text:
COCO mAP and CustomDataset mAP don't match on single IoU value.

By default COCO doesn't use a single IoU to calculate the mAP but instead averages over a range of IoUs (IoU=0.50:0.95).
The evaluate function also outputs the mAP for IoU 0.5 and 0.75.
However calculating the mAP for IoU 0.5 and 0.75 using the evaluate method from the super class CustomDataset  gives a different result.
This means that implementing a new dataset will give different evaluation results depending on whether it is implemented as a new subclass of CustomDataset or with COCO compatible labels as a CocoDataset.
Minimal example:
import os

import mmdet.datasets
import mmdet.apis
import mmcv

checkpoint_path = "checkpoints/ssd300_coco_20200307-a92d2092.pth"
config_path = "configs/ssd/ssd300_coco.py"

cfg = mmcv.Config.fromfile(config_path)
cfg.model.pretrained = None
cfg.data.test.test_mode = True
cfg.test_cfg.max_per_img = 100 # Set to 100 instead of 200 as CustomDataset.evaluate has no maxDets

test_dataset = mmdet.datasets.build_dataset(cfg.data.test)
test_dataloader = mmdet.datasets.build_dataloader(
    test_dataset,
    samples_per_gpu=1,
    workers_per_gpu=cfg.data.workers_per_gpu,
    dist=False,
    shuffle=False
)

loading annotations into memory...
Done (t=0.52s)
creating index...
index created!

raw_model = mmdet.models.build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = mmcv.runner.load_checkpoint(raw_model, checkpoint_path, map_location='cpu')
raw_model.CLASSES = test_dataset.CLASSES
model = mmcv.parallel.MMDataParallel(raw_model, device_ids=[0])

results = mmdet.apis.single_gpu_test(model, test_dataloader)

[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 39.0 task/s, elapsed: 128s, ETA:     0s

test_dataset.evaluate(results)

Evaluating bbox...
Loading and preparing results...
DONE (t=3.77s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=53.15s).
Accumulating evaluation results...
DONE (t=7.20s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.255
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.436
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.263
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.065
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.277
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.422
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.237
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.347
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.364
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.108
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.401
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582

{'bbox_mAP': 0.255,
 'bbox_mAP_50': 0.436,
 'bbox_mAP_75': 0.263,
 'bbox_mAP_s': 0.065,
 'bbox_mAP_m': 0.277,
 'bbox_mAP_l': 0.422,
 'bbox_mAP_copypaste': '0.255 0.436 0.263 0.065 0.277 0.422'}

super(type(test_dataset), test_dataset).evaluate(results, iou_thr=0.5)

+----------------+-------+--------+--------+-------+
| class          | gts   | dets   | recall | ap    |
+----------------+-------+--------+--------+-------+
| person         | 10777 | 151136 | 0.781  | 0.619 |
| bicycle        | 314   | 1935   | 0.503  | 0.376 |
| car            | 1918  | 17858  | 0.626  | 0.440 |
| motorcycle     | 367   | 2282   | 0.703  | 0.571 |
| airplane       | 143   | 3942   | 0.895  | 0.737 |
| bus            | 283   | 875    | 0.742  | 0.693 |
| train          | 190   | 4048   | 0.863  | 0.795 |
| truck          | 414   | 2610   | 0.693  | 0.431 |
| boat           | 424   | 8890   | 0.547  | 0.308 |
| traffic light  | 634   | 12564  | 0.465  | 0.236 |
| fire hydrant   | 101   | 274    | 0.703  | 0.662 |
| stop sign      | 75    | 479    | 0.640  | 0.581 |
| parking meter  | 60    | 147    | 0.583  | 0.498 |
| bench          | 411   | 12074  | 0.457  | 0.267 |
| bird           | 427   | 13891  | 0.433  | 0.296 |
| cat            | 202   | 1221   | 0.931  | 0.851 |
| dog            | 218   | 1027   | 0.821  | 0.713 |
| horse          | 272   | 2077   | 0.794  | 0.671 |
| sheep          | 354   | 5733   | 0.782  | 0.559 |
| cow            | 372   | 4083   | 0.766  | 0.534 |
| elephant       | 252   | 3908   | 0.897  | 0.738 |
| bear           | 71    | 308    | 0.831  | 0.777 |
| zebra          | 266   | 5540   | 0.880  | 0.797 |
| giraffe        | 232   | 5620   | 0.901  | 0.812 |
| backpack       | 371   | 2462   | 0.305  | 0.111 |
| umbrella       | 407   | 4079   | 0.634  | 0.462 |
| handbag        | 540   | 4092   | 0.274  | 0.079 |
| tie            | 252   | 4043   | 0.480  | 0.314 |
| suitcase       | 299   | 1934   | 0.515  | 0.334 |
| frisbee        | 115   | 566    | 0.687  | 0.499 |
| skis           | 240   | 5994   | 0.592  | 0.301 |
| snowboard      | 69    | 343    | 0.478  | 0.311 |
| sports ball    | 260   | 4571   | 0.504  | 0.382 |
| kite           | 327   | 7137   | 0.648  | 0.385 |
| baseball bat   | 145   | 1113   | 0.441  | 0.283 |
| baseball glove | 148   | 1161   | 0.480  | 0.296 |
| skateboard     | 179   | 1872   | 0.721  | 0.559 |
| surfboard      | 267   | 4453   | 0.592  | 0.413 |
| tennis racket  | 225   | 2015   | 0.631  | 0.524 |
| bottle         | 1013  | 7547   | 0.523  | 0.277 |
| wine glass     | 341   | 1171   | 0.408  | 0.290 |
| cup            | 895   | 4976   | 0.534  | 0.330 |
| fork           | 215   | 1029   | 0.414  | 0.228 |
| knife          | 325   | 1714   | 0.237  | 0.065 |
| spoon          | 253   | 1237   | 0.249  | 0.088 |
| bowl           | 623   | 3130   | 0.652  | 0.431 |
| banana         | 370   | 7206   | 0.635  | 0.300 |
| apple          | 236   | 2028   | 0.424  | 0.154 |
| sandwich       | 177   | 1156   | 0.678  | 0.462 |
| orange         | 285   | 2710   | 0.561  | 0.285 |
| broccoli       | 312   | 13007  | 0.769  | 0.325 |
| carrot         | 365   | 9667   | 0.573  | 0.193 |
| hot dog        | 125   | 470    | 0.480  | 0.363 |
| pizza          | 284   | 2390   | 0.736  | 0.600 |
| donut          | 328   | 2525   | 0.649  | 0.389 |
| cake           | 310   | 1742   | 0.626  | 0.412 |
| chair          | 1771  | 33923  | 0.537  | 0.279 |
| couch          | 261   | 1659   | 0.785  | 0.590 |
| potted plant   | 342   | 12017  | 0.661  | 0.308 |
| bed            | 163   | 1625   | 0.785  | 0.632 |
| dining table   | 695   | 8174   | 0.662  | 0.381 |
| toilet         | 179   | 3298   | 0.866  | 0.730 |
| tv             | 288   | 2276   | 0.795  | 0.671 |
| laptop         | 231   | 913    | 0.740  | 0.647 |
| mouse          | 106   | 627    | 0.708  | 0.557 |
| remote         | 283   | 2146   | 0.378  | 0.138 |
| keyboard       | 153   | 1376   | 0.784  | 0.624 |
| cell phone     | 262   | 2546   | 0.462  | 0.287 |
| microwave      | 55    | 366    | 0.727  | 0.562 |
| oven           | 143   | 3618   | 0.783  | 0.523 |
| toaster        | 9     | 16     | 0.111  | 0.007 |
| sink           | 225   | 8280   | 0.702  | 0.447 |
| refrigerator   | 126   | 2632   | 0.794  | 0.599 |
| book           | 1129  | 21431  | 0.417  | 0.100 |
| clock          | 267   | 4601   | 0.674  | 0.562 |
| vase           | 274   | 1612   | 0.504  | 0.315 |
| scissors       | 36    | 256    | 0.472  | 0.379 |
| teddy bear     | 190   | 2154   | 0.674  | 0.535 |
| hair drier     | 11    | 1      | 0.000  | 0.000 |
| toothbrush     | 57    | 326    | 0.263  | 0.146 |
+----------------+-------+--------+--------+-------+
| mAP            |       |        |        | 0.430 |
+----------------+-------+--------+--------+-------+

{'mAP': 0.43032437562942505}

super(type(test_dataset), test_dataset).evaluate(results, iou_thr=0.75)

