text:
KeyError: 'mask'

I encountered issues when using cascade mask rcnn and training with VOC datasets
Traceback (most recent call last):
  File "./tools/train.py", line 133, in <module>
    main()
  File "./tools/train.py", line 129, in main
    runner.train()
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\runner\runner.py", line 1745, in train
    model = self.train_loop.run()  # type: ignore
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\runner\loops.py", line 96, in run
    self.run_epoch()
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\runner\loops.py", line 111, in run_epoch
    for idx, data_batch in enumerate(self.dataloader):
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\utils\data\dataloader.py", line 628, in __next__
    data = self._next_data()
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\utils\data\dataloader.py", line 1333, in _next_data
    return self._process_data(data)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\utils\data\dataloader.py", line 1359, in _process_data
    data.reraise()
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\_utils.py", line 543, in reraise
    raise exception
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\utils\data\_utils\worker.py", line 302, in _worker_loop
    data = fetcher.fetch(index)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\utils\data\_utils\fetch.py", line 58, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\torch\utils\data\_utils\fetch.py", line 58, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\dataset\dataset_wrapper.py", line 278, in __getitem__
    return self.dataset[sample_idx]
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\dataset\dataset_wrapper.py", line 159, in __getitem__
    return self.datasets[dataset_idx][sample_idx]
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\dataset\base_dataset.py", line 408, in __getitem__
    data = self.prepare_data(idx)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\dataset\base_dataset.py", line 790, in prepare_data
    return self.pipeline(data_info)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmengine\dataset\base_dataset.py", line 58, in __call__
    data = t(data)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmcv\transforms\base.py", line 12, in __call__
    return self.transform(results)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmdet-3.1.0-py3.8.egg\mmdet\datasets\transforms\loading.py", line 447, in transform
    self._load_masks(results)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmdet-3.1.0-py3.8.egg\mmdet\datasets\transforms\loading.py", line 386, in _load_masks
    gt_masks = self._process_masks(results)
  File "C:\software\Anaconda3\envs\openmmlab\lib\site-packages\mmdet-3.1.0-py3.8.egg\mmdet\datasets\transforms\loading.py", line 348, in _process_masks
    gt_mask = instance['mask']
KeyError: 'mask'

@QZZZZZYF  I think you have configured it incorrectly.

@QZZZZZYF I think you have configured it incorrectly.
Thank you for your reply. Here is my configuration. Can you help me check where the problem is?
’‘’

cascade-mask-rcnn_r50_fpn_1x_coco.py
base = [
'../base/models/cascade-mask-rcnn_r50_fpn.py',
'../base/datasets/voc0712.py',
'../base/schedules/schedule_1x.py', '../base/default_runtime.py'
]
‘’‘
’‘’
voc0712.py
dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
Example to use different file client
Method 1: simply set the data root and let the file I/O module
automatically Infer from prefix (not support LMDB and Memcache yet)
data_root = 's3://openmmlab/datasets/detection/segmentation/VOCdevkit/'
Method 2: Use backend_args, file_client_args in versions before 3.0.0rc6
backend_args = dict(
backend='petrel',
path_mapping=dict({
'./data/': 's3://openmmlab/datasets/segmentation/',
'data/': 's3://openmmlab/datasets/segmentation/'
}))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(600, 600), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(600, 600), keep_ratio=True),
# avoid bboxes being resized
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type='ConcatDataset',
# VOCDataset will add different dataset_type in dataset.metainfo,
# which will get error if using ConcatDataset. Adding
# ignore_keys can avoid this error.
ignore_keys=['dataset_type'],
datasets=[
dict(
type=dataset_type,
data_root=data_root,
ann_file='VOC2007/ImageSets/Main/trainval.txt',
data_prefix=dict(sub_data_root='VOC2007/'),
filter_cfg=dict(
filter_empty_gt=True, min_size=32, bbox_min_size=32),
pipeline=train_pipeline,
backend_args=backend_args),
])))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='VOC2007/ImageSets/Main/test.txt',
data_prefix=dict(sub_data_root='VOC2007/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
Pascal VOC2007 uses 11points as default evaluate mode, while PASCAL
VOC2012 defaults to use 'area'.
val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points')
test_evaluator = val_evaluator
‘’‘

authors: QZZZZZYF hhaAndroid

repo: open-mmlab/mmdetection


text:
How to get the loss on validation set after each epoch?

Is there any ways to get the loss on validation set after each epoch when training?
Not just making COCO-style evaluation on validation set.
Thanks for any ideas!

Take 'Faster-R50' as example, you can modify the workflow in this line as
 worflow = [('train', 1), ('val', 1)]
https://github.com/open-mmlab/mmdetection/blob/900968046507c57d25d0d965474937ed4a16775e/configs/faster_rcnn_r50_fpn_1x.py#L156
Also you need to modify the data_loader in this line, your need to append your valset_loader at the end of data_loader
https://github.com/open-mmlab/mmdetection/blob/900968046507c57d25d0d965474937ed4a16775e/mmdet/apis/train.py#L64

@yhcao6 Thanks! That solved the problem. Another question is whether I can get this loss after a given iteration interval(such as every 200 iterations, not one epoch like 1000 iterations)? Because I need more points to plot loss curve on both training and validation set.

You need to modify mmcv, to log every n iter in val epoch, you need to add a method after_val_iter just similar to after_train_iter to log every n iters
https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/logger/base.py

I have tried to do some modification in base.py, but it didn't work successfully. The 'val epoch' is always after 'train epoch' like the following log output and I can't make them work alternately in the same epoch.
2019-04-17 18:50:43,930 - INFO - Epoch [1][600/772]	lr: 0.00500, eta: 0:01:34, time: 0.569, data_time: 0.004, memory: 5116, loss_rpn_cls: 0.0190, loss_rpn_reg: 0.0082, s0.loss_cls: 0.0461, s0.acc: 98.0684, s0.loss_reg: 0.0441, s0.loss_mask: 0.2014, s1.loss_cls: 0.0256, s1.acc: 97.9508, s1.loss_reg: 0.0803, s1.loss_mask: 0.1152, s2.loss_cls: 0.0169, s2.acc: 97.0288, s2.loss_reg: 0.0633, s2.loss_mask: 0.0582, loss: 0.6783
2019-04-17 18:51:41,695 - INFO - Epoch [1][700/772]	lr: 0.00500, eta: 0:00:39, time: 0.578, data_time: 0.004, memory: 5116, loss_rpn_cls: 0.0165, loss_rpn_reg: 0.0075, s0.loss_cls: 0.0470, s0.acc: 98.0078, s0.loss_reg: 0.0465, s0.loss_mask: 0.1982, s1.loss_cls: 0.0268, s1.acc: 97.7954, s1.loss_reg: 0.0817, s1.loss_mask: 0.1110, s2.loss_cls: 0.0168, s2.acc: 97.2259, s2.loss_reg: 0.0688, s2.loss_mask: 0.0556, loss: 0.6763
2019-04-17 18:52:53,718 - INFO - Epoch(val) [1][100]	eta: 0:00:00, time: 0.204, data_time: 0.005, memory: 5217, loss_rpn_cls: 0.0201, loss_rpn_reg: 0.0082, s0.loss_cls: 0.0562, s0.acc: 97.6641, s0.loss_reg: 0.0473, s0.loss_mask: 0.2076, s1.loss_cls: 0.0334, s1.acc: 97.2258, s1.loss_reg: 0.0849, s1.loss_mask: 0.1132, s2.loss_cls: 0.0187, s2.acc: 96.7638, s2.loss_reg: 0.0774, s2.loss_mask: 0.0572, loss: 0.7242
2019-04-17 18:53:13,847 - INFO - Epoch(val) [1][200]	eta: 0:00:00, time: 0.201, data_time: 0.004, memory: 5217, loss_rpn_cls: 0.0164, loss_rpn_reg: 0.0073, s0.loss_cls: 0.0591, s0.acc: 97.5947, s0.loss_reg: 0.0493, s0.loss_mask: 0.2116, s1.loss_cls: 0.0347, s1.acc: 97.3185, s1.loss_reg: 0.0888, s1.loss_mask: 0.1161, s2.loss_cls: 0.0195, s2.acc: 96.7061, s2.loss_reg: 0.0779, s2.loss_mask: 0.0584, loss: 0.7390
2019-04-17 18:53:17,392 - INFO - Epoch(val) [1][218]	eta: 0:00:00, time: 0.202, data_time: 0.004, memory: 5217, loss_rpn_cls: 0.0179, loss_rpn_reg: 0.0076, s0.loss_cls: 0.0568, s0.acc: 97.6536, s0.loss_reg: 0.0475, s0.loss_mask: 0.2110, s1.loss_cls: 0.0337, s1.acc: 97.3061, s1.loss_reg: 0.0856, s1.loss_mask: 0.1156, s2.loss_cls: 0.0190, s2.acc: 96.7671, s2.loss_reg: 0.0766, s2.loss_mask: 0.0583, loss: 0.7294
Then I found method run in https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/runner.py. It seems like train epoch and val epoch must be done at least in one epoch by following the order of cfg.workflow. Am I missing any important details?

