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20221101_012220.log
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2022-11-01 01:22:20,891 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: A100-SXM4-40GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.0, V11.0.221
GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5)
PyTorch: 1.10.1+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: AVX2
- 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=1.10.1, 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: 0.11.2+cu113
OpenCV: 4.5.4
MMCV: 1.6.2
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.25.2+
------------------------------------------------------------
2022-11-01 01:22:21,098 - mmdet - INFO - Distributed training: True
2022-11-01 01:22:21,417 - mmdet - INFO - Config:
model = dict(
type='RetinaNet',
backbone=dict(
type='MetaMobile',
depths=[3, 3, 9, 3],
stem_dim=24,
embed_dims=[48, 72, 160, 288],
exp_ratios=[2.0, 3.0, 4.0, 4.0],
norm_layers=['bn_2d', 'bn_2d', 'ln_2d', 'ln_2d'],
act_layers=['silu', 'silu', 'gelu', 'gelu'],
dw_kss=[3, 3, 5, 5],
group_size=1,
se_ratios=[0.0, 0.0, 0.0, 0.0],
dim_heads=[24, 24, 32, 32],
window_sizes=[10, 10, 10, 10],
attn_ss=[False, False, True, True],
attn_s_skips=[False, False, False, False],
qkv_bias=True,
attn_drop=0.0,
drop=0.0,
drop_path=0.05,
v_group=False,
attn_pre=True,
conv_l=True,
skip_l=True,
pre_dim=0,
sync_bn=True,
out_indices=(1, 2, 3, 4),
pretrained='../../ckpt_models/MetaMobile_5M/net_E.pth',
frozen_stages=1,
norm_eval=True),
neck=dict(
type='FPN',
in_channels=[48, 72, 160, 288],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type='RetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
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='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', 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 = '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=4,
workers_per_gpu=4,
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_config = dict(grad_clip=None)
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
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)
bs_ratio = 1
optimizer = dict(
type='AdamW',
lr=0.0002,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0))))
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
min_lr=0)
work_dir = './work_dirs/retinanet_metamobile5M_fpn_1x_coco'
auto_resume = False
gpu_ids = range(0, 8)
2022-11-01 01:22:21,418 - mmdet - INFO - Set random seed to 0, deterministic: False
2022-11-01 01:22:21,756 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-11-01 01:22:21,773 - mmdet - INFO - initialize RetinaHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'retina_cls', 'std': 0.01, 'bias_prob': 0.01}}
Name of parameter - Initialization information
backbone.stage0.0.convs.0.0.weight - torch.Size([24, 3, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.0.convs.0.0.bias - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.0.convs.0.1.weight - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.0.convs.0.1.bias - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.conv_local.conv.weight - torch.Size([24, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.conv_local.norm.weight - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.conv_local.norm.bias - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.se.conv_reduce.weight - torch.Size([24, 24, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.se.conv_reduce.bias - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.se.conv_expand.weight - torch.Size([24, 24, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.se.conv_expand.bias - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage0.1.proj.conv.weight - torch.Size([24, 24, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.norm.weight - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.norm.bias - torch.Size([24]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.v.conv.weight - torch.Size([96, 24, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.v.conv.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.conv_local.conv.weight - torch.Size([96, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.conv_local.norm.weight - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.conv_local.norm.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.0.proj.conv.weight - torch.Size([48, 96, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.norm.weight - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.norm.bias - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.v.conv.weight - torch.Size([96, 48, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.v.conv.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.conv_local.conv.weight - torch.Size([96, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.conv_local.norm.weight - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.conv_local.norm.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.proj.conv.weight - torch.Size([48, 96, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.norm.weight - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.norm.bias - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.v.conv.weight - torch.Size([96, 48, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.v.conv.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.conv_local.conv.weight - torch.Size([96, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.conv_local.norm.weight - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.conv_local.norm.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.2.proj.conv.weight - torch.Size([48, 96, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.norm.weight - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.norm.bias - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.v.conv.weight - torch.Size([288, 48, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.v.conv.bias - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.conv_local.conv.weight - torch.Size([288, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.conv_local.norm.weight - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.conv_local.norm.bias - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.proj.conv.weight - torch.Size([72, 288, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.norm.weight - torch.Size([72]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.norm.bias - torch.Size([72]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.v.conv.weight - torch.Size([216, 72, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.v.conv.bias - torch.Size([216]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.conv_local.conv.weight - torch.Size([216, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.conv_local.norm.weight - torch.Size([216]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.conv_local.norm.bias - torch.Size([216]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.proj.conv.weight - torch.Size([72, 216, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.norm.weight - torch.Size([72]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.norm.bias - torch.Size([72]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.v.conv.weight - torch.Size([216, 72, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.v.conv.bias - torch.Size([216]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.conv_local.conv.weight - torch.Size([216, 1, 3, 3]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.conv_local.norm.weight - torch.Size([216]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.conv_local.norm.bias - torch.Size([216]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.2.proj.conv.weight - torch.Size([72, 216, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.norm.norm.weight - torch.Size([72]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.norm.norm.bias - torch.Size([72]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.v.conv.weight - torch.Size([576, 72, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.v.conv.bias - torch.Size([576]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.conv_local.conv.weight - torch.Size([576, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.conv_local.norm.weight - torch.Size([576]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.conv_local.norm.bias - torch.Size([576]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.proj.conv.weight - torch.Size([160, 576, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.qk.conv.weight - torch.Size([320, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.qk.conv.bias - torch.Size([320]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.v.conv.weight - torch.Size([640, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.v.conv.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.conv_local.conv.weight - torch.Size([640, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.conv_local.norm.weight - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.conv_local.norm.bias - torch.Size([640]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.8.proj.conv.weight - torch.Size([160, 640, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.norm.norm.weight - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.norm.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.v.conv.weight - torch.Size([1280, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.v.conv.bias - torch.Size([1280]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.conv_local.conv.weight - torch.Size([1280, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.conv_local.norm.weight - torch.Size([1280]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.conv_local.norm.bias - torch.Size([1280]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.proj.conv.weight - torch.Size([288, 1280, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.norm.norm.weight - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.norm.norm.bias - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.qk.conv.weight - torch.Size([576, 288, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.qk.conv.bias - torch.Size([576]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.v.conv.weight - torch.Size([1152, 288, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.v.conv.bias - torch.Size([1152]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.conv_local.conv.weight - torch.Size([1152, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.conv_local.norm.weight - torch.Size([1152]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.conv_local.norm.bias - torch.Size([1152]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.proj.conv.weight - torch.Size([288, 1152, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.norm.norm.weight - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.norm.norm.bias - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.qk.conv.weight - torch.Size([576, 288, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.qk.conv.bias - torch.Size([576]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.v.conv.weight - torch.Size([1152, 288, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.v.conv.bias - torch.Size([1152]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.conv_local.conv.weight - torch.Size([1152, 1, 5, 5]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.conv_local.norm.weight - torch.Size([1152]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.conv_local.norm.bias - torch.Size([1152]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.proj.conv.weight - torch.Size([288, 1152, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.lateral_convs.0.conv.weight - torch.Size([256, 72, 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 RetinaNet
neck.lateral_convs.1.conv.weight - torch.Size([256, 160, 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 RetinaNet
neck.lateral_convs.2.conv.weight - torch.Size([256, 288, 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 RetinaNet
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 RetinaNet
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 RetinaNet
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 RetinaNet
neck.fpn_convs.3.conv.weight - torch.Size([256, 288, 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 RetinaNet
neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.4.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
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.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of RetinaNet
bbox_head.retina_cls.weight - torch.Size([720, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=-4.59511985013459
bbox_head.retina_cls.bias - torch.Size([720]):
NormalInit: mean=0, std=0.01, bias=-4.59511985013459
bbox_head.retina_reg.weight - torch.Size([36, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
bbox_head.retina_reg.bias - torch.Size([36]):
NormalInit: mean=0, std=0.01, bias=0
2022-11-01 01:22:55,556 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2022-11-01 01:22:56,023 - mmdet - INFO - Start running, host: root@ts-a926a14fa31f489fa5d6d7cef2b5bfe1-launcher, work_dir: /youtu_fuxi_team1_ceph/vtzhang/codes/pts_cls/down-stream-tasks/mmdetection/work_dirs/retinanet_metamobile5M_fpn_1x_coco
2022-11-01 01:22:56,023 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) NumClassCheckHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_epoch:
(NORMAL ) NumClassCheckHook
(NORMAL ) DistSamplerSeedHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
--------------------
2022-11-01 01:22:56,024 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2022-11-01 01:22:56,024 - mmdet - INFO - Checkpoints will be saved to /youtu_fuxi_team1_ceph/vtzhang/codes/pts_cls/down-stream-tasks/mmdetection/work_dirs/retinanet_metamobile5M_fpn_1x_coco by HardDiskBackend.