+----------------+-------+--------+--------+-------+
| class          | gts   | dets   | recall | ap    |
+----------------+-------+--------+--------+-------+
| person         | 10777 | 151136 | 0.424  | 0.322 |
| bicycle        | 314   | 1935   | 0.220  | 0.141 |
| car            | 1918  | 17858  | 0.301  | 0.209 |
| motorcycle     | 367   | 2282   | 0.351  | 0.275 |
| airplane       | 143   | 3942   | 0.664  | 0.538 |
| bus            | 283   | 875    | 0.625  | 0.589 |
| train          | 190   | 4048   | 0.679  | 0.623 |
| truck          | 414   | 2610   | 0.425  | 0.236 |
| boat           | 424   | 8890   | 0.200  | 0.094 |
| traffic light  | 634   | 12564  | 0.129  | 0.056 |
| fire hydrant   | 101   | 274    | 0.594  | 0.532 |
| stop sign      | 75    | 479    | 0.600  | 0.567 |
| parking meter  | 60    | 147    | 0.433  | 0.373 |
| bench          | 411   | 12074  | 0.221  | 0.138 |
| bird           | 427   | 13891  | 0.267  | 0.194 |
| cat            | 202   | 1221   | 0.757  | 0.617 |
| dog            | 218   | 1027   | 0.651  | 0.529 |
| horse          | 272   | 2077   | 0.540  | 0.418 |
| sheep          | 354   | 5733   | 0.432  | 0.290 |
| cow            | 372   | 4083   | 0.462  | 0.333 |
| elephant       | 252   | 3908   | 0.643  | 0.511 |
| bear           | 71    | 308    | 0.718  | 0.664 |
| zebra          | 266   | 5540   | 0.624  | 0.552 |
| giraffe        | 232   | 5620   | 0.668  | 0.582 |
| backpack       | 371   | 2462   | 0.127  | 0.029 |
| umbrella       | 407   | 4079   | 0.327  | 0.218 |
| handbag        | 540   | 4092   | 0.093  | 0.019 |
| tie            | 252   | 4043   | 0.183  | 0.118 |
| suitcase       | 299   | 1934   | 0.261  | 0.143 |
| frisbee        | 115   | 566    | 0.539  | 0.393 |
| skis           | 240   | 5994   | 0.133  | 0.046 |
| snowboard      | 69    | 343    | 0.217  | 0.134 |
| sports ball    | 260   | 4571   | 0.342  | 0.255 |
| kite           | 327   | 7137   | 0.321  | 0.175 |
| baseball bat   | 145   | 1113   | 0.172  | 0.067 |
| baseball glove | 148   | 1161   | 0.257  | 0.117 |
| skateboard     | 179   | 1872   | 0.358  | 0.237 |
| surfboard      | 267   | 4453   | 0.270  | 0.156 |
| tennis racket  | 225   | 2015   | 0.293  | 0.183 |
| bottle         | 1013  | 7547   | 0.247  | 0.116 |
| wine glass     | 341   | 1171   | 0.194  | 0.115 |
| cup            | 895   | 4976   | 0.345  | 0.214 |
| fork           | 215   | 1029   | 0.172  | 0.080 |
| knife          | 325   | 1714   | 0.098  | 0.029 |
| spoon          | 253   | 1237   | 0.119  | 0.046 |
| bowl           | 623   | 3130   | 0.456  | 0.293 |
| banana         | 370   | 7206   | 0.265  | 0.117 |
| apple          | 236   | 2028   | 0.250  | 0.094 |
| sandwich       | 177   | 1156   | 0.486  | 0.318 |
| orange         | 285   | 2710   | 0.411  | 0.210 |
| broccoli       | 312   | 13007  | 0.308  | 0.108 |
| carrot         | 365   | 9667   | 0.216  | 0.064 |
| hot dog        | 125   | 470    | 0.304  | 0.233 |
| pizza          | 284   | 2390   | 0.521  | 0.434 |
| donut          | 328   | 2525   | 0.463  | 0.293 |
| cake           | 310   | 1742   | 0.374  | 0.199 |
| chair          | 1771  | 33923  | 0.244  | 0.126 |
| couch          | 261   | 1659   | 0.521  | 0.321 |
| potted plant   | 342   | 12017  | 0.266  | 0.102 |
| bed            | 163   | 1625   | 0.534  | 0.405 |
| dining table   | 695   | 8174   | 0.397  | 0.230 |
| toilet         | 179   | 3298   | 0.659  | 0.549 |
| tv             | 288   | 2276   | 0.635  | 0.507 |
| laptop         | 231   | 913    | 0.576  | 0.499 |
| mouse          | 106   | 627    | 0.557  | 0.459 |
| remote         | 283   | 2146   | 0.166  | 0.073 |
| keyboard       | 153   | 1376   | 0.484  | 0.333 |
| cell phone     | 262   | 2546   | 0.282  | 0.177 |
| microwave      | 55    | 366    | 0.545  | 0.438 |
| oven           | 143   | 3618   | 0.413  | 0.277 |
| toaster        | 9     | 16     | 0.111  | 0.007 |
| sink           | 225   | 8280   | 0.351  | 0.179 |
| refrigerator   | 126   | 2632   | 0.556  | 0.424 |
| book           | 1129  | 21431  | 0.117  | 0.024 |
| clock          | 267   | 4601   | 0.468  | 0.339 |
| vase           | 274   | 1612   | 0.270  | 0.167 |
| scissors       | 36    | 256    | 0.250  | 0.189 |
| teddy bear     | 190   | 2154   | 0.374  | 0.240 |
| hair drier     | 11    | 1      | 0.000  | 0.000 |
| toothbrush     | 57    | 326    | 0.070  | 0.040 |
+----------------+-------+--------+--------+-------+
| mAP            |       |        |        | 0.259 |
+----------------+-------+--------+--------+-------+

{'mAP': 0.2592655122280121}

The customdataset uses VOC-style AP calculation, its implementation when calculating TP and FPs are a little bit different from COCO evaluation.

@Mxbonn If you use the official evaluation tool provided by COCO to evaluate the results of Pascal VOC dataset or ImageNet DET dataset, you will also get slightly different mAPs. The implementation of evaluation metrics of different datasets are not exactly the same.

authors: Mxbonn ZwwWayne hellock


text:
Seems to correct #8208

Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation
This appears to fix #8208 for me, but correct me if I'm wrong (if there's any unintended consequences to this modification)
Modification
Fix the error when using frozen stages in swin and cspnext:

RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one.

I believe this can be fixed by adding self._freeze_stages() at the end of the backbone __init__ methods
BC-breaking (Optional)
Not that I'm aware of
Checklist

Pre-commit or other linting tools are used to fix the potential lint issues.
The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls.
The documentation has been modified accordingly, like docstring or example tutorials.

@austinmw Please fix the CI

@hhaAndroid Can you help me understand why this is failing?

source: gharchive/pull-request

authors: austinmw hhaAndroid

repo: open-mmlab/mmdetection

license_type: permissive

license_source: github-api


text:
warning: no files found matching '*.cu' under directory 'mmdet3d/.mim/ops'

envs
branch: master
cuda:  11.1
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install mmcv-full==1.4.8 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html && pip install mmdet==2.19.0 && pip install mmsegmentation==0.20.0

Warning
warning: no files found matching '*.cpp' under directory 'mmdet3d/.mim/ops'
warning: no files found matching '*.cu' under directory 'mmdet3d/.mim/ops'
warning: no files found matching '*.h' under directory 'mmdet3d/.mim/ops'
warning: no files found matching '*.cc' under directory 'mmdet3d/.mim/ops'

Under this warning I can install mmdetection3d and train successly, but there is no build folder generated, and the traing speed is slow.

After installing ninja by conda install -c conda-forge ninja, the warning still exists.
Anyone know has this problem?

It does not matter and please just ignore them. We may clean these unused messages afterward.

source: gharchive/issue

authors: MaRongbo Tai-Wang


text:
spconv with regnet

Could you give me some advices about how to modify spconv with regnet? Thank you!

I think it is still an open question. RegNet is a kind of architecture composed of several basic convolutional layers. So basically what you need to do is to replace the conventional CNN with spconv.

authors: Tai-Wang zzyzzyzz


text:
[Docs] Update introduction and fix issues

Modification

add an introduction to docs.
fix links and typos.
update docs apis config.

Codecov Report

Merging #10 (b2df6fe) into main (486c8c2) will increase coverage by 0.07%.
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@@            Coverage Diff             @@
##             main      #10      +/-   ##
==========================================
+ Coverage   71.56%   71.63%   +0.07%
==========================================
  Files         116      116
  Lines        5546     5560      +14
  Branches     1055     1055
==========================================
+ Hits         3969     3983      +14
  Misses       1301     1301
  Partials      276      276

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mmfewshot/utils/infinite_sampler.py
76.59% <0.00%> (-0.65%)
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mmfewshot/classification/models/heads/base_head.py
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mmfewshot/detection/datasets/dataset_wrappers.py
80.40% <0.00%> (+0.08%)
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...ewshot/classification/datasets/dataset_wrappers.py
86.51% <0.00%> (+0.15%)
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87.33% <0.00%> (+0.16%)
:arrow_up:

mmfewshot/classification/datasets/base.py
48.31% <0.00%> (+0.58%)
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...tection/models/detectors/query_support_detector.py
30.10% <0.00%> (+0.75%)
:arrow_up:

mmfewshot/detection/models/detectors/meta_rcnn.py
32.65% <0.00%> (+1.40%)
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repo: open-mmlab/mmfewshot


text:
[Bug]

Hi !!
I am trying to pass this command in google colab :
!CUDA_VISIBLE_DEVICES=0 PORT=29500 ./tools/dist_train.sh \ {/content/mmpose/configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/cspnext-m_udp_8xb64-210e_coco-wholebody-256x192.py}/group_fisher_{act}prune{cspnext-m_udp_8xb64-210e_coco-wholebody-256x192}.py 1
--work-dir '/content/mmrazor/work_dir'
and I am receiving this error :
/usr/local/lib/python3.10/dist-packages/torch/distributed/launch.py:181: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use-env is set by default in torchrun.
If your script expects --local-rank argument to be set, please
change it to read from os.environ['LOCAL_RANK'] instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
usage: train.py [-h] [--work-dir WORK_DIR] [--amp] [--auto-scale-lr] [--resume]
[--cfg-options CFG_OPTIONS [CFG_OPTIONS ...]]
[--launcher {none,pytorch,slurm,mpi}] [--local_rank LOCAL_RANK]
config
train.py: error: unrecognized arguments: --local-rank=0
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 2) local_rank: 0 (pid: 10358) of binary: /usr/bin/python3
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launch.py", line 196, in
main()
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launch.py", line 192, in main
launch(args)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launch.py", line 177, in launch
run(args)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 134, in call
return launch_agent(self._config, self._entrypoint, list(args))
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
./tools/train.py FAILED
Failures:
<NO_OTHER_FAILURES>
Root Cause (first observed failure):
[0]:
time      : 2023-10-17_14:58:52
host      : 511a211dac06
rank      : 0 (local_rank: 0)
exitcode  : 2 (pid: 10358)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
Did somebody had the same problem ?
Thank you for your attention

What I am trying to do is prunning an model from cpsnext called mmpose, is there any way to do this using a command ?

It seems that you use torch >= 2.0. Please use torch <2.0

authors: LKJacky grpinto

repo: open-mmlab/mmrazor


text:
How to set "accum-iter" in MAE to keep effective batch size = 4096 if limited GPU / memory?

Hello,
In official MAE repo, accum_iter is used to maintain the effective batch size = 4096.
How could I maintain the effective batch size as 4096 in config, as I don't have enough resource as your setting (8xA100 80G)?
And, will the accuracy keep the same if effective batch size = 512  (256 batch size per gpu X 2 gpu )?
Thanks.

We did not use accum_iter to pre-train MAE, but theoretically, it should work the same as not using it. You can have a try and report the results here, if convenient. Thanks !

You can refer the this doc for the usage of accum_iter. Thanks!

