{"env_info": "sys.platform: linux\nPython: 3.7.0 (default, Oct  9 2018, 10:31:47) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: A100-SXM-80GB\nCUDA_HOME: /mnt/lustre/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.9.0+cu111\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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.1\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_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 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=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: 0.10.0+cu111\nOpenCV: 4.6.0\nMMCV: 1.4.2\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMDetection: 2.22.0+", "config": "model = dict(\n    type='CascadeRCNN',\n    backbone=dict(\n        type='DCNv3_5_Post',\n        deform_points=9,\n        embed_dim=192,\n        depths=[5, 5, 24, 5],\n        num_heads=[12, 24, 48, 96],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=3,\n        mlp_ratio=4.0,\n        drop_path_rate=0.4,\n        use_hw_scaler=2.0,\n        op_types=['D', 'D', 'D', 'D'],\n        out_indices=(0, 1, 2, 3),\n        dcnv3_core_type='MSDeformAttnGrid1_softmax',\n        layer_scale=1.0,\n        cpe_norm_layer='LN',\n        with_cp=False,\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            '/mnt/petrelfs/wangwenhai/workspace/DCNv3-dev/classification/work_dirs_shared/dcnv3_5_post_xl_v5_22k_192to384_ft/ckpt_epoch_best.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='Resize', img_scale=(1333, 800), keep_ratio=True),\n    dict(type='RandomFlip', flip_ratio=0.5),\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='Resize', img_scale=(1333, 800), keep_ratio=True),\n            dict(type='RandomFlip', flip_ratio=0.5),\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=0.0001,\n    weight_decay=0.05,\n    constructor='CustomLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=39, layer_decay_rate=0.94, depths=[5, 5, 24, 5]))\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=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1, max_keep_ckpts=3, save_last=True)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\npretrained = '/mnt/petrelfs/wangwenhai/workspace/DCNv3-dev/classification/work_dirs_shared/dcnv3_5_post_xl_v5_22k_192to384_ft/ckpt_epoch_best.pth'\nwork_dir = './work_dirs/cascade_mask_rcnn_dcnv3_post_xlarge_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 770811306, "exp_name": "cascade_mask_rcnn_dcnv3_post_xlarge_fpn_1x_coco.py", "hook_msgs": {}}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 42294, "data_time": 1.33273, "loss_rpn_cls": 0.62466, "loss_rpn_bbox": 0.10913, "s0.loss_cls": 2.4721, "s0.acc": 81.21143, "s0.loss_bbox": 0.02134, "s0.loss_mask": 0.70356, "s1.loss_cls": 1.3255, "s1.acc": 74.31396, "s1.loss_bbox": 0.00289, "s1.loss_mask": 0.35156, "s2.loss_cls": 0.64902, "s2.acc": 78.88794, "s2.loss_bbox": 0.00035, "s2.loss_mask": 0.17478, "loss": 6.43489, "time": 2.37908}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 42294, "data_time": 0.03722, "loss_rpn_cls": 0.30496, "loss_rpn_bbox": 0.09449, "s0.loss_cls": 0.43986, "s0.acc": 94.41895, "s0.loss_bbox": 0.18569, "s0.loss_mask": 0.68813, "s1.loss_cls": 0.13003, "s1.acc": 97.16821, "s1.loss_bbox": 0.0248, "s1.loss_mask": 0.34316, "s2.loss_cls": 0.04681, "s2.acc": 98.20337, "s2.loss_bbox": 0.00257, "s2.loss_mask": 0.17064, "loss": 2.43112, "time": 1.02362}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 42416, "data_time": 0.0337, "loss_rpn_cls": 0.22717, "loss_rpn_bbox": 0.08162, "s0.loss_cls": 0.42372, "s0.acc": 