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20221102_234131.log
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2022-11-02 23:41:31,723 - 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+
------------------------------------------------------------
2022-11-02 23:41:31,953 - mmdet - INFO - Distributed training: True
2022-11-02 23:41:32,339 - mmdet - INFO - Config:
model = dict(
type='RetinaNet',
backbone=dict(
type='MetaMobile',
depths=[2, 2, 8, 3],
stem_dim=24,
embed_dims=[32, 48, 80, 168],
exp_ratios=[2.0, 2.5, 3.0, 3.5],
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=[16, 16, 20, 21],
window_sizes=[7, 7, 7, 7],
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=0.04036,
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_1M/net_E.pth',
frozen_stages=1,
norm_eval=True),
neck=dict(
type='FPN',
in_channels=[32, 48, 80, 168],
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_metamobile1M_fpn_1x_coco'
auto_resume = False
gpu_ids = range(0, 8)
2022-11-02 23:41:32,340 - mmdet - INFO - Set random seed to 0, deterministic: False
2022-11-02 23:41:32,574 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-11-02 23:41:32,595 - 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([32, 96, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.norm.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.norm.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.v.conv.weight - torch.Size([64, 32, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.v.conv.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.conv_local.conv.weight - torch.Size([64, 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([64]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.conv_local.norm.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage1.1.proj.conv.weight - torch.Size([32, 64, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.norm.weight - torch.Size([32]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.norm.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.v.conv.weight - torch.Size([160, 32, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.v.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.conv_local.conv.weight - torch.Size([160, 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([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.conv_local.norm.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.0.proj.conv.weight - torch.Size([48, 160, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.norm.weight - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.norm.bias - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.v.conv.weight - torch.Size([120, 48, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.v.conv.bias - torch.Size([120]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.conv_local.conv.weight - torch.Size([120, 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([120]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.conv_local.norm.bias - torch.Size([120]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage2.1.proj.conv.weight - torch.Size([48, 120, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.norm.norm.weight - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.norm.norm.bias - torch.Size([48]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.v.conv.weight - torch.Size([288, 48, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.v.conv.bias - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.conv_local.conv.weight - torch.Size([288, 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([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.conv_local.norm.bias - torch.Size([288]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.0.proj.conv.weight - torch.Size([80, 288, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.1.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.2.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.3.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.4.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.5.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.6.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.qk.conv.weight - torch.Size([160, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.qk.conv.bias - torch.Size([160]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.v.conv.weight - torch.Size([240, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.v.conv.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.conv_local.conv.weight - torch.Size([240, 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([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.conv_local.norm.bias - torch.Size([240]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage3.7.proj.conv.weight - torch.Size([80, 240, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.norm.norm.weight - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.norm.norm.bias - torch.Size([80]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.v.conv.weight - torch.Size([560, 80, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.v.conv.bias - torch.Size([560]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.conv_local.conv.weight - torch.Size([560, 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([560]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.conv_local.norm.bias - torch.Size([560]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.0.proj.conv.weight - torch.Size([168, 560, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.norm.norm.weight - torch.Size([168]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.norm.norm.bias - torch.Size([168]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.qk.conv.weight - torch.Size([336, 168, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.qk.conv.bias - torch.Size([336]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.v.conv.weight - torch.Size([588, 168, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.v.conv.bias - torch.Size([588]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.conv_local.conv.weight - torch.Size([588, 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([588]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.conv_local.norm.bias - torch.Size([588]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.1.proj.conv.weight - torch.Size([168, 588, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.norm.norm.weight - torch.Size([168]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.norm.norm.bias - torch.Size([168]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.qk.conv.weight - torch.Size([336, 168, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.qk.conv.bias - torch.Size([336]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.v.conv.weight - torch.Size([588, 168, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.v.conv.bias - torch.Size([588]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.conv_local.conv.weight - torch.Size([588, 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([588]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.conv_local.norm.bias - torch.Size([588]):
The value is the same before and after calling `init_weights` of RetinaNet
backbone.stage4.2.proj.conv.weight - torch.Size([168, 588, 1, 1]):
The value is the same before and after calling `init_weights` of RetinaNet
neck.lateral_convs.0.conv.weight - torch.Size([256, 48, 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, 80, 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, 168, 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, 168, 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
2022-11-02 23:42:24,846 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2022-11-02 23:42:25,385 - mmdet - INFO - Start running, host: root@ts-8f168728d23e44df8b2aa939e7968482-launcher, work_dir: /youtu_fuxi_team1_ceph/vtzhang/codes/pts_cls/down-stream-tasks/mmdetection/work_dirs/retinanet_metamobile1M_fpn_1x_coco
2022-11-02 23:42:25,389 - 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
--------------------
2022-11-02 23:42:25,391 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2022-11-02 23:42:25,392 - mmdet - INFO - Checkpoints will be saved to /youtu_fuxi_team1_ceph/vtzhang/codes/pts_cls/down-stream-tasks/mmdetection/work_dirs/retinanet_metamobile1M_fpn_1x_coco by HardDiskBackend.
