pretrain/selfsup_cbv2_swin-L_1x_coco/20230128_220928.log.json

{"env_info": "sys.platform: linux\nPython: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-PCIE-40GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0\nPyTorch: 1.10.0\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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)\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: AVX512\n  - CUDA Runtime 11.3\n  - NVCC architecture flags: -gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n  - CuDNN 8.2.1\n    - Built with 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-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=1.10.0, 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: 0.11.1+cu113\nOpenCV: 4.6.0\nMMCV: 1.6.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.27.0+db771d3", "config": "train_dataset_type = 'MultiViewCocoDataset'\ntest_dataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nclasses = ['selective_search']\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\nload_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', with_bbox=True, with_mask=False)\n]\ntrain_pipeline1 = [\n    dict(\n        type='Resize',\n        img_scale=[(1600, 400), (1600, 1400)],\n        multiscale_mode='range',\n        keep_ratio=True),\n    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),\n    dict(type='Pad', size_divisor=32),\n    dict(type='RandFlip', flip_ratio=0.5),\n    dict(\n        type='OneOf',\n        transforms=[\n            dict(type='Identity'),\n            dict(type='AutoContrast'),\n            dict(type='RandEqualize'),\n            dict(type='RandSolarize'),\n            dict(type='RandColor'),\n            dict(type='RandContrast'),\n            dict(type='RandBrightness'),\n            dict(type='RandSharpness'),\n            dict(type='RandPosterize')\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='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntrain_pipeline2 = [\n    dict(\n        type='Resize',\n        img_scale=[(1600, 400), (1600, 1400)],\n        multiscale_mode='range',\n        keep_ratio=True),\n    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),\n    dict(type='Pad', size_divisor=32),\n    dict(type='RandFlip', flip_ratio=0.5),\n    dict(\n        type='OneOf',\n        transforms=[\n            dict(type='Identity'),\n            dict(type='AutoContrast'),\n            dict(type='RandEqualize'),\n            dict(type='RandSolarize'),\n            dict(type='RandColor'),\n            dict(type='RandContrast'),\n            dict(type='RandBrightness'),\n            dict(type='RandSharpness'),\n            dict(type='RandPosterize')\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='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=(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='MultiViewCocoDataset',\n        dataset=dict(\n            type='CocoDataset',\n            classes=['selective_search'],\n            ann_file=\n            'data/coco/filtered_proposals/train2017_ratio3size0008@0.5.json',\n            img_prefix='data/coco/train2017/',\n            pipeline=[\n                dict(type='LoadImageFromFile'),\n                dict(type='LoadAnnotations', with_bbox=True, with_mask=False)\n            ]),\n        num_views=2,\n        pipelines=[[{\n            'type': 'Resize',\n            'img_scale': [(1600, 400), (1600, 1400)],\n            'multiscale_mode': 'range',\n            'keep_ratio': True\n        }, {\n            'type': 'FilterAnnotations',\n            'min_gt_bbox_wh': (0.01, 0.01)\n        }, {\n            'type': 'Pad',\n            'size_divisor': 32\n        }, {\n            'type': 'RandFlip',\n            'flip_ratio': 0.5\n        }, {\n            'type':\n            'OneOf',\n            'transforms': [{\n                'type': 'Identity'\n            }, {\n                'type': 'AutoContrast'\n            }, {\n                'type': 'RandEqualize'\n            }, {\n                'type': 'RandSolarize'\n            }, {\n                'type': 'RandColor'\n            }, {\n                'type': 'RandContrast'\n            }, {\n                'type': 'RandBrightness'\n            }, {\n                'type': 'RandSharpness'\n            }, {\n                'type': 'RandPosterize'\n            }]\n        }, {\n            'type': 'Normalize',\n            