Sorry that I misunderstand your point. You may need to insert some code in train method at this line in "runner.py":
https://github.com/open-mmlab/mmcv/blob/506455af0bb4edf6b13cec5c7d171f26c9531d52/mmcv/runner/runner.py#L269
may be like this:
save_train_epoch_state
if (i + 1) % interval == 0:
    self.eval()
restore_train_epoch_state

Thank you for helping! Now the modification is working properly.

Take 'Faster-R50' as example, you can modify the workflow in this line as
 worflow = [('train', 1), ('val', 1)]
mmdetection/configs/faster_rcnn_r50_fpn_1x.py
Line 156 in 9009680
workflow = [('train', 1)]
Also you need to modify the data_loaders in this line, your need to append your valset_loader at the end of data_loaders
mmdetection/mmdet/apis/train.py
Line 64 in 9009680
data_loaders = [

Will it make valset contribute to the model weights ?
Because It works just look like trainset.

@forestriveral , Hi by "Also you need to modify the data_loaders in this line, your need to append your valset_loader at the end of data_loaders", could you be more specific about how to make a valset_loader?

@yhcao6 You mentioned here https://github.com/open-mmlab/mmdetection/issues/505#issuecomment-483954471 I have to add val_dataset in _non_dist_train but I do not understand how I can get it from this function
Could you please clarify it?

It works if you add this lines:
from mmdet.datasets import get_dataset

...
    data_loaders = [
        build_dataloader(
            dataset,
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False),
        build_dataloader(
            get_dataset(cfg.data.val),
            cfg.data.imgs_per_gpu,
            cfg.data.workers_per_gpu,
            cfg.gpus,
            dist=False
        )
    ]

Take 'Faster-R50' as example, you can modify the workflow in this line as
 worflow = [('train', 1), ('val', 1)]
https://github.com/open-mmlab/mmdetection/blob/900968046507c57d25d0d965474937ed4a16775e/configs/faster_rcnn_r50_fpn_1x.py#L156
Also you need to modify the data_loaders in this line, your need to append your valset_loader at the end of data_loaders
https://github.com/open-mmlab/mmdetection/blob/900968046507c57d25d0d965474937ed4a16775e/mmdet/apis/train.py#L64

I cannot understand exactly what you mean.
You mean I have to create and import a new python file on 'mmdet/datasets/loader' or add method in 'mmdet/datasets/loader/build_loader.py'?

Run tools/dist_train.sh with —validate will get you on the way～
Yet I dont know what is the relation between ‘work_flow’ in config file and —validate in command line。
发自我的iPhone

在 2019年12月1日，下午8:07，psh9002 notifications@github.com 写道：

Take 'Faster-R50' as example, you can modify the workflow in this line as
worflow = [('train', 1), ('val', 1)]
https://github.com/open-mmlab/mmdetection/blob/900968046507c57d25d0d965474937ed4a16775e/configs/faster_rcnn_r50_fpn_1x.py#L156
Also you need to modify the data_loaders in this line, your need to append your valset_loader at the end of data_loaders
https://github.com/open-mmlab/mmdetection/blob/900968046507c57d25d0d965474937ed4a16775e/mmdet/apis/train.py#L64
I cannot understand exactly what you mean.
You mean I have to create and import a new python file on 'mmdet/datasets/loader' or add method in 'mmdet/datasets/loader/build_loader.py'?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.

Hii, is this way also the same to get the validiation loss for mmdetection version 2.28.2? I am also not sure how to set this "Also you need to modify the data_loaders in this line, your need to append your valset_loader at the end of data_loaders", because in version 2.28.2 the data_loaders looks different. In version 2.28.2 there is no _dist_train function, here it looks like this:
`def train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
cfg = compat_cfg(cfg)
logger = get_root_logger(log_level=cfg.log_level)

# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]

runner_type = 'EpochBasedRunner' if 'runner' not in cfg else cfg.runner[
    'type']

train_dataloader_default_args = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    # `num_gpus` will be ignored if distributed
    num_gpus=len(cfg.gpu_ids),
    dist=distributed,
    seed=cfg.seed,
    runner_type=runner_type,
    persistent_workers=False)

train_loader_cfg = {
    **train_dataloader_default_args,
    **cfg.data.get('train_dataloader', {})
}

data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]`

I am new to this. Can somebody help me to get the validation loss in mmdetection version 2.28.2?

source: gharchive/issue

license_type: permissive

license_source: github-api


text:
Centerpoint training logs/model configs

Describe the feature
Would it be possible to describe the double flip procedure in more detail and/or provide a config that we can plug and play with in https://github.com/open-mmlab/mmdetection3d/tree/master/configs/centerpoint.  If you guys could provide the training logs as well that would be awesome. We are trying to replicate the centerpoint training curves for nuscenes :-) and test out some new training methods :-)

Oh I see this is the config and because it's test time pipeline's the training curves are exactly the same :-)! Thanks for providing such a good/well-documented codebase


text:
ImportError: cannot import name 'gcd' from 'fractions' (/usr/lib/python3.9/fractions.py)

Thanks for your error report and we appreciate it a lot.
Checklist

I have searched related issues but cannot get the expected help.
The bug has not been fixed in the latest version.

Describe the bug
I have installed the mmdet3d but can't be used. Some error occured:
     17 Note that most of these functions are only guaranteed to work for DAGs.
     18 In general, these functions do not check for acyclic-ness, so it is up
     19 to the user to check for that.
     20 """
     22 from collections import defaultdict, deque
---> 23 from fractions import gcd
     24 from functools import partial
     25 from itertools import chain

ImportError: cannot import name 'gcd' from 'fractions' (/usr/lib/python3.9/fractions.py)

Reproduction

What command or script did you run?

from mmdet3d.apis import *

Environment
sys.platform: linux
Python: 3.9.7 (default, Sep 10 2021, 14:59:43) [GCC 11.2.0]
CUDA available: True
GPU 0,1: NVIDIA GeForce RTX 2080
CUDA_HOME: /usr/local/cuda-11.3
NVCC: Build cuda_11.3.r11.3/compiler.29920130_0
GCC: gcc (Ubuntu 9.4.0-3ubuntu1.1) 9.4.0
PyTorch: 1.11.0+cu113
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - 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-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=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.12.0+cu113
OpenCV: 4.5.4
MMCV: 1.4.5
MMCV Compiler: GCC 11.2
MMCV CUDA Compiler: 11.1
MMDetection: 2.22.0
MMSegmentation: 0.22.1
MMDetection3D: 1.0.0rc0+2f88c12

I solved it by upgrading the networkx package. But another error occured:
ImportError: cannot import name 'SparseModule' from 'mmcv.ops' (/usr/local/lib/python3.9/dist-packages/mmcv/ops/__init__.py)

Please update mmcv verison to 1.4.7

Yes, I just solved it by upgrading mmcv to 1.4.7

authors: ZCMax ouening


text:
[Bug] RuntimeError: Trying to create tensor with negative dimension -512: [-512]

Describe the bug
A clear and concise description of what the bug is.
当我在运行剪枝任务，group fisher时，遇到了该错误，无法解决
To Reproduce
The command you executed.
python .\tools\train.py .\configs\pruning\base\group_fisher\group_fisher_prune_feces.py

Post related information

The output of pip list | grep "mmcv\|mmrazor\|^torch"

Your config file if you modified it or created a new one.

