System environment:
sys.platform: win32
Python: 3.8.20 (default, Oct 3 2024, 15:19:54) [MSC v.1929 64 bit (AMD64)]
CUDA available: True
MUSA available: False
numpy_random_seed: 42
GPU 0: NVIDIA GeForce MX350
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1
NVCC: Cuda compilation tools, release 12.1, V12.1.66
MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.42.34435 版
GCC: n/a
PyTorch: 2.0.0
PyTorch compiling details: PyTorch built with:
- C++ Version: 199711
- MSVC 193431937
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
- OpenMP 2019
- LAPACK is enabled (usually provided by MKL)
- CPU capability usage: AVX2
- CUDA Runtime 11.8
- 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_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.7
- Magma 2.5.4
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=C:/cb/pytorch_1000000000000/work/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj /FS -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=OFF, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.15.0
OpenCV: 4.11.0
MMEngine: 0.10.7
Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 42
Distributed launcher: none
Distributed training: False
GPU number: 1
------------------------------------------------------------
06/11 16:05:36 - mmengine - INFO - Config:
crop_size = (
512,
512,
)
data_preprocessor = dict(
bgr_to_rgb=True,
mean=[
130.9550538547,
140.2221399179,
149.2311794435,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
118.7814609013,
110.3165588617,
105.461818473,
],
type='SegDataPreProcessor')
data_root = 'D:/3D/data/voc/taihedian/data_dataset_voc'
dataset_type = 'SPRACAVOCDataset'
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=2000, type='CheckpointHook'),
logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'),
param_scheduler=dict(type='ParamSchedulerHook'),
sampler_seed=dict(type='DistSamplerSeedHook'),
timer=dict(type='IterTimerHook'),
visualization=dict(type='SegVisualizationHook'))
default_scope = 'mmseg'
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
img_ratios = [
0.5,
0.75,
1.0,
1.25,
1.5,
1.75,
]
launcher = 'none'
lazy_import = True
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
model = dict(
auxiliary_head=dict(
align_corners=False,
channels=256,
concat_input=False,
dropout_ratio=0.1,
in_channels=1024,
in_index=2,
loss_decode=dict(
class_weight=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
],
loss_weight=0.4,
type='CrossEntropyLoss',
use_sigmoid=False),
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=15,
num_convs=1,
type='FCNHead'),
backbone=dict(
contract_dilation=True,
depth=50,
dilations=(
1,
1,
2,
4,
),
norm_cfg=dict(requires_grad=True, type='BN'),
norm_eval=False,
num_stages=4,
out_indices=(
0,
1,
2,
3,
),
strides=(
1,
2,
1,
1,
),
style='pytorch',
type='ResNetV1c'),
data_preprocessor=dict(
bgr_to_rgb=True,
mean=[
130.9550538547,
140.2221399179,
149.2311794435,
],
pad_val=0,
seg_pad_val=255,
size=(
512,
512,
),
std=[
118.7814609013,
110.3165588617,
105.461818473,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
c1_channels=48,
c1_in_channels=256,
channels=512,
dilations=(
1,
12,
24,
36,
),
dropout_ratio=0.1,
in_channels=2048,
in_index=3,
loss_decode=[
dict(
class_weight=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
],
loss_weight=1.0,
type='CrossEntropyLoss',
use_sigmoid=False),
dict(loss_weight=0.8, type='DiceLoss'),
],
norm_cfg=dict(requires_grad=True, type='BN'),
num_classes=15,
type='DepthwiseSeparableASPPHead'),
pretrained='open-mmlab://resnet50_v1c',
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
norm_cfg = dict(requires_grad=True, type='BN')
optim_wrapper = dict(
clip_grad=None,
optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005),
type='OptimWrapper')
optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
dict(
begin=0,
by_epoch=False,
end=20000,
eta_min=0.0001,
power=0.9,
type='PolyLR'),
]
randomness = dict(seed=42)
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='ImageSets/Segmentation/val.txt',
data_prefix=dict(
img_path='JPEGImages', seg_map_path='SegmentationClass'),
data_root='D:/3D/data/voc/taihedian/data_dataset_voc',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
512,
512,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
reduce_zero_label=False,
type='SPRACAVOCDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
512,
512,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
]
train_cfg = dict(max_iters=20000, type='IterBasedTrainLoop', val_interval=2000)
train_dataloader = dict(
batch_size=4,
dataset=dict(
ann_file='ImageSets/Segmentation/train.txt',
data_prefix=dict(
img_path='JPEGImages', seg_map_path='SegmentationClass'),
data_root='D:/3D/data/voc/taihedian/data_dataset_voc',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
512,
512,
),
type='RandomResize'),
dict(
cat_max_ratio=0.75, crop_size=(
512,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
],
reduce_zero_label=False,
type='SPRACAVOCDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
512,
512,
),
type='RandomResize'),
dict(cat_max_ratio=0.75, crop_size=(
512,
512,
), type='RandomCrop'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs'),
]
tta_model = dict(type='SegTTAModel')
tta_pipeline = [
dict(backend_args=None, type='LoadImageFromFile'),
dict(
transforms=[
[
dict(keep_ratio=True, scale_factor=0.5, type='Resize'),
