finetune/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5/20221003_230350.log

2022-10-03 23:03:50,973 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: A100-SXM-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0
PyTorch: 1.10.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.3
  - 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
  - CuDNN 8.2
  - Magma 2.5.2
  - 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, 

TorchVision: 0.11.1+cu113
OpenCV: 4.6.0
MMCV: 1.6.1
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.25.2+87c120c
------------------------------------------------------------

2022-10-03 23:03:52,207 - mmdet - INFO - Distributed training: True
2022-10-03 23:03:53,323 - mmdet - INFO - Config:
model = dict(
    type='FCOS',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5,
        relu_before_extra_convs=True,
        norm_cfg=dict(type='SyncBN', requires_grad=True)),
    bbox_head=dict(
        type='FCOSHead',
        num_classes=20,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        strides=[8, 16, 32, 64, 128],
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0,
            ignore_iof_thr=-1),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Resize',
        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
                   (1333, 736), (1333, 768), (1333, 800)],
        multiscale_mode='value',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='VOCDataset',
        ann_file=[
            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
        ],
        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='Resize',
                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),
                           (1333, 736), (1333, 768), (1333, 800)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    val=dict(
        type='VOCDataset',
        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
        img_prefix='data/VOCdevkit/VOC2007/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='VOCDataset',
        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
        img_prefix='data/VOCdevkit/VOC2007/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(interval=12000, metric='mAP', save_best='auto')
optimizer = dict(
    type='SGD',
    lr=0.015,
    momentum=0.9,
    weight_decay=5e-05,
    paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0))
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[9000, 11000],
    by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=12000)
checkpoint_config = dict(interval=12000)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [
    dict(type='NumClassCheckHook'),
    dict(
        type='MMDetWandbHook',
        init_kwargs=dict(project='I2B', group='finetune'),
        interval=50,
        num_eval_images=0,
        log_checkpoint=False)
]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
custom_imports = None
norm_cfg = dict(type='SyncBN', requires_grad=True)
work_dir = 'work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5'
auto_resume = False
gpu_ids = range(0, 8)

2022-10-03 23:03:53,326 - mmdet - INFO - Set random seed to 42, deterministic: False
2022-10-03 23:03:53,597 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2022-10-03 23:04:07,756 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-10-03 23:04:07,780 - mmdet - INFO - initialize FCOSHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'conv_cls', 'std': 0.01, 'bias_prob': 0.01}}
Name of parameter - Initialization information

backbone.conv1.weight - torch.Size([64, 3, 7, 7]): 
PretrainedInit: load from torchvision://resnet50 

backbone.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn2.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn2.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn3.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn3.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.downsample.1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.downsample.1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn2.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn2.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn3.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn3.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn2.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn2.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn3.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn3.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.downsample.1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.downsample.1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.downsample.1.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.downsample.1.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn2.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn2.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn3.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn3.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.downsample.1.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.downsample.1.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn2.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn2.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn3.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn3.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn2.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn2.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn3.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn3.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

neck.lateral_convs.0.conv.weight - torch.Size([256, 512, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.0.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.lateral_convs.0.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.lateral_convs.1.conv.weight - torch.Size([256, 1024, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.1.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.lateral_convs.1.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.lateral_convs.2.conv.weight - torch.Size([256, 2048, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.2.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.lateral_convs.2.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.0.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.0.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.1.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.1.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.2.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.2.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.3.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.3.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.4.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

neck.fpn_convs.4.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.cls_convs.0.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.0.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.cls_convs.1.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.1.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.cls_convs.2.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.2.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.cls_convs.3.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.cls_convs.3.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.reg_convs.0.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.0.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.reg_convs.1.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.1.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.reg_convs.2.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.2.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.reg_convs.3.gn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.reg_convs.3.gn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.conv_cls.weight - torch.Size([20, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=-4.59511985013459 

bbox_head.conv_cls.bias - torch.Size([20]): 
NormalInit: mean=0, std=0.01, bias=-4.59511985013459 

bbox_head.conv_reg.weight - torch.Size([4, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.conv_reg.bias - torch.Size([4]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.conv_centerness.weight - torch.Size([1, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.conv_centerness.bias - torch.Size([1]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.scales.0.scale - torch.Size([]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.scales.1.scale - torch.Size([]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.scales.2.scale - torch.Size([]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.scales.3.scale - torch.Size([]): 
The value is the same before and after calling `init_weights` of FCOS  

bbox_head.scales.4.scale - torch.Size([]): 
The value is the same before and after calling `init_weights` of FCOS  
2022-10-03 23:04:09,685 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2022-10-03 23:04:10,655 - mmdet - INFO - load checkpoint from local path: pretrain/selfsup_fcos_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth
2022-10-03 23:04:10,809 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.bias, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.bias, neck.fpn_convs.4.conv.bias

missing keys in source state_dict: neck.lateral_convs.0.bn.weight, neck.lateral_convs.0.bn.bias, neck.lateral_convs.0.bn.running_mean, neck.lateral_convs.0.bn.running_var, neck.lateral_convs.1.bn.weight, neck.lateral_convs.1.bn.bias, neck.lateral_convs.1.bn.running_mean, neck.lateral_convs.1.bn.running_var, neck.lateral_convs.2.bn.weight, neck.lateral_convs.2.bn.bias, neck.lateral_convs.2.bn.running_mean, neck.lateral_convs.2.bn.running_var, neck.fpn_convs.0.bn.weight, neck.fpn_convs.0.bn.bias, neck.fpn_convs.0.bn.running_mean, neck.fpn_convs.0.bn.running_var, neck.fpn_convs.1.bn.weight, neck.fpn_convs.1.bn.bias, neck.fpn_convs.1.bn.running_mean, neck.fpn_convs.1.bn.running_var, neck.fpn_convs.2.bn.weight, neck.fpn_convs.2.bn.bias, neck.fpn_convs.2.bn.running_mean, neck.fpn_convs.2.bn.running_var, neck.fpn_convs.3.bn.weight, neck.fpn_convs.3.bn.bias, neck.fpn_convs.3.bn.running_mean, neck.fpn_convs.3.bn.running_var, neck.fpn_convs.4.bn.weight, neck.fpn_convs.4.bn.bias, neck.fpn_convs.4.bn.running_mean, neck.fpn_convs.4.bn.running_var, bbox_head.conv_cls.weight, bbox_head.conv_cls.bias, bbox_head.conv_reg.weight, bbox_head.conv_reg.bias, bbox_head.conv_centerness.weight, bbox_head.conv_centerness.bias

2022-10-03 23:04:10,814 - mmdet - INFO - Start running, host: tiger@n136-144-086, work_dir: /home/tiger/code/mmdet/work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5
2022-10-03 23:04:10,815 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(NORMAL      ) MMDetWandbHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) NumClassCheckHook                  
(NORMAL      ) MMDetWandbHook                     
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(NORMAL      ) MMDetWandbHook                     
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(NORMAL      ) MMDetWandbHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) NumClassCheckHook                  
(NORMAL      ) MMDetWandbHook                     
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(NORMAL      ) MMDetWandbHook                     
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_run:
(NORMAL      ) MMDetWandbHook                     
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2022-10-03 23:04:10,815 - mmdet - INFO - workflow: [('train', 1)], max: 12000 iters
2022-10-03 23:04:10,815 - mmdet - INFO - Checkpoints will be saved to /home/tiger/code/mmdet/work_dirs/finetune_fcos_12k_voc0712_lr1.5e-2_wd5e-5 by HardDiskBackend.