_base_ = 'mmseg::ddrnet/ddrnet_23-slim_in1k-nopre_2xb6-6k_feces-224x224-cbam.py'
**pretrained_path** = 'D:\Allcode\mmlab\mmsegmentation-dev-1.x\work_dirs\ddrnet_23-slim_in1k-nopre_2xb6-6k_feces-224x224-cbam\\best_mIoU_iter_6000.pth'
interval = 10
normalization_type = 'act'
lr_ratio = 0.1
target_flop_ratio = 0.5
**input_shape** = (1, 3, 224, 224)

Your train log file if you meet the problem during training.
10/09 19:24:58 - mmengine - INFO -

System environment:
sys.platform: win32
Python: 3.8.17 (default, Jul  5 2023, 20:44:21) [MSC v.1916 64 bit (AMD64)]
CUDA available: True
numpy_random_seed: 304
GPU 0: NVIDIA GeForce RTX 3080
CUDA_HOME: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7
NVCC: Cuda compilation tools, release 11.7, V11.7.64
MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.28.29334 版
GCC: n/a
PyTorch: 1.13.1+cu117
PyTorch compiling details: PyTorch built with:

C++ Version: 199711

MSVC 192829337

Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications

Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)

OpenMP 2019

LAPACK is enabled (usually provided by MKL)

CPU capability usage: AVX2

CUDA Runtime 11.7

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 8.5

Magma 2.5.4

Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/actions-runner/_work/pyt
orch/pytorch/builder/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, 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=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.14.1+cu117
OpenCV: 4.8.0
MMEngine: 0.8.4

Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0}
dist_cfg: {'backend': 'nccl'}
seed: 304
Distributed launcher: none
Distributed training: False
GPU number: 1
10/09 19:24:59 - mmengine - INFO - Config:
architecture = dict(
scope='mmseg',
backbone=dict(
align_corners=False,
channels=32,
in_channels=3,
init_cfg=None,
norm_cfg=dict(requires_grad=True, type='SyncBN'),
ppm_channels=128,
type='DDRNet1'),
data_preprocessor=dict(
scope='mmseg',
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
224,
224,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=64,
dropout_ratio=0.0,
in_channels=128,
loss_decode=[
dict(
class_weight=[
0.8,
0.9,
1.1,
1.1,
],
loss_weight=1.0,
min_kept=131072,
thres=0.9,
type='OhemCrossEntropy'),
dict(
class_weight=[
0.8,
0.9,
1.1,
1.1,
],
loss_weight=0.4,
min_kept=131072,
thres=0.9,
type='OhemCrossEntropy'),
],
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=4,
type='DDRHead'),
init_cfg=dict(
checkpoint=
'D:\Allcode\mmlab\mmsegmentation-dev-1.x\work_dirs\ddrnet_23-slim_in1k-nopre_2xb6-6k_feces-224x224-cbam\best_mIoU_iter_6000.pth',
type='Pretrained'),
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder')
checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth'
class_weight = [
0.8,
0.9,
1.1,
1.1,
]
crop_size = (
224,
224,
)
custom_hooks = [
dict(type='mmrazor.PruningStructureHook'),
dict(
demo_input=dict(
input_shape=(
1,
3,
224,
224,
), type='mmrazor.DefaultDemoInput'),
interval=10,
save_ckpt_thr=[
0.5,
],
type='mmrazor.ResourceInfoHook'),
]
data_preprocessor = dict(
scope='mmseg',
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
224,
224,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor')
data_root = 'D:\Allcode\mmlab\mmsegmentation-dev-1.x\data\data'
dataset_type = 'Feces'
default_hooks = dict(
checkpoint=dict(
scope='mmseg',
by_epoch=False,
interval=200,
max_keep_ckpts=3,
save_best='mIoU',
type='CheckpointHook'),
logger=dict(
scope='mmseg',
interval=5,
log_metric_by_epoch=False,
type='LoggerHook'),
param_scheduler=dict(scope='mmseg', type='ParamSchedulerHook'),
sampler_seed=dict(scope='mmseg', type='DistSamplerSeedHook'),
timer=dict(scope='mmseg', type='IterTimerHook'),
visualization=dict(scope='mmseg', 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,
]
img_scale = (
448,
224,
)
input_shape = (
1,
3,
224,
224,
)
interval = 10
iters = 6000
launcher = 'none'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
lr_ratio = 0.1
model = dict(
scope='mmrazor',
architecture=dict(
scope='mmseg',
backbone=dict(
align_corners=False,
channels=32,
in_channels=3,
init_cfg=None,
norm_cfg=dict(requires_grad=True, type='SyncBN'),
ppm_channels=128,
type='DDRNet1'),
data_preprocessor=dict(
scope='mmseg',
bgr_to_rgb=True,
mean=[
123.675,
116.28,
103.53,
],
pad_val=0,
seg_pad_val=255,
size=(
224,
224,
),
std=[
58.395,
57.12,
57.375,
],
type='SegDataPreProcessor'),
decode_head=dict(
align_corners=False,
channels=64,
dropout_ratio=0.0,
in_channels=128,
loss_decode=[
dict(
class_weight=[
0.8,
0.9,
1.1,
1.1,
],
loss_weight=1.0,
min_kept=131072,
thres=0.9,
type='OhemCrossEntropy'),
dict(
class_weight=[
0.8,
0.9,
1.1,
1.1,
],
loss_weight=0.4,
min_kept=131072,
thres=0.9,
type='OhemCrossEntropy'),
],
norm_cfg=dict(requires_grad=True, type='SyncBN'),
num_classes=4,
type='DDRHead'),
init_cfg=dict(
checkpoint=
'D:\Allcode\mmlab\mmsegmentation-dev-1.x\work_dirs\ddrnet_23-slim_in1k-nopre_2xb6-6k_feces-224x224-cbam\best_mIoU_iter_6000.pth',
type='Pretrained'),
test_cfg=dict(mode='whole'),
train_cfg=dict(),
type='EncoderDecoder'),
interval=10,
mutator=dict(
channel_unit_cfg=dict(
default_args=dict(normalization_type='act'),
type='GroupFisherChannelUnit'),
parse_cfg=dict(tracer_type='FxTracer', type='ChannelAnalyzer'),
type='GroupFisherChannelMutator'),
type='GroupFisherAlgorithm')
model_wrapper_cfg = dict(
broadcast_buffers=False, type='mmrazor.GroupFisherDDP')
norm_cfg = dict(scope='mmseg', requires_grad=True, type='SyncBN')
normalization_type = 'act'
optim_wrapper = dict(
scope='mmseg',
clip_grad=None,
optimizer=dict(lr=0.001, momentum=0.9, type='SGD', weight_decay=0.0005),
type='OptimWrapper')
optimizer = dict(
scope='mmseg', lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005)
param_scheduler = [
dict(
scope='mmseg',
begin=0,
by_epoch=False,
end=6000,
eta_min=0,
power=0.9,
type='PolyLR'),
]
pretrained_path = 'D:\Allcode\mmlab\mmsegmentation-dev-1.x\work_dirs\ddrnet_23-slim_in1k-nopre_2xb6-6k_feces-224x224-cbam\best_mIoU_iter_6000.pth'
randomness = dict(seed=304)
resume = False
target_flop_ratio = 0.5
test_cfg = dict(scope='mmseg', type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
scope='mmseg',
data_prefix=dict(
img_path='images/validation',
seg_map_path='annotations/validation'),
data_root='D:\Allcode\mmlab\mmsegmentation-dev-1.x\data\data',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
448,
224,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='Feces'),
num_workers=2,
persistent_workers=True,
sampler=dict(scope='mmseg', shuffle=False, type='DefaultSampler'))
test_evaluator = dict(
scope='mmseg',
iou_metrics=[
'mDice',
'mIoU',
'mFscore',
],
type='IoUMetric')
test_pipeline = [
dict(scope='mmseg', type='LoadImageFromFile'),
dict(scope='mmseg', keep_ratio=True, scale=(
448,
224,
), type='Resize'),
dict(scope='mmseg', type='LoadAnnotations'),
dict(scope='mmseg', type='PackSegInputs'),
]
train_cfg = dict(
scope='mmseg',
max_iters=6000,
type='IterBasedTrainLoop',
val_interval=200)
train_dataloader = dict(
batch_size=64,
dataset=dict(
scope='mmseg',
dataset=dict(
data_prefix=dict(
img_path='images/training',
seg_map_path='annotations/training'),
data_root='D:\Allcode\mmlab\mmsegmentation-dev-1.x\data\data',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
448,
224,
),
type='RandomResize'),
dict(prob=0.5, type='RandomFlip'),
dict(type='PhotoMetricDistortion'),
dict(
cat_max_ratio=0.75,
crop_size=(
224,
224,
),
type='RandomCrop'),
dict(type='PackSegInputs'),
],
type='Feces'),
times=40000,
type='RepeatDataset'),
num_workers=4,
persistent_workers=True,
sampler=dict(scope='mmseg', shuffle=True, type='InfiniteSampler'))
train_pipeline = [
dict(scope='mmseg', type='LoadImageFromFile'),
dict(scope='mmseg', type='LoadAnnotations'),
dict(
scope='mmseg',
keep_ratio=True,
ratio_range=(
0.5,
2.0,
),
scale=(
448,
224,
),
type='RandomResize'),
dict(scope='mmseg', prob=0.5, type='RandomFlip'),
dict(scope='mmseg', type='PhotoMetricDistortion'),
dict(
scope='mmseg',
cat_max_ratio=0.75,
crop_size=(
224,
224,
),
type='RandomCrop'),
dict(scope='mmseg', type='PackSegInputs'),
]
tta_model = dict(scope='mmseg', type='SegTTAModel')
tta_pipeline = [
dict(scope='mmseg', backend_args=None, type='LoadImageFromFile'),
dict(
scope='mmseg',
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(scope='mmseg', type='ValLoop')
val_dataloader = dict(
batch_size=1,
dataset=dict(
scope='mmseg',
data_prefix=dict(
img_path='images/validation',
seg_map_path='annotations/validation'),
data_root='D:\Allcode\mmlab\mmsegmentation-dev-1.x\data\data',
pipeline=[
dict(type='LoadImageFromFile'),
dict(keep_ratio=True, scale=(
448,
224,
), type='Resize'),
dict(type='LoadAnnotations'),
dict(type='PackSegInputs'),
],
type='Feces'),
num_workers=2,
persistent_workers=True,
sampler=dict(scope='mmseg', shuffle=False, type='DefaultSampler'))
val_evaluator = dict(
scope='mmseg',
iou_metrics=[
'mDice',
'mIoU',
'mFscore',
],
type='IoUMetric')
vis_backends = [
dict(scope='mmseg', type='LocalVisBackend'),
dict(scope='mmseg', type='TensorboardVisBackend'),
]
visualizer = dict(
scope='mmseg',
name='visualizer',
type='SegLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend'),
])
work_dir = './work_dirs\group_fisher_prune_feces'
2023-10-09 19:25:01.929852: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2023-10-09 19:25:01.929982: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
d:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\models\builder.py:36: UserWarning: build_loss would be deprecated soon, please use mmseg.registry.MODELS.build()
warnings.warn('build_loss would be deprecated soon, please use '
G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\mmengine\structures\pixel_data.py:83: UserWarning: The shape of value will convert from torch.Size([224, 224]) to torch.Size([1, 224, 224])
warnings.warn('The shape of value will convert from '
G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\fx_symbolic_trace.py:564: UserWarning: Was not able to add assertion to guarantee correct input data_samples to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with.
warnings.warn(
10/09 19:25:04 - mmengine - INFO - eq() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - assert() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getattr_1() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - floordiv() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getattr_2() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_1() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - floordiv_1() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_2_0_cbam1_ca_sigmoid(backbone.stem.2.0.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_1() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_2() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_3() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_2_0_cbam1_sa_sigmoid(backbone.stem.2.0.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_1() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_2_1_cbam1_ca_sigmoid(backbone.stem.2.1.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_2() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_1() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_2() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_4() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_5() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_2_1_cbam1_sa_sigmoid(backbone.stem.2.1.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_3() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_4_0_cbam1_ca_sigmoid(backbone.stem.4.0.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_4() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_2() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_3() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_6() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_7() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_4_0_cbam1_sa_sigmoid(backbone.stem.4.0.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_5() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_4_1_cbam1_ca_sigmoid(backbone.stem.4.1.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_6() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_3() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_4() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_8() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_9() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_stem_4_1_cbam1_sa_sigmoid(backbone.stem.4.1.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_7() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam2_ca_sigmoid(backbone.cbam2.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_8() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_4() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_5() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_10() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_11() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam2_sa_sigmoid(backbone.cbam2.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_9() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_10() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_0_0_cbam1_ca_sigmoid(backbone.context_branch_layers.0.0.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_11() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_5() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_6() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_12() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_13() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_0_0_cbam1_sa_sigmoid(backbone.context_branch_layers.0.0.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_12() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_0_1_cbam1_ca_sigmoid(backbone.context_branch_layers.0.1.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_13() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_6() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_7() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_14() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_15() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_0_1_cbam1_sa_sigmoid(backbone.context_branch_layers.0.1.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_14() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_0_0_cbam1_ca_sigmoid(backbone.spatial_branch_layers.0.0.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_15() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_7() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_8() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_16() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_17() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_0_0_cbam1_sa_sigmoid(backbone.spatial_branch_layers.0.0.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_16() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_0_1_cbam1_ca_sigmoid(backbone.spatial_branch_layers.0.1.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_17() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_8() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_9() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_18() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_19() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_0_1_cbam1_sa_sigmoid(backbone.spatial_branch_layers.0.1.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_18() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam6_ca_sigmoid(backbone.cbam6.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_19() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_9() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_10() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_20() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_21() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam6_sa_sigmoid(backbone.cbam6.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_20() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_21() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam7_ca_sigmoid(backbone.cbam7.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_22() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_10() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_11() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_22() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_23() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam7_sa_sigmoid(backbone.cbam7.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_23() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_24() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_1_0_cbam1_ca_sigmoid(backbone.context_branch_layers.1.0.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_25() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_11() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_12() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_24() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_25() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_1_0_cbam1_sa_sigmoid(backbone.context_branch_layers.1.0.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_26() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_1_1_cbam1_ca_sigmoid(backbone.context_branch_layers.1.1.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_27() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_12() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_13() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_26() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_27() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_1_1_cbam1_sa_sigmoid(backbone.context_branch_layers.1.1.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_28() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_1_0_cbam1_ca_sigmoid(backbone.spatial_branch_layers.1.0.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_29() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_13() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_14() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_28() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_29() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_1_0_cbam1_sa_sigmoid(backbone.spatial_branch_layers.1.0.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_30() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_1_1_cbam1_ca_sigmoid(backbone.spatial_branch_layers.1.1.cbam1.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_31() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_14() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_15() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_30() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_31() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_1_1_cbam1_sa_sigmoid(backbone.spatial_branch_layers.1.1.cbam1.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_32() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam8_ca_sigmoid(backbone.cbam8.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_33() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_15() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_16() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_32() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_33() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam8_sa_sigmoid(backbone.cbam8.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_34() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_35() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam9_ca_sigmoid(backbone.cbam9.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_36() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_16() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_17() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_34() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_35() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam9_sa_sigmoid(backbone.cbam9.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_37() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_38() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate_1() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_2_0_cbam2_ca_sigmoid(backbone.context_branch_layers.2.0.cbam2.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_39() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_17() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_18() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_36() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_37() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_context_branch_layers_2_0_cbam2_sa_sigmoid(backbone.context_branch_layers.2.0.cbam2.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_40() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_2_0_cbam2_ca_sigmoid(backbone.spatial_branch_layers.2.0.cbam2.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_41() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_18() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_19() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_38() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_39() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_spatial_branch_layers_2_0_cbam2_sa_sigmoid(backbone.spatial_branch_layers.2.0.cbam2.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_42() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getattr_3() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_40() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate_2() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getattr_4() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_41() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate_3() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getattr_5() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_42() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate_4() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getattr_6() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_43() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate_5() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam13_ca_sigmoid(backbone.cbam13.ca.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_43() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mean_19() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - max_20() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_44() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - getitem_45() node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - backbone_cbam13_sa_sigmoid(backbone.cbam13.sa.sigmoid) node can't find match type ofchannel_nodes,replaced with PassUnionChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_44() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - mul_45() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - INFO - interpolate_6() node can't find match type ofchannel_nodes,replaced with BindChannelNode by default.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_stem_2_0_cbam1_sa_sigmoid(1,1)uion and mul_1(32,32)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_stem_2_1_cbam1_sa_sigmoid(1,1)uion and mul_3(32,32)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_stem_4_0_cbam1_sa_sigmoid(1,1)uion and mul_5(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_stem_4_1_cbam1_sa_sigmoid(1,1)uion and mul_7(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_cbam2_sa_sigmoid(1,1)uion and mul_9(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_context_branch_layers_0_0_cbam1_sa_sigmoid(1,1)uion and mul_12(128,128)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_context_branch_layers_0_1_cbam1_sa_sigmoid(1,1)uion and mul_14(128,128)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_spatial_branch_layers_0_0_cbam1_sa_sigmoid(1,1)uion and mul_16(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_spatial_branch_layers_0_1_cbam1_sa_sigmoid(1,1)uion and mul_18(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_cbam6_sa_sigmoid(1,1)uion and mul_20(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_cbam7_sa_sigmoid(1,1)uion and mul_23(128,128)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_context_branch_layers_1_0_cbam1_sa_sigmoid(1,1)uion and mul_26(256,256)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_context_branch_layers_1_1_cbam1_sa_sigmoid(1,1)uion and mul_28(256,256)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_spatial_branch_layers_1_0_cbam1_sa_sigmoid(1,1)uion and mul_30(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_spatial_branch_layers_1_1_cbam1_sa_sigmoid(1,1)uion and mul_32(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_cbam8_sa_sigmoid(1,1)uion and mul_34(64,64)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_cbam9_sa_sigmoid(1,1)uion and mul_37(256,256)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_context_branch_layers_2_0_cbam2_sa_sigmoid(1,1)uion and mul_40(512,512)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_spatial_branch_layers_2_0_cbam2_sa_sigmoid(1,1)uion and mul_42(128,128)bind dismatch, we add an ExpandNode between them.
10/09 19:25:04 - mmengine - WARNING - As the channels of backbone_cbam13_sa_sigmoid(1,1)uion and mul_44(128,128)_bind dismatch, we add an ExpandNode between them.
10/09 19:25:12 - mmengine - INFO - Forward failed in loss mode as not enough values to unpack (expected 2, got 1)
10/09 19:25:13 - 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:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\engine\hooks\visualization_hook.py:61: 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 '
10/09 19:25:13 - mmengine - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) RuntimeInfoHook
(NORMAL      ) ResourceInfoHook
(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
(NORMAL      ) SegVisualizationHook
(NORMAL      ) PruningStructureHook
(NORMAL      ) ResourceInfoHook
(BELOW_NORMAL) LoggerHook
(LOW         ) ParamSchedulerHook
(VERY_LOW    ) CheckpointHook
after_train_epoch:
(NORMAL      ) IterTimerHook
(NORMAL      ) PruningStructureHook
(NORMAL      ) ResourceInfoHook
(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
10/09 19:25:38 - mmengine - WARNING - The prefix is not set in metric class IoUMetric.
Traceback (most recent call last):
File ".\tools\train.py", line 121, in
main()
File ".\tools\train.py", line 117, in main
runner.train()
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\mmengine\runner\runner.py", line 1720, in train
self.call_hook('before_run')
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\mmengine\runner\runner.py", line 1807, in call_hook
getattr(hook, fn_name)(self, **kwargs)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\implementations\pruning\group_fisher\hook.py", line 137, in before_run
original_resource = self._evaluate(model)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\implementations\pruning\group_fisher\hook.py", line 179, in _evaluate
res = self.estimator.estimate(
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\task_modules\estimators\resource_estimator.py", line 136, in estimate
flops, params = get_model_flops_params(model, **flops_params_cfg)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\task_modules\estimators\counters\flops_params_counter.py", line 88, in get_model_flops_params
_ = flops_params_model(**input)
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\nn\modules\module.py", line 1212, in _call_impl
result = forward_call(*input, **kwargs)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\algorithms\base.py", line 144, in forward
return self._forward(inputs, data_samples)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\algorithms\base.py", line 165, in _forward
return self.architecture(inputs, data_samples, mode='tensor')
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "d:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\models\segmentors\base.py", line 98, in forward
return self._forward(inputs, data_samples)
File "d:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\models\segmentors\encoder_decoder.py", line 241, in _forward
x = self.extract_feat(inputs)
File "d:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\models\segmentors\encoder_decoder.py", line 120, in extract_feat
x = self.backbone(inputs)  # 传入backbone计算时出错
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "d:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\models\backbones\ddrnet.py", line 359, in forward
x_c = self.spp(x_c)
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "d:\allcode\mmlab\mmsegmentation-dev-1.x\mmseg\models\utils\ppm.py", line 130, in forward
return self.compression(torch.cat(feats,
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\mmcv\cnn\bricks\conv_module.py", line 209, in forward
x = self.norm(x)
File "G:\softwares\path\Anaconda3\envs\mmseg\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\architectures\dynamic_ops\bricks\dynamic_norm.py", line 81, in forward
running_mean, running_var, weight, bias = self.get_dynamic_params()
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\architectures\dynamic_ops\mixins\dynamic_mixins.py", line 230, in get_dynamic_params
out_mask = self._get_num_features_mask()
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\architectures\dynamic_ops\mixins\dynamic_mixins.py", line 212, in _get_num_features_mask
out_mask = self.mutable_attrs['num_features'].current_mask.to(
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\mutables\mutable_channel\mutable_channel_container.py", line 63, in current_mask
return self.current_choice.bool()
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\mutables\mutable_channel\mutable_channel_container.py", line 43, in current_choice
self._fill_unregistered_range()
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\mutables\mutable_channel\mutable_channel_container.py", line 118, in _fill_unregistered_range
SimpleMutableChannel(last_end - start), last_end, start)
File "d:\allcode\mmlab\mmrazor-main\mmrazor\models\mutables\mutable_channel\simple_mutable_channel.py", line 23, in init
mask = torch.ones([self.num_channels])  # save bool as float for dist training
RuntimeError: Trying to create tensor with negative dimension -512: [-512].