2022-11-01 01:23:27,008 - mmdet - INFO - Epoch [1][50/3665] lr: 1.978e-05, eta: 7:33:34, time: 0.619, data_time: 0.082, memory: 7487, loss_cls: 1.1891, loss_bbox: 0.6921, loss: 1.8812
2022-11-01 01:23:48,240 - mmdet - INFO - Epoch [1][100/3665] lr: 3.976e-05, eta: 6:21:48, time: 0.425, data_time: 0.015, memory: 7487, loss_cls: 0.9998, loss_bbox: 0.6718, loss: 1.6716
2022-11-01 01:24:09,548 - mmdet - INFO - Epoch [1][150/3665] lr: 5.974e-05, eta: 5:58:01, time: 0.426, data_time: 0.015, memory: 7487, loss_cls: 0.8493, loss_bbox: 0.6059, loss: 1.4552
2022-11-01 01:24:30,623 - mmdet - INFO - Epoch [1][200/3665] lr: 7.972e-05, eta: 5:45:05, time: 0.421, data_time: 0.014, memory: 7487, loss_cls: 0.7971, loss_bbox: 0.5558, loss: 1.3529
2022-11-01 01:24:52,037 - mmdet - INFO - Epoch [1][250/3665] lr: 9.970e-05, eta: 5:38:11, time: 0.428, data_time: 0.014, memory: 7487, loss_cls: 0.7503, loss_bbox: 0.5270, loss: 1.2773
2022-11-01 01:25:12,975 - mmdet - INFO - Epoch [1][300/3665] lr: 1.197e-04, eta: 5:32:18, time: 0.419, data_time: 0.014, memory: 7487, loss_cls: 0.6951, loss_bbox: 0.5003, loss: 1.1954
2022-11-01 01:25:33,844 - mmdet - INFO - Epoch [1][350/3665] lr: 1.397e-04, eta: 5:27:52, time: 0.417, data_time: 0.014, memory: 7487, loss_cls: 0.6568, loss_bbox: 0.4792, loss: 1.1360
2022-11-01 01:25:54,903 - mmdet - INFO - Epoch [1][400/3665] lr: 1.596e-04, eta: 5:24:47, time: 0.421, data_time: 0.014, memory: 7487, loss_cls: 0.6466, loss_bbox: 0.4641, loss: 1.1106
2022-11-01 01:26:15,940 - mmdet - INFO - Epoch [1][450/3665] lr: 1.796e-04, eta: 5:22:17, time: 0.421, data_time: 0.014, memory: 7487, loss_cls: 0.6112, loss_bbox: 0.4554, loss: 1.0666
2022-11-01 01:26:37,068 - mmdet - INFO - Epoch [1][500/3665] lr: 1.996e-04, eta: 5:20:20, time: 0.423, data_time: 0.014, memory: 7487, loss_cls: 0.5960, loss_bbox: 0.4473, loss: 1.0432
2022-11-01 01:26:58,180 - mmdet - INFO - Epoch [1][550/3665] lr: 2.000e-04, eta: 5:18:40, time: 0.422, data_time: 0.014, memory: 7487, loss_cls: 0.6072, loss_bbox: 0.4418, loss: 1.0490
2022-11-01 01:27:19,294 - mmdet - INFO - Epoch [1][600/3665] lr: 2.000e-04, eta: 5:17:13, time: 0.422, data_time: 0.014, memory: 7487, loss_cls: 0.5921, loss_bbox: 0.4307, loss: 1.0229
2022-11-01 01:27:40,196 - mmdet - INFO - Epoch [1][650/3665] lr: 2.000e-04, eta: 5:15:42, time: 0.418, data_time: 0.014, memory: 7487, loss_cls: 0.5628, loss_bbox: 0.4255, loss: 0.9883
2022-11-01 01:28:01,248 - mmdet - INFO - Epoch [1][700/3665] lr: 2.000e-04, eta: 5:14:30, time: 0.421, data_time: 0.017, memory: 7487, loss_cls: 0.5284, loss_bbox: 0.4202, loss: 0.9486
