{"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-11.3\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\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+216ec8a", "config": "dataset_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),\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, 4200), (500, 4200), (600, 4200)],\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': False\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'])\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),\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, 4200), (500, 4200),\n                                            (600, 4200)],\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': False\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(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\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(interval=1, metric='bbox', classwise=True, save_best='auto')\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)]\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    weight_decay=0.05,\n    paramwise_cfg=dict(custom_keys=dict(backbone=dict(lr_mult=0.1))))\noptimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))\nlr_config = dict(\n    policy='step',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.001,\n    step=[11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\npretrained = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth'\nmodel = dict(\n    type='DINO',\n    backbone=dict(\n        type='InternImage',\n        core_op='DCNv3',\n        channels=160,\n        depths=[5, 5, 22, 5],\n        groups=[10, 20, 40, 80],\n        mlp_ratio=4.0,\n        drop_path_rate=0.4,\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=(1, 2, 3),\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth'\n        )),\n    neck=dict(\n        type='ChannelMapper',\n        in_channels=[320, 640, 1280],\n        kernel_size=1,\n        out_channels=256,\n        act_cfg=None,\n        norm_cfg=dict(type='GN', num_groups=32),\n        num_outs=4),\n    bbox_head=dict(\n        type='DINOHead',\n        num_query=900,\n        num_classes=80,\n        in_channels=2048,\n        sync_cls_avg_factor=True,\n        as_two_stage=True,\n        with_box_refine=True,\n        dn_cfg=dict(\n            type='CdnQueryGenerator',\n            noise_scale=dict(label=0.5, box=1.0),\n            group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100)),\n        transformer=dict(\n            type='DinoTransformer',\n            two_stage_num_proposals=900,\n            encoder=dict(\n                type='DetrTransformerEncoder',\n                num_layers=6,\n                transformerlayers=dict(\n                    type='BaseTransformerLayer',\n                    attn_cfgs=dict(\n                        type='MultiScaleDeformableAttention',\n                        embed_dims=256,\n                        dropout=0.0),\n                    feedforward_channels=2048,\n                    ffn_dropout=0.0,\n                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),\n            decoder=dict(\n                type='DinoTransformerDecoder',\n                num_layers=6,\n                return_intermediate=True,\n                transformerlayers=dict(\n                    type='DetrTransformerDecoderLayer',\n                    attn_cfgs=[\n                        dict(\n                            type='MultiheadAttention',\n                            embed_dims=256,\n                            num_heads=8,\n                            dropout=0.0),\n                        dict(\n                            type='MultiScaleDeformableAttention',\n                            embed_dims=256,\n                            dropout=0.0)\n                    ],\n                    feedforward_channels=2048,\n                    ffn_dropout=0.0,\n                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                     'ffn', 'norm')))),\n        positional_encoding=dict(\n            type='SinePositionalEncoding',\n            num_feats=128,\n            temperature=20,\n            normalize=True),\n        loss_cls=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=1.0),\n        loss_bbox=dict(type='L1Loss', loss_weight=5.0),\n        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),\n    train_cfg=dict(\n        assigner=dict(\n            type='HungarianAssigner',\n            cls_cost=dict(type='FocalLossCost', weight=2.0),\n            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),\n            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),\n    test_cfg=dict(max_per_img=300))\nwork_dir = './work_dirs/dino_4scale_internimage_l_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 325581724, "exp_name": "dino_4scale_internimage_l_1x_coco.py", "hook_msgs": {}}
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{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 40067, "data_time": 0.0048, "interm_loss_cls": 0.99866, "interm_loss_bbox": 1.3928, "interm_loss_iou": 1.696, "loss_cls": 0.97668, "loss_bbox": 1.35854, "loss_iou": 1.66972, "d0.loss_cls": 0.95986, "d0.loss_bbox": 1.41168, "d0.loss_iou": 1.71803, "d1.loss_cls": 0.96237, "d1.loss_bbox": 1.39101, "d1.loss_iou": 1.69851, "d2.loss_cls": 0.96096, "d2.loss_bbox": 1.3775, "d2.loss_iou": 1.68598, "d3.loss_cls": 0.96002, "d3.loss_bbox": 1.3696, "d3.loss_iou": 1.67815, "d4.loss_cls": 0.96791, "d4.loss_bbox": 1.36371, "d4.loss_iou": 1.67343, "dn_loss_cls": 0.87219, "dn_loss_bbox": 1.2385, "dn_loss_iou": 1.30097, "d0.dn_loss_cls": 0.8861, "d0.dn_loss_bbox": 1.2307, "d0.dn_loss_iou": 1.30328, "d1.dn_loss_cls": 0.8413, "d1.dn_loss_bbox": 1.23108, "d1.dn_loss_iou": 1.30201, "d2.dn_loss_cls": 0.82098, "d2.dn_loss_bbox": 1.23209, "d2.dn_loss_iou": 1.30123, "d3.dn_loss_cls": 0.83974, "d3.dn_loss_bbox": 1.23379, "d3.dn_loss_iou": 1.3007, "d4.dn_loss_cls": 0.85894, "d4.dn_loss_bbox": 1.23585, "d4.dn_loss_iou": 1.30062, "loss": 48.6012, "grad_norm": 72.52018, "time": 0.71264}
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