Other code you modified in the mmrazor folder.
No

Additional context
Add any other context about the problem here.
[here]

How to solve this problem ?

authors: HePengguang happygds

repo: open-mmlab/mmrazor


text:
Image Thumbnails/Zoom in Loop Publishing

When publishing, it is difficult to see what existing pictures you already uploaded (if you are doing a new version). The thumbnails in the preview window are too small to see - if you could click to zoom it would take care of the issue....want to see during publishing process what the prior screenshots are and if they will work for the new version or if certain ones need to be deleted and replaced, etc.

Thanks for the feedback! Created HELPS-2122 to track this.

authors: BrettMugglinOlive e-rotera

repo: open-olive/loop-development-kit

license: MIT


text:
Support 2023.3 IDEA Version and Fix Minor Error False Positives

Description

Compatibility: upgrade to 2023.3 / 233.2 version
Grammar: rules with multidimensional array heads support
Grammar: else expressions no longer consider ':=' invalid
tests: bump OPA version to v0.61.0

Fixes #165
Fixes #151

Thanks Shane! Much appreciated. Now just to try and figure out how to get this published, as I've never been involved in that before 😅 If you have experience publishing a plugin and could help provide some pointers, hit me up on the OPA Slack!

@spoage Thanks for the work :)
Just a  clarification,  this PR fixes compatibility for  IDEA 2024.1 ( aka 241 in gradle, the new EAP) version on dev was already compatible with version 2023.3 (aka 233 in gradle).
@anderseknert, I have a working PR that fixes the release-note generation and publishing of the plugin... But it is not opened yet as I have weird behavior.
TLDR:  the last version on dev should not have been published as tests failed due to missing opa binary.
I will open PR this afternoon or tomorrow.

