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* Enables detect.py to use Triton for inference
Triton Inference Server is an open source inference serving software
that streamlines AI inferencing.
https://github.com/triton-inference-server/server
The user can now provide a "--triton-url" argument to detect.py to use
a local or remote Triton server for inference.
For e.g., http://localhost:8000 will use http over port 8000
and grpc://localhost:8001 will use grpc over port 8001.
Note, it is not necessary to specify a weights file to use Triton.
A Triton container can be created by first exporting the Yolov5 model
to a Triton supported runtime. Onnx, Torchscript, TensorRT are
supported by both Triton and the export.py script.
The exported model can then be containerized via the OctoML CLI.
See https://github.com/octoml/octo-cli#getting-started for a guide.
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* Segmentation `polygons2masks_overlap()` in `np.int32`
May resolve https://github.com/ultralytics/yolov5/issues/9461
WARNING: Masks should be uint8 for fastest speed, change needs profiling results to determine impact.
@AyushExel @Laughing-q
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Per
```
/content/yolov5/utils/dataloaders.py:458: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
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If < 1 or > 1024 set output to default batch size 16.
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* Add remote ultralytics and check git status with that
* Simplify
* Update general.py
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* s fix
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Checking if ``/workspace`` exists is not a reliable method to check if
the process runs in a docker container.
Reusing the logic from the npm "is-docker" package to check if the
process runs in a container.
References: https://github.com/sindresorhus/is-docker/blob/main/index.jsFixes#8710.
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* Fix BGR->RGB Bug in albumentations https://github.com/ultralytics/yolov5/issues/8641
* Change transform methode from cv2 to numpy
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* Simplify
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* New `HUBDatasetStats()` class
Usage examples:
```
from utils.dataloaders import *
stats = HUBDatasetStats('coco128.yaml', autodownload=True) # method 1
stats = HUBDatasetStats('path/to/coco128_with_yaml.zip') # method 1
stats.get_json(save=False)
stats.process_images()
```
@kalenmike
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* assert torch!=1.12.0 for DDP training
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* Refactor optimizer initialization
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* Add `--hard-fail` list argument to benchmarks for CI
Will cause CI to fail on a benchmark failure for given indices.
* Update ci-testing.yml
* Attempt Failure (CI should fail)
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* attempt at reproducibility
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* use deterministic algs
* fix everything :)
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* revert dataloader changes
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* process_batch as np
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* Add filesize to benchmarks.py
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* HUB dataset_stats() error reporting
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* Reject webcam inference on Colab/Kaggle
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* AutoBatch checks against failed solutions
@kalenmike this is a simple improvement to AutoBatch to verify that returned solutions have not already failed, i.e. return batch-size 8 when 8 already produced CUDA out of memory.
This is a halfway fix until I can implement a 'final solution' that will actively verify the solved-for batch size rather than passively assume it works.
* Update autobatch.py
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
* Code refactor for general.py
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* Add PyTorch AMP check
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* Cleanup
* Cleanup
* Cleanup
* Robust for DDP
* Fixes
* Add amp enabled boolean to check_train_batch_size
* Simplify
* space to prefix
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* Improve mAP0.5-0.95
Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function
Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.
Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s]
all 5000 36335 0.743 0.626 0.682 0.506
from pycocotools
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685
These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.
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* Remove line to retain pycocotools results
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* Higher precision int conversion
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Glenn Jocher
Kalen Michael
SSTato
Vladislav Veklenko
Paul Guerrie
KristenKehrer
Anant Sakhare
Soumik Rakshit
Forever518
Gaz Iqbal
zombob
FeiGeChuanShu
Dhruv Nair
mucunwuxian
Junjie Zhang
Hoyeong-GenGenAI
Ayush Chaurasia
Katteria
黄不盈
VELC
Victor Sonck
spacewalk01
Yannick Merkli
HighMans
0zppd
Michel Promonet
Code-Bat
Amir Pourmand
jbutle55
Max Strobel
UnglvKitDe
Rüdiger Busche
Colin Wong
HRan2004
Anton Lebedev
xylieong