{"env_info": "sys.platform: linux\nPython: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]\nCUDA available: True\nGPU 0,1: GeForce GTX 1080 Ti\nCUDA_HOME: /cluster/apps/cuda/10.1\nNVCC: Cuda compilation tools, release 10.1, V10.1.243\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.7.0\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.1\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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\n  - CuDNN 7.6.3\n  - Magma 2.5.2\n  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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-variable -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.8.0\nOpenCV: 4.5.5\nMMCV: 1.4.4\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 10.1\nMMRotate: 0.1.0+unknown", "config": "dataset_type = 'DOTADataset'\ndata_root = '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/'\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=1,\n    workers_per_gpu=1,\n    train=dict(\n        type='DOTADataset',\n        ann_file=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/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=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/trainval/annfiles/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/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=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/test/images/',\n        img_prefix=\n        '/cluster/home/it_stu198/main/datasets/split_1024_dota1_0/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=12, metric='mAP')\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    paramwise_cfg=dict(\n        custom_keys=dict(\n            absolute_pos_embed=dict(decay_mult=0.0),\n            relative_position_bias_table=dict(decay_mult=0.0),\n            norm=dict(decay_mult=0.0))))\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=12)\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)]\nangle_version = 'le90'\nmodel = dict(\n    type='RoITransformer',\n    backbone=dict(\n        type='SwinTransformer',\n        embed_dims=96,\n        depths=[2, 2, 6, 2],\n        num_heads=[3, 6, 12, 24],\n        window_size=7,\n        mlp_ratio=4,\n        qkv_bias=True,\n        qk_scale=None,\n        drop_rate=0.0,\n        attn_drop_rate=0.0,\n        drop_path_rate=0.2,\n        patch_norm=True,\n        out_indices=(0, 1, 2, 3),\n        with_cp=False,\n        convert_weights=True,\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            '/cluster/home/it_stu190/.cache/torch/hub/checkpoints/swin_tiny_patch4_window7_224.pth'\n        )),\n    neck=dict(\n        type='FPN',\n        in_channels=[96, 192, 384, 768],\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='RoIAlignRotated',\n                    out_size=7,\n                    sample_num=2,\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                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                    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                    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(type='le90', iou_thr=0.1),\n            max_per_img=2000)))\nwork_dir = './work_dirs/roi_trans_swin_tiny_fpn_1x_dota_le90'\nauto_resume = False\ngpu_ids = range(0, 2)\n", "seed": 518, "exp_name": "roi_trans_swin_tiny_fpn_1x_dota_le90.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 4e-05, "memory": 4761, "data_time": 0.30718, "loss_rpn_cls": 0.44046, "loss_rpn_bbox": 0.06391, "s0.loss_cls": 0.44812, "s0.acc": 91.72461, "s0.loss_bbox": 0.24551, "s1.loss_cls": 0.45365, "s1.acc": 93.53906, "s1.loss_bbox": 0.1304, "loss": 1.78204, "grad_norm": 17.56705, "time": 0.90412}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 5e-05, "memory": 8202, "data_time": 0.0116, "loss_rpn_cls": 0.28081, "loss_rpn_bbox": 0.08549, "s0.loss_cls": 0.35419, "s0.acc": 91.75781, "s0.loss_bbox": 0.29466, "s1.loss_cls": 0.24008, "s1.acc": 95.19966, "s1.loss_bbox": 0.16855, "loss": 1.42378, "grad_norm": 11.14686, "time": 0.59958}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 5e-05, "memory": 8202, "data_time": 0.00896, "loss_rpn_cls": 0.20423, "loss_rpn_bbox": 0.07203, "s0.loss_cls": 0.33864, "s0.acc": 90.6875, "s0.loss_bbox": 0.29564, "s1.loss_cls": 0.2425, "s1.acc": 93.23633, "s1.loss_bbox": 0.20149, "loss": 1.35453, "grad_norm": 11.96647, "time": 0.60137}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 6e-05, "memory": 8202, "data_time": 0.00789, "loss_rpn_cls": 0.18941, "loss_rpn_bbox": 0.07165, "s0.loss_cls": 0.40325, "s0.acc": 89.54883, "s0.loss_bbox": 0.37365, "s1.loss_cls": 0.24348, "s1.acc": 94.27835, "s1.loss_bbox": 0.23104, "loss": 1.51249, "grad_norm": 11.47654, "time": 0.59683}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 7e-05, "memory": 