2022-10-03 23:04:17,705 - mmdet - INFO - Iter [50/12000]	lr: 1.484e-03, eta: 0:22:43, time: 0.114, data_time: 0.007, memory: 3374, loss_cls: 1.0537, loss_bbox: 4.8491, loss_centerness: 0.6754, loss: 6.5782, grad_norm: 12.3657
2022-10-03 23:04:23,217 - mmdet - INFO - Iter [100/12000]	lr: 2.982e-03, eta: 0:22:14, time: 0.110, data_time: 0.006, memory: 3374, loss_cls: 0.6791, loss_bbox: 0.8601, loss_centerness: 0.6573, loss: 2.1965, grad_norm: 4.5347
2022-10-03 23:04:28,829 - mmdet - INFO - Iter [150/12000]	lr: 4.481e-03, eta: 0:22:09, time: 0.112, data_time: 0.006, memory: 3374, loss_cls: 0.5333, loss_bbox: 0.7146, loss_centerness: 0.6428, loss: 1.8907, grad_norm: 5.2160
2022-10-03 23:04:34,160 - mmdet - INFO - Iter [200/12000]	lr: 5.979e-03, eta: 0:21:47, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.4749, loss_bbox: 0.7338, loss_centerness: 0.6323, loss: 1.8410, grad_norm: 7.3390
2022-10-03 23:04:39,394 - mmdet - INFO - Iter [250/12000]	lr: 7.478e-03, eta: 0:21:27, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.4591, loss_bbox: 0.6353, loss_centerness: 0.6251, loss: 1.7196, grad_norm: 6.4957
2022-10-03 23:04:44,999 - mmdet - INFO - Iter [300/12000]	lr: 8.976e-03, eta: 0:21:26, time: 0.112, data_time: 0.007, memory: 3374, loss_cls: 0.4273, loss_bbox: 0.6177, loss_centerness: 0.6211, loss: 1.6661, grad_norm: 6.4898
2022-10-03 23:04:50,570 - mmdet - INFO - Iter [350/12000]	lr: 1.047e-02, eta: 0:21:23, time: 0.111, data_time: 0.006, memory: 3374, loss_cls: 0.4455, loss_bbox: 0.6623, loss_centerness: 0.6199, loss: 1.7277, grad_norm: 6.3315
2022-10-03 23:04:56,146 - mmdet - INFO - Iter [400/12000]	lr: 1.197e-02, eta: 0:21:20, time: 0.112, data_time: 0.006, memory: 3374, loss_cls: 0.4226, loss_bbox: 0.5643, loss_centerness: 0.6222, loss: 1.6091, grad_norm: 4.8027
2022-10-03 23:05:01,689 - mmdet - INFO - Iter [450/12000]	lr: 1.347e-02, eta: 0:21:15, time: 0.111, data_time: 0.006, memory: 3374, loss_cls: 0.4131, loss_bbox: 0.6746, loss_centerness: 0.6191, loss: 1.7067, grad_norm: 6.1644
2022-10-03 23:05:08,483 - mmdet - INFO - Iter [500/12000]	lr: 1.497e-02, eta: 0:21:39, time: 0.136, data_time: 0.006, memory: 3374, loss_cls: 0.3854, loss_bbox: 0.6034, loss_centerness: 0.6164, loss: 1.6052, grad_norm: 5.1157
2022-10-03 23:05:13,624 - mmdet - INFO - Iter [550/12000]	lr: 1.500e-02, eta: 0:21:22, time: 0.103, data_time: 0.006, memory: 3374, loss_cls: 0.3810, loss_bbox: 0.6026, loss_centerness: 0.6148, loss: 1.5985, grad_norm: 5.2547
2022-10-03 23:05:18,837 - mmdet - INFO - Iter [600/12000]	lr: 1.500e-02, eta: 0:21:09, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.3759, loss_bbox: 0.6033, loss_centerness: 0.6159, loss: 1.5950, grad_norm: 5.4132
2022-10-03 23:05:24,127 - mmdet - INFO - Iter [650/12000]	lr: 1.500e-02, eta: 0:20:59, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.3536, loss_bbox: 0.5552, loss_centerness: 0.6144, loss: 1.5232, grad_norm: 4.6439
2022-10-03 23:05:29,329 - mmdet - INFO - Iter [700/12000]	lr: 1.500e-02, eta: 0:20:48, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.3611, loss_bbox: 0.5496, loss_centerness: 0.6123, loss: 1.5230, grad_norm: 4.9982
2022-10-03 23:05:34,597 - mmdet - INFO - Iter [750/12000]	lr: 1.500e-02, eta: 0:20:38, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3628, loss_bbox: 0.6455, loss_centerness: 0.6124, loss: 1.6208, grad_norm: 5.6424
2022-10-03 23:05:39,916 - mmdet - INFO - Iter [800/12000]	lr: 1.500e-02, eta: 0:20:30, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.3525, loss_bbox: 0.5528, loss_centerness: 0.6113, loss: 1.5166, grad_norm: 4.6742
2022-10-03 23:05:44,962 - mmdet - INFO - Iter [850/12000]	lr: 1.500e-02, eta: 0:20:19, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.3506, loss_bbox: 0.5777, loss_centerness: 0.6112, loss: 1.5394, grad_norm: 5.0777
2022-10-03 23:05:50,213 - mmdet - INFO - Iter [900/12000]	lr: 1.500e-02, eta: 0:20:11, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3315, loss_bbox: 0.6549, loss_centerness: 0.6092, loss: 1.5956, grad_norm: 5.1478
2022-10-03 23:05:55,528 - mmdet - INFO - Iter [950/12000]	lr: 1.500e-02, eta: 0:20:04, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.3390, loss_bbox: 0.5597, loss_centerness: 0.6095, loss: 1.5081, grad_norm: 4.3107
2022-10-03 23:06:00,561 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:06:00,562 - mmdet - INFO - Iter [1000/12000]	lr: 1.500e-02, eta: 0:19:54, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.3277, loss_bbox: 0.4881, loss_centerness: 0.6082, loss: 1.4240, grad_norm: 4.3779
2022-10-03 23:06:05,627 - mmdet - INFO - Iter [1050/12000]	lr: 1.500e-02, eta: 0:19:44, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.3301, loss_bbox: 0.5140, loss_centerness: 0.6098, loss: 1.4539, grad_norm: 4.5959
2022-10-03 23:06:10,997 - mmdet - INFO - Iter [1100/12000]	lr: 1.500e-02, eta: 0:19:39, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.3150, loss_bbox: 0.5068, loss_centerness: 0.6070, loss: 1.4287, grad_norm: 4.3244
2022-10-03 23:06:16,223 - mmdet - INFO - Iter [1150/12000]	lr: 1.500e-02, eta: 0:19:31, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3163, loss_bbox: 0.5167, loss_centerness: 0.6070, loss: 1.4400, grad_norm: 4.7621
2022-10-03 23:06:21,607 - mmdet - INFO - Iter [1200/12000]	lr: 1.500e-02, eta: 0:19:26, time: 0.108, data_time: 0.006, memory: 3374, loss_cls: 0.3054, loss_bbox: 0.4738, loss_centerness: 0.6063, loss: 1.3855, grad_norm: 3.7848
2022-10-03 23:06:26,663 - mmdet - INFO - Iter [1250/12000]	lr: 1.500e-02, eta: 0:19:18, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.2982, loss_bbox: 0.5563, loss_centerness: 0.6080, loss: 1.4626, grad_norm: 5.4164
2022-10-03 23:06:32,004 - mmdet - INFO - Iter [1300/12000]	lr: 1.500e-02, eta: 0:19:12, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.2959, loss_bbox: 0.4749, loss_centerness: 0.6046, loss: 1.3755, grad_norm: 4.0394
2022-10-03 23:06:37,278 - mmdet - INFO - Iter [1350/12000]	lr: 1.500e-02, eta: 0:19:06, time: 0.105, data_time: 0.006, memory: 3374, loss_cls: 0.3065, loss_bbox: 0.4300, loss_centerness: 0.6061, loss: 1.3425, grad_norm: 3.6915
2022-10-03 23:06:42,277 - mmdet - INFO - Iter [1400/12000]	lr: 1.500e-02, eta: 0:18:57, time: 0.100, data_time: 0.006, memory: 3374, loss_cls: 0.3188, loss_bbox: 0.4513, loss_centerness: 0.6068, loss: 1.3769, grad_norm: 3.7263
2022-10-03 23:06:47,648 - mmdet - INFO - Iter [1450/12000]	lr: 1.500e-02, eta: 0:18:52, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.3040, loss_bbox: 0.4776, loss_centerness: 0.6043, loss: 1.3859, grad_norm: 4.1767
2022-10-03 23:06:53,019 - mmdet - INFO - Iter [1500/12000]	lr: 1.500e-02, eta: 0:18:47, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.2978, loss_bbox: 0.4800, loss_centerness: 0.6047, loss: 1.3824, grad_norm: 4.1199
2022-10-03 23:06:58,344 - mmdet - INFO - Iter [1550/12000]	lr: 1.500e-02, eta: 0:18:41, time: 0.106, data_time: 0.006, memory: 3374, loss_cls: 0.2975, loss_bbox: 0.4485, loss_centerness: 0.6025, loss: 1.3485, grad_norm: 3.9451
2022-10-03 23:07:03,761 - mmdet - INFO - Iter [1600/12000]	lr: 1.500e-02, eta: 0:18:36, time: 0.108, data_time: 0.006, memory: 3374, loss_cls: 0.2885, loss_bbox: 0.4370, loss_centerness: 0.6048, loss: 1.3304, grad_norm: 3.3962
2022-10-03 23:07:09,400 - mmdet - INFO - Iter [1650/12000]	lr: 1.500e-02, eta: 0:18:32, time: 0.113, data_time: 0.006, memory: 3374, loss_cls: 0.2843, loss_bbox: 0.4135, loss_centerness: 0.6059, loss: 1.3038, grad_norm: 3.2543
2022-10-03 23:07:15,080 - mmdet - INFO - Iter [1700/12000]	lr: 1.500e-02, eta: 0:18:29, time: 0.114, data_time: 0.006, memory: 3374, loss_cls: 0.2856, loss_bbox: 0.4120, loss_centerness: 0.6029, loss: 1.3005, grad_norm: 3.1262
2022-10-03 23:07:20,292 - mmdet - INFO - Iter [1750/12000]	lr: 1.500e-02, eta: 0:18:22, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.2861, loss_bbox: 0.4194, loss_centerness: 0.6016, loss: 1.3071, grad_norm: 3.4000