2022-11-02 23:43:08,400 - mmdet - INFO - Epoch [1][50/3665] lr: 1.978e-05, eta: 10:29:23, time: 0.860, data_time: 0.087, memory: 4245, loss_cls: 1.1946, loss_bbox: 0.6888, loss: 1.8834
2022-11-02 23:43:41,302 - mmdet - INFO - Epoch [1][100/3665] lr: 3.976e-05, eta: 9:15:01, time: 0.658, data_time: 0.018, memory: 4245, loss_cls: 1.0033, loss_bbox: 0.6728, loss: 1.6762
2022-11-02 23:44:14,075 - mmdet - INFO - Epoch [1][150/3665] lr: 5.974e-05, eta: 8:49:11, time: 0.655, data_time: 0.015, memory: 4245, loss_cls: 0.8529, loss_bbox: 0.6097, loss: 1.4626
2022-11-02 23:44:47,556 - mmdet - INFO - Epoch [1][200/3665] lr: 7.972e-05, eta: 8:38:36, time: 0.670, data_time: 0.020, memory: 4245, loss_cls: 0.8244, loss_bbox: 0.5604, loss: 1.3848
2022-11-02 23:45:14,595 - mmdet - INFO - Epoch [1][250/3665] lr: 9.970e-05, eta: 8:13:11, time: 0.540, data_time: 0.019, memory: 4245, loss_cls: 0.7868, loss_bbox: 0.5296, loss: 1.3163
2022-11-02 23:45:47,412 - mmdet - INFO - Epoch [1][300/3665] lr: 1.197e-04, eta: 8:10:09, time: 0.656, data_time: 0.016, memory: 4245, loss_cls: 0.7703, loss_bbox: 0.5110, loss: 1.2813
2022-11-02 23:46:20,595 - mmdet - INFO - Epoch [1][350/3665] lr: 1.397e-04, eta: 8:08:37, time: 0.664, data_time: 0.018, memory: 4245, loss_cls: 0.7262, loss_bbox: 0.4965, loss: 1.2227
2022-11-02 23:46:53,899 - mmdet - INFO - Epoch [1][400/3665] lr: 1.596e-04, eta: 8:07:30, time: 0.666, data_time: 0.019, memory: 4245, loss_cls: 0.6954, loss_bbox: 0.4796, loss: 1.1750
2022-11-02 23:47:27,086 - mmdet - INFO - Epoch [1][450/3665] lr: 1.796e-04, eta: 8:06:20, time: 0.664, data_time: 0.016, memory: 4245, loss_cls: 0.7357, loss_bbox: 0.4747, loss: 1.2105
2022-11-02 23:48:00,342 - mmdet - INFO - Epoch [1][500/3665] lr: 1.996e-04, eta: 8:05:24, time: 0.665, data_time: 0.016, memory: 4245, loss_cls: 0.6736, loss_bbox: 0.4659, loss: 1.1395
2022-11-02 23:48:33,267 - mmdet - INFO - Epoch [1][550/3665] lr: 2.000e-04, eta: 8:04:06, time: 0.659, data_time: 0.018, memory: 4245, loss_cls: 0.6543, loss_bbox: 0.4590, loss: 1.1134
2022-11-02 23:49:06,561 - mmdet - INFO - Epoch [1][600/3665] lr: 2.000e-04, eta: 8:03:22, time: 0.666, data_time: 0.019, memory: 4245, loss_cls: 0.6339, loss_bbox: 0.4515, loss: 1.0855
2022-11-02 23:49:39,663 - mmdet - INFO - Epoch [1][650/3665] lr: 2.000e-04, eta: 8:02:27, time: 0.662, data_time: 0.018, memory: 4245, loss_cls: 0.6265, loss_bbox: 0.4475, loss: 1.0740
2022-11-02 23:50:13,118 - mmdet - INFO - Epoch [1][700/3665] lr: 2.000e-04, eta: 8:01:57, time: 0.669, data_time: 0.018, memory: 4245, loss_cls: 0.5862, loss_bbox: 0.4417, loss: 1.0279
2022-11-02 23:50:46,160 - mmdet - INFO - Epoch [1][750/3665] lr: 2.000e-04, eta: 8:01:02, time: 0.661, data_time: 0.019, memory: 4245, loss_cls: 0.5889, loss_bbox: 0.4351, loss: 1.0240
2022-11-02 23:51:19,877 - mmdet - INFO - Epoch [1][800/3665] lr: 2.000e-04, eta: 8:00:47, time: 0.674, data_time: 0.020, memory: 4245, loss_cls: 0.5838, loss_bbox: 0.4278, loss: 1.0116
2022-11-02 23:51:52,787 - mmdet - INFO - Epoch [1][850/3665] lr: 2.000e-04, eta: 7:59:48, time: 0.658, data_time: 0.019, memory: 4245, loss_cls: 0.5823, loss_bbox: 0.4245, loss: 1.0068