Ah, very happy to hear that Vincent! 👍 Looking forward to having Rego in my IDEA IDE's once more :)

source: gharchive/pull-request

authors: anderseknert spoage vgramer

repo: open-policy-agent/opa-idea-plugin


text:
fix: Submit button z-index is below the hot repos

Definitely my first pull request. I apologize if I did not use the right form, I'm trying!
**Also, I definitely only changed line 73, please point me in the right direction if I screwed this up somehow:
I literally just added:  z-[1]
What type of PR is this? (check all applicable)

[ ] 🍕 Feature
[x] 🐛 Bug Fix
[ ] 📝 Documentation Update
[x] 🎨 Style
[ ] 🧑💻 Code Refactor
[ ] 🔥 Performance Improvements
[ ] ✅ Test
[ ] 🤖 Build
[ ] 🔁 CI
[ ] 📦 Chore (Release)
[ ] ⏩ Revert

Description
This PR fixes the RepoSubmission.tsx component to be positioned above/in front of the hot repos. I changed the Z index rather than messing with a whole lot of CSS. (I thought it was best to keep my first attempt simple)
Related Tickets & Documents

Closes #237
Mobile & Desktop Screenshots/Recordings

Blocked Image:

Corrected Z Index

Added tests?

[ ] 👍 yes
[x] 🙅 no, because they aren't needed
[ ] 🙋 no, because I need help

Added to documentation?

[ ] 📜 README.md
[ ] 📓 docs.opensauced.pizza
[ ] 🍕 dev.to/opensauced
[ ] 📕 storybook
[x] 🙅 no documentation needed

[optional] Are there any post-deployment tasks we need to perform?
Nope, unless someone wants to use a different method other than z-index to resolve this. I also wanted to add a black border to show a bit more contrast...but I really don't want to start making unauthorized changes to a project that I'm just starting to get involved with.
[optional] What gif best describes this PR or how it makes you feel?

Changed z-[1] to z-10 to avoid having an on the fly generated CSS class, it should be good to go after this! 🍕

Thank you!! sorry, this was my first time using Tailwind and I appreciate all the help!
...also, should I delete this branch now?

Thank you!! sorry, this was my first time using Tailwind and I appreciate all the help!
...also, should I delete this branch now?

No worries, the deploy preview was broken and reading on z-index while reviewing made me realize it's a minor improvement, not really worth a review or change request - as a maintainer I could just add that along with the merge! 🍕
You can delete the branch of course, but this is all happening inside your fork so not needed. When you sync your fork with upstream your branches will remain, if you are not bothered by them while writing code I suggest keeping them around - they act like fixed snapshots in time of the code you write, sometimes it makes sense when you are writing a blog post to, for example, link a PR or a branch you are running in the fork for people to have additional context on your post! 🍕

This all makes sense now - thank you again for all the help. I used a ton of Git, but it is very different when you're working with others. I'm excited to contribute in a more meaningful way, but right now I'll fix whatever I can
:)

This all makes sense now - thank you again for all the help. I used a ton of Git, but it is very different when you're working with others. I'm excited to contribute in a more meaningful way, but right now I'll fix whatever I can
:)

Thank you, every contribution is meaningful!
Glad you are enjoying open source 🍕

repo: open-sauced/hot


text:
implicitly update boost

May fix #36

gcc9 fails here due to boost requiring statx. Not sure how to resolve....

I think it is because we are using old ubuntu-18.04. I have pushed a new commit and created PR into this branch.
I also removed gcc-7 and 8 from the build matrix because we have also removed them for libcosim recently.

repo: open-simulation-platform/proxy-fmu


text:
Bring back the term "content-agnostic"

"Content-agnostic" refers to the design of Anki being a content-free flashcard application allowing user to choose whatever deck they please and allowing them to create their own deck. Contrast to other flashcard applications like Wanikani where users of the application learn the same set of cards without any options to use the application for another purpose besides what it originally offered for.
The previous change to "highly customizable" lost this meaning.
Discussion: https://github.com/open-spaced-repetition/awesome-fsrs/commit/1d7bace84c449efb65bb34821e019bfe04515186#r146399797

Some other suggestions also include "content-neutral" and "content-independent". But I would prefer to the term introduced in https://apps.ankiweb.net/.

A glossary might be helpful.

Can we not write around that to avoid a noun string? To be more descriptive than that, can just tell people, simply, what Anki allows. Custom fonts, colours, emojis, photos, videos, audio, math symbols, etcetra, etcetra.

I think that's another discussion unrelated to this pull request

repo: open-spaced-repetition/awesome-fsrs


text:
[featureflag] - Dockerfile should build protobuf modules

Feature Request
Update Dockerfile to build artifacts similar to how other services are built.

[ ] Use the top-level build context, updating paths in Dockerfile accordingly
[ ] Copy the top-level demo.proto file into the appropriate build location
[ ] Run rebar3 grpc gen as part of the build

cc @tsloughter

Can we close this @puckpuck ?

#599 will close this

authors: cartersocha puckpuck

repo: open-telemetry/opentelemetry-demo


text:
chore(ot-operator): update version to v0.51.0

Update of the OpenTelemetry Operator to v0.51.0

I'm not sure but probably it is enough to restart tests.

I'm not sure but probably it is enough to restart tests.

@mat-rumian I've re-triggered the tests twice and the same test keeps failing. Can you look into why the test is failing? The failing test is one of the kuttl tests in the opentelemetry-operator repo.

@TylerHelmuth I found a reason, updated CRD and there's one more thing. target-allocator image used in the tests comes from local build in the opentelemetry-operator repository.
logger.go:42: 18:29:04 | targetallocator-features | 2022-05-22 18:26:42 +0000 UTC	Warning	Pod stateful-targetallocator-c7dc44688-79wtd.spec.containers{ta-container}		Failed	Failed to pull image "local/opentelemetry-operator-targetallocator:e2e": rpc error: code = Unknown desc = failed to pull and unpack image "docker.io/local/opentelemetry-operator-targetallocator:e2e": failed to resolve reference "docker.io/local/opentelemetry-operator-targetallocator:e2e": pull access denied, repository does not exist or may require authorization: server message: insufficient_scope: authorization failed

I'm not sure if I should overwrite it here in the test scripts or use public image of target-allocator in opentelemetry-operator repo. cc: @pavolloffay

authors: TylerHelmuth mat-rumian


text:
Declaring any preHandler hook breaks @opentelemetry/instrumentation-fastify

What version of OpenTelemetry are you using?
  "dependencies": {
    "@opentelemetry/api": "^1.9.0",
    "@opentelemetry/auto-instrumentations-node": "^0.48.0",
    "@opentelemetry/sdk-node": "^0.52.1",
    "fastify": "^4.28.1"
  }

What version of Node are you using?
Node 18.17.1
What did you do?
Debugging a full project, I wasn't able to add attributes to span inside a fastify request handler, because the span returned from trace.getActiveSpan() was already ended.
What did you expect to see?
Adding any number of hooks should be okay; when one calls trace.getActiveSpan() in a request handler, the span should not be ended yet to allow for adding attributes.
What did you see instead?
If ANY preHandler to fastify (even a no-op), calling trace.getActiveSpan() in a request handler will return an already ended span, preventing you from adding attributes.
It seems that for some reason, add other preHandler hook to fastify will cause the spans that should be auto-instrumented to end prematurely, before the handler actually runs.
I'm not totally sure why, but I'm guessing it has something to do with the fact the instrumentation seems to expect the handler to always run after the auto-instrumentation's own preHandler hook...I'm thinking declaring another hook adds a follow up to the first, which ends the span once it completes, instead of actually wrapping the handler.
It also wraps addHook, which might be trying to prevent this behaviour, but it doesn't seem to work.
https://github.com/open-telemetry/opentelemetry-js-contrib/blob/6e8989de4d60c32bc9b3e3ef760e92c68ae7f491/plugins/node/opentelemetry-instrumentation-fastify/src/instrumentation.ts#L193
Additional context
Sorry for the wall, but here's a small reproducible example using the Node SDK + Basic fastify setup:
"use strict";
const process = require("process");
const opentelemetry = require("@opentelemetry/sdk-node");
const {
  getNodeAutoInstrumentations,
} = require("@opentelemetry/auto-instrumentations-node");
const { ConsoleSpanExporter } = require("@opentelemetry/sdk-trace-base");
const { Resource } = require("@opentelemetry/resources");
const {
  SEMRESATTRS_SERVICE_NAME,
} = require("@opentelemetry/semantic-conventions");

const {
  trace,
  diag,
  DiagConsoleLogger,
  DiagLogLevel,
} = require("@opentelemetry/api");
diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.DEBUG);