2022-10-03 23:07:25,374 - mmdet - INFO - Iter [1800/12000]	lr: 1.500e-02, eta: 0:18:15, time: 0.102, data_time: 0.006, memory: 3374, loss_cls: 0.2892, loss_bbox: 0.3962, loss_centerness: 0.6040, loss: 1.2893, grad_norm: 2.8839
2022-10-03 23:07:30,704 - mmdet - INFO - Iter [1850/12000]	lr: 1.500e-02, eta: 0:18:10, time: 0.107, data_time: 0.006, memory: 3374, loss_cls: 0.2739, loss_bbox: 0.3708, loss_centerness: 0.6029, loss: 1.2476, grad_norm: 2.6212
2022-10-03 23:07:35,850 - mmdet - INFO - Iter [1900/12000]	lr: 1.500e-02, eta: 0:18:03, time: 0.103, data_time: 0.006, memory: 3374, loss_cls: 0.2914, loss_bbox: 0.4107, loss_centerness: 0.6050, loss: 1.3071, grad_norm: 3.1236
2022-10-03 23:07:40,977 - mmdet - INFO - Iter [1950/12000]	lr: 1.500e-02, eta: 0:17:56, time: 0.103, data_time: 0.006, memory: 3374, loss_cls: 0.2795, loss_bbox: 0.4096, loss_centerness: 0.6022, loss: 1.2914, grad_norm: 3.0628
2022-10-03 23:07:46,032 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:07:46,033 - mmdet - INFO - Iter [2000/12000]	lr: 1.500e-02, eta: 0:17:50, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.2805, loss_bbox: 0.3808, loss_centerness: 0.6005, loss: 1.2618, grad_norm: 2.8152
2022-10-03 23:07:51,065 - mmdet - INFO - Iter [2050/12000]	lr: 1.500e-02, eta: 0:17:43, time: 0.101, data_time: 0.006, memory: 3374, loss_cls: 0.2772, loss_bbox: 0.3860, loss_centerness: 0.5991, loss: 1.2624, grad_norm: 2.9319
2022-10-03 23:07:56,283 - mmdet - INFO - Iter [2100/12000]	lr: 1.500e-02, eta: 0:17:37, time: 0.104, data_time: 0.006, memory: 3374, loss_cls: 0.2703, loss_bbox: 0.3860, loss_centerness: 0.6015, loss: 1.2578, grad_norm: 2.6650
2022-10-03 23:08:01,375 - mmdet - INFO - Iter [2150/12000]	lr: 1.500e-02, eta: 0:17:30, time: 0.102, data_time: 0.006, memory: 3374, loss_cls: 0.2569, loss_bbox: 0.3632, loss_centerness: 0.5993, loss: 1.2194, grad_norm: 2.6891
2022-10-03 23:08:06,399 - mmdet - INFO - Iter [2200/12000]	lr: 1.500e-02, eta: 0:17:24, time: 0.100, data_time: 0.006, memory: 3374, loss_cls: 0.2682, loss_bbox: 0.3759, loss_centerness: 0.6009, loss: 1.2450, grad_norm: 2.7334
2022-10-03 23:08:11,513 - mmdet - INFO - Iter [2250/12000]	lr: 1.500e-02, eta: 0:17:17, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2589, loss_bbox: 0.3624, loss_centerness: 0.5985, loss: 1.2198, grad_norm: 2.7641
2022-10-03 23:08:16,930 - mmdet - INFO - Iter [2300/12000]	lr: 1.500e-02, eta: 0:17:12, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2619, loss_bbox: 0.3805, loss_centerness: 0.5989, loss: 1.2414, grad_norm: 2.8289
2022-10-03 23:08:22,089 - mmdet - INFO - Iter [2350/12000]	lr: 1.500e-02, eta: 0:17:06, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2718, loss_bbox: 0.3752, loss_centerness: 0.6001, loss: 1.2471, grad_norm: 2.7427
2022-10-03 23:08:27,145 - mmdet - INFO - Iter [2400/12000]	lr: 1.500e-02, eta: 0:17:00, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2640, loss_bbox: 0.3638, loss_centerness: 0.5997, loss: 1.2274, grad_norm: 2.5183
2022-10-03 23:08:32,180 - mmdet - INFO - Iter [2450/12000]	lr: 1.500e-02, eta: 0:16:54, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2464, loss_bbox: 0.3551, loss_centerness: 0.6002, loss: 1.2016, grad_norm: 2.4714
2022-10-03 23:08:37,449 - mmdet - INFO - Iter [2500/12000]	lr: 1.500e-02, eta: 0:16:48, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2611, loss_bbox: 0.3776, loss_centerness: 0.6007, loss: 1.2394, grad_norm: 2.7616
2022-10-03 23:08:42,790 - mmdet - INFO - Iter [2550/12000]	lr: 1.500e-02, eta: 0:16:43, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2558, loss_bbox: 0.3706, loss_centerness: 0.6010, loss: 1.2273, grad_norm: 2.6622
2022-10-03 23:08:48,168 - mmdet - INFO - Iter [2600/12000]	lr: 1.500e-02, eta: 0:16:38, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2528, loss_bbox: 0.3718, loss_centerness: 0.6008, loss: 1.2255, grad_norm: 2.6662
2022-10-03 23:08:53,369 - mmdet - INFO - Iter [2650/12000]	lr: 1.500e-02, eta: 0:16:32, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.2571, loss_bbox: 0.3540, loss_centerness: 0.5993, loss: 1.2104, grad_norm: 2.6098
2022-10-03 23:08:58,471 - mmdet - INFO - Iter [2700/12000]	lr: 1.500e-02, eta: 0:16:26, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2488, loss_bbox: 0.3670, loss_centerness: 0.5980, loss: 1.2138, grad_norm: 2.7511
2022-10-03 23:09:03,858 - mmdet - INFO - Iter [2750/12000]	lr: 1.500e-02, eta: 0:16:21, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2875, loss_bbox: 0.3567, loss_centerness: 0.5974, loss: 1.2417, grad_norm: 2.8518
2022-10-03 23:09:09,385 - mmdet - INFO - Iter [2800/12000]	lr: 1.500e-02, eta: 0:16:17, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.2600, loss_bbox: 0.3723, loss_centerness: 0.5989, loss: 1.2311, grad_norm: 2.7242
2022-10-03 23:09:14,773 - mmdet - INFO - Iter [2850/12000]	lr: 1.500e-02, eta: 0:16:12, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2638, loss_bbox: 0.3606, loss_centerness: 0.6012, loss: 1.2255, grad_norm: 2.5991
2022-10-03 23:09:19,960 - mmdet - INFO - Iter [2900/12000]	lr: 1.500e-02, eta: 0:16:06, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.2658, loss_bbox: 0.3596, loss_centerness: 0.5990, loss: 1.2244, grad_norm: 2.6344
2022-10-03 23:09:25,447 - mmdet - INFO - Iter [2950/12000]	lr: 1.500e-02, eta: 0:16:01, time: 0.110, data_time: 0.006, memory: 3375, loss_cls: 0.2623, loss_bbox: 0.3663, loss_centerness: 0.5994, loss: 1.2280, grad_norm: 2.5495
2022-10-03 23:09:30,697 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:09:30,697 - mmdet - INFO - Iter [3000/12000]	lr: 1.500e-02, eta: 0:15:56, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2467, loss_bbox: 0.3484, loss_centerness: 0.5985, loss: 1.1936, grad_norm: 2.3818
2022-10-03 23:09:35,762 - mmdet - INFO - Iter [3050/12000]	lr: 1.500e-02, eta: 0:15:50, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2489, loss_bbox: 0.3615, loss_centerness: 0.5986, loss: 1.2090, grad_norm: 2.6018
2022-10-03 23:09:40,840 - mmdet - INFO - Iter [3100/12000]	lr: 1.500e-02, eta: 0:15:44, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2385, loss_bbox: 0.3538, loss_centerness: 0.5981, loss: 1.1904, grad_norm: 2.6130
2022-10-03 23:09:46,209 - mmdet - INFO - Iter [3150/12000]	lr: 1.500e-02, eta: 0:15:38, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2298, loss_bbox: 0.3453, loss_centerness: 0.5973, loss: 1.1723, grad_norm: 2.5113
2022-10-03 23:09:51,589 - mmdet - INFO - Iter [3200/12000]	lr: 1.500e-02, eta: 0:15:33, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2480, loss_bbox: 0.3494, loss_centerness: 0.5984, loss: 1.1958, grad_norm: 2.5547
2022-10-03 23:09:56,892 - mmdet - INFO - Iter [3250/12000]	lr: 1.500e-02, eta: 0:15:28, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.2448, loss_bbox: 0.3371, loss_centerness: 0.5989, loss: 1.1808, grad_norm: 2.4596
2022-10-03 23:10:01,982 - mmdet - INFO - Iter [3300/12000]	lr: 1.500e-02, eta: 0:15:22, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2546, loss_bbox: 0.3607, loss_centerness: 0.5988, loss: 1.2141, grad_norm: 2.7104
2022-10-03 23:10:07,046 - mmdet - INFO - Iter [3350/12000]	lr: 1.500e-02, eta: 0:15:16, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2385, loss_bbox: 0.3381, loss_centerness: 0.5972, loss: 1.1738, grad_norm: 2.4207
2022-10-03 23:10:12,276 - mmdet - INFO - Iter [3400/12000]	lr: 1.500e-02, eta: 0:15:11, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2361, loss_bbox: 0.3271, loss_centerness: 0.5959, loss: 1.1592, grad_norm: 2.3731
2022-10-03 23:10:17,293 - mmdet - INFO - Iter [3450/12000]	lr: 1.500e-02, eta: 0:15:05, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2472, loss_bbox: 0.3672, loss_centerness: 0.5965, loss: 1.2109, grad_norm: 2.8299
2022-10-03 23:10:22,449 - mmdet - INFO - Iter [3500/12000]	lr: 1.500e-02, eta: 0:14:59, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2285, loss_bbox: 0.3372, loss_centerness: 0.5958, loss: 1.1614, grad_norm: 2.3995
2022-10-03 23:10:27,682 - mmdet - INFO - Iter [3550/12000]	lr: 1.500e-02, eta: 0:14:54, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2323, loss_bbox: 0.3379, loss_centerness: 0.5953, loss: 1.1656, grad_norm: 2.7294