Understood. Thanks for your quick response.

authors: YuanLiuuuuuu foolhard

repo: open-mmlab/mmselfsup


text:
Support for setting output location

Issue #157
default dir of "test-results" preserved
can be changed via cmdline -o/--output or env var OP_TEST_OUTPUT
with cmdline taking precedence
print output directory at start and end of run
msg was being created, but never written to screen
via --suffix allow changing of suffix added to files
default still current time
output and outsuffix load into conf so available to tests
for use if needed

Signed-off-by: Jason Albert albertj@us.ibm.com

This change is

thanks! Looks good and merged to master as of 839f0539d0c57aaa1b5436e7a0de1fc3075540dc

authors: stewart-ibm thejta

repo: open-power/op-test-framework

license_source: bigquery


text:
Port/double compose delivery

What's new

Port https://github.com/open-rmf/rmf-web/pull/963
Port https://github.com/open-rmf/rmf-web/pull/967

Self-checks

[ ] I have prototyped this new feature (if necessary) on Figma
[ ] I'm familiar with and follow this Typescript guideline
[ ] I added unit-tests for new components
[ ] I tried testing edge cases
[ ] I tested the behavior of the components that interact with the backend, with an e2e test

Closing after considerations that this should be in deployment forks instead

repo: open-rmf/rmf-web


text:
提供排程算法接口

请问能否提供排程算法的接口？我想尝试添加按照标签或者优先级排程的算法，甚至是按照双链相关性排程的算法，从而实践类似supermemo的优先级队列与神经回顾的功能

什么是排程算法接口？输入和输出是什么？

license: MIT


text:
[Logs SDK] Log Appender for glog

Implemented as per discussion https://github.com/open-telemetry/opentelemetry-cpp/issues/2047#issuecomment-1870815587:

Minor hack to accommodate older versions [0.3.5 - 0.5.0) which don't support higher precisions for timestamp.
Minimal examples provided to demonstrate the setup required.
Basic unit testing.
Tried using the same flags as those used in otel-cpp for configuring cmake project.
Add clang format file to help keep the style in line with otel-cpp repo

Resolved the conflict on .clang-format when merging from main (using web browser, not command line), not sure why this file stills shows as rewritten by the PR.

authors: chusitoo marcalff


text:
Pino 8 support

There was a previous request for this at #1147
but the related pr just removed testing on Pino 8

never mind see #1108 and related pr that adds pino 8

repo: open-telemetry/opentelemetry-js-contrib


text:
examples: provide an example for gRPC plugin

Same as #245 but blocked by #158.
Reference implementation: https://github.com/census-instrumentation/opencensus-node/tree/master/examples/grpc

@markwolff would you be willing to make a stub on this?

@mayurkale22 Sure I can take this

repo: open-telemetry/opentelemetry-js


text:
Update exporters.md

Fixed a typo

thanks @jclegras - can you sign the CLA? Then we can review.

authors: cartermp jclegras


text:
Change peer.service to service.origin.name and service.target.name?

@AlexanderWert to confirm that ECS doesn't have an equivalent to peer.service.

ECS service.* fields can be self-nested  under service.origin.* and service.target.*, which has a similar purpose as the peer.service in OTel. Similar to our previous discussions on client / server and source / destination, here service.origin.* and service.target.* make the relationship more explicit, without the need to know the context of the field.
_Originally posted by @AlexanderWert in https://github.com/open-telemetry/semantic-conventions/issues/1012

having service.origin.name and sevice.target.name would allow to:

use them on the same telemetry item (e.g. a log record that apply to both services)
having 1 attribute instead of 2 (span_kind would mean someone needs to set 2 attributes to identify which side peer.service applies to)
easier to query and more obvious when looking at the telemetry on what it means

authors: lmolkova trask

repo: open-telemetry/semantic-conventions


text:
Add geo fields from ECS

Fixes https://github.com/open-telemetry/opentelemetry-specification/issues/2834
Changes
This PR is to add geo fields from Elastic Common Schema(ECS) into otel general attributes. These fields can be used to carry data about a specific location such as city name, country name, longitude, latitude, postal code, and ect.
Merge requirement checklist

[x] CONTRIBUTING.md guidelines followed.
[x] CHANGELOG.md updated for non-trivial changes.
[ ] schema-next.yaml updated with changes to existing conventions.

I think it's better to change this PR to add geo to the registry

can we add geo.yaml instead?

can we add geo.yaml instead?

@trask In the registry we structure the files by the top-level namespace (in that case server and client). I'd like to keep it consistent.
But, as soon as we have a way of reusing / nesting attribute definitions under other namespaces, we would move all geo.* fields to a dedicated geo.yaml and only reuse in other yaml files like client and server.

can we add geo.yaml instead?

In addition to what Alex said, I just want to point out in ECS, geo fields are not expected to be used directly at the root of the events.

as soon as we have a way of reusing / nesting attribute definitions under other namespaces

can we use adding the geo fields as motivation to move this forward instead of adding the duplication? or is the proposal that we would still have duplication even after reuse / nesting is introduced?

can we use adding the geo fields as motivation to move this forward instead of adding the duplication? or is the proposal that we would still have duplication even after reuse / nesting is introduced?

We would like to use the geo fields as motivation to move forward with reusing / nesting attribute definitions. That's why we want to add geo fields into 2 attributes (client and server) in this PR to present the need for reusing definitions.
Do you prefer to have geo fields added into for example only client here, and then enable the reusing attribute definition feature, and then add geo fields for server?

I was thinking that by introducing reuse / nesting first, we might avoid some churn, but i'm not sure what the plan is for reuse / nesting so definitely could be mistaken

@trask We thought about this too but since we don't know when the nested fields will be implemented, we would like to add the geo fields first and then clean up later.

we don't know when the nested fields will be implemented

ya, this is a bit of my worry, so trying to leverage the motivation for adding geo fields into adding support for nested fields in general 😄
but really, I don't mean to hold this up, I'm happy to go along with whichever approach @open-telemetry/specs-semconv-maintainers want to go here

I wonder, could we add geo.yaml and then still have geoclient.yaml and geoserver.yaml and those two simple ref the attributes from geo.yaml but under the prefix server/client in each file?

I wonder, could we add geo.yaml and then still have geoclient.yaml and geoserver.yaml and those two simply ref the attributes from geo.yaml but under the prefix server/client in each file?

That exactly would be the idea with reuse under new namespace. But, IIUC, the following is not possible with the syntax / tooling yet:

and those two simply ref the attributes from geo.yaml but under the prefix server/client in each file

Hi @kaiyan-sheng !
We changed how the CHANGELOG.md is managed. Please take a look at https://github.com/open-telemetry/semantic-conventions/blob/main/CONTRIBUTING.md#adding-a-changelog-entry to see what needs to be done. Sorry for the disruption.

authors: AlexanderWert joaopgrassi kaiyan-sheng trask trisch-me


text:
How VRS relates to DNS?

This is a research project that requires discussion and research with DNS experts.

Each voice application is identified by a URL which is maintained by DNS. The DNS must be used to access a voice application.

We will wait until we have discussion with ICANN folks and get their POV.

What are the things we need to add in DNS so it accomodate the Voice Requirements.

destination point (voice.domain.name)
utterance (conversational agent)

repo: open-voice-network/docs


text:
docs: add https://iooxa.dev/ libraries

More component libraries more awesome!

hmm prettier complains 😅

authors: Westbrook daKmoR

repo: open-wc/open-wc


text:
feat: websearch

#2004

Thanks for all the hard work @cheahjs 🙌

repo: open-webui/open-webui


text:
Observing 500 error for /v1/provide-list-of-parallel-links API

Tag: test_v1.1.2
Observing '500 Internal Server Error' with API /v1/provide-list-of-parallel-links
http://xxx:xxx/v1/provide-list-of-parallel-links

curl -X POST "http://xxx:xxx/v1/provide-list-of-parallel-links" -H "accept: application/json" -H "user: User Name" -H "originator: Resolver" -H "x-correlator: 550e8400-e29b-11d4-a716-446655440000" -H "trace-indicator: 1.3.1" -H "customer-journey: Unknown value" -H "operation-key: Operation key not yet provided." -H "Content-Type: application/json" -d "{\"link-id\":\"513250006\"}"

{
  "code": 500,
  "message": "Internal Server Error"
}

Tested and it is working in v1.1.5
Logs:
curl -X POST "/v1/provide-list-of-parallel-links" -H "accept: application/json" -H "user: User Name" -H "originator: Resolver" -H "x-correlator: 550e8400-e29b-11d4-a716-446655440000" -H "trace-indicator: 1.3.1" -H "customer-journey: Unknown value" -H "operation-key: ---------------" -H "Content-Type: application/json" -d "{"link-id":"533616788"}"
{
"parallel-link-list": [
"533616788",
"533616790"
]
}

repo: openBackhaul/MicroWaveDeviceInventory


text:
PromptForBequeathingDataCausesALTbeingRequestedToNotifyLinkUpdatesToNewRelease OUTPUT port-direction uuid from "okm-0-0-1-op-c-3071" to  "okm-0-0-1-op-c-3070"

The PromptForBequeathingDataCausesALTbeingRequestedToNotifyLinkUpdatesToNewRelease is an invariant forwarding-construct.
This forwarding will help ALT to notify the link updates to the new release of OKM.
As of now , the OUTPUT Port-direction uuid is "okm-0-0-1-op-c-3071" . This uuid is for the service "/v1/end-subscription". So the intended redirection to new release will not happen.
The OUTPUT port-direction uuid should be "okm-0-0-1-op-c-3070" which corresponds to the service "/v1/notify-link-updates"
Proposal :

In the forwarding-construct PromptForBequeathingDataCausesALTbeingRequestedToNotifyLinkUpdatesToNewRelease

The OUTPUT port-direction uuid should be changed from "okm-0-0-1-op-c-3071" to  "okm-0-0-1-op-c-3070"

This will allow the ALT to notify the link updates  to the new release of OKM.

OKM LOADfile line 2226 and OKM forwardingList line 104 have to be changed from "okm-0-0-1-op-c-3071" to "okm-0-0-1-op-c-3070" for referencing the /v1/notify-link-updates/.

Already done.

repo: openBackhaul/OperationKeyManagement


text:
Validate the server side code

Take the content generated in the server side stub from the activity
Read Running Node.js application in VSCode
Run the NodeJs application and do a basic sanity
Verify whether the server is getting started without any error
Incase of any issues , Read Debugging Node.js application in VSCode and resolve the issue.
Close this issue

Done


text:
Small adjustments to economic variables

Changes of units on economic variables according to #76 and #77.