2022-11-02 23:52:25,696 - mmdet - INFO - Epoch [1][900/3665] lr: 2.000e-04, eta: 7:58:52, time: 0.658, data_time: 0.018, memory: 4245, loss_cls: 0.5591, loss_bbox: 0.4235, loss: 0.9826
2022-11-02 23:52:59,556 - mmdet - INFO - Epoch [1][950/3665] lr: 2.000e-04, eta: 7:58:42, time: 0.677, data_time: 0.019, memory: 4245, loss_cls: 0.5552, loss_bbox: 0.4235, loss: 0.9787
2022-11-02 23:53:32,561 - mmdet - INFO - Exp name: retinanet_metamobile1M_fpn_1x_coco.py
2022-11-02 23:53:32,561 - mmdet - INFO - Epoch [1][1000/3665] lr: 2.000e-04, eta: 7:57:53, time: 0.660, data_time: 0.019, memory: 4245, loss_cls: 0.5405, loss_bbox: 0.4177, loss: 0.9582
2022-11-02 23:54:01,185 - mmdet - INFO - Epoch [1][1050/3665] lr: 2.000e-04, eta: 7:54:07, time: 0.573, data_time: 0.016, memory: 4245, loss_cls: 0.5700, loss_bbox: 0.4138, loss: 0.9838
2022-11-02 23:54:33,375 - mmdet - INFO - Epoch [1][1100/3665] lr: 2.000e-04, eta: 7:52:56, time: 0.644, data_time: 0.019, memory: 4245, loss_cls: 0.5408, loss_bbox: 0.4115, loss: 0.9524
2022-11-02 23:55:06,787 - mmdet - INFO - Epoch [1][1150/3665] lr: 2.000e-04, eta: 7:52:36, time: 0.668, data_time: 0.019, memory: 4245, loss_cls: 0.5381, loss_bbox: 0.4132, loss: 0.9513
2022-11-02 23:55:40,285 - mmdet - INFO - Epoch [1][1200/3665] lr: 2.000e-04, eta: 7:52:16, time: 0.670, data_time: 0.018, memory: 4245, loss_cls: 0.5239, loss_bbox: 0.4064, loss: 0.9303
2022-11-02 23:56:13,581 - mmdet - INFO - Epoch [1][1250/3665] lr: 2.000e-04, eta: 7:51:49, time: 0.666, data_time: 0.018, memory: 4245, loss_cls: 0.5252, loss_bbox: 0.4080, loss: 0.9332
2022-11-02 23:56:46,549 - mmdet - INFO - Epoch [1][1300/3665] lr: 2.000e-04, eta: 7:51:11, time: 0.659, data_time: 0.018, memory: 4245, loss_cls: 0.5173, loss_bbox: 0.4090, loss: 0.9262
2022-11-02 23:57:19,283 - mmdet - INFO - Epoch [1][1350/3665] lr: 2.000e-04, eta: 7:50:25, time: 0.655, data_time: 0.019, memory: 4245, loss_cls: 0.5124, loss_bbox: 0.4028, loss: 0.9152
2022-11-02 23:57:52,561 - mmdet - INFO - Epoch [1][1400/3665] lr: 2.000e-04, eta: 7:49:58, time: 0.666, data_time: 0.020, memory: 4245, loss_cls: 0.5008, loss_bbox: 0.3982, loss: 0.8990
2022-11-02 23:58:26,057 - mmdet - INFO - Epoch [1][1450/3665] lr: 2.000e-04, eta: 7:49:36, time: 0.670, data_time: 0.021, memory: 4245, loss_cls: 0.4908, loss_bbox: 0.3938, loss: 0.8847
2022-11-02 23:58:57,974 - mmdet - INFO - Epoch [1][1500/3665] lr: 2.000e-04, eta: 7:48:29, time: 0.639, data_time: 0.019, memory: 4245, loss_cls: 0.4988, loss_bbox: 0.3950, loss: 0.8938
2022-11-02 23:59:31,372 - mmdet - INFO - Epoch [1][1550/3665] lr: 2.000e-04, eta: 7:48:04, time: 0.668, data_time: 0.021, memory: 4245, loss_cls: 0.4975, loss_bbox: 0.3948, loss: 0.8923
2022-11-03 00:00:04,450 - mmdet - INFO - Epoch [1][1600/3665] lr: 2.000e-04, eta: 7:47:30, time: 0.662, data_time: 0.018, memory: 4245, loss_cls: 0.4897, loss_bbox: 0.3910, loss: 0.8807
2022-11-03 00:00:37,653 - mmdet - INFO - Epoch [1][1650/3665] lr: 2.000e-04, eta: 7:47:00, time: 0.664, data_time: 0.019, memory: 4245, loss_cls: 0.4966, loss_bbox: 0.3920, loss: 0.8886
2022-11-03 00:01:10,456 - mmdet - INFO - Epoch [1][1700/3665] lr: 2.000e-04, eta: 7:46:20, time: 0.656, data_time: 0.019, memory: 4245, loss_cls: 0.4969, loss_bbox: 0.3925, loss: 0.8895