2022-10-03 23:10:32,728 - mmdet - INFO - Iter [3600/12000]	lr: 1.500e-02, eta: 0:14:48, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2352, loss_bbox: 0.3351, loss_centerness: 0.5969, loss: 1.1672, grad_norm: 2.6776
2022-10-03 23:10:37,762 - mmdet - INFO - Iter [3650/12000]	lr: 1.500e-02, eta: 0:14:42, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2406, loss_bbox: 0.3351, loss_centerness: 0.5969, loss: 1.1726, grad_norm: 2.5880
2022-10-03 23:10:42,994 - mmdet - INFO - Iter [3700/12000]	lr: 1.500e-02, eta: 0:14:37, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2356, loss_bbox: 0.3268, loss_centerness: 0.5963, loss: 1.1587, grad_norm: 2.3722
2022-10-03 23:10:48,338 - mmdet - INFO - Iter [3750/12000]	lr: 1.500e-02, eta: 0:14:31, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2314, loss_bbox: 0.3354, loss_centerness: 0.5952, loss: 1.1620, grad_norm: 2.5687
2022-10-03 23:10:53,688 - mmdet - INFO - Iter [3800/12000]	lr: 1.500e-02, eta: 0:14:26, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.2388, loss_bbox: 0.3303, loss_centerness: 0.5958, loss: 1.1649, grad_norm: 2.5392
2022-10-03 23:10:58,875 - mmdet - INFO - Iter [3850/12000]	lr: 1.500e-02, eta: 0:14:21, time: 0.104, data_time: 0.007, memory: 3375, loss_cls: 0.2336, loss_bbox: 0.3207, loss_centerness: 0.5965, loss: 1.1509, grad_norm: 2.3348
2022-10-03 23:11:04,134 - mmdet - INFO - Iter [3900/12000]	lr: 1.500e-02, eta: 0:14:15, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2293, loss_bbox: 0.3234, loss_centerness: 0.5946, loss: 1.1473, grad_norm: 2.3656
2022-10-03 23:11:09,316 - mmdet - INFO - Iter [3950/12000]	lr: 1.500e-02, eta: 0:14:10, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.2271, loss_bbox: 0.3254, loss_centerness: 0.5963, loss: 1.1489, grad_norm: 2.3502
2022-10-03 23:11:14,373 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:11:14,373 - mmdet - INFO - Iter [4000/12000]	lr: 1.500e-02, eta: 0:14:04, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2390, loss_bbox: 0.3419, loss_centerness: 0.5963, loss: 1.1772, grad_norm: 2.6043
2022-10-03 23:11:19,472 - mmdet - INFO - Iter [4050/12000]	lr: 1.500e-02, eta: 0:13:59, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2309, loss_bbox: 0.3362, loss_centerness: 0.5982, loss: 1.1653, grad_norm: 2.5254
2022-10-03 23:11:24,712 - mmdet - INFO - Iter [4100/12000]	lr: 1.500e-02, eta: 0:13:53, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2314, loss_bbox: 0.3494, loss_centerness: 0.5960, loss: 1.1768, grad_norm: 2.6908
2022-10-03 23:11:30,137 - mmdet - INFO - Iter [4150/12000]	lr: 1.500e-02, eta: 0:13:48, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2227, loss_bbox: 0.3214, loss_centerness: 0.5937, loss: 1.1378, grad_norm: 2.5844
2022-10-03 23:11:35,406 - mmdet - INFO - Iter [4200/12000]	lr: 1.500e-02, eta: 0:13:43, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2212, loss_bbox: 0.3142, loss_centerness: 0.5922, loss: 1.1276, grad_norm: 2.5120
2022-10-03 23:11:40,647 - mmdet - INFO - Iter [4250/12000]	lr: 1.500e-02, eta: 0:13:38, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2243, loss_bbox: 0.3261, loss_centerness: 0.5936, loss: 1.1441, grad_norm: 2.5729
2022-10-03 23:11:45,717 - mmdet - INFO - Iter [4300/12000]	lr: 1.500e-02, eta: 0:13:32, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2274, loss_bbox: 0.3149, loss_centerness: 0.5937, loss: 1.1361, grad_norm: 2.5403
2022-10-03 23:11:50,857 - mmdet - INFO - Iter [4350/12000]	lr: 1.500e-02, eta: 0:13:26, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2301, loss_bbox: 0.3325, loss_centerness: 0.5947, loss: 1.1573, grad_norm: 2.6587
2022-10-03 23:11:55,876 - mmdet - INFO - Iter [4400/12000]	lr: 1.500e-02, eta: 0:13:21, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2274, loss_bbox: 0.3267, loss_centerness: 0.5945, loss: 1.1486, grad_norm: 2.4360
2022-10-03 23:12:00,878 - mmdet - INFO - Iter [4450/12000]	lr: 1.500e-02, eta: 0:13:15, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2180, loss_bbox: 0.3155, loss_centerness: 0.5942, loss: 1.1278, grad_norm: 2.4964
2022-10-03 23:12:05,906 - mmdet - INFO - Iter [4500/12000]	lr: 1.500e-02, eta: 0:13:09, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2253, loss_bbox: 0.3083, loss_centerness: 0.5952, loss: 1.1288, grad_norm: 2.3991
2022-10-03 23:12:10,944 - mmdet - INFO - Iter [4550/12000]	lr: 1.500e-02, eta: 0:13:04, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2151, loss_bbox: 0.3198, loss_centerness: 0.5954, loss: 1.1303, grad_norm: 2.4637
2022-10-03 23:12:16,026 - mmdet - INFO - Iter [4600/12000]	lr: 1.500e-02, eta: 0:12:58, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2297, loss_bbox: 0.3213, loss_centerness: 0.5944, loss: 1.1455, grad_norm: 2.5831
2022-10-03 23:12:21,092 - mmdet - INFO - Iter [4650/12000]	lr: 1.500e-02, eta: 0:12:53, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2188, loss_bbox: 0.3081, loss_centerness: 0.5925, loss: 1.1193, grad_norm: 2.2491
2022-10-03 23:12:26,399 - mmdet - INFO - Iter [4700/12000]	lr: 1.500e-02, eta: 0:12:47, time: 0.106, data_time: 0.007, memory: 3375, loss_cls: 0.2252, loss_bbox: 0.3300, loss_centerness: 0.5961, loss: 1.1514, grad_norm: 2.5577
2022-10-03 23:12:31,477 - mmdet - INFO - Iter [4750/12000]	lr: 1.500e-02, eta: 0:12:42, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2234, loss_bbox: 0.3177, loss_centerness: 0.5950, loss: 1.1361, grad_norm: 2.4445
2022-10-03 23:12:36,463 - mmdet - INFO - Iter [4800/12000]	lr: 1.500e-02, eta: 0:12:36, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2094, loss_bbox: 0.3308, loss_centerness: 0.5942, loss: 1.1344, grad_norm: 2.5108
2022-10-03 23:12:41,538 - mmdet - INFO - Iter [4850/12000]	lr: 1.500e-02, eta: 0:12:31, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2180, loss_bbox: 0.2990, loss_centerness: 0.5935, loss: 1.1104, grad_norm: 2.3976
2022-10-03 23:12:46,590 - mmdet - INFO - Iter [4900/12000]	lr: 1.500e-02, eta: 0:12:25, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.2187, loss_bbox: 0.3108, loss_centerness: 0.5938, loss: 1.1232, grad_norm: 2.4357
2022-10-03 23:12:51,755 - mmdet - INFO - Iter [4950/12000]	lr: 1.500e-02, eta: 0:12:20, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2167, loss_bbox: 0.3118, loss_centerness: 0.5939, loss: 1.1224, grad_norm: 2.4076
2022-10-03 23:12:56,821 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:12:56,821 - mmdet - INFO - Iter [5000/12000]	lr: 1.500e-02, eta: 0:12:14, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2298, loss_bbox: 0.3248, loss_centerness: 0.5956, loss: 1.1501, grad_norm: 2.5838
2022-10-03 23:13:01,852 - mmdet - INFO - Iter [5050/12000]	lr: 1.500e-02, eta: 0:12:09, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2233, loss_bbox: 0.3420, loss_centerness: 0.5964, loss: 1.1616, grad_norm: 2.4916
2022-10-03 23:13:06,953 - mmdet - INFO - Iter [5100/12000]	lr: 1.500e-02, eta: 0:12:03, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2165, loss_bbox: 0.3110, loss_centerness: 0.5922, loss: 1.1196, grad_norm: 2.4828
2022-10-03 23:13:11,976 - mmdet - INFO - Iter [5150/12000]	lr: 1.500e-02, eta: 0:11:58, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2346, loss_bbox: 0.3290, loss_centerness: 0.5967, loss: 1.1603, grad_norm: 2.5321
2022-10-03 23:13:16,971 - mmdet - INFO - Iter [5200/12000]	lr: 1.500e-02, eta: 0:11:52, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2181, loss_bbox: 0.3112, loss_centerness: 0.5931, loss: 1.1224, grad_norm: 2.4412
2022-10-03 23:13:22,234 - mmdet - INFO - Iter [5250/12000]	lr: 1.500e-02, eta: 0:11:47, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2033, loss_bbox: 0.2971, loss_centerness: 0.5904, loss: 1.0909, grad_norm: 2.3256
2022-10-03 23:13:27,473 - mmdet - INFO - Iter [5300/12000]	lr: 1.500e-02, eta: 0:11:42, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2147, loss_bbox: 0.3131, loss_centerness: 0.5909, loss: 1.1187, grad_norm: 2.7216
