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faster_rcnn_r50_fpn_alphagiou_1x_coco_eps1e-9.log
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2022-07-08 09:24:05,915 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.10 (default, Jun 4 2021, 15:09:15) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3: NVIDIA GeForce RTX 3070
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
PyTorch: 1.10.0+cu113
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_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
- 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: 1.5.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.25.0+5631088
------------------------------------------------------------
2022-07-08 09:24:06,282 - mmdet - INFO - Distributed training: True
2022-07-08 09:24:06,573 - mmdet - INFO - Config:
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', 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,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(
type='AlphaIoULoss', loss_weight=10.0, mode='giou', eps=1e-09),
reg_decoded_bbox=True)),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
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, 800), 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='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), 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='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
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=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
work_dir = './work_dirs/faster_rcnn_r50_fpn_alphagiou_1x_coco'
auto_resume = False
gpu_ids = range(0, 4)
2022-07-08 09:24:06,574 - mmdet - INFO - Set random seed to 0, deterministic: False
2022-07-08 09:24:06,982 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2022-07-08 09:24:12,886 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-07-08 09:24:12,909 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2022-07-08 09:24:12,914 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
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, 256, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of FasterRCNN
rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_conv.bias - torch.Size([256]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_cls.bias - torch.Size([3]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_reg.bias - torch.Size([12]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_cls.weight - torch.Size([81, 1024]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_cls.bias - torch.Size([81]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_reg.weight - torch.Size([320, 1024]):
NormalInit: mean=0, std=0.001, bias=0
roi_head.bbox_head.fc_reg.bias - torch.Size([320]):
NormalInit: mean=0, std=0.001, bias=0
roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=uniform, bias=0
2022-07-08 09:24:33,271 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2022-07-08 09:24:34,297 - mmdet - INFO - Start running, host: root@container-7beb118352-6153ce43, work_dir: /root/autodl-tmp/mmdetection/work_dirs/faster_rcnn_r50_fpn_alphagiou_1x_coco
2022-07-08 09:24:34,297 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) NumClassCheckHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_val_epoch:
(NORMAL ) NumClassCheckHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook
--------------------
2022-07-08 09:24:34,297 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2022-07-08 09:24:34,297 - mmdet - INFO - Checkpoints will be saved to /root/autodl-tmp/mmdetection/work_dirs/faster_rcnn_r50_fpn_alphagiou_1x_coco by HardDiskBackend.
2022-07-08 09:24:53,921 - mmdet - INFO - Epoch [1][50/14659] lr: 9.890e-04, eta: 19:04:05, time: 0.390, data_time: 0.075, memory: 3706, loss_rpn_cls: 0.5643, loss_rpn_bbox: 0.1210, loss_cls: 1.0979, acc: 86.8535, loss_bbox: 0.1280, loss: 1.9112
2022-07-08 09:25:09,539 - mmdet - INFO - Epoch [1][100/14659] lr: 1.988e-03, eta: 17:09:27, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.2066, loss_rpn_bbox: 0.0883, loss_cls: 0.4251, acc: 94.3354, loss_bbox: 0.3463, loss: 1.0664
2022-07-08 09:25:25,362 - mmdet - INFO - Epoch [1][150/14659] lr: 2.987e-03, eta: 16:35:06, time: 0.316, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1802, loss_rpn_bbox: 0.0973, loss_cls: 0.4306, acc: 93.6846, loss_bbox: 0.3849, loss: 1.0930
2022-07-08 09:25:41,087 - mmdet - INFO - Epoch [1][200/14659] lr: 3.986e-03, eta: 16:16:09, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1575, loss_rpn_bbox: 0.0907, loss_cls: 0.4206, acc: 93.5977, loss_bbox: 0.4023, loss: 1.0711
2022-07-08 09:25:56,706 - mmdet - INFO - Epoch [1][250/14659] lr: 4.985e-03, eta: 16:03:37, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1535, loss_rpn_bbox: 0.1022, loss_cls: 0.4677, acc: 92.3867, loss_bbox: 0.4832, loss: 1.2066
2022-07-08 09:26:12,247 - mmdet - INFO - Epoch [1][300/14659] lr: 5.984e-03, eta: 15:54:33, time: 0.311, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1210, loss_rpn_bbox: 0.0895, loss_cls: 0.4875, acc: 91.9395, loss_bbox: 0.5376, loss: 1.2356
2022-07-08 09:26:27,907 - mmdet - INFO - Epoch [1][350/14659] lr: 6.983e-03, eta: 15:48:48, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1308, loss_rpn_bbox: 0.0977, loss_cls: 0.4835, acc: 91.6650, loss_bbox: 0.5564, loss: 1.2685
2022-07-08 09:26:43,620 - mmdet - INFO - Epoch [1][400/14659] lr: 7.982e-03, eta: 15:44:49, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1135, loss_rpn_bbox: 0.0852, loss_cls: 0.4890, acc: 91.6060, loss_bbox: 0.5787, loss: 1.2663
2022-07-08 09:26:59,227 - mmdet - INFO - Epoch [1][450/14659] lr: 8.981e-03, eta: 15:41:00, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1098, loss_rpn_bbox: 0.0897, loss_cls: 0.5097, acc: 91.0273, loss_bbox: 0.6172, loss: 1.3263
2022-07-08 09:27:14,976 - mmdet - INFO - Epoch [1][500/14659] lr: 9.980e-03, eta: 15:38:42, time: 0.315, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1140, loss_rpn_bbox: 0.0911, loss_cls: 0.5291, acc: 90.4570, loss_bbox: 0.6601, loss: 1.3944
2022-07-08 09:27:30,669 - mmdet - INFO - Epoch [1][550/14659] lr: 1.000e-02, eta: 15:36:30, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1207, loss_rpn_bbox: 0.0978, loss_cls: 0.5673, acc: 89.2666, loss_bbox: 0.7298, loss: 1.5157
2022-07-08 09:27:46,347 - mmdet - INFO - Epoch [1][600/14659] lr: 1.000e-02, eta: 15:34:40, time: 0.314, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.1041, loss_rpn_bbox: 0.0838, loss_cls: 0.5162, acc: 90.7109, loss_bbox: 0.6515, loss: 1.3555