2022-11-03 00:01:44,099 - mmdet - INFO - Epoch [1][1750/3665] lr: 2.000e-04, eta: 7:46:00, time: 0.673, data_time: 0.021, memory: 4245, loss_cls: 0.4879, loss_bbox: 0.3874, loss: 0.8753
2022-11-03 00:02:17,684 - mmdet - INFO - Epoch [1][1800/3665] lr: 2.000e-04, eta: 7:45:38, time: 0.672, data_time: 0.022, memory: 4245, loss_cls: 0.4839, loss_bbox: 0.3896, loss: 0.8735
2022-11-03 00:02:50,037 - mmdet - INFO - Epoch [1][1850/3665] lr: 2.000e-04, eta: 7:44:48, time: 0.647, data_time: 0.022, memory: 4245, loss_cls: 0.4759, loss_bbox: 0.3813, loss: 0.8572
2022-11-03 00:03:18,816 - mmdet - INFO - Epoch [1][1900/3665] lr: 2.000e-04, eta: 7:42:39, time: 0.575, data_time: 0.021, memory: 4245, loss_cls: 0.4824, loss_bbox: 0.3820, loss: 0.8644
2022-11-03 00:03:51,670 - mmdet - INFO - Epoch [1][1950/3665] lr: 2.000e-04, eta: 7:42:03, time: 0.657, data_time: 0.019, memory: 4245, loss_cls: 0.4785, loss_bbox: 0.3904, loss: 0.8689
2022-11-03 00:04:24,757 - mmdet - INFO - Exp name: retinanet_metamobile1M_fpn_1x_coco.py
2022-11-03 00:04:24,758 - mmdet - INFO - Epoch [1][2000/3665] lr: 2.000e-04, eta: 7:41:32, time: 0.662, data_time: 0.018, memory: 4245, loss_cls: 0.4721, loss_bbox: 0.3779, loss: 0.8501
2022-11-03 00:04:57,504 - mmdet - INFO - Epoch [1][2050/3665] lr: 2.000e-04, eta: 7:40:54, time: 0.655, data_time: 0.018, memory: 4245, loss_cls: 0.4775, loss_bbox: 0.3770, loss: 0.8545
2022-11-03 00:05:30,532 - mmdet - INFO - Epoch [1][2100/3665] lr: 2.000e-04, eta: 7:40:22, time: 0.660, data_time: 0.016, memory: 4245, loss_cls: 0.4646, loss_bbox: 0.3793, loss: 0.8439
2022-11-03 00:06:03,074 - mmdet - INFO - Epoch [1][2150/3665] lr: 2.000e-04, eta: 7:39:41, time: 0.651, data_time: 0.018, memory: 4245, loss_cls: 0.4591, loss_bbox: 0.3744, loss: 0.8336
2022-11-03 00:06:36,016 - mmdet - INFO - Epoch [1][2200/3665] lr: 2.000e-04, eta: 7:39:07, time: 0.659, data_time: 0.018, memory: 4245, loss_cls: 0.4551, loss_bbox: 0.3755, loss: 0.8306
2022-11-03 00:07:08,832 - mmdet - INFO - Epoch [1][2250/3665] lr: 2.000e-04, eta: 7:38:31, time: 0.656, data_time: 0.017, memory: 4245, loss_cls: 0.4613, loss_bbox: 0.3768, loss: 0.8381
2022-11-03 00:07:41,375 - mmdet - INFO - Epoch [1][2300/3665] lr: 2.000e-04, eta: 7:37:51, time: 0.651, data_time: 0.020, memory: 4245, loss_cls: 0.4727, loss_bbox: 0.3775, loss: 0.8502
2022-11-03 00:08:14,344 - mmdet - INFO - Epoch [1][2350/3665] lr: 2.000e-04, eta: 7:37:18, time: 0.659, data_time: 0.019, memory: 4245, loss_cls: 0.4554, loss_bbox: 0.3700, loss: 0.8254
2022-11-03 00:08:47,336 - mmdet - INFO - Epoch [1][2400/3665] lr: 2.000e-04, eta: 7:36:46, time: 0.660, data_time: 0.018, memory: 4245, loss_cls: 0.4408, loss_bbox: 0.3720, loss: 0.8128
2022-11-03 00:09:20,612 - mmdet - INFO - Epoch [1][2450/3665] lr: 2.000e-04, eta: 7:36:18, time: 0.665, data_time: 0.018, memory: 4245, loss_cls: 0.4513, loss_bbox: 0.3728, loss: 0.8241
2022-11-03 00:09:53,818 - mmdet - INFO - Epoch [1][2500/3665] lr: 2.000e-04, eta: 7:35:49, time: 0.664, data_time: 0.018, memory: 4245, loss_cls: 0.4478, loss_bbox: 0.3747, loss: 0.8225