2022-10-03 23:13:32,537 - mmdet - INFO - Iter [5350/12000]	lr: 1.500e-02, eta: 0:11:36, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2090, loss_bbox: 0.3241, loss_centerness: 0.5906, loss: 1.1237, grad_norm: 2.6115
2022-10-03 23:13:37,672 - mmdet - INFO - Iter [5400/12000]	lr: 1.500e-02, eta: 0:11:31, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1970, loss_bbox: 0.2998, loss_centerness: 0.5933, loss: 1.0901, grad_norm: 2.3276
2022-10-03 23:13:42,654 - mmdet - INFO - Iter [5450/12000]	lr: 1.500e-02, eta: 0:11:25, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.2139, loss_bbox: 0.3126, loss_centerness: 0.5936, loss: 1.1201, grad_norm: 2.6115
2022-10-03 23:13:47,792 - mmdet - INFO - Iter [5500/12000]	lr: 1.500e-02, eta: 0:11:20, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.2196, loss_bbox: 0.2999, loss_centerness: 0.5937, loss: 1.1133, grad_norm: 2.3717
2022-10-03 23:13:52,833 - mmdet - INFO - Iter [5550/12000]	lr: 1.500e-02, eta: 0:11:14, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2144, loss_bbox: 0.3060, loss_centerness: 0.5949, loss: 1.1154, grad_norm: 2.4405
2022-10-03 23:13:57,884 - mmdet - INFO - Iter [5600/12000]	lr: 1.500e-02, eta: 0:11:09, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.2133, loss_bbox: 0.3024, loss_centerness: 0.5918, loss: 1.1075, grad_norm: 2.5034
2022-10-03 23:14:03,262 - mmdet - INFO - Iter [5650/12000]	lr: 1.500e-02, eta: 0:11:04, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1986, loss_bbox: 0.3024, loss_centerness: 0.5913, loss: 1.0922, grad_norm: 2.3927
2022-10-03 23:14:08,662 - mmdet - INFO - Iter [5700/12000]	lr: 1.500e-02, eta: 0:10:59, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.2162, loss_bbox: 0.3056, loss_centerness: 0.5944, loss: 1.1162, grad_norm: 2.5149
2022-10-03 23:14:13,780 - mmdet - INFO - Iter [5750/12000]	lr: 1.500e-02, eta: 0:10:54, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2090, loss_bbox: 0.3121, loss_centerness: 0.5934, loss: 1.1145, grad_norm: 2.3781
2022-10-03 23:14:18,881 - mmdet - INFO - Iter [5800/12000]	lr: 1.500e-02, eta: 0:10:48, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2121, loss_bbox: 0.3099, loss_centerness: 0.5922, loss: 1.1142, grad_norm: 2.6576
2022-10-03 23:14:24,445 - mmdet - INFO - Iter [5850/12000]	lr: 1.500e-02, eta: 0:10:43, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.2230, loss_bbox: 0.3037, loss_centerness: 0.5928, loss: 1.1195, grad_norm: 2.5658
2022-10-03 23:14:29,669 - mmdet - INFO - Iter [5900/12000]	lr: 1.500e-02, eta: 0:10:38, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2123, loss_bbox: 0.3129, loss_centerness: 0.5938, loss: 1.1190, grad_norm: 2.6781
2022-10-03 23:14:34,710 - mmdet - INFO - Iter [5950/12000]	lr: 1.500e-02, eta: 0:10:33, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2086, loss_bbox: 0.3025, loss_centerness: 0.5915, loss: 1.1027, grad_norm: 2.4293
2022-10-03 23:14:39,785 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:14:39,785 - mmdet - INFO - Iter [6000/12000]	lr: 1.500e-02, eta: 0:10:27, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2063, loss_bbox: 0.3019, loss_centerness: 0.5926, loss: 1.1008, grad_norm: 2.4104
2022-10-03 23:14:44,827 - mmdet - INFO - Iter [6050/12000]	lr: 1.500e-02, eta: 0:10:22, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2097, loss_bbox: 0.3115, loss_centerness: 0.5933, loss: 1.1145, grad_norm: 2.4511
2022-10-03 23:14:49,852 - mmdet - INFO - Iter [6100/12000]	lr: 1.500e-02, eta: 0:10:16, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2069, loss_bbox: 0.2894, loss_centerness: 0.5897, loss: 1.0860, grad_norm: 2.3739
2022-10-03 23:14:54,937 - mmdet - INFO - Iter [6150/12000]	lr: 1.500e-02, eta: 0:10:11, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.2026, loss_bbox: 0.3039, loss_centerness: 0.5934, loss: 1.0999, grad_norm: 2.4252
2022-10-03 23:15:00,058 - mmdet - INFO - Iter [6200/12000]	lr: 1.500e-02, eta: 0:10:06, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1982, loss_bbox: 0.2990, loss_centerness: 0.5923, loss: 1.0895, grad_norm: 2.4156
2022-10-03 23:15:05,099 - mmdet - INFO - Iter [6250/12000]	lr: 1.500e-02, eta: 0:10:00, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1977, loss_bbox: 0.3022, loss_centerness: 0.5897, loss: 1.0897, grad_norm: 2.5230
2022-10-03 23:15:10,411 - mmdet - INFO - Iter [6300/12000]	lr: 1.500e-02, eta: 0:09:55, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.2001, loss_bbox: 0.2945, loss_centerness: 0.5892, loss: 1.0838, grad_norm: 2.5836
2022-10-03 23:15:15,557 - mmdet - INFO - Iter [6350/12000]	lr: 1.500e-02, eta: 0:09:50, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1997, loss_bbox: 0.2807, loss_centerness: 0.5900, loss: 1.0703, grad_norm: 2.3159
2022-10-03 23:15:21,104 - mmdet - INFO - Iter [6400/12000]	lr: 1.500e-02, eta: 0:09:45, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.1946, loss_bbox: 0.2813, loss_centerness: 0.5894, loss: 1.0652, grad_norm: 2.4935
2022-10-03 23:15:26,515 - mmdet - INFO - Iter [6450/12000]	lr: 1.500e-02, eta: 0:09:40, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1993, loss_bbox: 0.2951, loss_centerness: 0.5912, loss: 1.0856, grad_norm: 2.3950
2022-10-03 23:15:31,748 - mmdet - INFO - Iter [6500/12000]	lr: 1.500e-02, eta: 0:09:35, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.2053, loss_bbox: 0.3171, loss_centerness: 0.5932, loss: 1.1157, grad_norm: 2.6241
2022-10-03 23:15:36,910 - mmdet - INFO - Iter [6550/12000]	lr: 1.500e-02, eta: 0:09:29, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1946, loss_bbox: 0.2933, loss_centerness: 0.5913, loss: 1.0792, grad_norm: 2.3415
2022-10-03 23:15:41,932 - mmdet - INFO - Iter [6600/12000]	lr: 1.500e-02, eta: 0:09:24, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1969, loss_bbox: 0.2923, loss_centerness: 0.5900, loss: 1.0792, grad_norm: 2.4594
2022-10-03 23:15:47,006 - mmdet - INFO - Iter [6650/12000]	lr: 1.500e-02, eta: 0:09:19, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1952, loss_bbox: 0.2876, loss_centerness: 0.5895, loss: 1.0723, grad_norm: 2.4592
2022-10-03 23:15:52,065 - mmdet - INFO - Iter [6700/12000]	lr: 1.500e-02, eta: 0:09:13, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2037, loss_bbox: 0.2893, loss_centerness: 0.5903, loss: 1.0833, grad_norm: 2.5001
2022-10-03 23:15:57,122 - mmdet - INFO - Iter [6750/12000]	lr: 1.500e-02, eta: 0:09:08, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2060, loss_bbox: 0.3081, loss_centerness: 0.5938, loss: 1.1079, grad_norm: 2.4866
2022-10-03 23:16:02,117 - mmdet - INFO - Iter [6800/12000]	lr: 1.500e-02, eta: 0:09:03, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1931, loss_bbox: 0.2915, loss_centerness: 0.5892, loss: 1.0738, grad_norm: 2.4755
2022-10-03 23:16:07,139 - mmdet - INFO - Iter [6850/12000]	lr: 1.500e-02, eta: 0:08:57, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1999, loss_bbox: 0.3040, loss_centerness: 0.5905, loss: 1.0945, grad_norm: 2.5293
2022-10-03 23:16:12,233 - mmdet - INFO - Iter [6900/12000]	lr: 1.500e-02, eta: 0:08:52, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1905, loss_bbox: 0.3017, loss_centerness: 0.5918, loss: 1.0839, grad_norm: 2.4225
2022-10-03 23:16:17,543 - mmdet - INFO - Iter [6950/12000]	lr: 1.500e-02, eta: 0:08:47, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1933, loss_bbox: 0.2995, loss_centerness: 0.5909, loss: 1.0837, grad_norm: 2.4788
2022-10-03 23:16:22,605 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:16:22,605 - mmdet - INFO - Iter [7000/12000]	lr: 1.500e-02, eta: 0:08:41, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.2045, loss_bbox: 0.2978, loss_centerness: 0.5918, loss: 1.0941, grad_norm: 2.5168
2022-10-03 23:16:27,624 - mmdet - INFO - Iter [7050/12000]	lr: 1.500e-02, eta: 0:08:36, time: 0.100, data_time: 0.007, memory: 3375, loss_cls: 0.1988, loss_bbox: 0.2890, loss_centerness: 0.5895, loss: 1.0774, grad_norm: 2.4041
2022-10-03 23:16:32,749 - mmdet - INFO - Iter [7100/12000]	lr: 1.500e-02, eta: 0:08:31, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1999, loss_bbox: 0.2849, loss_centerness: 0.5911, loss: 1.0759, grad_norm: 2.4509