2022-07-08 09:28:01,890 - mmdet - INFO - Epoch [1][650/14659] lr: 1.000e-02, eta: 15:32:18, time: 0.311, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1026, loss_rpn_bbox: 0.0941, loss_cls: 0.5034, acc: 90.5625, loss_bbox: 0.6646, loss: 1.3647
2022-07-08 09:28:17,516 - mmdet - INFO - Epoch [1][700/14659] lr: 1.000e-02, eta: 15:30:40, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.1034, loss_rpn_bbox: 0.0858, loss_cls: 0.5058, acc: 90.2070, loss_bbox: 0.6949, loss: 1.3899
2022-07-08 09:28:33,134 - mmdet - INFO - Epoch [1][750/14659] lr: 1.000e-02, eta: 15:29:06, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0981, loss_rpn_bbox: 0.0859, loss_cls: 0.5099, acc: 90.3521, loss_bbox: 0.6839, loss: 1.3777
2022-07-08 09:28:48,701 - mmdet - INFO - Epoch [1][800/14659] lr: 1.000e-02, eta: 15:27:39, time: 0.312, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.0834, loss_rpn_bbox: 0.0805, loss_cls: 0.5083, acc: 89.8179, loss_bbox: 0.7506, loss: 1.4228
2022-07-08 09:29:04,373 - mmdet - INFO - Epoch [1][850/14659] lr: 1.000e-02, eta: 15:26:35, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0933, loss_rpn_bbox: 0.0828, loss_cls: 0.5036, acc: 89.7036, loss_bbox: 0.7314, loss: 1.4110
2022-07-08 09:29:20,015 - mmdet - INFO - Epoch [1][900/14659] lr: 1.000e-02, eta: 15:25:33, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0959, loss_rpn_bbox: 0.0847, loss_cls: 0.5176, acc: 89.3760, loss_bbox: 0.7539, loss: 1.4522
2022-07-08 09:29:35,672 - mmdet - INFO - Epoch [1][950/14659] lr: 1.000e-02, eta: 15:24:42, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0916, loss_rpn_bbox: 0.0829, loss_cls: 0.5048, acc: 89.5962, loss_bbox: 0.7367, loss: 1.4160
2022-07-08 09:29:51,361 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_alphagiou_1x_coco.py
2022-07-08 09:29:51,361 - mmdet - INFO - Epoch [1][1000/14659] lr: 1.000e-02, eta: 15:23:53, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0946, loss_rpn_bbox: 0.0821, loss_cls: 0.4767, acc: 90.2056, loss_bbox: 0.7011, loss: 1.3544
2022-07-08 09:30:07,014 - mmdet - INFO - Epoch [1][1050/14659] lr: 1.000e-02, eta: 15:23:07, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0766, loss_rpn_bbox: 0.0732, loss_cls: 0.4555, acc: 90.2026, loss_bbox: 0.7228, loss: 1.3281
2022-07-08 09:30:22,658 - mmdet - INFO - Epoch [1][1100/14659] lr: 1.000e-02, eta: 15:22:22, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0858, loss_rpn_bbox: 0.0802, loss_cls: 0.4873, acc: 89.3491, loss_bbox: 0.7789, loss: 1.4322
2022-07-08 09:30:38,260 - mmdet - INFO - Epoch [1][1150/14659] lr: 1.000e-02, eta: 15:21:29, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0814, loss_rpn_bbox: 0.0845, loss_cls: 0.4651, acc: 89.7910, loss_bbox: 0.7462, loss: 1.3771
2022-07-08 09:30:53,947 - mmdet - INFO - Epoch [1][1200/14659] lr: 1.000e-02, eta: 15:20:55, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0871, loss_rpn_bbox: 0.0816, loss_cls: 0.4438, acc: 90.0249, loss_bbox: 0.7311, loss: 1.3436
2022-07-08 09:31:09,762 - mmdet - INFO - Epoch [1][1250/14659] lr: 1.000e-02, eta: 15:20:41, time: 0.317, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.0828, loss_rpn_bbox: 0.0818, loss_cls: 0.4740, acc: 89.0898, loss_bbox: 0.7933, loss: 1.4319
