{"env_info": "sys.platform: linux\nPython: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/lustre/share/cuda-11.1\nNVCC: Cuda compilation tools, release 11.1, V11.1.74\nGCC: gcc (GCC) 7.3.0\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: 4.7.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.28.1+", "config": "model = dict(\n    type='CascadeRCNN',\n    backbone=dict(\n        type='InternImage',\n        core_op='DCNv3',\n        channels=192,\n        depths=[5, 5, 24, 5],\n        groups=[12, 24, 48, 96],\n        mlp_ratio=4.0,\n        drop_path_rate=0.5,\n        norm_layer='LN',\n        layer_scale=1.0,\n        offset_scale=2.0,\n        post_norm=True,\n        with_cp=False,\n        out_indices=(0, 1, 2, 3),\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            '/mnt/petrelfs/share_data/huangzhenhang/code/internimage/checkpoint_dir/cls/internimage_xl_22k_192to384.pth'\n        )),\n    neck=dict(\n        type='FPN',\n        in_channels=[192, 384, 768, 1536],\n        out_channels=256,\n        num_outs=5),\n    rpn_head=dict(\n        type='RPNHead',\n        in_channels=256,\n        feat_channels=256,\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='CascadeRoIHead',\n        num_stages=3,\n        stage_loss_weights=[1, 0.5, 0.25],\n        bbox_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        bbox_head=[\n            dict(\n                type='ConvFCBBoxHead',\n                num_shared_convs=4,\n                num_shared_fcs=1,\n                in_channels=256,\n                conv_out_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=80,\n                bbox_coder=dict(\n                    type='DeltaXYWHBBoxCoder',\n                    target_means=[0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.1, 0.1, 0.2, 0.2]),\n                reg_class_agnostic=False,\n                reg_decoded_bbox=True,\n                norm_cfg=dict(type='SyncBN', requires_grad=True),\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),\n            dict(\n                type='ConvFCBBoxHead',\n                num_shared_convs=4,\n                num_shared_fcs=1,\n                in_channels=256,\n                conv_out_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=80,\n                bbox_coder=dict(\n                    type='DeltaXYWHBBoxCoder',\n                    target_means=[0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.05, 0.05, 0.1, 0.1]),\n                reg_class_agnostic=False,\n                reg_decoded_bbox=True,\n                norm_cfg=dict(type='SyncBN', requires_grad=True),\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),\n            dict(\n                type='ConvFCBBoxHead',\n                num_shared_convs=4,\n                num_shared_fcs=1,\n                in_channels=256,\n                conv_out_channels=256,\n                fc_out_channels=1024,\n                roi_feat_size=7,\n                num_classes=80,\n                bbox_coder=dict(\n                    type='DeltaXYWHBBoxCoder',\n                    target_means=[0.0, 0.0, 0.0, 0.0],\n                    target_stds=[0.033, 0.033, 0.067, 0.067]),\n                reg_class_agnostic=False,\n                reg_decoded_bbox=True,\n                norm_cfg=dict(type='SyncBN', requires_grad=True),\n                loss_cls=dict(\n                    type='CrossEntropyLoss',\n                    use_sigmoid=False,\n                    loss_weight=1.0),\n                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))\n        ],\n        mask_roi_extractor=dict(\n            type='SingleRoIExtractor',\n            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n            out_channels=256,\n            featmap_strides=[4, 8, 16, 32]),\n        mask_head=dict(\n            type='FCNMaskHead',\n            num_convs=4,\n            in_channels=256,\n            conv_out_channels=256,\n            num_classes=80,\n            loss_mask=dict(\n                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\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                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                    match_low_quality=False,\n                    ignore_iof_thr=-1),\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                mask_size=28,\n                pos_weight=-1,\n                debug=False),\n            dict(\n                assigner=dict(\n                    type='MaxIoUAssigner',\n                    pos_iou_thr=0.6,\n                    neg_iou_thr=0.6,\n                    min_pos_iou=0.6,\n                    match_low_quality=False,\n                    ignore_iof_thr=-1),\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                mask_size=28,\n                pos_weight=-1,\n                debug=False),\n            dict(\n                assigner=dict(\n                    