logs/redet_ssp_dotav10.json

{"env_info": "sys.platform: linux\nPython: 3.8.0 (default, Nov  6 2019, 21:49:08) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.2, V11.2.67\nGCC: gcc (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0\nPyTorch: 1.11.0+cu113\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.3\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.2\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.12.0+cu113\nOpenCV: 4.8.1\nMMCV: 1.7.0\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMRotate: 0.3.4+bd27831", "config": "dataset_type = 'DOTADataset'\ndata_root = 'datasets/dota/split_ss_dota_v10/'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True),\n    dict(type='RResize', img_scale=(1024, 1024)),\n    dict(\n        type='RRandomFlip',\n        flip_ratio=[0.25, 0.25, 0.25],\n        direction=['horizontal', 'vertical', 'diagonal'],\n        version='le90'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='Pad', size_divisor=32),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1024, 1024),\n        flip=False,\n        transforms=[\n            dict(type='RResize'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ndata = dict(\n    samples_per_gpu=2,\n    workers_per_gpu=2,\n    train=dict(\n        type='DOTADataset',\n        ann_file='pseudo_labels/ssp_dotav10_hybrid/',\n        img_prefix='datasets/dota/split_ss_dota_v10/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(type='RResize', img_scale=(1024, 1024)),\n            dict(\n                type='RRandomFlip',\n                flip_ratio=[0.25, 0.25, 0.25],\n                direction=['horizontal', 'vertical', 'diagonal'],\n                version='le90'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='Pad', size_divisor=32),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n        ],\n        version='le90'),\n    val=dict(\n        type='DOTADataset',\n        ann_file='datasets/dota/split_ss_dota_v10/trainval/annfiles/',\n        img_prefix='datasets/dota/split_ss_dota_v10/trainval/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='DefaultFormatBundle'),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ],\n        version='le90'),\n    test=dict(\n        type='DOTADataset',\n        ann_file='datasets/dota/split_ss_dota_v10/test/images/',\n        img_prefix='datasets/dota/split_ss_dota_v10/test/images/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1024, 1024),\n                flip=False,\n                transforms=[\n                    dict(type='RResize'),\n                    dict(\n                        type='Normalize',\n                        mean=[123.675, 116.28, 103.53],\n                        std=[58.395, 57.12, 57.375],\n                        to_rgb=True),\n                    dict(type='Pad', size_divisor=32),\n                    dict(type='DefaultFormatBundle'),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ],\n        version='le90'))\nevaluation = dict(interval=1, metric='mAP')\noptimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.3333333333333333,\n    step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nangle_version = 'le90'\nclasses = ('plane', 'baseball-diamond', 'bridge', 'ground-track-field',\n           'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',\n           'basketball-court', 'storage-tank', 'soccer-ball-field',\n           'roundabout', 'harbor', 'swimming-pool', 'helicopter')\nmodel = dict(\n    type='ReDet',\n    backbone=dict(\n        type='ReResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        frozen_stages=1,\n        style='pytorch',\n        pretrained='pretrained/re_resnet50_c8_batch256-25b16846.pth'),\n    neck=dict(\n        type='ReFPN',\n        in_channels=[256, 512, 1024, 