2022-07-08 09:31:25,581 - mmdet - INFO - Epoch [1][1300/14659] lr: 1.000e-02, eta: 15:20:21, time: 0.316, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0874, loss_rpn_bbox: 0.0838, loss_cls: 0.4458, acc: 89.4683, loss_bbox: 0.7687, loss: 1.3857
2022-07-08 09:31:41,409 - mmdet - INFO - Epoch [1][1350/14659] lr: 1.000e-02, eta: 15:20:10, time: 0.317, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.0840, loss_rpn_bbox: 0.0879, loss_cls: 0.4491, acc: 89.8276, loss_bbox: 0.7484, loss: 1.3694
2022-07-08 09:31:57,216 - mmdet - INFO - Epoch [1][1400/14659] lr: 1.000e-02, eta: 15:19:52, time: 0.316, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0794, loss_rpn_bbox: 0.0773, loss_cls: 0.4479, acc: 89.9243, loss_bbox: 0.7080, loss: 1.3126
2022-07-08 09:32:12,929 - mmdet - INFO - Epoch [1][1450/14659] lr: 1.000e-02, eta: 15:19:26, time: 0.315, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0911, loss_rpn_bbox: 0.0766, loss_cls: 0.4328, acc: 89.9897, loss_bbox: 0.7190, loss: 1.3196
2022-07-08 09:32:28,678 - mmdet - INFO - Epoch [1][1500/14659] lr: 1.000e-02, eta: 15:19:02, time: 0.315, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0900, loss_rpn_bbox: 0.0847, loss_cls: 0.4648, acc: 89.1641, loss_bbox: 0.7739, loss: 1.4134
2022-07-08 09:32:44,315 - mmdet - INFO - Epoch [1][1550/14659] lr: 1.000e-02, eta: 15:18:26, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0721, loss_rpn_bbox: 0.0757, loss_cls: 0.4524, acc: 89.7847, loss_bbox: 0.7336, loss: 1.3339
2022-07-08 09:32:59,983 - mmdet - INFO - Epoch [1][1600/14659] lr: 1.000e-02, eta: 15:17:57, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0850, loss_rpn_bbox: 0.0771, loss_cls: 0.4154, acc: 90.2124, loss_bbox: 0.7152, loss: 1.2926
2022-07-08 09:33:15,617 - mmdet - INFO - Epoch [1][1650/14659] lr: 1.000e-02, eta: 15:17:21, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0795, loss_rpn_bbox: 0.0766, loss_cls: 0.4263, acc: 90.0000, loss_bbox: 0.7306, loss: 1.3129
2022-07-08 09:33:31,299 - mmdet - INFO - Epoch [1][1700/14659] lr: 1.000e-02, eta: 15:16:55, time: 0.314, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.0816, loss_rpn_bbox: 0.0786, loss_cls: 0.4341, acc: 89.6133, loss_bbox: 0.7495, loss: 1.3438
2022-07-08 09:33:46,958 - mmdet - INFO - Epoch [1][1750/14659] lr: 1.000e-02, eta: 15:16:26, time: 0.313, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.0750, loss_rpn_bbox: 0.0724, loss_cls: 0.4327, acc: 89.6338, loss_bbox: 0.7488, loss: 1.3289
2022-07-08 09:34:02,685 - mmdet - INFO - Epoch [1][1800/14659] lr: 1.000e-02, eta: 15:16:05, time: 0.315, data_time: 0.008, memory: 3707, loss_rpn_cls: 0.0780, loss_rpn_bbox: 0.0809, loss_cls: 0.4288, acc: 89.5439, loss_bbox: 0.7616, loss: 1.3493
2022-07-08 09:34:18,397 - mmdet - INFO - Epoch [1][1850/14659] lr: 1.000e-02, eta: 15:15:43, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0855, loss_rpn_bbox: 0.0780, loss_cls: 0.4410, acc: 89.4658, loss_bbox: 0.7484, loss: 1.3530
2022-07-08 09:34:34,090 - mmdet - INFO - Epoch [1][1900/14659] lr: 1.000e-02, eta: 15:15:19, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0762, loss_rpn_bbox: 0.0781, loss_cls: 0.4226, acc: 89.8662, loss_bbox: 0.7285, loss: 1.3053