2022-11-01 01:28:22,142 - mmdet - INFO - Epoch [1][750/3665] lr: 2.000e-04, eta: 5:13:16, time: 0.418, data_time: 0.017, memory: 7487, loss_cls: 0.5472, loss_bbox: 0.4143, loss: 0.9615
2022-11-01 01:28:43,106 - mmdet - INFO - Epoch [1][800/3665] lr: 2.000e-04, eta: 5:12:12, time: 0.419, data_time: 0.017, memory: 7487, loss_cls: 0.5430, loss_bbox: 0.4094, loss: 0.9524
2022-11-01 01:29:03,985 - mmdet - INFO - Epoch [1][850/3665] lr: 2.000e-04, eta: 5:11:10, time: 0.418, data_time: 0.018, memory: 7487, loss_cls: 0.5102, loss_bbox: 0.4066, loss: 0.9168
2022-11-01 01:29:24,874 - mmdet - INFO - Epoch [1][900/3665] lr: 2.000e-04, eta: 5:10:12, time: 0.418, data_time: 0.018, memory: 7487, loss_cls: 0.4967, loss_bbox: 0.4061, loss: 0.9028
2022-11-01 01:29:45,670 - mmdet - INFO - Epoch [1][950/3665] lr: 2.000e-04, eta: 5:09:14, time: 0.416, data_time: 0.018, memory: 7487, loss_cls: 0.4897, loss_bbox: 0.4010, loss: 0.8907
2022-11-01 01:30:06,688 - mmdet - INFO - Exp name: retinanet_metamobile5M_fpn_1x_coco.py
2022-11-01 01:30:06,689 - mmdet - INFO - Epoch [1][1000/3665] lr: 2.000e-04, eta: 5:08:29, time: 0.420, data_time: 0.018, memory: 7487, loss_cls: 0.4786, loss_bbox: 0.3975, loss: 0.8761
2022-11-01 01:30:27,814 - mmdet - INFO - Epoch [1][1050/3665] lr: 2.000e-04, eta: 5:07:51, time: 0.423, data_time: 0.019, memory: 7487, loss_cls: 0.4932, loss_bbox: 0.3915, loss: 0.8847
2022-11-01 01:30:48,414 - mmdet - INFO - Epoch [1][1100/3665] lr: 2.000e-04, eta: 5:06:53, time: 0.412, data_time: 0.018, memory: 7487, loss_cls: 0.4694, loss_bbox: 0.3883, loss: 0.8577
2022-11-01 01:31:09,200 - mmdet - INFO - Epoch [1][1150/3665] lr: 2.000e-04, eta: 5:06:06, time: 0.416, data_time: 0.018, memory: 7487, loss_cls: 0.4872, loss_bbox: 0.3882, loss: 0.8754
2022-11-01 01:31:30,167 - mmdet - INFO - Epoch [1][1200/3665] lr: 2.000e-04, eta: 5:05:28, time: 0.419, data_time: 0.017, memory: 7487, loss_cls: 0.4718, loss_bbox: 0.3841, loss: 0.8559
2022-11-01 01:31:51,408 - mmdet - INFO - Epoch [1][1250/3665] lr: 2.000e-04, eta: 5:05:00, time: 0.425, data_time: 0.017, memory: 7487, loss_cls: 0.4702, loss_bbox: 0.3855, loss: 0.8557
2022-11-01 01:32:12,484 - mmdet - INFO - Epoch [1][1300/3665] lr: 2.000e-04, eta: 5:04:28, time: 0.422, data_time: 0.017, memory: 7487, loss_cls: 0.4537, loss_bbox: 0.3900, loss: 0.8437
2022-11-01 01:32:33,701 - mmdet - INFO - Epoch [1][1350/3665] lr: 2.000e-04, eta: 5:04:01, time: 0.424, data_time: 0.017, memory: 7487, loss_cls: 0.4645, loss_bbox: 0.3823, loss: 0.8468