type='MaxIoUAssigner',\n                    pos_iou_thr=0.7,\n                    neg_iou_thr=0.7,\n                    min_pos_iou=0.7,\n                    match_low_quality=False,\n                    ignore_iof_thr=-1),\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                mask_size=28,\n                pos_weight=-1,\n                debug=False)\n        ]),\n    test_cfg=dict(\n        rpn=dict(\n            nms_pre=1000,\n            max_per_img=1000,\n            nms=dict(type='nms', iou_threshold=0.7),\n            min_bbox_size=0),\n        rcnn=dict(\n            score_thr=0.05,\n            nms=dict(type='nms', iou_threshold=0.5),\n            max_per_img=100,\n            mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\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, with_mask=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\n    dict(\n        type='AutoAugment',\n        policies=[[{\n            'type':\n            'Resize',\n            'img_scale': [(480, 1333), (512, 1333), (544, 1333), (576, 1333),\n                          (608, 1333), (640, 1333), (672, 1333), (704, 1333),\n                          (736, 1333), (768, 1333), (800, 1333)],\n            'multiscale_mode':\n            'value',\n            'keep_ratio':\n            True\n        }],\n                  [{\n                      'type': 'Resize',\n                      'img_scale': [(400, 1333), (500, 1333), (600, 1333)],\n                      'multiscale_mode': 'value',\n                      'keep_ratio': True\n                  }, {\n                      'type': 'RandomCrop',\n                      'crop_type': 'absolute_range',\n                      'crop_size': (384, 600),\n                      'allow_negative_crop': True\n                  }, {\n                      'type':\n                      'Resize',\n                      'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n                                    (576, 1333), (608, 1333), (640, 1333),\n                                    (672, 1333), (704, 1333), (736, 1333),\n                                    (768, 1333), (800, 1333)],\n                      'multiscale_mode':\n                      'value',\n                      'override':\n                      True,\n                      'keep_ratio':\n                      True\n                  }]]),\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', 'gt_masks'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(1333, 800),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='RandomFlip'),\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='ImageToTensor', keys=['img']),\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='CocoDataset',\n        ann_file='data/coco/annotations/instances_train2017.json',\n        img_prefix='data/coco/train2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\n            dict(\n                type='AutoAugment',\n                policies=[[{\n                    'type':\n                    'Resize',\n                    'img_scale': [(480, 1333), (512, 1333), (544, 1333),\n                                  (576, 1333), (608, 1333), (640, 1333),\n                                  (672, 1333), (704, 1333), (736, 1333),\n                                  (768, 1333), (800, 1333)],\n                    'multiscale_mode':\n                    'value',\n                    'keep_ratio':\n                    True\n                }],\n                          [{\n                              'type': 'Resize',\n                              'img_scale': [(400, 1333), (500, 1333),\n                                            (600, 1333)],\n                              'multiscale_mode': 'value',\n                              'keep_ratio': True\n                          }, {\n                              'type': 'RandomCrop',\n                              'crop_type': 'absolute_range',\n                              'crop_size': (384, 600),\n                              'allow_negative_crop': True\n                          }, {\n                              'type':\n                              'Resize',\n                              'img_scale': [(480, 1333), (512, 1333),\n                                            (544, 1333), (576, 1333),\n                                            (608, 1333), (640, 1333),\n                                            (672, 1333), (704, 1333),\n                                            (736, 1333), (768, 1333),\n                                            (800, 1333)],\n                              'multiscale_mode':\n                              'value',\n                              'override':\n                              True,\n                              'keep_ratio':\n                              True\n                          }]]),\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(\n                