2022-11-03 00:10:26,337 - mmdet - INFO - Epoch [1][2550/3665] lr: 2.000e-04, eta: 7:35:09, time: 0.650, data_time: 0.017, memory: 4245, loss_cls: 0.4480, loss_bbox: 0.3686, loss: 0.8166
2022-11-03 00:10:59,643 - mmdet - INFO - Epoch [1][2600/3665] lr: 2.000e-04, eta: 7:34:42, time: 0.666, data_time: 0.021, memory: 4245, loss_cls: 0.4395, loss_bbox: 0.3685, loss: 0.8080
2022-11-03 00:11:33,256 - mmdet - INFO - Epoch [1][2650/3665] lr: 2.000e-04, eta: 7:34:19, time: 0.672, data_time: 0.019, memory: 4245, loss_cls: 0.4385, loss_bbox: 0.3680, loss: 0.8065
2022-11-03 00:12:00,901 - mmdet - INFO - Epoch [1][2700/3665] lr: 2.000e-04, eta: 7:32:24, time: 0.553, data_time: 0.020, memory: 4245, loss_cls: 0.4508, loss_bbox: 0.3729, loss: 0.8237
2022-11-03 00:12:34,013 - mmdet - INFO - Epoch [1][2750/3665] lr: 2.000e-04, eta: 7:31:55, time: 0.662, data_time: 0.026, memory: 4245, loss_cls: 0.4277, loss_bbox: 0.3648, loss: 0.7925
2022-11-03 00:13:07,123 - mmdet - INFO - Epoch [1][2800/3665] lr: 2.000e-04, eta: 7:31:25, time: 0.662, data_time: 0.021, memory: 4245, loss_cls: 0.4286, loss_bbox: 0.3728, loss: 0.8014
2022-11-03 00:13:40,344 - mmdet - INFO - Epoch [1][2850/3665] lr: 2.000e-04, eta: 7:30:57, time: 0.665, data_time: 0.020, memory: 4245, loss_cls: 0.4457, loss_bbox: 0.3652, loss: 0.8109
2022-11-03 00:14:13,466 - mmdet - INFO - Epoch [1][2900/3665] lr: 2.000e-04, eta: 7:30:28, time: 0.662, data_time: 0.018, memory: 4245, loss_cls: 0.4376, loss_bbox: 0.3671, loss: 0.8046
2022-11-03 00:14:46,067 - mmdet - INFO - Epoch [1][2950/3665] lr: 2.000e-04, eta: 7:29:51, time: 0.652, data_time: 0.019, memory: 4245, loss_cls: 0.4320, loss_bbox: 0.3614, loss: 0.7934
2022-11-03 00:15:19,270 - mmdet - INFO - Exp name: retinanet_metamobile1M_fpn_1x_coco.py
2022-11-03 00:15:19,270 - mmdet - INFO - Epoch [1][3000/3665] lr: 2.000e-04, eta: 7:29:22, time: 0.664, data_time: 0.020, memory: 4245, loss_cls: 0.4393, loss_bbox: 0.3626, loss: 0.8019
2022-11-03 00:15:52,365 - mmdet - INFO - Epoch [1][3050/3665] lr: 2.000e-04, eta: 7:28:52, time: 0.662, data_time: 0.019, memory: 4245, loss_cls: 0.4469, loss_bbox: 0.3653, loss: 0.8122
2022-11-03 00:16:25,636 - mmdet - INFO - Epoch [1][3100/3665] lr: 2.000e-04, eta: 7:28:24, time: 0.666, data_time: 0.021, memory: 4245, loss_cls: 0.4442, loss_bbox: 0.3637, loss: 0.8079
2022-11-03 00:16:59,333 - mmdet - INFO - Epoch [1][3150/3665] lr: 2.000e-04, eta: 7:28:01, time: 0.674, data_time: 0.021, memory: 4245, loss_cls: 0.4449, loss_bbox: 0.3679, loss: 0.8128
2022-11-03 00:17:32,709 - mmdet - INFO - Epoch [1][3200/3665] lr: 2.000e-04, eta: 7:27:34, time: 0.667, data_time: 0.020, memory: 4245, loss_cls: 0.4371, loss_bbox: 0.3607, loss: 0.7977
2022-11-03 00:18:05,014 - mmdet - INFO - Epoch [1][3250/3665] lr: 2.000e-04, eta: 7:26:53, time: 0.646, data_time: 0.019, memory: 4245, loss_cls: 0.4265, loss_bbox: 0.3618, loss: 0.7883
2022-11-03 00:18:37,718 - mmdet - INFO - Epoch [1][3300/3665] lr: 2.000e-04, eta: 7:26:18, time: 0.654, data_time: 0.018, memory: 4245, loss_cls: 0.4261, loss_bbox: 0.3605, loss: 0.7866
2022-11-03 00:19:10,817 - mmdet - INFO - Epoch [1][3350/3665] lr: 2.000e-04, eta: 7:25:47, time: 0.662, data_time: 0.018, memory: 4245, loss_cls: 0.4301, loss_bbox: 0.3607, loss: 0.7908