Hi all,
Is there any progress in accepting these changes?
Greets, Hettie

I was waiting for a decision on an agreed spelling of Euro, see #85 - please vote there (or suggest an alternative). Then we can close that discussion and continue with the implementation of the changes.

@erikfilias & @sebastianzwickl, per the outcome of the vote in #85, can you please work through the entire nomenclature and harmonize to the new spelling (allowing several currencies where relevant). Also, please keep the orders of magnitude (million, billion) as lower-case for consistency.

@erikfilias & @sebastianzwickl, per the outcome of the vote in #85, can you please work through the entire nomenclature and harmonize to the new spelling (allowing several currencies where relevant). Also, please keep the orders of magnitude (million, billion) as lower-case for consistency.

I've just updated the units for all variables in economy.yaml, @hettieboonman & @sebastianzwickl, please check it.
I also keep EUR/kWh for Price|Final Energy|Residential|Electricity:

authors: danielhuppmann erikfilias hettieboonman

repo: openENTRANCE/nomenclature


text:
New Category: Lists and New Rule: List markup must be explicit

New Category: Lists
New Rule:  List markup must be explicit
Requirement or recommendation:  Recommended
There is no standard for the list markup available in Section 508 so this is highly recommended to ensure that the list markup is explicit to users of screen readers.
Purpose/description:
Lists that appear to be implicit without the proper list markup. Common practices involve tables that list items, visually indented list (without list markup) or bullet or image that marks each list item visually as a list.
A proper list structure helps users of assistive technologies like screen readers understand the hierarchical structure and position of list items. This structure helps them determine the number of list items in the list and the list hierarchy of nested lists as well.
Standards:
WCAG 1.0 Priority 2
3.6 Mark up lists and list items properly
WCAG 2.0 Level A
1.3.1 Information and Relationships

List rule 1 and 2 address the use of list markup


text:
[0.4] Adds stream support

This PR adds stream support.
This resolves #19 #21 #80
Since the library is HTTP client agnostic now it was a bit more complicated than expected. See explanations below.
Here are the key changes:
Examples
Completion Create
(copy from README)
$stream = $client->completions()->createStreamed([
        'model' => 'text-davinci-003',
        'prompt' => 'Hi',
        'max_tokens' => 10,
    ]);

foreach($stream->read() as $response){
    $response->choices[0]->text;
}
// 1. iteration => 'I'
// 2. iteration => ' am'
// 3. iteration => ' very'
// 4. iteration => ' excited'
// ...

Chat Create
(copy from README)
$stream = $client->chat()->createStreamed([
    'model' => 'gpt-4',
    'messages' => [
        ['role' => 'user', 'content' => 'Hello!'],
    ],
]);

foreach($stream->read() as $response){
    $response->choices[0]->toArray();
}
// 1. iteration => ['index' => 0, 'delta' => ['role' => 'assistant'], 'finish_reason' => null]
// 2. iteration => ['index' => 0, 'delta' => ['content' => 'Hello'], 'finish_reason' => null]
// 3. iteration => ['index' => 0, 'delta' => ['content' => '!'], 'finish_reason' => null]
// ...

FineTunes Events List
(copy from README)
$stream = $client->fineTunes()->listEventsStreamed('ft-y3OpNlc8B5qBVGCCVsLZsDST');

foreach($stream->read() as $response){
    $response->message;
}
// 1. iteration => 'Created fine-tune: ft-y3OpNlc8B5qBVGCCVsLZsDST'
// 2. iteration => 'Fine-tune costs $0.00'
// ...
// xx. iteration => 'Uploaded result file: file-ajLKUCMsFPrT633zqwr0eI4l'
// xx. iteration => 'Fine-tune succeeded'

Explanations

I finally decided to go with dedicated methods for the stream requests: create() -> createStreamed() etc.
All streams return a StreamResponse object which has a method read() that returns a Generator instance for all the incoming responses.
As far as possible I reused the existing response objects. For Chat Create this was not possible as the response structure is very different from the non streaming response.

Async requests and PSR-18
PSR-18 sadly does not cover async requests. Therefore it is not possible to write generic code which will work with all the HTTP clients.
To work around this issue I have extended the OpenAI::factory() with a new method withAsyncRequest where a closure can be passed which will be used for async requests.
If GuzzleHttp is used there is an implementation available and it is not necessary to provide it manually.
To be honest, this is not very developer friendly but so far I had no better idea. The only thing we could do is to provide more implementations for various HTTP clients.
Example factory usage (with GuzzleHttp which is redundant because it is already baked in):
$client = OpenAI::factory()
    ->withApiKey($yourApiKey)
    ->withHttpClient($client = new \GuzzleHttp\Client([]))
    ->withAsyncRequest(fn (RequestInterface $request): ResponseInterface => $client->send($request, ['stream' => true]))
    ->make();

Thanks
Many thanks to @slavarazum for giving me a starting point here #21

Great job, looks like we are close!
Do we really need an extra call to the read method?
Maybe it makes sense to return Generator to stay closer to native types or make StreamResponse object as iterable.
I'm a little confused about the name of the factory method withAsyncRequest for streaming requests.
For example GuzzleHttp\Client::send method is synchronous but can be streamed. Async != stream. Perhaps it would be better to call it withStreamRequest.

Just a heads up with the streaming: the key off of choices is delta, not text

Great job, looks like we are close!
Do we need an extra call to the read method? Maybe it makes sense to return Generator to stay closer to native types or make StreamResponse object as iterable.
I'm a little confused about the name of the factory method withAsyncRequest for streaming requests. For example GuzzleHttp\Client::send method is synchronous but can be streamed. Async != stream. Perhaps it would be better to call it withStreamRequest.

Thanks @slavarazum . @nunomaduro already updated the parts you mentioned.

Just a heads up with the streaming: the key off of choices is delta, not text (https://github.com/openai/openai-cookbook/blob/main/examples/How_to_stream_completions.ipynb)

@jhull Thanks for your hint but the key in completion responses is still text. It only changes for chat completions from message to delta.

When will you release this PR?

When will you release this PR?

Later today.

authors: gehrisandro jhull slavarazum vigstudio

repo: openai-php/client


text:
There is no provision to run ddpg.

I typed the policy as 'lol' and it still gives the same result. The readme says that we can run the experiments for ddpg as well as maddpg which is definitely not working.

I also find the same problem

I typed the policy as 'lol' and it still gives the same result. The readme says that we can run the experiments for ddpg as well as maddpg which is definitely not working.

Hi,do you know how we can run the experiments for ddpg now?

repo: openai/maddpg

license: mit


text:
[Whisper] Update docs to better explain use of prompt and temperature for transcription and translation

Describe the bug
The docs do not clearly explain the use of prompt and temperature when it comes to the transcription and translation endpoints.
Include a sample file with various prompt values to clarify.
To Reproduce
Read docs
Code snippets
No response
OS
Windows 11
Node version
Node v16.16.0
Library version
openai v3.2.0

The only way to use it is to set it like this:
const resp = await openai.createTranscription( 	fs.createReadStream(audioFile.filepath) as any, 	'whisper-1', // model 	'', // prompt 	'', // responseFormat 	0.2, // temperature 	'en' // language  );
My proposition would be just use options as an object, so we can omit any unused params.

yes thats messy, you need to check the TS files.
 createTranscription(file: File, model: string, prompt?: string, responseFormat?: string, temperature?: number, language?: string, options?: AxiosRequestConfig): Promise<import("axios").AxiosResponse<CreateTranscriptionResponse, any>>;
    /**
     *
     * @summary Translates audio into into English.
     * @param {File} file The audio file to translate, in one of these formats: mp3, mp4, mpeg, mpga, m4a, wav, or webm.
     * @param {string} model ID of the model to use. Only `whisper-1` is currently available.
     * @param {string} [prompt] An optional text to guide the model\\\'s style or continue a previous audio segment. The [prompt](/docs/guides/speech-to-text/prompting) should be in English.
     * @param {string} [responseFormat] The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.
     * @param {number} [temperature] The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use [log probability](https://en.wikipedia.org/wiki/Log_probability) to automatically increase the temperature until certain thresholds are hit.
     * @param {*} [options] Override http request option.
     * @throws {RequiredError}
     * @memberof OpenAIApi
     */

 createTranslation(file: File, model: string, prompt?: string, responseFormat?: string, temperature?: number, options?: AxiosRequestConfig): Promise<import("axios").AxiosResponse<CreateTranslationResponse, any>>;
    /**
     *
     * @summary Delete a file.
     * @param {string} fileId The ID of the file to use for this request
     * @param {*} [options] Override http request option.
     * @throws {RequiredError}
     * @memberof OpenAIApi

This is documented in the reference, eg: https://platform.openai.com/docs/api-reference/audio/create#audio/create-prompt

authors: rattrayalex rmtuckerphx romain130492 spnq

repo: openai/openai-node


text:
You've made too many requests, please wait one minute and try again.

Request ID: ..
Please contact us through our help center
if this issue persists.
Return to homepage

Hi @insoxin, thanks for writing in!
This error looks like our system working as intended to rate limit users.  Is there a different response you are expecting?

I'm going to close this due to inactivity. Please let me know if there's additional information!

authors: hallacy insoxin

repo: openai/openai-python


text:
AttributeError: module 'openai' has no attribute 'ChatCompletion'. Did you mean: 'Completion'?

Describe the bug
Trying to run gpt-3.5-turbo model but it's throwing next error: AttributeError: module 'openai' has no attribute 'ChatCompletion'. Did you mean: 'Completion'?
Updated to latest version, not working.
To Reproduce

Install latest openai
Copy code from official documentation
Run

Code snippets
import openai
openai.api_key = 'sk-***'

completion = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[{"role": "user", "content": "Tell the world about the ChatGPT API in the style of a pirate."}]
)

print(completion)

OS
Arch Linux - 6.1.4-arch1-1
Python version
Python v3.10.9
Library version
openai-python v0.27.0

Can you try making a new virtual env and seeing if the issue persists?

Can you try making a new virtual env and seeing if the issue persists?

Fixed


text:
Ai answers itself in my android app

Hello guys, I program in C# and use your example. Please tell me how you achieved that when AI speaks, its voice is not recorded.

I have been having the exact same issue and I am hoping one of you lovely gents has solved it somehow

👋  You will need to apply AEC onto the Audio input stream .... and I have only been able to achieve this when my recording input method is VOICE_COMMUNICATION.

👋 You will need to apply AEC onto the Audio input stream .... and I have only been able to achieve this when my recording input method is VOICE_COMMUNICATION.