2022-07-08 09:34:49,699 - mmdet - INFO - Epoch [1][1950/14659] lr: 1.000e-02, eta: 15:14:48, time: 0.312, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0891, loss_rpn_bbox: 0.0734, loss_cls: 0.4310, acc: 89.8926, loss_bbox: 0.7086, loss: 1.3023
2022-07-08 09:35:05,417 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_alphagiou_1x_coco.py
2022-07-08 09:35:05,417 - mmdet - INFO - Epoch [1][2000/14659] lr: 1.000e-02, eta: 15:14:27, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0749, loss_rpn_bbox: 0.0789, loss_cls: 0.4239, acc: 89.5356, loss_bbox: 0.7638, loss: 1.3415
2022-07-08 09:35:20,927 - mmdet - INFO - Epoch [1][2050/14659] lr: 1.000e-02, eta: 15:13:50, time: 0.310, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0776, loss_rpn_bbox: 0.0820, loss_cls: 0.4199, acc: 89.2339, loss_bbox: 0.7871, loss: 1.3666
2022-07-08 09:35:36,626 - mmdet - INFO - Epoch [1][2100/14659] lr: 1.000e-02, eta: 15:13:28, time: 0.314, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0837, loss_rpn_bbox: 0.0836, loss_cls: 0.4043, acc: 89.5840, loss_bbox: 0.7724, loss: 1.3441
2022-07-08 09:35:52,369 - mmdet - INFO - Epoch [1][2150/14659] lr: 1.000e-02, eta: 15:13:10, time: 0.315, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0736, loss_rpn_bbox: 0.0743, loss_cls: 0.4039, acc: 90.1147, loss_bbox: 0.7114, loss: 1.2633
2022-07-08 09:36:08,117 - mmdet - INFO - Epoch [1][2200/14659] lr: 1.000e-02, eta: 15:12:53, time: 0.315, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0729, loss_rpn_bbox: 0.0747, loss_cls: 0.4161, acc: 89.9146, loss_bbox: 0.7315, loss: 1.2952
2022-07-08 09:36:23,744 - mmdet - INFO - Epoch [1][2250/14659] lr: 1.000e-02, eta: 15:12:26, time: 0.313, data_time: 0.007, memory: 3707, loss_rpn_cls: 0.0759, loss_rpn_bbox: 0.0745, loss_cls: 0.3890, acc: 90.1738, loss_bbox: 0.7188, loss: 1.2582
2022-07-08 09:36:39,539 - mmdet - INFO - Epoch [1][2300/14659] lr: 1.000e-02, eta: 15:12:12, time: 0.316, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0794, loss_rpn_bbox: 0.0825, loss_cls: 0.3981, acc: 89.9478, loss_bbox: 0.7293, loss: 1.2893
2022-07-08 09:36:55,181 - mmdet - INFO - Epoch [1][2350/14659] lr: 1.000e-02, eta: 15:11:48, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0773, loss_rpn_bbox: 0.0762, loss_cls: 0.4291, acc: 89.1426, loss_bbox: 0.7890, loss: 1.3717
2022-07-08 09:37:10,850 - mmdet - INFO - Epoch [1][2400/14659] lr: 1.000e-02, eta: 15:11:24, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0753, loss_rpn_bbox: 0.0748, loss_cls: 0.4054, acc: 89.9023, loss_bbox: 0.7438, loss: 1.2992
2022-07-08 09:37:26,555 - mmdet - INFO - Epoch [1][2450/14659] lr: 1.000e-02, eta: 15:11:04, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0722, loss_rpn_bbox: 0.0759, loss_cls: 0.3947, acc: 89.9614, loss_bbox: 0.7251, loss: 1.2679
2022-07-08 09:37:42,134 - mmdet - INFO - Epoch [1][2500/14659] lr: 1.000e-02, eta: 15:10:37, time: 0.312, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0711, loss_rpn_bbox: 0.0734, loss_cls: 0.4038, acc: 89.7451, loss_bbox: 0.7575, loss: 1.3057
2022-07-08 09:37:57,755 - mmdet - INFO - Epoch [1][2550/14659] lr: 1.000e-02, eta: 15:10:13, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0774, loss_rpn_bbox: 0.0755, loss_cls: 0.4190, acc: 89.4624, loss_bbox: 0.7656, loss: 1.3374