93.5896, "s0.loss_bbox": 0.22148, "s0.loss_mask": 0.6732, "s1.loss_cls": 0.11977, "s1.acc": 96.8479, "s1.loss_bbox": 0.03014, "s1.loss_mask": 0.33207, "s2.loss_cls": 0.03994, "s2.acc": 98.08569, "s2.loss_bbox": 0.0033, "s2.loss_mask": 0.16482, "loss": 2.31723, "time": 1.03341}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0, "memory": 42673, "data_time": 0.02969, "loss_rpn_cls": 0.17086, "loss_rpn_bbox": 0.07432, "s0.loss_cls": 0.45813, "s0.acc": 92.80347, "s0.loss_bbox": 0.25857, "s0.loss_mask": 0.65639, "s1.loss_cls": 0.12398, "s1.acc": 96.6106, "s1.loss_bbox": 0.03487, "s1.loss_mask": 0.31988, "s2.loss_cls": 0.03915, "s2.acc": 98.06201, "s2.loss_bbox": 0.00364, "s2.loss_mask": 0.15735, "loss": 2.29713, "time": 1.03532}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0, "memory": 42673, "data_time": 0.03931, "loss_rpn_cls": 0.14427, "loss_rpn_bbox": 0.07821, "s0.loss_cls": 0.50767, "s0.acc": 91.67236, "s0.loss_bbox": 0.30472, "s0.loss_mask": 0.63673, "s1.loss_cls": 0.1377, "s1.acc": 96.11621, "s1.loss_bbox": 0.04183, "s1.loss_mask": 0.31164, "s2.loss_cls": 0.0425, "s2.acc": 97.84644, "s2.loss_bbox": 0.00447, "s2.loss_mask": 0.15264, "loss": 2.36238, "time": 1.03708}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 1e-05, "memory": 42673, "data_time": 0.04315, "loss_rpn_cls": 0.11427, "loss_rpn_bbox": 0.07052, "s0.loss_cls": 0.52082, "s0.acc": 91.06934, "s0.loss_bbox": 0.33683, "s0.loss_mask": 0.59874, "s1.loss_cls": 0.13725, "s1.acc": 95.97314, "s1.loss_bbox": 0.04577, "s1.loss_mask": 0.29746, "s2.loss_cls": 0.04089, "s2.acc": 97.87305, "s2.loss_bbox": 0.00481, "s2.loss_mask": 0.1469, "loss": 2.31425, "time": 1.02501}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 1e-05, "memory": 42673, "data_time": 0.03303, "loss_rpn_cls": 0.09492, "loss_rpn_bbox": 0.06774, "s0.loss_cls": 0.53899, "s0.acc": 90.22241, "s0.loss_bbox": 0.38825, "s0.loss_mask": 0.54684, "s1.loss_cls": 0.14205, "s1.acc": 95.49219, "s1.loss_bbox": 0.05507, "s1.loss_mask": 0.2732, "s2.loss_cls": 0.04019, "s2.acc": 97.77686, "s2.loss_bbox": 0.0058, "s2.loss_mask": 0.13716, "loss": 2.29021, "time": 1.01666}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 1e-05, "memory": 42752, "data_time": 0.03193, "loss_rpn_cls": 0.09397, "loss_rpn_bbox": 0.06933, "s0.loss_cls": 0.57513, "s0.acc": 89.17065, "s0.loss_bbox": 0.44335, "s0.loss_mask": 0.49871, "s1.loss_cls": 0.1587, "s1.acc": 94.4834, "s1.loss_bbox": 0.07057, "s1.loss_mask": 0.24876, "s2.loss_cls": 0.04482, "s2.acc": 97.31592, "s2.loss_bbox": 0.00765, "s2.loss_mask": 0.12555, "loss": 2.33653, "time": 1.02758}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 1e-05, "memory": 42752, "data_time": 0.03067, "loss_rpn_cls": 0.0806, "loss_rpn_bbox": 0.06289, "s0.loss_cls": 0.54216, "s0.acc": 89.09912, "s0.loss_bbox": 0.4475, "s0.loss_mask": 0.45942, "s1.loss_cls": 0.15903, "s1.acc": 93.85083, "s1.loss_bbox": 0.08523, "s1.loss_mask": 0.22755, "s2.loss_cls": 0.04464, "s2.acc": 96.92139, "s2.loss_bbox": 0.01075, "s2.loss_mask": 0.11429, "loss": 2.23405, "time": 1.03274}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 1e-05, "memory": 42858, "data_time": 0.02991, "loss_rpn_cls": 0.08062, "loss_rpn_bbox": 0.0639, "s0.loss_cls": 0.54374, "s0.acc": 88.72925, "s0.loss_bbox": 0.45826, "s0.loss_mask": 0.44013, "s1.loss_cls": 0.17519, "s1.acc": 93.0083, "s1.loss_bbox": 0.10188, "s1.loss_mask": 0.2145, "s2.loss_cls": 0.05022, "s2.acc": 96.29785, "s2.loss_bbox": 0.01433, "s2.loss_mask": 0.1072, "loss": 2.24997, "time": 1.03968}
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