2022-10-03 23:16:37,770 - mmdet - INFO - Iter [7150/12000]	lr: 1.500e-02, eta: 0:08:25, time: 0.100, data_time: 0.007, memory: 3375, loss_cls: 0.2015, loss_bbox: 0.2874, loss_centerness: 0.5904, loss: 1.0793, grad_norm: 2.3829
2022-10-03 23:16:42,849 - mmdet - INFO - Iter [7200/12000]	lr: 1.500e-02, eta: 0:08:20, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.2032, loss_bbox: 0.3085, loss_centerness: 0.5915, loss: 1.1032, grad_norm: 2.7239
2022-10-03 23:16:47,768 - mmdet - INFO - Iter [7250/12000]	lr: 1.500e-02, eta: 0:08:15, time: 0.098, data_time: 0.007, memory: 3375, loss_cls: 0.1963, loss_bbox: 0.2936, loss_centerness: 0.5903, loss: 1.0802, grad_norm: 2.4589
2022-10-03 23:16:53,108 - mmdet - INFO - Iter [7300/12000]	lr: 1.500e-02, eta: 0:08:10, time: 0.107, data_time: 0.007, memory: 3375, loss_cls: 0.1790, loss_bbox: 0.2900, loss_centerness: 0.5881, loss: 1.0571, grad_norm: 2.4370
2022-10-03 23:16:58,574 - mmdet - INFO - Iter [7350/12000]	lr: 1.500e-02, eta: 0:08:04, time: 0.109, data_time: 0.006, memory: 3375, loss_cls: 0.1870, loss_bbox: 0.2812, loss_centerness: 0.5882, loss: 1.0564, grad_norm: 2.4754
2022-10-03 23:17:03,591 - mmdet - INFO - Iter [7400/12000]	lr: 1.500e-02, eta: 0:07:59, time: 0.100, data_time: 0.007, memory: 3375, loss_cls: 0.1883, loss_bbox: 0.2878, loss_centerness: 0.5876, loss: 1.0637, grad_norm: 2.4153
2022-10-03 23:17:08,694 - mmdet - INFO - Iter [7450/12000]	lr: 1.500e-02, eta: 0:07:54, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1916, loss_bbox: 0.2811, loss_centerness: 0.5907, loss: 1.0635, grad_norm: 2.3504
2022-10-03 23:17:13,736 - mmdet - INFO - Iter [7500/12000]	lr: 1.500e-02, eta: 0:07:49, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1960, loss_bbox: 0.2863, loss_centerness: 0.5908, loss: 1.0732, grad_norm: 2.4362
2022-10-03 23:17:19,114 - mmdet - INFO - Iter [7550/12000]	lr: 1.500e-02, eta: 0:07:43, time: 0.107, data_time: 0.007, memory: 3375, loss_cls: 0.1868, loss_bbox: 0.2858, loss_centerness: 0.5872, loss: 1.0598, grad_norm: 2.5911
2022-10-03 23:17:24,208 - mmdet - INFO - Iter [7600/12000]	lr: 1.500e-02, eta: 0:07:38, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1832, loss_bbox: 0.2850, loss_centerness: 0.5891, loss: 1.0573, grad_norm: 2.4779
2022-10-03 23:17:29,438 - mmdet - INFO - Iter [7650/12000]	lr: 1.500e-02, eta: 0:07:33, time: 0.105, data_time: 0.007, memory: 3375, loss_cls: 0.1894, loss_bbox: 0.2913, loss_centerness: 0.5899, loss: 1.0707, grad_norm: 2.5428
2022-10-03 23:17:34,437 - mmdet - INFO - Iter [7700/12000]	lr: 1.500e-02, eta: 0:07:28, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1969, loss_bbox: 0.2840, loss_centerness: 0.5881, loss: 1.0691, grad_norm: 2.6974
2022-10-03 23:17:39,440 - mmdet - INFO - Iter [7750/12000]	lr: 1.500e-02, eta: 0:07:22, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1931, loss_bbox: 0.3048, loss_centerness: 0.5913, loss: 1.0892, grad_norm: 2.6059
2022-10-03 23:17:44,815 - mmdet - INFO - Iter [7800/12000]	lr: 1.500e-02, eta: 0:07:17, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1955, loss_bbox: 0.2941, loss_centerness: 0.5893, loss: 1.0789, grad_norm: 2.6701
2022-10-03 23:17:50,063 - mmdet - INFO - Iter [7850/12000]	lr: 1.500e-02, eta: 0:07:12, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1865, loss_bbox: 0.2720, loss_centerness: 0.5877, loss: 1.0462, grad_norm: 2.3621
2022-10-03 23:17:54,985 - mmdet - INFO - Iter [7900/12000]	lr: 1.500e-02, eta: 0:07:07, time: 0.098, data_time: 0.006, memory: 3375, loss_cls: 0.1968, loss_bbox: 0.2950, loss_centerness: 0.5900, loss: 1.0818, grad_norm: 2.6020
2022-10-03 23:18:00,052 - mmdet - INFO - Iter [7950/12000]	lr: 1.500e-02, eta: 0:07:01, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1923, loss_bbox: 0.2947, loss_centerness: 0.5895, loss: 1.0765, grad_norm: 2.6275
2022-10-03 23:18:05,119 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:18:05,119 - mmdet - INFO - Iter [8000/12000]	lr: 1.500e-02, eta: 0:06:56, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1962, loss_bbox: 0.2897, loss_centerness: 0.5913, loss: 1.0771, grad_norm: 2.4957
2022-10-03 23:18:10,151 - mmdet - INFO - Iter [8050/12000]	lr: 1.500e-02, eta: 0:06:51, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1973, loss_bbox: 0.2891, loss_centerness: 0.5909, loss: 1.0773, grad_norm: 2.5776
2022-10-03 23:18:15,373 - mmdet - INFO - Iter [8100/12000]	lr: 1.500e-02, eta: 0:06:46, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1929, loss_bbox: 0.2841, loss_centerness: 0.5903, loss: 1.0673, grad_norm: 2.5122
2022-10-03 23:18:20,522 - mmdet - INFO - Iter [8150/12000]	lr: 1.500e-02, eta: 0:06:40, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1960, loss_bbox: 0.2843, loss_centerness: 0.5869, loss: 1.0673, grad_norm: 2.6011
2022-10-03 23:18:25,561 - mmdet - INFO - Iter [8200/12000]	lr: 1.500e-02, eta: 0:06:35, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1907, loss_bbox: 0.2878, loss_centerness: 0.5906, loss: 1.0691, grad_norm: 2.4059
2022-10-03 23:18:30,632 - mmdet - INFO - Iter [8250/12000]	lr: 1.500e-02, eta: 0:06:30, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1973, loss_bbox: 0.2953, loss_centerness: 0.5907, loss: 1.0833, grad_norm: 2.3690
2022-10-03 23:18:35,602 - mmdet - INFO - Iter [8300/12000]	lr: 1.500e-02, eta: 0:06:24, time: 0.099, data_time: 0.006, memory: 3375, loss_cls: 0.1849, loss_bbox: 0.2971, loss_centerness: 0.5895, loss: 1.0716, grad_norm: 2.5134
2022-10-03 23:18:40,600 - mmdet - INFO - Iter [8350/12000]	lr: 1.500e-02, eta: 0:06:19, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1811, loss_bbox: 0.2672, loss_centerness: 0.5855, loss: 1.0339, grad_norm: 2.4261
2022-10-03 23:18:45,619 - mmdet - INFO - Iter [8400/12000]	lr: 1.500e-02, eta: 0:06:14, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1758, loss_bbox: 0.2833, loss_centerness: 0.5882, loss: 1.0473, grad_norm: 2.6022
2022-10-03 23:18:50,700 - mmdet - INFO - Iter [8450/12000]	lr: 1.500e-02, eta: 0:06:09, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1811, loss_bbox: 0.2762, loss_centerness: 0.5893, loss: 1.0466, grad_norm: 2.3019
2022-10-03 23:18:55,706 - mmdet - INFO - Iter [8500/12000]	lr: 1.500e-02, eta: 0:06:03, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1827, loss_bbox: 0.3011, loss_centerness: 0.5893, loss: 1.0731, grad_norm: 2.5493
2022-10-03 23:19:00,820 - mmdet - INFO - Iter [8550/12000]	lr: 1.500e-02, eta: 0:05:58, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1774, loss_bbox: 0.2718, loss_centerness: 0.5866, loss: 1.0358, grad_norm: 2.5059
2022-10-03 23:19:06,073 - mmdet - INFO - Iter [8600/12000]	lr: 1.500e-02, eta: 0:05:53, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1807, loss_bbox: 0.2822, loss_centerness: 0.5873, loss: 1.0503, grad_norm: 2.5063
2022-10-03 23:19:11,427 - mmdet - INFO - Iter [8650/12000]	lr: 1.500e-02, eta: 0:05:48, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1833, loss_bbox: 0.2826, loss_centerness: 0.5887, loss: 1.0546, grad_norm: 2.5176
2022-10-03 23:19:16,627 - mmdet - INFO - Iter [8700/12000]	lr: 1.500e-02, eta: 0:05:43, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1896, loss_bbox: 0.2769, loss_centerness: 0.5886, loss: 1.0551, grad_norm: 2.4687
2022-10-03 23:19:21,661 - mmdet - INFO - Iter [8750/12000]	lr: 1.500e-02, eta: 0:05:37, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1801, loss_bbox: 0.2793, loss_centerness: 0.5897, loss: 1.0492, grad_norm: 2.3412
2022-10-03 23:19:26,707 - mmdet - INFO - Iter [8800/12000]	lr: 1.500e-02, eta: 0:05:32, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1750, loss_bbox: 0.2684, loss_centerness: 0.5876, loss: 1.0310, grad_norm: 2.4336
2022-10-03 23:19:31,728 - mmdet - INFO - Iter [8850/12000]	lr: 1.500e-02, eta: 0:05:27, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1809, loss_bbox: 0.2817, loss_centerness: 0.5880, loss: 1.0506, grad_norm: 2.6375