Hi, big thx, but i already tried it without success :(

I realized most non-Samsung devices are OK without additional audio treatments.
Samsung devices, from what I can tell, needed audioManager.setMode(AudioManager.MODE_IN_COMMUNICATION) in addition to AEC. But this could throw the phone into phone-call mode where the audio only comes out quietly from the top earpiece.
...If you've gotten this far, then the last hurdle is to force the audio out from the built-in speakers.

authors: Kioshilol kingstonfung sleechie

repo: openai/openai-realtime-console


text:
What is EMA_RATE for jerk agent?

https://github.com/openai/retro-baselines/blob/master/agents/jerk_agent.py#L15
as far as I can tell, it is not used in the code. Am I missing something?

It looks like this is left over from an old implementation.

authors: tristansokol unixpickle

repo: openai/retro-baselines


text:
MPI Issue

when running ppo example, this issue raises,
`There are not enough slots available in the system to satisfy the 4 slots
that were requested by the application:
/Library/Frameworks/Python.framework/Versions/3.6/bin/python3
Either request fewer slots for your application, or make more slots available
for use.`
I'm currently using macbook air (2017 model), macOS Mojave, python 3.6

Hi @ruifengma! This is not a bug---what it's saying is that you have less than 4 physical CPU cores and so you can't run 4 parallel processes with one on each core. Set --num_cpu to 2 or 1 instead.

authors: jachiam ruifengma

repo: openai/spinningup


text:
JITFunction treats any annotated arg as constexpr

Using triton.__version__ == '2.0.0'.
JITFunction will treat any annotated argument to the decorated kernel as constexpr, regardless of what it's annotated with.
See example failures below.
This seems like a blunt decision mechanism, given that it is so simple to actually examine the annotations and determine which of them are constexpr.
I can only guess that perhaps no test was made for the constexpr class because it is impossible to import at module level, due to the impossible circular import tangle (#884).
I have opened #1345 with a simple proposed fix.
With this fix applied, the two examples below work without issue.
Failure examples:
@triton.jit
def some_kernel(
        block_size: tl.constexpr,
        input_matrix: Tensor,
        input_matrix_outer_stride: int,
        output_matrix: Tensor,
        output_matrix_outer_stride: int,
        iteration_count: int,
):
    ... do stuff ...

Will result in the (unjustified, IMHO) cryptic error:
  File ".../triton/runtime/jit.py", line 423, in jit
    return decorator(fn)
  File ".../triton/runtime/jit.py", line 416, in decorator
    return JITFunction(
  File ".../triton/runtime/jit.py", line 325, in __init__
    self.run = self._make_launcher()
  File ".../triton/runtime/jit.py", line 291, in _make_launcher
    exec(src, scope)
  File "<string>", line 3
    sig_key =  ,
               ^
SyntaxError: invalid syntax

Removing int annotations above as follows:
@triton.jit
def some_kernel(
        block_size: tl.constexpr,
        input_matrix: Tensor,
        input_matrix_outer_stride,
        output_matrix,
        output_matrix_outer_stride,
        iteration_count,
):
    tl.store(output_matrix, tl.load(input_matrix))

Results in the even more cryptic exception chain below, as it will attempt to treat the entire input_matrix as constexpr:
Traceback ...:
  File "<string>", line 21, in some_kernel
KeyError: (... long list of data including the content of the passed `input_matrix` ...)

During handling of the above exception, another exception occurred:

Traceback ...:
  ... long traceback ..
  File ".../triton/language/core.py", line 872, in load
    return semantic.load(pointer, mask, other, cache_modifier, eviction_policy, volatile, _builder)
  File ".../triton/language/semantic.py", line 757, in load
    if not ptr.type.scalar.is_ptr():
AttributeError: 'constexpr' object has no attribute 'type'

The above exception was the direct cause of the following exception:

Traceback ...:
triton.compiler.CompilationError: at 9:36:
def some_kernel(
	...

I got
Traceback (most recent call last):
  File "/home/shshao/Documents/triton_test.py", line 10, in <module>
    def kernel(Z: tl.pointer_type(tl.float32), X: tl.pointer_type(tl.float32), SIZE: tl.constexpr):
  File "/home/shshao/triton/python/triton/runtime/jit.py", line 424, in jit
    return decorator(fn)
  File "/home/shshao/triton/python/triton/runtime/jit.py", line 416, in decorator
    return JITFunction(
  File "/home/shshao/triton/python/triton/runtime/jit.py", line 321, in __init__
    self.constexprs = [index for index, ty in self.annotations.items() if issubclass(ty, constexpr)]
  File "/home/shshao/triton/python/triton/runtime/jit.py", line 321, in <listcomp>
    self.constexprs = [index for index, ty in self.annotations.items() if issubclass(ty, constexpr)]
TypeError: issubclass() arg 1 must be a class

now with the program below
import torch
import triton
import triton.language as tl
import numpy as np

SIZE = 128
# define the kernel / launch-grid

@triton.jit
def kernel(Z: tl.pointer_type(tl.float32), X: tl.pointer_type(tl.float32), SIZE: tl.constexpr):
    off = tl.arange(0, SIZE)
    x = tl.load(X + off)
    z = x
    tl.store(Z + off, z)

# inputs
x = np.random.normal(SIZE)
# triton result
x_tri = torch.tensor(x, device='cuda')
z_tri = torch.tensor(np.empty_like(x), device='cuda')
kernel[(1, )](z_tri, x_tri, SIZE=SIZE, num_warps=4)

My bad. Thanks for finding it. Submitted #1352 to fix this.

resolved by #1352

repo: openai/triton


text:
[AutoPR deviceupdate] apiview demo

Create to sync https://github.com/openapi-env-test/azure-rest-api-specs/pull/3072

API change check for Azure.IoT.DeviceUpdate
API changes have been detected in Azure.IoT.DeviceUpdate. You can review API changes here