2048],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='RotatedRPNHead',\n        in_channels=256,\n        feat_channels=256,\n        version='le90',\n        anchor_generator=dict(\n            type='AnchorGenerator',\n            scales=[8],\n            ratios=[0.5, 1.0, 2.0],\n            strides=[4, 8, 16, 32, 64]),\n        bbox_coder=dict(\n            type='DeltaXYWHBBoxCoder',\n            target_means=[0.0, 0.0, 0.0, 0.0],\n            target_stds=[1.0, 1.0, 1.0, 1.0]),\n        loss_cls=dict(\n            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n        loss_bbox=dict(\n            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),\n    roi_head=dict(\n        type='RoITransRoIHead',\n        version='le90',\n        num_stages=2,\n        stage_loss_weights=[1, 1],\n        bbox_roi_extractor=[\n            dict(\n                type='SingleRoIExtractor',\n                roi_layer=dict(\n                    type='RoIAlign', output_size=7, sampling_ratio=0),\n                out_channels=256,\n                featmap_strides=[4, 8, 16, 32]),\n            dict(\n                type='RotatedSingleRoIExtractor',\n                roi_layer=dict(\n                    type='RiRoIAlignRotated',\n                    out_size=7,\n                    num_samples=2,\n                    num_orientations=8,\n                    clockwise=True),\n                out_channels=256,\n                featmap_strides=[4, 8, 16, 32])\n        ],\n        bbox_head=[\n            dict(\n                type='RotatedShared2FCBBoxHead',\n                in_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=15,\n                bbox_coder=dict(\n                    type='DeltaXYWHAHBBoxCoder',\n                    angle_range='le90',\n                    norm_factor=2,\n                    edge_swap=True,\n                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.1, 0.1, 0.2, 0.2, 1]),\n                reg_class_agnostic=True,\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,\n                               loss_weight=1.0)),\n            dict(\n                type='RotatedShared2FCBBoxHead',\n                in_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=15,\n                bbox_coder=dict(\n                    type='DeltaXYWHAOBBoxCoder',\n                    angle_range='le90',\n                    norm_factor=None,\n                    edge_swap=True,\n                    proj_xy=True,\n                    target_means=[0.0, 0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.05, 0.05, 0.1, 0.1, 0.5]),\n                reg_class_agnostic=False,\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))\n        ]),\n    train_cfg=dict(\n        rpn=dict(\n            assigner=dict(\n                type='MaxIoUAssigner',\n                pos_iou_thr=0.7,\n                neg_iou_thr=0.3,\n                min_pos_iou=0.3,\n                gpu_assign_thr=600,\n                match_low_quality=True,\n                ignore_iof_thr=-1),\n            sampler=dict(\n                type='RandomSampler',\n                num=256,\n                pos_fraction=0.5,\n                neg_pos_ub=-1,\n                add_gt_as_proposals=False),\n            allowed_border=0,\n            pos_weight=-1,\n            debug=False),\n        rpn_proposal=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=[\n            dict(\n                assigner=dict(\n                    type='MaxIoUAssigner',\n                    pos_iou_thr=0.5,\n                    neg_iou_thr=0.5,\n                    min_pos_iou=0.5,\n                    gpu_assign_thr=600,\n                    match_low_quality=False,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='BboxOverlaps2D')),\n                