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Burak Mandira
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ADD soft teacher code
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‎LICENSE‎

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MIT License
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3-
Copyright (c) 2022 utkutpcgl
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Copyright (c) 2021 Microsoft
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎Makefile‎

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pre:
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python -m pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
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mkdir -p thirdparty
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git clone https://github.com/open-mmlab/mmdetection.git thirdparty/mmdetection
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cd thirdparty/mmdetection && python -m pip install -e .
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install:
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make pre
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python -m pip install -e .
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clean:
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rm -rf thirdparty
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rm -r ssod.egg-info

‎configs/baseline/base.py‎

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mmdet_base = "../../thirdparty/mmdetection/configs/_base_"
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_base_ = [
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f"{mmdet_base}/models/faster_rcnn_r50_fpn.py",
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f"{mmdet_base}/datasets/coco_detection.py",
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f"{mmdet_base}/schedules/schedule_1x.py",
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f"{mmdet_base}/default_runtime.py",
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]
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model = dict(
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backbone=dict(
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norm_cfg=dict(requires_grad=False),
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norm_eval=True,
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style="caffe",
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init_cfg=dict(
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type="Pretrained", checkpoint="open-mmlab://detectron2/resnet50_caffe"
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),
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)
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)
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img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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train_pipeline = [
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dict(type="LoadImageFromFile"),
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dict(type="LoadAnnotations", with_bbox=True),
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dict(
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type="Sequential",
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transforms=[
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dict(
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type="RandResize",
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img_scale=[(1333, 400), (1333, 1200)],
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multiscale_mode="range",
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keep_ratio=True,
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),
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dict(type="RandFlip", flip_ratio=0.5),
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dict(
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type="OneOf",
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transforms=[
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dict(type=k)
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for k in [
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"Identity",
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"AutoContrast",
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"RandEqualize",
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"RandSolarize",
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"RandColor",
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"RandContrast",
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"RandBrightness",
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"RandSharpness",
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"RandPosterize",
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]
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],
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),
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],
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),
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dict(type="Pad", size_divisor=32),
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dict(type="Normalize", **img_norm_cfg),
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dict(type="ExtraAttrs", tag="sup"),
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dict(type="DefaultFormatBundle"),
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dict(
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type="Collect",
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keys=["img", "gt_bboxes", "gt_labels"],
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meta_keys=(
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"filename",
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"ori_shape",
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"img_shape",
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"img_norm_cfg",
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"pad_shape",
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"scale_factor",
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"tag",
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),
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),
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]
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test_pipeline = [
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dict(type="LoadImageFromFile"),
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dict(
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type="MultiScaleFlipAug",
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type="Resize", keep_ratio=True),
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dict(type="RandomFlip"),
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dict(type="Normalize", **img_norm_cfg),
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dict(type="Pad", size_divisor=32),
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dict(type="ImageToTensor", keys=["img"]),
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dict(type="Collect", keys=["img"]),
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],
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),
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]
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data = dict(
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samples_per_gpu=1,
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workers_per_gpu=1,
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline),
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)
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optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
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lr_config = dict(step=[120000, 160000])
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runner = dict(_delete_=True, type="IterBasedRunner", max_iters=180000)
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checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=10)
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evaluation = dict(interval=4000)
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fp16 = dict(loss_scale="dynamic")
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log_config = dict(
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interval=50,
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hooks=[
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dict(type="TextLoggerHook", by_epoch=False),
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dict(
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type="WandbLoggerHook",
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init_kwargs=dict(
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project="pre_release",
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name="${cfg_name}",
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config=dict(
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work_dirs="${work_dir}",
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total_step="${runner.max_iters}",
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),
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),
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by_epoch=False,
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),
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],
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)

‎configs/baseline/faster_rcnn_r101_caffe_fpn_coco_full_720k.py‎

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_base_ = "base.py"
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model = dict(
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backbone=dict(
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depth=101,
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init_cfg=dict(checkpoint="open-mmlab://detectron2/resnet101_caffe"),
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)
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)
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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ann_file="data/coco/annotations/instances_train2017.json",
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img_prefix="data/coco/train2017/",
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),
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)
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optimizer = dict(lr=0.02)
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lr_config = dict(step=[120000 * 4, 160000 * 4])
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runner = dict(_delete_=True, type="IterBasedRunner", max_iters=180000 * 4)

‎configs/baseline/faster_rcnn_r50_caffe_fpn_coco_full_720k.py‎

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_base_ = "base.py"
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data = dict(
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samples_per_gpu=2,
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workers_per_gpu=2,
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train=dict(
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ann_file="data/coco/annotations/instances_train2017.json",
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img_prefix="data/coco/train2017/",
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),
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)
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optimizer = dict(lr=0.02)
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lr_config = dict(step=[120000 * 4, 160000 * 4])
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runner = dict(_delete_=True, type="IterBasedRunner", max_iters=180000 * 4)

‎configs/baseline/faster_rcnn_r50_caffe_fpn_coco_partial_180k.py‎

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_base_ = "base.py"
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fold = 1
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percent = 1
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data = dict(
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samples_per_gpu=1,
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workers_per_gpu=1,
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train=dict(
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ann_file="data/coco/annotations/semi_supervised/instances_train2017.${fold}@${percent}.json",
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img_prefix="data/coco/train2017/",
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),
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)
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work_dir = "work_dirs/${cfg_name}/${percent}/${fold}"
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log_config = dict(
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interval=50,
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hooks=[
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dict(type="TextLoggerHook"),
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dict(
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type="WandbLoggerHook",
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init_kwargs=dict(
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project="pre_release",
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name="${cfg_name}",
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config=dict(
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fold="${fold}",
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percent="${percent}",
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work_dirs="${work_dir}",
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total_step="${runner.max_iters}",
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),
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),
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by_epoch=False,
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),
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],
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)

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