2022-11-03 00:19:43,596 - mmdet - INFO - Epoch [1][3400/3665] lr: 2.000e-04, eta: 7:25:12, time: 0.655, data_time: 0.019, memory: 4245, loss_cls: 0.4242, loss_bbox: 0.3533, loss: 0.7774
2022-11-03 00:20:16,325 - mmdet - INFO - Epoch [1][3450/3665] lr: 2.000e-04, eta: 7:24:37, time: 0.655, data_time: 0.017, memory: 4245, loss_cls: 0.4218, loss_bbox: 0.3530, loss: 0.7748
2022-11-03 00:20:43,832 - mmdet - INFO - Epoch [1][3500/3665] lr: 2.000e-04, eta: 7:23:02, time: 0.550, data_time: 0.020, memory: 4245, loss_cls: 0.4314, loss_bbox: 0.3570, loss: 0.7885
2022-11-03 00:21:16,716 - mmdet - INFO - Epoch [1][3550/3665] lr: 2.000e-04, eta: 7:22:30, time: 0.658, data_time: 0.020, memory: 4245, loss_cls: 0.4230, loss_bbox: 0.3594, loss: 0.7824
2022-11-03 00:21:50,089 - mmdet - INFO - Epoch [1][3600/3665] lr: 2.000e-04, eta: 7:22:03, time: 0.668, data_time: 0.019, memory: 4245, loss_cls: 0.4359, loss_bbox: 0.3576, loss: 0.7935
2022-11-03 00:22:23,601 - mmdet - INFO - Epoch [1][3650/3665] lr: 2.000e-04, eta: 7:21:37, time: 0.670, data_time: 0.020, memory: 4245, loss_cls: 0.4302, loss_bbox: 0.3589, loss: 0.7891
2022-11-03 00:22:34,198 - mmdet - INFO - Saving checkpoint at 1 epochs
2022-11-03 00:23:57,758 - mmdet - INFO - Evaluating bbox...
2022-11-03 00:25:05,793 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.174
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.309
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.175
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.089
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.191
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.233
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.376
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.376
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.192
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.401
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.525
2022-11-03 00:25:07,409 - mmdet - INFO - Exp name: retinanet_metamobile1M_fpn_1x_coco.py
2022-11-03 00:25:07,409 - mmdet - INFO - Epoch(val) [1][625] bbox_mAP: 0.1740, bbox_mAP_50: 0.3090, bbox_mAP_75: 0.1750, bbox_mAP_s: 0.0890, bbox_mAP_m: 0.1910, bbox_mAP_l: 0.2330, bbox_mAP_copypaste: 0.174 0.309 0.175 0.089 0.191 0.233
2022-11-03 00:25:43,170 - mmdet - INFO - Epoch [2][50/3665] lr: 1.966e-04, eta: 7:19:39, time: 0.715, data_time: 0.077, memory: 4245, loss_cls: 0.4124, loss_bbox: 0.3498, loss: 0.7622
2022-11-03 00:26:16,083 - mmdet - INFO - Epoch [2][100/3665] lr: 1.966e-04, eta: 7:19:08, time: 0.658, data_time: 0.016, memory: 4245, loss_cls: 0.4196, loss_bbox: 0.3503, loss: 0.7699
2022-11-03 00:26:49,472 - mmdet - INFO - Epoch [2][150/3665] lr: 1.966e-04, eta: 7:18:42, time: 0.668, data_time: 0.017, memory: 4245, loss_cls: 0.4131, loss_bbox: 0.3531, loss: 0.7662
2022-11-03 00:27:22,107 - mmdet - INFO - Epoch [2][200/3665] lr: 1.966e-04, eta: 7:18:07, time: 0.653, data_time: 0.018, memory: 4245, loss_cls: 0.4066, loss_bbox: 0.3442, loss: 0.7508
2022-11-03 00:27:54,932 - mmdet - INFO - Epoch [2][250/3665] lr: 1.966e-04, eta: 7:17:35, time: 0.656, data_time: 0.018, memory: 4245, loss_cls: 0.4120, loss_bbox: 0.3579, loss: 0.7698