8202, "data_time": 0.01236, "loss_rpn_cls": 0.15623, "loss_rpn_bbox": 0.0684, "s0.loss_cls": 0.43112, "s0.acc": 88.66602, "s0.loss_bbox": 0.36292, "s1.loss_cls": 0.28912, "s1.acc": 92.53348, "s1.loss_bbox": 0.35746, "loss": 1.66524, "grad_norm": 12.72457, "time": 0.59307}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 7e-05, "memory": 8202, "data_time": 0.00667, "loss_rpn_cls": 0.12321, "loss_rpn_bbox": 0.06678, "s0.loss_cls": 0.39767, "s0.acc": 89.38867, "s0.loss_bbox": 0.32798, "s1.loss_cls": 0.26092, "s1.acc": 93.10339, "s1.loss_bbox": 0.27073, "loss": 1.44728, "grad_norm": 10.25779, "time": 0.59676}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 8e-05, "memory": 8202, "data_time": 0.00559, "loss_rpn_cls": 0.12403, "loss_rpn_bbox": 0.06464, "s0.loss_cls": 0.41021, "s0.acc": 88.80664, "s0.loss_bbox": 0.35587, "s1.loss_cls": 0.29553, "s1.acc": 91.97535, "s1.loss_bbox": 0.33361, "loss": 1.58389, "grad_norm": 13.13041, "time": 0.59542}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 9e-05, "memory": 8202, "data_time": 0.00709, "loss_rpn_cls": 0.12207, "loss_rpn_bbox": 0.06363, "s0.loss_cls": 0.38154, "s0.acc": 88.42578, "s0.loss_bbox": 0.3083, "s1.loss_cls": 0.27034, "s1.acc": 91.9994, "s1.loss_bbox": 0.33249, "loss": 1.47837, "grad_norm": 12.71413, "time": 0.5956}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 9e-05, "memory": 8202, "data_time": 0.00693, "loss_rpn_cls": 0.13478, "loss_rpn_bbox": 0.07968, "s0.loss_cls": 0.42826, "s0.acc": 88.71289, "s0.loss_bbox": 0.30807, "s1.loss_cls": 0.26112, "s1.acc": 92.85486, "s1.loss_bbox": 0.30524, "loss": 1.51716, "grad_norm": 11.03777, "time": 0.5993}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0001, "memory": 8202, "data_time": 0.00693, "loss_rpn_cls": 0.10352, "loss_rpn_bbox": 0.06183, "s0.loss_cls": 0.35522, "s0.acc": 89.63281, "s0.loss_bbox": 0.31894, "s1.loss_cls": 0.24011, "s1.acc": 93.25996, "s1.loss_bbox": 0.34021, "loss": 1.41984, "grad_norm": 10.83163, "time": 0.59265}
{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 8202, "data_time": 0.0062, "loss_rpn_cls": 0.10609, "loss_rpn_bbox": 0.05718, "s0.loss_cls": 0.35988, "s0.acc": 89.81055, "s0.loss_bbox": 0.30765, "s1.loss_cls": 0.23041, "s1.acc": 93.61211, "s1.loss_bbox": 0.31687, "loss": 1.37809, "grad_norm": 9.84241, "time": 0.59384}
{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0001, "memory": 8202, "data_time": 0.00753, "loss_rpn_cls": 0.09088, "loss_rpn_bbox": 0.05191, "s0.loss_cls": 0.30595, "s0.acc": 90.26367, "s0.loss_bbox": 0.2822, "s1.loss_cls": 0.18031, "s1.acc": 94.4535, "s1.loss_bbox": 0.23929, "loss": 1.15054, "grad_norm": 10.02131, "time": 0.59309}
{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0001, "memory": 8202, "data_time": 0.00852, "loss_rpn_cls": 0.08273, "loss_rpn_bbox": 0.06571, "s0.loss_cls": 0.34817, "s0.acc": 89.26172, "s0.loss_bbox": 0.32776, "s1.loss_cls": 0.21314, "s1.acc": 93.92028, "s1.loss_bbox": 0.30957, "loss": 1.34708, "grad_norm": 9.05957, "time": 0.59893}
{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0001, "memory": 8202, "data_time": 0.00653, "loss_rpn_cls": 0.08217, "loss_rpn_bbox": 0.0566, "s0.loss_cls": 0.3424, "s0.acc": 89.44336, "s0.loss_bbox": 0.33086, "s1.loss_cls": 0.2282, "s1.acc": 93.16338, "s1.loss_bbox": 0.3263, "loss": 1.36654, "grad_norm": 10.52943, "time": 0.5942}
{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0001, "memory": 8202, "data_time": 0.00561, "loss_rpn_cls": 0.10655, "loss_rpn_bbox": 0.0594, "s0.loss_cls": 0.30364, "s0.acc": 90.43945, "s0.loss_bbox": 0.3197, "s1.loss_cls": 0.19503, "s1.acc": 94.17793, "s1.loss_bbox": 0.31578, "loss": 1.30011, "grad_norm": 9.52272, "time": 0.59421}
{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0001, "memory": 8202, "data_time": 0.00841, "loss_rpn_cls": 0.08222, "loss_rpn_bbox": 0.06476, "s0.loss_cls": 0.35975, "s0.acc": 88.23828, "s0.loss_bbox": 0.3448, "s1.loss_cls": 0.22058, "s1.acc": 93.30744, "s1.loss_bbox": 0.36312, "loss": 1.43522, "grad_norm": 10.71887, "time": 0.60287}
{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0001, "memory": 8202, "data_time": 0.00542, "loss_rpn_cls": 0.0962, "loss_rpn_bbox": 0.06157, "s0.loss_cls": 0.31639, "s0.acc": 89.73438, "s0.loss_bbox": 0.28293, "s1.loss_cls": 0.19428, "s1.acc": 94.23434, "s1.loss_bbox": 0.26672, "loss": 1.21808, "grad_norm": 9.18873, "time": 0.59252}
{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0001, "memory": 9450, "data_time": 0.00649, "loss_rpn_cls": 0.09843, "loss_rpn_bbox": 0.06121, "s0.loss_cls": 0.30603, "s0.acc": 89.79883, "s0.loss_bbox": 0.29476, "s1.loss_cls": 0.18491, "s1.acc": 94.36349, "s1.loss_bbox": 0.28447, "loss": 1.22982, "grad_norm": 10.0461, "time": 0.60231}
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