2022-07-08 09:38:13,434 - mmdet - INFO - Epoch [1][2600/14659] lr: 1.000e-02, eta: 15:09:51, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0683, loss_rpn_bbox: 0.0754, loss_cls: 0.3888, acc: 89.6416, loss_bbox: 0.7613, loss: 1.2938
2022-07-08 09:38:29,168 - mmdet - INFO - Epoch [1][2650/14659] lr: 1.000e-02, eta: 15:09:35, time: 0.315, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0755, loss_rpn_bbox: 0.0743, loss_cls: 0.4158, acc: 89.5532, loss_bbox: 0.7457, loss: 1.3112
2022-07-08 09:38:44,889 - mmdet - INFO - Epoch [1][2700/14659] lr: 1.000e-02, eta: 15:09:16, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0861, loss_rpn_bbox: 0.0806, loss_cls: 0.3772, acc: 90.1104, loss_bbox: 0.7164, loss: 1.2604
2022-07-08 09:39:00,482 - mmdet - INFO - Epoch [1][2750/14659] lr: 1.000e-02, eta: 15:08:51, time: 0.312, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0691, loss_rpn_bbox: 0.0720, loss_cls: 0.3980, acc: 89.7422, loss_bbox: 0.7465, loss: 1.2856
2022-07-08 09:39:16,130 - mmdet - INFO - Epoch [1][2800/14659] lr: 1.000e-02, eta: 15:08:30, time: 0.313, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0800, loss_rpn_bbox: 0.0773, loss_cls: 0.3942, acc: 90.1421, loss_bbox: 0.7053, loss: 1.2567
2022-07-08 09:39:31,847 - mmdet - INFO - Epoch [1][2850/14659] lr: 1.000e-02, eta: 15:08:13, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0733, loss_rpn_bbox: 0.0744, loss_cls: 0.3787, acc: 90.3267, loss_bbox: 0.7231, loss: 1.2496
2022-07-08 09:39:47,710 - mmdet - INFO - Epoch [1][2900/14659] lr: 1.000e-02, eta: 15:08:04, time: 0.317, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0757, loss_rpn_bbox: 0.0717, loss_cls: 0.3928, acc: 89.6113, loss_bbox: 0.7556, loss: 1.2958
2022-07-08 09:40:03,539 - mmdet - INFO - Epoch [1][2950/14659] lr: 1.000e-02, eta: 15:07:53, time: 0.316, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0700, loss_rpn_bbox: 0.0765, loss_cls: 0.4025, acc: 89.7231, loss_bbox: 0.7388, loss: 1.2878
2022-07-08 09:40:19,320 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_alphagiou_1x_coco.py
2022-07-08 09:40:19,320 - mmdet - INFO - Epoch [1][3000/14659] lr: 1.000e-02, eta: 15:07:39, time: 0.316, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0715, loss_rpn_bbox: 0.0759, loss_cls: 0.3672, acc: 90.4238, loss_bbox: 0.6886, loss: 1.2032
2022-07-08 09:40:35,015 - mmdet - INFO - Epoch [1][3050/14659] lr: 1.000e-02, eta: 15:07:20, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0768, loss_rpn_bbox: 0.0780, loss_cls: 0.3971, acc: 89.2383, loss_bbox: 0.7744, loss: 1.3263
2022-07-08 09:40:50,611 - mmdet - INFO - Epoch [1][3100/14659] lr: 1.000e-02, eta: 15:06:55, time: 0.312, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0658, loss_rpn_bbox: 0.0663, loss_cls: 0.3645, acc: 90.7832, loss_bbox: 0.6701, loss: 1.1668
2022-07-08 09:41:06,314 - mmdet - INFO - Epoch [1][3150/14659] lr: 1.000e-02, eta: 15:06:39, time: 0.314, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0736, loss_rpn_bbox: 0.0693, loss_cls: 0.3745, acc: 90.4263, loss_bbox: 0.6917, loss: 1.2091
2022-07-08 09:41:22,092 - mmdet - INFO - Epoch [1][3200/14659] lr: 1.000e-02, eta: 15:06:24, time: 0.315, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0738, loss_rpn_bbox: 0.0761, loss_cls: 0.3845, acc: 89.7954, loss_bbox: 0.7444, loss: 1.2788
