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20220227_224600.log
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2022-02-27 22:46:00,017 - mmseg - INFO - Multi-processing start method is `None`
2022-02-27 22:46:00,017 - mmseg - INFO - OpenCV num_threads is `<built-in function getNumThreads>
2022-02-27 22:46:00,017 - mmseg - INFO - OMP num threads is 1
2022-02-27 22:46:00,056 - mmseg - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.12 (default, Oct 12 2021, 13:49:34) [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: Build cuda_11.1.TC455_06.29069683_0
GCC: gcc (GCC) 9.3.1 20200408 (Red Hat 9.3.1-2)
PyTorch: 1.10.2
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
- 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=1.10.2, 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: 0.11.3
OpenCV: 4.5.5
MMCV: 1.4.6
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.1
MMSegmentation: 0.21.1+c0442f1
------------------------------------------------------------
2022-02-27 22:46:00,056 - mmseg - INFO - Distributed training: True
2022-02-27 22:46:00,286 - mmseg - INFO - Config:
custom_imports = dict(
imports=['mmseg.models.backbones.resnet', 'mmseg.models.necks.sf_neck'],
allow_failed_imports=False)
norm_cfg = dict(type='SyncBN', requires_grad=True)
sampler = dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet18_v1c',
backbone=dict(
type='ResNetV1cc',
stem_channels=128,
depth=18,
dilations=(1, 1, 2, 4),
norm_cfg=dict(type='SyncBN', requires_grad=True)),
neck=dict(
type='SFNeck',
in_channels=[64, 128, 256, 512],
channels=128,
align_corners=True,
norm_cfg=dict(type='SyncBN', requires_grad=True)),
decode_head=dict(
type='FCNHead',
in_channels=512,
in_index=4,
channels=128,
num_convs=1,
kernel_size=3,
concat_input=False,
num_classes=19,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
align_corners=True),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
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='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='CityscapesDataset',
data_root='data/cityscapes/',
img_dir='leftImg8bit/train',
ann_dir='gtFine/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]),
val=dict(
type='CityscapesDataset',
data_root='data/cityscapes/',
img_dir='leftImg8bit/val',
ann_dir='gtFine/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
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='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CityscapesDataset',
data_root='data/cityscapes/',
img_dir='leftImg8bit/test',
ann_dir='gtFine/test',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
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='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
log_config = dict(
interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.0)
lr_config = dict(policy='poly', power=1.0, min_lr=0.0001, by_epoch=True)
runner = dict(type='EpochBasedRunner', max_iters=None, max_epochs=300)
checkpoint_config = dict(by_epoch=True, interval=30)
evaluation = dict(interval=30, metric='mIoU', pre_eval=True)
fp16 = dict()
work_dir = './work_dirs/sfnet_r18-d8_512x1024_50k_cityscapes'
gpu_ids = range(0, 8)
auto_resume = False
2022-02-27 22:46:07,344 - mmseg - INFO - Set random seed to 455188243, deterministic: False
2022-02-27 22:46:07,435 - mmseg - INFO - initialize ResNetV1cc with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://resnet18_v1c'}
2022-02-27 22:46:07,472 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
Name of parameter - Initialization information
backbone.stem.0.weight - torch.Size([64, 3, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.1.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.1.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.3.weight - torch.Size([64, 64, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.4.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.4.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.6.weight - torch.Size([128, 64, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.7.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.stem.7.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.layer1.0.conv1.weight - torch.Size([64, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.layer1.0.bn1.weight - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.0.bn1.bias - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.0.bn2.weight - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.0.bn2.bias - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.0.downsample.0.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.layer1.0.downsample.1.weight - torch.Size([64]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.layer1.0.downsample.1.bias - torch.Size([64]):
The value is the same before and after calling `init_weights` of EncoderDecoder
backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.1.bn1.weight - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.1.bn1.bias - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.1.bn2.weight - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer1.1.bn2.bias - torch.Size([64]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.bn1.weight - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.bn1.bias - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.bn2.weight - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.bn2.bias - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.downsample.1.weight - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.0.downsample.1.bias - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.1.bn1.weight - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.1.bn1.bias - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.1.bn2.weight - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer2.1.bn2.bias - torch.Size([128]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.bn1.weight - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.bn1.bias - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.bn2.weight - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.bn2.bias - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.downsample.1.weight - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.0.downsample.1.bias - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.1.bn1.weight - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.1.bn1.bias - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.1.bn2.weight - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer3.1.bn2.bias - torch.Size([256]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.bn1.weight - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.bn1.bias - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.bn2.weight - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.bn2.bias - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.downsample.1.weight - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.0.downsample.1.bias - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.1.bn1.weight - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.1.bn1.bias - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.1.bn2.weight - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
backbone.layer4.1.bn2.bias - torch.Size([512]):
PretrainedInit: load from open-mmlab://resnet18_v1c
neck.ppm.0.1.conv.weight - torch.Size([128, 512, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.1.1.conv.weight - torch.Size([128, 512, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.2.1.conv.weight - torch.Size([128, 512, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.3.1.conv.weight - torch.Size([128, 512, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.3.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.ppm.3.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.bottleneck.conv.weight - torch.Size([128, 1024, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.bottleneck.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.bottleneck.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.low_conv.conv.weight - torch.Size([128, 64, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.low_conv.conv.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.low_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.low_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.low_down_conv.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.high_down_conv.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.0.flow_conv.weight - torch.Size([2, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.low_conv.conv.weight - torch.Size([128, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.low_conv.conv.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.low_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.low_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.low_down_conv.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.high_down_conv.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.1.flow_conv.weight - torch.Size([2, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.low_conv.conv.weight - torch.Size([128, 256, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.low_conv.conv.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.low_conv.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.low_conv.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.low_down_conv.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.high_down_conv.weight - torch.Size([64, 128, 1, 1]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.fams.2.flow_conv.weight - torch.Size([2, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.0.conv.weight - torch.Size([128, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.1.conv.weight - torch.Size([128, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.1.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.1.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.2.conv.weight - torch.Size([128, 128, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.2.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
neck.convs.2.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
decode_head.conv_seg.weight - torch.Size([19, 128, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
decode_head.conv_seg.bias - torch.Size([19]):
NormalInit: mean=0, std=0.01, bias=0
decode_head.convs.0.conv.weight - torch.Size([128, 512, 3, 3]):
The value is the same before and after calling `init_weights` of EncoderDecoder
decode_head.convs.0.bn.weight - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
decode_head.convs.0.bn.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of EncoderDecoder
2022-02-27 22:46:07,474 - mmseg - INFO - EncoderDecoder(
(backbone): ResNetV1cc(
(stem): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(7): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): ResLayer(
(0): BasicBlock(
(conv1): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): SyncBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer2): ResLayer(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer3): ResLayer(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
(bn2): SyncBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(layer4): ResLayer(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
(bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
(bn2): SyncBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
)