'mean': [123.675, 116.28, 103.53],\n            'std': [58.395, 57.12, 57.375],\n            'to_rgb': True\n        }, {\n            'type': 'DefaultFormatBundle'\n        }, {\n            'type': 'Collect',\n            'keys': ['img', 'gt_bboxes', 'gt_labels']\n        }],\n                   [{\n                       'type': 'Resize',\n                       'img_scale': [(1600, 400), (1600, 1400)],\n                       'multiscale_mode': 'range',\n                       'keep_ratio': True\n                   }, {\n                       'type': 'FilterAnnotations',\n                       'min_gt_bbox_wh': (0.01, 0.01)\n                   }, {\n                       'type': 'Pad',\n                       'size_divisor': 32\n                   }, {\n                       'type': 'RandFlip',\n                       'flip_ratio': 0.5\n                   }, {\n                       'type':\n                       'OneOf',\n                       'transforms': [{\n                           'type': 'Identity'\n                       }, {\n                           'type': 'AutoContrast'\n                       }, {\n                           'type': 'RandEqualize'\n                       }, {\n                           'type': 'RandSolarize'\n                       }, {\n                           'type': 'RandColor'\n                       }, {\n                           'type': 'RandContrast'\n                       }, {\n                           'type': 'RandBrightness'\n                       }, {\n                           'type': 'RandSharpness'\n                       }, {\n                           'type': 'RandPosterize'\n                       }]\n                   }, {\n                       'type': 'Normalize',\n                       'mean': [123.675, 116.28, 103.53],\n                       'std': [58.395, 57.12, 57.375],\n                       'to_rgb': True\n                   }, {\n                       'type': 'DefaultFormatBundle'\n                   }, {\n                       'type': 'Collect',\n                       'keys': ['img', 'gt_bboxes', 'gt_labels']\n                   }]]),\n    val=dict(\n        type='CocoDataset',\n        classes=['selective_search'],\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        classes=['selective_search'],\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(interval=65535, gpu_collect=True)\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=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)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(type='MomentumUpdateHook'),\n    dict(\n        type='MMDetWandbHook',\n        init_kwargs=dict(project='I2B', group='pretrain'),\n        interval=50,\n        num_eval_images=0,\n        log_checkpoint=False)\n]\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'\nauto_scale_lr = dict(enable=True, base_batch_size=16)\ncustom_imports = dict(\n    imports=[\n        'mmselfsup.datasets.pipelines',\n        'selfsup.core.hook.momentum_update_hook',\n        'selfsup.datasets.pipelines.selfsup_pipelines',\n        'selfsup.datasets.pipelines.rand_aug',\n        'selfsup.datasets.single_view_coco',\n        'selfsup.datasets.multi_view_coco',\n        'selfsup.models.losses.contrastive_loss',\n        'selfsup.models.dense_heads.fcos_head',\n        'selfsup.models.dense_heads.retina_head',\n        'selfsup.models.dense_heads.detr_head',\n        'selfsup.models.dense_heads.deformable_detr_head',\n        'selfsup.models.roi_heads.bbox_heads.convfc_bbox_head',\n        'selfsup.models.roi_heads.standard_roi_head',\n        'selfsup.models.roi_heads.htc_roi_head',\n        'selfsup.models.roi_heads.cbv2_roi_head',\n        'selfsup.models.necks.cb_fpn', 'selfsup.models.backbones.cbv2',\n        'selfsup.models.backbones.swinv1',\n        'selfsup.models.detectors.selfsup_detector',\n        'selfsup.models.detectors.selfsup_fcos',\n        'selfsup.models.detectors.selfsup_detr',\n        'selfsup.models.detectors.selfsup_deformable_detr',\n        'selfsup.models.detectors.selfsup_retinanet',\n        'selfsup.models.detectors.selfsup_mask_rcnn',\n        'selfsup.models.detectors.selfsup_htc',\n        'selfsup.models.detectors.selfsup_cbv2',\n        'selfsup.models.detectors.cbv2',\n        'selfsup.core.bbox.assigners.hungarian_assigner',\n        