repo: openapi-env-test/azure-sdk-for-net


text:
[AutoPR-sdkreleaseplannertest]SDK Release Planner Test PR

Create to sync https://github.com/Azure/azure-rest-api-specs/pull/21720

/azp run prepare-pipelines

/azp run net - sdkreleaseplannertest - ci


text:
Fix Energy scale lib and resource path

The path for energy scale is tests/energy_scale/
so the lib and resources need to be
Resource          ../../lib/energy_scale_utils.robot
Resource          ../../lib/rest_client.robot
Resource          ../../lib/openbmc_ffdc.robot
Resource          ../../lib/boot_utils.robot
Resource          ../../lib/ipmi_client.robot
Resource          ../../syslib/utils_os.robot
```

In review https://gerrit.openbmc-project.xyz/#/c/9125/

repo: openbmc/openbmc-test-automation


text:
[Redfish] redfish/update_service/test_firmware_tftp_upload_image.robot check FirmwareInventory state failed

I use openbmc-test-automation v3.0-stable branch and run the redfish/update_service/test_firmware_tftp_upload_image.robot test case.
After uploading the BMC image via TFTP, when checking the FirmwareInventory status, it is confirmed that it is "Enabled" and not "Disabled" & "Updating". Do we need to validate the "Disabled" & "Updating" status in the robot source code?

robot -v OPENBMC_HOST:100.100.100.64 -v TFTP_SERVER:x.x.x.x -v IMAGE_FILE_NAME:v2.12.2.all.tar --include TFTP_Download_Install_With_ApplyTime_OnReset_Policy redfish/update_service/test_firmware_tftp_upload_image.robot

$ curl -H "X-Auth-Token: $token" -k https://${bmc}/redfish/v1/UpdateService/FirmwareInventory/ece98953
{
  "@odata.id": "/redfish/v1/UpdateService/FirmwareInventory/ece98953",
  "@odata.type": "#SoftwareInventory.v1_1_0.SoftwareInventory",
  "Description": "BMC image",
  "Id": "ece98953",
  "Name": "Software Inventory",
  "RelatedItem": [
    {
      "@odata.id": "/redfish/v1/Managers/bmc"
    }
  ],
  "RelatedItem@odata.count": 1,
  "Status": {
    "Health": "OK",
    "HealthRollup": "OK",
    "State": "Enabled"
  },
  "Updateable": true,
  "Version": "v2.12.2"
}

@susilsi7 please take a look into this.

This code is designed on old API and has to be migrated on Task API.


text:
Journalctl Output Verification

https://github.com/openbmc/openbmc/issues/241
https://github.com/openbmc/openbmc/issues/240
Please update the issue in template format.

The changes for journal and test are in place.. closing

authors: gkeishin sivassrr


text:
Use getObjectMetadata to get object version when uploading file to S3 archive (fixes #4161)

Your pull request should…

[ ] have a concise title
[ ] close an accompanying issue if one exists
[ ] be against the correct branch
[ ] include migration scripts and documentation, if appropriate
[ ] pass automated tests
[ ] have a clean commit history
[ ] have proper commit messages (title and body) for all commits

Fixes #4161

Actually, if we could get this into r/11.x that would be awesome.  It would affect all OC versions, and there's a new 11.x coming at some point.

authors: gregorydlogan mtneug rute-santos

repo: opencast/opencast


text:
Use legacy rest-docs per default

The new OpenAPI implementation has problems with the FormParams, which are not easily solvable, we may need to wait for the Karaf 4.4.7 update. In the meantime, we should use the old rest-docs implementation.
Your pull request should…

[ ] have a concise title
[ ] close an accompanying issue if one exists
[ ] be against the correct branch
[ ] include migration scripts and documentation, if appropriate
[ ] pass automated tests
[ ] have a clean commit history
[ ] have proper commit messages (title and body) for all commits
[ ] explain why it needs to be merged into the legacy branch, if it is targeting the legacy branch

[…] we may need to wait for the Karaf 4.4.7

Out of curiosity: What's the problem, and what's the Karaf 4.4.7-feature that fixes the problem?

It will only solve some problems; the version of the whole swagger stuff seems too old.

authors: geichelberger lkiesow


text:
Require address for >$5k/yr contributors when using Add Funds

User story
Related to #4692 - we want this to apply to add funds transactions too. US fiscal hosts have to report addresses for all donors giving >$5k per calendar year, but we sometimes miss collecting this info when manually adding funds. This was in the original issue spec but was not completed before the issue was closed.
Best solution for this problem
Require address in add funds modal if contributor has given over $5,000 this calendar year to that Fiscal Host. Ideally this would be a setting at the Host level, because non-US or non-nonprofits Hosts may not have this requirement.
MVP
We could hard-code this for OSC and OCF only if we don't want to take on building a user-facing optional setting.
Metrics
Fewer unknown addresses at tax reporting time.
P2 low frequency, high impact

More and more, "add funds" is duplicating code from the contribution flow. I honestly wonder if we should find a way to merge them.

Yes if the threshold is crossed when using add funds we need to ensure we have the address on file.
Correct, this is an IRS reporting requirement. We have to give them a big list of all >$5k donors at the end of the year, with their addresses. This year we were missing a lot because they were added funds.

Yes that solution sounds right. I don't know what the user interface should be, we just need to ensure we have these addresses on file.

authors: Betree alanna

repo: opencollective/opencollective


text:
Add import alias @ for simplifying frontend imports

An import alias (suggested: @) could make importing files easier and more consistent (and enable smoother copying and moving of components from one place to another).
From this
import Avatar from '../../Avatar';
import helper from '../../../lib/helper';

to this:
import Avatar from '@/components/Avatar';
import helper from '@/lib/helper';

I'm up for it; it's a pretty standard pattern that simplifies refactors (especially since we're sorting imports alphabetically)

authors: Betree gustavlrsn


text:
[Project] Transaction Statement:  Alternate Views & Implementation for Collectives

### Tasks
- [ ] Test performance for biggest non-fiscal host account to understand if a materialized view is necessary
- [x] Implement version with same grouping as for Hosts for Collectives

Last week
A first version of the transaction statement was deployed, publicly available behind an opt-in preview feature flag.


text:
navigate over unresolved issues

Actions before raising this issue

[X] I searched the existing issues and did not find anything similar.
[X] I read/searched the docs

Is your feature request related to a problem? Please describe.
There are a some resolved issues in the job. I've gone through the annotations and added some new issues. Now annotator to fix them should somehow find all frames with unresolved issues.
In the latest version of cvat (2.10.2) this is impossible. There is only a way to navigate over frames with issues (both resolved and unresolved):

If there are a lot of resolved issues, it's hard and annoying to find frames with new issues.
Describe the solution you'd like
Cheking show/hide resolved issue should also hide frames with them:

This will allow you to skip frames with unresolved issues.
Describe alternatives you've considered
No response
Additional context
No response

@nmanovic can you assign please me this issue?

@ArMaxik can you please help me recreate the issue?

@tahamukhtar20 Glad to hear you are working on this issue!
Small steps:

As a reviewer open a job and create some issues.
As an annotator fix issues and resolve them. Or the reviewer can resolve them, it doesn't matter
As a reviewer you found new mistakes, so create some new issues on the same job.
As an annotator, I want to find new issues and skip already resolved. Now it is impossible, I should look through all of them.

There is a perfect button "show/hide resolved issues", but buttons "find the next/previous frame with issues" ignore it

@nmanovic @bsekachevv is the issue still open, if so can you please assign it to me

@nmanovic you can unassign. I am not able to work on this currently. Thanks

@thekavikumar , please let me know if you want to take the issue.

@nmanovic yeah, I will take the issue

@nmanovic @klakhov please close this issue as it has been resolved!

Resolved in #7653

authors: ArMaxik klakhov nmanovic tahamukhtar20 thekavikumar

repo: opencv/cvat


text:
Add mqtt in Cmake

Dear all,
I am trying to integrate the paho mqtt library with the demo code. I have installed and build the paho MQTT file according to 1.
And now I am trying to modify demos/CMakeList.txt with the reference of 2. I used build_demos.sh and it could not be compiled properly.
The OS is ubuntu 16.04 LTS.
Or is there any way to add the flag in CMakeList.txt for compilation?
because the compile command of mqtt need "-lpaho-mqtt3as -lpaho-mqtt3"
I hope someone could suggest me the solution.
Thank you

Does it not work because of some specifics in the Open Model Zoo CMake scripts? If not, you'd be better off asking the developers of the library you're trying to integrate.

Thanks for the reply.
I am new to Cmake so I might need help as the cmake of this is quite different with others.
I tried use find(PahoQqttC REQUIRED) but it does not work.
Thank you

This does sound like it's more related to that project than to this one. You should contact the developers of Paho MQTT.

authors: CzJakethedog IRDonch


text:
Add okapi-tck to release artifacts

It's required by custom back ends in order to run TCK tests, however we don't release it at the moment.

fixed by https://github.com/opencypher/cypher-for-apache-spark/pull/477

repo: opencypher/cypher-for-apache-spark


text:
Development:  Video Freezes as soon as you press start recording

I know we have discussed this in other threads, but I am starting this thread to log the issue as we work on it in the hopes that someone sees something and goes, ah ha!  Anyway, here is the log when the freeze happens:
FPS:18.4         AVG_FPS:18.6
known client: 26, sent = 19213, must be sent outlen = 19213
Ask YOLO to restart to record on a file demo.mp4
Process YOLO restart
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.5         AVG_FPS:18.6
known client: 26, sent = 19309, must be sent outlen = 19309
problem with request:  Error: read ECONNRESET
    at TCP.onStreamRead (internal/stream_base_commons.js:209:20) {
  errno: 'ECONNRESET',
  code: 'ECONNRESET',
  syscall: 'read'
}
YOLO stream error: Error: read ECONNRESET
Too much retries, YOLO took more than 3 min to start, likely an error
Process YOLO exit
Process YOLO stopped
Process YOLO started
 CUDA-version: 10020 (10020), cuDNN: 8.2.1, GPU count: 1
 OpenCV version: 4.1.1
 0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 2    416 x 416 x   3 ->  208 x 208 x  32 0.075 BF
   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF
   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
   3 route  2                                  1/2 ->  104 x 104 x  32
   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF
   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF
   6 route  5 4                                    ->  104 x 104 x  64
   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
   8 route  2 7                                    ->  104 x 104 x 128
   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF
  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  11 route  10                                 1/2 ->   52 x  52 x  64
  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF
  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF
  14 route  13 12                                  ->   52 x  52 x 128
  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  16 route  10 15                                  ->   52 x  52 x 256
  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF
  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  19 route  18                                 1/2 ->   26 x  26 x 128
  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF
  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF
  22 route  21 20                                  ->   26 x  26 x 256
  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  24 route  18 23                                  ->   26 x  26 x 512
  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF
  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  29 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  30 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
  31 route  27                                     ->   13 x  13 x 256
  32 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  33 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  34 route  33 23                                  ->   26 x  26 x 384
  35 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  36 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  37 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 6.910
avg_outputs = 310203
 Allocate additional workspace_size = 26.22 MB
Loading weights from yolov4-tiny.weights...Demo
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
Create CUDA-stream - 0
 Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
Done! Loaded 38 layers from weights-file

 seen 64, trained: 0 K-images (0 Kilo-batches_64)
video file: opendatacam_videos_uploaded/demo.mp4
Video stream: 640 x 360
Objects:

 JSON-stream sent.

FPS:0.0 	 AVG_FPS:0.0
Objects:

 JSON-stream sent.

8070 still spits out data and the video is not frozen on 8090.

Would it be possible to see the config.JSON and the complete log from the beginning until you get the error?
Also do you run ODC from a live feed or a video file?

We are just running the demo video for now.  Here is the config file:
{
  "OPENDATACAM_VERSION": "3.0.2",
  "PATH_TO_YOLO_DARKNET" : "/home/mark/ODC/darknet",
  "CMD_TO_YOLO_DARKNET" : "/home/mark/ODC/darknet/darknet",
  "VIDEO_UPLOAD_FOLDER": "/home/mark/ODC/darknet/opendatacam_videos_uploaded",
  "VIDEO_INPUT": "file",
  "NEURAL_NETWORK": "yolov4-tiny",
  "VIDEO_INPUTS_PARAMS": {
    "file": "opendatacam_videos_uploaded/demo.mp4",
    "usbcam": "v4l2src device=/dev/video0 ! video/x-raw, framerate=30/1, width=640, height=360 ! videoconvert ! appsink",
    "raspberrycam": "nvarguscamerasrc ! video/x-raw(memory:NVMM),width=1280, height=720, framerate=30/1, format=NV12 ! nvvidconv ! video/x-raw, format=BGRx, width=640, height=360 ! videoconvert ! video/x-raw, format=BGR ! appsink",