init_cfg={'type': 'Pretrained', 'checkpoint': 'open-mmlab://resnet18_v1c'}
(neck): SFNeck(
(ppm): PPM(
(0): Sequential(
(0): AdaptiveAvgPool2d(output_size=1)
(1): ConvModule(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Sequential(
(0): AdaptiveAvgPool2d(output_size=2)
(1): ConvModule(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): Sequential(
(0): AdaptiveAvgPool2d(output_size=3)
(1): ConvModule(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): Sequential(
(0): AdaptiveAvgPool2d(output_size=6)
(1): ConvModule(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(bottleneck): ConvModule(
(conv): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(fams): ModuleList(
(0): FAM(
(low_conv): ConvModule(
(conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1))
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU()
)
(low_down_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(high_down_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(flow_conv): Conv2d(128, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(1): FAM(
(low_conv): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU()
)
(low_down_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(high_down_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(flow_conv): Conv2d(128, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
(2): FAM(
(low_conv): ConvModule(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU()
)
(low_down_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(high_down_conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(flow_conv): Conv2d(128, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
)
(convs): ModuleList(
(0): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(1): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
(2): ConvModule(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
)
)
(decode_head): FCNHead(
input_transform=None, ignore_index=255, align_corners=True
(loss_decode): CrossEntropyLoss()
(conv_seg): Conv2d(128, 19, kernel_size=(1, 1), stride=(1, 1))
(dropout): Dropout2d(p=0.1, inplace=False)
(convs): Sequential(
(0): ConvModule(
(conv): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): SyncBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(activate): ReLU(inplace=True)
)
)
)
init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}}
)
2022-02-27 22:46:07,526 - mmseg - INFO - Loaded 2975 images
2022-02-27 22:46:07,884 - mmseg - INFO - Loaded 500 images
2022-02-27 22:46:07,885 - mmseg - INFO - Start running, host: root@vm-0-12-tlinux, work_dir: /mnt/jyang/4_lab/mmsegmentation/work_dirs/sfnet_r18-d8_512x1024_50k_cityscapes
2022-02-27 22:46:07,885 - mmseg - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) PolyLrUpdaterHook
(ABOVE_NORMAL) Fp16OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) PolyLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) PolyLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) Fp16OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) DistEvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_epoch:
(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
--------------------
2022-02-27 22:46:07,885 - mmseg - INFO - workflow: [('train', 1)], max: 300 epochs
2022-02-27 22:46:07,886 - mmseg - INFO - Checkpoints will be saved to /mnt/jyang/4_lab/mmsegmentation/work_dirs/sfnet_r18-d8_512x1024_50k_cityscapes by HardDiskBackend.
2022-02-27 22:46:31,734 - mmseg - INFO - Iter [50/55800] lr: 1.000e-02, eta: 7:23:05, time: 0.477, data_time: 0.178, memory: 9112, decode.loss_ce: 1.8166, decode.acc_seg: 41.2486, loss: 1.8166
2022-02-27 22:46:39,597 - mmseg - INFO - Iter [100/55800] lr: 1.000e-02, eta: 4:54:19, time: 0.157, data_time: 0.003, memory: 9112, decode.loss_ce: 1.2450, decode.acc_seg: 59.3965, loss: 1.2450
2022-02-27 22:46:47,263 - mmseg - INFO - Iter [150/55800] lr: 1.000e-02, eta: 4:03:27, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 1.0710, decode.acc_seg: 65.0872, loss: 1.0710
2022-02-27 22:46:57,087 - mmseg - INFO - Iter [200/55800] lr: 9.967e-03, eta: 4:16:50, time: 0.321, data_time: 0.166, memory: 9112, decode.loss_ce: 1.0438, decode.acc_seg: 64.7686, loss: 1.0438
2022-02-27 22:47:04,862 - mmseg - INFO - Iter [250/55800] lr: 9.967e-03, eta: 3:54:05, time: 0.156, data_time: 0.002, memory: 9112, decode.loss_ce: 0.9714, decode.acc_seg: 67.3863, loss: 0.9714
2022-02-27 22:47:12,416 - mmseg - INFO - Iter [300/55800] lr: 9.967e-03, eta: 3:38:09, time: 0.151, data_time: 0.002, memory: 9112, decode.loss_ce: 0.9082, decode.acc_seg: 70.0973, loss: 0.9082
2022-02-27 22:47:20,019 - mmseg - INFO - Iter [350/55800] lr: 9.967e-03, eta: 3:26:55, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.8374, decode.acc_seg: 71.9009, loss: 0.8374
2022-02-27 22:47:29,964 - mmseg - INFO - Iter [400/55800] lr: 9.934e-03, eta: 3:27:54, time: 0.234, data_time: 0.123, memory: 9112, decode.loss_ce: 0.8445, decode.acc_seg: 71.6778, loss: 0.8445
2022-02-27 22:47:37,777 - mmseg - INFO - Iter [450/55800] lr: 9.934e-03, eta: 3:20:39, time: 0.156, data_time: 0.048, memory: 9112, decode.loss_ce: 0.7470, decode.acc_seg: 74.8154, loss: 0.7470
2022-02-27 22:47:45,357 - mmseg - INFO - Iter [500/55800] lr: 9.934e-03, eta: 3:14:24, time: 0.152, data_time: 0.007, memory: 9112, decode.loss_ce: 0.7275, decode.acc_seg: 75.6483, loss: 0.7275
2022-02-27 22:47:52,880 - mmseg - INFO - Iter [550/55800] lr: 9.934e-03, eta: 3:09:09, time: 0.150, data_time: 0.008, memory: 9112, decode.loss_ce: 0.7190, decode.acc_seg: 75.7440, loss: 0.7190
2022-02-27 22:48:02,640 - mmseg - INFO - Iter [600/55800] lr: 9.901e-03, eta: 3:09:02, time: 0.206, data_time: 0.059, memory: 9112, decode.loss_ce: 0.6972, decode.acc_seg: 76.3893, loss: 0.6972