'selfsup.core.bbox.assigners.pseudo_hungarian_assigner',\n        'selfsup.core.bbox.match_costs.match_cost'\n    ],\n    allow_failed_imports=False)\nmodel = dict(\n    type='SelfSupDetector',\n    backbone=dict(\n        type='SelfSupCBv2',\n        backbone=dict(\n            type='CBSwinTransformer',\n            embed_dim=192,\n            depths=[2, 2, 18, 2],\n            num_heads=[6, 12, 24, 48],\n            window_size=7,\n            mlp_ratio=4.0,\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            ape=False,\n            patch_norm=True,\n            out_indices=(0, 1, 2, 3),\n            pretrained=\n            'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',\n            use_checkpoint=False),\n        neck=dict(\n            type='CBFPN',\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,\n                loss_weight=1.0)),\n        roi_head=dict(\n            type='SelfSupCBv2Head',\n            interleaved=True,\n            mask_info_flow=True,\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(\n                    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='SelfSupShared4Conv1FCBBoxHead',\n                    in_channels=256,\n                    fc_out_channels=1024,\n                    roi_feat_size=7,\n                    num_classes=256,\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=True,\n                    loss_cls=dict(\n                        type='ContrastiveLoss',\n                        loss_weight=1.0,\n                        temperature=0.5),\n                    loss_bbox=dict(\n                        type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),\n                dict(\n                    type='SelfSupShared4Conv1FCBBoxHead',\n                    in_channels=256,\n                    fc_out_channels=1024,\n                    roi_feat_size=7,\n                    num_classes=256,\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=True,\n                    loss_cls=dict(\n                        type='ContrastiveLoss',\n                        loss_weight=1.0,\n                        temperature=0.5),\n                    loss_bbox=dict(\n                        type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),\n                dict(\n                    type='SelfSupShared4Conv1FCBBoxHead',\n                    in_channels=256,\n                    fc_out_channels=1024,\n                    roi_feat_size=7,\n                    num_classes=256,\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=True,\n                    loss_cls=dict(\n                        type='ContrastiveLoss',\n                        loss_weight=1.0,\n                        temperature=0.5),\n                    loss_bbox=dict(\n                        type='SmoothL1Loss', beta=1.0, loss_weight=1.0))\n            ],\n            mask_roi_extractor=None,\n            mask_head=None),\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                    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                        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                        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                        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=0.001,\n                nms=dict(type='nms', iou_threshold=0.5),\n                max_per_img=100,\n                mask_thr_binary=0.5))))\nfind_unused_parameters = True\nfp16 = dict(loss_scale='dynamic')\nwork_dir = 'work_dirs/selfsup_cbv2_swin-L_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 64)\n", "seed": 42, "exp_name": "cbv2.py"}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 8e-05, "memory": 11679, "data_time": 0.07384, "s0.loss_cls0_w0.5": 3.8848, "s0.loss_bbox0_w0.5": 0.20454, "s1.loss_cls0_w0.5": 1.95859, "s1.loss_bbox0_w0.5": 0.10232, "s2.loss_cls0_w0.5": 0.97823, "s2.loss_bbox0_w0.5": 0.01934, "loss_rpn_cls0_w0.50_w0.5": 0.112, "loss_rpn_bbox0_w0.50_w0.5": 0.04817, "loss_rpn_cls10_w0.5": 0.22343, "loss_rpn_bbox10_w0.5": 0.09616, "s0.loss_cls1": 7.76648, "s0.loss_bbox1": 0.4093, "s1.loss_cls1": 3.91711, "s1.loss_bbox1": 0.20474, "s2.loss_cls1": 1.95592, "s2.loss_bbox1": 0.03867, "loss_rpn_cls0_w0.51": 0.22399, "loss_rpn_bbox0_w0.51": 0.09634, "loss_rpn_cls11": 0.44686, "loss_rpn_bbox11": 0.19232, "loss": 22.87894, "time": 4.18734}
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