2022-07-08 09:41:37,799 - mmdet - INFO - Epoch [1][3250/14659] lr: 1.000e-02, eta: 15:06:07, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0752, loss_rpn_bbox: 0.0718, loss_cls: 0.4054, acc: 89.3428, loss_bbox: 0.7638, loss: 1.3162
2022-07-08 09:41:53,373 - mmdet - INFO - Epoch [1][3300/14659] lr: 1.000e-02, eta: 15:05:42, time: 0.311, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0666, loss_rpn_bbox: 0.0720, loss_cls: 0.3726, acc: 90.0957, loss_bbox: 0.7315, loss: 1.2426
2022-07-08 09:42:09,055 - mmdet - INFO - Epoch [1][3350/14659] lr: 1.000e-02, eta: 15:05:23, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0740, loss_rpn_bbox: 0.0763, loss_cls: 0.3815, acc: 89.9604, loss_bbox: 0.7498, loss: 1.2815
2022-07-08 09:42:24,775 - mmdet - INFO - Epoch [1][3400/14659] lr: 1.000e-02, eta: 15:05:06, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0717, loss_rpn_bbox: 0.0731, loss_cls: 0.3797, acc: 89.6826, loss_bbox: 0.7472, loss: 1.2717
2022-07-08 09:42:40,550 - mmdet - INFO - Epoch [1][3450/14659] lr: 1.000e-02, eta: 15:04:52, time: 0.315, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0672, loss_rpn_bbox: 0.0710, loss_cls: 0.3630, acc: 90.1211, loss_bbox: 0.7121, loss: 1.2133
2022-07-08 09:42:56,333 - mmdet - INFO - Epoch [1][3500/14659] lr: 1.000e-02, eta: 15:04:38, time: 0.316, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0695, loss_rpn_bbox: 0.0731, loss_cls: 0.3597, acc: 90.4917, loss_bbox: 0.6993, loss: 1.2015
2022-07-08 09:43:11,917 - mmdet - INFO - Epoch [1][3550/14659] lr: 1.000e-02, eta: 15:04:15, time: 0.312, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0700, loss_rpn_bbox: 0.0784, loss_cls: 0.3692, acc: 90.1787, loss_bbox: 0.7196, loss: 1.2372
2022-07-08 09:43:27,680 - mmdet - INFO - Epoch [1][3600/14659] lr: 1.000e-02, eta: 15:04:01, time: 0.315, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0619, loss_rpn_bbox: 0.0694, loss_cls: 0.3656, acc: 90.3960, loss_bbox: 0.6829, loss: 1.1799
2022-07-08 09:43:43,407 - mmdet - INFO - Epoch [1][3650/14659] lr: 1.000e-02, eta: 15:03:44, time: 0.315, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0697, loss_rpn_bbox: 0.0718, loss_cls: 0.3594, acc: 90.4307, loss_bbox: 0.6847, loss: 1.1857
2022-07-08 09:43:58,920 - mmdet - INFO - Epoch [1][3700/14659] lr: 1.000e-02, eta: 15:03:18, time: 0.310, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0651, loss_rpn_bbox: 0.0685, loss_cls: 0.3514, acc: 90.5312, loss_bbox: 0.6853, loss: 1.1704
2022-07-08 09:44:14,528 - mmdet - INFO - Epoch [1][3750/14659] lr: 1.000e-02, eta: 15:02:56, time: 0.312, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0727, loss_rpn_bbox: 0.0751, loss_cls: 0.3674, acc: 90.0342, loss_bbox: 0.7239, loss: 1.2391
2022-07-08 09:44:30,232 - mmdet - INFO - Epoch [1][3800/14659] lr: 1.000e-02, eta: 15:02:38, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0699, loss_rpn_bbox: 0.0720, loss_cls: 0.3572, acc: 90.1069, loss_bbox: 0.7152, loss: 1.2142
2022-07-08 09:44:45,854 - mmdet - INFO - Epoch [1][3850/14659] lr: 1.000e-02, eta: 15:02:17, time: 0.312, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0694, loss_rpn_bbox: 0.0771, loss_cls: 0.3765, acc: 90.0098, loss_bbox: 0.7235, loss: 1.2465