sampler=dict(\n                    type='RandomSampler',\n                    num=512,\n                    pos_fraction=0.25,\n                    neg_pos_ub=-1,\n                    add_gt_as_proposals=True),\n                pos_weight=-1,\n                debug=False),\n            dict(\n                assigner=dict(\n                    type='MaxIoUAssigner',\n                    pos_iou_thr=0.5,\n                    neg_iou_thr=0.5,\n                    min_pos_iou=0.5,\n                    gpu_assign_thr=600,\n                    match_low_quality=False,\n                    ignore_iof_thr=-1,\n                    iou_calculator=dict(type='RBboxOverlaps2D')),\n                sampler=dict(\n                    type='RRandomSampler',\n                    num=512,\n                    pos_fraction=0.25,\n                    neg_pos_ub=-1,\n                    add_gt_as_proposals=True),\n                pos_weight=-1,\n                debug=False)\n        ]),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=2000,\n            max_per_img=2000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            nms_pre=2000,\n            min_bbox_size=0,\n            score_thr=0.05,\n            nms=dict(iou_thr=0.1),\n            max_per_img=2000)))\nwork_dir = './work_dirs/redet_ssp_dotav10'\nauto_resume = False\ngpu_ids = range(0, 4)\n", "seed": 2423, "exp_name": "redet_ssp_dotav10.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.00797, "memory": 6679, "data_time": 0.05411, "loss_rpn_cls": 0.36769, "loss_rpn_bbox": 0.07577, "s0.loss_cls": 0.53929, "s0.acc": 91.39209, "s0.loss_bbox": 0.21109, "s1.loss_cls": 0.40506, "s1.acc": 93.11328, "s1.loss_bbox": 0.1506, "loss": 1.7495, "grad_norm": 7.11657, "time": 0.53532}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.00931, "memory": 6679, "data_time": 0.004, "loss_rpn_cls": 0.18509, "loss_rpn_bbox": 0.05937, "s0.loss_cls": 0.34487, "s0.acc": 92.46387, "s0.loss_bbox": 0.18029, "s1.loss_cls": 0.3002, "s1.acc": 93.6693, "s1.loss_bbox": 0.24838, "loss": 1.3182, "grad_norm": 2.68916, "time": 0.48454}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.01064, "memory": 6679, "data_time": 0.00466, "loss_rpn_cls": 0.15912, "loss_rpn_bbox": 0.06481, "s0.loss_cls": 0.39342, "s0.acc": 89.99658, "s0.loss_bbox": 0.23775, "s1.loss_cls": 0.34883, "s1.acc": 91.24312, "s1.loss_bbox": 0.39042, "loss": 1.59435, "grad_norm": 3.09525, "time": 0.48101}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.01197, "memory": 6679, "data_time": 0.00396, "loss_rpn_cls": 0.11889, "loss_rpn_bbox": 0.06098, "s0.loss_cls": 0.39636, "s0.acc": 89.40967, "s0.loss_bbox": 0.24799, "s1.loss_cls": 0.34526, "s1.acc": 90.79223, "s1.loss_bbox": 0.43487, "loss": 1.60435, "grad_norm": 3.32112, "time": 0.46823}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.01331, "memory": 7287, "data_time": 0.00399, "loss_rpn_cls": 0.10681, "loss_rpn_bbox": 0.06683, "s0.loss_cls": 0.39162, "s0.acc": 88.99072, "s0.loss_bbox": 0.2565, "s1.loss_cls": 0.339, "s1.acc": 90.65487, "s1.loss_bbox": 0.4671, "loss": 1.62785, "grad_norm": 3.18658, "time": 0.48744}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.01464, "memory": 7287, "data_time": 0.00436, "loss_rpn_cls": 0.10564, "loss_rpn_bbox": 0.06258, "s0.loss_cls": 0.35538, "s0.acc": 89.41797, "s0.loss_bbox": 0.26369, "s1.loss_cls": 0.30493, "s1.acc": 91.08369, "s1.loss_bbox": 0.45686, "loss": 1.54908, "grad_norm": 2.90278, "time": 0.47764}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.01597, "memory": 7287, "data_time": 0.00412, "loss_rpn_cls": 0.08956, "loss_rpn_bbox": 0.05604, "s0.loss_cls": 0.31536, "s0.acc": 90.15527, "s0.loss_bbox": 0.24077, "s1.loss_cls": 0.27599, "s1.acc": 91.54889, "s1.loss_bbox": 0.44467, "loss": 1.42238, "grad_norm": 2.65448, "time": 