2022-02-27 22:48:10,334 - mmseg - INFO - Iter [650/55800] lr: 9.901e-03, eta: 3:05:14, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.6831, decode.acc_seg: 76.4788, loss: 0.6831
2022-02-27 22:48:18,031 - mmseg - INFO - Iter [700/55800] lr: 9.901e-03, eta: 3:01:58, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.6224, decode.acc_seg: 78.7682, loss: 0.6224
2022-02-27 22:48:27,960 - mmseg - INFO - Iter [750/55800] lr: 9.868e-03, eta: 3:22:38, time: 0.539, data_time: 0.428, memory: 9112, decode.loss_ce: 0.6700, decode.acc_seg: 76.5194, loss: 0.6700
2022-02-27 22:48:35,645 - mmseg - INFO - Iter [800/55800] lr: 9.868e-03, eta: 3:18:36, time: 0.154, data_time: 0.008, memory: 9112, decode.loss_ce: 0.6524, decode.acc_seg: 77.9333, loss: 0.6524
2022-02-27 22:48:43,267 - mmseg - INFO - Iter [850/55800] lr: 9.868e-03, eta: 3:14:58, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.6310, decode.acc_seg: 78.7747, loss: 0.6310
2022-02-27 22:48:50,810 - mmseg - INFO - Iter [900/55800] lr: 9.868e-03, eta: 3:11:38, time: 0.151, data_time: 0.026, memory: 9112, decode.loss_ce: 0.5914, decode.acc_seg: 79.7040, loss: 0.5914
2022-02-27 22:49:00,717 - mmseg - INFO - Iter [950/55800] lr: 9.835e-03, eta: 3:14:18, time: 0.269, data_time: 0.151, memory: 9112, decode.loss_ce: 0.5854, decode.acc_seg: 79.7347, loss: 0.5854
2022-02-27 22:49:08,185 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 22:49:08,185 - mmseg - INFO - Iter [1000/55800] lr: 9.835e-03, eta: 3:11:14, time: 0.149, data_time: 0.042, memory: 9112, decode.loss_ce: 0.5724, decode.acc_seg: 80.2485, loss: 0.5724
2022-02-27 22:49:15,929 - mmseg - INFO - Iter [1050/55800] lr: 9.835e-03, eta: 3:08:42, time: 0.155, data_time: 0.006, memory: 9112, decode.loss_ce: 0.5807, decode.acc_seg: 80.1137, loss: 0.5807
2022-02-27 22:49:23,616 - mmseg - INFO - Iter [1100/55800] lr: 9.835e-03, eta: 3:06:19, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.5566, decode.acc_seg: 80.6151, loss: 0.5566
2022-02-27 22:49:33,394 - mmseg - INFO - Iter [1150/55800] lr: 9.802e-03, eta: 3:06:48, time: 0.221, data_time: 0.076, memory: 9112, decode.loss_ce: 0.5310, decode.acc_seg: 81.2584, loss: 0.5310
2022-02-27 22:49:41,063 - mmseg - INFO - Iter [1200/55800] lr: 9.802e-03, eta: 3:04:40, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.5289, decode.acc_seg: 81.5645, loss: 0.5289
2022-02-27 22:49:48,683 - mmseg - INFO - Iter [1250/55800] lr: 9.802e-03, eta: 3:02:39, time: 0.152, data_time: 0.003, memory: 9112, decode.loss_ce: 0.5483, decode.acc_seg: 80.6640, loss: 0.5483
2022-02-27 22:49:56,284 - mmseg - INFO - Iter [1300/55800] lr: 9.802e-03, eta: 3:00:47, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4930, decode.acc_seg: 82.7060, loss: 0.4930
2022-02-27 22:50:06,098 - mmseg - INFO - Iter [1350/55800] lr: 9.769e-03, eta: 3:00:39, time: 0.200, data_time: 0.055, memory: 9112, decode.loss_ce: 0.5224, decode.acc_seg: 81.4544, loss: 0.5224
2022-02-27 22:50:13,774 - mmseg - INFO - Iter [1400/55800] lr: 9.769e-03, eta: 2:59:00, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.5054, decode.acc_seg: 82.2175, loss: 0.5054
2022-02-27 22:50:21,586 - mmseg - INFO - Iter [1450/55800] lr: 9.769e-03, eta: 2:57:34, time: 0.156, data_time: 0.010, memory: 9112, decode.loss_ce: 0.4810, decode.acc_seg: 82.9584, loss: 0.4810
2022-02-27 22:50:31,530 - mmseg - INFO - Iter [1500/55800] lr: 9.736e-03, eta: 3:02:06, time: 0.352, data_time: 0.227, memory: 9112, decode.loss_ce: 0.5291, decode.acc_seg: 81.2433, loss: 0.5291
2022-02-27 22:50:39,179 - mmseg - INFO - Iter [1550/55800] lr: 9.736e-03, eta: 3:00:32, time: 0.153, data_time: 0.004, memory: 9112, decode.loss_ce: 0.4882, decode.acc_seg: 83.0983, loss: 0.4882
2022-02-27 22:50:46,874 - mmseg - INFO - Iter [1600/55800] lr: 9.736e-03, eta: 2:59:04, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4913, decode.acc_seg: 82.6136, loss: 0.4913
2022-02-27 22:50:54,500 - mmseg - INFO - Iter [1650/55800] lr: 9.736e-03, eta: 2:57:39, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4807, decode.acc_seg: 83.0475, loss: 0.4807
2022-02-27 22:51:04,421 - mmseg - INFO - Iter [1700/55800] lr: 9.703e-03, eta: 2:58:45, time: 0.244, data_time: 0.099, memory: 9112, decode.loss_ce: 0.4697, decode.acc_seg: 82.5275, loss: 0.4697
2022-02-27 22:51:12,356 - mmseg - INFO - Iter [1750/55800] lr: 9.703e-03, eta: 2:57:34, time: 0.159, data_time: 0.016, memory: 9112, decode.loss_ce: 0.4665, decode.acc_seg: 82.9084, loss: 0.4665
2022-02-27 22:51:20,148 - mmseg - INFO - Iter [1800/55800] lr: 9.703e-03, eta: 2:56:22, time: 0.156, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4712, decode.acc_seg: 83.2047, loss: 0.4712
2022-02-27 22:51:27,780 - mmseg - INFO - Iter [1850/55800] lr: 9.703e-03, eta: 2:55:09, time: 0.153, data_time: 0.003, memory: 9112, decode.loss_ce: 0.4612, decode.acc_seg: 83.2735, loss: 0.4612
2022-02-27 22:51:37,535 - mmseg - INFO - Iter [1900/55800] lr: 9.670e-03, eta: 2:55:18, time: 0.208, data_time: 0.060, memory: 9112, decode.loss_ce: 0.4379, decode.acc_seg: 84.1820, loss: 0.4379
2022-02-27 22:51:45,071 - mmseg - INFO - Iter [1950/55800] lr: 9.670e-03, eta: 2:54:07, time: 0.151, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4597, decode.acc_seg: 83.3862, loss: 0.4597
2022-02-27 22:51:52,570 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 22:51:52,570 - mmseg - INFO - Iter [2000/55800] lr: 9.670e-03, eta: 2:52:58, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4519, decode.acc_seg: 84.1401, loss: 0.4519
2022-02-27 22:52:02,489 - mmseg - INFO - Iter [2050/55800] lr: 9.637e-03, eta: 3:04:32, time: 0.729, data_time: 0.616, memory: 9112, decode.loss_ce: 0.4207, decode.acc_seg: 84.2000, loss: 0.4207