2022-10-03 23:19:36,771 - mmdet - INFO - Iter [8900/12000]	lr: 1.500e-02, eta: 0:05:22, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1835, loss_bbox: 0.2802, loss_centerness: 0.5887, loss: 1.0524, grad_norm: 2.5153
2022-10-03 23:19:41,994 - mmdet - INFO - Iter [8950/12000]	lr: 1.500e-02, eta: 0:05:16, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1850, loss_bbox: 0.2791, loss_centerness: 0.5898, loss: 1.0540, grad_norm: 2.4977
2022-10-03 23:19:47,205 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:19:47,206 - mmdet - INFO - Iter [9000/12000]	lr: 1.500e-02, eta: 0:05:11, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1873, loss_bbox: 0.2851, loss_centerness: 0.5910, loss: 1.0634, grad_norm: 2.3387
2022-10-03 23:19:52,498 - mmdet - INFO - Iter [9050/12000]	lr: 1.500e-03, eta: 0:05:06, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1636, loss_bbox: 0.2461, loss_centerness: 0.5873, loss: 0.9969, grad_norm: 1.9895
2022-10-03 23:19:57,639 - mmdet - INFO - Iter [9100/12000]	lr: 1.500e-03, eta: 0:05:01, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1572, loss_bbox: 0.2387, loss_centerness: 0.5853, loss: 0.9812, grad_norm: 1.8355
2022-10-03 23:20:02,708 - mmdet - INFO - Iter [9150/12000]	lr: 1.500e-03, eta: 0:04:56, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1616, loss_bbox: 0.2397, loss_centerness: 0.5860, loss: 0.9873, grad_norm: 1.8689
2022-10-03 23:20:08,033 - mmdet - INFO - Iter [9200/12000]	lr: 1.500e-03, eta: 0:04:50, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1540, loss_bbox: 0.2367, loss_centerness: 0.5874, loss: 0.9782, grad_norm: 1.8470
2022-10-03 23:20:13,407 - mmdet - INFO - Iter [9250/12000]	lr: 1.500e-03, eta: 0:04:45, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1511, loss_bbox: 0.2405, loss_centerness: 0.5854, loss: 0.9771, grad_norm: 1.8256
2022-10-03 23:20:18,697 - mmdet - INFO - Iter [9300/12000]	lr: 1.500e-03, eta: 0:04:40, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1552, loss_bbox: 0.2260, loss_centerness: 0.5852, loss: 0.9664, grad_norm: 1.7953
2022-10-03 23:20:23,716 - mmdet - INFO - Iter [9350/12000]	lr: 1.500e-03, eta: 0:04:35, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1468, loss_bbox: 0.2259, loss_centerness: 0.5828, loss: 0.9555, grad_norm: 1.8571
2022-10-03 23:20:28,841 - mmdet - INFO - Iter [9400/12000]	lr: 1.500e-03, eta: 0:04:30, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1413, loss_bbox: 0.2216, loss_centerness: 0.5825, loss: 0.9454, grad_norm: 1.7513
2022-10-03 23:20:33,859 - mmdet - INFO - Iter [9450/12000]	lr: 1.500e-03, eta: 0:04:24, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1407, loss_bbox: 0.2201, loss_centerness: 0.5808, loss: 0.9416, grad_norm: 1.7782
2022-10-03 23:20:39,102 - mmdet - INFO - Iter [9500/12000]	lr: 1.500e-03, eta: 0:04:19, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1407, loss_bbox: 0.2198, loss_centerness: 0.5811, loss: 0.9416, grad_norm: 1.7229
2022-10-03 23:20:44,395 - mmdet - INFO - Iter [9550/12000]	lr: 1.500e-03, eta: 0:04:14, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1402, loss_bbox: 0.2166, loss_centerness: 0.5823, loss: 0.9391, grad_norm: 1.7978
2022-10-03 23:20:49,562 - mmdet - INFO - Iter [9600/12000]	lr: 1.500e-03, eta: 0:04:09, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1398, loss_bbox: 0.2233, loss_centerness: 0.5835, loss: 0.9466, grad_norm: 1.7395
2022-10-03 23:20:54,590 - mmdet - INFO - Iter [9650/12000]	lr: 1.500e-03, eta: 0:04:04, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1310, loss_bbox: 0.2175, loss_centerness: 0.5841, loss: 0.9325, grad_norm: 1.7419
2022-10-03 23:20:59,841 - mmdet - INFO - Iter [9700/12000]	lr: 1.500e-03, eta: 0:03:58, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1369, loss_bbox: 0.2151, loss_centerness: 0.5828, loss: 0.9348, grad_norm: 1.7701
2022-10-03 23:21:05,135 - mmdet - INFO - Iter [9750/12000]	lr: 1.500e-03, eta: 0:03:53, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1395, loss_bbox: 0.2123, loss_centerness: 0.5825, loss: 0.9343, grad_norm: 1.8823
2022-10-03 23:21:10,242 - mmdet - INFO - Iter [9800/12000]	lr: 1.500e-03, eta: 0:03:48, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1400, loss_bbox: 0.2160, loss_centerness: 0.5829, loss: 0.9390, grad_norm: 1.7530
2022-10-03 23:21:15,244 - mmdet - INFO - Iter [9850/12000]	lr: 1.500e-03, eta: 0:03:43, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1402, loss_bbox: 0.2162, loss_centerness: 0.5829, loss: 0.9394, grad_norm: 1.8387
2022-10-03 23:21:20,277 - mmdet - INFO - Iter [9900/12000]	lr: 1.500e-03, eta: 0:03:38, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1377, loss_bbox: 0.2241, loss_centerness: 0.5840, loss: 0.9459, grad_norm: 1.7914
2022-10-03 23:21:25,462 - mmdet - INFO - Iter [9950/12000]	lr: 1.500e-03, eta: 0:03:32, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1374, loss_bbox: 0.2235, loss_centerness: 0.5838, loss: 0.9447, grad_norm: 1.7652
2022-10-03 23:21:31,026 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:21:31,026 - mmdet - INFO - Iter [10000/12000]	lr: 1.500e-03, eta: 0:03:27, time: 0.111, data_time: 0.006, memory: 3375, loss_cls: 0.1387, loss_bbox: 0.2153, loss_centerness: 0.5808, loss: 0.9347, grad_norm: 1.8058
2022-10-03 23:21:36,386 - mmdet - INFO - Iter [10050/12000]	lr: 1.500e-03, eta: 0:03:22, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1394, loss_bbox: 0.2170, loss_centerness: 0.5833, loss: 0.9398, grad_norm: 1.8790
2022-10-03 23:21:41,757 - mmdet - INFO - Iter [10100/12000]	lr: 1.500e-03, eta: 0:03:17, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1397, loss_bbox: 0.2223, loss_centerness: 0.5848, loss: 0.9468, grad_norm: 1.8400
2022-10-03 23:21:47,164 - mmdet - INFO - Iter [10150/12000]	lr: 1.500e-03, eta: 0:03:12, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1342, loss_bbox: 0.2153, loss_centerness: 0.5826, loss: 0.9321, grad_norm: 1.7860
2022-10-03 23:21:52,515 - mmdet - INFO - Iter [10200/12000]	lr: 1.500e-03, eta: 0:03:07, time: 0.107, data_time: 0.006, memory: 3375, loss_cls: 0.1382, loss_bbox: 0.2165, loss_centerness: 0.5828, loss: 0.9375, grad_norm: 1.8351
2022-10-03 23:21:57,603 - mmdet - INFO - Iter [10250/12000]	lr: 1.500e-03, eta: 0:03:01, time: 0.102, data_time: 0.007, memory: 3375, loss_cls: 0.1360, loss_bbox: 0.2164, loss_centerness: 0.5822, loss: 0.9347, grad_norm: 1.8078
2022-10-03 23:22:02,711 - mmdet - INFO - Iter [10300/12000]	lr: 1.500e-03, eta: 0:02:56, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1381, loss_bbox: 0.2194, loss_centerness: 0.5833, loss: 0.9407, grad_norm: 1.8150
2022-10-03 23:22:07,799 - mmdet - INFO - Iter [10350/12000]	lr: 1.500e-03, eta: 0:02:51, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1409, loss_bbox: 0.2201, loss_centerness: 0.5846, loss: 0.9456, grad_norm: 1.8660
2022-10-03 23:22:12,820 - mmdet - INFO - Iter [10400/12000]	lr: 1.500e-03, eta: 0:02:46, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1360, loss_bbox: 0.2176, loss_centerness: 0.5821, loss: 0.9357, grad_norm: 1.8105
2022-10-03 23:22:17,815 - mmdet - INFO - Iter [10450/12000]	lr: 1.500e-03, eta: 0:02:41, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1321, loss_bbox: 0.2089, loss_centerness: 0.5828, loss: 0.9239, grad_norm: 1.7748
2022-10-03 23:22:22,853 - mmdet - INFO - Iter [10500/12000]	lr: 1.500e-03, eta: 0:02:35, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1363, loss_bbox: 0.2266, loss_centerness: 0.5853, loss: 0.9483, grad_norm: 1.7853
2022-10-03 23:22:27,885 - mmdet - INFO - Iter [10550/12000]	lr: 1.500e-03, eta: 0:02:30, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1343, loss_bbox: 0.2127, loss_centerness: 0.5820, loss: 0.9290, grad_norm: 1.8488
2022-10-03 23:22:32,971 - mmdet - INFO - Iter [10600/12000]	lr: 1.500e-03, eta: 0:02:25, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1314, loss_bbox: 0.2115, loss_centerness: 0.5817, loss: 0.9246, grad_norm: 1.8739
2022-10-03 23:22:38,025 - mmdet - INFO - Iter [10650/12000]	lr: 1.500e-03, eta: 0:02:20, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1326, loss_bbox: 0.2134, loss_centerness: 0.5831, loss: 0.9291, grad_norm: 1.8480
