Add pre-commit CI actions (#4982)
* define pre-commit * add CI code * configure * apply pre-commit * fstring * apply MD * pre-commit * Update torch_utils.py * Update print strings * notes * Cleanup code-format.yml * Update setup.cfg * Update .pre-commit-config.yaml Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -61,10 +61,10 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
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<details>
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<summary> <b>Usage</b> </summary>
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<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
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</details>
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<h3> 2: Train and Log Evaluation simultaneousy </h3>
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This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
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Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
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@@ -72,31 +72,31 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
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<details>
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<summary> <b>Usage</b> </summary>
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<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data .. --upload_data </code>
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</details>
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<h3> 3: Train using dataset artifact </h3>
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When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
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When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
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can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
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<details>
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<summary> <b>Usage</b> </summary>
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<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data {data}_wandb.yaml </code>
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</details>
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<h3> 4: Save model checkpoints as artifacts </h3>
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To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
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To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
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You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
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<details>
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<summary> <b>Usage</b> </summary>
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<b>Code</b> <code> $ python train.py --save_period 1 </code>
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</details>
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</details>
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<h3> 5: Resume runs from checkpoint artifacts. </h3>
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@@ -105,28 +105,28 @@ Any run can be resumed using artifacts if the <code>--resume</code> argument sta
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<details>
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<summary> <b>Usage</b> </summary>
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<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
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</details>
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<h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
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<b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
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The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
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The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
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train from <code>_wandb.yaml</code> file and set <code>--save_period</code>
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<details>
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<summary> <b>Usage</b> </summary>
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<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
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</details>
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</details>
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<h3> Reports </h3>
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W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).
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<img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">
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