2022-11-01 01:32:54,858 - mmdet - INFO - Epoch [1][1400/3665] lr: 2.000e-04, eta: 5:03:32, time: 0.423, data_time: 0.017, memory: 7487, loss_cls: 0.4547, loss_bbox: 0.3769, loss: 0.8316
2022-11-01 01:33:16,213 - mmdet - INFO - Epoch [1][1450/3665] lr: 2.000e-04, eta: 5:03:10, time: 0.427, data_time: 0.017, memory: 7487, loss_cls: 0.4339, loss_bbox: 0.3741, loss: 0.8080
2022-11-01 01:33:37,205 - mmdet - INFO - Epoch [1][1500/3665] lr: 2.000e-04, eta: 5:02:37, time: 0.420, data_time: 0.017, memory: 7487, loss_cls: 0.4476, loss_bbox: 0.3743, loss: 0.8219
2022-11-01 01:33:58,244 - mmdet - INFO - Epoch [1][1550/3665] lr: 2.000e-04, eta: 5:02:07, time: 0.421, data_time: 0.017, memory: 7487, loss_cls: 0.4357, loss_bbox: 0.3740, loss: 0.8097
2022-11-01 01:34:19,371 - mmdet - INFO - Epoch [1][1600/3665] lr: 2.000e-04, eta: 5:01:39, time: 0.423, data_time: 0.017, memory: 7487, loss_cls: 0.4260, loss_bbox: 0.3691, loss: 0.7952
2022-11-01 01:34:40,402 - mmdet - INFO - Epoch [1][1650/3665] lr: 2.000e-04, eta: 5:01:09, time: 0.421, data_time: 0.017, memory: 7487, loss_cls: 0.4274, loss_bbox: 0.3691, loss: 0.7966
2022-11-01 01:35:01,385 - mmdet - INFO - Epoch [1][1700/3665] lr: 2.000e-04, eta: 5:00:39, time: 0.420, data_time: 0.016, memory: 7487, loss_cls: 0.4345, loss_bbox: 0.3705, loss: 0.8050
2022-11-01 01:35:22,388 - mmdet - INFO - Epoch [1][1750/3665] lr: 2.000e-04, eta: 5:00:10, time: 0.420, data_time: 0.017, memory: 7487, loss_cls: 0.4335, loss_bbox: 0.3650, loss: 0.7985
2022-11-01 01:35:43,504 - mmdet - INFO - Epoch [1][1800/3665] lr: 2.000e-04, eta: 4:59:44, time: 0.422, data_time: 0.017, memory: 7487, loss_cls: 0.4278, loss_bbox: 0.3695, loss: 0.7972
2022-11-01 01:36:04,508 - mmdet - INFO - Epoch [1][1850/3665] lr: 2.000e-04, eta: 4:59:15, time: 0.420, data_time: 0.017, memory: 7487, loss_cls: 0.4238, loss_bbox: 0.3591, loss: 0.7828
2022-11-01 01:36:25,666 - mmdet - INFO - Epoch [1][1900/3665] lr: 2.000e-04, eta: 4:58:50, time: 0.423, data_time: 0.017, memory: 7487, loss_cls: 0.4294, loss_bbox: 0.3599, loss: 0.7892
2022-11-01 01:36:46,683 - mmdet - INFO - Epoch [1][1950/3665] lr: 2.000e-04, eta: 4:58:23, time: 0.420, data_time: 0.017, memory: 7487, loss_cls: 0.4244, loss_bbox: 0.3697, loss: 0.7942
2022-11-01 01:37:07,874 - mmdet - INFO - Exp name: retinanet_metamobile5M_fpn_1x_coco.py
2022-11-01 01:37:07,875 - mmdet - INFO - Epoch [1][2000/3665] lr: 2.000e-04, eta: 4:57:59, time: 0.424, data_time: 0.017, memory: 7487, loss_cls: 0.4151, loss_bbox: 0.3565, loss: 0.7716