2022-02-27 22:52:10,076 - mmseg - INFO - Iter [2100/55800] lr: 9.637e-03, eta: 3:03:13, time: 0.152, data_time: 0.005, memory: 9112, decode.loss_ce: 0.4226, decode.acc_seg: 84.6410, loss: 0.4226
2022-02-27 22:52:17,692 - mmseg - INFO - Iter [2150/55800] lr: 9.637e-03, eta: 3:01:57, time: 0.152, data_time: 0.029, memory: 9112, decode.loss_ce: 0.4204, decode.acc_seg: 84.5930, loss: 0.4204
2022-02-27 22:52:25,363 - mmseg - INFO - Iter [2200/55800] lr: 9.637e-03, eta: 3:00:46, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4127, decode.acc_seg: 84.9829, loss: 0.4127
2022-02-27 22:52:35,177 - mmseg - INFO - Iter [2250/55800] lr: 9.604e-03, eta: 3:02:07, time: 0.279, data_time: 0.150, memory: 9112, decode.loss_ce: 0.3901, decode.acc_seg: 85.6775, loss: 0.3901
2022-02-27 22:52:42,798 - mmseg - INFO - Iter [2300/55800] lr: 9.604e-03, eta: 3:00:56, time: 0.152, data_time: 0.015, memory: 9112, decode.loss_ce: 0.4386, decode.acc_seg: 83.9233, loss: 0.4386
2022-02-27 22:52:50,379 - mmseg - INFO - Iter [2350/55800] lr: 9.604e-03, eta: 2:59:48, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4317, decode.acc_seg: 84.5697, loss: 0.4317
2022-02-27 22:52:57,990 - mmseg - INFO - Iter [2400/55800] lr: 9.604e-03, eta: 2:58:42, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4091, decode.acc_seg: 84.7353, loss: 0.4091
2022-02-27 22:53:07,939 - mmseg - INFO - Iter [2450/55800] lr: 9.571e-03, eta: 2:59:03, time: 0.229, data_time: 0.109, memory: 9112, decode.loss_ce: 0.4022, decode.acc_seg: 85.0842, loss: 0.4022
2022-02-27 22:53:15,531 - mmseg - INFO - Iter [2500/55800] lr: 9.571e-03, eta: 2:58:00, time: 0.152, data_time: 0.023, memory: 9112, decode.loss_ce: 0.3897, decode.acc_seg: 85.5207, loss: 0.3897
2022-02-27 22:53:23,228 - mmseg - INFO - Iter [2550/55800] lr: 9.571e-03, eta: 2:57:01, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3952, decode.acc_seg: 85.5751, loss: 0.3952
2022-02-27 22:53:30,839 - mmseg - INFO - Iter [2600/55800] lr: 9.571e-03, eta: 2:56:03, time: 0.152, data_time: 0.004, memory: 9112, decode.loss_ce: 0.3940, decode.acc_seg: 85.3744, loss: 0.3940
2022-02-27 22:53:40,699 - mmseg - INFO - Iter [2650/55800] lr: 9.538e-03, eta: 2:55:58, time: 0.204, data_time: 0.061, memory: 9112, decode.loss_ce: 0.3891, decode.acc_seg: 85.6800, loss: 0.3891
2022-02-27 22:53:48,201 - mmseg - INFO - Iter [2700/55800] lr: 9.538e-03, eta: 2:55:01, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3991, decode.acc_seg: 85.2101, loss: 0.3991
2022-02-27 22:53:55,826 - mmseg - INFO - Iter [2750/55800] lr: 9.538e-03, eta: 2:54:07, time: 0.152, data_time: 0.001, memory: 9112, decode.loss_ce: 0.4135, decode.acc_seg: 84.7266, loss: 0.4135
2022-02-27 22:54:05,784 - mmseg - INFO - Iter [2800/55800] lr: 9.505e-03, eta: 2:57:00, time: 0.391, data_time: 0.233, memory: 9112, decode.loss_ce: 0.4091, decode.acc_seg: 84.5408, loss: 0.4091
2022-02-27 22:54:13,431 - mmseg - INFO - Iter [2850/55800] lr: 9.505e-03, eta: 2:56:06, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3867, decode.acc_seg: 85.7593, loss: 0.3867
2022-02-27 22:54:21,043 - mmseg - INFO - Iter [2900/55800] lr: 9.505e-03, eta: 2:55:13, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.4087, decode.acc_seg: 84.9767, loss: 0.4087
2022-02-27 22:54:28,565 - mmseg - INFO - Iter [2950/55800] lr: 9.505e-03, eta: 2:54:20, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3926, decode.acc_seg: 85.4821, loss: 0.3926
2022-02-27 22:54:38,387 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 22:54:38,387 - mmseg - INFO - Iter [3000/55800] lr: 9.472e-03, eta: 2:54:54, time: 0.248, data_time: 0.099, memory: 9112, decode.loss_ce: 0.3629, decode.acc_seg: 86.2238, loss: 0.3629
2022-02-27 22:54:45,993 - mmseg - INFO - Iter [3050/55800] lr: 9.472e-03, eta: 2:54:04, time: 0.152, data_time: 0.003, memory: 9112, decode.loss_ce: 0.3715, decode.acc_seg: 86.2061, loss: 0.3715
2022-02-27 22:54:53,532 - mmseg - INFO - Iter [3100/55800] lr: 9.472e-03, eta: 2:53:13, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3922, decode.acc_seg: 85.6644, loss: 0.3922
2022-02-27 22:55:01,262 - mmseg - INFO - Iter [3150/55800] lr: 9.472e-03, eta: 2:52:28, time: 0.155, data_time: 0.003, memory: 9112, decode.loss_ce: 0.3634, decode.acc_seg: 86.4812, loss: 0.3634
2022-02-27 22:55:11,055 - mmseg - INFO - Iter [3200/55800] lr: 9.439e-03, eta: 2:52:33, time: 0.214, data_time: 0.062, memory: 9112, decode.loss_ce: 0.3696, decode.acc_seg: 85.9496, loss: 0.3696
2022-02-27 22:55:18,491 - mmseg - INFO - Iter [3250/55800] lr: 9.439e-03, eta: 2:51:44, time: 0.149, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3606, decode.acc_seg: 86.4150, loss: 0.3606
2022-02-27 22:55:26,056 - mmseg - INFO - Iter [3300/55800] lr: 9.439e-03, eta: 2:50:58, time: 0.151, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3808, decode.acc_seg: 86.0093, loss: 0.3808
2022-02-27 22:55:35,849 - mmseg - INFO - Iter [3350/55800] lr: 9.406e-03, eta: 3:05:17, time: 1.305, data_time: 1.189, memory: 9112, decode.loss_ce: 0.4461, decode.acc_seg: 83.4333, loss: 0.4461
2022-02-27 22:55:43,469 - mmseg - INFO - Iter [3400/55800] lr: 9.406e-03, eta: 3:04:21, time: 0.152, data_time: 0.012, memory: 9112, decode.loss_ce: 0.3499, decode.acc_seg: 87.0628, loss: 0.3499
2022-02-27 22:55:51,051 - mmseg - INFO - Iter [3450/55800] lr: 9.406e-03, eta: 3:03:25, time: 0.151, data_time: 0.008, memory: 9112, decode.loss_ce: 0.3632, decode.acc_seg: 86.4913, loss: 0.3632
2022-02-27 22:55:58,573 - mmseg - INFO - Iter [3500/55800] lr: 9.406e-03, eta: 3:02:30, time: 0.151, data_time: 0.007, memory: 9112, decode.loss_ce: 0.3659, decode.acc_seg: 86.6140, loss: 0.3659