dict(keep_ratio=True, scale_factor=0.75, type='Resize'),
dict(keep_ratio=True, scale_factor=1.0, type='Resize'),
dict(keep_ratio=True, scale_factor=1.25, type='Resize'),
dict(keep_ratio=True, scale_factor=1.5, type='Resize'),
dict(keep_ratio=True, scale_factor=1.75, type='Resize'),
],
[
dict(direction='horizontal', prob=0.0, type='RandomFlip'),
dict(direction='horizontal', prob=1.0, type='RandomFlip'),
],
[
dict(type='LoadAnnotations'),
],
[
dict(type='PackSegInputs'),
],
],
type='TestTimeAug'),
]
val_cfg = dict(type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='ImageSets/Segmentation/val.txt',
data_prefix=dict(
img_path='JPEGImages', seg_map_path='SegmentationClass'),
data_root='D:/3D/data/voc/taihedian/data_dataset_voc',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
512,
512,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
reduce_zero_label=False,
type='SPRACAVOCDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
iou_metrics=[
'mIoU',
], type='IoUMetric')
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = './work_dirs\\deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512_myvoc'
d:\ab\mmsegmentation\mmseg\models\backbones\resnet.py:431: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
d:\ab\mmsegmentation\mmseg\models\losses\cross_entropy_loss.py:251: UserWarning: Default ``avg_non_ignore`` is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set ``avg_non_ignore=True``.
warnings.warn(
06/11 16:05:41 - mmengine - INFO - Distributed training is not used, all SyncBatchNorm (SyncBN) layers in the model will be automatically reverted to BatchNormXd layers if they are used.
d:\ab\mmsegmentation\mmseg\engine\hooks\visualization_hook.py:60: UserWarning: The draw is False, it means that the hook for visualization will not take effect. The results will NOT be visualized or stored.
warnings.warn('The draw is False, it means that the '
06/11 16:05:41 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) RuntimeInfoHook
(BELOW_NORMAL) LoggerHook
--------------------
before_train:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(VERY_LOW ) CheckpointHook
--------------------
before_train_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(NORMAL ) DistSamplerSeedHook
--------------------
before_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
--------------------
after_train_iter:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_train_epoch:
(NORMAL ) IterTimerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
before_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_val_epoch:
(NORMAL ) IterTimerHook
--------------------
before_val_iter:
(NORMAL ) IterTimerHook
--------------------
after_val_iter:
(NORMAL ) IterTimerHook
(NORMAL ) SegVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_val_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
(LOW ) ParamSchedulerHook
(VERY_LOW ) CheckpointHook
--------------------
after_val:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_train:
(VERY_HIGH ) RuntimeInfoHook
(VERY_LOW ) CheckpointHook
--------------------
before_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
before_test_epoch:
(NORMAL ) IterTimerHook
--------------------
before_test_iter:
(NORMAL ) IterTimerHook
--------------------
after_test_iter:
(NORMAL ) IterTimerHook
(NORMAL ) SegVisualizationHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test_epoch:
(VERY_HIGH ) RuntimeInfoHook
(NORMAL ) IterTimerHook
(BELOW_NORMAL) LoggerHook
--------------------
after_test:
(VERY_HIGH ) RuntimeInfoHook
--------------------
after_run:
(BELOW_NORMAL) LoggerHook
--------------------
06/11 16:05:57 - mmengine - WARNING - The prefix is not set in metric class IoUMetric.
06/11 16:05:58 - mmengine - INFO - load model from: open-mmlab://resnet50_v1c
06/11 16:05:58 - mmengine - INFO - Loads checkpoint by openmmlab backend from path: open-mmlab://resnet50_v1c
06/11 16:05:58 - mmengine - WARNING - The model and loaded state dict do not mate dict do not match exactly
unexpected key in source state_dict: fc.weight, fc.bias
06/11 16:05:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBaunexpected key in source state_dict: fc.weight, fc.bias
06/11 16:05:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/11 16:05:58 - mmengine - INFO - Checkpoints will be saved to D:\AB\mmsegmentation\work_dirs\deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512_myvoc.
unexpected key in source state_dict: fc.weight, fc.bias
06/11 16:05:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/11 16:05:58 - mmengine - INFO - Checkpoints will be saved to D:\AB\mmsegmentaunexpected key in source state_dict: fc.weight, fc.bias
06/11 16:05:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/11 16:05:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBa06/11 16:05:58 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBa Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBa.html#file-io
06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBa06/11 16:05:58 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future.
06/11 16:05:58 - mmengine - INFO - Checkpoints will be saved to D:\AB\mmsegmenta06/11 16:06/11 16:05:58 - mmengine - INFO - Checkpoints will be saved to D:\AB\mmsegmentation\work_dirs\deeplabv3plus_r50-d8_4xb4-20k_voc12aug-512x512_myvoc.
这个是什么意思
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