2022-11-01 01:37:28,798 - mmdet - INFO - Epoch [1][2050/3665] lr: 2.000e-04, eta: 4:57:30, time: 0.418, data_time: 0.017, memory: 7487, loss_cls: 0.4128, loss_bbox: 0.3502, loss: 0.7631
2022-11-01 01:37:49,818 - mmdet - INFO - Epoch [1][2100/3665] lr: 2.000e-04, eta: 4:57:04, time: 0.420, data_time: 0.018, memory: 7487, loss_cls: 0.4045, loss_bbox: 0.3557, loss: 0.7602
2022-11-01 01:38:10,988 - mmdet - INFO - Epoch [1][2150/3665] lr: 2.000e-04, eta: 4:56:40, time: 0.423, data_time: 0.017, memory: 7487, loss_cls: 0.4060, loss_bbox: 0.3495, loss: 0.7555
2022-11-01 01:38:32,454 - mmdet - INFO - Epoch [1][2200/3665] lr: 2.000e-04, eta: 4:56:23, time: 0.429, data_time: 0.017, memory: 7487, loss_cls: 0.4103, loss_bbox: 0.3542, loss: 0.7644
2022-11-01 01:38:53,755 - mmdet - INFO - Epoch [1][2250/3665] lr: 2.000e-04, eta: 4:56:02, time: 0.426, data_time: 0.017, memory: 7487, loss_cls: 0.4081, loss_bbox: 0.3536, loss: 0.7617
2022-11-01 01:39:14,949 - mmdet - INFO - Epoch [1][2300/3665] lr: 2.000e-04, eta: 4:55:39, time: 0.424, data_time: 0.017, memory: 7487, loss_cls: 0.4093, loss_bbox: 0.3536, loss: 0.7630
2022-11-01 01:39:36,342 - mmdet - INFO - Epoch [1][2350/3665] lr: 2.000e-04, eta: 4:55:20, time: 0.428, data_time: 0.017, memory: 7487, loss_cls: 0.3987, loss_bbox: 0.3470, loss: 0.7458
2022-11-01 01:39:57,513 - mmdet - INFO - Epoch [1][2400/3665] lr: 2.000e-04, eta: 4:54:56, time: 0.423, data_time: 0.017, memory: 7487, loss_cls: 0.3905, loss_bbox: 0.3485, loss: 0.7391
2022-11-01 01:40:19,017 - mmdet - INFO - Epoch [1][2450/3665] lr: 2.000e-04, eta: 4:54:39, time: 0.430, data_time: 0.017, memory: 7487, loss_cls: 0.3918, loss_bbox: 0.3509, loss: 0.7427
2022-11-01 01:40:40,500 - mmdet - INFO - Epoch [1][2500/3665] lr: 2.000e-04, eta: 4:54:21, time: 0.430, data_time: 0.017, memory: 7487, loss_cls: 0.3947, loss_bbox: 0.3521, loss: 0.7468
2022-11-01 01:41:02,585 - mmdet - INFO - Epoch [1][2550/3665] lr: 2.000e-04, eta: 4:54:12, time: 0.442, data_time: 0.017, memory: 7487, loss_cls: 0.3987, loss_bbox: 0.3480, loss: 0.7468
2022-11-01 01:41:24,215 - mmdet - INFO - Epoch [1][2600/3665] lr: 2.000e-04, eta: 4:53:56, time: 0.433, data_time: 0.017, memory: 7487, loss_cls: 0.3921, loss_bbox: 0.3464, loss: 0.7385
2022-11-01 01:41:45,927 - mmdet - INFO - Epoch [1][2650/3665] lr: 2.000e-04, eta: 4:53:41, time: 0.434, data_time: 0.017, memory: 7487, loss_cls: 0.3986, loss_bbox: 0.3433, loss: 0.7419