2022-02-27 22:56:08,465 - mmseg - INFO - Iter [3550/55800] lr: 9.373e-03, eta: 3:03:29, time: 0.305, data_time: 0.148, memory: 9112, decode.loss_ce: 0.3367, decode.acc_seg: 87.2329, loss: 0.3367
2022-02-27 22:56:16,169 - mmseg - INFO - Iter [3600/55800] lr: 9.373e-03, eta: 3:02:38, time: 0.154, data_time: 0.017, memory: 9112, decode.loss_ce: 0.3450, decode.acc_seg: 86.9361, loss: 0.3450
2022-02-27 22:56:23,754 - mmseg - INFO - Iter [3650/55800] lr: 9.373e-03, eta: 3:01:46, time: 0.151, data_time: 0.043, memory: 9112, decode.loss_ce: 0.3717, decode.acc_seg: 86.1649, loss: 0.3717
2022-02-27 22:56:31,264 - mmseg - INFO - Iter [3700/55800] lr: 9.373e-03, eta: 3:00:54, time: 0.151, data_time: 0.048, memory: 9112, decode.loss_ce: 0.3499, decode.acc_seg: 87.0242, loss: 0.3499
2022-02-27 22:56:41,132 - mmseg - INFO - Iter [3750/55800] lr: 9.340e-03, eta: 3:01:00, time: 0.231, data_time: 0.094, memory: 9112, decode.loss_ce: 0.3376, decode.acc_seg: 87.1852, loss: 0.3376
2022-02-27 22:56:48,968 - mmseg - INFO - Iter [3800/55800] lr: 9.340e-03, eta: 3:00:14, time: 0.157, data_time: 0.003, memory: 9112, decode.loss_ce: 0.3419, decode.acc_seg: 87.3538, loss: 0.3419
2022-02-27 22:56:56,530 - mmseg - INFO - Iter [3850/55800] lr: 9.340e-03, eta: 2:59:25, time: 0.151, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3555, decode.acc_seg: 86.8108, loss: 0.3555
2022-02-27 22:57:04,101 - mmseg - INFO - Iter [3900/55800] lr: 9.340e-03, eta: 2:58:38, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3322, decode.acc_seg: 87.5273, loss: 0.3322
2022-02-27 22:57:13,898 - mmseg - INFO - Iter [3950/55800] lr: 9.307e-03, eta: 2:58:27, time: 0.205, data_time: 0.057, memory: 9112, decode.loss_ce: 0.3361, decode.acc_seg: 87.3650, loss: 0.3361
2022-02-27 22:57:21,612 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 22:57:21,612 - mmseg - INFO - Iter [4000/55800] lr: 9.307e-03, eta: 2:57:42, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3325, decode.acc_seg: 87.4137, loss: 0.3325
2022-02-27 22:57:29,481 - mmseg - INFO - Iter [4050/55800] lr: 9.307e-03, eta: 2:57:01, time: 0.157, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3266, decode.acc_seg: 87.8036, loss: 0.3266
2022-02-27 22:57:39,282 - mmseg - INFO - Iter [4100/55800] lr: 9.274e-03, eta: 2:59:21, time: 0.443, data_time: 0.322, memory: 9112, decode.loss_ce: 0.3325, decode.acc_seg: 87.0961, loss: 0.3325
2022-02-27 22:57:46,948 - mmseg - INFO - Iter [4150/55800] lr: 9.274e-03, eta: 2:58:37, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3295, decode.acc_seg: 87.6349, loss: 0.3295
2022-02-27 22:57:54,731 - mmseg - INFO - Iter [4200/55800] lr: 9.274e-03, eta: 2:57:54, time: 0.156, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3372, decode.acc_seg: 87.4547, loss: 0.3372
2022-02-27 22:58:02,223 - mmseg - INFO - Iter [4250/55800] lr: 9.274e-03, eta: 2:57:09, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3298, decode.acc_seg: 87.8067, loss: 0.3298
2022-02-27 22:58:12,060 - mmseg - INFO - Iter [4300/55800] lr: 9.241e-03, eta: 2:57:30, time: 0.258, data_time: 0.114, memory: 9112, decode.loss_ce: 0.3030, decode.acc_seg: 88.3734, loss: 0.3030
2022-02-27 22:58:19,637 - mmseg - INFO - Iter [4350/55800] lr: 9.241e-03, eta: 2:56:47, time: 0.152, data_time: 0.006, memory: 9112, decode.loss_ce: 0.3273, decode.acc_seg: 87.5693, loss: 0.3273
2022-02-27 22:58:27,101 - mmseg - INFO - Iter [4400/55800] lr: 9.241e-03, eta: 2:56:03, time: 0.149, data_time: 0.006, memory: 9112, decode.loss_ce: 0.3377, decode.acc_seg: 87.2617, loss: 0.3377
2022-02-27 22:58:34,756 - mmseg - INFO - Iter [4450/55800] lr: 9.241e-03, eta: 2:55:23, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3344, decode.acc_seg: 87.3209, loss: 0.3344
2022-02-27 22:58:44,546 - mmseg - INFO - Iter [4500/55800] lr: 9.208e-03, eta: 2:55:20, time: 0.218, data_time: 0.075, memory: 9112, decode.loss_ce: 0.3134, decode.acc_seg: 88.0814, loss: 0.3134
2022-02-27 22:58:51,993 - mmseg - INFO - Iter [4550/55800] lr: 9.208e-03, eta: 2:54:38, time: 0.149, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3243, decode.acc_seg: 87.6641, loss: 0.3243
2022-02-27 22:58:59,527 - mmseg - INFO - Iter [4600/55800] lr: 9.208e-03, eta: 2:53:58, time: 0.151, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3088, decode.acc_seg: 88.3094, loss: 0.3088
2022-02-27 22:59:06,962 - mmseg - INFO - Iter [4650/55800] lr: 9.208e-03, eta: 2:53:17, time: 0.149, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3038, decode.acc_seg: 88.3107, loss: 0.3038
2022-02-27 22:59:16,970 - mmseg - INFO - Iter [4700/55800] lr: 9.175e-03, eta: 2:53:05, time: 0.200, data_time: 0.075, memory: 9112, decode.loss_ce: 0.3100, decode.acc_seg: 88.1567, loss: 0.3100
2022-02-27 22:59:24,563 - mmseg - INFO - Iter [4750/55800] lr: 9.175e-03, eta: 2:52:28, time: 0.152, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3266, decode.acc_seg: 87.6729, loss: 0.3266
2022-02-27 22:59:32,120 - mmseg - INFO - Iter [4800/55800] lr: 9.175e-03, eta: 2:51:50, time: 0.151, data_time: 0.002, memory: 9112, decode.loss_ce: 0.2959, decode.acc_seg: 88.8069, loss: 0.2959
2022-02-27 22:59:41,948 - mmseg - INFO - Iter [4850/55800] lr: 9.142e-03, eta: 2:52:42, time: 0.320, data_time: 0.170, memory: 9112, decode.loss_ce: 0.3035, decode.acc_seg: 88.0536, loss: 0.3035
2022-02-27 22:59:49,381 - mmseg - INFO - Iter [4900/55800] lr: 9.142e-03, eta: 2:52:03, time: 0.148, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3040, decode.acc_seg: 88.4259, loss: 0.3040
2022-02-27 22:59:56,947 - mmseg - INFO - Iter [4950/55800] lr: 9.142e-03, eta: 2:51:26, time: 0.152, data_time: 0.003, memory: 9112, decode.loss_ce: 0.3183, decode.acc_seg: 87.9387, loss: 0.3183