0.48852}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.01731, "memory": 7287, "data_time": 0.00434, "loss_rpn_cls": 0.09336, "loss_rpn_bbox": 0.06547, "s0.loss_cls": 0.34715, "s0.acc": 88.89502, "s0.loss_bbox": 0.27276, "s1.loss_cls": 0.30013, "s1.acc": 90.6959, "s1.loss_bbox": 0.49096, "loss": 1.56983, "grad_norm": 2.87844, "time": 0.4859}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.01864, "memory": 7287, "data_time": 0.00418, "loss_rpn_cls": 0.08221, "loss_rpn_bbox": 0.05656, "s0.loss_cls": 0.301, "s0.acc": 90.10254, "s0.loss_bbox": 0.2486, "s1.loss_cls": 0.26402, "s1.acc": 91.45245, "s1.loss_bbox": 0.47347, "loss": 1.42586, "grad_norm": 2.61819, "time": 0.47868}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.01997, "memory": 7287, "data_time": 0.00444, "loss_rpn_cls": 0.08645, "loss_rpn_bbox": 0.05701, "s0.loss_cls": 0.29701, "s0.acc": 90.29492, "s0.loss_bbox": 0.24979, "s1.loss_cls": 0.25918, "s1.acc": 91.48217, "s1.loss_bbox": 0.47982, "loss": 1.42926, "grad_norm": 2.52553, "time": 0.65152}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.02, "memory": 7287, "data_time": 0.00421, "loss_rpn_cls": 0.09085, "loss_rpn_bbox": 0.05966, "s0.loss_cls": 0.30204, "s0.acc": 90.10107, "s0.loss_bbox": 0.24442, "s1.loss_cls": 0.26668, "s1.acc": 91.45487, "s1.loss_bbox": 0.46935, "loss": 1.433, "grad_norm": 2.56601, "time": 0.47874}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.02, "memory": 7287, "data_time": 0.00421, "loss_rpn_cls": 0.08132, "loss_rpn_bbox": 0.05989, "s0.loss_cls": 0.29465, "s0.acc": 89.8418, "s0.loss_bbox": 0.25177, "s1.loss_cls": 0.26389, "s1.acc": 91.14422, "s1.loss_bbox": 0.47912, "loss": 1.43063, "grad_norm": 2.52113, "time": 0.4866}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.02, "memory": 7287, "data_time": 0.00406, "loss_rpn_cls": 0.08408, "loss_rpn_bbox": 0.05871, "s0.loss_cls": 0.28385, "s0.acc": 90.18018, "s0.loss_bbox": 0.24779, "s1.loss_cls": 0.2516, "s1.acc": 91.33636, "s1.loss_bbox": 0.48179, "loss": 1.40782, "grad_norm": 2.29889, "time": 0.48921}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.02, "memory": 7287, "data_time": 0.00416, "loss_rpn_cls": 0.07166, "loss_rpn_bbox": 0.04925, "s0.loss_cls": 0.26409, "s0.acc": 90.75342, "s0.loss_bbox": 0.22136, "s1.loss_cls": 0.23675, "s1.acc": 91.84888, "s1.loss_bbox": 0.4877, "loss": 1.3308, "grad_norm": 2.18524, "time": 0.48627}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.02, "memory": 7287, "data_time": 0.00448, "loss_rpn_cls": 0.06905, "loss_rpn_bbox": 0.05443, "s0.loss_cls": 0.27363, "s0.acc": 90.604, "s0.loss_bbox": 0.23346, "s1.loss_cls": 0.24344, "s1.acc": 91.94449, "s1.loss_bbox": 0.4486, "loss": 1.3226, "grad_norm": 2.29082, "time": 0.67614}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.02, "memory": 7287, "data_time": 0.00472, "loss_rpn_cls": 0.0667, "loss_rpn_bbox": 0.05195, "s0.loss_cls": 0.25624, "s0.acc": 91.07324, "s0.loss_bbox": 0.20872, "s1.loss_cls": 0.23213, "s1.acc": 92.07942, "s1.loss_bbox": 0.44344, "loss": 1.25918, "grad_norm": 2.22955, "time": 0.51385}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.02, "memory": 7287, "data_time": 0.0048, "loss_rpn_cls": 0.06491, "loss_rpn_bbox": 0.05365, "s0.loss_cls": 0.25158, "s0.acc": 90.96143, "s0.loss_bbox": 0.21971, "s1.loss_cls": 0.2283, "s1.acc": 91.98397, "s1.loss_bbox": 0.45505, "loss": 1.27319, "grad_norm": 2.14411, "time": 0.67496}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.02, "memory": 7287, "data_time": 0.00405, "loss_rpn_cls": 0.05962, "loss_rpn_bbox": 0.04817, "s0.loss_cls": 0.24601, "s0.acc": 91.20898, "s0.loss_bbox": 0.22653, "s1.loss_cls": 0.22066, "s1.acc": 92.17732, "s1.loss_bbox": 0.46266, "loss": 1.26365, "grad_norm": 2.06953, "time": 0.49217}
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