsq+Ql35pVvWW5JkEy+uAYh47j8cRlObPdHeS9Gs/x5ci//vnPeGPZ9QPb/Z73X3xBBY7Pn/n4+YlvvvmGYbuRBi4XssJJyzxzuVzYbHbiURe8LOWbfF2pjceHNxhr+Lc//Zn7uwPjZsPdwz2TZlGU0rgUIRpF04tUQfVMzlg6jUWg6qdfm4R9IWw0L4rG19uylIzzMnIUhN1UTaWlyo8//sTdYccXX3/Ny8sLy7zg1DOpllcVaDPrUrphxl7OCIv4C1lLzvKwBt/h3GtV88bydHzh6ekzXd/xcP+AtaLSLLnQBX+DTFwznKdJltz9oA6y+tE5z3Q+cn9/T3OWPDVKSsQY+MNvfss8T9QiLqbT+Ygzjt1mQ0lJqWOO5bqANUzHE+frhc24YbPd8PBwz3KdeTmf+PT5M89PT+x2O/6X/+V/ZVGVb0ozqSwEIxqGy3zFGEfse9Kibo1dvC3Dqj7wxVpenp4Zhp7jNBGjZ56n2y7CGTnASmlcLlexLalV8jXUjqEok8QbJ2Kikm9jedP/w6CimUYrM8eLiPpuyB8CDzorS8B1khAKceN0utKPPc9PL/z402eOTyeWyeHjFuctDotfHC4afAYTNJCqNWFRUSnGSeJgbVSv9hUr7p+tBjklrA3q35P0cEiyAwkDrcyUlmVS0FQ1u8qkDEhwmlE/poKzYl9vnLtZfDsA93Mtw2sec1OYR+Cuhq3iEXY7JBTtcFog1EtOeli1CDHa6/88tKciRVIpZdI0aFUx2vQYzZmWgmZ1KSGHnrXr3m0VLRaqijFzEctvasLaqIU0yJ5RKFQ6DRlaNZSab21B+9k/AvnIg1uR6Ud6coE7jDKA0PdeqGo1A6a6W4FoTcK+bvY87XWXJ+6ua2UWH6iWDbdLY4w2aMpU+hk7Ci1kzXis8bdiK38sTYWxKJTmKMUS4gMPb3+D7+9YmsPYzND1vHnzjqHrsUYcb1OpfPrwEecd3aanGMnwXtJCHztMM0xZPNrSnKRhjTKN1mo4ny+02nh4cy+WQc6yHXv5cxrPz0845+k3I3lObMaNFB9NtixVipSNhmm68r//7/8Hf/+Hv+erb77m6cMH+q5ns9tibMO2yqfPnxjHnof7O3GBTgtd7Dkc7iSLwyhKEAe89+ScybUSvRcKe84crxdpcvd7Wmt8Pr5wvVx5vL/DCyNUKrN44Ivz51ISzx8/MAwD++2O43Ll6fmJ+7sD1EoXOqx1dF0nXkBqRpWWRCrpNhrJDS9d+HQ9M4wbmRSbkchAy03QlVrlww8f+Pj8kaHvePPwKBTTXjDxpRRSWsTH6HRiul558+6tdOPWKX5tuc4TzVo+v7wQnKeLHoq4ll7nhW6I7Lcb8XtvEGLEuUBwkcvxTDdI4MdmHMW4LkaC95xPZ5aUhJvvPdFLHnU/SDDI6XoShXht9DEylcw8T3IjWSkIJYvt9qefPpFq4u3btzfqYMuJcegxxrDbbIjecj17RmWf5VLJSYJsQgzi4JgysUH2Cp/kSk1FFZTSofq1YOsBVmnkVvBNFnmFQr4sdHojCw3RSDeRxbpjVWpiDLt+5M9/+Z7//q//yn5/j4sbPr0ccf5KayOHuwFTZ7VUzgxWePveFPxq34y44Lq+h7oe7RWD/EwxNC1gMq02Up1uB3JrlrpcAS+qayPdqNBirR60mWohrjClMcpC8RizUoTrzWbitiAzK/wvB5Ix8rlZlaqv99r667YYlZehhWotFlIU67p5tq84eV47elRjsjKVahNdQ4NWhPRgNbvIWQ+u0XLV7vxnC2Hk64X6GW6HalPfpKo054pOxk08m8qKINQiXf26/2OlvDYtEGiBaDQk9wTdD8i9ZjG6Vm5UhZXWXYESVVbUwhhadQKrAbdkuaZEFvkiLc6vC3G49UuaNWKUY6BQGKsSvWJbVXgpUmvAhTvu3/wK3++xcSQ62TWV0ghdj/GeaV4I1dH5yHW50i6V+/sHyXLHELqBKS2yeHaRKSWaqcxLwU2ii1ouZ9I88+FyYdiM9L3h8eGBoetYloxtjcv5Qhg6+r4n7WVCCDHKgjwXjJVoXYzsL/7jP/6juEgY0UmlKiFC1kihfXp6IqUt93d3WAc//vATw3bLnRHISFyoZQfq1s8b0UPlnHh+OfL//T//P+Ql83//n/9nDvf3fP74kZfjkbdv30p8KdboclE6F+cdronDa9cPzDlhGvzHf/yfCFFUzDFEcJZcMyHIYZmzdPYSU7maczU1ZYOuG25LLHlWpLto6CLQWHb7HZfrmS/ev6cfB9KsORK1Mqckxn8IjfPt+3fEEATbRT3mW+Kw2zG3wv/7//h/YZrhqy/e8+UXXxC9w/VRFrGl0UJju93SDYPASxYus1jrhi6SihQAMemTG9roAxOcox9G3r17x2WeuV7OfPjwkfdffAlNdhHzZea6TBjtyEvOlCzXBQt5UXsNa2k5s6Ry44I7Y/A+st0Fui7QlHrcTAVlklwvZ8nv3h+wVbonivRUwWoUaFtV23LFV/feVrR7BfbbHS8cmS4T/aa/4Xrr51QWtbb2igkHx3WapUeOljItVBP4fCykfGLJkbIHu/f4PlNqwxsozVJJxJ9RXNNSqK4K59yIKFN+qjBaBLfWUZSg+Pa6YBUufTWiF7FqvUJrWOcIqrtpTd2LG8p40dxk5f5bPZikM+dmiV9VVW5NpTUrLJz1dZmKrTK9NsWpzO0A06VyE6jEaFERgz45sGVyERSuNW5uyLU23UQIkcMZz2qvPucLvnZk4YjegCnTGqaqdYZZtygCd63vCdAYTrFQNxh5RvG8yvMUnrmdyDLpZJ1wqkI9VRYfN92AYGBZoCItuGUtCuuzXlVJXQvtFY+S6aLoVKCz1s+ADZmmftZs3oq2zmKytihYhIou71ns10uxFEaG4Q2xuye3gToXfKgsSRwCrpdZooErTOcrdB3d0PF4/0CtjWBF2Z9bYimF6/nCy+nEOI7EKs3j4W4jmphiGHdbvv/ue4btqo3SfIgu4ryn85F+uyUnAT+7rhM7FoVhSsl0vQSh5WXCOctX33wt+5/WGHYbulpZpgUTxLLm7u6O1oROH1zP4e6OUjK1JJ5fLhz2B2LwnKcZoxkXTs+HJSU2fcdXX37Fv/3pT/zX//bf+L/9x//IZrvl/RdfSK5Ka0CRkfZ8PZOmKw8Pb3DOsdttBXNfZlqpLLVigsdamPNC0Y4Gq4svMkuacd7rg6TOi0YsAISRIV2KMdqFaOfSjGDTj2/fst1v6fuBMmd8jILdG3FFNSYRQmAcJTNWcMOOzm9Z1rCNUliWiVqE9TP925VuHPniiy8UY7a6VA/0h5G8JF7SCwdj2G23OGfJOYtQ6zrdGEub7YaldORcuJxO5NUqm0bXD3z5/j0fPn4kpSwmhsETWi+eLWkRtkiD63Viu9uz3e6ppWoHJ099MQZKYZoSl/ORfuwwydz2PdWsfPdMboV+6BVTFkx9KhIaU42l7zqlDvKKia+dLypO0iix7bDjYq+kksTCoyhTxrlbQEsFzscL3kkaVuw/s0wLOUsnv2THp+dCWhZ82NH3FT+phYeVQuNB7MeN5DXnNFNrksPURAzgWpUJgUZbPYuaBElJt7jKh2X28LqUNethbQqmQnUa2QlYHFkPMqs0TlHm2ptHmbEGp11LI1NNlqJgRC1dm9rP6zG6Wqq4qhCKAbOaFjWoyP0oi2OB6zBGsgdMohkPzqhvV7kxenI1+pr11VdhCpbSqHW5HZCmiU17a7DUBMZh22sBFnvvFQ6y+uxVXQwXsEG6/FW8WHWB3gwNmSwEvpTprVS5Hq1IguHP0yhXDURpVemp69SkYrNiVDSoe491t6A1prTXwiZDmwZZGcBJUyCsrp/tWSzQCka+SJl3ospOWFLriJs3PD1X+rzwfi97mVKqinmF4nxZFmJtYqVRC/OS6Lqey3XieD2L47LvKHkilYLDME0TIXhCFxWudzQL227D17/4hm7oGEMUa3Gl0ZZUWeyC81HvVUOInZAJWqU1o8FYDVMSl9OFbhiwtioTTNAQayF29iY0nKaJoe9xTnJzrBEHWpxnM25orbKkInY6CsiqCTz7zY5Pz5/57W9+y1dff83Hjx+wwOPjo+jMSsHnJAo9a2RZ1PWjWPjWLJt0Zxm7jhYN12XmerlCE9bMqr4vWXx2Qoz4Jvbb3onqb13qOe+EXrvym2U216WtZlUrBrrdbEk5kw2U85VmYOg7WdpowlrL6p2/LgK9x1YJxMEagtvw9//u3/PD999TS+Zw2Msh5VV56sPNgdYFx2azESX5MrHdbbHZgBdRW71kwbGtlShCMjFEuq7j6elJTLucox835B9/YpquHO7usBic9yKmWRacczc2F02wQ4Bpnm8PtjWGl/OF4APee9KSsQ1SrmSqiB0N5JTpu0G/Rr73Uirn45F5uhJ9JL55hBUEsGuHrojzyuVvMNfMEAIhOJZF4mKLmn7RKiUVQpAez1tHTom/fvcdqQBFlt7iry//vs4LLy8z28HRdQW44k0iRA92NUlc8GbQw96JmVu9yoLWVqioEZ3V1wvrzmL9ZYzkkWO92h2LmR9GDpVSilrYCxFhtb0oFFwVfNhYYR8Fm6H62wHfkLNNMST93iKklAw0UdEa1kQ0bhBTMAJlWIVKbqLSptCK6gAakhJotJM0ugwW6ZwRV+YiD31tVZ8fmQZWLUQRmaYWfkuliN/RumCqSbp65O+tsFa7WWyjOwzZQdJk8kpNvg/OUpqXTBVWBpgskVeDwdKyLq2LTjRGp95XONmgSXMrfVeN69bCKDAbNxHhCuG9TiPtVpTNmh1RVphS6cgyvJGI5Nax3X1BPz7w449/5TR95v2X7wnB3yalGCLDKFOEaVIkaEjyJEVijufpdn3RCOVhO1JSIcaIkLcF4jLVkFrl8e6B1ipTXjTaF+ZplonIWUbvyQk+ffrI4+MbYhdYlisGf0Nbcil0w8D5dGSZ9UxywtQU3ytDq3If3N/fsywLp+ORZZp4+9VXGkRW6LpIKQInDoN879XjarpKYuP1fOF6ufLrX/6S+/2BTBNYqok+xIfYkXPG0xjGXimA8sH54JmXxPVy5vn5mdP5RE6Z3/7+9+KnjihFS8nkIqymEILYPyQRmK2Mjoo4jMr4X3UnITBMW6fdZnDWMWu2RQiB8+VIWhLx3Tt8EKvv0sTmGu/ptNqdL2cu5zNv3ryh1kpaFu73Bx72B47XC6UULtOF7bjBecGhxcNE0vUOuz3P84V9EL1FdoKF2wY+Rk7HowpgLJfjmafnJ0l3Gkfa5SJMp1L48t0XXOZJHmBlgtSab3i+93LIL2lhzZMwznK9TLLHyZLGF73FuoFaFhbdC9jaFCJYEXNxiEXtHIKzjGPP0A2STaHFKyfxlbfWvHZsinFKAmAjZcGNrXHMadaFuChZnVh5Qm30u5Ef/vaRaU6ErheRH8JttC4I9bkII0bS9rLy3MXxdwkJ14RVIXbZTqCLlrlOibEXd9c137o2YQeteg5r1+Wl2EqUVnE100KUa2IEjsQp/bKpA21eaLYq/Vb2MlaN5AySoe1MXmsEkBD7BsXlrcFR5Htp1Gexa+EptwMTGtkq9dIahQHFDsUazapujsyaC+CgriJTcyuCZi1sxtzEd/IlVo3q1KxSvYwqa8oa2tWLOjnf2Ekr3LX+mRyUtbWfvWdpCnKFhiM1YbqsGS4VhddWhi5iy7J6M63aEtFJOL33m+y91LZbipUUoopML/nWBSgU2JSCrK9dFuFZ73qvP0tmr6zFSV6NTAe1bdhu3tINbzC2549//HtKSoTQiQUQkJYsdPvaQI3upIGV56YgYsTQ92KjXjMez91hT6nidRe9ZtI3oTKvNODiGg1xsJ7OVzEp7QfVyzRagZfnJ77//jtO5wu//PZbrFPY3K1Qp6X8/7j6sydJsuzME/vdTRcz8zUiI7P2KjTQy8j0NGREyP//nSIUPpAzJKe7gepCAZVbLO5uZqp6Nz58Ry1ymCIQAJkRvpip3XvOt+ZVibPjrG26tRs0qARtx5YX5sOBaZopeaUejrheuLxe2PLG/d2DDXCea1mJPlGcOLZ9E3z3/Mx1Wzi/nfnp5RPReb55957mHOfLWeqmGDWViOjSw7nLTAEdRM7z7ukdw6iOhbb30FZY1oVSCncPD4oHqJUYBxmIevvFQ16NVLSV14rZe1PQ11YKgylhxkER2/d3j9pIQsC1zmaS0TREvAsk79lqpeS8j36A3ujL50+8f/cNd/MBnGPNCt/rXbhvzpnoA8OoCG11DHTKTZYoNyoODscjeVm5vl1YLlf+7a//qkwpNCnmnM2sUm9ywLzlG1HdcJKf5mrKna4I466peUxRD1jrFmgnvbtlBNzgosu6KAAsBoXtmaO25EIIkTROOmaaJYfiyMtGjY3jUfJlWfD3Cc1TcRSTUUbv2S5q5/I+KLs+Jsl/q4IZf/zpk+DD2oneyz3rFGddeyV4x+EhkWJmL6ZxPur3rvJLONf139j7nROHQ5KMua87RP5/UhPhjAC3yd35RtgDzFqR+it4enWSElrSnfc7yr9zM9XIbjnIhehUmv0+N8lnE2chJZDXDlEzIVhfdFPiqhrOEKvX+QUhaxO32GlNuX2/3n9BCrsqWEakgvEKpirrIoCVE/X1cN+3ElpnD9LXMKb7vduhXH23Q9Sr2rb2GzQmqLlTumAkfUwl0doNl3uPNei917TuzMPgkKmu3C7jZhsN+4XSO273TtlEvs+E7Taw7H4GhydQTQnmuhoV9XPFXeAFLih23HucwW2tJ2qfCHFmGp4I4x2dxOW6cneauX94svdRZ0lK+m47PN1rU52AVxJqGBTxfX678Pz8zOlwElyJzsZau1JbW6XWqnpeAi/nLzw+PDCmgdaurMuVeHr4Gm/iHOu2suaNx6f3xBhY14X5OJniUJf5HrWfhgFHZV0zPogf2bbV8pn2c6Dw+vLCMCTmw4G8FU73d+x+lBijWutqvQ2U2OsQcPQQeHx44uePH/l//N/+71yXC//uT3/H7//0J/1dPZn+hjnvZdtbzoTgGceBp+dnvYEh8PLyhVYa0IwAg/PrmVIKD6c7OUpbB1dvnbr7kx1C0rTkAnVdGeNEA9ZyxVdHHAaCgzAkWf+bwVQGAxdTXxCtDc9V/DDizGT28Phg+K5ym86XK6VJNz6MA4ek3JLz9Up0sFxn7k53nO7vFBA4JNKovujgJf1dl4WYIkNMbA6u28IwjXzz4QM+BHIubOZ5WMvG5bqQ0qiSn3Vh2zbSoGm91XarAw1OZDV2qeqD3W4H+7Ip+RavyIZWNZVFZ2UtODOfIezWujudKUlwGFYPLgSW5co4yj2+lCJxgvNUpzbCbJHmgg4dtWi6298/j6O0wj//+V/59OUVF0VCDmO0S79Rq3iX013iODdCXHHeCEv0mvYm13Xv0Q5uhaQJVoDmMwEpO9hhGC/DlhQwRYofwLlRX9vLzd2cIzQ5uRvGD/hmE9wOt+lW8N0ZBu9uLvJ9u1GKhrdDWLh42OGu3qmGi/tm7Q7NqAbkYu67ka95qrd012akv3M76M6+edhdoHnADvS+h/iZzNXhbgeNx0QH1Vn4nv59bRpYdLlEfQabDn1nqjYd3u0mqd2tdb03ct9/Tx1mdJkYjQEwl7YTL7DDek0HUTc+YH8o98LR3vdQPrsc9s2OvlMVhO7Ypb/Vfn/Xld5rnD1th5TsjNLvAPRErZ7cJ4bxHXG8YxhmQhA0fclXPn1cOR2O+BAtWE8c6pYz+IQ3DijGwPmqifrp+ZEpjby1V8XrTLMJKaAGwV5bzhrAamUNmW0rfP/D9zrYXWAcZ+Iw4F1ky5sNRo5hHHh6fNLz7y2vq3ma77RaBLPS8DGCg+W64lNkDF8VYb2jVIQgKPB8veKAeeamUi1FicTBe4qvDCHoPYxaAILzUlIZ3/H4cM/v/vR7/tv/8V/53/4//1/uH+/5/R/+SPSGleIc0TXWWrlcz0pJHUTS5tykMKKyrSvj4WAeicA4zzw9PynR0Ik89VFf05m7ttRyS31trZBSgj4oXx7wVeqRtOewgLBNWzdrLdYNrayWfQopQOqN0pqwaZt+nE0wYxoom3KLgpWeL8vCmlcy0q6Do9WioOkQ2DYZ3qL31CJDy5gGwRoxMDNSa+N0Oin/aVlpeROHUox8ypltWTVl9E5sjWVbycum3yMELtuCR7Hk1YwxXZs5S175+PPPvHv3TlLH2uS2DmaOcxjmiylqHKH722EutZrbz0NCDPjNqVyoCc5RSYsFyxEYXGel0WvG7X/n63CL6/Dxywt/+/6jUniryo/onhAjvjcu25U5OX736xPTWInBGXiyk5xGm3XLJPUiTxvNYisK9CgzlvkEAHwTvBT2n8WED811vOv4fQpHA7kqSJVnEJs6oXcYp3ePD5WOxYJ0b4d+p6GJ0vdJv5ttEeIKzG8C+G5bt3O4vsMf7bbR0B2NZBe4Dme1+hVTVGki1F2+K/ycHfA2VbtGtXdxl3fup0PDmdqu0fY5j2abmf083tFRWY94iK8XBvY67hCWeAXLUjLyX/CXShC5QXzcBCmdYHdmux1at46G28XDjSSFjvv/UzU1m/6a6Wg94sokznC3P6/WPQ1/gpMq2biU0hK1zAzTE4SZEJR7BBIMjDGQhoM4HvtdnUmZMXw+BAVX5qxAxNPdnc6M5G8cW1rVmx6ix1VPL40hBFbdVFwvCx8/feR0uie4wOfPn5lM5rrlTDRFX2uZFBJhjvvHU++Ba7YdOnwVoR1qpWQZ6w7jBB3ezlfSODAGj4+R3tSnfZhmqSi9hr8QAst1Y1muTOM79lc+t0zM2lZ9Gm4BjSF4gk/8/d/9O54fnvjbv/2r+OnWiDqcDPvzji8vn+XCu7/XgetkdCq1QOlMszT70QdCSjg64zwxtImSN2o2RYeeGnCBGBQ1LqzNsSzC0ZN9wMbjSTrtWq0cRgenKRT1ybeDwdMl4e4ikUoTOR6HQaFiMeF6I6XE89PIy/mNbV1lhfeO83Kltcbd4aj4365Vv3fwKZKXleY8fUiElBjHkd6bzjMf6K5QgVILa84yppSO71aRWQvZh5ujN9pk17JhsCai3zHo3Bo1Z0QYCgaopbJt2XLdtepjE0fVJ+b2Yd3xEucMk22mfy/KRRpiYkgRDgfoOwT1NfjMdxnwfAj46pVl1ET/9apI99pEpV6vCzFGNjNseTpbzYoEcYHDEPnVb0ee3wVCeNUh7Lwdjnsjndm0nEeE9R6rYJOpTjhqkwzYd2eRENKPO2QU69Hjaqe7oh4UZ6kS7EOPDsYKIqlbo/uO95Va1J0QgnKMxIc6mit4F9R5sE/UBgkZYqQPepfhSWVGDXqC3pRNxU5Qq+yl+WrSX4OfDEO/BRd6k3JWUw7ZZbVHewvzFxbv+Ip9K4FVRWHY5qOjXLxG16IBLpgZUje930M2u703bk9Zxi4VPaT7Rq4NwjaZim0PNsz13T1tHMrN9MZXOGu/oKyv4ga78DUBVuZDU5t1fTHX+k2663vVQNlkuGt1YKudViNDumM6PuPcxHK5cpqgJy/3eGnMhyPz6XTD31Vi5alFQZaKUs9c1w0fpFQMKbJmHebPj08i77H3rnRJop3EL4/3DyzXK99//z/Y1pU//OEPROvDyZd6U0b2W6R7oO37mz3bIPJbzyS3s9g1JVOnHA3pUYtkeTtTxyITXtTZOI0Hcl7s7FjIOXCL8ffOzKOdIcgDcj1fuXu443g4CEEIgVaU8Pz+3Ts+fPONfucO8dOXT2yXlfvHO66XK7lU3n/3nWoTs148gBAiy7YwpEG48aTC8lL0wjrnCEnpnN19JQj7DXd2t8ujlFXreRz0UNeqFFJvBSO9kNcV7xN+SnQa21oY3cBWK9Ggit471+sZ7z2TqWKCg+o8W6nQClOaTCKng8LVRhoHHh4eKbmwLqsySkIgNvBxIOBpW2U+TJKnOWfGnMrHLy+8vrxyvl5Zr4uhwcJ7e99ozZHLSquVFAIZnbnDkBjcQK3V8pUkBfRdIWg0gzuaIIn3797h+Jp55Ty6ILouCBGjuiBKFw/igyCZ1y8vXK5XUkq8f//esOcGUYbD6vR9YfcYBDaT8oYYhFsb5q9nuYvviIFcVHhUGxA986DiGUdlGiL3p0DwK1BxVdCgc9HMaP6WwaOAu0Jnh52+hsvtJUns+UndCZLq2HbRzGugyOfQO91H/V61EJxaweSTU4GTDF7e+IGvByn25+jaZnaSr1qla7OP9J5h5LwXTu/STVqqg1oHvKp1JU0srkAzg1mvVINfXRf3Fbpg02bSKNVoKna62jbU2eHbTCCSwWo9vQxjts2xk+pd8FXzTc1zRmjvh7H5yG/biifQb5uEDq39+RIUpuzVXQZ7g+yMR9QDYlBW/+p7olvardOr4pr8GSonko9EW5YgLUe0N8M2FtD24Pdwv4C8zY7SB/LqcX7Cp2e8P+BcYBj0nk3DRK2d43xkmCTYyLV9DW60DSiGwPntjbfLhXfffKPQzWrwJGp1vHUzbIVxGtnWTByiiqJKowVt0qf7e44nOBxmam3c39+Rc6FshTiMghudQY8mcPRR4Z7dSRXm0USf88ZlkZ8oRFWNrusb0zgyDaoQbq1y3RZmJlxyTIeJVNTw9/Zm8P/DI3dP97ogWlcyLDAPA+u6WtKD4Mx1XfAG0we7MFT4FYitVMZ5pONJ08S7uzuC97y9XXh5Udzy48O9oKVhJA5SL4Xu5Ii1FU7bqQxIHsFMrTVy3khpMMxRF8UYJ2qt1KwM8xQixamEpZqawvlAbRvLtTIcZuq6cT5fhKn1xunh3mSNtor3TnRxn1noQEG9F7FGSm9MIXC6u2eaJryDdVs4HGZKaVzZCEnE6VoyNNWo7quyiP3G50+f+cu//IvyqFxknEZpnvs+5QtfjUmFP5j8r1QplCiNXPdoR8hm2DGQjV6+lrzsHQLOfXWdSiQl2KZ164ZonrWIeO6tU0rleDzigy4dvCNOg4XlCb7ZDXut6aLoTfn2AWnWQ+jU0qlG7nWP6lfbbs7rpOhJaVAPMJn7uwMpgesZ3z09jgaHSSbqu6cb6L3XOnpXaC3SfTcYqdG6x2PksSWbYpJKZQgN2oDgttm5PabCK6NHXRgiNgvNXrMdmWkmjf0KZzkvklZqIE1+JS/U1hnSTA+CQVxTC54WOF3urXuR5l2GQY+ju4gaBM2xjvDovvfGlUDzxTwKRmRbQVbpHYIuY9cULeF7pO51EyYEqV2wCnbot17Nt2MXlTM+Cx3wNwVVrxYZbpyB/U/bDXn23rRmeUr6iprGd/bYLthySybEvk4Hb5Ed3X+dlnceAm0c3i4DKYNk0K3s0k79qX2r9M1RiLTmqHUgl4kURFKHMIB3TNNAvJv11Z34zXFUMutmr02pRTlkrUpCPk382w8/0crG+w/fAoJZVe/rZEy1yJSX1zfepcR1W7hPR1otbIb5+xB5fveOsmawLcs7qzBtzjhcbioy/U9luxTm496JU9jNzP/jL3/hx7/9wD/+r/8Fb50mJW9cF23y43EkxElcZm90q3kWZaC6g/NFfTBTsr5710l0mvOUBqfjEdc727aZ+ETx7QHHEBNxmDTY9EZ8fn6nMKmYboTHsix8/PFHXIy8f/eOcZrJeWMcRchsrVji4o4+7q+AM+lnpW8KGZPzUUS3clm6eQ4c1+sFeme4u9PmMIzsUjThwUH8RVN6YfAeP5rSouvwDTHx9vZG8EmRFt5bxIhj8FIMXbeV5fMXvvvwntPpQKmVXCpHw/l6h5wrec3Ew0yMgVIyy1nWem9y0W3bGNLAw/09D09PxJh4fX1lvS6SAdsDVWpVu1VtlK4IwlI7ronM96hjt7lOr1WwgElb9zTe3qua4sJX4l7wn5ck1Q69nVBuVYGEwzTw/OG9HTA6IIJJPiV37LeHsdiZEQzmURRHt6hpB7FZNIaULc/fvOPDxwt/++lHXTIhmdChEVxnGKWsKV29yRAorhLQh0lHpDO+QQ5xHRgF1zzdCWZx7P3CIidvpszeLYoA09UbBu5uZ9at0EcSSRUqBVcl1fZBh1CvpIa2QAeNQm/2d+uK9yN42JaMD4kSIVZnmndBUQKJoh3aluJvCr6C+ituFTu968B0ImKbwSstQ6OaI1tTeO5yU7uiLKqb0dI3fHPiX5z21z3FtpnCScoikDS4m+PeYkfc158Xp0PcmVGhW3d2N3gBHKX1GwqwqxH1tFW7nDSQyARmzyi6IPY470olEASP2c/lurRP+8ZgIJDktZZvLimtXNuhBzYcrQ0sq6MxaXMYJnEtIeKSxye5iKPXpRGj8r/WvIlXjFHBfUHfOw2J3hvv3z8yxJHonAkHYE8eVm6cwu5ckEhgCFHx+Dc+xe/FcKRRfKUhwHo9m4qeUtLAWW2DqdXx08vP+KCAPW+w4Lpt/Nf/+n9weTvzf4n/V4IPzNOBmEZyKVDFXQbvb4pOpWT02+t5vLvTmds7W8niVS5XcopM42Tcr2OrVfyajyQSwXXWslJrNWhVz0d0dgBs28o8zzhUvnF8uOf56QlwnF9fqb0yjpMmlk2XhPPepjtILhiRKDx5Wzda324Ss50YO55OjDFRKESntbtZpEStBRfDL3oh1BRXsqKRYwrW8KUHqZpS4TQfSYOySRKRQrN4Az2LMUgyu64LKSXVYnpw5qLFO1JMbLUQq3ovhiFRc6YjtcDb6yuXZeHdu2dTQ3XWVdlP13Vl6xmPVrR1WZimiWVbqauC6aRUCnjThXv6jZQqeaOWyjROdLp8AlXEprek1q/QA3K05yw8ssNWiiKAgzOlj9yxzfMVg3bcJkFvH4jr+cw4TYRR+Lf6G6C3Kj4kKJYhG0Q0DxP//j/8A+flwpfXN9ZNCcCxd2KEGFBlaXTUoGHBUQUvdINkK7Soy6FjHzAkeFAbmscbzIW58ptrtwtmL7qq9mEWWbwrfAxWsomU7mlOguaAeKVu2GxxVS14dmHjquJRms15veDSIEy/FF0U7atUs7pgB1qnt4LrjeIEi7jehEHbwdkd7KVevRVtml5FWA51sXdTUqkOs9mWU2xw02tVSsH5atBRB9tKaqnaxGzbrU6dHN0Zh7PLn5xtCe3rxN58xRVvx7/UcArw27fbr5uF68pD6qauwzbK/YLTxSyiX36cr/DWrn7o5k3B/vvNO7HzKqbeqQDVU3ok18h17azVK/stTRqSvGL9nfOkMOCdYxhnQmjIRhVYt03Cihj09aoiYnzaY4AeBPlhog8Tf3Q6MQVa94x+4ng86XyaEm/nK4dxkIM5eFzwLG9npmnEe0fySW2LFXrsxF9clD10vb7B8fz4aJ/fSoq6eLdtI8WBP/677/BBG3ipGUdgjGqp80a811IJMeGd5PZDGnTWOM/pdCce2UHA8c//7b/z7a9/zenbI0vexBO3DZcStYj3CiEyD5P8HduF43xgGCJx2yTh2tYrw6Bf8vHhwYLUlH7YnaNsepBLa7e0SxDBRDelQofuAzF4uajtsIY9j6UxjMoGSj0QDoN9cCq9Vi69cozHm+GsN7her0zTLG2ylW1sW4YO4zTYgT5qMmvFipN02LkkfX8IiaenE9EykgiSCLpR0khpp3VALNczoR/AO4ZxvEWHaPhx4D0pRDyVTy+2RZRNk3kTnHZ/ulPP9WpTIe6Gc+6Kit6hbCu5ZBnkvA4SZ/uB9+32IaVDLpWyFcIYb9LWPV5jGkZxPN4R8AqPM1nmPvHQu0lebbsJgel0IthGmFuzk9dRu6P1QnAjTXbfW/9Brpu1qKmzYh4OOFcg7Fk93nTtHag6rNqeheogdMO+AZepFnRxM5EhgvIGt9HwzZ4lkincKnKeREFnVjWp2IYolNVMm67pkMZ7c0tb6nHrFlPuKfZ391KHVjURY4R9M+VVMbc+XY7U2pWn1XqzaDspXXzQ+ytzl7PzsN02nE5ja9gUboclgeraDZIQfKUBznkNUV2nMMEgE25eaqDWW5f5/lzolLL48f0xqLLhtX2Gb+H253f5tO/Rtor9L6Hssd4MthJfVZAybr+ELN1MJj+DgesOMWlV1WfA7T/OLpzAXOjc/mxrkVoirY1c1s75WonTQIwDwQclEgziG1KITEMipIGUksLvvKn/gmfZMofDxDDKhWysmBKv8eRcSMmqDpw2K7tLAfXIbFsmt415GAX1Nse6rKRBr9/nz584TAee372jU8ANMs713Qfjb3yE6l49aUxKOGiNUgSJpiHxj//4XzgdVZHaWuPtcuYwzaQhEVAtbKmNlCKlVVUyXC9U7wlpUFR8UImQpM2ZrWz0pvTX9XphvLtnng9EHNdlJZfGMEroovKjwYrRIKZhYAqefjwYaaNfrOyrlxfGNU0TZStsTaatOARyXtR2FRKvXz7TkelsPhwoobItm4xfaSAlYcAxBmUxEWg0tnxlmg5a/1q3VE8IPpCbAvH2i2ZXvIQYLTTQ4AY92ry+fObl5YVvPnwDznGK8jfEmBSjXWXAqbnSQieaKUX9F5qwx6i+B9cc1RXiEKGok7qUyufPn4hRqqfSKosFAuqwKMrxd+IFvNfE2Cx36bqqwjBF5bmHlL7mrZjevcPNM9BLNf9Dt8DEKlimKcCudh1yDRFkaRxoHlxVV60UNLpt3U1aqPgA7x2HJHjv8nbhcn6lNzjOB4L3uKTkXRcjoQIu8Pp25f/1//x/83K+ElMAbybE5JlGyLUwdkU8qO9Y6gq8fnYZ9PY8L10M+6Sru8wbTt5Ndnm7I5EhT9O5oit0ubRusKbT91dnObtuko6XAqop9bX1IlgK8Qs17JN5tIgLRwzBUk29CHOQT8Wkur07mi82STfozf6mx7lALeYLsIle2Ox+GeiSwP58906bU8WmaGGARX9AysjuoGZAm3upFsTp/G0C94D37RdQkG0QFtHdW//aQcHOB9zmfH2+tG7quuu6DLtJMyuSd+9/v7WdrdOvpt75cFMkAfoM+a7IdrgFINL3QcF4HiOTJapwtBYodSTnxNYjLgaGyeOiw0crxUkyiQ1jEsLhPD50E8xgr6tI7Ov1Qs6FISaKB98UceJ6oJhKUKU7+p28Mzl96zLqlkquGrxKLuStMCSIybMuG3HoxDRSt8IQA5e3C80LmVEOXAenNk7fRE6XXii5WnWCYn68dyQ3cZgPdBB6cblwvlxwHdIQGVISlJg16JW8kWIgjuNNQRq9o9jbqvM58t2vfoXzgVI2xvlg6i0HacQFz5QGei2sWRaAIaqJ1LdOLKVQqz00Tvivnhk9rLUq7dF5r5Vt2VjrFR+PcjW/vfHw+MRWM+uy0ps8BL43rstF1Z/ekVuVIW2Tm7f2bsmxlVQKRJUc6QFXTlEIgWFUbMg4jSQfLYvQMZpdvnfhnut25c9//RfatvLttx8otRJNNtqK6jb3SU666MZlyYyjTHQgBVf33eR8jdfLG4/DI3gZU5Zt4Xy5MsRNue+9KuKjJUqRWqP3Tt5Eog5JmVT75QvdJKpf8ePuZXorXVkprSoQLAZ7gHvVoYXVTdqf9V0ZMz4ElJrgiSEKQ0VbjzKE9IFpTnyEI+DHoA+1HR7n8xuue+ZJ5SOO3eValTfjla/1+dNnPr98MVKrM0+DFDIekwqv2p6CZ/KeHiPNNXwvMrXZRV0jJBfZdat+P9Q0R4Jd2nilmzZfDI6yC4B4m/b2aA2B6lIVycS5h0gAPdJbpZj6zbskYxvta/eBr/KHub1JTtO9s/SBSlX0+f4zVk9rmT0YUPHUOtUVkmfx13A7iJ1xCAbW4G1r6s3ZlqOcqdqKeJtd1Fsre7nWni4Lt8UHTIEVbMrvpjbS5mEn8y4zRlueYDz72b4urBIIfL2fac3dypO4/R52Udqr5C2+ZP/vhrQATTLl4MVFtH4TLnS/T0R66wz4o5VAKYlSZiqjBpYQOR0inkgMgzbEEAgxkuJgP+hGb0IUvHeUYkS7U0mYtuB665cecBTvKFmXeK0N5ztDGiwgNJOGgcvlwmGcGeYRuuJ/cpX6K4UBP8oKML070IuCD3OpfPz0kV/95jdEP1HqSu+OECZC1KVweTsTh8QQR3kRgtRdpWyKw6mwx7Z/eH5H6ZXXL68cTydiTILlI8R0NKd75XJZFPhpfOX5fFamXkw8Pz2Tc+XtfOH+eOJ8PfPD6wt39w/cnR6YppnL5ZXz65k+N4ZB9cHVO3EStSqsan+oQVkl3gc7ONstIM2FgM/KIp/H6dYr8fz4jlYqW1m5Xq9A143YO8vlwvV6YTqM9CZFzpgSdds4zAeuy1WrTzT5Bka2eW+ytKyLo3aZ8szkhPPkWvCt0IEPz8+M08QwTQy1sWyb2eMbl3UhRXUz7NK9YVAMdwq6oVtrtNJJY2LNmXXd+PLpM/cPDwwp8fTwSOudrRYulzPFtpyaM703lrzSUHCfsw9p3jJ53SA4hhjZSmEvGep0yRS9Y3m96OAKnpYr8XjQh6ZXcHqAnDmwva3uNpRZH7W6mkErtB8StMa6XGk4xmFQa1xKN5VP93C5XgA4nY5EF8k13yYSRUc0fA+ULZOGxDypcyIkRwqQonKRconiI3onDRM5Nsa+E+Kq2IzO4V21/gQ7XC32c3cp7zlAmo51iexQpZ0p2nBNRdK8EaFYz3KP7FEw3TgB3wFT87TmwIt4bHxdWHoJOC+PibOVbnc2926bW81QO4GgfkeS4CwavUW5G7w3DUegOhHszb6Jb/4GSUmh5PX+R5Nmur05zgvGlX5LsleDdHrvhBZoHrvUdlhI3E6z79ctGbViEFZzN9VXt21Fm0uwfKX98tkhKynQev/Fe9SDXTRy/u7vV987O5o3KEvDB/tr0fbtURcxnZtCq2GgWR/JNbJVz1YjtEi36PdcG3dRXfSdThoPdCRsiWGg9k5pzWTwDucD4xiU5Izyl1JSQ9z33//At99+S4xJvJt5jZz3bNeFnOVMXpeNcZyYpqMpGZXZ5UJkTEqPUBWvN+LYE8eB5jyH48H6QjTQpRg14NGopbFcLtRWGf0ENA3HDmrNLNvGPA6AqgiGUXlTow10Ptrl4f2tarq7indwPBxtv1ZMz//+v/3vQOfv/uHveX56R+uOjz/8TBySPEKtc7muPJykivI+UntnzasQFDtgYrTiHHZyCiWzLterfkAj87oTLh59IMxm7mhaQXvja9/A2viXv/4LUxx4fv9OklbXOBxnXTqjpY+GyBSsX9JLJre3oYFI1PPbG7V0bSNx/wAJulLQmRrYmncc4sT8699wXa63D8x6XUhBPbPXy4VzLfh3z8yjonrnecJ7T66Zvuj75JqFF4ZgPIPcwrUKDrq7O/Hlyysf316Ud4R6n3POfPr5I6WJ57gbZz6f33j5+JFlXTgd73h6/459m3B+N77J5XtdrxwPR8ZhIAdtCN6ilb3zjNMkAx3OVC/mvwjR3OydveeYEHF0qveaKrpeT9clcStVq28vjW3JzPPMPMsTkm2r3J34Keoy2srCh+cH6r/7Ay9vF9blTKsb02Hm8nallEwpjdQcrTp6dbJmeMlKBwIEj4vJqiY9t6Kgfeq/6fBN3dTc11Pc+B4dPtyGGUejmUzT9SY4QZov1AEBxWnLiwZltdZvP0Pzbcd0bBO0zQSLr3by1vSmJj8cCvar6IClm+xRF/+tRtUDXVWxFBkYJcZNGlK8bQwARZtrsIMfjHj3/TatSx0laWb1ujR2k5tk3/6G/9Ps9bhdrwC/zE+yC7cZzEcziLNSTNHUu7txEl0vtV3EX9viLE5QPiTnDMq0P2sXJISbdLl5iVekN243k2EuAzBQ6sCSTRXmImPwuOipW1FsvfeEkDgMkxkgBe8lD24AnMft3dD2Hjgjoh2Ow3Tg7k7ST8XIVFMHKongL3/9V5bLhf/yv/4jd9Gk/sE4NuSmH0IiWxq7952OhmgHOBeNvIfj6Wjepq9ydt885/OZ8/nM3d0d4zBSWyOvi34X7ziMsyZ4J7YuuZG3tzde3t6YhoHT6Siy3kQeUiYayRMEP3rveXn9zA/f/wgR7h8eeb5/ZBwT799/YIoRguebb79ljIM8cTbs1py5f3pgmiYcjlw2aeJ24tIH6+ztjvlwpLXK588f9UvPR2IIROfIXcYQcpPkzWuizlkmMlc7zNLWpxCYxif87SDr1G2DWnBBWPJx0v9uVYegN5L0er4yzhPH00GHIp0tr9Ri0Jd5AqZpYi9buZtPFCreO+6PJxE7IXCYJ3748UfedSwSoZnqCkqpuACHcVK0SPAsy8oQB54fHimmc66ls23iGsZpwuMIfuOyLuAjPkbK5SosMyReP33m9XpWFEfSNO5xN0WXbCyVbds4Hg8W2eFJIXHb+Q1XdgjyUbn9rhhpxstoSnHd+gDYUx470zwbnNNuSinXO657ruuF6DzH40FnlTPfhSX6ll5JMcpIliZKK/zut78BGn/5y585n1+Z55GWEzUbsThCC9YKtlU2GnPwCt5DxrPmOmmHZdi5mN0L0sAuDkwCu/8j8t8OUq/Dudtx5TQOQnVUb8IEM2P5lmjBOC3vcNVTvF4fmvEaO6ls5