2022-07-08 09:45:01,592 - mmdet - INFO - Epoch [1][3900/14659] lr: 1.000e-02, eta: 15:02:03, time: 0.315, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0698, loss_rpn_bbox: 0.0730, loss_cls: 0.3654, acc: 90.1255, loss_bbox: 0.7196, loss: 1.2277
2022-07-08 09:45:17,253 - mmdet - INFO - Epoch [1][3950/14659] lr: 1.000e-02, eta: 15:01:43, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0676, loss_rpn_bbox: 0.0741, loss_cls: 0.3583, acc: 90.1689, loss_bbox: 0.7196, loss: 1.2195
2022-07-08 09:45:32,992 - mmdet - INFO - Exp name: faster_rcnn_r50_fpn_alphagiou_1x_coco.py
2022-07-08 09:45:32,992 - mmdet - INFO - Epoch [1][4000/14659] lr: 1.000e-02, eta: 15:01:29, time: 0.315, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0718, loss_rpn_bbox: 0.0732, loss_cls: 0.3855, acc: 89.7119, loss_bbox: 0.7442, loss: 1.2747
2022-07-08 09:45:48,695 - mmdet - INFO - Epoch [1][4050/14659] lr: 1.000e-02, eta: 15:01:11, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0691, loss_rpn_bbox: 0.0666, loss_cls: 0.3494, acc: 90.6694, loss_bbox: 0.6693, loss: 1.1545
2022-07-08 09:46:04,375 - mmdet - INFO - Epoch [1][4100/14659] lr: 1.000e-02, eta: 15:00:53, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0654, loss_rpn_bbox: 0.0739, loss_cls: 0.3632, acc: 90.3228, loss_bbox: 0.6787, loss: 1.1812
2022-07-08 09:46:20,156 - mmdet - INFO - Epoch [1][4150/14659] lr: 1.000e-02, eta: 15:00:40, time: 0.316, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0665, loss_rpn_bbox: 0.0750, loss_cls: 0.3817, acc: 90.1304, loss_bbox: 0.7015, loss: 1.2246
2022-07-08 09:46:35,847 - mmdet - INFO - Epoch [1][4200/14659] lr: 1.000e-02, eta: 15:00:22, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0670, loss_rpn_bbox: 0.0753, loss_cls: 0.3659, acc: 90.2036, loss_bbox: 0.7155, loss: 1.2238
2022-07-08 09:46:51,582 - mmdet - INFO - Epoch [1][4250/14659] lr: 1.000e-02, eta: 15:00:07, time: 0.315, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0657, loss_rpn_bbox: 0.0708, loss_cls: 0.3540, acc: 90.4170, loss_bbox: 0.6931, loss: 1.1836
2022-07-08 09:47:07,261 - mmdet - INFO - Epoch [1][4300/14659] lr: 1.000e-02, eta: 14:59:49, time: 0.314, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0634, loss_rpn_bbox: 0.0638, loss_cls: 0.3473, acc: 90.5269, loss_bbox: 0.6935, loss: 1.1679
2022-07-08 09:47:22,929 - mmdet - INFO - Epoch [1][4350/14659] lr: 1.000e-02, eta: 14:59:31, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0587, loss_rpn_bbox: 0.0671, loss_cls: 0.3518, acc: 90.2686, loss_bbox: 0.7118, loss: 1.1894
2022-07-08 09:47:38,583 - mmdet - INFO - Epoch [1][4400/14659] lr: 1.000e-02, eta: 14:59:11, time: 0.313, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0663, loss_rpn_bbox: 0.0727, loss_cls: 0.3745, acc: 89.8623, loss_bbox: 0.7371, loss: 1.2506
2022-07-08 09:47:54,224 - mmdet - INFO - Epoch [1][4450/14659] lr: 1.000e-02, eta: 14:58:53, time: 0.313, data_time: 0.008, memory: 3940, loss_rpn_cls: 0.0691, loss_rpn_bbox: 0.0714, loss_cls: 0.3647, acc: 90.1333, loss_bbox: 0.7151, loss: 1.2202
2022-07-08 09:48:09,842 - mmdet - INFO - Epoch [1][4500/14659] lr: 1.000e-02, eta: 14:58:33, time: 0.312, data_time: 0.007, memory: 3940, loss_rpn_cls: 0.0663, loss_rpn_bbox: 0.0677, loss_cls: 0.3510, acc: 90.2915, loss_bbox: 0.6986, loss: 1.1837