type='Collect',\n                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n        ]),\n    val=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_val2017.json',\n        img_prefix='data/coco/val2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\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='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='CocoDataset',\n        ann_file='data/coco/annotations/instances_val2017.json',\n        img_prefix='data/coco/val2017/',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(1333, 800),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='RandomFlip'),\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='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]))\nevaluation = dict(metric=['bbox', 'segm'], classwise=True, save_best='auto')\noptimizer = dict(\n    type='AdamW',\n    lr=0.0002,\n    weight_decay=0.05,\n    constructor='CustomLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=39,\n        layer_decay_rate=0.9,\n        depths=[5, 5, 24, 5],\n        offset_lr_scale=0.01))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[27, 33])\nrunner = dict(type='EpochBasedRunner', max_epochs=36)\ncheckpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(\n        type='ExpMomentumEMAHook',\n        resume_from=None,\n        momentum=0.0001,\n        priority=49)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\npretrained = '/mnt/petrelfs/share_data/huangzhenhang/code/internimage/checkpoint_dir/cls/internimage_xl_22k_192to384.pth'\nwork_dir = 'work_dirs/internimage_xl_fpn_3x_coco_try2'\nauto_resume = False\ngpu_ids = range(0, 32)\n", "seed": 663131696, "exp_name": "internimage_xl_fpn_3x_coco_try2.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 50641, "data_time": 0.09174, "loss_rpn_cls": 0.64459, "loss_rpn_bbox": 0.08164, "s0.loss_cls": 2.10562, "s0.acc": 75.28992, "s0.loss_bbox": 0.0533, "s0.loss_mask": 0.70349, "s1.loss_cls": 1.04634, "s1.acc": 74.65295, "s1.loss_bbox": 0.0072, "s1.loss_mask": 0.35097, "s2.loss_cls": 0.54918, "s2.acc": 72.16394, "s2.loss_bbox": 0.00082, "s2.loss_mask": 0.17349, "loss": 5.71666, "time": 1.0642}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 56238, "data_time": 0.01996, "loss_rpn_cls": 0.24075, "loss_rpn_bbox": 0.07164, "s0.loss_cls": 0.48235, "s0.acc": 93.46124, "s0.loss_bbox": 0.22965, "s0.loss_mask": 0.68133, "s1.loss_cls": 0.13808, "s1.acc": 96.78156, "s1.loss_bbox": 0.03279, "s1.loss_mask": 0.33598, "s2.loss_cls": 0.04419, "s2.acc": 98.11829, "s2.loss_bbox": 0.00376, "s2.loss_mask": 0.16597, "loss": 2.42648, "time": 0.96265}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 56238, "data_time": 0.01799, "loss_rpn_cls": 0.17755, "loss_rpn_bbox": 0.0649, "s0.loss_cls": 0.47169, "s0.acc": 92.45374, "s0.loss_bbox": 0.27309, "s0.loss_mask": 0.64736, "s1.loss_cls": 0.12992, "s1.acc": 96.40503, "s1.loss_bbox": 0.03879, "s1.loss_mask": 0.31733, "s2.loss_cls": 0.04022, "s2.acc": 97.99481, "s2.loss_bbox": 0.00436, "s2.loss_mask": 0.1565, "loss": 2.32173, "time": 0.96712}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0, "memory": 56238, "data_time": 0.02343, "loss_rpn_cls": 0.13209, "loss_rpn_bbox": 0.06459, "s0.loss_cls": 0.51814, "s0.acc": 91.27161, "s0.loss_bbox": 0.3263, "s0.loss_mask": 0.59351, "s1.loss_cls": 0.13894, "s1.acc": 95.99414, "s1.loss_bbox": 0.0458, "s1.loss_mask": 0.29487, "s2.loss_cls": 0.04131, "s2.acc": 97.87524, "s2.loss_bbox": 0.00509, "s2.loss_mask": 0.14573, "loss": 2.30637, "time": 0.97657}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0, "memory": 56238, "data_time": 0.0181, "loss_rpn_cls": 0.1067, "loss_rpn_bbox": 0.0575, "s0.loss_cls": 0.54378, "s0.acc": 90.1629, "s0.loss_bbox": 0.38967, "s0.loss_mask": 0.52134, "s1.loss_cls": 0.14736, "s1.acc": 95.31586, "s1.loss_bbox": 0.05736, "s1.loss_mask": 0.26191, "s2.loss_cls": 0.04255, "s2.acc": 97.65936, "s2.loss_bbox": 0.00649, "s2.loss_mask": 0.13128, "loss": 2.26593, "time": 0.9758}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.0, "memory": 56238, "data_time": 0.0201, "loss_rpn_cls": 0.0895, "loss_rpn_bbox": 0.05413, "s0.loss_cls": 0.54009, "s0.acc": 89.70447, "s0.loss_bbox": 0.42381, "s0.loss_mask": 0.4625, "s1.loss_cls": 0.15512, "s1.acc": 94.50653, "s1.loss_bbox": 0.07369, "s1.loss_mask": 0.23049, "s2.loss_cls": 0.04328, "s2.acc": 97.33337, "s2.loss_bbox": 0.00873, "s2.loss_mask": 0.11637, "loss": 2.19772, "time": 0.98611}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.0, "memory": 56238, "data_time": 0.02322, "loss_rpn_cls": 0.08276, "loss_rpn_bbox": 0.05098, "s0.loss_cls": 0.49555, "s0.acc": 89.49463, "s0.loss_bbox": 0.43507, "s0.loss_mask": 0.43955, "s1.loss_cls": 0.16627, "s1.acc": 93.47949, "s1.loss_bbox": 0.09522, "s1.loss_mask": 0.21544, "s2.loss_cls": 0.04761, "s2.acc": 96.64642, "s2.loss_bbox": 0.01306, "s2.loss_mask": 0.10816, "loss": 2.14966, "time": 0.99964}
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