UK7M16nrbepWkGGnkRtWQ5r7yxKw75e7wa+R/N771CKyVXJ+iNl3wwM6/fOgvC8/d8Ku7O3GbF4XxVHFUusNV/Gnpq7q432etZqt1Bvv8yDktsd25IkD7BNwmA/1/vte9KDficHrhqjsquV6HT3VYzQvDZP+Vl2SKmj+HcFFe7ptzp3BrYyUuvAViS9HdKIi+JUUhoYx5mQJqYkP4UPnvEwmSBAn9dazZjapK4qXqBedwoODb2TUuD9+/e391wqR8foNISejjPff/9vtFxwMcpIi2VVoSHs9fVF+U9z5Xg44by2bvGd9eu1rCmH7qDkypYz0zQY/5DET8QA1TFMs7YDoro1qtz63els+uGHH/nx+7+R6fzHf/gH3r//hnVdqWUlHk7kbeX18sZ2WXl8/477OfD5ywtpjLx//57f/va3GhZ9YJyVI+cbzMOsHLSmZ3stG8MkwrpYU+G2bUStMInWwPVKaR0/BmIMfP7yynndmKeRLa8M8WgPtvT9W16Z5gO1VkIQJh6Pkfl45HI5k9eNOB3wKEZAeK8eyNIqkU7wCcK+w0iL3Gshec/90yPRJ3zwXN8u6EJrdC9XcHAykTmn6OlaK9mZ4qRUGFBDU1On9MPDvbBs+q0BLpeVznDDZYdxtJVcWfCvlyvBeeZxpOMoWR/ygOdqrlzXxWc8PjziumfLK87D87tn7u9OhDSwE5LVCc/09jC1UjRFuqiHIwR6qfgYhNvWpgdGOzp09KDFQBgGTYG37KGv22C1jcV7BSPWWojm1Iwh6pJ3nmmetb1V029vlfOyME0j0SdIDlecJK7Ise1957e//g3f//BvdFc5zider69mw9bPUoHNzvKtejqF7gIhNAoO55MERHahydBmk2cTGdwwEvE2T4tJ0DZmc21z2gaQKauzT/lOl1JvNFcJJBpmduoVVwVLdMS5tN7ppYHXlqHuBfFYrnb7/zvqat40tJgIoO3ksR2UtSp6v9kku9eCCpaRRFa/iV0UTnzGDThr4lCalfzISxHlp8HR+2YRFvvft7h19pffPDh9h8K+Es7gbyqi/d/pefdQTTDRxTlIGNDtIg56ew0K7vALb4WeON2pxifdSG4U1tfFSTXA9URrgesWyH1g2SKd4SatD4eR4MGHSAiJGCIhRpz13afgSTHKm1QUAd67AWHB4kOqItrlM3Js24r32npBKELyik3RRdz59W9+c5vNBPMKjchNEOmUBqiNbZUaaDocCE2gZNt5imShlJZVl0vmcllZz2c+t0bpmW8+fMfxeHcbbLzT56CaX23bKiF00pA4rwvLcuVtuTKn4QbheweOSOjw5csLf/2Xv7AuGx0Yfj0wDom//4e/57e//o1ENwaLykxYyLUo9bpWiusa6GPg/niku27BhYoeis57YpQBbCv6kEe0shzv7jkd71i2hXmccE5yse6FfYagUu1xHCi5qmR7y7y9vXC6f8AFM7RcF9JhJoZgvQqRui3ElHBxvx4s5fW6ct108N/d3eNCp+bONA9g3oSYhFV2B+OUTBLYjY3XrV5aZWAgxGQPT+Dx/sHgpcphUCTI5XxhHCtumrisKylnDjZZr+tCK5FxHMi1aGrxkXy+8vLyRS70IOiNpt7v4+lAXKShn8aROgy0bt3UTi7grpJhXEuE1DnNM7Q9ilkQRQrCBPesfde8cPAqXX7vEfVDiBB1xlE4h7aaJlGAY28JdPgU6EXBfr6r+2OIVh4THK50lu1qUewjpcHgAzVUgkv4IG12pXM8HfmGD7y8vpBi5FrO9LrRW6IUtSDjHTV7vK926BRWYHQefGPY/Qk9CIGwq+C2RPVOIRN80ia0n3M2qd209QY9NK+E3WZXhD79OshKz9xMiV3xL+KyGntDnZ3wOszNRe/sa7WW8XuxksPiQfYMVQ++mxeiU8qGXOHxdtgqQ2oP9Qv4W55Ssw3BeCoLM2zYhkPDEXBd+DoNy73CHOXWyeKCcpH2WaLa70/fNShflxy3v77YFuFl9Oz7NqLnyt9+HlM3AXuwYrfXyXfBTLux9msZURNEhqNTLGsqUXrA9YE1R3KJLNWRS2CMgTBI+TPEhDNl4B79E6IjRE9AW7yzbLSff/gb9fGRu5N8DMnEJ82UW641QhKMWGsRx2Wb2/5jtmDG3BD57e9+S69NMfzO40Pg/OWFAMTHJ+6fnjjmTdemi5Sm3uoYk3K27PnrWZyu7451uXJdr6zLosy368r494n5cGKfTl0ItLWy1dX4GxV8zePA6f6B+8uV+9OJ+XAQ2kLABzmlT8cTv/v9H/jy8iLOsFZ+9atf45O31Ghuqd7RRXGkTsN+7VVS4JaZhknPTuu4aL6M1kRcDzGxXBe2kklpEFlXpc4IMTL5A3nbRMCYxCxEqyU0Lfe6XhU/HZIZXDB5ZrUavH7Dl533HOfpdrCH7iEEQmu85ZXruuB94NQqJXecK0zDZNyFJRh6bQKDH6wQ3mK0EaxyGkZiFDwjDFIk7WE+EFwkl0q/XPjrX//KH//4B759943C/ULgcJjZSiGFwOPTE9fzmX/7/gce7+/14TB1SIoqdhmGEe/ObDnTnHDIXAtjTLSS1SRlqbIhStKqKsFq6bqI5HMNfGSKkxneGoNXmiih0UojjAODc18Pdqf12gX5AryXI7s75TiFFK19rRFdwA0KH8N5xlFrfa8V14N6ElLk/v6OlEb2EMEWzEmeV+G7MeIcHOc7hjCxlgu9Zz5/+ZlaK7HLYb7lDskb0auco97ly3Clk1EseHCNViM9dJNg72a7Tu8yQOn/s65l+o5+3KZnTcxN5GZztzm9h2Joh9WUIhzZdbmkXat4F2nmlgBwreH87nHZu8fU1teDv5GD2ga8OGzZm1U/64Km2V7ofq8atcC9X0qHENnrDE4UpNpuf94Rbr9fbt2ErsYSdE/FE2+XoN2I7H4VDDoSdNW96k/Dvmkg45q2VakNvWU01d7NGY44H7fTIf5WWrXrC8p+63j9LjLq2eXjJDNtZCBQa2JZE3kbyHi26khhUPmPD5zGQa+vRUx0O4jTaeAwH8V70rWRoiHx06cXvE/cne4VteI0S3XvVUXrTABhMPIQByrGd2FS3yLe1Bu+X5raGF3ojMPEN+/fczlfFDjpPYSBvneUNITpYyS/ZUStuTAMCh4dUuLny0VDTYMvL1/42/ff84ffzxpELOkijQMvX77gveM4nLgu8mD99te/41fffitJfZSHpTWvDCcUQfI8jTw/Pd3Q2ZILg08034m+iZd02npyFRJRaVJKZW0gMYrDjF7Pt/caJKOzmyOkxOwDw2EihsDrl8/UnHl8fAKw1TcSgFwlT/U+UUqm984wzyQfTaf8wHW54qNc1Q3H29sLj4/P7OQfMdG3FdcdWysk5Nb1PvDr735FDJHrcuF6XZgPJ5EoqyRmMSTreUjqPLEHU3Cu9NMKZ+PWueuCJ1oio3Mi3a/bxvlyIVgbm/OOISVeLxdePn8ipIHnd8+03tjWhVyPNGCeZ8ZxZFkWLtfzTamlVMmv+f65Kben9UZ00WS4/lZU0p0UT8KIPeW64mOQRqk1s8/vNaJyVPuOacIdaZwsjE193N1w1m4f0uN01MFbPW4r5FqZhiRTkXE/y7IwDoEUEj0OfGMqktrKjSCOPprb12SXhnu7jtb2KgNOD57Xlx+oVQPClqvgpgQjNp0HQUWNLly+6msG75QHZhhw2/cKU93oSt1BJL0uatNT5WlAnoa2M/reYCg7zSQtrQy22mPKIHr8hZRUG4mglKD/y1m4xn6210JzCUkjdJpXIOzeBuMwOp3cYC/L7Du4dDvMjY9wu9kOerUsMvt9la/UUUCev8WQCGLsBsDZIW2bQms7Sa5Lla4LtlVrqpNN1r6uRb53j3ojpHZy9v27VjQzle0y1/0399ajYXvbTsdgUA0WJ949tU3UFsklcb14lrURh4HaCsn6UmqvEOUVCEGKw2qa5GkYmQf5AIIp44q9z7/7/W85TEcNqPYaV7jByfrHE7xMqOits88N+GDGRAvepIGLltSaC9FHhjiS7pN65Y2niPZMhBCpLUtW75D03g0QPEveOMWRh/tHlnWzSA3Jd58eH2+/R89ZkFrwnE4nZdfZkPjl5RXnI4fDbK16lZo1ZLoOITmdHa1wXa7EkHg4Hbm0lXVbGceR2m0gDHpvvRUSBSdVnYuRMSRylvKyxCTD3TgxzhOx5IIPA9M4aj1zjm3d8MB0PMmE1grjeIDerIVMb4BuNDNq5UyfdUCFqKjsmAaSD2yrurEPx4UYB+iV6CLDMIlP8IGyLUZaJZO9SSs+DBMx6GDd8ioyL3STwVqpt9P0M7honxd3i1cITiac0irzdDAOQQ9PGgd+/atfs5aV9bpxmEZCDCTnqK3T88a2bIzjyO9+81s2k7q6Hmk0kUdVUF2aJs6fP7Jse1aMorFz8dx81ONAdw23wjxOYGTntm0M4yyiqhYLGnR2vnmLn9CaLLliJ6aItwDFaZ7kj8ibKaKgl6qk3tJYlovMgl4lSTFFfBef01ulF0c6aQOEkVoKFMWb6+JRrEFiwLVNZsFcoKt72+M4He+UGtkyl+WN2jaSNzYVSXlr0/rqvVNtZdN7GL3CIrtPEjjQoTX2NCvnDYtCYogG0AQf9NZxrdDiQGwmLXVeOUqtm+/FmVRUkOZev0PfYSDPnkLanUhaZ1lae3y5+IliZrWdoNRxS68UU4N1p9RZ8d67akich/foQLIDvhn82HrFt8CuRtq5CY3wRjCDeR6Mc0Zbh+s7R9PtOLTgQ4PKFE2yE8je/DL7z63v5fteQdq/fi+7GPRRkXMek9U6Z6DKDcLSNEwHjwIXtalEti1wXSOVqMbAMOBSo3vHEFRHHIZBeUhh4HQ44UO4eavWos09t6pk07sJamfbFryPfHj/jd7zJk5u90LlLeODDa1dGWgNEcH7ZyEEf4v5aS5Qt035Tw78OGsAds66QPbtVXBtzg28miu3sjEy6Oq03pvoAqU3Ss2kYeTbb94TUlKaBAi6NFPO5XxmOhwYnUrBrpcLvcE4Djw/v8MHz7pu+JxpXSKE9fJGa/D4+Ej3nm1ZOb++8nD/SBxGxq7K3Gp8mkcJD8554hCJMdl/1/PoouP1y5naGsfDgR8/feTD+284MBN/+v4n3n374aar760Sk2cIJ4KV8GxbYRjtQXOOIQSWslLtaV2WC8uycKRxOOogvjuc2OtQUxr57le/IqbI68sXWrNij6YO5tY7pXWmeSJGJY0G7/ApQndKZl0y26WQZi+HITvRY/WZDYr3N/nbDYrYMb/9yW+mqmnWD02jrBvrdmWeJ8WgB8fvf/cbrkZaBfSzxFZxjOSmAMPuPddtZc2Z4zzyw/eZcUhq0wuBumWil3LMJUE0KY5MUcmp1XXWZeHl5cw3HybGWY123mnSCV3FJ757K1sSZr70yrJklssXhmnQ9BEq3utCXfNGSNr6Pl/fWNeVIUbBF8FTtsy6ZqZh4NsP3yqTRjkZ+uz7SI+Wt29SSD3YnjCOBpt1eunkqmykwUWOhzv45g98/vQTb5ePOLdhCK3eieJpZjwrYH2+nmzQfnKNWAMtyJlN1UTqaweSTavNSEGFGmJ/39VGi8GmLSHtOsDUFCdNu8Vs4CXbtGS2Rre8KA+9aMi4Haw2rd84BH0GunMEu7g73jYTHdqdqt+TLoVT18nempG+3f5724uvlBF0+/rdKBEsEt0C9PZAid2rIYtdNzJ/994YDAImce23bar2jquCtW7dGGAEqpEZ7L4Lq9g1qEYXCjhnpUP2x1uH2IWlNYv0a82z1ImaB5Y1sFWHjyOlZkL0+AAxRlJIknAPgclHjrPCNb3BHdF5XIBpkBxziEmCl+AZ5wPzON4i/HcFYO9SSV63lTFawnRwRG/prxWS13PSSqFFb4GWHYZkJLKUeMG6r+l2+djv7b22PPE9TWrEHQbHRBXe3y4QQfDKpHOWVbXlzC5nH6aZXhvn5YX5dNLumPTnw7RzPPDx40deXl748M0HQho5v37hzqE05nFgXEe6c+R14XJ+ZRylmmq9sQE//vA3ti3zxz/8EWK4ff9S9w0JylYoQ+G3v/4NY5IQJIYhGC/QrRbTYsBvL75jGJIIMqWx0b0nhsSyXO22Fo64LAsxha91nX1HkfUwtdqJKUkJ4JzUMx5olWEaNXmvhZiCIBVnD6j3jAaTeK/JrbbOtmWGYcA7b12vlcuycZxn+RAw5YxhfqVVlpw5jjN4x+FwYBxH7qaR4/Go1ZFKIuJ9ZJomci68rVdoMB9FvmPEzjwNPJxOOOD1qtyV8TCTt4xrcM1F2uNamYaR/fMG0nyP0XN5e2PX7g8p2jot6K2PkeCCRZEbYh6lHz9//ogfIo9PT3JoZp1o3gdSGmX0c17qKJsa4xBJaeTL9RNrXm4YyhAHTeVd8tFeFD3gvBHtfMXUg3lcQM5wAzvsjPGMcea7D7/l5e3A6+uPOLdS+4bvjoT8Jhn1CXfLPJKzQZlS1XrQQ3dEBDlobs3iALo3Nr/RXcV1j7fyqtYqvYCLewqtoD66FCRCHgXhBNe/GrusWAnfxBF448oI9Fu+s+H8Cs0SrBEkW3XOSPcur4FGbA/OIAwXkBLLNp1m4XqucQswt80X4/xa3y8M23ssZgQUzVE9t80A+5x1Z1tyd8YxcJPEVrcXhNo988vhaf9ee4SHbAz07ixBWAaOsP8IDmjJ7pTOZoNALZ7eD1xzYCmJsgUTVii563xZmY+R0+FEcIGUNMmHkBiHiE8eH/e9WxfwPE/cn44stXJ5O0PvDMeDeh7sd2ut6hk22ZaPnuM8sy4btEx03pRhMCX93J7Op5dXQoqMhxmFw7qbabGWIoVhM1ixNplkQyB6j08DtWZKybfY84CjWpWypM1Yw6ba45xTDpzznmXN9JK5f3xg8p5tU1KB956Hxyfyllm3lcPhQClq5pymmZ8/fuR6ufDNd9+qf9pCPvfLYB4HCEGZUKEymaQ14AjDSLKhZ8uKlaH3m8x9y4Xz+RXnURW1E3IRn56fCUE5SV//cbSmuNhofdLX68I0D8QYqKioZogDPgZqtZu1e0FVzt8K7H3vlNopRcqTIQxkJ+dq9YUdSXTOs22bQjBRxHfzFllRNN2lPWDNB7zrpNRpJeOCqijx6ote11XmsK3QvCO6ePvNWi2cL2eGNHC6OzEOifFwMKhFa6J3Du9hiNah7KDmTCsNN0dChut6ucWFhOuVFCNPz8+A48KF8/VCa41l3bg7HdVQhXDG2pVT780BO48jQwj4lBgHx7auuBBJXook52yqtoEv+sQ3H77F20NdctYqHAIhDrhaSUHyv+fnd+SSab2qycoHPn3+KG6jF9XRTmrocy7oA+UFuXQzSQnq8vRccNEIYDdAD7fgt9oiIep186Hz8PCOVgprOdP7GVzRJFx1IU7D3msdFP9tH57uHK4pUjrrUcSZAMJhfoCuTCEXIlSLLDFTZyGT8kTzwbq0dyLV9E7GX2FTn6AradRd37OQhOUXg2ZulonOV/GFHWKaw40rsu8TnZRSKrQ3OIbdEW7bgHP6YLu9me+X8JFNqc1MeU3S9FtWk6gSevdf48K9Y29a8t24lS54qNvPcIvZdLo89k4OUAOgNgFV4XaDk6oddLhAwxPNILZRoQYgUmrAtcRaGtc6sG0RmJQ7lqUgq7UxTiPzNBGSSalDUK5aFN+SYmBIM7tqrEfHkAaac4wxUccBZ0R0p7OVYvXKG8PDoIZTW3n2Ui4fjC/rziqOdRneWha3TdWgcc/q0kDVXKeuK4BVJzurSnCsrZKvr0SD3RV3rz8jcZpSr0spVgHgmCbMnd2lHu2d87Jw1+4oWZ3d8+HIuq5E66RPw8RtC++dw2HmV999yzjNpBgJdyfF0NiQGaM2Gt+F1MQYb1KH0poSMp4eocur5u338l6elOAdcUzkbeO6bYLEgTgEuUYd4gGccyQvyehO+6iiM9N7UgpsB7yjuMroEjF5khMMUWsXjmtrfPCe1BtxHKi1GfnobZEVQdk6DFEF4TusUUpleTtzd//AtmZaq9Sha+II2gzCOMg93BrFyRwSjpHPL68013j58kppld/86tfsRo3jdOTHTz9zcJ2jO0rr3ZWvtLWVJToOYdamEwJ+gHmayDkrNK8LT+0dWlFN6vl84e16YRxHrvZgpRAZDonTSX0UpRbhrjjp+L3qV6f5wOHoiVHTWnMeFyMTEONA9FIz+KQ3fK91VcquLq/eGmFQwYp3js0isIMPxMkzOanLQtQwEJ1nczCkgTQk82ogFQPdBADCcxvNUnLDDaPwaMtzyVywN+F9l3oLyOtGKZ6H0wdKO3NdPlPaIgOaSxRrYCu+Ewzr9fa1e2+S6YJa5nqnBVNx2aHm+u7Aj1KC2cFHKxSna0U9Fl7bhGvg+u1i7jTLEfImC935ALgZyuw5/WV8eWseF/pt2r2BaeZVoDeKD9xo067Cm+73BFl3u+wVb9FpzQxbLtllo58veEev3eK3Rep7p5TeYpd6t8uJKm9MRBvd7cP7ddej20XxdSHqdvlCddpY9lTWW0u6qRW0THlK8WytkZuj1wiM5Bapm6c0BcsFP1IJeMssCylIvTSMxDgSvTDxwfKX8rZRKxynwDgkSg3knJlGnTd125gOR5uqsx3o4nlqzoxptIY/64Sx7mff9+KzwjBYNhJ7bpVT09ssiIbayL0Qe9DF0mEthTENUjS2LuFL8PRtZb1cCHcnevfErpTsOAXamuVa74K1rmUj5zdrdoz6XqZSGqfJkhMaPVeIjVz1P6U15iBuVM+rIKz7h0d5G4ok7sEO+CFFjsd3OK84/R2xIehCLiWThsi2rBTvmcwPlotBu131DiFEnh+fuT+dKL3RSyPusFBwzvKNEu3W2SATXEpJWSW+s25i0GOIXF8vhDtHTCOOroyTOKo/tmWSHwljsNpGC6pr+01v6Z0gyVXvt0gMrW1SWuRSNW2E0VIs3U1B1Ju3F0kDXrb89OM8kku+9VA0Wx1blSnm8e7eJjSROpfLhYfHe3xzCvayKlPnAzVnnKusxdqfumeMA+1wJIeB8/XCDz/9xHW58uHbb6m1sm6b5K/ImFW7wga3JROGKLennavRe03y3iYQYEyJraGiEnNfOyDFRN5WrtvK8TTog29O+SFGnJNhKQQz9Bhu6p2jW2lK7J7H52fazz9xerhnmAbydWEI8iJUg2KmQet7aIHmGqUV7qaZ2hvLqnpSLLlWGLU5wYO04a0CRMbxwDHd00ms189Un4mhAJ3arzKWOckVW7MK0Q5qRGtUb5N83bsidjhI0EtwNm40wVQuBHqr1CauS/CB1GOqRd3b/jytihehe8FKdhjiFHmOXQPdefEJ3GB7dp+3Jamb0khQoYxxOzaDff+GAEP/Fd/vYk/2P6VZ3tvK4ix4U7+bfJSdZkS3Pp+mbXKwZz4VJ45gV2Pp1ZE/QynAznwi+vs4k6q2eoMM2R2/Vi9Kg9IjrUVylbv5ukWcG+hhoFSoVaokVVE76OILu1cY3RATrWv4DCkyDhPJ1H6ZTMRzmI/4lEiuUFshxoF/++u/cH93xzfW1dBLp/lGnCS+GMcBonrJm3XCOxw+KhjQdcdyudB6Z5wGy2vSa7aVgsQjjV4r63XF9c7j+/cEU1XtUvRqHSshqLtimhrrRQU9j09PlGXR85sioTpTfB6J88z59ZVbkVqXx8QHbw2ZjpgSuXdyqSTbNgbzpan0S/hfCtZa6D1bLqzrxuEwazjZB7z9Uumd89uZu9MB5wdLDFDYqQsKV00hsF5XbXLAP/3lz4Irq0JahxDZghVs7RHA8zBLe9/tEd9VNV5qg7UVxmHa0Uzmw2yqhGq2b82Cra605imusV3OzPPhZtRR8qSm3BACtWjCL6XSS8clW8mGRBoGM8Do50tBqqLtutCdZ4hRBK0PFBqTYe/yRjl+/atfMaSBLW9StmyZnoTBpZg4v70xPD4ShkjPjflwYBxGWitKZc2ZrTamKJNPcp7qddMH5+gx0EpliIG7D98yjSN/+etfAblGXS3kstk6p49sCIES6y26fDpMDGlQokRv5Ks6xefjTLL6z0qlGrvrgz4Q2k4qKSnWFx8oPZOvV+bDgb0cR/go5CpsNqTAfbyjl8owaLW8hcdhLt2ig+PzywtjjEzzrBpNZ5JIVJiUohQi3VbqjleghoNtXejdSlGAw3jHnCZKudDqhc6mtZeCZwGXCChXP+6uOaeDrGLOZxK7yqY7kYaWyGRReE1hcgY7doMN98P+qwtBB3Xotr1FrDfapu7uLIlCyj3XmxnpbuO5XQHNJv89N0lbiy3aXw/q28X0lRfYvenOfS3m+frl642E7XYLdHNfe9++/judO5Zaq6/fkPRWwriA657im4kPzBVuv0C1DKA9EVaPqPEeTT6MWh1rDrTiKC2xVWguUmrk5eVMd5nD3YOwcWcy3f0SckH1vsbDjUOUb2CYdKaEgXFKTAZBpTRBr7QYOPgDwTi3FOPtfVlqYfv4xrfffUtwgeLl6cit0otiZZyZdp0zlaN3tLxRY6DWwrXIAJdz4Xy98vTwYDW8igv3vbO0omRWpzSA5pVr5ZviMqb5AGElFMlVx2lmWa5c140xROaDIKG8raQ0SPFp0G1D+P92vdJmaKVQqczzjA+CwEpT+7t66zXlx2PCt2aiCUcK8l6lYVDkeJUC7DQfqL0xH2ZcUHBpt7qCmCSsoXdCGri7l7flh48/07bMsq3893/6b1yuV373m9/z8HBP3EqxD4Dgjx0nvU2gXdhfiNHCpDpY1s00H8jLKlllDNSilNE0zkTv+f5vf+Nv//ZX/vCnP3K6P2nSoN8upb5HnBQ9qKUUQhwkTXNayVJKN19BGJylwTY1x3U5rFNKN+NcbpVmmUMuKA4gWcLrPM/4GIg+fCXs8KRBJsFcK65WjpNMLj5FnMuG7UWVeXS4vl1Z15XjUevvn/70d7iU+Of/8Wd678zzQQR0UHR1LRWXAg+mjR7HSdEdOCkjvFzJniAy1AXGYRZ84iHUIEdv16R9f3+HD3Ygl64PnLNQNh9IPrGWlS1njscTJa/ULcvXYtBFiOFW3jSM6r/W1NjYaiUFkerueGSaZ9as3KlhTBzGiZdylgGwO5pzu2uA2jvR60MTR8Up9N6Y40BukegixScqK21LtHqmmWKuskFfVQzliiAvp+sHWwjAfVUMIcy87JyJTfndYjno3bYFYy3xCkWzQ77ZQe5Kx0dnX1sXRajQLXXUO8hdG9YuFK0UQo82mesSq10tzR7oVQe27rrA7j+Q90MXUPV2IRkEhBPE0/ouPdVUrF81aervsDuZW+/sJT664rUNeJO9evsdfbfCpQ44q7RtVVOjNyGGHV7eOVr1bDmQiy6KS+7kLQKJGAZ7rwvrVrnmSm1nPnx4Z36lQHP1Fv8SUyLY6z+Ns+DIoN9qnJS+HCbxbk7SOsYY2LaVkgsfvv0AwHK9spXM4DznbePl0yt3j/JPSfGija5bRJCPguKWdeFwOlJyFfzSHclHyT+bzoZKp9fCeDyRgsQAQwriEEpnWVfezm8457l/eNDv5j1TGlnboogZB8FnqAvXmiEGhnGidA0sjUY0X4xr8o+9bTo7p3liHMbbEDHEQZxl0LPavGOeR5sEuobioqBO3zrDOLLlzOV8odXMOAy3NOPr5UqM2lpiUmePs62d1uh1z2vTAJq8TNL/+te/cT0v/Pt/+PdfNwlnEridgQc5inMp5CLX6t4Y53zgfHkjhoSPkV4Lb2+vtNp4fHy6BdV1L8zrv//zn3n//h33dyeent4pnz9X1qzazXVbmA8HQouomCYCHu9356cjJa1awXse7k64ECjVDmDnNC1Y9n90kx76UsBMNs38Ha7DXm36/PjIbOmqW4H704nDKIzSeR2kQwj6oNVOK4WcM2OKkjGWxnw8ELfMJa8MKXE6nW5EF7UwjjMlyHC4q6xqrVqD80qN5gj24kCmeaZhzlGnW36tm8qRaBSle+B9NO1/leIiRHz1hMNAo3A9n6m1cJgV7S05XGW9LPRW8Un+EWcwV/NabUtW21mujad3z5YC3G7wmZrNwCHxQgOoSiHt3rJ/HJxOJ5oZ0TSZe4KHpci8NcZHtiFRykDoK01gDcFvqsgs3aIutJ06B6EZWW1Be6AgSGc8hhRI+veNnWYw6MXGe+eaIiecNz+BztiaGz066R8CZDqpYaF1AdfV+LCbzJyT2kuBeTchKaYGtX92Xs/SCfC3Kd7hcbtyaVeQ3Tg6u3j8TozbyuDM/OTtWulfv6/MdvpuzWkt2PvH2w7h4fDNfEWW1yV5sabV0Bu5B5bFsSyRykDxkU6AFHAkip0R3QWOd3f48yLeqgNeKhnXAzEFxmlgGAaS8Vk+eK7XC4fjHWlIECN+j653QfAUDnxgmIKpkLR7revG2/mN+7sH7h+eaXUzCXQjEugxEKpMZZ3dD1F5+fzC/d2JOIysdqn6FAm9MQ6jOLk9NLNWcmsM43BLHN56IZfMy+sruWTCGLk73UPviiLvCBLCEWNgPsj7NU9H6HCYRoYoM2ruisZYclHM/jBQi4zJtWW2RWSxM+ShtyrxBuJpfdv73StjjPTDTK9V9brbRsuZFhzbtvHl82da79zdP+BjoOTCtm1Qu3o1tpXaG08PT5ymCR8D8zhJDo+g7s9fvvDTp5+JwbJ7mjgOOjqsWm83GWzJK7msHE93RBc4X8789PMnfv3ddzjveX0940AYW1Sm+3K58vz0xDgkfv75Z758/sS2XLk7PUBM7CU8MUWmPslxeAjUNRuOKk1zuGXDVz7+8AOtVB7fPfNwOjEOAz99+ahY2+YtKkMM/5IXBeG5QRknVTIyTWiKG2nojdiWqoDAu6b8d+t+wOuhrTlT1g0XFRMw+ImtVC7X601eviw6fK+vrzy8e3+DD3xwuOqhKXhwnAbOb19IQ6JeC+tyxXVHnBLfPL/He11+OVdy12t/OV85nWbuTneEUFm29YY9piQpYG8mPxCoLk6BZHpsbxeLDvzcKsGkoK7rMnegqPCS5WSvhSkEmvOs20Z3MCRLJS2d+7u7mzhhx1i7Ya7NYJ3YHaXrVpNJzRFD0vfskRCO+DBC0QRWfTMJaSAmlfr0tldeihBONOiBZimmzn3lGGqXYmz3BHf7vbsZ8RrNRHCO2vbcJRvEUXYVeMEwtZKjIh720p2+R4OjSV7KJx3kN/ezs9eyK1/H2Z/11sSof8xFbQRqwCSviBwWbQ2+6U864VL2mXFGaJiazy7A3SyoO9m2dVP4uLrLY81Y16SAKXSTqFZadWx5oBTHWiJbG8HP9B7JTvDqfvH2JiL3dJyZ00C1nzPiFOvvHD45hjQSUjR/QWOOiUttlJz1LHulCyixJ9NRdH9taqiMqPa3bCu9Nxl+jft6eHrC0VkWRcakccQFKItCF6/XKz4GHh4frNJAv0PtKj9KKbGyUXLWaxqcCr96E3KQIlve6LUTh4GYohWfJbzToe6DZ4zqhFBlsqfVSsmFw+FODFqTj8G5pEqC3pUDFQOnu3tq2XBO/jFd7N0k/MZ59a7L0iBh7xy5N5LzTCnxcr1yLVWdE4dZz4LFaby9vRGjUJnLduXPf/4nLucL27LSnePx4ZHDfOIBz+PTI703Xl4/s1yuyFckKD+2JnIae1B7U1DfXiNZa5ZMzGotL9cL3//wPYdxpvXOED3zYSb4eEtQzbnw6eNHjqcjDw9PhDSwblfuDgfBHMtKrYXJwqpC3PuNIU5yG9faqDmDFZiFGLi/u+ev//pXXv75jedv3vHtN9/w9PhgHyBxG93roEouaJMuFUJkHidNjzXz+nbmV999S82FEgJxlNPx88sXEdDP73DjaHHRwn63kpnCyBgHStG6OYRID5Bb0wXpnbwUpkbw00C+rmxlZUgjrYuEj8PA8XRiGIVjNjqn+cQwqp82knj5/IV1WZhPB4YpsW4bT4bNjj6Y2auZ21wKHPremewY7x7I61Vel4BWSzxhCLx9fmWKB4aQuK5XRh/VHoZC1Hrv7E7j3lQIH5wC1dSxULSNOc2xQ0qUriTMaOtq3GWHTbk4wXVadcQgx31rciaLZAyUkKAGCgO4M96Bp+K8olhqF7moqAdnNNUunxZ3J7e5tgW9b90uErsG7F6Qd+c27ivN1fhi5wQ/dRwuB1p0mrjDV8ms29VD3VSgHcvysbRbuA0P3eI/dhir3xL2pN7yOzmM3ODi1H4RCohBTzec392CDV3btzrxRN5p0vT7NuMcfb9MbVvBeCfxqFHBgNWR88A1B3JzVDeAj+aPMBmw/9pEZy5ISaanqMr4Cj450pBM2qrNIcZkNZuF7gP390/ULLhlmmbWdSENg2CsKjhGg0iTCa6p5z6EwOFwwnvYrpuym7rlpJXMeDwQnGdEaQA+7UOE5/x2JsTE8TjjnHxdW9nIy4ZPiXW5UnLh+emJgWDbtDgNnzzenM3DMNz8GbUU1m1lPh5pVa+pD4HpeIRWVYHcHe7m+WryPGEd6CjKJSOhwMdPP3M6njTApJE0uJtPLHkJdFqz4cuLa8y1WrqthCrH052Ujs4xPD9zPJ3oXXCZEJbItmzkWnh++obf//73nI4Haw0d+dWvf8W77Zm385laOofjgbwV4p6ZnlJkWTbU0+xv6+1ocRHDNJKXlc8fP9Jb5/X8yrat/OGPf+R4uLPcIk3LdStMh5laG0uW6/b983vBTKXydn5juVz5ME1EL7VEb50QEzFopd4jF5qv1OZM/nXP+fzGjz/+yE8//MAYB37z29+K6Nky12VlPkwc5wNL2UhWX5prYfQys63rxs8//8Tz0zPDOBBTYh4nNnNOH6aZkBLXy5VxGIhjYhoGYgr87V//xt3DCfAMgw7MshPALYkr8EpdXdeNrVTWvCpq/DDjkLvxeDgyThMxFvEpVnD0+vpKSpGYJo53J5prPDzck7eVL59e5cJOgZINT2yNHqP6ObykdMu66EBZBW0NoxoH11xslfSkYdIH0ltHgdcUhblzW5e6pbVCrU2hj71jNRwkb1JCF4iu033g5dMntnXh6d076qbgOClMPaFJ+uytf3rAkVuntIJzTTk8RFy8o9WIJ5FbIyoGDucLwWValRQa409usSxeuP7OvrrudamxH67yWQDsgYjNoMc9+mQ31rHbFBBp7kswQt/Rgg750LFtF3klunkS3P4FkI9jn3xwt01BX1cfr9r3noi+J27oH9dp/pdxLBj0gpHZUrz0HfrZv03fU626HOAWV81tBBPx0JGUt7nEsgS2LRLcxGobjgx7gldjMOi0dxH9COrsQdyTcwGfdJENQRP3nggbkz4/QxopqfDp02fe3z0Sp0kXQwo4P984yKUu+KaBsDfrcR480/FAraYuBOLgWJbMVjblP00zJWf8MFOKNmWPBs0UPWke+fLzR+7ujrTS2LL9pjEwjapFXlZFVrgYqXnVIe8c3iVK3+wV1PPWa741z1Hl55LvSBeBT+mr8gy0vZeNXippGvF48pbZuj5b3RV+/atfMx+PUnU6boS190qUaEEciavmmfHyWkyTcuRAUDy71NpqDGqtN5Tn7//9P/D0/MR123i8f+Dp8Un86Lax1copTKQQeTidBPmFgTpWYkxJ04rzeL+R4mAEctTt1TvzKCL2y3rmermybAuX68Lz05N0+8jQVWydAsV8V+2hzMcHXIdty9S6yRF5d9IkVspNB0xvtKopJaV0U+Z45+lVtYz3j0+s60ouFee7EVXiTuIQiV7wme+NZVv59Okjd6cH3AmGYWQcJx7fvaPUgl/hJRfVnU6zLstR1v9/+du/8t279zyMz5rW12JR58Iq1yL99RgjMU4seSX4aNNTZMkFHyXJi8MgjfImU2AYdPPrAyjcupbGzz9/VkHS4cDD0yNPz4/kvHI+n3k5f2F8GXn37huiz3QX1FDWG8Fw3doD3m1KK226gARbdFNIZBGG46CDqDVSFISylUowItc7uThL7UiBVMXFuJHkhWf7Ha7pmjZ//vlH1i3z+PxkcfCaNHVK7VQxgh6iPlSxerZtoXaIHhwR7ycdyCbP9K3Q2Aj9QgwjnUZpG55m2TPNSlwMv++yOeyCU9cl3fY9Ucl2wejAU9GOA5OumorQeAabum8Hu2Nfuetu0nOO0DrdVVM5NVpz1oGRvv7dm5z2F9vK7RUx65gzD7RN/th7Vv3+OssLsvvP/Z611HfWwqBAACdhba8iXAR9OXofKM3RSmQpkdoDn79cab1xd0oGU+2JtE0Ng1E/qSo+Ct4F8xzoawfvGaZRlwUOHxIpadAbp5FkMORyueK7YzzMTONkUmUNOs5JwTelQK6KBnKm3tEW5agVZZwNA9OQrEuiEdIIrfP9Dz/zcH+vz5ePdCQN73iO8z3pm6ictVx4eX3h8eFBqka7lE7xQKmVoSohN+diPRZVF1qMvL68EFNiuV45HA9Mh5kvL18IPnB3fyepcNkYpkkx4rWqs9pgz+y6gkxbZ1mufPr8wh/+8FtCnLg76rPaBwcWS7TlDWJkWxZGhwWE6t0upXC9CCkIBxmcJUSwwaDL1tDswS69M8TIb3/9W0ot6vEumRgkAKqWkeW94EEVWYkOiHSorhM9jNNEKRuX85n5eCCMI+u6aMKPgZpXnGuqATze8c23H4SNB5Fs0UUcgpvAqRikd0oR0ai13zGlkTTNLNcLb2+vvHv/nhAiaynQNg6H8SaZlavYzE2tM08j3/7qV/Tab6QsdA7jxN6yBY1hGCnLwuFwZJxU0VdKIcXIN8/v1ekaI9e3N7ZtY5pnhmFkWzIXf+Xp4Ul1m63RvDM1k8ioUpRfE4Ky7a/b8lVX7x1bzbroYuJ4POlnyZVWKofDZCocTSK9FSVYzipFWq4LS1l5wDGEpEt2OnA8PrBcNtZTZhiMW6lQ1pVaMiEO4D3jONHrZhNfotUiZUuQ+uy6Ltw7TxrUASwVjGX+Y07hLmgq+gAmO3ZBpCB4ko9Up5nVu0ihUktjiIpXyMF8Lh6sz1WGx9agVUWNE3ChwJaJQVp+HWSO6gciIpwLGe9GYdPIFBf9SHeF6Ff6ttJQMdOO/rte1HlgxLE3TD64ZgS58RNdhOkuicVB78Huh3zD0vWfGl8ZYEE9DmTI63Y4968bw86FODu0sUto54x+SXjvK8xXJdPOT3SLhbBIHMwZ3eMt4BL7O3sMYKP+QsXUoQV6C6xtZFtHtqxU5oYHF+kUdQpgIuYufD6R8OPX3C1xWk5ySzu8nQ2EyUq1QkhM48QQE2stDDHdYnO4v+PdNxOH8XALoay18OnjT4QwcP94YhwnGlkbhME0O2mfhsRylSKwOB26wzQyjxNvb2cub2daLdzfPzAPJ5TdJVgt9EpKI+t65fx2lkHOOSvLa+QibjBvhdNhZpxmJQm0Rl0bKYH3gWVbOVmVwrZu5Fq5XC7mVzhQ6ZxfX3hOkeo9edusdlnFSNRN79C6gXPUstGqmuqqTRA3EY7TM1tzRR8bNWmmpAK1nDflNTmp3CbjUEFpx9F7cu+kLnXU+e1MSZHTdOCyXIhpAGDJK702sxeAa43leuXl7Y3H+3umw0HqJgc3otr7wOF0lMUbOByFa63rxvF0L2WBPdzRKv66kVrBR2LQL7hHEXvk4O3NsawXvINxPpJi5NPHq/iNw4HH50dNC9UCoVu1aNtA9JIm7loO7wKH06RI665sInXzYlJQcCFyd3fHcT5oLcdxXq/cBavq7J3ZwePTE2OSYuYwqzC91srT+yeSj/QQCa1xOOjhzjnTvaNsmWXR5eAcrNcrwzBwNJzYuwvee10srXNZLjiioLXeSOPATz/+xOfPL/zpj3/gMB14907vhneeaRwoFoD4/PTE48Mjl+tqZLiiMWILvH75wpfPnzmcDkzTQXHYwTgLq0nd1VwxBVJJcu8WKUckSW7mcIDWpGYLcbC+DDuyQlest63Xws01gffaFfHsdDkE58ilQVQE/a7oUmR7RN3A4Elys5cqmKRVnE+4vYeh7hCRF9LfKiFUqpNqJ7qRNKw4Kv529CkGzxv8s8/w3Vzh3YxlnmDtbWamAsOeMqoQjRb/YeuV6xS7THwD3+STsTmbvbPE1Q6Wp7NDXL1be6FtzK114i5ndb+4Fm7rxe6+t0YJw5uaM/ksUqB99XHY9tOhukQz3qb2SN5GlqWBH9gqODfq0nN6Xaf5wNvbG70UfIg0+v/p7tr71HfpdvJB9z7tFvkUozZ4uYhHXBMUFYdERfH1Tw8Pv3gfAodpYKuOx4enm+BgWVYTgVw5xBPR4v6T22XW2h5P8yzFY/ByqzvHw8M9wzjjnafVbDHrAerGtnfDd8d8PDDME9TGsiwM40h0nmtuzONI8BZVEcIt4gdUA/z++b14x3mibpnzcmE10y7OUa6rJDetK5zQ6UxsBj2dl4XD4cBwOBBy4f7p0c7OTjKfQ72Zdh2Xtwu9w9PzI947Sm7KtWudvBXmeTaF4VcnfamVvK7E45G6rizLynx34nh31BjiHcM4gvN8+vyRMU08PDzo0t4sIdr4nJe3N8Hf9mzRinCzaRQ54/3ubHZ2eDSGYQDvCJaF1F2/Re362mm90Cy3PbeGK1llQnYwrMvK/f09Kcow11qTljomaDCOIykpwpZuMQzeSkY6uOhZrisff/6J8O23nA5yBbfaaLWoCQ9ozRGCohsUDSLj3mGvJjWDRq6NcdQ6PIRIHBOHgypagw/gbcr24mxyqxbQhta1ZWGaJk4nKX3O5zdCHIkxEDbP5XzleDjI7h8j//rXf+X19Y0//d0fOKYDL29vFoqog3SaphvAUU1mGVzQBO8VWlb63lfRyWWjlcKXlxeulwu/++Of1I1RFpI3x2y1Ep8uRceUBkWM7K9Xs1A3+i2hMsagVb07w1ar1Tp0XPT6fdAFUlE65bZlnp+fqd2ZaU9qmv2W8T7gfbXXv95m5jBMlHalm5RZvA2kgJy0ffcXCIuvWNyGkxIFtxEoVDa8W3U4u93hLNHpHui2/5tqkI9O56/kL1+pWTPseZQK+1UV540wbr0TDM5xXREfe57SjV+wn8M1bbclBFyVx6M4r0C3XdW0bxldfEbfyXn7esFJy++9U+9EMD6mezpRai8XKM1Tc1JgYU1ci2NZM9PhQPdmeuzmh6ATY+R4POrfO3Sw2uvknHpLBufVlOiVmJpqNxlxF+9guWfJ4N7mOyNyWedN4pPeGp9fX3m8e1DFKIqt8PNIsNKe8/mK945hGBQtT7j9nM45Hu7uxHl2R9lWWkrUqs/A6XQnLrV11m3lelk4nY7swZ7RB/yYiDhc7Yqc8DIAuxAhePKyMU+zImiAUuVy9jZ0SBmm88RPM1OtTGngWpQ9FofE81EX2HVbeHl55d3TwDiO5E2bVa6NIXmWtvHhm2e2RTL+4JsN1rpkPEoLDjFaKi7EQb6J+osnFqD1TnTgfCBfrxQ6vRSGOLBFhYwe7056bku1n6MQu2oaQP0US9m4G06MYeDd0xNrUVVz9JYc2YIjdkVGXC4XuSBDlAEFJcGCoJZuW7e05p0YPERZ5ntHhTW1m8sXXJAb8+HuAZ88b6+vdoDDfDgSQmDdsr5Hd1ze3hjHGTcp4KrsrkI8MQWOxxO7u9R3R3Eoq926oNf1KrVOGoXrotTRcRw1UTjIOUsiGwJrWZk/fKvckyIs3/VO2QqXcrUIjcaQRg7HA21upKvCvsZhpLkuhZfz/PTlE87NRBe5LlfFn7uIS47XtzM//fwT/8s//mdSSPzpD3+g1c40jDeMe28VK1Uqs4os971VevdSH+26bhx394/8MUaWnMllY1s3fv74E4P3TNPMdDD8t+rCz6WyFWmmj/PR3sNqR6RIrr2YCTo1N9Kg+bxXm4J91BTcGt68NHd3d3z77QcZMB3EnmQu26WY9vu53d6LqUd8oKUoGtgm1mFnaVslOlMjhUTD04tKfxzmn3YJeoW+UUuk9o0hNlM/FYph+vW2aexeid39HGj2f8v/EH5xoXRdEPuZD/J+tB2n3/+Lt5QCu2CKkcSoJ1s8nbfOBy9IyxJs98IjH/YD3FnIH9AFArq6q6MUQV+6tshKADdRt0hu+ozlikFM+umbhzD5W56TNigLA+mmsgnR8qus6MgFbYwt41wijknprM4T0ihVm4lL0jTzdjlTS+bu/l7S7m5x4k5GzXmeuCwLvXfzWjl6LVzWq/iBu0fm44E0REpujPNEK5W8LAzzQWnEpmCUObPDJj9AtdSB3jtlywzTyORGrlylPpoPJLvk5ZuSetL3wGEM+DjgeuPp7p7z5Wx+K93W3cE4TqozQGrLrWTujnfgHNPpxHfjyLZJvah7P7JtC3/585/5+aePzP/LxHx4oqNO6eAiznemIRF8wrQ11KpNrteNLRcOh5lpniyB2x6HWqlBIZyn05HaJNmPv4Dkcy6s60rZCo8PDxzGkUZnWRa8UQAdmW4f371TOm2HmNDP5MwMCUxplKKrtj0CWdNVa51xGG+FLT5YZaGT6mW/ZfcIZ993uMHjoqc0TfZ7CUpMSe1PueAsenddM84XDtPINA6s68LhcAIcPnQOx5MeXCfCsNWGqw1ioFd4vH8kpkjOhRC+atars0MgREIwF2evXC5n1suVx8dHYpTn4tOnz7TeFDI2REouXMqF1S88PT2xkqE1rucrETje3cuZHQJ0xWQv20YIgS+fX/HeM8wjvChV0YfAh/ffaPoyruR0fyTn9QYHnQ5HZfwHL+y+a2OLPlBrI3rpxdvOKTRFa4vQF4Y8DJEYHxm3lVozpMhyufLz5UIaEt9884H7O2XU19I1pXg1hpVaSUOkFZmSeu0Kd3Pgq4g/bzW1yQfeyoIr4JKkOcErT+nd8zO5yoW/lqxQwt7p1QDCoGM3BKlvtlKovRF9otEIPinunIqEO0EFPX0/nAXwexzV788FOB/ZapOem4HXt8L5vPD+wz1pdLiW8bXifTViOAuytK+rOtHKDgyFm+lNOG83poTd32CwpZRL1im9hzcZG92dtX95VUzu4WnNQt+c9WS3pufIB324ZQwDiIJzm8Vm90GkMQHvPFuPbDWpryTD1kzm2iPOR0E3XkPbfrvt7Squt9s22Jt+Ftc0Ve/mvf01qL3gOowpEpNBdD4yxkgaBsZRAXKlVA11w6Bh0cN2EWQyT4MaAWtlHkfmaSR69YJ8eXnhL3/+Jz5+/MTf/d3f8ae7PzKliT50rtcrL19eOZ8vPLx75u505JoL23VhGBR9XVxneT3jYySlQRte0HvhomqHS85cLmeCj+rHDslEBZ2SM8Gbe6U30jwSt42as6XGqq9mGAa6CyTvyMDlTY1t0zAJYh8GXWIW862a2Mr17cJ3333DmALbsuFc53Q8iDeqyrAieOZpotZq5T9KqI4uwGFimkYr3JJasLhOapUeh103QM5ZHS+tkVzgeDxQq+DbWgvVtry6ZaqTXD04L6Ou9xZ+mOjFomfolCaDnkuJbduI67qqvzolDgf7ZpbYuv+jeAmLcm6Yc9hI5aA4i2hToG+A3UbDOBPNyJbzhq860O/uH0SamkPa2029LFeGYVT+uXc3CZgbREC+vH1hWTbu7k+EwVbCLbMaQRRjvFX/zbMm+23b+PHjT9zf3fHEIwGY5okv48CXz5/pwUsDbTlQx+mgYD0zBa3LyuFw4nQ60op4iFIKb5ezkl174+PLF+b5QOmFYZz48vbCelkZxsQ8z1RFzfK73/yG7969t4hrnUc+eUlNS2FIA2XbKMFxOhyMeJTuXiRmNyXIrRdQeH1TIqSIxcTj8zO5Zm0bJkve29HO1wvOReZptrAx+RccggnkHg0seSP0wDyMuKZdo1nsiicKpumNsik0cQwGP+WKH4MdensyqW0StsXFEGnrQqkbveqCdB68EakdbpWZ0v87m3AcIWiyNnZZdbUm+xvGIyGNuEG8U3OZ6jKtFoJreCdXNr7jzVfjvTyt7kbeajoDxVhUJAff9y1923DzL/g982mnDuwzkzuC75wuUppFblT0GjcvQpKKjxHfoxJUQwA30JsjtKAUVDzez+Ai21apPUmvTzMfhhdP5DvLm3wH02BkqG43GiarpRO6QidxwvNDUJZayZkaJGX1g8Qee1po9EpRGMfEPA2mhnQc706kMXJ3vCeNUhD54LheLuI2fcDOZQ2PSTDz9//2b/z1X/9GGiIhJVp3UrmFQK+dz58+8bosvJ3P/Onv/h3buvLp8yc+fPj2JrN3IXA8zIKBvDNoteKKVIUepeimgzmoS2VdVpa80XLheDySEITWqgQ8e4lSCILLHFJ45ib56t3jPc4rYr117DOpKoQQpBQ9HO/4n//xP/Py+kItjZfLJ9bLwjfffoDg2daVYRwZk3lP7AxcFnGO8SjeqNRKcvIn9dYZ48hWNwbbiHOpjCkJtagXvHeMadCWgba2AfkpwuDJW8G6gsX/uc66LkQfdIbklWkcdUkNI9MwUFojjqYKyrXgmRSlkQutVsVAmyqm1mJuWW4YWW1dhHfJODOCXS5n0jBqeqJxuWz0jnDH3rle3hiGgZ9+/kQvhcfnJ7UxlUozDGyaJimiGqTI7RJ5fXlhy4WHh3t9/1r44fvvqa3y7Yfv6LVyvS483t+bCqTzl7/+C//03/47/+E//Sf1P6REc45f/+pbvnl+FgmVC8FLehqdDriQYFs3xmnmcDgoF6pWlvOVcRqUJHt3z08/f1TKbHDcDXf885//B3/+8/+g9M7z4yPfffhwOziOxzvC/YM8Dp6b/PXl5ZW//uu/8R///t/TXWeKSVfAtpmMzwjo2ileUrWSixHywhmxyae2xjyNfHj/gfP5jcN8UMxF6+I5miMk08F7NOEGeL1cOEyTAvkc9D6YwUyQhNbhQApRQoFWTZ2zY+ae3Cx1t0mJ4b0mFsE5Oilc08Pb+8i6ZCs7gtg8DFiYpPK5QDI+F61JrmXWTVhrMEx+9oNh8w1/usNgfbW2+Yj3A9SsLmevKd13R2kbMURVvgZT3/XNOIgOvemA7x2Hekn2bNdgEd0gVeytMW5X4TlHK+Z6LoLpsoPQA7mpJwIPLXtqK7dehd4gjgMpzjjnuTrY8kZpMEyT4NTUabkZVDfSXd5ZaGppjMNA7bCVppykHapoUiPV1nBB4FV0UY5sVC7UoziKcRaBuzfBOeeYRoXIxZQIaWSzLXqeZ053J7z3LNervAkhsgWFUMYkOLBieSDA9Xzl8nZhHmfuH+/55t17alZcfa2V492J3/3hD/z0+Qs//u1vcjCf7gSBm6AGr+9tUgRoCgmNKd3ELG6MHOIdY5JR71pWVQ5bqq6gtT1+XllMtUlgUXqhx0AMyFvS5BMaJ5lycdBy0fbuOst14XA8AoP4UDzH6cAwTfz0wxvT8WDPQ1WNwO49kiZB6V728/QsWev5fFHKc8Jgy6p+iiCnvouR0rsZoJU0sVUJR4agBOkvb69cXl54enxmWRbmSec9dokejyK0g4uUsnG9LNzfnRitH2OMSVHh4zgyu4E1b/qAt2bThVbliJroSlWGfS1NL7DvUDulZgan6sxlU5/C/eMjed1Y88YQB1wMjC6wbQtvlwvTkJge7jge7yhWgOGH4Wb+yLVCU0zE5e3M/f0d7775ht46Q0zUWlmumupTCNpoSmVbV/kDnA7Pl89feH565u504nq9crle8b3z8PzMw92diGgHW22EdcOlQbc3gs8GArmsChIcR+4fhW/GGHl7e8M5x+9/+zvelis4/fuHx0ee372TqofOPE7kvIkI7CKmtloZoqPkynZZWN5eKb3y9PCEA14vb7xcLjw83OthbNroFO0bRDi3DlUF6MHBukoOuuXCdFTrHkHql7oVrutKmkaGoA9/6EixUwt//ctf+O1vfsPD/SPdQ4ymxjEXfvAB3wutdCpZQX4W9eyplLypZMWb+sd7QZINYrfIDuORaKqiZPa8nS9yakdPdJ4WO8uqS5cg6MOHgE+O9XXh7fWNw/HI3ekkotj3mwop9AANMpVAuFF7Pgy4oIk8OHUc5+ZoBEreGMKkDygHXKsseaOW5VaclaIzPsMk1t3pstW+gbMNxePIdHyFQqU1p8m/6SDu3alXwskj4pIu3bxttDCS5oRzkYJgC98FZ0RLV5YktBKCmul8L1TTw7tuBUV+oCwrmOT8Bq3ZaxGCyGD5MEwd5Tw+RY6TzKU0wZsxBSKRGAIxydNzmEdKrVYeFG+kLgY70xrTYWaeD0ZAd3JvBOOmeu+4oLDL092Jx+cnfIxsmwaMMA3QOsf5RIgDh1lbYc2ZOEwWTlfpW6aleBPY5Jz58vkzd3dqVKuhaFPritwJlhMVSiavhRDUdNl3cQWVNA6kLrjFIhfVj2OXawrBNgugVq7XC703jrMMbd5FTeuDN+hGEOHz4wdCkkrT1UoYRxmLLQVBplAJS4ZhUgV072qZA4soB1clAFE9gPiFsqkdL4XIcln54Ye/cf8oo5xrjU8//si2Fb779leEIbEsK8475nFi9wNV85qdrAVPWhOR4811Iugm6l6NVqVpGvEh2HRqJeBYR7Bz5G0hjQPeil+ii5Yd1KF0aqzEkOixaUJy4FrHD555mhnHyT40gg1ECq1yIVtB+DgJP7y8Xnh9eaH3xvv0DaVUSqpW6+n5/R/+gOvw8fNnhmHgw7cfbomwAP/Tf/6fGQeRzH/729/4+NNP5Fx4fnrmP/6n/8j93Z0+4Dv0EppxM5pKFJ2tZNzWKqXKVCelh/D2XArny4XkA+8/fODp6ZlaG8XkbLt/YwiJSiGFgZYt/K517p6e+I8P9zwcNQmvecV7SV997+TurNBpY1lWdVrnzPnyxpQm8nWhpEAaoxHaUpM0L26ha7y9VaP2pgMPlI9TQ+D+cGTNmdwKoVlsRZBD/nw+czoetRnEwFYLvVVezm+c7o701nh7eZVv5DjTkOKi1ypyzYQP3nn78KlnOiVJYJdlhagLRPCJPqAhDqZOEqaeyyYj0vXKmCameZD73Bm009V/7WwL8T5SkZTWOZHFrXaTJsoRHucTLcg/PTi58tt6Zms6mFMKxCQTXyWrFnv/LGC8w80QpvMm+67DOwZSVFBgbiLF1Ueg9Fx2wtGtJqqKisPoBg8CKYx4JAJxruEN/xaItMu0tLWDxCTjpMRQpb+aSo4gjhFT9OzQm+hGUyZFvCkPhxQtyTUxzTLLDSkRUmLdzhyOo2n6TaYaAsM8KRLDB8Y04H2Uj2C7ikAO+jwdxpk//v6P+ASEQHKBHAQN91LISAHnXeTucOS6LMpUalIkdeBaMpND0DjaQL/57lvWNXN+eyGGSO4VamccJ3xTVEzzkeLV356CSsuKTBvm32gEHLkVapMBrZo9oNeKj2amXa/2jEZCUuKzPDWCTmlYQCPgBYunaYRhoNvzsOaNcRyJ7mvu125gvtqw643TcXi697RaKLlziJKyZxvgSi/89a//wn/7p//O3//p73j3/I5spUlxHOkdDsPMula862xlJaXRODb9mOu6EpI4v9okUArNa0hpBgX4lPQAe+WUdzC+YrjpqvGeMAxc1pXRsslDSIAml9PDSQ9nkVZ5J7qdZY+4bv4KY+VxUlfUJvVQjJE98hjgcH/ku+HXnF9fuS6L5acn4hg5HHSDX69X6pZx46hyE7rMXNEzh1HqATPANaeQr59+/onPnz9zfzrhOqzbxjzPjOMeLdCgCj93UVj7tm74GNhqxbWF0qsI9FZ5enxgywV/vXItmeACwzDYzSwS1yWF/Q3DQPCV3tHvEr6SsS/nV67XhU5jtsDEYbAa1bpPOJ3z5Y0vX14Z342keaSVSq1S6Gylcn57IwTP4XDAdR1qwyi4cHfV6vYQ9v7w+Ewu6ubeLKY4da3VaRiVQFAr6/pG6ZW3tzeW85W70xFCJA3RoBYvOK03ldQXwWItRjvPpBpppWpTG6z7d9lY+8oQEzENtG6Te+cWzOa6x7ugbbYWYLROZMmlS3XQFAW9u/X77r3wjpaLxVmDa45pnHXJW3WvN3hpnEdiH0xNM0JQBELrkcu6MiQZF7vlVxWTVDsvGMk5TwKc0/BxyVdK6cTY8K7fNiSBEo5o0SnOeI79Pd5KZRzU7cA+VFVp3ZvDPDfqr44+WG6H5Nu9d0X+NyRZtxBHb+nGwbak2gopDeywc4rhFuewLpmSOg8PjyrxMc4lxV0YYkCNHYZjHEw9s1P9egZAiqFm0E6tmyZiF/CtqTudQGudt3VhHEa9Pr6Dj4zDLGd+24hupLXKcRjp0VNzoZYqHtEnDnMkWRBlLcXiNnbVoKNHz8hgsJPBhE35bt45SllpUVN/7x1CIqIq0m3bIGd9fuPAMMkXRq94Y60EyXcNXHmzylPlWDXnLGlWzvmyZm0bB2/qpK7hWysfAKVVXl7fuJsmhoMCEJs5prsNnvIhFT5//sTz4xMPz480i0J/ev8N8zjeLqa7gyoI3q5vTMndxBjeC0LuWyeM4pFTh+t2JZac9SYbC76nDbb2dQIlmKKmKu6i9cbrl8+EhwdSOpJztV7owDjO1FLYcmZIWumojXVdVUiCI9SmS2QcCd0pHrtW4bOmr+3NWYIlemhOMpEVcwSO40DeMjUXXHA8f/NOh/0qOCsNiYfjvX4Pr797nA/85Kym00lKqmrWQmqDeis6pO6txa7beufxUW/sMIxc14VeCiFEjnd3bMtCKZUxjVzOF/7pn/5Z8rPTkWVbGUelYW7bRqWTuhRf4jvtI+XUa/v2+ibHZFB/9K7cAtiKEi97LkxpIj1J79zQwbNaO9aPP37P+Xzm/bv30MTNYJti7vKUJGuvK1VZXcfDjPNHtfE1xV0kJ1JuTAPJR671yo8//MCW1Rx2f3cHsm6pgGrJLNcLPniGmMxDbFMh3By6Ak2qEcNVA0jyDC0oKK83tusVphFvAwQOjseTZUw5hl02bNN7a93MYPrA9g7dKc4hJf0kN+jAeXwc8KM+0NENhk8rUXYej3bItK/KE+0VeIoMni5QXFGekUFrwQX63mrXTfhEM229Dm/voy66Dg6ZQeldvfAumj9AWHVZV1yDeRwhJS7nN6noxlmXScB+/kApUm6p21k9JR5v4YVOg4j3Xy+JGGm1MceZcUx4r0N/H/q898zzIkHIqC2wmFHyeHfS69v3NkEn5ZTBfr13M7pJHRl7sk1LU/paKslLAeYa+Bi4Xq+koOw2S6TUhRc6YYx0ogVZVqEWTgkPS7twXVeepyd6z6xrYauK3I4pcRomPE38aats10Ub7DzfMsqoMvPVKOPg6CN7vCJUXl5eKTkzzTrbYtAQ2XPB9cE8QDKn7gGFraqgTQI6qaf2fp5SC84FxuN0q4mWL0IQknOOnjPbdaXRefvymbJNPKV3DLvct6oZ8jgfJU5xjX/4D/+Bwzxxmg98OZ+JQQkIzjnWujC4EeeDMvZqJRvflPPG7Gfujid71qXQ+vLlC3nLxNfzqwLn4j4JijyJKekFGQfJPpsePuFhnnePT7ghgrMD38lso6pMiX97rZpgHCzbAsDpcDJzk4Om4nXX3U2aWFu1ciOUFRMiDpinkWVZbaLTh2jnUKZJ2u23tzdiitRcuDueKF0Be6fpiA+B0/2Ru/lEnSrH+cjD6UTZMi6o5a4WlXC0oAkx+sD1/AbeczgeGLwRfathdTFRW1aaZV65XK9M08Tj3Yn705HmPTlnogtctyvTMJFC4Hw+M9gGUasuhNIqn758ZiuFFIUBBwv+yxY/nPNG8ImlrPjowTvq9aLWKuv1fXl94fPHz3z73bfc3z3qsLOMeNcV81Brl+u5FlrNxKCQs+5UfzmNI8u2kVvRVlkrISD1Rky0ZWWIgcfHB5xTQONlXShbobVySwPGO5KLdDM17jlbpTXysjEeJ8lInSdFYd3ew7oWuimhnOu0pg0gDgOPj4/aRLpMSLu8urVGCP0W9ldatYlUpqgxJkL0ZJP6jWZg1IpvmIs52naJcUhKEu6rNfYFzzwf9GyjQ2rfMnYoTWnENjVb5ek4H26bRnTBvCYodbkbxGZSbnzEtUx02qjOb69KHB5Gehc567xkmiHsRffyL13WjVoy0zByeJp1OQ3Ctb1N/t5HmT2d5/9H1X82SZakV5rgo+wyI84iImkRNNDTO9M9Ivv/f8V+2p2Zlp5GA0UyK4O4u5FLlO2Ho2bZA0EJgEJWhLvZvaovOec5iUw/aPyRc2Z/ODQTKXR9r9Abo8vNNyl05yXFTk023Q8DKcPr+0nL6N3IbpwQkUVgFN/GXzEKdTJ0QlBnJJhp8eNgLYN3TVnZcuFLVda8kToqplV+AtPUlNUytvF4WlXwXk9XOh8IXUu6o8r4ayRPz0XLcYoRu6kfWLcVlwzBBnXtRfpBqiVtibfzCec9u2mvd2JbNZJvBr7UFITqsKAfJHdd4sb5/E639XRdYH840jslWsp/gTpvp9G9M56tVraYCC106OXDB4ZBBOolJXwLWasVYsuaCdbz8fmJglL6utCRY2GtUbsNQ7vQ9T2mFmdo23grlUyN9Z7q9/5+4vXbV56eX/C7cdKU+E49tOTm0uX24KKZmW+SuVoK3OZlsbBuCzl7hlH/7K2K03jJskUdpBij/Ow2cjAFtiSMbRcUen7b9l/ma1umeFIqrDmztGjOdV25zEvLdVZl+vr6yvl84sOHj3S9xgFpU2a2sYYtRXof+PGnH/Ft4eM6uY8DIrHi7D2y0BhH3BKvr6+8fPqAt56UEyRNgL2XGcb07WBYV7brSh86/vk//ieWdeF0PtH3PaVot3O9zvRjz2W+sGeH7RvWIWW2pPl9H0IDo+kQM7l1GtYwDKNQ4kncpVoyS5MJHvZ7+i5wOV+0lD8+3kc+10Xa7pQzvQ+E0AxJGIzvKE0JZo0MYtKJF3KOjP0IXpeY6z0fP36U4iEoAtaUwrpFrpe5tfyudQzt0qdqFlwS8zwLdugcOFWhSsZycuvniLOBrvPS4dMCg1pFV3JuBYv4SwmpvLa0qRKvtjnFxZHSgS0tPA5Gv6NrmRnah6mG4zaCM9x/dj3I6h+ctzLfVdFjb0JXYzThSdT25xnWuAllYDxb2igl04cBYw3LtgjuaGiub4OzPc4VjbxKafTc5sAPA6lPvJ3PvDzKZ5CaFN0242jFYo3gldfrVYqUviN0GmH9PiBvgMuuux+W1jmsdcS4cr7MjNPAOE13fEPcNlyvwib4QOcD1UK6LBhTmecN7zs65znFxJevX3lIWhxPdtQ+Mislct024hLJORKGgXHw7WOUSCT0gTVtOBPAVppCl23dwHli3nANcFeLUB8GheNg5NnQUt6yHyec08I9JVkcjdEzsD8cNDZqk4qUKx7Iq7p8u98RjAEv71Q14PqO76fvMd7f93Q+9Hp3S+W6LlCrYoMNd1xJSYVf/v2vvJ9OVCpPj0+M4ySFWKcMditPAQaNeUuslJy5Xs94Ywmh10FuDEtUFHLXpgA3T05tbLzcZOnCggg7k3Kkmk4dU6soNIYeGirGyhCcEl/e3tntduz3O4IPTLsdw9DhndfSeVlWUk4c9ocW0qHbN5aEM52Y8cYQc2LbVs3tqxQrneuIObKtsQWYB0lnkXV/XWZcM9OklMkpSXppPWEQilzwt6LAl5KpWeMczU4TKUa6vmO3n+TMNnA6X+g66YJDFxinkf1hx7dvr439LrlmzhptxJLwXS+3aImQM1sufPv2jXVZ+OMf/wjG8P7+zsNhT997Pn36ROj61ppaYtzIS2J8GEUh3fSzXS4XhqEnpkRJEdM6hMfDgfM8c/rHidfXbwTfMYwTD0fhCWI0zMsCpnLYH4DauFnaG2w5kkvFYcmm7SSs4Xx+l37dew57EXW3lHg4PBD6XrLXdkh44ylNeaKxk4Zcy10Zppmo5KP67hX5aoltobbFDdOC5j9+/EihsK4L1XvJOdvOytt6b7nlm1GIvMFIXqsxOT70rOsmtcckxHO1jQ11lyXeDu8GxKsVI2eZgH2+klftErxzhE5GrdRc4nGLzYhWMQm6QRnUREMi01V5DYyUCpIU3xzSukP1HrR7I7VgH9sOeO11Gp/JVHLRHk4/fL3v4HIz0uWUqV2b0VvB+jRyM8QtYWpbstobNdew3x24Xq+UKtOhMfLFl0Zczs08NvmOw+MRb8M9qjKlSE6FaRzbmEod97KtrVqX8u75+YUQAuAIoaHBMXJQB6/vqY2PDruJOlYul8sdWbGlyP5w4M9dz3W+cn0/0XU9IcDlOoPVGWF2huusIlLGUHMftyQiNSWyoZnSNG1wXYcxDnIkpSLptTMcdgcFouXCMPaQqoxsbXzsc8TQtQLLKrkPjfrHYbhLYH2By/XM+/nEOAz4bSO0c+c6X+mCKnjamNpU05I6q5bWQNxUrYfQFKDc7z+2rCJmHCemhgm5XWg5CddjjXZqwWrXNnQd04ePpFJ4f3vHJIk6luuVnDMfv/9eiq0Gfcw5quu1VlHQpbA0Flfn++YgL7qAgeuysG2RZDOh77HV8vn1jfPpnfl6YRr+SD90DP0TIXSiupWb1rYpFSTd1EIq0FQpXrI5bx3J/g5AoyJZpRlI60a2Dmcb36b98gUwObW5ayE7S+eHxo2X3MrUpghp7fc07fUAVemuzc42GabkW4XK6gXoui3dpmnC+8Bxf+CWkW2MZLnWOnoTiHGl7zseH56xPhDnd94vF+K2MU4Tz09PfPv6leM04UKHa0vRUtVt5RixgDeS3Zay8fr6SkqZ6eHA+i7WfGmzYaqj9x0vzy/kUpivV67zmbfXd7rnZ0i3CtISvGNeV26BOKVhQhTQY3S6VkhxY1k3rJN5xlmngyQX9rvpnhS2xowPEEYt740J6oYqGG8InTABtornc8OcqEEXgyk3BZpt5ybONcRDaRWxFDPBOGwVwM9Z+/sl1ZbjBS3pjZHPIKdE3Da8c2wxkdaVflTs7O8Roa2LbQ7QG9b8BjkyFbo+QBbB8gZAC10gxdSMaXJH+6B8gZKEiryZ4RQHapuiTYW3bbknoKX0zVhtELkzN/GFd6apqyqmaPzU7ndijHLPB40VqeAmVbrynlShv6sq3T507RCzdxl6ypE+9Oym3f0/A20XgSVF7Uv2w4gfep78jhtw0jnxympXmcZRFWTLNd+WyBoXxdJ2PTZ4nl9eNOtuuzYoYh/V25hC3eC2Na/RON1T8eKyaQ819Lyf3og54QzMl6t+l5jxXp+zC+ric/FUGxTEZR05a2dRN3ml+rEHlFdiXWNHBceXv/0d1wUe9o+8nd748vUz//Tnf8bapuiLseEspLKTuEDqQo3SPaWqC8cErJcRzjdT6zJf6foBkyXOcPbCy4ePEt5s+j1D1zf1lZbL434iuE7ClhsBAnlO/vmf/4VlvuCcV6dxL3poQUyWvnUK3rkmfrBYH+irxnnbtmjv6zuwUZfAttEFcaHWlMm10lm5kTKGlCKD7+iDb7ktIh9/+fKZv//732QxMJU///nPPD09icvXnsdcC7smPS4l65JIccNYRwgBRyXVqiCboGVXihvrvEjm1lC69yS427KqaCTi2kNl9K2xrpGvX77QTRNd37f5sRbENgTxlJpSRC+1XshtXTV3tSPF1LtKCqcqxNTKh+dncq68v73x9vbOxw8v2GqYponzfKGrkjpmJH/DGFIt7EJQO14qu/0eYyCu4rNvMbHf73DBQZMtFiCuC9fLQvCOYRra61r/p6qvcno/8fXbK/vjkY3IZZ7pQwcOnl6e6YaB5TpjjLAEa3NedtY2+uXC+Xzi8fggtEIuzbyoB6+YQq4ateyb/b5WuUFrpSEHTBvn2abC0cdmWsWX4oYLHuJNBVNYY6QfBmLJwob4HmMqSSN7mQ0BQid3eNXlUNvvbr3yjI0BHxr8sepnvy3Hamn5GaA2uM1H623k4FyTeXq809owp9L2MJaYsn7uqtm0abPgagVzvF5m3t/f+f6HT2glLJ9LIAD2rpYyQbuJnACEj5EEUDuQkm+OgkqNCg5yzlJyIRhDNcoq0MXuWodjNA5xDoeqwbUdGO5/+gJM1cLeNOez/m39T281Z9dospBa4BPNO0ar7g03k2lP7w3W0STeIg7U1tF1QbN925bR2ikoKW4cJ1LesMbJOOVFGKhVEwXr2iIdo6xqBkyBJS387S9/5cOnj7w8P7PFRFw3hk5ZCCF4Pn3/fZMXO95O7xhjJbdMeo+C9cJtVfGhUozY3mCcMj+CkzIrxoQPDe5XBTQ0Fb779JFUsjxR28bb2xtvb9/48OGFZVnYYmTqd4pirtqtYgzxeqHv+/tFqE+z3FMxrbXsDkc6L6lviivX+cK8rDjvOR4PGj0ZwzAIi5GLchiUQKcEva4bsO1n9Z069P3xQUo0bpq2lsPRsrQV6qSiyBujd6MZ5Pb7HWkLrElZNH0vVzcuk3Lk0O24zhc+f/7K09MjwzhKWdkry6OgRXmtlZgS//2//Xc+f/ksyGfMHA97Pr584NOHD4TQ0XVK8cy3n8cGvOijI+smY5pv/ghnle41z3r5lm0jbivfffrE4eGJWw9ujFHmQr3Nv8CapmaIivH77sfvW3xnQzlEhYu7tNF3HRRIKerh7npyWsgp0k/7lnhn1LF4R01wupzlFciFt9OZ09s727ZhbQvxMYJT3R6IEIQxKMBht29z2ERFRsIffvyJuKzEXFiXhYfjAeNaN0Uhbxvz+cp1vjINI+O0o5RMjKlF/ymi8bffPvP6+o1xt5Nqo+ogckhJlWOklsJht2M/TffRhjGaAYcQeHp6vo/Kl2Ul14Q3oe0QNI6xnacvA+uysG4rNWrua8rtIVR6WsG0hZoOGWMNfTeQS2GbL/TDxGW+SIrbB2JSdvFNLmnaIRabos3oZIWm2KEJCDCV0If2PDRnKILLUTQqMvb3YJ9assJX0PLdWHBdIG4bxkSyFeiRFPUc7EZcq7QzunwcN9w4VOP4+vUz5/OF777/JA9IsKRYaPBSTNGYpglBqTlKB+VuMMNGB6apdrLaCouYVmSJLDrvyIk26sl3NdFtHl6MPDUhiHEkvtWtpGh5De3/FqDQthFcaUqwQjH5vvjVrsjcOzLxrrKWz03K6ltAmG3qJe/8nW2mkYaKIk0KanvPjsLKhw7XRCvWOcZp0BjM3y7t20iskHPhcjkTXgNPz88yMPYdeYtcT2e5kZ1hGPf3AgcnVEysFVf1LNzc5SC/AgjJEqy9d0JznAldj3dS2znjWdaZ/fFAXDe+vX7Be8thf+Dbt288PT9Rs4qKlAZqRWNu7yUlDx210audEyBS93dmmkbez1lqzGHU91Erp/d3LvOVqe/EXbLyiLlpwNjQCicj4U6F3uuMC82ndQtv8y2ILMWIrQXvOj0v7azVudimB0bvrwKLEn3L3nGNTJBS5LLOWGMkojDynnXe8f76SvAB23t8y2O3GXp/GzPN/PDdJ7rQcTq/g4Gnlw8MjR/1/PA7Lom2uK/O4m+uy64bJAnNkiTadqNbapufRXLOdH1D++bfK9RiDLZUqT2QK9CUSq6Z4HvGaVAlVwx2bG7EVQdKqW1RWmoDp4lYmLMMQubGvGkP3OVy5R+//YNppyXRNs+s68Lx4YHdNFGqmPNSYql1kqImqYoxGiVYa8C0eD9jefn4UXnDpWp+317lmBMpJoZRrHrRWV3jIVXiujCvG8M4cDgeyCgX3HoZz3799Tc+fnyhFhjHkWVZmbeN54cH8epTUqtsxN9njS3bRmMeAfnUtZk2ly8NKFeRCqILXgeZt5h0W36aVr3oqrOApzF+SsH5cI9sHfueaoRDp1E6TaFFWArfkE1pVXZtG1vRTKky4aSUtOTyGl0ZIxnxTVFhTKW2HG4wQsh3YJzBNUmsDzdMNdSasJ1j8CPgwBly3MhJ6V7lfuBXKJV1jcKn5HYcN6Vc3rLMZUXRsTc/h5aLumb08wq8Zp1eUmNvPgA9LzFGGU2LXvCYI94GWsmqkVJ7aJxzwmPXer8QbgUVzZ2sZz7dR4vGtLrLiAeVbcY72zoZr3vZVELvsW5o+x+5op1rB2xLh3HOMfQDfQelauTljKEbRPxcllW7mtISINuJ2XntB2otzMtKSpmw1xI9Jb1Hn777nuA71m2V+a22p8vI5FcLrPOCBeZ1wXsvIUnV3spWfQSlyb7NffmEFtZVz5j3jQ+WM8v5yjgObW+in+90uVCbQshiiIuCtlRUCq3yj99+I6fEH//0J4Lv2mTAtqLFUGuixMo07hj6SYVNzBIcAJ9++J7eB6b9jr6fKCVpJ1IEO82aVzY8Prg+QBL00LQv1RqddxI46Pu5iWNu562DJt6Q+tDUtl8phbfTmy7vYeC6LqzXC19++8puNzGEjpITwzDy/fc/cD6fGYbxTieuNyWosThr6HzHP/2Hf+EPf4wsm2KVbTvLbtEEpgEKjWtIHcBfzmdsu22Ntfc/3FQRQcdpj3VB8+lc2E+Tvk/rWpqSFiWpzahvN0euldBJG3yDBRqLdOV9wLrfLfXWGLpBeOAtRVzXMzU34E1tQ6vunPV8/PBJlXPrTLou8Pz0IBVjLGzLgu9aKlZtNMRUMUEBJtaaxpXxdJ1nXWb2o7Kthz7Qe0nbnDEyqaWIDYIfjtPIbakoA4zFOcO4G7Q83triLATeX9/4y7/9Kx9eXjAYpq7HPD5xPb+zxK05n01LR1NhE7pATplCobMBwk1xIwOVDoxMTImu69k3ZzdWMuJiwHrbzDbiPVknY9a8XNjt9gyhJ2IFVHSe0A/6fkqhuFbRlHK/EDIZqmCOYNpcv4okaSpbjGxNnmymG/kUHIbSOEe3Rd4tsObGcKIpg2qh5frmdmjrUXJGhq9aKjE2hZN1uGxwXjLSklMTGARMLS2yUeO3SgMjopdPWh/tqUotPByOrYOwGKsRlw8ee5sSVahO/h/Wre3+5WnQZdouAfN7ilq9dQW3HqJyv+RzVRd9U69VU9ps2+Bcw0AY8HiSU7RvNwSN2IzHB6nTvLWtKrdNKGLvqjQouIb2vy3gS2oZFc4RBqBW+tBrAeokRy3N7b5uiV///g9ePr5gjWdNq/I+rMe7jmma2JaNeV4IXUPglEJwgXm7UIrGWw+PD8RNE4Jb12m8uud5XXk47jHIHGecxSIYYAW6zrQutPDl7Rvf+Q/sj0cAQj/wxz/8gd9+U2Ru1wc9t8VQsnZL/dDRdT12JzVRqQUausQCxla2JWGrEvVyG58bo73gfhz4+YefgN+9MtbA2A9SRQnZdB8RudYml+aKd5i76VBsvExctBsdB2Sqi1FdnBHiJd4UEtaKFN0MqDULC9I5T9gf+fZ64tvbO/vDEWcd4wRd6NjtBCH17axSOqShu4VedaIue+/Z3ywPSIwki4Ml9B3r+wlbHG6QxNqnWiiLIkpDJ0nj5Xqh73qhao2VWsILfFcK1NqW3Dn+znFCubjWOeIWFUQfQlve6qB2PsiRWi1dkOxya47I3nuWOFOTIGegXUdJuc1VO1Uz1nPcd3qpjeXp6RHvdCGQMilGPHJ/5rbYK0ahIKPT71cQ+logNyl3LstC8B13ZssW5d/IhbFX0Lozht1u3xQxUiEN49hmyW3BWgrGOUpKDGPPp+++xzdlSK6Vse9ZVi25zucTh8OBse9J9aakqVznK2vcWNeFcZoaZkR4kFSSDhHvoVSic9BywvXhlnsV44NXIl6S0XFbIt7MhP2B4D12mthyovOSZbYJANVxR4pYRFottyW0gXlbcUDXD1hjCM6RvFVuRa0t27y20YFK5FpVJHhjtJhuB0tpnZ72FlDR91JbzrbghIkt64KorUvQB+6wVgE4h4cDpbm7Ta04bynOt5jRyhJ1abatCMY4xtBjMSRlOt2NfL+rnHKrBitbLmxxU+FjoVYVTbpMbh6J5j9prbppv0MsiWBltEqlsi2R7DNd3xHo7heSawdozJJuK5Crwf+MqmvnJVsNXhJP40TBdb4dgFbyaNfGOsZZvNWiT51labsoI0Xi9cpunPDOMW+RPnS8v1/4v//bfyNukeN/etQ+oDh6Y+kfDvpsWqqf856uCwJJZsEMLYVUzD3/2jXfSa1yf9fadlw4vK8KSGoKttJ2WbVWtrgAlU/PH+j7vhkGW5dhDceHhxYzoIjiaB2+m7U0dp6P331iCL2Cy4ppu0ztTi2GZV6I28Zh1wjX1hE6y2G3V7HSLg5rA6Vs/PVvv/D0+Mjj06OeoYZIcd61PRP3KALb8CGlVOXr1MputyfFyOvbK74pPffec7lchB8fOoKTdDsVfVeHaU/MmZoSNTjmbeWnH39gXdU5XeeZEDqGUXk6l7iyrLFl59z62HrH9eRSMA01UlJmmEZM0vQlhACl8Pn1lbJu/PTzTxrVj+MOYyA4yVa3mPj82z84THt4eqDvZR7yPiAMzu+Lvfuc1DjwVtP3XIlVhhe1l6qMSimKb2n7hWIa0/0GAUsRgyezKSXOBal6rGsjosTpdKJWUWIfHw5UWxnHHQ7J+mrT+9quw3nH17c3MJWn44Naf+dIVYlixUpJEWPkuN+r6m2Hm3AHUMvKr//4B99/+Mh0PDDe2tRyW8kUKYTCjtNJSo7SYlfBMI0TP/7wI7cQn5ozNTh2g0JYbq72DKzbinee8/nCL7/+IqwyBarh8eEBa+BtmSUF9lIYJTLBO9Imt7xp1uwtpwaFU+dh28z64fGBlGMzeqmOCC5QjMUXNDppUmSlx/nWBdyqZVU167yw3+91+DcXsWuvva1WS+8qh+tdcEBpn1kVrdN7qNqBiNhqf0cnCOBELlHMm+YWNkUMG+daZ8kttLFQEtLE24azsB3OinSrf19VYm0k3GmQTryA8pRpzCfjdFEYoZV9EFZBZGSNMqAHU+8AwNKECwaN3kKnVMBafo9SLcbijWFbF7Zt4TgcG0tL3ZkPXuOa2hz5DbzXdTJOuSATXtd1bYek7OjgFQBlvWvZ5/JAGOfE3rH6ZzJgjA6GXAoueGJKAkyGRClVqIqYsBQ+fvrE+/nCui6EoZcirQpaGJqy63pdSEk8K9cIwSFo32eNEbrHd/hB+yRvdPiv68qwn/SscZMZ6L9S0aI1JYWCTdNE9do1lBWWdWV33GHwWO9wsSFIsoq74/GhfdeFqR9akaHCNhWRBmqBYmG/n3j/snK9Xtg/PBKsZ1uXpj6Sd+nhcMQAKSX+r//r/+Cf//lfeHh6vI8HTQvIKqUItEjrxlKWizx0dEOPd4ok3ixNJq9LKJfCl89feP7wwmG3Ux5Pgy+Czttv374Blb7v2E07Ykz0w9CgiRCC1IKu8+zdgffTCVtVbFdqi0G2d/m5MYZt3Xh7f+fD7Uxr05/L+wln4LotvJ/PfHh+xlsnFyZtxlmcIGNzTuRvb3z82NG5oKo/bnJUW8O2RZnZmnpAWALRK4d+0q4hF6rXzFYyMy/sdyqYqHlZZ8SgmWNmNw6E4sFb5mVWWMm0J5fC6fTK9XwhJY03/D//Bx4e5Om4LeMqBbza55Qyx/1erV9zNJciE5wx8mWkhjsuB0kaU0qs80Kxld2gOf31OnOaZ8bDDoO9e0jWRtqkgu0CvpljXNcpvjVGaN6DNca7HpyawIlKudurKpuXmS/fvvD09MyWFr0I007wr6BbHyBui9zPpmOLWioqcU5jkiXnBuMqVGebKUquTYxh6Huozf1qhEevRqHu2Vp670ltzhOGoTm+1G0VUyCr0hv68Y5+d0Ji4p3c9rn+Ph6koRpM/T2kygBbjFRTWba17YmaaNBo3pyK8A/rpoRD7y29h+QVx2rQ6Esz3CadBkKv0VZAu4VSa3PzahRqreJtrdMuIseCzTdchH6fTCHgKVviuiyMBfogI5Lkqq5dDFqeZ9qfT5Emft3o+j1QFdjivdhmbeQ0jTsK0PdKYqu18ZSCSMsYiROcNzjfK6Fs6NvY0AjKZmg7EIUZuTa2M8046rsB75WMqCq3XXzVgpVyaegGSpRnyTlBBeMayTayOxz5T//pKFm1qYTqKM5Qk8F1VsmEDYCZt8Q8rw0x3gkjnprkIxXmolwVqZwS63ri/XTisT5gdntM1my+OO2naswarRX1lLkW/vVf/xVT4acff+R8vTJvCx8/fFCOQmnkZy/sDRambt8EGDdzrgooX7STNOid8b7j0/ffq0OpOodK0WdfMvz6t7+z/+cdLqiT/w//4Z/56cef7t+ZXqva9pga8Rkr6bg18H5Z1bEagzWO67xgrWEcRoopbJs6zJcPLxzGHVuKrTvV6HCLTQ7czJF919N5xT2XUiAVrLeKVTCG3lqwmcfDUeqlJLItFUwDO8okLsRKnFf+9f1feX75wMdnSaBd56XubOuAbVvxqVRcUY5xbRJK1yz4uWimXYMhbYXT6yt9m/EZY9Sem+Y4TpnUDkDvlEmdjcGm1Mw7QjS7G/2goTBS3CilMoQgF6nVIXA5nzidL/z0k1Q88/lKbIC74KXjvi2DHAbTML7e25vw6j4PVPZtJha5GYOzDbQlf8L57Z3BD+ymwNvlhAUe9opI/fmHHxh3I9sW9We3isV5wzKrY3JFD8q2CkBoQsOuVxluctKcfd1WQg1SHeUkTbUzfP36SkURmtMw8eHlA9N+0sK4SkhQqTw/PbKlzHK5tGAkx2oMrmqBXUqiG3dt+Spl041Cq6pZM3NnDAGNmKhGv4NRR2OrDFry8jZCMA6HJ5UNcmUYJ3IWoth0WlLfnomSN6xV9oBAyJBKYlkWjFO17LuWPe4CxlWc9e1B1kHsbKDWIuVOrwGRIHxSq+EcHgtZ+BS1+i10xpr7oUmuZFRZVwPZybudUtG8uAvihRWjpW9B0lkj7MrQDTjTwl+8pwxdy8LIrduQCzu3TivFKK1/U37d4I0pt7/PO3zv5U3wzVxo9L3fksp6ekxPk6qOisstmb5XSljNEZzHd6Fdvlk7gi2TozqdhcqHpwcMlpgrrlNa5K1Dtl5dgQuOrnZCmSTY7yYp55z8C8M0Mp9nhqcej1zythrO1zPbsuCcZ5p2xLhxvc463HxgmCZyyuz27bCukoeqQ2ga/Ao1tsx4I5OaKRVTFR9rvFDkn7/9g1//9gvjbsc//VPPxw8vbFuk5EroLM8vzxq9tJF3igk7tjCmUpijSKimeSJK0W6BalpGSsB1HXlLLNuMteBdT9cPgFEGg90RQs+//PO/kNDFap29d70Gg+18y/PRc1jQJYrViN6agi1VqYw5c9jv6CZPzJXjTjL8krPeRSzOON7nd4ah57g/tPEtUkGpLVTRkkTfzhS6PmCzJVudh7b5rNYYmaZRExwqFAW8rXFtuxZ1stu2KQPDBmLMDF2P9R4fnETpcdvQfSPNh5MEQ5vyahqFsRC8cMGlFFUh3rMsV+LWUAfVMDZPhKkKj7lerryf3nkphYfHJ1UwrX3Sf7XDtOQmfqn0Q4/zglnFTYtZ7zuOuz3Tbs84Dk191ZRPtVCMDHcKskmkKt3/Y1AriYXz+QpDQxPUqvyAbeP17ZVpHKQSCk2yVrNCzFOm66R8yLkQBunSx2lqP/3v0t5glcMckw6163LFGae/nxv219z10c449tNO/CnvqXXjw4v8H1+/fWPstYAz1pBrJDhL7jvB1pxn6PQgTPuJiZ12P+1y1KhhEJelxaNWtKAyrnlcEA45VQUX6TuPmFyotrJtEVyWXI/altftBZznpolXYE2pwNYOXJqsz5SmhhKoTyFCjlwaubN1gPWuejLNHS+TXs7qeGwx1GoJ1oBrHsRa7qqi2l58o/0kOZVGiNWLlYvkgNn+7qA2GEqs0rPjqUTJsVsBYnwQN6qNn8Z+ai/qTVtlmrtcrt4bIcC0hXnfaMcpZ6ahBxe0M+o0PvXGyrBZikazWIqzHA8D424n6W5DdN8AfIqb7OiHvo2kYOh6+l5KtPf3V75+/kzJWe9I1rirAtcYoVambiI2afg8L7y/nvDOcHx4UldaMxXb4JeReV2UP5Aza974x6+fuV7OjLsd33//Ha7rCKUIa2GFktHzZ5SJbStxeyN0fWNQoY7IKcL1JmoAi+vAJsg54n1gmVdC8HTO3uf/tVZC1zXiiHLmqQW7GxWQFpPS2LxGfPUW6VphXRYlw3Ud+/2kgKBqcd6wLjOlVol1MHz//fd67g0NP5MwppBSVuCRZjd33pHeFb1G3nU4E/F+YEuivXrrKWmTGrFqNHm5nPjw4ZMa79ty35omqjjoM3JOgU4ls5t+z0g0Rl20D17qvXVTRHSSIjV0gffLmV9/+TsfP33HbhwpuXDZFkUn58y02/GwF6pEZ73ozM+PT02gYfC2tcFd16MUSENnHLFmuqoXsZbMdZlJObOsM4ehZ42rogmN2sp7Epmz3KEKrYrfH/ZMR2mnv3z9rKSpcdRyfBxIufy+62iyWGc9rtNheng8MqwD/dBz2O3u80AF4vze1mO0BM21ELdE3wWKFfgLo9Agf8Mcg0yDFbxttvdlZRh6QvCkWtiWjd1hBzHp8rLmPnIyRodmKWqTv375yul84uXTd6zrSt8F/V4xU1yrNFMSy6kt325I5v1+rxezSUxzhde3V16/fYXjA6fTmeenJxl0jNrGm4P4dtnmnLUz2KTfd9ZpSWugxkr1bSGfsi6clDlf5zYrHQhNV11LJhhLdZU1KdQm3MZH2ihCVVp05wNLTFKMOS8GTrCNrSPZbWo46y5YVAQ7jHeErKovWkmK71LQKhWXt0ptK9UKeW1psuqKS0Yky9uuJP+eOSHgtg7PG7dHazFD8VUy7M5C1fw/lYJHSYBGZnR5MbDYlukeU9uVmKaMMcLZq0s1lKIDKHQdDksI2ofgDHXLSnXzvbIurDhDxRp81zW1lRbOAu95ht1IF9SBxBipXmyzwfX0w0DnPZ2Ty5wiPlZKiRgzT4+PeO/5+vpFleDopM6BxmWL8hPVgjdB3ofribGfKEXsLIejoD1F6DtqFn7eGMuXr1/59vpN/hxr7s9H1wlJ4Z1ljpXT6Z0udByOWswfDw/gLNYFdRc3P8At5CZ4XfJIfl+aTPbT8zMueKa+ows92xaJKdKbAWcD6nM1V3DGYL3l7e2NdV0Z+57944OUO+Y2i195fXsjWKH8x7GT4sfocnetCKq1cDgcm/CmQt+zbguduynJHLEUqaPQn92FwLKpcBqn4S5FtVUmwpskvOsHShaSY7fTbkpXpM6+0ozJGEuwkpKfTyedpeOod6PeDMvCu2BFyA7VKeCrFHKWO77vB95eX1kWpXY6p7yPB/vAcXeQVLzFPitptDbXfeV0veBjznS28earXuwuBEoW40QRhpXgHL9+/abc10cjjnlrX/qxl3oiJaoxzbxhWt4t+NDRhY7z5cTbt3f2hwP7w0FSsqSFoHFOrtzbBgtJP60xkucOEza4hgqudy0/xuA630gNwohXaxl74Z8727HmjW9fX/n5xx+ZdlrEUwod4ELAW8tuGHFOrtPz+czhuCduG++v6Y5FsE1XTFM3lQLX84kwDFgMp9OJ3fGBEDqNBqhM+12rbFIL9LjlgP8e8+mCU5VrDGvaKFnE02m3Z5xGfvv1V659YBgEY3Te443h7fWtVelWih7rqBprN5e2Lt1aGm6heSBCCLzO7/zlL3+hGwI//fCT5p4AxZFyJhfH0DlSkDIjt7ln4nZQGvzQUbaGmqeybokYo+SH7uY/kEw2bYlYkxLjisZjMSaldd08Bu1gMG15YVzr9jL3rAZ3w4ZYR2esAqFo71QRWqH62+hKgUdO8zpcBhskPLiNiHzbmYEuJ4zDV0uq6jDF4tdnq32czHO2SMVXmmqLWnE+tN1IUDjMtrHFxG4c6YaAdaFp0YV18d4zzwvWD3jaCwAAUdJJREFUwW6/b6ZMeyd82qZiK0UU227QEj03ObBGto68JX755Ve+fP7C9z/8wE8//sQwTZJDusCW4+9elSYQkE9BwoZpd+Rxt9duso0kz6crMUXGUZkbxVacs4zTSD8MVCr74/H+OccYdVl3Ciza7w86+KzUZtb7+7MICJfR5unv5zMfn18ojakWfGCbZ0x27B+OjLudlFhxxTl5uiTVqNjb79IuW9Oo06Vktip5uME0kqxj2k14625JqlBk6Ot8x/HhAdCyP6Wm+quV6hzeGCw9WNvy3CskXfY364D1DrOJGZa6TNcFapKx8nZZTNOoBM+iZ2A8jOry28+9Rv28XRdad+05XU4sceXD8wvWS4G0xYTHYUxhTZuyqduzYrWAI2eBRY8PR759eyXlyDAMTNZqZFcKqeY7wry20a1vK4dlWXj99oo3pWhQW9XygqEbB3zymp2n2DoNfYj7pwfdOk2hZEohhAHvAuu6kKKqT9v8DYZKjBu3mNQ//elPTTrmpZqpv7dr0ZTm2FZlVaqInSxaPndUqtUvoyQ5I8hbTqSqF/7t9E4InunpAzGtYKBzoRngahsV3GbAjs45fN8rp7cdvHoZDWuKnF/P7FprhjX4YDG1haNvkW9v7/jLmf3jgT/+4Y8UaygpkVJm2yJfvn7BVHh+fla1l+Nd5mbbw0iV/NEDnfOYzvMYOsWLxkg/TryfLuynPWvcOM+iuhpjGMaJuG1ctyvZ3tAMGucYKxyJDzo1b5RfY9qFnhPbtTBfL+ynqTHmY9PgZ833kyPWqAfWWsLNAKlznDAIUTKfr8S43S/3elt2V9VI1WjJfL3ODKFjGHuCt5L+FmEXCrld/NxHl9ZYis3QOjYXLBRLihGMDnuhGyQxdc5j2lgs4FhTq5yNJRNx1RCzxkVdcBRMw3uogvVO9GBnBTe8mT2tdfq7/yegX2oz6ePuoNhSa6lRLKKYIilnSirN9TtCA+JZY1QwVIln53lm6DvBJ41klaZUjDeYZHDOctsd9l4V8KVIHlor/PLrZ/7tf/wby7LydjoxjCO7nXw/xknC6YwH47AxkmJk2xKmN/T9yH6yeO9aNamvLMbYjHg3zAqQ9RmZanh4fORhfyDFjHXN5+L1zuYG3ZMEWJ3xffTYeqdajH5/53BGu6iUNs6nM7v9ntAHcixcr1dC6NrPHJmmAecs87bRD7JhltI6EKtLYphGXCdvVy2mjVILoZeEfbcTELPW1HZ5yq0Yhr6NjnWRSZV4E2TQCtkmxjBgOycaccPj9F2Ps4brPPP2/srD8ZnDbic+2RYxVELoqMYw9IMydHImpo2+H6UAc64VkBrB336Wl4dHjbeLdkt5W8EqkOz8+t6UeDI5+9C1C0NKtsvlyvn0zg8//KDOtKr4mPqRmovwO65hbkqmNqHP++nUnPrO3dsig4xIve8ozvPt2ze5Gntx0D9+/52cvCmpgq9NkVKE7u2HEWtv6IYmC6xNFVIEWttyYp1nHh6fdMoYmqGsmbNaglXfy9X5/vYNg+Hx6QMYWOar5oHekEsiXSLn04Wnl0e871m3mePDJ3KO5FrYDzsqlT/8+LPUK2vCBkvXqb1dtwjOtrlrj/GWp+ODfAyXC/3YUzsLUZdTjumOKTYGHg4HrsvCl1+/ME4DKVeuacVUUVIv1wun81nZC63cybbcZXXWAk5jFCmVFGjzdnoXhAt4OB54e3vDO8fbeeVyOjNOE8M4NgT4bfylz3pNG947sfGtI4SuKXtqQ4XA4bDn4eFB7WTQUjWlzLpFkrfEuDJNe4qtxCW25WbGBFXJ67YpYL6o8k9kbJAc1rQdR2luducNg1VqYIoJ45scz1hCO+S3bSH41gbR1E7tkKZV36adYNaAD0EejuwoKVJcEYjMy4fBje+ERonBA1USaH13DTuQS0sibMqwasA5+uAVH4mhOoszks6mZszLxdE57Ueql4fAe4fr29jMWZY1sT/uZHpqL3+1RpGgXdB7F4KYaV1PCO4+1fPBczkpInPaTVIIrglnZfTrnCVXmp5engvQmPVyPpG2yLQfmbqdTFweOXRNkGmVjS1vd95Xqq0lqppLPzwcRGaut9c5N7u0dg2Px0dFh7bx5e0yEQ7YtkM4q1gopV18uihq1bhU+7DA8/MHrLNYW+mnXuOQfuCaLpyvM48PPUM/YcpVl1UtzJcLfQhKY6T5WazEK0M/UMuMD1L21KTkyVJpSYg3NH7zONRCjMqqGEdFK998Jrogm0gmpXskacUoxdNYLm/vxJhwDw+UVPn822f+8dtnQvgLf/6nf+LPf/qTitSm9tMUWIl8xohv5lrxYa0lx0gqlc6PFGMYpx3W0OjZDUVTJAjZTQceDkfmddVlaY34a8FjrOO3L19IKfHd9z+Qa+W//49/xVnHf/inf1K3sC2Srfsm7olycKc18vnzZ477A7cVI3CL+GvdhAu8vHyg1ETKmbhFrIUtbZpXVWmNSZJbmlK5c4javKC0xehtUxhLhFyYt41dSi1cvf1zsUhWSSVvwtzWUhoWo9LNM0PJnE4nXOjojCHFzNevX1iXlcPxgJtumQKW6zZLh26NrO9GbJ21ZnrjGkNHTZSplX/841c+vXzg6emJab9na/mzQyeVg+/ULl8vyrJ2ncxs3lr2w6DwDxdY0yKDWeiIpbBdV7Z5IW7SMbvqtBy3rUqxTrd5wwY75wTFqzB0gcFLfTGMcrp7Hzg8HOl1w1OK8oVDCCKYOkeK4vFIxVKwwYGxbNcLYRyhKpLS/JPj25evPOwFFFyTkNApZlKUCmzbCvO88OH5ue0XMtsapTBrbaqx5s7Euj1NcduouWKCpIzF3qhJ7i6VFdix3HMnbiw7CRIk56RIWyUh/e3EkkQySxxG9bYtwCFtmW5o+wNj74Yz2xzRtRrWEnXYGoftbOM0iTbqrKO7xZ1mjZl0AMG2LaQ1KYCqKtjIWtNw680eZhXo03UdadsIITAM4z0n5ZY5XdD83PvAONCEAYWMCiHfdfzj11/ph4FhGNhNA6ZJZmPctNeyjmXZ+PjxIw+HB5Z54e39jQx8+fob1n/Hbj/dpb3UQHCwVSHCY4zCobQMkLtvBNd2dTdvRGlKHslfv/vheyGmjcV2Rh6VLJRFvQUptcvXe8cWtUzGGhUCbe5urJY8N/GBMSI41yr8tXGezrUxHxU/jHdUST8oQ/u8nint76IttY2V+MHdlsrWqhg1ijag+YRyk7vaYtlylH9j7JTOmAomxSaZ1w4gl9p8X4YQXDOc9vh+gLpwC8ryXlke58uFbV11+DvtbqtrqH5rGYL2m/SCY1rU5dL2EBkgRRk+iyKJg+94PBw1DWjjJD/0jM7dF803RWeulcNR+R6Pxwf+8fkLf//73/n5Z8l4axNLxJJw0dANGqd5r+f5p59/Vkqm3kpzG8tKeuhv0lGZgpblxGg1jd62ja4TwTDnzNYS53yQVLHminEduCKXoDV3XbW1Dtt5Pn2YWq1oidvKti6/zzhvt+GmA+v5+MSyRrXfxvDh4ydKyaRYWLetLXEVgAPw3U8/6INK6m5iTtLAI7Kks7qYUi067IPX+KEdMs56/d3W8PT4pKVqoTmDRQQ16M9PObFmuZ2fX15Y54V41kWYq/Y43//wPT/7n5sZjOa6TS21rDY6KQRnNYuNG+fXV43JQsC0hDrnHG/v70zTSJc7/ezWsqXC2GnRWUshVxl1bjkAtuolzC6TUiTYvS7eHHk4PnA87FrGR6bmyjQM2MmS6w68pQ+ex8OeLnQEn9myZxylgpqvV3ZNnleK3NDG6MDxfY/3+dYqYnPzKjR5NU1dlqyFnOWMNTSEhmmLauVTxJT0PTTcscVRnELujXOKm6xtT+J+z1THGkY/Cl1eEjmZJtXVqDEEqeuWy1W4ihCaPlyIDqk99PRIsdVBToROUbej8VSrpXK1le26yvzpNdv/4ccfG+Qy0nUDfdex5dyos5WSioyf1kpjXyolbmxrZL4KcHc8HkgpMV9mDocHrDPM66y5unWEYAkhcNjtcc6zbgtUw7/95d9xDaUTnKN3gZwjayqk1N4bHyhGKptSKr7zBBeozrSFf9WYitiECMrTtkYpiNWWVtSsYvxYS6mJdVm5LgvH3Z79ca8JnWk4Fx9UIKB9ozOOHEU09V6FTqmZUjR6sdP+/t1Sqqo66wAl5H35+pnr+cqf//xP3DLKKdCNgvzdxoV9K6IASlYhq0GG/AgpboKQZss6z/zjl1/xQT6Kvu/I1VBLJBtH3oS8uF4v9EOm7wLj8UjOUkP+9Mc/cN0WQvB8//PPbDlhnSMbeXi2Kn/OTaShd0aWgq4LUgv2VeOwnJljbNy3jeQLL4+PDP0gU16t2jfEyPPTEzYIjhmzEEfTMIIVhubxcOB/+1//Nx6ORxVcWZ9NsOrKT6d3lnnhxx9+wqCzYFlmGW1vc1bT7PrKDtZHWEoWm8Q7zpcTcWv27da29/2gQJ5q2zJBLZMAWNJud6EjxcQ4dg0caLGmEtPG6f2dlArjbrhrqm9OVO89xYm+aY29p7yJgdPCcTo5xWU6o+nOtUPJVUucVDOsia+vb1hjeXoQAbO2ZLViBLfzXUcuiXnWoUQIBB9YLjNb3Dg8PtC5gHWGdd3IMUJDWN9UGt5avp1O5JgZ2t5AHZZtbaZ2JClpLLdFGfp2fiBtG7/++gvzvPDwcERZw65BywrztjIQWJcG5+oU2J5K0WgpwHWRNyIEtdWS0+nAnXZa/nsL82WDuooQ6j3BVx6fajtkLSlv2kOEjgcfZMQ2hlAi1Xod1tYo46FUUlyJUXGYwQW8geLlBK2l5Uw082Ep7VKxAo8VgxbOhhZXqWfPeL1IaYnUahh618Yj4KrHWVWFNwOSOpMkDWwVvdV433AzDbRmpD6yxt7HrLuDUCvFGBm/EJLDG0s1mrM7d8t80M/mncOGgCvl7ooeu4FtXvD+9z/bOcsyZ4bRyblcajNuZmFtWjJf53S5bDkSt4VUC7YBLTsf+TrPGOc47mTALA2gaZyjYJnXlc6rCAo+8MNPP5JTIqeibgvYUuF8PilS1zqeXp5FAbBGI44lkbzUZiH06gpLucueSzHyiRhFgGZ+p4V2Tt1qcKF1ODO//uNXeUOap2e5KKDrBuozreu/LBfmeWa33zEMk/7MmqEKTVKaJPmmaLDGCLiYNvqu519/+x98/8P37A4jqZl0q9EZY4qyTJxVV72tm/Knx/EuwLgRBayXC31ZFj6/v5K2xMPjc8PGWKb9gXVZySkp5KyM2qk4T3B61pOB3WHHf/nf/wvVGKbQkWkjeVXk2ArLfOG9GWJ9U3Z1wdOFAdeW1rVaet+xbZGaMvvDnqG9i13f0w2DfBcp0w8jvuE2bl0rRZ1GsJZl3eiGnpcPLyqIc2aJG6fzO9M4sdvtsPNCjCu1FrZt43K5KDcFaltY65J2bdx0Y+44U7VraC/nNA6E0N0PLhDSoyBX9b2Ky1rS+DaCCqHTD10z1kGpVpm0zjMECNbdw7nJBd/3mJqZrxfeX8/sDw/sj3usKdRiW7arY7/f3fkoPtvfQzuskemkQo7bHQVhmmS2lkx/fCCXwjRNxJZed4Ou1Vr59u0buyQOSt91OKu4w2XemqMaapPr1SaTtN7z5ctXUiocHvas6yojC9wPjlJvuviW6ZAK17zy7dtXvn39CqDfq+pimIYB6xzPj49akIW+VUTNjNRMgZ2x+Ar9OOKs432Lbd5exd/Z7RsDJ3O5nKmp4q3HBB18UnFpYOQyxC2xRi3/a0OIdJ1gaL2XHNMZy1ojMWW8l/JEl0DBWxnEcPVOl7TGYkrSd28k+6ylknNTupWE68a7Zpy2jA7BNdmkvqOCciRMkg+hH7TDyrGALcQigmiZZz62yM5i2wXhAlTlNDsjRd111Q5lfHi6q5ayEdhw7DoqUqAM/SjGTgOkLdsmr4O19L0CdRRta5mXha7rOByP9H2Ps47QqdMRy0ih9DFCDJ5p6FoIUKArlV3fcZmvzOvCdb4CRiPbUrDBY7GkmvRi50w2HcZ4sZ+spTQIJsY0oxaMu5EQO/kHciFtQlXYwZC2jdRMkgIQWv1nq20TAttYXpZUNE7M1mGqFZp93QjdRLWKufXdG9sWGacRg2FovCJrhTmpNZKSPo8QvEaRVgbZXGhyehV+SlhsEw5j2OaFLUd2w8SHDx90cSUFndlaNbRvP3870jBFiYtbKWzXhXE/Ya1lGgfK0GMRMsPtD/z8w48sy4ZrzDo/eElsnSEEyZDDvnXF6O/1IXC+XLDeUluWRwlGy+DaFuKl3jHhy+nUUupsG/hr//Dy9HxXe6aSCUF5EkM/MnadIJbl9yC4p8fHhvuWUtQZ4XbMYH6XtlJZrjOH/QFr4evpnbfXd2JcieuMqZndbrxnbthq7imLDS5sGtGwdXRGpq/ceODOS4lx2B3u6ocaMzFHnHfk6n5fQts2QoobQz9qbq4VJFvSbiDnQtd39OPYoHCFbhCQKl4v5Jx56IUiGIaJ7rsdfRfkoLJGOAM6vPMMfXc3guUqOqp3WgAqGczhbCCMPdNompZbF9K6rs39uL8nM21RWbpbk416Yxi7nuwK67qxxZW//u0XtnXhTz//CWe0J9hyIq/qDqZparhgwzxfscZIjdQ6s1wbH8kIs4ATv+Xb16/QFlX7aSKEjrfLBYrgYC5YctzuHYu+fDCh4b2tZZpG7nStthMKvXLCTZEnxIfAbtqrxe9UOada7rkFpmpkuBF5P594Pj7S7UZVncYwBKjOtIICSJLshaDL9DovxG0ljDtCuOlZtHjznadk7RAwopZWahuLaNyTUiIVYV5CF3g4PkrJlOWNcEb7Aosl2o1+7Aljx/lylrKugima/adNXUg/dPik/ASdm66Z6HSoOJPwgzTw611UKcJnN3TaZ2MYJ0k6sXK0Pgzte23guGGa7svRQ98ROo+RSYGaKzZokbslVYeuSaFj0pK39wNPD88s6ywTp++pNeJ9p92GccqbqLAV7YacMWTr1OHVRIqFL99emfqRofMUJ/RLP4w46ynWsJtUhVbTDv9iCEE59rFRELw3gMNaAedKXFlKwfsOFwKpATXfL+8cd0cZxJaFrh9IJfHy9NKUjUJin95PMr71A5fLCWsdwzAQuoHgJa6oWbsErOoLECpD54tc+ljHWjLkjOk7/vDjzzp0V6GCcEoKlBmxYjFkY3DBSZ2XFeh0G016K2WcsfLOrLnw+PgsSqvTfqszmpT0veb2oijoMjLGYFFI0OA75tNF77b3+CCRj2n/WlMmNLXobr8XO2ub73vbm8gkZXHggvP4zrG2c4kGCzUI3y4kUTuv2+i63YmklMgxSlrdPGyXZWEYOmJMnM/veO9YN7hGRawaZ2T+a6TsbY342ma8N+mXMzJclSouTPABUw3zLFKhD+5u5wbownBfbDmcFsSNcX9LTXNtoWJam22QOxlg2zacbTsRC/tpT6ayLDOh6xj6oY1pKvO2NsQ49MHLuVszrs24b8C3GBOX60UmtdTGIhiy0ZyztoOmWmnSt5LpcVQjF3mxht04Mg3PojpWzb+/fPmGdXK4Wmv4/O0LIXR8/PiRvETe3t+VbDdN9N3A6fzOscWwmlKpRlVQsJ7X0zfNqXtJSEvODMPAMIx8+PACTar6sD+yxU3gQIyMXDXpMXAWU7Twz6WQEHTOtu9y93DQSM45qFF5E81E9vT4KExCaelvqem8a2WLmVJVgUy7PYeHB0opLFX0VzuOcrVTOJ0vLMuVl+cXaRCxTOPIEPqWACfkuLHKb3bOCufiPZnK5Xzm/f2MA/b7EdPovn2nPUPXOERxEx+oFtPiRtWq97bH9U6YERyVlVq9goBw0Bv6rqNzPaYTYfZ0PXPoB1xTW9ku8DQNv7trrcVTKY3B3/sO6z2ZJMZ+Azl643Be3KO6VSiF/X4vAOFNPouheC3EY1ReuXFNrJATeGWBzNeFXT/hgufx+YmSj7y9vXPLe7nJnUvNpOKgGNZ1VSRqteS8iUbsHMZ4Rt9TDbzPM701QrQvC74fiXFmvhZcJyTH2A/kkomb9pDS2TeYodPuItWFeW7Z2Fm/t7WZWDPzZaH3HeM48vnrN14eH0UbtbaZ2Byv377y9n7ieNjR9wP7nVSHoevIsWC8a2ayTHUoq6MlGK7rjDWW/eGgMXABXyFWS46Zab+n63vp/nPGFdP2PvouC7DNV3lQQmikB+Gzq9U50GRTFITYGYaOuK3kXDlMPcY1/0PJ9N5TmuDmNs5NKbNzHbvdRDxluiCsu3LWM+MgmnPdJEG2zjH1A4yGdQ2S+nb9vcBMTW13Y+KFoDGeKkAD1lLjpjHdNCnbonlGghVyXXy11rnmih0CNQpqejjsyemF17dXFTSDCvY4r3ROXfn1ciXlhFfQTmGJm1zOSHXi2xzOWWnsb7F8N0hebQeyDpWVYRg1emnzPW8dqSYxWYwInMZavNGicl1naDpq29yjAQvB40tiq7e2tunOc5aLund8/frKfr9ntCNY00KPDFi5P6PJLTdh0OFqfKOSttmjazfytjGvC3Geefn0seELKuu2qV0L6kaME+U250jwPZ+++0QphS9fvnI8HonbxrpsfPntM1/e39ntdhyPltf397uRqOaFZVsYx4lcKq/v7/zx58c7zuTlwwcxr5yDkjldrlpYhyASZhsbWFvvn3/wjpQKW2yGmFIxQQ98Kpo7p9JS4rJ+f1NVfeSq2NGhyjBEMHTOtZnrQoqRSuXl+Zlu6JnnmeU6048d+/FAqqJ0/t//9f/DnCKfPn0nA5+1lCiGkuIPtRgsuagbtMJxb2skNYTI4+OBXb+n1HSPfN3t9wL9NSns+7zgvWM8HBTxmGx7CYTVrsYw7Sccu2YadO0y9XShI+cqtEoqDKGTQStFghEiX+quhK2G3TRS226s60Lz8ThctTSl8T3EXpJchx1s05/rH9hioiR1yN5KCZZKhlg5DEdyn3jPiZzr3bey5I2h+RWcdxyPBznnbzHCtVJiy/YIDl8CSkqSC7+0+bPzjt3DgbhGluuV3A/0fWjy1Mr1fOZSCx9eXuQVyTd6cWBZF97e3piGkUxlmnb4vsdFjYRMGx3WllfQ9yMfP36Q/t7AeFvqDxMxJtoqjt00NdVfAGtacZluEhslOTrJl/19oWsgqaspSCBSym0sWcg5UpIh9IX97jbH1zjZGEO2lX70d0VlSonH44Eu9CJXG+FKTu8nrtcLwzgwTTudD83fk9KKc0dKqfz1b38lb4k//8t/0Ggqxvs/G7dI7XochsM0EawTCLUU3t5ngnX0w0Dx7j7uvs4z47RjGgb6XoFQoAtOewWN/Ev9Pe+9Iil+aLs80VsLrqhbDy5oV1Uqwfl7mJG1Bm8V3xBzZOwGfvj+B7quw1nDfn+QudXr+ylUpv2eHBO+GjidTvz7X/5CqfC4P/DHP/8JYx3kKN6OU9tPrZrTItmgtU01tCqHONwXe3Jim2xkkiqFUsQxMW1+Z6xv/grN6FyR9Isinn83jiIhLhtm7Mml0vW9FkVYti1BnhnHUSYnoOn2iOuqlLMqJYa1jnme5SDdTwQbWhxlQwBYS4wb52Vm6jopl9ImtYYt1G1lciNPLx80t02q5D+8fABnSZuS6XwXCMG2lk9jhJozaVupVaMsjMUGz8dPH/XFO12czuhCwiiTexoGxYbmQinbnXOUMPiuZUM7Bzljvbgwt6V/SpGaM/9z/nbXB6a2MCwpcjlfFXTvrSqrIsVJzkXQxSqyZQ0C7fVDz/64b9p+RyiWlCKn84lP333H0IvJY61hbcoJ12jBMSVhq4Gu7UjWy0zcVp4eHhv+OpNWJygatOr55t8xuEb3nTot87PT752LF3LaWp4fHshUhq5viqbC6e2ED460tn2Ig2k34pzjcjrT992dABxahnbfZMK2QSNrzRgnJYxt1alvKPyb6sYZgQlNm9tuW+SyXCm1SjDgAtO4Y5mvAPTjQL+tAkc6Szdq5JKbE7rWCnfQZtWuxjlln5wvTPsdtRph0fuAC45tnvFNLOGdZyMy7vYcpuk+vpA3QFJxGf89uY3XSspcz2f+z//f/8Ef//SnFjqkDok2R98PA7VaRXYiodG0G6FOulgfmpvZgAsWazQKHXcT/TBSSVKDUbHWk1IkblHerKJFeWnycGN00IXdgVxFBL7OFwoymW7XFWMND804ELMmAXqeKjllXTjOczwcqFkRy2tZ8cOEwfDt/Y1ffvl7w3r3/PM//5OUXY23JSCf6L5vr9+4BTsZKz5XvqXjWdNkvQ5vrM64WulCx/PLY3uOLY7CNAyauGAIXhGto3dc55ktb4zdCF6O7GKaNLmIjtx1Ha7K1+WcYz9OrRCWYqtW0Q1qgM4HHJl12zjPF43UNbVj3mY634mz5y1tvYy3vnU1HQ/7AzFF/Lat/PLLryzbend7WiPY1eV0IvSBcTroDisRHzpyzYxNt6/Q8sh1Wfjw8kGLSDQTTG3emtLGfDlj7Ew/yIk5DpNufqsDoVZaNGigltxUE4aUMl1uM/imd989HMktZpDNMfmR1DC7vbH3X3LoBoL1rHHj67dvyr4eB4prS9hxYDATIYQWmfrO+PzCYX/QwrdWcpQiY5kXpgb0u6lpQFCteZ4xDvb7Pbv9Aeccy7zSeU8NgbxciVlyYJxhN+rvvF5n1rhx3O11kVoFxNhq2WrG4chWL6l0z6oQrPU4r5GG7To6VE34YtuLkgVZQx6DWDJjGNrPrsxs51zba9jblAhvPN5vlKTZfzf0TXZpmS9yEJsKNlfw0rz/4U9/5vHpiS2LS2UxLN/eGHcDwQfiumo23MZgru2+xrHn6fGoTIOSZPQqlcF2YOFyucqtHBS4czNvWevamEZ67s5IXn2rmNZ5wbVxZ74uGKMM4NobpaK16FphqMS+ciHo8wudqvYqk5ouH2n/TdusFGqja2ZMKYzjjq1EUky4oWU4W8/x+ECMSh8zyBtjMYRuICZhMvb7g7qp4OhcR6ZikiznN8XNVmVIvF7P7HfHdkCpOg8htBAnLaZLloseq649DB0hB8CSsxRxUHl4eGrjC7TcbSj66tT17h+O2M5xOB4pucgj5awk5zhMmy4Yp0uA5j3YlkUwyqbo885zY5zN1wv9MBBc3zaUEqtkk5SRvWVV084Rc8WYpIiCktuIyet9ixvOB7p+YJkFHdWtCiVuXE4nHp+fm1Nfv5cHZVz7Qt0Sy3lhCDIvWmPoTMBN6sTW65Xj8VHxtN4rAM1YrHN8+PARWjEn340h0faLRbuTnCPGQLDiYmFaprbRRSbfjUK9zG0yYPQ8eufJ20oNWu7fdjE3YGfJGnsXW3l7e2e33zWWlG/UAXUaJRftAq0hV0c/jhyxfHt75bg/EPqemhLztmgM7jpi3rBVBIiv317lXB+0U/Y5ZQ6HPU/PT0zTxGG3J5fM+f2k9ts5Xr99aWCqQPAb52Xh+4+f9KBUHbxsGzlrRBGbicg5vRzB98xc2y2mhefNbm5zRUYEtUs0Y5lpH04fAl+/fMaHnsPuQDc0lk5oLXyKpCijWx/UZTw9i2ZpgGL053WhYzyMhEawNSh9bmvqq77vGZq3oOv6Jnt0aicxXC5nrDOMw9RQGpWtFH777TOvr195fz9zfDjy8vKBbdsESBy1r6l1wOfM0pZIzjlS1IJ9y6Z1a46aE7Va1m0llSxUNfJc5Kz871xLq9RMkyHrInZGo55gDZWO7X6YGGzWwj44p8UhYIMnbVuz4hcp0ZzGK7eLzvdyBtcqE+XldFZ1GTSi8qHjn/78R17fTpAlN7bWcbqcmPYToR/oc5E02bWuxlqGEKSiMJZyvUo10ubXad3wvVQ+c5nxgyeviX4aFJ6TMzfcw3KZGaaeru8080+JabdTsEyFfpwIvqcfBy13qZheljVTYX846OegSGqbhOOoVuBFezPIOS1AU1LO92AD23yVwsnpkMnOEePGOq9UZzm0PJDdTuMLg3YpnZHKLaXIljLTpNjbkjMpakc09IGcDGtciGuScmteCb2wJ4f9kVQiOYLtQ4P2yROxzAsGmUit9OgYY5TBYiylsdI6b8hVm5xUbzZFy/H4wH/+z/+FkhPLfNWl13m8dbw8PHGeFy5vrzw9P4GxrPOGdZZh6Im5YFNhGKSMujGdas2cThfe3k48vzwzDEOTtKoViTFyfrtweDzwcHwimEpsopPaip7Q/EKH47GN+CzbuIhf1SnvBiyHw4NMa01oQJViyVo5L2uBbhhk3rTw+PAAJfF+OhO3RCqVeb4yzzMfXl5UXrddx8ePn6hUBWFNbbKypTvn639mJ7nOU71hnVfGybFer6RaedodWOIm5WhjdGWkzuz7TkbbIhl7Ntqy1lp0nlrXEOUw9h22Vi7XK9M4tN2PlSAjtPFpXPWzBM84jlyXmVwrg5HyzafMebmwxo3dOGK6Bh/tPMt1Yb5c2e1G/DhpMbjNK7txkuRrk6Hu49MDwXfM64XX93ec91y3DVMrychJm4rmkkM3sKbIL7/8nRhXfvrpRy1sW2fSj4NmgUEUzHpn4Jj7zRpCJ0mhqfTjSPCOdau8n8/0YWMYejrTKYuhaCbovSR/NRfcNCpLwWiul1rQ+RAC/fOjDuLSwsFBY6leI4LQ5r4Z+O3zF66XC7vdxMdPHxXD2PXCSOfUXJ3w/n7it9/+wflylhPbOeLxAazl7fWtMZMMoesZgycsi0ictdzNhWPoWyiK5tkFWuhO4+kkOK0LRx8k721Viwt6ES+XEzkVqZpyZOg6vO+pJd8hf9YqmN0527wIwhgscaP3QZV2gw2O3UB46SimUnO9K9MOxwec8+x3B0Ln2WLEJrDOizQZpJLyPvD9Dz/do2/7vlNyFvK29P7WDUTN28cRrKdS8KXlKIeASRtL8gyhIzp7l5z6dliA4Zdf/kG4Wn7+WWZFcYRuVCnxv0LftRyQ5uUBghWraZxGCob5qlb8eFQC4PlyYVlmpmlH6GSuVKZER3aikA5Dr2VpypLSOkMthtN15nw68xp6nj88aRFcwQaLwxCRbNcGjVi3dWFdN4IXCqQk7Y7isipbulRy6TTPjpHf/vEb3//4ozK/04YLjopwIuOw146iZmptkUjW3Meuwm/JzVxKR+etKuG2N4wpAoWu79lWyClSKaQVtmVjv98Jr20lv6wZluXK++XMEJQG2QePdZIwlyQwZM6ZvikAt3XTJWEbinxNWO+xnSc1/0yqDYpnIebEFqN8T01tGZov6fnlg4B/QQ7n0i4d17I+hmHgvMx8/fLlXnReLjO1Qe8OxyPDMPDw8oILPZfrmcNhrxVd2ohFuejOSVoeEIyyOCcjmvPEeOF6uai7HJtcddvoYsS2gDOMYV63RqhueeCtWMyNZgEw9CPFzOQ1SmBjoImAKSVJBFSAXNv7Xnk7fwFbRXOthVKThCyp8PnrV3wX+PT8gUTh8fhArvXevf/bX/+d//Gv/4r3nv/8X/53vv/+iK2G/W4vooN3Gtlta2Q5X9ly4Xh0reIxDLup2eULvuv58PSkhVPf4XzQrI+2UDGyuP/tr3/j2+s3/vjzH+n7kZS2ln9t6Vx3Xyi13oubMSavka0pl1wjVS7zjNtPeO/4w89/kJHLar+RUxJR3Ul3/jvSQpA4W83vjJNqpJiw+qIrUGJs8DPbfibJclX9dSynM1+/fuV8OpFS5vnlkcPhKIDdJvPLvGysm7j0Ye3o+sTD8yPWW5ZlI8YN67W7mZeZnd9r9lsrQ/UY5yQ1K1UuVyMToCmFoRvuDuOtbOzGqdEtTQubMW3UV/j8+WtDDu+IcWXoAhbL1KvtT02ffTebVXkjaq0cpn3bLTnO15laiqqZ26igAVK3pJfUtO/fYhh6y2YN07Tj8fFJxpvgmtTXsm1XOfaNpxuGNtXQEKXUgsNL7dHC3nMqZJ/xfcvJrp6XwwE39PgY774JZ0Nb4Fv6PnC5vGtk5gLjBNTmlTGa6QYnUuYlrhp1VP2Ol2UhLiu7hwOP/aN2cE6z/K4LpNirUzNCsjvrpaKqivj13tH3PTkXtuWMwTINA/n4gK2GJa58+e0LH7/7jmnnlWQYtERNZaPzI13fsVwXvHN4r1FL6As5SprqnSdM/m5GrKVyXWbWZW5L3AoN83EzT3WtGmz/jauS554vJ2qGcRjlIbKGJS503UCyqWFQKmmr+KAkt27oSCnxt7/+jRwTf/jjHzkcj/hewTjFVjnujeHrt1f2u1HFFJV5XliXWReUs2xVktNp2hG3RNcFtnWlZHWfH56fJYWxDoc6LesdXR/o+tDiA+S4z61L7kNHLqWN4GRG25YV5z1YkU5/++VX3k/v6gjaGCylxLwuvJ/OHPY7Hh4f5Sg3Uj855xi6wLquWuY61/584dANYL3RvrSooO6GgW7ohccJjpgLXa1KoSuFx6cnnV1VKkZ9dxXvHdd5k4rUGOIWeXt/ZV0XbOu4+9CxpY11WemHXp0GHX3f8d3LB40n9WKpm3fyka3ryn63l02hgUS9sWBhjZm//+1vrMvK7sOe/TSRthWs4bjbS8gUGpm7VOjHHQ+DZsG56CYarWbEqeX6hq6XbrcLGnE0qdgtQnNZZrZ15eef/8jj0xP3LNthFGajJGLjIRmj29D4ZtqzhnGcmqcC3l6/8tvn3/jDzz8x9tKGG9SCkrTAK0VwsBA0knIhyOTRnDOiberBv15njVJ2Qbr5BulKKTNOI2BYrjOmLcd3xyP781UYg3lhPs/sdwcxbXIhbnNbWIpme9zveX554unhiWIt5/MFh+W6zFKJVAX0eCP1UjH1jlYutbCuKz4JmifGvXDmKWW+vb7Rd4GH41GLfmMbVygzz1IhPR2PzPPML7/+Svn0kcPhIKVcbUqW5crj4aFJlVX9x1oIfU9c1pYdXVpHl1oVrkVxbKTKzimbW5+bQGPOelWFznI8HOVhCV7dnDOQEt4ryjFXmrO0Uqvqo2otJUbWJfJ+emXsB/bDSM4o5AagREzfkUtt45hI5we2bWMcJnLcAMl8TW2zXqsoXgxUI/XP+e2dp+cPWC93dBc04x6GgYI+p9FMFN9MoCGyxY1u6NgdD5K+ZsHshm4gFlWAyzJTKHR+IFWFuOx2O5Z54fX1K6HzOOu4zFdCVDe0zgscFLizxYVpGnFBh2Uhk1BWxy2K93K+YK1UhZ8+ftLzbQMhVKxs6YJtBtNGk2LjOq/sbipiKbUx3LJtXJdV+4knSU9rexet17O1LAvjMPD69ZV1WXn59JHQ94JEoguqomhN646E0DGM3c1bxraupJjpOpkIe99xna9Y5zm9vYHZsa2x7Rd6xtDfcw1qKzpDrW38G7guF4ZhahBG6DoZ89YYGfuArQr2WpBMd9u2e2CSdU2NWaEajW5yEghxvp6ZTxc+/vSD9hztIPZN9ZmzTJfXy4nhpxGo2imZlh8+CjG036v6vnmrjNPCPtfC+XTS8nvZCMPAdx8/Uksk1lYk14JHl13X91gb+PrtlZQijy8vHA9H+mGgDwO1ZkpJbNuKy6VdyvJJ1Ap3zj6Gp+dn9sfjfbSvEdvNSQFDP7D78cD/8i//IkNxjJgsukbnfEMbWfw4TtgKuRRiXHBOPBdTcuPr1/sczDYTCZS7DyKXhDWO4Ht+/PEntTpZ8/yuybpq1Z8xjHIe11R4e39jGAYOxweMF8q5mOakXDamfmBe14YJXtRKt9bN1ooPDutkKKm1MHrZ9311YKoQwbYSmvuX1jV8e33l44cXUspc5wtd3/H19Stfv3zDe8//u//f+fTxE/t/2fH12zdp0btey2Anuuh1k/KpH3qeG/2yC4E1Rpb5yrdvr7jOE4r0zPtpR04ahWQKZivEbaXreoINXOYLb6eFZVnY7ffs9ztJI4HOwHK9sut7buE0wyBdtHOWl5cPyupYLhSEBd7v9xrJWctWkvI9rOXf//3feXl+5vn5GVdF5G2rDYyxpKy2dxwnUlUEJKVQYmLOiuXsBy2kc8m8vb+TS+Hp6RGCOgVrpT+/rkpB6/rA6XLm/XLi04dPDIMq9GoM27JgquH1/Rv//f/6b/wv/6//iA/PeG+oVfLQWAvBelWRVg5hY6DrPMPQMV81erBef6/JGYvlFt0YGoix78dmchNcL/QD095LfbWtnN/PhI8dvnrWdcFYtd3OCdhXq+jEDkWbLsvC189f+Pb2jU8fXrSo3hIlJWKKLOvCukS2ZSH1PcE65su1JbNV5suVfpwYx13bJ6DfOTVeUmexRT6A0Fy2CvA6NI9RbqKDpn6y6qJSuu0FI6MPgvAFy6cfvsNVx/U68/75ndP7K9/98KP2IbcDtGrpbqw6qmqM9pSHI8M0aeRX5KXItULOVCdDp/cWqvhjlcowDHTd0M6VDRs8UxnIMbXUSuRUd56hjRKdC6S0ErwlOC+11fVKKQOX01lL8/F3+WrOUVialBr2XfLm1CYWIfS8PD5yma+kLHx8SS3moEdJkt7x8PTMfhhZm4yXlLhuG+fzha4LHA4HXJCqSay40HwrA32pfFmumEakMEbFZwhBz0oprPPCsi5s28pj1+l8dZ44z3TBc2j8syVpL/HDD9/zenrn119/IafYVHd6Z0zVpRLXSN/Au9bKKhBTZAiDiNrOcdwfsFWRqQ4twmOJd+/Fx0+fpLIblV6JNZL/m3BXg3Yu4NV+5eZm9MoAAIqz5DXShY7fEzogplWSRNekiqgCccHhi2tZugmMMqtzybj2v5fmBU5FevjrMtOPIznFFiRisFSeP3y4p0zlrDl1oRC8upnSHLclrpS4MW8z3336TnnCNkFpRiqh2djv9CJerxfe377y6eNHTBU+gVwoqXI8HKRtLkUqEQzPT4/EmHRR5YqjEKuMeLlWtlwoDT5XDVwuZy7zlb//41f++Ic/ErqO60UobtuckvO2MF9njseDTEoptkAgkUN7L1WEaSM5Gzp657m0TGFqVLVhHOMwMPQD27YydSPmg6UPPUuMbccgF/N0GDmdL3z5/JkPL88yMDbZZ4wbfpiw1uGdZtbWOVxGOALnmPqe6ypkg90kravA6+s3jHV8fHm5O/RrNpyvM7EkdsPA6XTh3//6F67zhanv8f6ZYjQq2GKiH3o+ffhInFf2+4f2jMXW8Rg8Gi85W/G2pw5ydntr2Ja5RTcqH8I7ixt6UlOfee8Yuo55i/gQqEaI8XVb8UFFzroom/nwcLzLiMXjCTivYKHqm/8h17b8rJxP76QUeTge2R8Ocqpby/l8Zr5c2NZIrpktRta0sT8eWvyvJ6bIZZmbNLa1/5cF412rQtWlGmfIqRLavP429ig5sq6JaZIhzRiawsvTdW0CsCWu7ycOj9oluXaAFSqddzw+PbMbRzAt1KtW3udZVaQPTLsdxljFjEqTK7UOog5TirrJaptoQsbEmdr2GzK5XU9naqnsp4kYJGEPskLTdVquy5DuMG3c2bXvaNsSh8OhSZQbaiRloqt0xrOukXHsG1vJ6NntLK6isewtJOl8bp4MKZP6cVJkb05s88rzw4PAmynh+sCXL1/x1tF1PV2QKMI2MOjdx2DVIRvv+fThI7VJYMFwOV8kL7ce5z1937OtK9Y6np4f6Vwglk0dSefvAV21ZJYklddht6N++iRBzTAqo6aIFtH1g1zc1rGuC33X32WyLohwXZtU97ptSsULpimqpJLsOu3MaiPJklUEdT5wOp85vb7fuzpvjCHWQs1SAJmWEeGqrOzVcFfQKIaybwC/1JzQWhQ6oPMddtCM2bV5eCkKIKoVHE3KZbRE7X3H6fyu28pmAfXunKjSgnMctSRyzHSDnINx21jmC6VU+nFgN+yYlxVjNzof6IYABYK1rfmQZrkU+O67n1QZdj19LMRS+OH77/ANEeC94/PXr3f3s2166FoypVpiTtojhKBqu7YIzCIC5Dwv+rtuUaQUzJ2fgi6lVh1bqzCl/bQn58ylaHSxbVtb/CXOpxPXWeOy0lysvuvlWG17IQ3gHQd/ACeKpXGe63ImLUpXO5/feXwQNnierwz7HeTM9TLThZ6hoSSqleu1VM1+c5KywxhhBqosEMo4nnbsW9eBNQTjWdPGul4ZhwnnHJ8//4NaEx+eP+B8xy9/+xvd0PPp03eM4yAVlff8/Kc/qoDImS9fv+AMfPf991hjSDlinBQb9YbbKJKuPj49M+6POO8JxhJT4np5Y3h4aLrwijeWy/Uqd/HLC8m1wwawNtD34R5sv62buqssKsB1vrIuK49PjwQfSFGJXofjA0/Pz1Qj2XIxlRQ3tutVjmsHh2nH8XiQL6CB5ow1TOPINI13I1aJVXucdaU4x9gQNdZYQueI2yac+WEnL4wLdN3vpNqbiumWnuicY9xNvL2+kqpyNqq1UtRZw76NBq0XWqMYGlcq6BKs+nlovoObkpA2wky357BNLnrvmVPjiFUJeJfrpe0gxEUy1uG55UtUBRVVSFW59tPgJa2t8mWt64oLHbuuI5XCw8MTVL07OWeMDzw8HO9FqnWay9dcwDuuyyI/THA8Pj5AKcqdb4Y+RyUmRzBOMnxgN40SjqTEMAWeHp/khaA2igEKz6qNEQUYCtYHcqM8GKvkwBwzprOYnDBOkEnnXYsxTaxx066rwRqN1di+1kJJGkEfjg+6eM9XjDW8X955fHgSLt2quCj19nNp32AqxJI0oq+/74xNaTJ5Z4lLwhTofU80kRw3ileOfCqV129v2iebtgeVS9RTs2k3emkVuDgiJWWsVcWVamHsBxlrSst+aKMlqjDVCq/P4LwumxZlqhc7CXyFEMHWWYLVB1iNXKzOB3FwjOTbpSR8aCarJtec56taOt8xhI5sG+xuzSS/MY0fwGSWecZ7h2/jEdeIjTGmm+ZPclrnBeMqhViM5pjWEnPmcDxSc+J6vWKBZAUrrElu1i4ETIa1Fl0aFp6fX/AGtpRaDnjLN6h6QI9OWv+0/n74Wu94DFpgO2MaHrvj+fmJYRYKfFnXhudoOR1GbKJlW1nWlf1hpDd9k/9VteUhMG8rfT+w++HA2Pcs28o8z/S+43g83Em41eglKEV+AO89cd2akcvSN0TKNi/8+o/fOD48MB32ko46jdakEjoQml7+8eWJfdzx4dN3vL2+8tuXLxwfHnh6EjhRipMLNNNZSllomKqgGuccJjUAmrHK8WheCxcGHsed4jStJRjDvCxcrlf2xyPeWtamjvHB83Y60+8mxq5jTSs5ZaZpvDtaSy68vr+Tto24bRzbRZOaKW/a70gpsS7aI5Tb0nQc8VgZkJ4fMNXhLyd2Oy2n70EztXC5XNgdD3jjmsInUopMp1M3NYZam2c3MJw1DtfLv1EbRVbBTTq49b0V0pK1A/HqhI7HBz2fpVCq4Xy5EJersB/6T977bWskY12WhdN1xlYl4DkH2yJNfzcqVe1m+ryp53LrouIy0+2me+ecUma339+pAsY4KvpeKaWlzW3333ccx2YerRwOB+3dkjAStRV7Ifjmq6ptCXsz3gkddDpdCcEJpx7kps+lNISGnqN1WRQq5R2T7+5IG4Oirp5ePghkaSoOi21jpnlbBLV0Ch2TjNdSc+KyXO+XVIor9rBnGkZqLeyNUeeSkjJtFiFUwtBrJ5EzvQ2YLuCzWHQFSzBeU5dtofOev/7lb5xPJ37++Y+YhuLpuh6CJRRD9VVqsG1j7Aau64xzTgDWWhpOyPG+vrPZjYf9ASLgvLqbqHCxfuhgpSE+PP5mZceK4FQqrNuM8m6BKlzG5XJVHGMp7L3MJSa1zAmKBtu3qt0o4tIYBWxocCamUkoy4j1/eGFt4wI5oq9axjW1gHdBf0czC9VS2JaVbuiFtsiZfui1sE5JFcgwMPSdMrOBb6+v7KaJvs8s68rQD5zetAvZ7cTJsU2HnFsOQiiFvh+IcePyvujBtJYvv30Ga9kdtV/IOfHlt8/89ONP2GBJccOUineBDy/PrFvEkdv8NuOxLNsiCNwwsKXI27c3Hh+OeuFLaXJhmZu8V+Sq8zu6XnP8fhh0wTXFzQ3q571j54RYTjlr+WyrllHrxm+ff2OadhjgMs9sccX3PbtBOyIah8tUeTas1tjklsudose5FhBFYWns+sPxyDxfyaWw3+9uX7MotVWLvR+//4FUC50LzNeZjx8+aKeRVZFVYBhH4rrqd+x7fvjxxztkzhiZxlITLIDUUSBDnlhD2qltVvLIT58+qespAuGdz2e+vb1zvZx4+fBEbnsKY6Q+yzXjbEcsiS+/fWk04wrO8d3HTzweNQYTejtRa2EYJ7Z1oxaoKZO8EQZh2sv1G4c2es2a78NdcOFaRyFSqKcFnKvrbc7m1BDrYmipApWLXQmDNRZyM2emLao7NqVh+2/cq16LzvYeGvSd2CalNsYqsxuNJt7eXvn67ZWyiShw2O95fHrGej3H3lrWGMlJLC4XQsOFKGb08z9+w3x64cPLx9at3Ob0kgf7YKi5Lb2tpOhLXMlb4nq98On77xi8uj+ZyTKmimqajQK+bFsKb9vG2yLiwn6/xxvL+/nMl8+fmwIIfqu/8fDwyOV8xjrP49MjtkIkYbAtH7ve5nCA8DbjqPczrZHdMCoPJ3S8n090fa+xL62jiELcd8FDaFt7cyB4T1qjwKXNv0XXg6mEzuGK1FIWCQksQpWLM+fxk56D3WFHWDQa3e/2/Nf/47/iQuAPP/xMKYXLMtPbAayYcjcZe9tdt5FRIypbFevHw0GXSRKLypTK0CkLPRiYxqntlgwlRvwyX3FeEYq5FK7XCzlmfMjsd4e2LFHlEHMkX6ELIrjmmpXClCVbDU5y01wy3nhyTTijeT0VQhgIgfvBSz/ivUxIaUukvNAPbcHYN01yqZRbGh7KC+6be7LvVImuubC0jseFTkuYnPj08QXvO86X9/uS/bdvv3HYHxh3TQpIZdsifW91KDotN6tzWrK23INpv+N8vlCqIW0rl8sV64VmCBhSbp0EImZa5yT3pOBLS8xqc8RUCq+vb3z5/BvGwsemEMopU61iXIMVciFlyXuXGMlR1bc4TKVJSqXGkQIys21bS6wr979T+QnwfnpnnhceHx942B+132lB8DVpHixYIjgjVMm6LazrSui6exhVGDoeHh90kCHq7boIn37Y78itK/RA8EHL3JJ5enpmt9vJMY0kvDRpoxmGNuagaeKFNKFdDNapepy3VbGi1qrKQ16b3LhL3inAJbeCRqZOy3a98nh8Zr8/yKPiVJys28b5dGKcBvbTHh8C1+sF5xzn05lPHz4yNuXH9SK+WTcNeGOJRlLIWqFsCeNpFFJ5RuZtoe8nqlNew35/bB1ybTC6guscaVOOvHdBBF7jWBdhS5wPLPOJEAK7w4FhCMynN/76t194fHzkw4cP5KqDqncaa6VNaWa2dabW1ObvyTy/fNAOUvNfcqxEr1l1zBVvA/4wsh/3dJ0n5yR3dugoDmqsYISf2JqpVB3iZ+bLhf1xRzKSfe/H/d2fYEwW76i51p21OC8fTPGBGJV9YQJ3Sbs1jnHUTurmLTLG4jG8riuX04Xz6Uwp8Pj4wDSMfMVorBY879/e2e0k0Z2vF/a7CWcs06hdTokZawu1IUBEH2mQx/YOxy3K2d95DsdDe/aUCvft9ZX304nDbmK/1/8Po9hTj6TSdd0k/mndEtWQchIGpAuE0BGcRCShydy3FqZmjEb03ktl+PLhI3/95W/89V//je9ePmGccqpzzorVDeZuYahG6qUcN9FyndDypWicfTqfWK5XcIHnlydqhSH0pJKwRgbpsR8UhWydlQ77Jh90CoJZlsg0NridtTweD5izWsyuH+4VgtzRBlMqqWa89WzLRu0raStitbe4zlslWGtly7UF7gx437E7OKEvhol5vapj2e+pFta44Uti3Ilqmk0lxUzMVw6HI77rGJpk0xtDRESFVDRL9KEDq1HQ99/9oBczJ8URGoM3heTbyCkmcK4paCT5M1mRlTkL6ZCyKtxpmqg5E63a7bwl5mXTZ5YztakILstFhFUnKJ2pkNaV19c3rLV8//E7qVNu+59aebte+fb2jWXd+P7TJ7ZmPrPW8vZ+4nkYqDUxb83FbZsZb9uw3iuVbF0xPvD9999LrdDGSjQneU5RKIwQWIt+bkttAUpCNGyXK6HvtNxsc2k5Sytb1uFmjOH99KYlf9fReRkTS62kUklRqinf+UYPvcWeylRXs6qkUjI5yy3bt4vCG6MRR5aUOW8RPxl811GcLuecYqPsujvVNqdCN4hV04Wen//0Rynp2s5FmcoOH/Tdnd5PGOt5fHjQTsc6XXCNabOuUbsnr4XoZVn49uULruvYjcr+GNpCsxqoFDrncc3h/346s9spr6OUTNf31Jr+H54eoaDVvndjT9cFYspgkPzTSVixbQrGuXU7WEEzjbc6LARqJUDDQFQwlWno2nK1JQgaK5R4kaP8w+Mz20GO5jEETtcLb2+vfP/dD4DS1FxLxLNGhq1lvnB6f6fmyvFw5On5CVcMv/z9Fx4fHvj5x5/UGTjh+Uut1BzJVbyyYZzuGSfOKrd+i5FME0g0a7bEMi3mNFdy3ORUXjc+f/mN3bTj6fjIt91XTpeLul5T6bqgzmCLbFsSZ+5R6qllVYqf6zuKreSctMMJnr6XzPrr21f2+wO7caIPHZfrBec8X16/8l//v/8np8uJH3/+mX/68z/x9PQo6bUVbbmUwrotTRnq8c7CLUZ53di2yMvzoKV3G71ll+97xtvgX/h7jar/1//0n0XAAGzOuK6nVgskvHEkUtvTWIK1VKtcjVoFBvzy7St/+8tf7xTvUirTODD08niE6qE3dypE5wK+83L8FqSE6UMHGKahMC8r87LweNwzDCPPfS+Ojbll1LoWqNGyWevvlanDYnpPNY55eZfJygpPABDXWYym9n+XWtkd9gTfae7cLhOHvAiuBcar8nRkExs8TulUzitPN2fJQEst9MVrhOM93ioEx44iqm5xY1kXjbtCr1Ada5jjxi7stKBO4hHpZ1FuBAZ2u0mO7U3MKhttO9Q0Wqu50S2Batt8vVhy0sLIecfTxw8cnx7pQ0eqCmIPVjPW0+srnz9/5f39nUrl4eGB3TiK924qMa1czydC6Di9v1NK5ofvvsNhGHZ7Yoq4CqkanLPEokPx+HAkWN/Q4zLR1RqpRj9/uX3mjRVjS9F+wSlEZ8sirlIKl+VCsJ7DtMMYw/HwoMOtUz60p2VntOhG8bzAYYkoAEqGQIkg5nXB+8D7RcoQ5xzrulJ9wHvLuqycTu8Kf/JefoBtY77MjLtRoDzv74lxKS9454kxEvqO54ZYEOJb+7SSs16ODx/5y1/+wvV85eXDB0n9mwmx6wSidMawGya6Qal287yw5UxYN962jffXV/74pz+3alEHW0aLdhqcbb5qTp5ilKy1LZlv1NktRcZhgKILxgZHV4pEBW10k2oFY3l5eeF4eFDFi5aVrnYaefWuKZI0Pqy2skXJcXfj7e9tihrbmEUx0fUD3gVKFpRxuc7M11mXCoaN0gCMFbLBj0HIjqHHGhUpw9DzfnpnWxb+sa18+PjpLuHEKMlwSQlDVeyw8/fPoZQiM6Sx5Bix93TJqoIOjcVxFuND+1wL6/nKeb7w9PDAbr/nfJ0xztK7XnsAp5wVV+Hi9ffMaVFEgPfE2Ax4bWw5Wkc1hvPpwtvriYf9g9I1U+TL6yu9d/z9r39jiQshBF6enpimURdTO0fI2tONw8i6LeQcyRnW08rT0xNPT4+kmDBOOw0XOoIVAtC3tEeQSKDmrN9z2Xg8Hnh4ONDdRAel4L3FIBLFmjZFDVcryXajXqRtoxC5nC/KBqmayjinfJm+JVneoKAxRl5PbxyPR3zM2/9DznobUVjruVy/sc0L5aDIxNqMVtU6SoWaVN0WMpaiB7gWpt2Id514TCVTcpu1VmmVl2Xhuqjdqgh9fD2dMM7x8eWDWD80GLm1TJOqKNt4MLQXZZp2uqgA33TAtt6kglLNuIYbv5n2bsu/WyKUbQjzbCqXZcYUGKdJhNHY5GEGXIV/fPnKfj/x/PjAfhx52yLrtlFyxh808pChTwfkbe58mPYsy8IyLxwOO5yzTGbANLx0SYXQFtZvr2/88uuvxC3SjR3juGO3m+g6LZzXeSanzPly4enRczju7yqpWEoz0yDkRt9LadX2Qt60w9p7KSnQ6BDTlGwl33dJueFHFDdbmqzUKYejjUyq059bqPSjmFrGGEnwDKr8iioYBbPIiVxLJSNwW0qJr6/f2I87pmkkbQvG7Di9n/i3//FvDGPPhw8f6IaBaRLuwbfqvKZ0Lx7WeWUrG36amJzH9lVS5lrpQqAkVafeeazzkDOXVeH1wTvthqyMmd//8FOL1n2Taikl+rG/e4NqKYSh49OHTyzLlRgjp8uZNa10JbDFCDXTB0WfxpI57vZyEztL9Kqqc/tX3/fULM5RKcImAHSDdmYEXXZxS+x2I5fLmZITQ99hmrpl3SLWW/owKNSqKXHWORI6MZDev53ow8huGPUdk+9hO/JAJYU5WSOVYNdz2O/luB8GQmnO+JL0WVW1v97Zu3+gFFjOi0Z9LqjiLVUehKxOdtsUcNM18N2WhKFQOqV+d3UNeqeLqZze3zHWctzvMDdXvKlYbxmHiaHroFYO0w4+FWjqQe9c25tWtlrpO+UyrJs4WC4EvDVcF42FKm0C4SzTbqTrvqMfeqW+1cLz4QjO8YefDd/98AMlJb77/juGfmwdMngDscLlegFgHIQ+yrnydnrjr3//O999+siSIyxoT9kN7fkq9wx0aqWmrMx6Y7GDLvFaS/NMGXJtJrq2I+l9B1UycmeF/fBNWVmr4fDwAAbe396FAx8nfNDF6VxLNcRyvl5Zlo2no0WAFKM5Xh9ck1VKHeOMbXAvGd1qe/mF0i1crzOh93d+UnCB0LUPC4hpIzgvzTC0iEGlcgUnpG5NyjUYdxPruhHbDZprJXgdosFqP+Gc4XKa2daNw0HkVIpmldkBRbLBrg9yZjaJ3k1DboqAf5flKgu+dVqS+yAHY1PnlKzlufFOKYhVMrj30xt9cErYc8IydFXQvdRMKzkXXPAKM2l7gWqANtu9ERvvrPhWKQiaVzhdLuSU6frA99/9yOOjnNJvr2+8nd45n0+ELsi42DhG98vTQI56yGzwmnPSlrRO33OhtPSx+Ht+BZX1etXsvCnWeicMxzYv+E6VRm1u3nmeOb2f2R93mK7JHs3vQS+F+rtaLEUpebyQ1DVr3JFLQ2s7HWA+BILzPD08MviOX8+/8Pr2lTD3pJj56Y8/8/T0fK/2AIL32sXkzDCNxC2yriv9vqPve2KMdF6z4GXbRObtA9MwqYhw2rtsm0B7FXVdxgjkt2vPbS4t2br8PxPrfC9qbNpW/uN/+k/spolqDd4GulHZApKH13tEbCmFkjJ0OhBSQ564Bld8e/vG6e3E49MTU9+TQCNR5zCdOpzD4ahscbQAD10gRK+Ap9FQtqw435bHbrH4oG7KWmVv1IaIMe2SVG50JsXtjs+fxgEbAlvK2KaxX7eN5XphHA8tZzrdPS/ys1hijvTDxHefPjJ2gZIy27Jyww1P49Rw7m0S1pbVipoxXC/zXd3oxw7fcBnzfOG6OA67PeM48nQ4Uo3h4fGRsevv0vTdoam6bkqsdh7VEjHe433H9Tpzen8npczT4yNj33FdV7Z1pZbCNO3YTztckw7nIhruOAzgHX3fsS6LSABbpIQObwOpZtakhTsoVK3vlIbog+Pp40c+//ILS87aeaIEuMs8M47qIvO6qMBrcxapsozICMZoxFsrWMflcsX7ICRMk8JqUpLIZeF8PhO85/H4IP/POPJ4OHB6vDBfrwzD0KwIJ/YNPVRqZepHOq89pDfAOl/lJnTTzdQM1rE/TMRNS8KSs4J6FEbMLUbUYkVdLMqasCVjjMYywXmKEU/JIPrrul2pMeOCFkTLthBqIHS97PNN9ne5nO+UTutFq9xi4uvnz8zrwjD0gr15LZ1q1eETa2qkx/aQIB35tY2gQgicXt95/viiQ9u4BthTClkxlXm54rpw1x1bKwnt9x+/owvSRNfmu8DZ5ma8xRjqS0rNT+GbczTXyjRqLJKpDK29W9eV3gdsraRVecDDODAOA7vdjhA818usxeAy04XAYf+gQ3CN5FrZt3HFvG3tsmsZt40Ce7qcGceRbvTUrSihz3spgrZFlXBb1j4cjqSqea6z7n4RpZJ5fX1lXVcu5zPbugmQ5/XZBxewxmjhnRNV6e9aIpMhCSy4tfD3rpnZpn7g48ePDKFv0krLLdZ02u3VVpfE++sr3nl80NHrjGFOic+fP3M4HDk+HKhFhcu8rm13lKlRIzAhKjK//fbK8fjA49Nj87VchfvwQTPjqjwT1zqokjPtPic1yahGNEkNba7YLnAc+rspSclncvQ6o6yBWJQYdkNbOPj/F3Umy20cQRB9vc2CjQwRFCVLVDjCYf//H0nhsLUQlgQQmL0XH6oI3XECMD3VWZkvsVWlQELoxoHvhwOXvschdvQlJ4VhCqakjz1d3+uLWBPl6jBar9fy8kxFE9xWnTiWIlc5bNtSOcF8l1KuATCTrU7dXnD7XtAvzjhWztENA925A2NpmiDYGANxTkSVyCp1WeE9+/1r7vb3wpHKWXqfjWW32XI4nShGbncOkUSKuox8ETt4VQdOxyPzPHBfPWCDBDq9MUyLNLtVVeBuf69yklebayaWxPl8oVmJzm5eHFGI+6cYwzLLf7cAfX9mijNN1bDGaIPgzDD05LqmdhUxR7zq/CkXmIQr5auKlXNEHZ4W6xiHQaZy66QTJ3hOfcd2A61vaauG12/eXgc0i6GbJ75++ZcP7z9gvVeirJxfyXB9jo0VdDoGjA4/Xp1qgJ7TIm0WJSYcDodrFsuYTKgrggvcbLfsdlvIhX4aOB5PVNoc6YwTpaaqRKWRa1TB+/pqYy2pUIjUoaautZQ+Z/nBTydwjpvNhqI6HgaCrYTgOC8UZ/BOpn+bpT60qAQjU+0vj/XL4rsO9dW7HWPk6emJ7z9+8vj+kd0usJTM4XDg0nWkFHk6HPj98VG1YxD7moSxxmGimMK6aeWWQeZweNJrrGMaRjKFNw+viSRcFgSB845x6CXIst0SiywTkyuA+LilTnGBaSGbQk1FQnYx8yxIBJeFcDnNEze7W4buwrevX7m/v2NJ4viZ1IkzTxOVSkXd0HOz3VFXNbHId7ksgre42+/lD1gKm+2aEjPH5yPWOLarliVnlnHipbmqqPd/Gkf+/vSJN7+95aF+4HnsCVVg5VvGsedy6eS25MRdIwUqogtTMikrZwr4+PGTINOt5eHhQXVaWZRNWeStjLjIYkYtvI7j8SSsreAJtQwWc1wwMWEbS+XrKxEzLgt121DyS02s5FWmeZbRMslLvThHyoXa+6sLxVorS8plZhoHhR4uhEqaD5u6wW7F/TFNsrNoWoUpWo1G6U1WwrMShnTaWHh+vtDq4TNOC01dMWvX8sBLaBOsS9dJNqrsGHTh+/KZRetqhQTQ8/PHT54vF5FpVi3BOaZxph97dusN5+7C538+k4H3796y3WxYNORIzkyzJv+xLHEmp6KqrHAMUpJnzwYr+xxkR56s+O29cxSn3CcjFk+ZFguhCswp6Q3iFXUr8LtcEqt1K13Q2u8RnLCqnO59lqVIz7Ii0//78o1zd+H+/o4P797JIaQL+ZQyKWfWq5amrhjGEWERCuPIqOsuqpQS40LXSS5is9mKaSElYpqp/eZXAFAHuKzPhfdyQIcQ9LaogTMDlbNYZOeVU6K4TDAOCgq/hHmZOF8uQjr2nlotsUnl2qAav7WG6J2wm7LA/GLOrOpGh0zZBZUUub19Rd02gtzfbMTAkcUIFEsUY4N2nRhjZAHvHFinEqj8XsZb4kXOpnXb8tcff15lpO7SscoN6F5WAi4QrOduvxfJKqUrZXdOCecD/wO6KoanIvmT/gAAAABJRU5ErkJggg==", "text/plain": [ "" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } + ], + "source": [ + "from PIL import Image\n", + "Image.open('demo/banana.png')" ] }, { @@ -794,6 +420,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -801,23 +428,22 @@ "id": "VvRoZpBGkgpC", "outputId": "68282782-015e-4f5c-cef2-79be3bf6a9b7" }, - "source": [ - "# Confirm the config file exists\n", - "!ls configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n", - "\n", - "# Specify the path of the config file and checkpoint file.\n", - "config_file = 'configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py'\n", - "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth'" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n" ] } + ], + "source": [ + "# Confirm the config file exists\n", + "!ls configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n", + "\n", + "# Specify the path of the config file and checkpoint file.\n", + "config_file = 'configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py'\n", + "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth'" ] }, { @@ -835,6 +461,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -856,23 +483,10 @@ "id": "KwJWlR2QkpiV", "outputId": "982b365e-d3be-4e3d-dee7-c507a8020292" }, - "source": [ - "import mmcv\n", - "from mmcls.apis import inference_model, init_model, show_result_pyplot\n", - "\n", - "# Specify the device, if you cannot use GPU, you can also use CPU \n", - "# by specifying `device='cpu'`.\n", - "device = 'cuda:0'\n", - "# device = 'cpu'\n", - "\n", - "# Build the model according to the config file and load the checkpoint.\n", - "model = init_model(config_file, checkpoint_file, device=device)" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", @@ -887,60 +501,67 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Use load_from_http loader\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n" ] }, { - "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "badf240bbb7d442fbd214e837edbffe2", - "version_minor": 0, - "version_major": 2 + "version_major": 2, + "version_minor": 0 }, "text/plain": [ " 0%| | 0.00/13.5M [00:00" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "%matplotlib inline\n", + "# Visualize the inference result\n", + "show_result_pyplot(model, img, result)" ] }, { @@ -1048,6 +674,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1055,17 +682,10 @@ "id": "3vBfU8GGlFPS", "outputId": "b12dadb4-ccbc-45b4-bb08-3d24977ed93c" }, - "source": [ - "# Download the cats & dogs dataset\n", - "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", - "!mkdir -p data\n", - "!unzip -qo cats_dogs_dataset.zip -d ./data/" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "--2021-10-21 03:57:58-- https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0\n", "Resolving www.dropbox.com (www.dropbox.com)... 162.125.80.18, 2620:100:6018:18::a27d:312\n", @@ -1093,6 +713,12 @@ "\n" ] } + ], + "source": [ + "# Download the cats & dogs dataset\n", + "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", + "!mkdir -p data\n", + "!unzip -qo cats_dogs_dataset.zip -d ./data/" ] }, { @@ -1108,13 +734,18 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "id": "WCfnDavFlWrK" }, + "outputs": [], "source": [ "# Load the base config file\n", "from mmcv import Config\n", + "from mmcls.utils import auto_select_device\n", + "\n", "cfg = Config.fromfile('configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py')\n", + "cfg.device = auto_select_device()\n", "\n", "# Modify the number of classes in the head.