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{"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: 1.6.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.25.2+87c120c", "config": "model = dict(\n type='FCOS',\n backbone=dict(\n type='ResNet',\n depth=50,\n num_stages=4,\n out_indices=(0, 1, 2, 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 neck=dict(\n type='FPN',\n in_channels=[256, 512, 1024, 2048],\n out_channels=256,\n start_level=1,\n add_extra_convs='on_output',\n num_outs=5,\n relu_before_extra_convs=True,\n norm_cfg=dict(type='SyncBN', requires_grad=True)),\n bbox_head=dict(\n type='FCOSHead',\n num_classes=20,\n in_channels=256,\n stacked_convs=4,\n feat_channels=256,\n strides=[8, 16, 32, 64, 128],\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='IoULoss', loss_weight=1.0),\n loss_centerness=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),\n train_cfg=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.4,\n min_pos_iou=0,\n ignore_iof_thr=-1),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n test_cfg=dict(\n nms_pre=1000,\n min_bbox_size=0,\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n 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=12000, metric='mAP', save_best='auto')\noptimizer = dict(\n type='SGD',\n lr=0.015,\n momentum=0.9,\n weight_decay=5e-05,\n paramwise_cfg=dict(bias_lr_mult=2.0, bias_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=0.001,\n step=[9000, 11000],\n by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=12000)\ncheckpoint_config = dict(interval=12000)\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_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/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)\nwork_dir = 'work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 42, "exp_name": "fcos_mstrain_12k_voc0712.py", "hook_msgs": {}}
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