    "remote_cam": "YOUR IP CAM STREAM (can be .m3u8, MJPEG ...), anything supported by opencv",
    "remote_hls_gstreamer": "souphttpsrc location=http://YOUR_HLSSTREAM_URL_HERE.m3u8 ! hlsdemux ! decodebin ! videoconvert ! videoscale ! appsink",
    "simulation": "--yolo_json public/static/placeholder/alexeydetections30FPS.json --video_file_or_folder public/static/placeholder/frames --isLive true --jsonFps 20 --mjpgFps 0.2"
  },
  "TRACKER_SETTINGS": {
    "objectMaxAreaInPercentageOfFrame": 80,
    "confidence_threshold": 0.2,
    "iouLimit": 0.05,
    "unMatchedFrameTolerance": 5,
    "fastDelete": true
  },
  "COUNTER_SETTINGS": {
    "countingAreaMinFramesInsideToBeCounted": 1,
    "countingAreaVerifyIfObjectEntersCrossingOneEdge": true,
    "minAngleWithCountingLineThreshold": 5,
    "computeTrajectoryBasedOnNbOfPastFrame": 5
  },
  "VALID_CLASSES": ["*"],
  "DISPLAY_CLASSES": [
    { "class": "bicycle", "hexcode": "1F6B2"},
    { "class": "person", "hexcode": "1F6B6"},
    { "class": "truck", "hexcode": "1F69B"},
    { "class": "motorbike", "hexcode": "1F6F5"},
    { "class": "car", "hexcode": "1F697"},
    { "class": "bus", "hexcode": "1F68C"}
  ],
  "PATHFINDER_COLORS": [
    "#1f77b4",
    "#ff7f0e",
    "#2ca02c",
    "#d62728",
    "#9467bd",
    "#8c564b",
    "#e377c2",
    "#7f7f7f",
    "#bcbd22",
    "#17becf"
  ],
  "COUNTER_COLORS": {
    "yellow": "#FFE700",
    "turquoise": "#A3FFF4",
    "green": "#a0f17f",
    "purple": "#d070f0",
    "red": "#AB4435"
  },
  "NEURAL_NETWORK_PARAMS": {
    "yolov4": {
      "data": "cfg/coco.data",
      "cfg": "cfg/yolov4-416x416.cfg",
      "weights": "yolov4.weights"
    },
    "yolov4-tiny": {
      "data": "cfg/coco.data",
      "cfg": "cfg/yolov4-tiny.cfg",
      "weights": "yolov4-tiny.weights"
    }
  },
  "TRACKER_ACCURACY_DISPLAY": {
    "nbFrameBuffer": 300,
    "settings": {
      "radius": 3.1,
      "blur": 6.2,
      "step": 0.1,
      "gradient": {
        "0.4":"orange",
        "1":"red"
      },
      "canvasResolutionFactor": 0.1
    }
  },
  "DATABASE": "mongo",
  "DATABASE_PARAMS": {
    "mongo": {
      "url": "mongodb://127.0.0.1:27017"
    }
  },
  "PORTS": {
    "app": 8080,
    "darknet_json_stream": 8070,
    "darknet_mjpeg_stream": 8090
  },
  "GPS": {
    "enabled": false,
    "port": 2947,
    "hostname": "localhost",
    "signalLossTimeoutSeconds": 60,
    "csvExportOpenStreetMapsUrl": true
  }
}

Here is the log from the beginning:
> Ready on http://localhost:8080
> Ready on http://192.168.1.15:8080
Success init db
(node:2178) [DEP0001] DeprecationWarning: OutgoingMessage.flush is deprecated. Use flushHeaders instead.
Process YOLO started
 CUDA-version: 10020 (10020), cuDNN: 8.2.1, GPU count: 1
 OpenCV version: 4.1.1
 0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1
   layer   filters  size/strd(dil)      input                output
   0 conv     32       3 x 3/ 2    416 x 416 x   3 ->  208 x 208 x  32 0.075 BF
   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF
   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
   3 route  2 		                       1/2 ->  104 x 104 x  32
   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF
   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF
   6 route  5 4 	                           ->  104 x 104 x  64
   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
   8 route  2 7 	                           ->  104 x 104 x 128
   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF
  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  11 route  10 		                       1/2 ->   52 x  52 x  64
  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF
  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF
  14 route  13 12 	                           ->   52 x  52 x 128
  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  16 route  10 15 	                           ->   52 x  52 x 256
  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF
  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  19 route  18 		                       1/2 ->   26 x  26 x 128
  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF
  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF
  22 route  21 20 	                           ->   26 x  26 x 256
  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  24 route  18 23 	                           ->   26 x  26 x 512
  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF
  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  29 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  30 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
  31 route  27 		                           ->   13 x  13 x 256
  32 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  33 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  34 route  33 23 	                           ->   26 x  26 x 384
  35 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  36 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  37 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 6.910
avg_outputs = 310203
 Allocate additional workspace_size = 26.22 MB
Loading weights from yolov4-tiny.weights...Demo
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
Create CUDA-stream - 0
 Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
Done! Loaded 38 layers from weights-file
Got first JSON chunk
Didn't get video resolution yet, not sending tracker info
setvideoresolution

 seen 64, trained: 0 K-images (0 Kilo-batches_64)
video file: opendatacam_videos_uploaded/demo.mp4
Video stream: 640 x 360
Objects:

 JSON-stream sent.

FPS:0.0 	 AVG_FPS:0.0
Objects:

JSON_sender: new client 24
SSE: Sending update to clients
 JSON-stream sent.

FPS:1.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:3.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:4.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:6.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:7.5 	 AVG_FPS:0.0
MJPG_sender: new client 26
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:8.5 	 AVG_FPS:0.0
known client: 26, sent = 19460, must be sent outlen = 19460
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:9.3 	 AVG_FPS:0.0
known client: 26, sent = 19435, must be sent outlen = 19435
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:10.1 	 AVG_FPS:0.0
known client: 26, sent = 19511, must be sent outlen = 19511
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:10.9 	 AVG_FPS:0.0
known client: 26, sent = 19450, must be sent outlen = 19450
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:11.2 	 AVG_FPS:0.0
known client: 26, sent = 19487, must be sent outlen = 19487
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:11.7 	 AVG_FPS:0.0
known client: 26, sent = 19515, must be sent outlen = 19515
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:12.3 	 AVG_FPS:0.0
known client: 26, sent = 19704, must be sent outlen = 19704
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:12.8 	 AVG_FPS:0.0
known client: 26, sent = 19587, must be sent outlen = 19587
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:13.2 	 AVG_FPS:0.0
known client: 26, sent = 19749, must be sent outlen = 19749
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:13.8 	 AVG_FPS:0.0
known client: 26, sent = 19734, must be sent outlen = 19734
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:14.2 	 AVG_FPS:0.0
known client: 26, sent = 19833, must be sent outlen = 19833
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:14.5 	 AVG_FPS:0.0
known client: 26, sent = 19796, must be sent outlen = 19796
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.0 	 AVG_FPS:0.0
known client: 26, sent = 19928, must be sent outlen = 19928
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.3 	 AVG_FPS:0.0
known client: 26, sent = 19989, must be sent outlen = 19989
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.6 	 AVG_FPS:0.0
known client: 26, sent = 20073, must be sent outlen = 20073
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.8 	 AVG_FPS:0.0
known client: 26, sent = 20100, must be sent outlen = 20100
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.1 	 AVG_FPS:0.0
known client: 26, sent = 20201, must be sent outlen = 20201
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.1 	 AVG_FPS:0.0
known client: 26, sent = 20153, must be sent outlen = 20153
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.2 	 AVG_FPS:0.0
known client: 26, sent = 20275, must be sent outlen = 20275
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.3 	 AVG_FPS:0.0
known client: 26, sent = 20277, must be sent outlen = 20277
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.5 	 AVG_FPS:0.0
known client: 26, sent = 20375, must be sent outlen = 20375
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.7 	 AVG_FPS:0.0
known client: 26, sent = 20264, must be sent outlen = 20264
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.9 	 AVG_FPS:0.0
known client: 26, sent = 20268, must be sent outlen = 20268
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.1 	 AVG_FPS:0.0
known client: 26, sent = 20234, must be sent outlen = 20234
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.3 	 AVG_FPS:0.0
known client: 26, sent = 20394, must be sent outlen = 20394
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.3 	 AVG_FPS:0.0
known client: 26, sent = 20263, must be sent outlen = 20263
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.4 	 AVG_FPS:0.0
known client: 26, sent = 20283, must be sent outlen = 20283
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.4 	 AVG_FPS:0.0
known client: 26, sent = 20346, must be sent outlen = 20346
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.5 	 AVG_FPS:0.0
known client: 26, sent = 20479, must be sent outlen = 20479
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.5 	 AVG_FPS:0.0
known client: 26, sent = 20280, must be sent outlen = 20280
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.5 	 AVG_FPS:0.0
known client: 26, sent = 20263, must be sent outlen = 20263
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.4 	 AVG_FPS:0.0
known client: 26, sent = 20259, must be sent outlen = 20259
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.4 	 AVG_FPS:0.0
known client: 26, sent = 20412, must be sent outlen = 20412
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.5 	 AVG_FPS:0.0
known client: 26, sent = 20282, must be sent outlen = 20282
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.5 	 AVG_FPS:0.0
known client: 26, sent = 20330, must be sent outlen = 20330
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.6 	 AVG_FPS:0.0
known client: 26, sent = 20270, must be sent outlen = 20270
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.6 	 AVG_FPS:0.0
known client: 26, sent = 20416, must be sent outlen = 20416
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.7 	 AVG_FPS:0.0
known client: 26, sent = 20327, must be sent outlen = 20327
Ask YOLO to restart to record on a file demo.mp4
Process YOLO restart
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.6 	 AVG_FPS:0.0
known client: 26, sent = 20418, must be sent outlen = 20418
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.3 	 AVG_FPS:0.0
known client: 26, sent = 20481, must be sent outlen = 20481
problem with request:  Error: read ECONNRESET
    at TCP.onStreamRead (internal/stream_base_commons.js:209:20) {
  errno: 'ECONNRESET',
  code: 'ECONNRESET',
  syscall: 'read'
}
YOLO stream error: Error: read ECONNRESET
Too much retries, YOLO took more than 3 min to start, likely an error
Process YOLO exit
Process YOLO stopped
Stopped so going to try to start()
Process YOLO started
 CUDA-version: 10020 (10020), cuDNN: 8.2.1, GPU count: 1
 OpenCV version: 4.1.1
 0 : compute_capability = 530, cudnn_half = 0, GPU: NVIDIA Tegra X1
   layer   filters  size/strd(dil)      input                output
   0 First request on console stream
conv     32       3 x 3/ 2    416 x 416 x   3 ->  208 x 208 x  32 0.075 BF
   1 conv     64       3 x 3/ 2    208 x 208 x  32 ->  104 x 104 x  64 0.399 BF
   2 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF
   3 route  2 		                       1/2 ->  104 x 104 x  32
   4 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF
   5 conv     32       3 x 3/ 1    104 x 104 x  32 ->  104 x 104 x  32 0.199 BF
   6 route  5 4 	                           ->  104 x 104 x  64
   7 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF
   8 route  2 7 	                           ->  104 x 104 x 128
   9 max                2x 2/ 2    104 x 104 x 128 ->   52 x  52 x 128 0.001 BF
  10 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF
  11 route  10 		                       1/2 ->   52 x  52 x  64
  12 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF
  13 conv     64       3 x 3/ 1     52 x  52 x  64 ->   52 x  52 x  64 0.199 BF
  14 route  13 12 	                           ->   52 x  52 x 128
  15 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF
  16 route  10 15 	                           ->   52 x  52 x 256
  17 max                2x 2/ 2     52 x  52 x 256 ->   26 x  26 x 256 0.001 BF
  18 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF
  19 route  18 		                       1/2 ->   26 x  26 x 128
  20 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF
  21 conv    128       3 x 3/ 1     26 x  26 x 128 ->   26 x  26 x 128 0.199 BF
  22 route  21 20 	                           ->   26 x  26 x 256
  23 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF
  24 route  18 23 	                           ->   26 x  26 x 512
  25 max                2x 2/ 2     26 x  26 x 512 ->   13 x  13 x 512 0.000 BF
  26 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF
  27 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  28 conv    512       3 x 3/ 1     13 x  13 x 256 ->   13 x  13 x 512 0.399 BF
  29 conv    255       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 255 0.044 BF
  30 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
  31 route  27 		                           ->   13 x  13 x 256
  32 conv    128       1 x 1/ 1     13 x  13 x 256 ->   13 x  13 x 128 0.011 BF
  33 upsample                 2x    13 x  13 x 128 ->   26 x  26 x 128
  34 route  33 23 	                           ->   26 x  26 x 384
  35 conv    256       3 x 3/ 1     26 x  26 x 384 ->   26 x  26 x 256 1.196 BF
  36 conv    255       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 255 0.088 BF
  37 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
Total BFLOPS 6.910
avg_outputs = 310203
 Allocate additional workspace_size = 26.22 MB