2022-11-03 00:28:28,169 - mmdet - INFO - Epoch [2][300/3665] lr: 1.966e-04, eta: 7:17:07, time: 0.664, data_time: 0.020, memory: 4245, loss_cls: 0.4018, loss_bbox: 0.3518, loss: 0.7537
2022-11-03 00:29:01,235 - mmdet - INFO - Epoch [2][350/3665] lr: 1.966e-04, eta: 7:16:37, time: 0.662, data_time: 0.018, memory: 4245, loss_cls: 0.4118, loss_bbox: 0.3527, loss: 0.7645
2022-11-03 00:29:28,818 - mmdet - INFO - Epoch [2][400/3665] lr: 1.966e-04, eta: 7:15:14, time: 0.552, data_time: 0.020, memory: 4245, loss_cls: 0.4036, loss_bbox: 0.3500, loss: 0.7537
2022-11-03 00:30:01,862 - mmdet - INFO - Epoch [2][450/3665] lr: 1.966e-04, eta: 7:14:44, time: 0.661, data_time: 0.017, memory: 4245, loss_cls: 0.4051, loss_bbox: 0.3496, loss: 0.7547
2022-11-03 00:30:34,718 - mmdet - INFO - Epoch [2][500/3665] lr: 1.966e-04, eta: 7:14:13, time: 0.657, data_time: 0.018, memory: 4245, loss_cls: 0.4003, loss_bbox: 0.3438, loss: 0.7440
2022-11-03 00:31:08,255 - mmdet - INFO - Epoch [2][550/3665] lr: 1.966e-04, eta: 7:13:48, time: 0.671, data_time: 0.017, memory: 4245, loss_cls: 0.4019, loss_bbox: 0.3522, loss: 0.7541
2022-11-03 00:31:40,927 - mmdet - INFO - Epoch [2][600/3665] lr: 1.966e-04, eta: 7:13:14, time: 0.653, data_time: 0.017, memory: 4245, loss_cls: 0.3967, loss_bbox: 0.3525, loss: 0.7493
2022-11-03 00:32:13,463 - mmdet - INFO - Epoch [2][650/3665] lr: 1.966e-04, eta: 7:12:40, time: 0.651, data_time: 0.018, memory: 4245, loss_cls: 0.3929, loss_bbox: 0.3450, loss: 0.7379
2022-11-03 00:32:46,320 - mmdet - INFO - Epoch [2][700/3665] lr: 1.966e-04, eta: 7:12:08, time: 0.657, data_time: 0.018, memory: 4245, loss_cls: 0.4036, loss_bbox: 0.3506, loss: 0.7541
2022-11-03 00:33:19,268 - mmdet - INFO - Epoch [2][750/3665] lr: 1.966e-04, eta: 7:11:38, time: 0.659, data_time: 0.017, memory: 4245, loss_cls: 0.4038, loss_bbox: 0.3483, loss: 0.7521
2022-11-03 00:33:52,631 - mmdet - INFO - Epoch [2][800/3665] lr: 1.966e-04, eta: 7:11:10, time: 0.667, data_time: 0.019, memory: 4245, loss_cls: 0.3936, loss_bbox: 0.3366, loss: 0.7302
2022-11-03 00:34:25,668 - mmdet - INFO - Epoch [2][850/3665] lr: 1.966e-04, eta: 7:10:40, time: 0.661, data_time: 0.020, memory: 4245, loss_cls: 0.4027, loss_bbox: 0.3402, loss: 0.7429
2022-11-03 00:34:58,080 - mmdet - INFO - Epoch [2][900/3665] lr: 1.966e-04, eta: 7:10:05, time: 0.648, data_time: 0.017, memory: 4245, loss_cls: 0.3967, loss_bbox: 0.3401, loss: 0.7368
2022-11-03 00:35:30,838 - mmdet - INFO - Epoch [2][950/3665] lr: 1.966e-04, eta: 7:09:32, time: 0.655, data_time: 0.018, memory: 4245, loss_cls: 0.3952, loss_bbox: 0.3453, loss: 0.7404
2022-11-03 00:36:03,773 - mmdet - INFO - Epoch [2][1000/3665] lr: 1.966e-04, eta: 7:09:01, time: 0.659, data_time: 0.019, memory: 4245, loss_cls: 0.3918, loss_bbox: 0.3418, loss: 0.7336
2022-11-03 00:36:37,353 - mmdet - INFO - Epoch [2][1050/3665] lr: 1.966e-04, eta: 7:08:36, time: 0.672, data_time: 0.020, memory: 4245, loss_cls: 0.3996, loss_bbox: 0.3442, loss: 0.7438
2022-11-03 00:37:10,552 - mmdet - INFO - Epoch [2][1100/3665] lr: 1.966e-04, eta: 7:08:06, time: 0.664, data_time: 0.019, memory: 4245, loss_cls: 0.3964, loss_bbox: 0.3435, loss: 0.7399