2022-02-27 23:00:04,667 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 23:00:04,667 - mmseg - INFO - Iter [5000/55800] lr: 9.142e-03, eta: 2:50:52, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.2971, decode.acc_seg: 88.7702, loss: 0.2971
2022-02-27 23:00:14,556 - mmseg - INFO - Iter [5050/55800] lr: 9.109e-03, eta: 2:50:58, time: 0.234, data_time: 0.091, memory: 9112, decode.loss_ce: 0.3039, decode.acc_seg: 88.4278, loss: 0.3039
2022-02-27 23:00:22,210 - mmseg - INFO - Iter [5100/55800] lr: 9.109e-03, eta: 2:50:24, time: 0.153, data_time: 0.003, memory: 9112, decode.loss_ce: 0.3032, decode.acc_seg: 88.2400, loss: 0.3032
2022-02-27 23:00:29,855 - mmseg - INFO - Iter [5150/55800] lr: 9.109e-03, eta: 2:49:50, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3157, decode.acc_seg: 88.0021, loss: 0.3157
2022-02-27 23:00:37,330 - mmseg - INFO - Iter [5200/55800] lr: 9.109e-03, eta: 2:49:14, time: 0.149, data_time: 0.002, memory: 9112, decode.loss_ce: 0.2972, decode.acc_seg: 88.6889, loss: 0.2972
2022-02-27 23:00:47,365 - mmseg - INFO - Iter [5250/55800] lr: 9.076e-03, eta: 2:49:09, time: 0.211, data_time: 0.060, memory: 9112, decode.loss_ce: 0.3156, decode.acc_seg: 88.0083, loss: 0.3156
2022-02-27 23:00:55,063 - mmseg - INFO - Iter [5300/55800] lr: 9.076e-03, eta: 2:48:37, time: 0.154, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3069, decode.acc_seg: 88.4005, loss: 0.3069
2022-02-27 23:01:02,564 - mmseg - INFO - Iter [5350/55800] lr: 9.076e-03, eta: 2:48:03, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.2992, decode.acc_seg: 88.7950, loss: 0.2992
2022-02-27 23:01:12,412 - mmseg - INFO - Iter [5400/55800] lr: 9.043e-03, eta: 2:50:30, time: 0.536, data_time: 0.412, memory: 9112, decode.loss_ce: 0.3023, decode.acc_seg: 87.9809, loss: 0.3023
2022-02-27 23:01:20,085 - mmseg - INFO - Iter [5450/55800] lr: 9.043e-03, eta: 2:49:57, time: 0.154, data_time: 0.006, memory: 9112, decode.loss_ce: 0.3055, decode.acc_seg: 88.3884, loss: 0.3055
2022-02-27 23:01:27,762 - mmseg - INFO - Iter [5500/55800] lr: 9.043e-03, eta: 2:49:24, time: 0.153, data_time: 0.002, memory: 9112, decode.loss_ce: 0.3075, decode.acc_seg: 88.4018, loss: 0.3075
2022-02-27 23:01:35,231 - mmseg - INFO - Iter [5550/55800] lr: 9.043e-03, eta: 2:48:51, time: 0.150, data_time: 0.002, memory: 9112, decode.loss_ce: 0.2925, decode.acc_seg: 88.7604, loss: 0.2925
2022-02-27 23:01:39,806 - mmseg - INFO - Saving checkpoint at 30 epochs
2022-02-27 23:02:08,560 - mmseg - INFO - per class results:
2022-02-27 23:02:08,562 - mmseg - INFO -
+---------------+-------+-------+
| Class | IoU | Acc |
+---------------+-------+-------+
| road | 94.85 | 98.93 |
| sidewalk | 64.12 | 71.28 |
| building | 85.4 | 92.14 |
| wall | 18.57 | 20.91 |
| fence | 39.53 | 51.14 |
| pole | 42.0 | 54.0 |
| traffic light | 19.07 | 19.9 |
| traffic sign | 49.4 | 59.0 |
| vegetation | 86.88 | 95.44 |
| terrain | 47.96 | 62.24 |
| sky | 89.84 | 96.98 |
| person | 61.56 | 79.66 |
| rider | 18.92 | 20.32 |
| car | 86.87 | 96.23 |
| truck | 14.15 | 14.55 |
| bus | 41.81 | 44.91 |
| train | 37.18 | 57.56 |
| motorcycle | 10.91 | 12.37 |
| bicycle | 62.14 | 74.89 |
+---------------+-------+-------+
2022-02-27 23:02:08,562 - mmseg - INFO - Summary:
2022-02-27 23:02:08,562 - mmseg - INFO -
+-------+-------+-------+
| aAcc | mIoU | mAcc |
+-------+-------+-------+
| 91.58 | 51.11 | 59.08 |
+-------+-------+-------+
2022-02-27 23:02:08,563 - mmseg - INFO - Iter(val) [63] aAcc: 0.9158, mIoU: 0.5111, mAcc: 0.5908, IoU.road: 0.9485, IoU.sidewalk: 0.6412, IoU.building: 0.8540, IoU.wall: 0.1857, IoU.fence: 0.3953, IoU.pole: 0.4200, IoU.traffic light: 0.1907, IoU.traffic sign: 0.4940, IoU.vegetation: 0.8688, IoU.terrain: 0.4796, IoU.sky: 0.8984, IoU.person: 0.6156, IoU.rider: 0.1892, IoU.car: 0.8687, IoU.truck: 0.1415, IoU.bus: 0.4181, IoU.train: 0.3718, IoU.motorcycle: 0.1091, IoU.bicycle: 0.6214, Acc.road: 0.9893, Acc.sidewalk: 0.7128, Acc.building: 0.9214, Acc.wall: 0.2091, Acc.fence: 0.5114, Acc.pole: 0.5400, Acc.traffic light: 0.1990, Acc.traffic sign: 0.5900, Acc.vegetation: 0.9544, Acc.terrain: 0.6224, Acc.sky: 0.9698, Acc.person: 0.7966, Acc.rider: 0.2032, Acc.car: 0.9623, Acc.truck: 0.1455, Acc.bus: 0.4491, Acc.train: 0.5756, Acc.motorcycle: 0.1237, Acc.bicycle: 0.7489
2022-02-27 23:02:13,795 - mmseg - INFO - Iter [5600/55800] lr: 9.010e-03, eta: 2:49:07, time: 0.261, data_time: 0.130, memory: 33642, decode.loss_ce: 0.2954, decode.acc_seg: 88.7022, loss: 0.2954
2022-02-27 23:02:21,408 - mmseg - INFO - Iter [5650/55800] lr: 9.010e-03, eta: 2:48:35, time: 0.152, data_time: 0.006, memory: 33642, decode.loss_ce: 0.2980, decode.acc_seg: 88.4674, loss: 0.2980
2022-02-27 23:02:28,959 - mmseg - INFO - Iter [5700/55800] lr: 9.010e-03, eta: 2:48:03, time: 0.151, data_time: 0.045, memory: 33642, decode.loss_ce: 0.2978, decode.acc_seg: 88.8340, loss: 0.2978
2022-02-27 23:02:36,568 - mmseg - INFO - Iter [5750/55800] lr: 9.010e-03, eta: 2:47:31, time: 0.152, data_time: 0.041, memory: 33642, decode.loss_ce: 0.2980, decode.acc_seg: 88.6072, loss: 0.2980
2022-02-27 23:02:46,349 - mmseg - INFO - Iter [5800/55800] lr: 8.977e-03, eta: 2:47:30, time: 0.222, data_time: 0.109, memory: 33642, decode.loss_ce: 0.2926, decode.acc_seg: 88.4892, loss: 0.2926
2022-02-27 23:02:53,861 - mmseg - INFO - Iter [5850/55800] lr: 8.977e-03, eta: 2:46:58, time: 0.150, data_time: 0.006, memory: 33642, decode.loss_ce: 0.2873, decode.acc_seg: 89.2410, loss: 0.2873
2022-02-27 23:03:01,429 - mmseg - INFO - Iter [5900/55800] lr: 8.977e-03, eta: 2:46:28, time: 0.152, data_time: 0.026, memory: 33642, decode.loss_ce: 0.3000, decode.acc_seg: 88.5955, loss: 0.3000