// configure the SDK to export telemetry data to the console
// enable all auto-instrumentations from the meta package
const traceExporter = new ConsoleSpanExporter();
const sdk = new opentelemetry.NodeSDK({
  resource: new Resource({
    [SEMRESATTRS_SERVICE_NAME]: "my-service",
  }),
  traceExporter,
  instrumentations: [getNodeAutoInstrumentations()],
});

// initialize the SDK and register with the OpenTelemetry API
// this enables the API to record telemetry
sdk.start();

// gracefully shut down the SDK on process exit
process.on("SIGTERM", () => {
  sdk
    .shutdown()
    .then(() => console.log("Tracing terminated"))
    .catch((error) => console.log("Error terminating tracing", error))
    .finally(() => process.exit(0));
});

// Server + Route
const Fastify = require("fastify");
const fastify = Fastify({
  logger: true,
});

fastify.addHook("preHandler", async () => {});

// Declare a route
fastify.get("/", async (request, reply) => {
  const span = trace.getActiveSpan();
  console.log("This span will already be ended :(", span);
  span?.setAttribute("attribute", "value");
  return reply.status(200).send("hello world");
});

// Run the server!
try {
  fastify.listen({ port: 3000 });
} catch (err) {
  fastify.log.error(err);
  process.exit(1);
}

I've got the same problem with Fastify v4 and v5.

authors: NicholasGWK luismiramirez

repo: open-telemetry/opentelemetry-js-contrib


text:
Provide auto-instrumentation for fs module

Describe the solution you'd like to see
I think this really doesn't need an explanation. fs module is quite a large one and has functionality across all paradigms:

callback-based,
promise-based,
synchronous and
streaming.

I'm sure there are differences across node versions, etc.

+1
I use this module in one of my personal projects, and would really be happy to have instrumentation for it.

Initial version added here: https://github.com/open-telemetry/opentelemetry-js-contrib/pull/872


text:
fix(instrumentation-fs): fix instrumentation of fs/promises

Which problem is this PR solving?
This PR fixes @opentelemetry/instrumentation-fs when used with fs/promises.
Short description of the changes
This PR adds logic to @opentelemetry/instrumentation-fs that instruments fs/promises. This will properly instrument fs methods when they come from fs/promises; currently, that instrumentation is not happening.

I just merged #1332 and it seems to create conflicts in this PR, sorry about that

Sorry, I merged this PR but now I see there is still a comment about it: https://github.com/open-telemetry/opentelemetry-js-contrib/pull/1375#discussion_r1118094721. If you want to address this comment, I suggest to do it on another PR.

authors: blumamir haddasbronfman mhassan1


text:
Plugin warning starting SDK configured with exporter-collector-grpc

What version of OpenTelemetry are you using?
0.16.0
What version of Node are you using?
v12.20.1
Please provide the code you used to setup the OpenTelemetry SDK
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { CollectorTraceExporter } = require('@opentelemetry/exporter-collector-grpc');

const sdk = new NodeSDK({
    traceExporter: new CollectorTraceExporter(),
});
sdk.start().then(() => {
    console.log('SDK configured');
});

What did you do?
Ran the snippet above
What did you expect to see?
Expected the SDK to be setup with no issues.  stdout should read
SDK configured

What did you see instead?
There was a warning from the plugin loader in stdout
Some modules (grpc) were already required when their respective plugin was loaded, some plugins might not work. Make sure the SDK is setup before you require in other modules.
SDK configured

Additional context
I'm not sure how big a problem this is but think it should be possible to configure GRPC without warnings.  Exporting traces via GRPC all seems to work as expected.
If I configure the HTTP exporter instead (i.e. const { CollectorTraceExporter } = require('@opentelemetry/exporter-collector) or disable the GRPC plugin (as below), there's no such warning.
    plugins: {
        grpc: { enabled: false, path: '@opentelemetry/plugin-grpc' },
    },

The core of the issue here is that the grpc exporter depends on grpc. If the dependency is loaded before the instrumentation then the instrumentation can fail. One option would be to use the newer @opentelemetry/instrumentation-grpc instead which would allow you to enable the instrumentation before the SDK.
Otherwise you may need to use the sdk components directly rather than using the NodeSDK package so you can control the load order.
const { NodeTracerProvider } = require('@opentelemetry/node');
const { GrpcInstrumentation } = require('@opentelemetry/instrumentation-grpc');

const provider = new NodeTracerProvider({
  // be sure to disable old plugin
  plugins: {
    grpc: { enabled: false, path: '@opentelemetry/plugin-groc' }
  },
});

const grpcInstrumentation = new GrpcInstrumentation({
  // see https://github.com/open-telemetry/opentelemetry-js/blob/main/packages/opentelemetry-instrumentation-grpc/README.md for available configuration
});
grpcInstrumentation.enable();

provider.addSpanProcessor(new SimpleSpanProcessor(new ConsoleSpanExporter()));
provider.register();

Oops forgot to provide the NodeSDK example. Try this:
const { GrpcInstrumentation } = require('@opentelemetry/instrumentation-grpc');

const grpcInstrumentation = new GrpcInstrumentation({
  // see https://github.com/open-telemetry/opentelemetry-js/blob/main/packages/opentelemetry-instrumentation-grpc/README.md for available configuration
});
grpcInstrumentation.enable();

const { NodeSDK } = require('@opentelemetry/sdk-node');
const { CollectorTraceExporter } = require('@opentelemetry/exporter-collector-grpc');

const sdk = new NodeSDK({
    traceExporter: new CollectorTraceExporter(),
});
sdk.start().then(() => {
    console.log('SDK configured');
});

Thanks for the quick response.  That all makes sense and the example works well for me.

authors: dyladan neilfordyce

repo: open-telemetry/opentelemetry-js


text:
Java layer-wrapper script for SQS-triggerd functions

The current Java wrapper Lamdba layer drops the SQS message before it gets to the handler. This update enables the special handler for SQS-triggered functions as documented here.

sure, no problem. I'll put a sample app together as soon as I get a chance

I've added a very basic sample app to demonstrate how you can instrument Lambdas that use SQS as an event source. This includes using the otel-sqs-handler and the SQSEvent type. I did not do anything with metrics or custom tracing as the idea was just to show support for SQS events. Let me know if you think we should anything more to this.

@rapphil can you review this?

repo: open-telemetry/opentelemetry-lambda


text:
add link to 'instrumenting' concept and to k8s operator injection to …

Two changes for https://opentelemetry.io/docs/instrumentation/:

I linked back from that page to the concept of instrumenting. Idea is that someone landing on that page might not know about the concept and can go back from there
I added a short paragraph that highlights that auto instrumentation libraries for java, node.js & python can be injected via the kubernetes operator. For someone running apps on k8s, who "just" wants to get services instrumented, this is a live saver. Let me know what you think, maybe this could also have it's own section within the docs and not pointing out?

Preview: https://deploy-preview-1147--opentelemetry.netlify.app/docs/instrumentation/

I think adding it to each SIG page would be decent. It makes me wonder if there should be a SIG page-equivalent for kubernetes alone?

I'd suggest that we merge this as is. I can then followup with a PR that distributes this comment to the Java, NodeJS and Python pages. How does that sound?

I'd suggest that we merge this as is. I can then followup with a PR that distributes this comment to the Java, NodeJS and Python pages. How does that sound?

sounds good to me

It makes me wonder if there should be a SIG page-equivalent for kubernetes alone?

