{"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: A100-SXM-80GB\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;-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-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: 2.25.3+04d855b", "config": "model = dict(\n    type='DETR',\n    backbone=dict(\n        type='ResNet',\n        depth=50,\n        num_stages=4,\n        out_indices=(3, ),\n        frozen_stages=1,\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        norm_eval=True,\n        style='pytorch',\n        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),\n    bbox_head=dict(\n        type='DETRHead',\n        num_classes=20,\n        in_channels=2048,\n        transformer=dict(\n            type='Transformer',\n            encoder=dict(\n                type='DetrTransformerEncoder',\n                num_layers=6,\n                transformerlayers=dict(\n                    type='BaseTransformerLayer',\n                    attn_cfgs=[\n                        dict(\n                            type='MultiheadAttention',\n                            embed_dims=256,\n                            num_heads=8,\n                            dropout=0.1)\n                    ],\n                    feedforward_channels=2048,\n                    ffn_dropout=0.1,\n                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),\n            decoder=dict(\n                type='DetrTransformerDecoder',\n                return_intermediate=True,\n                num_layers=6,\n                transformerlayers=dict(\n                    type='DetrTransformerDecoderLayer',\n                    attn_cfgs=dict(\n                        type='MultiheadAttention',\n                        embed_dims=256,\n                        num_heads=8,\n                        dropout=0.1),\n                    feedforward_channels=2048,\n                    ffn_dropout=0.1,\n                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                     'ffn', 'norm')))),\n        positional_encoding=dict(\n            type='SinePositionalEncoding', num_feats=128, normalize=True),\n        loss_cls=dict(\n            type='CrossEntropyLoss',\n            bg_cls_weight=0.1,\n            use_sigmoid=False,\n            loss_weight=1.0,\n            class_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='ClassificationCost', weight=1.0),\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=100))\ndataset_type = 'VOCDataset'\ndata_root = 'data/VOCdevkit/'\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(\n        type='Resize',\n        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n                   (1333, 736), (1333, 768), (1333, 800)],\n        multiscale_mode='value',\n        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'])\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='VOCDataset',\n        ann_file=[\n            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',\n            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'\n        ],\n        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', with_bbox=True),\n            dict(\n                type='Resize',\n                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),\n                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),\n                           (1333, 736), (1333, 768), (1333, 800)],\n                multiscale_mode='value',\n                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'])\n        ]),\n    val=dict(\n        type='VOCDataset',\n        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n        img_prefix='data/VOCdevkit/VOC2007/',\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='VOCDataset',\n        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',\n        img_prefix='data/VOCdevkit/VOC2007/',\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='mAP', save_best='auto')\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(type='NumClassCheckHook'),\n    dict(\n        type='MMDetWandbHook',\n        init_kwargs=dict(project='I2B', group='finetune'),\n        interval=50,\n        num_eval_images=0,\n        log_checkpoint=False)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'pretrain/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0/final_model.pth'\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=16)\ncustom_imports = None\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\noptimizer = dict(\n    type='AdamW',\n    lr=0.0001,\n    weight_decay=0.0001,\n    paramwise_cfg=dict(\n        custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0))))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='step', step=[70])\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\nwork_dir = 'work_dirs/finetune_detr_100e_voc0712'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "detr_mstrain_100e_voc0712.py", "hook_msgs": {}}
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