2022-02-27 23:03:09,047 - mmseg - INFO - Iter [5950/55800] lr: 8.977e-03, eta: 2:45:58, time: 0.152, data_time: 0.026, memory: 33642, decode.loss_ce: 0.2828, decode.acc_seg: 89.2068, loss: 0.2828
2022-02-27 23:03:18,859 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 23:03:18,859 - mmseg - INFO - Iter [6000/55800] lr: 8.944e-03, eta: 2:45:48, time: 0.200, data_time: 0.062, memory: 33642, decode.loss_ce: 0.2933, decode.acc_seg: 88.8834, loss: 0.2933
2022-02-27 23:03:26,307 - mmseg - INFO - Iter [6050/55800] lr: 8.944e-03, eta: 2:45:17, time: 0.149, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2926, decode.acc_seg: 88.6116, loss: 0.2926
2022-02-27 23:03:33,916 - mmseg - INFO - Iter [6100/55800] lr: 8.944e-03, eta: 2:44:48, time: 0.152, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2760, decode.acc_seg: 89.4708, loss: 0.2760
2022-02-27 23:03:43,665 - mmseg - INFO - Iter [6150/55800] lr: 8.911e-03, eta: 2:45:37, time: 0.347, data_time: 0.229, memory: 33642, decode.loss_ce: 0.2703, decode.acc_seg: 89.8173, loss: 0.2703
2022-02-27 23:03:51,214 - mmseg - INFO - Iter [6200/55800] lr: 8.911e-03, eta: 2:45:08, time: 0.151, data_time: 0.004, memory: 33642, decode.loss_ce: 0.2780, decode.acc_seg: 89.3098, loss: 0.2780
2022-02-27 23:03:58,843 - mmseg - INFO - Iter [6250/55800] lr: 8.911e-03, eta: 2:44:39, time: 0.153, data_time: 0.005, memory: 33642, decode.loss_ce: 0.2949, decode.acc_seg: 88.7034, loss: 0.2949
2022-02-27 23:04:06,502 - mmseg - INFO - Iter [6300/55800] lr: 8.911e-03, eta: 2:44:11, time: 0.153, data_time: 0.011, memory: 33642, decode.loss_ce: 0.2890, decode.acc_seg: 88.9348, loss: 0.2890
2022-02-27 23:04:16,387 - mmseg - INFO - Iter [6350/55800] lr: 8.878e-03, eta: 2:44:18, time: 0.243, data_time: 0.122, memory: 33642, decode.loss_ce: 0.2917, decode.acc_seg: 88.8694, loss: 0.2917
2022-02-27 23:04:23,963 - mmseg - INFO - Iter [6400/55800] lr: 8.878e-03, eta: 2:43:49, time: 0.152, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2885, decode.acc_seg: 88.7922, loss: 0.2885
2022-02-27 23:04:31,656 - mmseg - INFO - Iter [6450/55800] lr: 8.878e-03, eta: 2:43:22, time: 0.154, data_time: 0.003, memory: 33642, decode.loss_ce: 0.2967, decode.acc_seg: 88.6690, loss: 0.2967
2022-02-27 23:04:39,153 - mmseg - INFO - Iter [6500/55800] lr: 8.878e-03, eta: 2:42:54, time: 0.150, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2759, decode.acc_seg: 89.4460, loss: 0.2759
2022-02-27 23:04:48,910 - mmseg - INFO - Iter [6550/55800] lr: 8.845e-03, eta: 2:42:48, time: 0.209, data_time: 0.060, memory: 33642, decode.loss_ce: 0.2734, decode.acc_seg: 89.4363, loss: 0.2734
2022-02-27 23:04:56,619 - mmseg - INFO - Iter [6600/55800] lr: 8.845e-03, eta: 2:42:22, time: 0.154, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2803, decode.acc_seg: 89.0107, loss: 0.2803
2022-02-27 23:05:04,314 - mmseg - INFO - Iter [6650/55800] lr: 8.845e-03, eta: 2:41:55, time: 0.154, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2766, decode.acc_seg: 89.5504, loss: 0.2766
2022-02-27 23:05:14,042 - mmseg - INFO - Iter [6700/55800] lr: 8.812e-03, eta: 2:45:00, time: 0.727, data_time: 0.592, memory: 33642, decode.loss_ce: 0.2694, decode.acc_seg: 90.0130, loss: 0.2694
2022-02-27 23:05:21,722 - mmseg - INFO - Iter [6750/55800] lr: 8.812e-03, eta: 2:44:32, time: 0.154, data_time: 0.035, memory: 33642, decode.loss_ce: 0.2745, decode.acc_seg: 89.4475, loss: 0.2745
2022-02-27 23:05:29,318 - mmseg - INFO - Iter [6800/55800] lr: 8.812e-03, eta: 2:44:04, time: 0.152, data_time: 0.033, memory: 33642, decode.loss_ce: 0.2796, decode.acc_seg: 89.3611, loss: 0.2796
2022-02-27 23:05:36,832 - mmseg - INFO - Iter [6850/55800] lr: 8.812e-03, eta: 2:43:36, time: 0.150, data_time: 0.005, memory: 33642, decode.loss_ce: 0.2765, decode.acc_seg: 89.3911, loss: 0.2765
2022-02-27 23:05:46,624 - mmseg - INFO - Iter [6900/55800] lr: 8.779e-03, eta: 2:43:55, time: 0.283, data_time: 0.144, memory: 33642, decode.loss_ce: 0.2574, decode.acc_seg: 89.9971, loss: 0.2574
2022-02-27 23:05:54,192 - mmseg - INFO - Iter [6950/55800] lr: 8.779e-03, eta: 2:43:28, time: 0.151, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2689, decode.acc_seg: 89.5929, loss: 0.2689
2022-02-27 23:06:01,881 - mmseg - INFO - Exp name: sfnet_r18-d8_512x1024_50k_cityscapes.py
2022-02-27 23:06:01,882 - mmseg - INFO - Iter [7000/55800] lr: 8.779e-03, eta: 2:43:01, time: 0.154, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2860, decode.acc_seg: 89.2335, loss: 0.2860
2022-02-27 23:06:09,421 - mmseg - INFO - Iter [7050/55800] lr: 8.779e-03, eta: 2:42:34, time: 0.151, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2606, decode.acc_seg: 89.8616, loss: 0.2606
2022-02-27 23:06:19,324 - mmseg - INFO - Iter [7100/55800] lr: 8.746e-03, eta: 2:42:34, time: 0.230, data_time: 0.072, memory: 33642, decode.loss_ce: 0.2900, decode.acc_seg: 88.9726, loss: 0.2900
2022-02-27 23:06:26,931 - mmseg - INFO - Iter [7150/55800] lr: 8.746e-03, eta: 2:42:08, time: 0.152, data_time: 0.003, memory: 33642, decode.loss_ce: 0.2711, decode.acc_seg: 89.4209, loss: 0.2711
2022-02-27 23:06:34,572 - mmseg - INFO - Iter [7200/55800] lr: 8.746e-03, eta: 2:41:42, time: 0.153, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2664, decode.acc_seg: 89.7821, loss: 0.2664
2022-02-27 23:06:42,259 - mmseg - INFO - Iter [7250/55800] lr: 8.746e-03, eta: 2:41:17, time: 0.154, data_time: 0.002, memory: 33642, decode.loss_ce: 0.2687, decode.acc_seg: 89.6084, loss: 0.2687
2022-02-27 23:06:52,174 - mmseg - INFO - Iter [7300/55800] lr: 8.713e-03, eta: 2:41:08, time: 0.205, data_time: 0.077, memory: 33642, decode.loss_ce: 0.2654, decode.acc_seg: 89.5904, loss: 0.2654