2022-11-03 00:37:43,878 - mmdet - INFO - Epoch [2][1150/3665] lr: 1.966e-04, eta: 7:07:38, time: 0.666, data_time: 0.019, memory: 4245, loss_cls: 0.3987, loss_bbox: 0.3441, loss: 0.7428
2022-11-03 00:38:11,690 - mmdet - INFO - Epoch [2][1200/3665] lr: 1.966e-04, eta: 7:06:26, time: 0.556, data_time: 0.019, memory: 4245, loss_cls: 0.3879, loss_bbox: 0.3421, loss: 0.7300
2022-11-03 00:38:44,705 - mmdet - INFO - Epoch [2][1250/3665] lr: 1.966e-04, eta: 7:05:56, time: 0.660, data_time: 0.017, memory: 4245, loss_cls: 0.3976, loss_bbox: 0.3464, loss: 0.7440
2022-11-03 00:39:17,955 - mmdet - INFO - Epoch [2][1300/3665] lr: 1.966e-04, eta: 7:05:27, time: 0.665, data_time: 0.019, memory: 4245, loss_cls: 0.3940, loss_bbox: 0.3414, loss: 0.7354
2022-11-03 00:39:50,794 - mmdet - INFO - Epoch [2][1350/3665] lr: 1.966e-04, eta: 7:04:55, time: 0.656, data_time: 0.018, memory: 4245, loss_cls: 0.3922, loss_bbox: 0.3487, loss: 0.7409
2022-11-03 00:40:23,704 - mmdet - INFO - Epoch [2][1400/3665] lr: 1.966e-04, eta: 7:04:24, time: 0.659, data_time: 0.019, memory: 4245, loss_cls: 0.3910, loss_bbox: 0.3424, loss: 0.7334
2022-11-03 00:40:56,791 - mmdet - INFO - Epoch [2][1450/3665] lr: 1.966e-04, eta: 7:03:54, time: 0.662, data_time: 0.020, memory: 4245, loss_cls: 0.3800, loss_bbox: 0.3369, loss: 0.7169
2022-11-03 00:41:29,313 - mmdet - INFO - Epoch [2][1500/3665] lr: 1.966e-04, eta: 7:03:20, time: 0.650, data_time: 0.018, memory: 4245, loss_cls: 0.3901, loss_bbox: 0.3369, loss: 0.7270
2022-11-03 00:42:02,157 - mmdet - INFO - Epoch [2][1550/3665] lr: 1.966e-04, eta: 7:02:48, time: 0.657, data_time: 0.018, memory: 4245, loss_cls: 0.3896, loss_bbox: 0.3466, loss: 0.7363
2022-11-03 00:42:35,376 - mmdet - INFO - Epoch [2][1600/3665] lr: 1.966e-04, eta: 7:02:19, time: 0.664, data_time: 0.020, memory: 4245, loss_cls: 0.3778, loss_bbox: 0.3403, loss: 0.7181
2022-11-03 00:43:09,210 - mmdet - INFO - Epoch [2][1650/3665] lr: 1.966e-04, eta: 7:01:54, time: 0.677, data_time: 0.020, memory: 4245, loss_cls: 0.3826, loss_bbox: 0.3339, loss: 0.7165
2022-11-03 00:43:42,199 - mmdet - INFO - Epoch [2][1700/3665] lr: 1.966e-04, eta: 7:01:23, time: 0.660, data_time: 0.021, memory: 4245, loss_cls: 0.3808, loss_bbox: 0.3356, loss: 0.7164
2022-11-03 00:44:15,201 - mmdet - INFO - Epoch [2][1750/3665] lr: 1.966e-04, eta: 7:00:53, time: 0.660, data_time: 0.019, memory: 4245, loss_cls: 0.3850, loss_bbox: 0.3430, loss: 0.7280
2022-11-03 00:44:48,174 - mmdet - INFO - Epoch [2][1800/3665] lr: 1.966e-04, eta: 7:00:22, time: 0.660, data_time: 0.017, memory: 4245, loss_cls: 0.3800, loss_bbox: 0.3374, loss: 0.7174
2022-11-03 00:45:20,617 - mmdet - INFO - Epoch [2][1850/3665] lr: 1.966e-04, eta: 6:59:47, time: 0.649, data_time: 0.019, memory: 4245, loss_cls: 0.3844, loss_bbox: 0.3360, loss: 0.7203
2022-11-03 00:45:53,896 - mmdet - INFO - Epoch [2][1900/3665] lr: 1.966e-04, eta: 6:59:18, time: 0.666, data_time: 0.018, memory: 4245, loss_cls: 0.3998, loss_bbox: 0.3431, loss: 0.7429
2022-11-03 00:46:27,510 - mmdet - INFO - Epoch [2][1950/3665] lr: 1.966e-04, eta: 6:58:51, time: 0.672, data_time: 0.018, memory: 4245, loss_cls: 0.3805, loss_bbox: 0.3332, loss: 0.7137