Loading weights from yolov4-tiny.weights...Demo
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
Create CUDA-stream - 0
 Create cudnn-handle 0
nms_kind: greedynms (1), beta = 0.600000
nms_kind: greedynms (1), beta = 0.600000
Done! Loaded 38 layers from weights-file

 seen 64, trained: 0 K-images (0 Kilo-batches_64)
video file: opendatacam_videos_uploaded/demo.mp4
Video stream: 640 x 360
Objects:

 JSON-stream sent.

FPS:0.0 	 AVG_FPS:0.0
Objects:

 JSON-stream sent.

FPS:1.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:3.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:5.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:6.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:7.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:8.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:9.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:10.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:11.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:12.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:12.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:13.2 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:13.8 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:14.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:14.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:15.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:16.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.2 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.8 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:17.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.1 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.1 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.1 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.2 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.2 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:18.2 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sNew client, close previous stream

 seen 64, trained: 0 K-images (0 Kilo-batches_64)
video file: opendatacam_videos_uploaded/demo.mp4
Video stream: 640 x 360
Objects:

 JSON-stream sent.

FPS:0.0 	 AVG_FPS:0.0
Objects:

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FPS:1.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:3.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:5.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:6.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:7.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:8.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:9.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:10.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:11.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:12.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:12.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:13.2 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:13.8 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:14.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:14.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:15.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:15.4 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:15.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:16.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:16.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:16.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:16.7 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:16.9 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:17.0 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:17.2 	 AVG_FPS:0.0
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Objects:

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FPS:17.3 	 AVG_FPS:0.0
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Objects:

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FPS:17.5 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:17.6 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:17.7 	 AVG_FPS:0.0
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Objects:

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FPS:17.7 	 AVG_FPS:0.0
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Objects:

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FPS:17.8 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

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FPS:17.9 	 AVG_FPS:0.0
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Objects:

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FPS:18.0 	 AVG_FPS:0.0
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Objects:

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FPS:18.0 	 AVG_FPS:0.0
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Objects:

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FPS:18.1 	 AVG_FPS:0.0
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Objects:

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FPS:18.1 	 AVG_FPS:0.0
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Objects:

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FPS:18.0 	 AVG_FPS:0.0
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Objects:

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FPS:18.1 	 AVG_FPS:0.0
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FPS:18.2 	 AVG_FPS:0.0
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FPS:18.2 	 AVG_FPS:0.0
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FPS:18.3 	 AVG_FPS:0.0
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FPS:18.3 	 AVG_FPS:0.0
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FPS:18.3 	 AVG_FPS:0.0
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Objects:

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FPS:18.4 	 AVG_FPS:0.0
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Objects:

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FPS:18.4 	 AVG_FPS:0.0
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Objects:

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FPS:18.5 	 AVG_FPS:0.0
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FPS:18.4 	 AVG_FPS:0.0
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Objects:

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FPS:18.3 	 AVG_FPS:0.0
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FPS:18.3 	 AVG_FPS:0.0
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FPS:18.3 	 AVG_FPS:0.0
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Objects:

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FPS:18.3 	 AVG_FPS:0.0
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FPS:18.2 	 AVG_FPS:0.0
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Objects:

 JSON-stream sNew client, close previous stream
ent.

FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.3 	 AVG_FPS:0.0
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.4 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.4 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.5 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.3 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.1 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.0 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.0 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.0 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.1 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.1 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.2 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.2 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.3 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.4 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.4 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

FPS:18.4 	 AVG_FPS:18.5
 MJPEG-stream sent.
Objects:

 JSON-stream sent.

Aha! Now I know what's going on!
It's just another variation of https://github.com/opendatacam/opendatacam/issues/368 you're seeing!
When running from File, ODC restarts Darknet to process the file from the beginning when you hit record. After restarting Darknet, ODC makes a few attempts to connect to Darknet. If that does not work it will not try again and not show any video or tracker data.
So to fix this I suggest you look at Opendatacam.js and look for the Darknet connection in the listenToYolo function!

Excellent!  Ok, so this is happening even in headless mode but we will dive into that thread!

authors: munsterlander vsaw

repo: opendatacam/opendatacam


text:
Revert the patch of pipeline_version_id as pipeline_id

Revert the patch of pipeline_version_id as pipeline_id
Description
Based on a3fa683c, pipeline_version_id was set the same as pipeline_id to show proper display on the ODH Dashboard. This feature is now corrected on ODH Dashboard.
Fixes: https://github.com/opendatahub-io/notebooks/issues/255
Related-to: https://issues.redhat.com/browse/RHOAIENG-2469
How Has This Been Tested?

A dev image was created: quay.io/harshad16/data-science-runtime-ubi9:pv
Use this as Notebook imagestream in ODH cluster.
To setup compatible ODH Dashboard, set the below config in DSC:
    dashboard:
      devFlags:
        manifests:
          - contextDir: manifests
            sourcePath: base
            uri: >-
              https://github.com/opendatahub-io/odh-dashboard/tarball/v2.19.1-incubation-release
      managementState: Managed

Execute a pipeline, with same pipeline name , just with few little changes,
see how it gets displayed on the ODH dashboard.

On the above example:
The pipeline new is created with these changes, and one can click on view runs and see the changes.
whereas the pipeline hello-generic-world is created with old setup,
if we click on view runs, the additional versions would not shows execution runs.
for pipeline new it would function well.
If you want to check,  feel free to see the already set setup:
https://odh-dashboard-opendatahub.apps.hnalla.dev.datahub.redhat.com/pipelines/test
reach out for access.
Merge criteria:

[x] The commits are squashed in a cohesive manner and have meaningful messages.
[x] Testing instructions have been added in the PR body (for PRs involving changes that are not immediately obvious).
[x] The developer has manually tested the changes and verified that the changes work

Thank you for the review.
/approve

/cherrypick 2023b

repo: opendatahub-io/notebooks


text:
[Bug]: DSG Project creation disables label for modelmesh

Is there an existing issue for this?

[X] I have searched the existing issues

Current Behavior
Right now, we cannot add the label modelmesh-enabled to a new namespace created, that label is used by the modelmesh controller to inspect for namespaces with ServingRuntime and InferenceServices.
One possible solution is to modify the controller to support annotations.
Expected Behavior
Being able to add a label/annotation to  enable model serving in the namespace.
Steps To Reproduce

Create a new dsg project
Check that the label modelmesh-enabled is missing in the spec.

Workaround (if any)
Manually add that label.
What browsers are you seeing the problem on?
No response
Open Data Hub Version
No response
Anything else
No response

This will be addressed in main and will be merged into model mesh.

authors: andrewballantyne lucferbux

repo: opendatahub-io/odh-dashboard


text:
[RHOAIENG-9754] Use the npm-Version of the Segment libraries.

Closes https://issues.redhat.com/browse/RHOAIENG-9754
Description
Use the npm-version of the Segment client (Analytics(-next).js) and don't load it on the fly
on the first load of the UI.
This change has no impact of the visible appearance and functionality of the UI.
How Has This Been Tested?
Manual testing by looking at network tab of the browser tools in non-dev mode
Test Impact
See previous item
Request review criteria:
Self checklist (all need to be checked):

[X] The developer has manually tested the changes and verified that the changes work
[X] Commits have been squashed into descriptive, self-contained units of work (e.g. 'WIP' and 'Implements feedback' style messages have been removed)
[ ] Testing instructions have been added in the PR body (for PRs involving changes that are not immediately obvious).
[X] The developer has added tests or explained why testing cannot be added (unit or cypress tests for related changes)

After the PR is posted & before it merges:

[ ] The developer has tested their solution on a cluster by using the image produced by the PR to main

cc @christianvogt @andrewballantyne
Superseeds #3012

@andrewballantyne are we gtg with this yet?

@andrewballantyne are we gtg with this yet?

I was having issues getting a key to test this -- seems we have that as an option now. I have not yet tested this and I'm not sure I'll have today to do so. Maybe tomorrow.
@christianvogt feel free to test this and remove the hold whenever you see fit.

/retest

/retest

/lgtm

authors: andrewballantyne ashley-o0o christianvogt pilhuhn


text:
Remove logic - kube:admin as default username

Description

Fixes #360
How Has This Been Tested?

Merge criteria:

[X] The commits are squashed in a cohesive manner and have meaningful messages.
[ ] Testing instructions have been added in the PR body (for PRs involving changes that are not immediately obvious).
[ ] The developer has manually tested the changes and verified that the changes work

I renamed the PR -- it will be less alarming. Maybe we also change the commit message so it doesn't show up as delete kube:admin... in the commit tree


text:
Speed up Typescript tests

Use Javascript in some cases
Remove test cases not absolutely necessary
Disable SQ scan in all except one case

Closes #509.
Tasks:

[ ] Updated design documents in docs/design directory or not applicable
[ ] Updated user-facing documentation in docs directory or not applicable
[ ] Ran tests (e.g. make test) or not applicable
[ ] Updated changelog or not applicable

From 703.53s (see #509), to:
--- PASS: TestTaskODSBuildTypescript (503.67s)
    --- PASS: TestTaskODSBuildTypescript/fail_pulling_image_if_unsupported_node_version_is_specified (4.33s)
    --- PASS: TestTaskODSBuildTypescript/build_backend_javascript_app (161.26s)
    --- PASS: TestTaskODSBuildTypescript/build_javascript_app_with_custom_build_directory (74.49s)
    --- PASS: TestTaskODSBuildTypescript/build_typescript_app_with_SQ_scan (161.21s)
    --- PASS: TestTaskODSBuildTypescript/build_javascript_app_in_subdirectory_with_build_caching (70.45s)
    --- PASS: TestTaskODSBuildTypescript/fail_linting_typescript_app_and_generate_lint_report (30.14s)

repo: opendevstack/ods-pipeline