\n", "cfg.model.head.num_classes = 2\n", @@ -1171,9 +802,7 @@ "set_random_seed(0, deterministic=True)\n", "\n", "cfg.gpu_ids = range(1)" - ], - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", @@ -1188,6 +817,7 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1195,39 +825,10 @@ "id": "P7unq5cNmN8G", "outputId": "bf32711b-7bdf-45ee-8db5-e8699d3eff91" }, - "source": [ - "import time\n", - "import mmcv\n", - "import os.path as osp\n", - "\n", - "from mmcls.datasets import build_dataset\n", - "from mmcls.models import build_classifier\n", - "from mmcls.apis import train_model\n", - "\n", - "# Create the work directory\n", - "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", - "# Build the classifier\n", - "model = build_classifier(cfg.model)\n", - "model.init_weights()\n", - "# Build the dataset\n", - "datasets = [build_dataset(cfg.data.train)]\n", - "# Add `CLASSES` attributes to help visualization\n", - "model.CLASSES = datasets[0].CLASSES\n", - "# Start fine-tuning\n", - "train_model(\n", - " model,\n", - " datasets,\n", - " cfg,\n", - " distributed=False,\n", - " validate=True,\n", - " timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),\n", - " meta=dict())" - ], - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "2021-10-21 04:04:12,758 - mmcv - INFO - initialize MobileNetV2 with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', 'prefix': 'backbone'}\n", "2021-10-21 04:04:12,759 - mmcv - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", @@ -1619,15 +1220,15 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Use load_from_http loader\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "2021-10-21 04:04:12,965 - mmcv - INFO - \n", "backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): \n", @@ -1946,92 +1547,494 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 104.1 task/s, elapsed: 15s, ETA: 0s" ] }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "2021-10-21 04:05:27,767 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", - "2021-10-21 04:05:32,987 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:57, time: 0.505, data_time: 0.238, memory: 1709, loss: 0.1764\n", - "2021-10-21 04:05:35,779 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:54, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1514\n", - "2021-10-21 04:05:38,537 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:51, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1395\n", - "2021-10-21 04:05:41,283 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:48, time: 0.275, data_time: 0.020, memory: 1709, loss: 0.1508\n", - "2021-10-21 04:05:44,017 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:44, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.1771\n", - "2021-10-21 04:05:46,800 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:41, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1438\n", - "2021-10-21 04:05:49,570 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:38, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1321\n", - "2021-10-21 04:05:52,314 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:35, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1629\n", - "2021-10-21 04:05:55,052 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:32, time: 0.273, data_time: 0.021, memory: 1709, loss: 0.1574\n", - "2021-10-21 04:05:57,791 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:29, time: 0.274, data_time: 0.019, memory: 1709, loss: 0.1220\n", - "2021-10-21 04:06:00,534 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:26, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.2550\n", - "2021-10-21 04:06:03,295 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:23, time: 0.276, data_time: 0.019, memory: 1709, loss: 0.1528\n", - "2021-10-21 04:06:06,048 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.022, memory: 1709, loss: 0.1223\n", - "2021-10-21 04:06:08,811 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.276, data_time: 0.021, memory: 1709, loss: 0.1734\n", - "2021-10-21 04:06:11,576 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1527\n", - "2021-10-21 04:06:14,330 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1910\n", - "2021-10-21 04:06:17,106 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.277, data_time: 0.019, memory: 1709, loss: 0.1922\n", - "2021-10-21 04:06:19,855 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.274, data_time: 0.023, memory: 1709, loss: 0.1760\n", - "2021-10-21 04:06:22,638 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.278, data_time: 0.019, memory: 1709, loss: 0.1739\n", - "2021-10-21 04:06:25,367 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.020, memory: 1709, loss: 0.1654\n", - "2021-10-21 04:06:25,410 - mmcls - INFO - Saving checkpoint at 2 epochs\n" - ] + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:05:27,767 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", + "2021-10-21 04:05:32,987 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:57, time: 0.505, data_time: 0.238, memory: 1709, loss: 0.1764\n", + "2021-10-21 04:05:35,779 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:54, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1514\n", + "2021-10-21 04:05:38,537 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:51, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1395\n", + "2021-10-21 04:05:41,283 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:48, time: 0.275, data_time: 0.020, memory: 1709, loss: 0.1508\n", + "2021-10-21 04:05:44,017 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:44, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.1771\n", + "2021-10-21 04:05:46,800 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:41, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1438\n", + "2021-10-21 04:05:49,570 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:38, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1321\n", + "2021-10-21 04:05:52,314 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:35, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1629\n", + "2021-10-21 04:05:55,052 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:32, time: 0.273, data_time: 0.021, memory: 1709, loss: 0.1574\n", + "2021-10-21 04:05:57,791 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:29, time: 0.274, data_time: 0.019, memory: 1709, loss: 0.1220\n", + "2021-10-21 04:06:00,534 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:26, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.2550\n", + "2021-10-21 04:06:03,295 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:23, time: 0.276, data_time: 0.019, memory: 1709, loss: 0.1528\n", + "2021-10-21 04:06:06,048 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.022, memory: 1709, loss: 0.1223\n", + "2021-10-21 04:06:08,811 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.276, data_time: 0.021, memory: 1709, loss: 0.1734\n", + "2021-10-21 04:06:11,576 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1527\n", + "2021-10-21 04:06:14,330 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1910\n", + "2021-10-21 04:06:17,106 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.277, data_time: 0.019, memory: 1709, loss: 0.1922\n", + "2021-10-21 04:06:19,855 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.274, data_time: 0.023, memory: 1709, loss: 0.1760\n", + "2021-10-21 04:06:22,638 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.278, data_time: 0.019, memory: 1709, loss: 0.1739\n", + "2021-10-21 04:06:25,367 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.020, memory: 1709, loss: 0.1654\n", + "2021-10-21 04:06:25,410 - mmcls - INFO - Saving checkpoint at 2 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 105.5 task/s, elapsed: 15s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:06:40,694 - mmcls - INFO - Epoch(val) [2][51]\taccuracy_top-1: 97.5016\n" + ] + } + ], + "source": [ + "import time\n", + "import mmcv\n", + "import os.path as osp\n", + "\n", + "from mmcls.datasets import build_dataset\n", + "from mmcls.models import build_classifier\n", + "from mmcls.apis import train_model\n", + "\n", + "# Create the work directory\n", + "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", + "# Build the classifier\n", + "model = build_classifier(cfg.model)\n", + "model.init_weights()\n", + "# Build the dataset\n", + "datasets = [build_dataset(cfg.data.train)]\n", + "# Add `CLASSES` attributes to help visualization\n", + "model.CLASSES = datasets[0].CLASSES\n", + "# Start fine-tuning\n", + "train_model(\n", + " model,\n", + " datasets,\n", + " cfg,\n", + " distributed=False,\n", + " validate=True,\n", + " timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),\n", + " meta=dict())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "HsoGBZA3miui", + "outputId": "eb2e09f5-55ce-4165-b754-3b75dbc829ab" + }, + "outputs": [ + { + "data": { + "image/png": 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[2][51]\taccuracy_top-1: 97.5016\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 304 + "e1a3dce90c1a4804a9ef0c687a9c0703": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - "id": "HsoGBZA3miui", - "outputId": "eb2e09f5-55ce-4165-b754-3b75dbc829ab" - }, - "source": [ - "%matplotlib inline\n", - "# Validate the fine-tuned model\n", - "\n", - "img = mmcv.imread('data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg')\n", - "\n", - "model.cfg = cfg\n", - "result = inference_model(model, img)\n", - "\n", - "show_result_pyplot(model, img, result)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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] + } } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb b/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb index adbbeeb3f5c..b81bf870d47 100755 --- a/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb +++ b/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb @@ -1,557 +1,183 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "MMClassification_python_cn.ipynb", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XjQxmm04iTx4" + }, + "source": [ + "\"Open" + ] }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + { + "cell_type": "markdown", + "metadata": { + "id": "UdMfIsMpiODD" + }, + "source": [ + "# MMClassification Python API 教程\n", + "\n", + "在本教程中会介绍如下内容：\n", + "\n", + "* 如何安装 MMClassification\n", + "* 使用 Python API 进行模型推理\n", + "* 使用 Python API 进行模型微调" + ] }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 + { + "cell_type": "markdown", + "metadata": { + "id": "iOl0X9UEiRvE" }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" + "source": [ + "## 安装 MMClassification\n", + "\n", + "在使用 MMClassification 之前，我们需要配置环境，步骤如下：\n", + "\n", + "- 安装 Python, CUDA, C/C++ compiler 和 git\n", + "- 安装 PyTorch (CUDA 版)\n", + "- 安装 mmcv\n", + "- 克隆 mmcls github 代码库然后安装\n", + "\n", + "因为我们在 Google Colab 进行实验，Colab 已经帮我们完成了基本的配置，我们可以直接跳过前面两个步骤 。" + ] }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "badf240bbb7d442fbd214e837edbffe2": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "HBoxView", - "_dom_classes": [], - "_model_name": "HBoxModel", - "_view_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - 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"model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_view_name": "StyleView", - "_model_name": "DescriptionStyleModel", - "description_width": "", - "_view_module": "@jupyter-widgets/base", - "_model_module_version": "1.5.0", - "_view_count": null, - "_view_module_version": "1.2.0", - "_model_module": "@jupyter-widgets/controls" - } - }, - "e1a3dce90c1a4804a9ef0c687a9c0703": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_view_name": "LayoutView", - "grid_template_rows": null, - "right": null, - "justify_content": null, - "_view_module": "@jupyter-widgets/base", - "overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "XjQxmm04iTx4" + "id": "DMw7QwvpiiUO", + "outputId": "33fa5eb8-d083-4a1f-d094-ab0f59e2818e" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Mon_Oct_12_20:09:46_PDT_2020\n", + "Cuda compilation tools, release 11.1, V11.1.105\n", + "Build cuda_11.1.TC455_06.29190527_0\n" + ] + } + ], "source": [ - "\"Open" + "# 检查 nvcc 版本\n", + "!nvcc -V" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 10, "metadata": { - "id": "UdMfIsMpiODD" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VIBU7Fain4D", + "outputId": "ec20652d-ca24-4b82-b407-e90354d728f8" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ], "source": [ - "# MMClassification Python API 教程\n", - "\n", - "在本教程中会介绍如下内容：\n", - "\n", - "* 如何安装 MMClassification\n", - "* 使用 Python API 进行模型推理\n", - "* 使用 Python API 进行模型微调" + "# 检查 GCC 版本\n", + "!gcc --version" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 11, "metadata": { - "id": "iOl0X9UEiRvE" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24lDLCqFisZ9", + "outputId": "30ec9a1c-cdb3-436c-cdc8-f2a22afe254f" }, - "source": [ - "## 安装 MMClassification\n", - "\n", - "在使用 MMClassification 之前，我们需要配置环境，步骤如下：\n", - "\n", - "- 安装 Python, CUDA, C/C++ compiler 和 git\n", - "- 安装 PyTorch (CUDA 版)\n", - "- 安装 mmcv\n", - "- 克隆 mmcls github 代码库然后安装\n", - "\n", - "因为我们在 Google Colab 进行实验，Colab 已经帮我们完成了基本的配置，我们可以直接跳过前面两个步骤 。" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_i7cjqS_LtoP" - }, - "source": [ - "### 检查环境" - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c6MbAw10iUJI", - "outputId": "dd37cdf5-7bcf-4a03-f5b5-4b17c3ca16de" - }, - "source": [ - "%cd /content" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4IyFL3MaiYRu", - "outputId": "5008efdf-0356-4d93-ba9d-e51787036213" - }, - "source": [ - "!pwd" - ], - "execution_count": 8, "outputs": [ { - "output_type": "stream", "name": "stdout", - "text": [ - "/content\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DMw7QwvpiiUO", - "outputId": "33fa5eb8-d083-4a1f-d094-ab0f59e2818e" - }, - "source": [ - "# 检查 nvcc 版本\n", - "!nvcc -V" - ], - "execution_count": 9, - "outputs": [ - { "output_type": "stream", - "name": "stdout", "text": [ - "nvcc: NVIDIA (R) Cuda compiler driver\n", - "Copyright (c) 2005-2020 NVIDIA Corporation\n", - "Built on Mon_Oct_12_20:09:46_PDT_2020\n", - "Cuda compilation tools, release 11.1, V11.1.105\n", - "Build cuda_11.1.TC455_06.29190527_0\n" + "1.9.0+cu111\n", + "True\n" ] } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4VIBU7Fain4D", - "outputId": "ec20652d-ca24-4b82-b407-e90354d728f8" - }, - "source": [ - "# 检查 GCC 版本\n", - "!gcc --version" ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", - "Copyright (C) 2017 Free Software Foundation, Inc.\n", - "This is free software; see the source for copying conditions. There is NO\n", - "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "24lDLCqFisZ9", - "outputId": "30ec9a1c-cdb3-436c-cdc8-f2a22afe254f" - }, "source": [ "# 检查 PyTorch 的安装情况\n", "import torch, torchvision\n", "print(torch.__version__)\n", "print(torch.cuda.is_available())" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1.9.0+cu111\n", - "True\n" - ] - } ] }, { @@ -573,6 +199,7 @@ }, { "cell_type": "code", + "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -580,16 +207,10 @@ "id": "nla40LrLi7oo", "outputId": "162bf14d-0d3e-4540-e85e-a46084a786b1" }, - "source": [ - "# 安装 mmcv\n", - "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", - "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.9.0/index.html" - ], - "execution_count": 17, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", "Collecting mmcv\n", @@ -614,6 +235,11 @@ "Successfully installed addict-2.4.0 mmcv-1.3.15 yapf-0.31.0\n" ] } + ], + "source": [ + "# 安装 mmcv\n", + "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.9.0/index.html" ] }, { @@ -629,6 +255,7 @@ }, { "cell_type": "code", + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -636,19 +263,10 @@ "id": "Bwme6tWHjl5s", "outputId": "eae20624-4695-4cd9-c3e5-9c59596d150a" }, - "source": [ - "# 下载 mmcls 代码库\n", - "!git clone https://github.com/open-mmlab/mmclassification.git\n", - "%cd mmclassification/\n", - "\n", - "# 从源码安装 MMClassification\n", - "!pip install -e . " - ], - "execution_count": 12, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'mmclassification'...\n", "remote: Enumerating objects: 4152, done.\u001b[K\n", @@ -659,10 +277,19 @@ "Resolving deltas: 100% (2524/2524), done.\n" ] } + ], + "source": [ + "# 下载 mmcls 代码库\n", + "!git clone https://github.com/open-mmlab/mmclassification.git\n", + "%cd mmclassification/\n", + "\n", + "# 从源码安装 MMClassification\n", + "!pip install -e . " ] }, { "cell_type": "code", + "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -670,20 +297,19 @@ "id": "hFg_oSG4j3zB", "outputId": "05a91f9b-d41c-4ae7-d4fe-c30a30d3f639" }, - "source": [ - "# 检查 MMClassification 的安装情况\n", - "import mmcls\n", - "print(mmcls.__version__)" - ], - "execution_count": 18, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "0.16.0\n" ] } + ], + "source": [ + "# 检查 MMClassification 的安装情况\n", + "import mmcls\n", + "print(mmcls.__version__)" ] }, { @@ -709,6 +335,7 @@ }, { "cell_type": "code", + "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -716,15 +343,10 @@ "id": "nDQchz8CkJaT", "outputId": "9805bd7d-cc2a-4269-b43d-257412f1df93" }, - "source": [ - "# 获取示例图片\n", - "!wget https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0 -O demo/banana.png" - ], - "execution_count": 19, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "--2021-10-21 03:52:36-- https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0\n", "Resolving www.dropbox.com (www.dropbox.com)... 162.125.3.18, 2620:100:601b:18::a27d:812\n", @@ -748,10 +370,15 @@ "\n" ] } + ], + "source": [ + "# 获取示例图片\n", + "!wget https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0 -O demo/banana.png" ] }, { "cell_type": "code", + "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -760,23 +387,22 @@ "id": "o2eiitWnkQq_", "outputId": "192b3ebb-202b-4d6e-e178-561223024318" }, - "source": [ - "from PIL import Image\n", - "Image.open('demo/banana.png')" - ], - "execution_count": 20, "outputs": [ { - "output_type": "execute_result", "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } + ], + "source": [ + "from PIL import Image\n", + "Image.open('demo/banana.png')" ] }, { @@ -797,6 +423,7 @@ }, { "cell_type": "code", + "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -804,6 +431,15 @@ "id": "VvRoZpBGkgpC", "outputId": "68282782-015e-4f5c-cef2-79be3bf6a9b7" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n" + ] + } + ], "source": [ "# 检查确保配置文件存在\n", "!ls configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n", @@ -812,16 +448,6 @@ "# 其中，权重参数文件的路径可以是一个 url，会在加载权重时自动下载。\n", "config_file = 'configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py'\n", "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth'" - ], - "execution_count": 21, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n" - ] - } ] }, { @@ -839,6 +465,7 @@ }, { "cell_type": "code", + "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -860,21 +487,10 @@ "id": "KwJWlR2QkpiV", "outputId": "982b365e-d3be-4e3d-dee7-c507a8020292" }, - "source": [ - "import mmcv\n", - "from mmcls.apis import inference_model, init_model, show_result_pyplot\n", - "\n", - "# 指明设备，如果你没有开启 GPU，可以使用 CPU， `device='cpu'`.