2022-10-03 23:22:43,120 - mmdet - INFO - Iter [10700/12000]	lr: 1.500e-03, eta: 0:02:14, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1348, loss_bbox: 0.2162, loss_centerness: 0.5823, loss: 0.9333, grad_norm: 1.8150
2022-10-03 23:22:48,378 - mmdet - INFO - Iter [10750/12000]	lr: 1.500e-03, eta: 0:02:09, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1357, loss_bbox: 0.2097, loss_centerness: 0.5817, loss: 0.9272, grad_norm: 1.8364
2022-10-03 23:22:53,464 - mmdet - INFO - Iter [10800/12000]	lr: 1.500e-03, eta: 0:02:04, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1358, loss_bbox: 0.2174, loss_centerness: 0.5816, loss: 0.9347, grad_norm: 1.8194
2022-10-03 23:22:58,782 - mmdet - INFO - Iter [10850/12000]	lr: 1.500e-03, eta: 0:01:59, time: 0.106, data_time: 0.007, memory: 3375, loss_cls: 0.1340, loss_bbox: 0.2063, loss_centerness: 0.5809, loss: 0.9212, grad_norm: 1.8542
2022-10-03 23:23:04,036 - mmdet - INFO - Iter [10900/12000]	lr: 1.500e-03, eta: 0:01:54, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1306, loss_bbox: 0.2119, loss_centerness: 0.5821, loss: 0.9246, grad_norm: 1.8321
2022-10-03 23:23:09,311 - mmdet - INFO - Iter [10950/12000]	lr: 1.500e-03, eta: 0:01:49, time: 0.106, data_time: 0.006, memory: 3375, loss_cls: 0.1319, loss_bbox: 0.2146, loss_centerness: 0.5822, loss: 0.9287, grad_norm: 1.8227
2022-10-03 23:23:14,275 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:23:14,275 - mmdet - INFO - Iter [11000/12000]	lr: 1.500e-03, eta: 0:01:43, time: 0.099, data_time: 0.006, memory: 3375, loss_cls: 0.1372, loss_bbox: 0.2184, loss_centerness: 0.5836, loss: 0.9391, grad_norm: 1.8240
2022-10-03 23:23:19,434 - mmdet - INFO - Iter [11050/12000]	lr: 1.500e-04, eta: 0:01:38, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1325, loss_bbox: 0.2064, loss_centerness: 0.5815, loss: 0.9204, grad_norm: 1.8704
2022-10-03 23:23:24,889 - mmdet - INFO - Iter [11100/12000]	lr: 1.500e-04, eta: 0:01:33, time: 0.109, data_time: 0.006, memory: 3375, loss_cls: 0.1328, loss_bbox: 0.2112, loss_centerness: 0.5817, loss: 0.9256, grad_norm: 1.7883
2022-10-03 23:23:30,285 - mmdet - INFO - Iter [11150/12000]	lr: 1.500e-04, eta: 0:01:28, time: 0.108, data_time: 0.006, memory: 3375, loss_cls: 0.1279, loss_bbox: 0.2029, loss_centerness: 0.5819, loss: 0.9127, grad_norm: 1.7456
2022-10-03 23:23:35,379 - mmdet - INFO - Iter [11200/12000]	lr: 1.500e-04, eta: 0:01:23, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1327, loss_bbox: 0.2078, loss_centerness: 0.5804, loss: 0.9210, grad_norm: 1.7849
2022-10-03 23:23:40,596 - mmdet - INFO - Iter [11250/12000]	lr: 1.500e-04, eta: 0:01:17, time: 0.104, data_time: 0.006, memory: 3375, loss_cls: 0.1286, loss_bbox: 0.2092, loss_centerness: 0.5804, loss: 0.9182, grad_norm: 1.7203
2022-10-03 23:23:45,639 - mmdet - INFO - Iter [11300/12000]	lr: 1.500e-04, eta: 0:01:12, time: 0.101, data_time: 0.006, memory: 3375, loss_cls: 0.1283, loss_bbox: 0.2036, loss_centerness: 0.5813, loss: 0.9133, grad_norm: 1.7688
2022-10-03 23:23:50,736 - mmdet - INFO - Iter [11350/12000]	lr: 1.500e-04, eta: 0:01:07, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1354, loss_bbox: 0.2096, loss_centerness: 0.5816, loss: 0.9266, grad_norm: 1.7905
2022-10-03 23:23:55,741 - mmdet - INFO - Iter [11400/12000]	lr: 1.500e-04, eta: 0:01:02, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1284, loss_bbox: 0.2049, loss_centerness: 0.5813, loss: 0.9146, grad_norm: 1.6940
2022-10-03 23:24:00,882 - mmdet - INFO - Iter [11450/12000]	lr: 1.500e-04, eta: 0:00:57, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1310, loss_bbox: 0.2129, loss_centerness: 0.5837, loss: 0.9276, grad_norm: 1.7064
2022-10-03 23:24:06,044 - mmdet - INFO - Iter [11500/12000]	lr: 1.500e-04, eta: 0:00:51, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1273, loss_bbox: 0.2013, loss_centerness: 0.5800, loss: 0.9086, grad_norm: 1.7567
2022-10-03 23:24:11,063 - mmdet - INFO - Iter [11550/12000]	lr: 1.500e-04, eta: 0:00:46, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1315, loss_bbox: 0.2136, loss_centerness: 0.5837, loss: 0.9288, grad_norm: 1.7617
2022-10-03 23:24:16,060 - mmdet - INFO - Iter [11600/12000]	lr: 1.500e-04, eta: 0:00:41, time: 0.100, data_time: 0.006, memory: 3375, loss_cls: 0.1280, loss_bbox: 0.2022, loss_centerness: 0.5803, loss: 0.9105, grad_norm: 1.7358
2022-10-03 23:24:21,325 - mmdet - INFO - Iter [11650/12000]	lr: 1.500e-04, eta: 0:00:36, time: 0.105, data_time: 0.007, memory: 3375, loss_cls: 0.1357, loss_bbox: 0.2126, loss_centerness: 0.5832, loss: 0.9316, grad_norm: 1.8105
2022-10-03 23:24:26,491 - mmdet - INFO - Iter [11700/12000]	lr: 1.500e-04, eta: 0:00:31, time: 0.103, data_time: 0.006, memory: 3375, loss_cls: 0.1233, loss_bbox: 0.2019, loss_centerness: 0.5799, loss: 0.9051, grad_norm: 1.6989
2022-10-03 23:24:31,573 - mmdet - INFO - Iter [11750/12000]	lr: 1.500e-04, eta: 0:00:25, time: 0.102, data_time: 0.006, memory: 3375, loss_cls: 0.1286, loss_bbox: 0.2183, loss_centerness: 0.5824, loss: 0.9292, grad_norm: 1.6568
2022-10-03 23:24:36,544 - mmdet - INFO - Iter [11800/12000]	lr: 1.500e-04, eta: 0:00:20, time: 0.099, data_time: 0.007, memory: 3375, loss_cls: 0.1289, loss_bbox: 0.2059, loss_centerness: 0.5816, loss: 0.9164, grad_norm: 1.7113
2022-10-03 23:24:41,573 - mmdet - INFO - Iter [11850/12000]	lr: 1.500e-04, eta: 0:00:15, time: 0.101, data_time: 0.007, memory: 3375, loss_cls: 0.1272, loss_bbox: 0.2092, loss_centerness: 0.5814, loss: 0.9177, grad_norm: 1.7427
2022-10-03 23:24:46,727 - mmdet - INFO - Iter [11900/12000]	lr: 1.500e-04, eta: 0:00:10, time: 0.103, data_time: 0.007, memory: 3375, loss_cls: 0.1298, loss_bbox: 0.2030, loss_centerness: 0.5815, loss: 0.9143, grad_norm: 1.7562
2022-10-03 23:24:51,989 - mmdet - INFO - Iter [11950/12000]	lr: 1.500e-04, eta: 0:00:05, time: 0.105, data_time: 0.006, memory: 3375, loss_cls: 0.1301, loss_bbox: 0.2108, loss_centerness: 0.5819, loss: 0.9228, grad_norm: 1.7248
2022-10-03 23:24:57,103 - mmdet - INFO - Saving checkpoint at 12000 iterations
2022-10-03 23:24:57,589 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:24:57,589 - mmdet - INFO - Iter [12000/12000]	lr: 1.500e-04, eta: 0:00:00, time: 0.112, data_time: 0.006, memory: 3375, loss_cls: 0.1293, loss_bbox: 0.2050, loss_centerness: 0.5805, loss: 0.9149, grad_norm: 1.7602
2022-10-03 23:25:20,639 - mmdet - INFO - 
+-------------+------+-------+--------+-------+
| class       | gts  | dets  | recall | ap    |
+-------------+------+-------+--------+-------+
| aeroplane   | 285  | 6095  | 0.993  | 0.874 |
| bicycle     | 337  | 9704  | 0.988  | 0.819 |
| bird        | 459  | 9515  | 0.980  | 0.833 |
| boat        | 263  | 12281 | 0.962  | 0.736 |
| bottle      | 469  | 17289 | 0.932  | 0.705 |
| bus         | 213  | 7837  | 0.981  | 0.851 |
| car         | 1201 | 23829 | 0.990  | 0.883 |
| cat         | 358  | 6674  | 0.992  | 0.885 |
| chair       | 756  | 31606 | 0.963  | 0.661 |
| cow         | 244  | 6770  | 1.000  | 0.861 |
| diningtable | 206  | 17352 | 0.942  | 0.701 |
| dog         | 489  | 8899  | 1.000  | 0.880 |
| horse       | 348  | 8452  | 0.989  | 0.822 |
| motorbike   | 325  | 9942  | 0.972  | 0.826 |
| person      | 4528 | 57741 | 0.978  | 0.854 |
| pottedplant | 480  | 19546 | 0.931  | 0.586 |
| sheep       | 242  | 6822  | 0.983  | 0.837 |
| sofa        | 239  | 11389 | 0.975  | 0.716 |
| train       | 282  | 7815  | 0.986  | 0.861 |
| tvmonitor   | 308  | 10825 | 0.974  | 0.841 |
+-------------+------+-------+--------+-------+
| mAP         |      |       |        | 0.802 |
+-------------+------+-------+--------+-------+
2022-10-03 23:25:21,153 - mmdet - INFO - Now best checkpoint is saved as best_mAP_iter_12000.pth.
2022-10-03 23:25:21,154 - mmdet - INFO - Best mAP is 0.8015 at 12000 iter.
2022-10-03 23:25:21,154 - mmdet - INFO - Exp name: fcos_mstrain_12k_voc0712.py
2022-10-03 23:25:21,154 - mmdet - INFO - Iter(val) [619]	mAP: 0.8015, AP50: 0.8020