I think that would be a great addition, and might at some point replace the pointers out to the operator.

authors: cartermp chalin svrnm


text:
[pt] Add /pt/docs/concepts/signals/logs.md

Add localization signals logs.
Issue: #4922

Hello @svrnm
Can we help me?
Can I ignore these warnings? Or, do I need to fix this?
cc: @edsoncelio

Como sugestão:
Um **log** é um registro de texto com um carimbo de data e hora,
Um **log** é um registro de texto com  marcação de data e hora,

@EzzioMoreira Seems like you need to add cSpell:ignore: exceptions to the docs. For more info, see https://opentelemetry.io/docs/contributing/style-guide/#spell-checking

Just adding some more information - we already have a file with the list of words to ignore; if I'm not wrong, you just need to add the words inside this file @EzzioMoreira: https://github.com/open-telemetry/opentelemetry.io/blob/main/.cspell/pt-palavras.txt

Thanks, @edsoncelio and @theletterf 🙏


text:
add Azure resource log mapping to OpenTelemetry

What are you trying to achieve?
A mapping between the Azure top-level common schema for resource logs and OpenTelemetry was implemented in the Azure Event Hub receiver. During the review of the pull request (issue), the maintainers requested that the mapping be added to Appendix A of the logs data model.
Additional context.
For convenience, the proposed table is below.

Field
Type
Description
Maps to Unified Model Field

callerIpAddress
string
The caller IP address, if the operation corresponds to an API call that would come from an entity with a publicly available IP address.
Attributes["net.sock.peer.addr"]

correlationId
string
A GUID that's used to group together a set of related events. Typically, if two events have the same operationName value but two different statuses (for example, Started and Succeeded), they share the same correlationID value. This might also represent other relationships between events.
Attribute["azure.correlation.id"]

category
string
The log category of the event. Category is the granularity at which you can enable or disable logs on a particular resource. The properties that appear within the properties blob of an event are the same within a particular log category and resource type. Typical log categories are Audit, Operational, Execution, and Request.
Attributes["azure.category"]

durationMs
string
The duration of the operation in milliseconds.
Attributes["azure.duration"]

Level
string
The severity level of the event. Must be one of Informational, Warning, Error, or Critical.
fields["severity_number"], fields["severity_text"]

location
string
The region of the resource emitting the event; for example, East US or France South.
Attributes["cloud.region"]

operationName
string
The name of the operation that this event represents. If the event represents an Azure role-based access control (RBAC) operation, this is the Azure RBAC operation name (for example, Microsoft.Storage/storageAccounts/blobServices/blobs/Read). This name is typically modeled in the form of an Azure Resource Manager operation, even if it's not a documented Resource Manager operation: (Microsoft.{providerName}/{resourceType}/{subtype}/{Write|Read|Delete|Action}).
Attributes["azure.operation.name"]

operationVersion
string
The API version associated with the operation, if operationName was performed through an API (for example, http://myservice.windowsazure.net/object?api-version=2016-06-01). If no API corresponds to this operation, the version represents the version of that operation in case the properties associated with the operation change in the future.
Attributes["azure.operation.version"]

properties
map
Any extended properties related to this category of events. All custom or unique properties must be put inside this "Part B" of the schema.
Attributes["azure.properties"]

resourceId
string
The resource ID of the resource that emitted the event. For tenant services, this is of the form /tenants/tenant-id/providers/provider-name.
Resource["azure.resource.id"]

resultDescription
string
The static text description of this operation; for example, Get storage file.
Attributes["azure.result.description"]

resultSignature
string
The substatus of the event. If this operation corresponds to a REST API call, this field is the HTTP status code of the corresponding REST call.
Attributes["azure.result.signature"]

resultType
string
The status of the event. Typical values include Started, In Progress, Succeeded, Failed, Active, and Resolved.
Attributes["azure.result.type"]

tenantId
string
The tenant ID of the Active Directory tenant that this event is tied to. This property is used only for tenant-level logs. It does not appear in resource-level logs.
Attributes["azure.tenant.id"]

time
string
The timestamp (UTC) of the event.
fields["time_unix_nano"]

identity
map
A JSON blob that describes the identity of the user or application that performed the operation. Typically, this field includes the authorization and claims or JWT token from Active Directory.
Attributes["azure.identity"]

@loomis-relativity, Azure resource logs are being defined in #1028, please take a look if you're still interested.

authors: lmolkova loomis-relativity

repo: open-telemetry/semantic-conventions


text:
Traffic apply error if flow is configured with only tx port instead of tx&rx ports

Traffic apply error if flow is configured with only tx port instead of tx&rx ports

Implemention should be as such that tx port shouldn't receive back packets it sent out (no self-destined or loopback impl)

Issue should not present in v0.4.11

authors: ANISH-GOTTAPU alakendu ashutshkumr

repo: open-traffic-generator/snappi-ixnetwork


text:
doc(es-dev-server): update files-extensions type

files-extensions to string/array

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.

authors: claassistantio pishonx

repo: open-wc/open-wc


text:
remove dest_weight from xTokens::transfer

this param is not very pratical and was used only for paying more fees, pallet-xcm has removed dest_weight now. Is there a way to remove it also for xtokens?

Why do you want to remove it? pallet-xcm have added method to allow specify dest weight to avoid issues https://github.com/paritytech/polkadot/blob/58e4ef8ebdc2be83c60bf6891044a1a5d00bed34/xcm/pallet-xcm/src/lib.rs#L701

authors: GopherJ xlc


text:
fix: docstring event emitter

Pull Request Checklist
Note to first-time contributors: Please open a discussion post in Discussions and describe your changes before submitting a pull request.
Before submitting, make sure you've checked the following:

[x] Target branch: Please verify that the pull request targets the dev branch.
[x] Description: Provide a concise description of the changes made in this pull request.
[x] Changelog: Ensure a changelog entry following the format of Keep a Changelog is added at the bottom of the PR description.
[ ] Documentation: Have you updated relevant documentation Open WebUI Docs, or other documentation sources?
[x] Dependencies: Are there any new dependencies? Have you updated the dependency versions in the documentation?
[ ] Testing: Have you written and run sufficient tests for validating the changes?
[x] Code review: Have you performed a self-review of your code, addressing any coding standard issues and ensuring adherence to the project's coding standards?
[x] Prefix: To cleary categorize this pull request, prefix the pull request title, using one of the following:

BREAKING CHANGE: Significant changes that may affect compatibility
build: Changes that affect the build system or external dependencies
ci: Changes to our continuous integration processes or workflows
chore: Refactor, cleanup, or other non-functional code changes
docs: Documentation update or addition
feat: Introduces a new feature or enhancement to the codebase
fix: Bug fix or error correction
i18n: Internationalization or localization changes
perf: Performance improvement
refactor: Code restructuring for better maintainability, readability, or scalability
style: Changes that do not affect the meaning of the code (white-space, formatting, missing semi-colons, etc.)
test: Adding missing tests or correcting existing tests
WIP: Work in progress, a temporary label for incomplete or ongoing work

Changelog Entry
Description

Do not parse reserved params in docstring

@Simi5599 @michaelpoluektov Thanks everyone!

authors: michaelpoluektov tjbck

repo: open-webui/open-webui


text:
Implemented behavior based on "operationName" value

This is behavior for each value:

string-profile-1-0:STRING_VALUE_TYPE_PROTECTION

update key is done in every 5min
/v1/regard-updated-link does not update keys and returns status code 500

string-profile-1-0:STRING_VALUE_TYPE_REACTIVE

update key does not execute periodically
/v1/regard-updated-link updates keys

string-profile-1-0:STRING_VALUE_TYPE_OFF

update key does not execute periodically
/v1/regard-updated-link does not update keys and returns status code 500

Fixes #46
Signed-off-by: Martin Sunal martin.sunal@paxet.io

Changes are as per the latest decision in the issue https://github.com/openBackhaul/OperationKeyManagement/issues/39.
But the current branch is having merge conflict after merging the pull request https://github.com/openBackhaul/OperationKeyManagement/pull/45. Kindly please resolve the merge conflict. Many thanks.

authors: MartinSunal PrathibaJee

repo: openBackhaul/OperationKeyManagement