\n", - "device = 'cuda:0'\n", - "# device = 'cpu'\n", - "# 通过配置文件和权重参数文件构建模型\n", - "model = init_model(config_file, checkpoint_file, device=device)" - ], - "execution_count": 22, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", @@ -889,45 +505,56 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Use load_from_http loader\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n" ] }, { - "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "badf240bbb7d442fbd214e837edbffe2", - "version_minor": 0, - "version_major": 2 + "version_major": 2, + "version_minor": 0 }, "text/plain": [ " 0%| | 0.00/13.5M [00:00" ] }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } + ], + "source": [ + "%matplotlib inline\n", + "# 可视化分类结果\n", + "show_result_pyplot(model, img, result)" ] }, { @@ -1050,6 +676,7 @@ }, { "cell_type": "code", + "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1057,17 +684,10 @@ "id": "3vBfU8GGlFPS", "outputId": "b12dadb4-ccbc-45b4-bb08-3d24977ed93c" }, - "source": [ - "# 下载分类数据集文件\n", - "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", - "!mkdir -p data\n", - "!unzip -qo cats_dogs_dataset.zip -d ./data/" - ], - "execution_count": 29, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "--2021-10-21 03:57:58-- https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0\n", "Resolving www.dropbox.com (www.dropbox.com)... 162.125.80.18, 2620:100:6018:18::a27d:312\n", @@ -1095,6 +715,12 @@ "\n" ] } + ], + "source": [ + "# 下载分类数据集文件\n", + "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", + "!mkdir -p data\n", + "!unzip -qo cats_dogs_dataset.zip -d ./data/" ] }, { @@ -1110,13 +736,18 @@ }, { "cell_type": "code", + "execution_count": 31, "metadata": { "id": "WCfnDavFlWrK" }, + "outputs": [], "source": [ "# 载入已经存在的配置文件\n", "from mmcv import Config\n", + "from mmcls.utils import auto_select_device\n", + "\n", "cfg = Config.fromfile('configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py')\n", + "cfg.device = auto_select_device()\n", "\n", "# 修改模型分类头中的类别数目\n", "cfg.model.head.num_classes = 2\n", @@ -1172,9 +803,7 @@ "set_random_seed(0, deterministic=True)\n", "\n", "cfg.gpu_ids = range(1)" - ], - "execution_count": 31, - "outputs": [] + ] }, { "cell_type": "markdown", @@ -1189,6 +818,7 @@ }, { "cell_type": "code", + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1196,39 +826,10 @@ "id": "P7unq5cNmN8G", "outputId": "bf32711b-7bdf-45ee-8db5-e8699d3eff91" }, - "source": [ - "import time\n", - "import mmcv\n", - "import os.path as osp\n", - "\n", - "from mmcls.datasets import build_dataset\n", - "from mmcls.models import build_classifier\n", - "from mmcls.apis import train_model\n", - "\n", - "# 创建工作目录\n", - "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", - "# 创建分类器\n", - "model = build_classifier(cfg.model)\n", - "model.init_weights()\n", - "# 创建数据集\n", - "datasets = [build_dataset(cfg.data.train)]\n", - "# 添加类别属性以方便可视化\n", - "model.CLASSES = datasets[0].CLASSES\n", - "# 开始微调\n", - "train_model(\n", - " model,\n", - " datasets,\n", - " cfg,\n", - " distributed=False,\n", - " validate=True,\n", - " timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),\n", - " meta=dict())" - ], - "execution_count": 32, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "2021-10-21 04:04:12,758 - mmcv - INFO - initialize MobileNetV2 with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', 'prefix': 'backbone'}\n", "2021-10-21 04:04:12,759 - mmcv - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", @@ -1620,15 +1221,15 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Use load_from_http loader\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "2021-10-21 04:04:12,965 - mmcv - INFO - \n", "backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): \n", @@ -1947,92 +1548,494 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 104.1 task/s, elapsed: 15s, ETA: 0s" ] }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "2021-10-21 04:05:27,767 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", - "2021-10-21 04:05:32,987 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:57, time: 0.505, data_time: 0.238, memory: 1709, loss: 0.1764\n", - "2021-10-21 04:05:35,779 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:54, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1514\n", - "2021-10-21 04:05:38,537 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:51, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1395\n", - "2021-10-21 04:05:41,283 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:48, time: 0.275, data_time: 0.020, memory: 1709, loss: 0.1508\n", - "2021-10-21 04:05:44,017 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:44, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.1771\n", - "2021-10-21 04:05:46,800 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:41, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1438\n", - "2021-10-21 04:05:49,570 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:38, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1321\n", - "2021-10-21 04:05:52,314 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:35, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1629\n", - "2021-10-21 04:05:55,052 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:32, time: 0.273, data_time: 0.021, memory: 1709, loss: 0.1574\n", - "2021-10-21 04:05:57,791 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:29, time: 0.274, data_time: 0.019, memory: 1709, loss: 0.1220\n", - "2021-10-21 04:06:00,534 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:26, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.2550\n", - "2021-10-21 04:06:03,295 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:23, time: 0.276, data_time: 0.019, memory: 1709, loss: 0.1528\n", - "2021-10-21 04:06:06,048 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.022, memory: 1709, loss: 0.1223\n", - "2021-10-21 04:06:08,811 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.276, data_time: 0.021, memory: 1709, loss: 0.1734\n", - "2021-10-21 04:06:11,576 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1527\n", - "2021-10-21 04:06:14,330 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1910\n", - "2021-10-21 04:06:17,106 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.277, data_time: 0.019, memory: 1709, loss: 0.1922\n", - "2021-10-21 04:06:19,855 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.274, data_time: 0.023, memory: 1709, loss: 0.1760\n", - "2021-10-21 04:06:22,638 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.278, data_time: 0.019, memory: 1709, loss: 0.1739\n", - "2021-10-21 04:06:25,367 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.020, memory: 1709, loss: 0.1654\n", - "2021-10-21 04:06:25,410 - mmcls - INFO - Saving checkpoint at 2 epochs\n" - ] + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:05:27,767 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", + "2021-10-21 04:05:32,987 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:57, time: 0.505, data_time: 0.238, memory: 1709, loss: 0.1764\n", + "2021-10-21 04:05:35,779 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:54, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1514\n", + "2021-10-21 04:05:38,537 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:51, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1395\n", + "2021-10-21 04:05:41,283 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:48, time: 0.275, data_time: 0.020, memory: 1709, loss: 0.1508\n", + "2021-10-21 04:05:44,017 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:44, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.1771\n", + "2021-10-21 04:05:46,800 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:41, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1438\n", + "2021-10-21 04:05:49,570 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:38, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1321\n", + "2021-10-21 04:05:52,314 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:35, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1629\n", + "2021-10-21 04:05:55,052 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:32, time: 0.273, data_time: 0.021, memory: 1709, loss: 0.1574\n", + "2021-10-21 04:05:57,791 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:29, time: 0.274, data_time: 0.019, memory: 1709, loss: 0.1220\n", + "2021-10-21 04:06:00,534 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:26, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.2550\n", + "2021-10-21 04:06:03,295 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:23, time: 0.276, data_time: 0.019, memory: 1709, loss: 0.1528\n", + "2021-10-21 04:06:06,048 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.022, memory: 1709, loss: 0.1223\n", + "2021-10-21 04:06:08,811 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.276, data_time: 0.021, memory: 1709, loss: 0.1734\n", + "2021-10-21 04:06:11,576 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1527\n", + "2021-10-21 04:06:14,330 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1910\n", + "2021-10-21 04:06:17,106 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.277, data_time: 0.019, memory: 1709, loss: 0.1922\n", + "2021-10-21 04:06:19,855 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.274, data_time: 0.023, memory: 1709, loss: 0.1760\n", + "2021-10-21 04:06:22,638 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.278, data_time: 0.019, memory: 1709, loss: 0.1739\n", + "2021-10-21 04:06:25,367 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.020, memory: 1709, loss: 0.1654\n", + "2021-10-21 04:06:25,410 - mmcls - INFO - Saving checkpoint at 2 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 105.5 task/s, elapsed: 15s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:06:40,694 - mmcls - INFO - Epoch(val) [2][51]\taccuracy_top-1: 97.5016\n" + ] + } + ], + "source": [ + "import time\n", + "import mmcv\n", + "import os.path as osp\n", + "\n", + "from mmcls.datasets import build_dataset\n", + "from mmcls.models import build_classifier\n", + "from mmcls.apis import train_model\n", + "\n", + "# 创建工作目录\n", + "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", + "# 创建分类器\n", + "model = build_classifier(cfg.model)\n", + "model.init_weights()\n", + "# 创建数据集\n", + "datasets = [build_dataset(cfg.data.train)]\n", + "# 添加类别属性以方便可视化\n", + "model.CLASSES = datasets[0].CLASSES\n", + "# 开始微调\n", + "train_model(\n", + " model,\n", + " datasets,\n", + " cfg,\n", + " distributed=False,\n", + " validate=True,\n", + " timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),\n", + " meta=dict())" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "HsoGBZA3miui", + "outputId": "eb2e09f5-55ce-4165-b754-3b75dbc829ab" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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"cell_type": "code", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 304 + "e1a3dce90c1a4804a9ef0c687a9c0703": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - "id": "HsoGBZA3miui", - "outputId": "eb2e09f5-55ce-4165-b754-3b75dbc829ab" - }, - "source": [ - "%matplotlib inline\n", - "# 验证训练好的模型\n", - "\n", - "img = mmcv.imread('data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg')\n", - "\n", - "model.cfg = cfg\n", - "result = inference_model(model, img)\n", - "\n", - "show_result_pyplot(model, img, result)" - ], - "execution_count": 34, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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(#857) --- mmcls/apis/train.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mmcls/apis/train.py b/mmcls/apis/train.py index ed3a37b9b75..40e9531d400 100644 --- a/mmcls/apis/train.py +++ b/mmcls/apis/train.py @@ -209,6 +209,7 @@ def train_model(model, **loader_cfg, 'shuffle': False, # Not shuffle by default 'sampler_cfg': None, # Not use sampler by default + 'drop_last': False, # Not drop last by default **cfg.data.get('val_dataloader', {}), } val_dataloader = build_dataloader(val_dataset, **val_loader_cfg) From 8c7b7b15a37e10a5692d63f34c8919f3fbe6b67d Mon Sep 17 00:00:00 2001 From: takuoko Date: Fri, 30 Sep 2022 15:20:53 +0900 Subject: [PATCH 18/25] [Enhance] RepVGG for YOLOX-PAI. (#1025) * repvgg add ppf for yoloxpai * fix by review * update stem_channels * fix doc Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com> --- mmcls/models/backbones/repvgg.py | 103 ++++++++++++++++-- .../test_models/test_backbones/test_repvgg.py | 78 +++++++++++-- 2 files changed, 159 insertions(+), 22 deletions(-) diff --git a/mmcls/models/backbones/repvgg.py b/mmcls/models/backbones/repvgg.py index ca8cc605006..bbdbda2f48e 100644 --- a/mmcls/models/backbones/repvgg.py +++ b/mmcls/models/backbones/repvgg.py @@ -2,9 +2,11 @@ import torch import torch.nn.functional as F import torch.utils.checkpoint as cp -from mmcv.cnn import build_activation_layer, build_conv_layer, build_norm_layer +from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer, + build_norm_layer) from mmcv.runner import BaseModule, Sequential from mmcv.utils.parrots_wrapper import _BatchNorm +from torch import nn from ..builder import BACKBONES from ..utils.se_layer import SELayer @@ -254,6 +256,51 @@ def _norm_to_conv3x3(self, branch_norm): return tmp_conv3x3 +class MTSPPF(nn.Module): + """MTSPPF block for YOLOX-PAI RepVGG backbone. + + Args: + in_channels (int): The input channels of the block. + out_channels (int): The output channels of the block. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU'). + kernel_size (int): Kernel size of pooling. Default: 5. + """ + + def __init__(self, + in_channels, + out_channels, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + kernel_size=5): + super().__init__() + hidden_features = in_channels // 2 # hidden channels + self.conv1 = ConvModule( + in_channels, + hidden_features, + 1, + stride=1, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.conv2 = ConvModule( + hidden_features * 4, + out_channels, + 1, + stride=1, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.maxpool = nn.MaxPool2d( + kernel_size=kernel_size, stride=1, padding=kernel_size // 2) + + def forward(self, x): + x = self.conv1(x) + y1 = self.maxpool(x) + y2 = self.maxpool(y1) + return self.conv2(torch.cat([x, y1, y2, self.maxpool(y2)], 1)) + + @BACKBONES.register_module() class RepVGG(BaseBackbone): """RepVGG backbone. @@ -262,17 +309,24 @@ class RepVGG(BaseBackbone): `_ Args: - arch (str | dict): The parameter of RepVGG. - If it's a dict, it should contain the following keys: + arch (str | dict): RepVGG architecture. If use string, + choose from 'A0', 'A1`', 'A2', 'B0', 'B1', 'B1g2', 'B1g4', 'B2' + , 'B2g2', 'B2g4', 'B3', 'B3g2', 'B3g4' or 'D2se'. If use dict, + it should have below keys: - num_blocks (Sequence[int]): Number of blocks in each stage. - width_factor (Sequence[float]): Width deflator in each stage. - group_layer_map (dict | None): RepVGG Block that declares the need to apply group convolution. - - se_cfg (dict | None): Se Layer config + - se_cfg (dict | None): Se Layer config. + - stem_channels (int, optional): The stem channels, the final + stem channels will be + ``min(stem_channels, base_channels*width_factor[0])``. + If not set here, 64 is used by default in the code. + in_channels (int): Number of input image channels. Default: 3. - base_channels (int): Base channels of RepVGG backbone, work - with width_factor together. Default: 64. + base_channels (int): Base channels of RepVGG backbone, work with + width_factor together. Defaults to 64. out_indices (Sequence[int]): Output from which stages. Default: (3, ). strides (Sequence[int]): Strides of the first block of each stage. Default: (2, 2, 2, 2). @@ -292,6 +346,7 @@ class RepVGG(BaseBackbone): norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. + add_ppf (bool): Whether to use the MTSPPF block. Default: False. init_cfg (dict or list[dict], optional): Initialization config dict. """ @@ -323,7 +378,8 @@ class RepVGG(BaseBackbone): num_blocks=[4, 6, 16, 1], width_factor=[1, 1, 1, 2.5], group_layer_map=None, - se_cfg=None), + se_cfg=None, + stem_channels=64), 'B1': dict( num_blocks=[4, 6, 16, 1], @@ -383,7 +439,14 @@ class RepVGG(BaseBackbone): num_blocks=[8, 14, 24, 1], width_factor=[2.5, 2.5, 2.5, 5], group_layer_map=None, - se_cfg=dict(ratio=16, divisor=1)) + se_cfg=dict(ratio=16, divisor=1)), + 'yolox-pai-small': + dict( + num_blocks=[3, 5, 7, 3], + width_factor=[1, 1, 1, 1], + group_layer_map=None, + se_cfg=None, + stem_channels=32), } def __init__(self, @@ -400,6 +463,7 @@ def __init__(self, with_cp=False, deploy=False, norm_eval=False, + add_ppf=False, init_cfg=[ dict(type='Kaiming', layer=['Conv2d']), dict( @@ -427,9 +491,9 @@ def __init__(self, if arch['se_cfg'] is not None: assert isinstance(arch['se_cfg'], dict) + self.base_channels = base_channels self.arch = arch self.in_channels = in_channels - self.base_channels = base_channels self.out_indices = out_indices self.strides = strides self.dilations = dilations @@ -441,7 +505,12 @@ def __init__(self, self.with_cp = with_cp self.norm_eval = norm_eval - channels = min(64, int(base_channels * self.arch['width_factor'][0])) + # defaults to 64 to prevert BC-breaking if stem_channels + # not in arch dict; + # the stem channels should not be larger than that of stage1. + channels = min( + arch.get('stem_channels', 64), + int(self.base_channels * self.arch['width_factor'][0])) self.stem = RepVGGBlock( self.in_channels, channels, @@ -459,7 +528,7 @@ def __init__(self, num_blocks = self.arch['num_blocks'][i] stride = self.strides[i] dilation = self.dilations[i] - out_channels = int(base_channels * 2**i * + out_channels = int(self.base_channels * 2**i * self.arch['width_factor'][i]) stage, next_create_block_idx = self._make_stage( @@ -471,6 +540,16 @@ def __init__(self, channels = out_channels + if add_ppf: + self.ppf = MTSPPF( + out_channels, + out_channels, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + kernel_size=5) + else: + self.ppf = None + def _make_stage(self, in_channels, out_channels, num_blocks, stride, dilation, next_create_block_idx, init_cfg): strides = [stride] + [1] * (num_blocks - 1) @@ -507,6 +586,8 @@ def forward(self, x): for i, stage_name in enumerate(self.stages): stage = getattr(self, stage_name) x = stage(x) + if i + 1 == len(self.stages) and self.ppf is not None: + x = self.ppf(x) if i in self.out_indices: outs.append(x) diff --git a/tests/test_models/test_backbones/test_repvgg.py b/tests/test_models/test_backbones/test_repvgg.py index f4c78abcac4..beecdffc906 100644 --- a/tests/test_models/test_backbones/test_repvgg.py +++ b/tests/test_models/test_backbones/test_repvgg.py @@ -202,18 +202,36 @@ def test_repvgg_backbone(): # Test RepVGG forward with layer 3 forward model = RepVGG('A0', out_indices=(3, )) model.init_weights() - model.train() + model.eval() for m in model.modules(): if is_norm(m): assert isinstance(m, _BatchNorm) - imgs = torch.randn(1, 3, 224, 224) + imgs = torch.randn(1, 3, 32, 32) + feat = model(imgs) + assert isinstance(feat, tuple) + assert len(feat) == 1 + assert isinstance(feat[0], torch.Tensor) + assert feat[0].shape == torch.Size((1, 1280, 1, 1)) + + # Test with custom arch + cfg = dict( + num_blocks=[3, 5, 7, 3], + width_factor=[1, 1, 1, 1], + group_layer_map=None, + se_cfg=None, + stem_channels=16) + model = RepVGG(arch=cfg, out_indices=(3, )) + model.eval() + assert model.stem.out_channels == min(16, 64 * 1) + + imgs = torch.randn(1, 3, 32, 32) feat = model(imgs) assert isinstance(feat, tuple) assert len(feat) == 1 assert isinstance(feat[0], torch.Tensor) - assert feat[0].shape == torch.Size((1, 1280, 7, 7)) + assert feat[0].shape == torch.Size((1, 512, 1, 1)) # Test RepVGG forward model_test_settings = [ @@ -233,7 +251,7 @@ def test_repvgg_backbone(): dict(model_name='D2se', out_sizes=(160, 320, 640, 2560)) ] - choose_models = ['A0', 'B1', 'B1g2', 'D2se'] + choose_models = ['A0', 'B1', 'B1g2'] # Test RepVGG model forward for model_test_setting in model_test_settings: if model_test_setting['model_name'] not in choose_models: @@ -241,23 +259,23 @@ def test_repvgg_backbone(): model = RepVGG( model_test_setting['model_name'], out_indices=(0, 1, 2, 3)) model.init_weights() + model.eval() # Test Norm for m in model.modules(): if is_norm(m): assert isinstance(m, _BatchNorm) - model.train() - imgs = torch.randn(1, 3, 224, 224) + imgs = torch.randn(1, 3, 32, 32) feat = model(imgs) assert feat[0].shape == torch.Size( - (1, model_test_setting['out_sizes'][0], 56, 56)) + (1, model_test_setting['out_sizes'][0], 8, 8)) assert feat[1].shape == torch.Size( - (1, model_test_setting['out_sizes'][1], 28, 28)) + (1, model_test_setting['out_sizes'][1], 4, 4)) assert feat[2].shape == torch.Size( - (1, model_test_setting['out_sizes'][2], 14, 14)) + (1, model_test_setting['out_sizes'][2], 2, 2)) assert feat[3].shape == torch.Size( - (1, model_test_setting['out_sizes'][3], 7, 7)) + (1, model_test_setting['out_sizes'][3], 1, 1)) # Test eval of "train" mode and "deploy" mode gap = nn.AdaptiveAvgPool2d(output_size=(1)) @@ -275,11 +293,49 @@ def test_repvgg_backbone(): torch.allclose(feat[i], feat_deploy[i]) torch.allclose(pred, pred_deploy) + # Test RepVGG forward with add_ppf + model = RepVGG('A0', out_indices=(3, ), add_ppf=True) + model.init_weights() + model.train() + + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + + imgs = torch.randn(1, 3, 64, 64) + feat = model(imgs) + assert isinstance(feat, tuple) + assert len(feat) == 1 + assert isinstance(feat[0], torch.Tensor) + assert feat[0].shape == torch.Size((1, 1280, 2, 2)) + + # Test RepVGG forward with 'stem_channels' not in arch + arch = dict( + num_blocks=[2, 4, 14, 1], + width_factor=[0.75, 0.75, 0.75, 2.5], + group_layer_map=None, + se_cfg=None) + model = RepVGG(arch, add_ppf=True) + model.stem.in_channels = min(64, 64 * 0.75) + model.init_weights() + model.train() + + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + + imgs = torch.randn(1, 3, 64, 64) + feat = model(imgs) + assert isinstance(feat, tuple) + assert len(feat) == 1 + assert isinstance(feat[0], torch.Tensor) + assert feat[0].shape == torch.Size((1, 1280, 2, 2)) + def test_repvgg_load(): # Test output before and load from deploy checkpoint model = RepVGG('A1', out_indices=(0, 1, 2, 3)) - inputs = torch.randn((1, 3, 224, 224)) + inputs = torch.randn((1, 3, 32, 32)) ckpt_path = os.path.join(tempfile.gettempdir(), 'ckpt.pth') model.switch_to_deploy() model.eval() From 27b0bd5a724cd439cc098c4d31ee471396dbcfed Mon Sep 17 00:00:00 2001 From: tpoisonooo Date: Fri, 30 Sep 2022 14:22:42 +0800 Subject: [PATCH 19/25] [Fix] Add matplotlib minimum version requriments. (#909) --- requirements/runtime.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/runtime.txt b/requirements/runtime.txt index 80565dbe372..0df372f7fa3 100644 --- a/requirements/runtime.txt +++ b/requirements/runtime.txt @@ -1,3 +1,3 @@ -matplotlib +matplotlib>=3.1.0 numpy packaging From 2102d09dfc96df7206e559b0e25df069bd5a0691 Mon Sep 17 00:00:00 2001 From: JongYoon Lim Date: Fri, 30 Sep 2022 19:30:45 +1300 Subject: [PATCH 20/25] [Docs] Fixed typo for `--out-dir` option of analyze_results.py. (#898) --- docs/en/tools/analysis.md | 4 ++-- docs/zh_CN/tools/analysis.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/en/tools/analysis.md b/docs/en/tools/analysis.md index 13eeea0a4c3..0e583b04afb 100644 --- a/docs/en/tools/analysis.md +++ b/docs/en/tools/analysis.md @@ -157,7 +157,7 @@ python tools/analysis_tools/analyze_results.py \ - `config` : The path of the model config file. - `result`: Output result file in json/pickle format from `tools/test.py`. -- `--out_dir`: Directory to store output files. +- `--out-dir`: Directory to store output files. - `--topk`: The number of images in successful or failed prediction with the highest `topk` scores to save. If not specified, it will be set to 20. - `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md) @@ -171,7 +171,7 @@ In `tools/test.py`, we support using `--out-items` option to select which kind o python tools/analysis_tools/analyze_results.py \ configs/resnet/resnet50_b32x8_imagenet.py \ result.pkl \ - --out_dir results \ + --out-dir results \ --topk 50 ``` diff --git a/docs/zh_CN/tools/analysis.md b/docs/zh_CN/tools/analysis.md index 5f7fcfa3a5b..840ff39cb70 100644 --- a/docs/zh_CN/tools/analysis.md +++ b/docs/zh_CN/tools/analysis.md @@ -157,7 +157,7 @@ python tools/analysis_tools/analyze_results.py \ - `config` ：配置文件的路径。 - `result` ： `tools/test.py` 的输出结果文件。 -- `--out_dir` ：保存结果分析的文件夹路径。 +- `--out-dir` ：保存结果分析的文件夹路径。 - `--topk` ：分别保存多少张预测成功/失败的图像。如果不指定，默认为 `20`。 - `--cfg-options`: 额外的配置选项，会被合入配置文件，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 @@ -171,7 +171,7 @@ python tools/analysis_tools/analyze_results.py \ python tools/analysis_tools/analyze_results.py \ configs/resnet/resnet50_xxxx.py \ result.pkl \ - --out_dir results \ + --out-dir results \ --topk 50 ``` From 4eaaf896182d77c3df894eb5008687127214c856 Mon Sep 17 00:00:00 2001 From: HinGwenWoong Date: Fri, 30 Sep 2022 14:31:45 +0800 Subject: [PATCH 21/25] [Docs] Add version for torchvision to avoide error. (#903) * Add version for torchvision * Add version for torchvision --- README.md | 2 +- README_zh-CN.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1eab19a399e..8401b6b524d 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ Please refer to [changelog.md](docs/en/changelog.md) for more details and other Below are quick steps for installation: ```shell -conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y +conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y conda activate open-mmlab pip3 install openmim mim install mmcv-full diff --git a/README_zh-CN.md b/README_zh-CN.md index 6fee274c7ae..16f56d09114 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -80,7 +80,7 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O 以下是安装的简要步骤： ```shell -conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y +conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y conda activate open-mmlab pip3 install openmim mim install mmcv-full From 1b4e9cd22af563abdffffe11956fa67253f5f4c6 Mon Sep 17 00:00:00 2001 From: Hakjin Lee Date: Fri, 30 Sep 2022 15:41:07 +0900 Subject: [PATCH 22/25] [Improve] replace loop of progressbar in api/test. (#878) --- mmcls/apis/test.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mmcls/apis/test.py b/mmcls/apis/test.py index 3b20c809f92..621962c40ab 100644 --- a/mmcls/apis/test.py +++ b/mmcls/apis/test.py @@ -79,8 +79,7 @@ def single_gpu_test(model, **show_kwargs) batch_size = data['img'].size(0) - for _ in range(batch_size): - prog_bar.update() + prog_bar.update(batch_size) return results From 7dca27dd572176085ce2edac67bd94c8d7c6663b Mon Sep 17 00:00:00 2001 From: Philipp Allgeuer <5592992+pallgeuer@users.noreply.github.com> Date: Fri, 30 Sep 2022 09:01:36 +0200 Subject: [PATCH 23/25] [Fix] Fix warning with `torch.meshgrid`. (#860) * Fix warning with torch.meshgrid * Add torch_meshgrid_ij wrapper * Use `digit_version` instead of packaging package. Co-authored-by: mzr1996 --- tests/test_models/test_utils/test_attention.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/tests/test_models/test_utils/test_attention.py b/tests/test_models/test_utils/test_attention.py index 9626f66fecd..cc37d13415d 100644 --- a/tests/test_models/test_utils/test_attention.py +++ b/tests/test_models/test_utils/test_attention.py @@ -1,18 +1,25 @@ # Copyright (c) OpenMMLab. All rights reserved. +from functools import partial from unittest import TestCase from unittest.mock import ANY, MagicMock import pytest import torch +from mmcv.utils import TORCH_VERSION, digit_version from mmcls.models.utils.attention import ShiftWindowMSA, WindowMSA +if digit_version(TORCH_VERSION) >= digit_version('1.10.0a0'): + torch_meshgrid_ij = partial(torch.meshgrid, indexing='ij') +else: + torch_meshgrid_ij = torch.meshgrid # Uses indexing='ij' by default + def get_relative_position_index(window_size): """Method from original code of Swin-Transformer.""" coords_h = torch.arange(window_size[0]) coords_w = torch.arange(window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords = torch.stack(torch_meshgrid_ij([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww # 2, Wh*Ww, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] From c5bcd4801afadf3f46d4de65b2179c1f7ef94d55 Mon Sep 17 00:00:00 2001 From: Mengyang Liu <49838178+liu-mengyang@users.noreply.github.com> Date: Fri, 30 Sep 2022 15:02:24 +0800 Subject: [PATCH 24/25] [Docs] Fix typo in config.md. (#827) --- docs/en/tutorials/config.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en/tutorials/config.md b/docs/en/tutorials/config.md index 26740467d3b..16e43ac27ef 100644 --- a/docs/en/tutorials/config.md +++ b/docs/en/tutorials/config.md @@ -324,7 +324,7 @@ data = dict( Sometimes, you need to set `_delete_=True` to ignore some domain content in the basic configuration file. You can refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) for more instructions. -The following is an example. If you wangt to use cosine schedule in the above ResNet50 case, just using inheritance and directly modify it will report `get unexcepected keyword'step'` error, because the `'step'` field of the basic config in `lr_config` domain information is reserved, and you need to add `_delete_ =True` to ignore the content of `lr_config` related fields in the basic configuration file: +The following is an example. If you want to use cosine schedule in the above ResNet50 case, just using inheritance and directly modify it will report `get unexcepected keyword'step'` error, because the `'step'` field of the basic config in `lr_config` domain information is reserved, and you need to add `_delete_ =True` to ignore the content of `lr_config` related fields in the basic configuration file: ```python _base_ = '../../configs/resnet/resnet50_8xb32_in1k.py' From 7b45eb10cdeeec14d01c656f100f3c6edde04ddd Mon Sep 17 00:00:00 2001 From: Ma Zerun Date: Fri, 30 Sep 2022 18:03:53 +0800 Subject: [PATCH 25/25] Bump version to v0.24.0 (#1067) --- README.md | 18 +++++++++-------- README_zh-CN.md | 15 +++++++------- docker/serve/Dockerfile | 4 ++-- docs/en/changelog.md | 44 +++++++++++++++++++++++++++++++++++++++++ docs/en/faq.md | 3 ++- docs/zh_CN/faq.md | 3 ++- mmcls/version.py | 2 +- 7 files changed, 68 insertions(+), 21 deletions(-) diff --git a/README.md b/README.md index 8401b6b524d..d129e7d04e5 100644 --- a/README.md +++ b/README.md @@ -58,6 +58,16 @@ The master branch works with **PyTorch 1.5+**. ## What's new +The MMClassification 1.0 has released! It's still unstable and in release candidate. If you want to try it, go +to [the 1.x branch](https://github.com/open-mmlab/mmclassification/tree/1.x) and discuss it with us in +[the discussion](https://github.com/open-mmlab/mmclassification/discussions). + +v0.24.0 was released in 30/9/2022. +Highlights of the new version: + +- Support **HorNet**, **EfficientFormerm**, **SwinTransformer V2** and **MViT** backbones. +- Support Standford Cars dataset. + v0.23.0 was released in 1/5/2022. Highlights of the new version: @@ -65,14 +75,6 @@ Highlights of the new version: - Support training on IPU. - New style API docs, welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html). -v0.22.0 was released in 30/3/2022. - -Highlights of the new version: - -- Support a series of **CSP Network**, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet. -- A new `CustomDataset` class to help you **build dataset of yourself**! -- Support new backbones - **ConvMixer**, **RepMLP** and new dataset - **CUB dataset**. - Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history. ## Installation diff --git a/README_zh-CN.md b/README_zh-CN.md index 16f56d09114..9e407d1c22f 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -57,6 +57,13 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O ## 更新日志 +MMClassification 1.0 已经发布！目前仍在公测中，如果希望试用，请切换到 [1.x 分支](https://github.com/open-mmlab/mmclassification/tree/1.x)，并在[讨论版](https://github.com/open-mmlab/mmclassification/discussions) 参加开发讨论！ + +2022/9/30 发布了 v0.24.0 版本 + +- 支持了 **HorNet**，**EfficientFormerm**，**SwinTransformer V2**，**MViT** 等主干网络。 +- 支持了 Support Standford Cars 数据集。 + 2022/5/1 发布了 v0.23.0 版本 新版本亮点： @@ -65,14 +72,6 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O - 支持在 IPU 上进行训练。 - 更新了 API 文档的样式，更方便查阅，[欢迎查阅](https://mmclassification.readthedocs.io/en/master/api/models.html)。 -2022/3/30 发布了 v0.22.0 版本 - -新版本亮点： - -- 支持了一系列 **CSP Net**，包括 CSP-ResNet，CSP-ResNeXt 和 CSP-DarkNet。 -- 我们提供了一个新的 `CustomDataset` 类，这个类将帮助你轻松使用**自己的数据集**！ -- 支持了新的主干网络 **ConvMixer**、**RepMLP** 和一个新的数据集 **CUB dataset**。 - 发布历史和更新细节请参考 [更新日志](docs/en/changelog.md) ## 安装 diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile index c80d95d0e2a..b7481d2f5e6 100644 --- a/docker/serve/Dockerfile +++ b/docker/serve/Dockerfile @@ -3,8 +3,8 @@ ARG CUDA="10.2" ARG CUDNN="7" FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel -ARG MMCV="1.4.2" -ARG MMCLS="0.23.2" +ARG MMCV="1.6.2" +ARG MMCLS="0.24.0" ENV PYTHONUNBUFFERED TRUE diff --git a/docs/en/changelog.md b/docs/en/changelog.md index 8025e1631db..f478cfab74e 100644 --- a/docs/en/changelog.md +++ b/docs/en/changelog.md @@ -1,5 +1,49 @@ # Changelog +## v0.24.0(30/9/2022) + +### Highlights + +- Support HorNet, EfficientFormerm, SwinTransformer V2 and MViT backbones. +- Support Standford Cars dataset. + +### New Features + +- Support HorNet Backbone. ([#1013](https://github.com/open-mmlab/mmclassification/pull/1013)) +- Support EfficientFormer. ([#954](https://github.com/open-mmlab/mmclassification/pull/954)) +- Support Stanford Cars dataset. ([#893](https://github.com/open-mmlab/mmclassification/pull/893)) +- Support CSRA head. ([#881](https://github.com/open-mmlab/mmclassification/pull/881)) +- Support Swin Transform V2. ([#799](https://github.com/open-mmlab/mmclassification/pull/799)) +- Support MViT and add checkpoints. ([#924](https://github.com/open-mmlab/mmclassification/pull/924)) + +### Improvements + +- \[Improve\] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878)) +- \[Enhance\] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025)) +- \[Enhancement\] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017)) +- \[Refactor\] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965)) +- \[Improve\] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915)) +- \[Improve\] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985)) +- \[Improve\] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964)) +- \[Improve\] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953)) + +### Bug Fixes + +- Fix warning with `torch.meshgrid`. ([#860](https://github.com/open-mmlab/mmclassification/pull/860)) +- Add matplotlib minimum version requriments. ([#909](https://github.com/open-mmlab/mmclassification/pull/909)) +- val loader should not drop last by default. ([#857](https://github.com/open-mmlab/mmclassification/pull/857)) +- Fix config.device bug in toturial. ([#1059](https://github.com/open-mmlab/mmclassification/pull/1059)) +- Fix attenstion clamp max params ([#1034](https://github.com/open-mmlab/mmclassification/pull/1034)) +- Fix device mismatch in Swin-v2. ([#976](https://github.com/open-mmlab/mmclassification/pull/976)) +- Fix the output position of Swin-Transformer. ([#947](https://github.com/open-mmlab/mmclassification/pull/947)) + +### Docs Update + +- Fix typo in config.md. ([#827](https://github.com/open-mmlab/mmclassification/pull/827)) +- Add version for torchvision to avoide error. ([#903](https://github.com/open-mmlab/mmclassification/pull/903)) +- Fixed typo for `--out-dir` option of analyze_results.py. ([#898](https://github.com/open-mmlab/mmclassification/pull/898)) +- Refine the docstring of RegNet ([#935](https://github.com/open-mmlab/mmclassification/pull/935)) + ## v0.23.2(28/7/2022) ### New Features diff --git a/docs/en/faq.md b/docs/en/faq.md index e14ee3d1343..6e48a550352 100644 --- a/docs/en/faq.md +++ b/docs/en/faq.md @@ -18,7 +18,8 @@ and make sure you fill in all required information in the template. | MMClassification version | MMCV version | | :----------------------: | :--------------------: | | dev | mmcv>=1.6.0, \<1.7.0 | - | 0.23.2 (master) | mmcv>=1.4.2, \<1.7.0 | + | 0.24.0 (master) | mmcv>=1.4.2, \<1.7.0 | + | 0.23.2 | mmcv>=1.4.2, \<1.7.0 | | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | | 0.20.1 | mmcv>=1.4.2, \<=1.5.0 | diff --git a/docs/zh_CN/faq.md b/docs/zh_CN/faq.md index f1f6e1194a8..3ea4f38b58b 100644 --- a/docs/zh_CN/faq.md +++ b/docs/zh_CN/faq.md @@ -16,7 +16,8 @@ | MMClassification version | MMCV version | | :----------------------: | :--------------------: | | dev | mmcv>=1.6.0, \<1.7.0 | - | 0.23.2 (master) | mmcv>=1.4.2, \<1.6.0 | + | 0.24.0 (master) | mmcv>=1.4.2, \<1.7.0 | + | 0.23.2 | mmcv>=1.4.2, \<1.7.0 | | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | | 0.20.1 | mmcv>=1.4.2, \<=1.5.0 | diff --git a/mmcls/version.py b/mmcls/version.py index fbf17262db5..8980d13f498 100644 --- a/mmcls/version.py +++ b/mmcls/version.py @@ -1,6 +1,6 @@ # Copyright (c) OpenMMLab. All rights reserved -__version__ = '0.23.2' +__version__ = '0.24.0' def parse_version_info(version_str):