Use pre-commit to update code style.

.github/pr-labeler-config.yml

@@ -1,55 +1,55 @@
 # Add 'docker' to any changes within 'docker' folder or any subfolders
 docker:
   - changed-files:
-    - any-glob-to-any-file: docker/**
+      - any-glob-to-any-file: docker/**
 
 # Add 'docs' to any changes within 'docs' folder
 docs:
   - changed-files:
-    - any-glob-to-any-file: docs/**
+      - any-glob-to-any-file: docs/**
 
 # Add 'ci' to any changes in '.github' folder
 ci:
   - changed-files:
-    - any-glob-to-any-file: .github/**
+      - any-glob-to-any-file: .github/**
 
 # Add 'examples' to any changes within 'examples' folder
 examples:
   - changed-files:
-    - any-glob-to-any-file: examples/**
+      - any-glob-to-any-file: examples/**
 
 # Add 'base' to any changes within 'base' folder
 "module: base":
   - changed-files:
-    - any-glob-to-any-file: ignite/base/**/*
+      - any-glob-to-any-file: ignite/base/**/*
 
 # Add 'contrib' to any changes within 'contrib' folder
 "module: contrib":
   - changed-files:
-    - any-glob-to-any-file: ignite/contrib/**/*
+      - any-glob-to-any-file: ignite/contrib/**/*
 
 # Add 'distributed' to any changes within 'distributed' folder
 "module: distributed":
   - changed-files:
-    - any-glob-to-any-file: ignite/distributed/**/*
+      - any-glob-to-any-file: ignite/distributed/**/*
 
 # Add 'engine' to any changes within 'engine' folder
 "module: engine":
   - changed-files:
-    - any-glob-to-any-file: ignite/engine/**/*
+      - any-glob-to-any-file: ignite/engine/**/*
 
 # Add 'handlers' to any changes within 'handlers' folder
 "module: handlers":
   - changed-files:
-    - any-glob-to-any-file: ignite/handlers/**/*
+      - any-glob-to-any-file: ignite/handlers/**/*
 
 # Add 'metrics' to any changes within 'metrics' folder
 "module: metrics":
   - changed-files:
-    - any-glob-to-any-file: ignite/metrics/**/*
+      - any-glob-to-any-file: ignite/metrics/**/*
 
   -
 # Add 'utils' to any changes within 'utils' module
 "module: utils":
   - changed-files:
-    - any-glob-to-any-file: ignite/utils.py
+      - any-glob-to-any-file: ignite/utils.py

.github/workflows/discord_issues.yml

@@ -7,7 +7,7 @@ on:
   workflow_dispatch:
     inputs:
       issue_number:
-        description: 'Issue number'
+        description: "Issue number"
         required: true
 
 permissions:
@@ -26,5 +26,3 @@ jobs:
           issue_number: ${{ github.event.inputs.issue_number || github.event.issue.number }}
           issue_comment: Hey 👋, I've just created a [thread]($THREAD_LINK$) for this issue on [PyTorch-Ignite Discord](https://pytorch-ignite.ai/chat) where you can quickly talk to the community on the topic.
           discord_message: New issue created in `${{ github.repository }}`:<https://github.com/${{ github.repository }}/issues/${{ github.event.issue.number }}>
-
-

.github/workflows/discord_pull_requests.yaml

@@ -7,7 +7,7 @@ on:
   workflow_dispatch:
     inputs:
       pull_request_number:
-        description: 'Pull request number'
+        description: "Pull request number"
         required: true
 
 permissions:

.github/workflows/docker-build.yml

@@ -9,14 +9,12 @@ on:
     types: [published]
   workflow_dispatch:
 
-
 concurrency:
   # <workflow_name>-<branch_name>-<true || commit_sha (if branch is protected)>
   group: docker-build-publish-${{ github.ref_name }}-${{ !(github.ref_protected) || github.sha }}
   cancel-in-progress: true
 
 jobs:
-
   setup:
     name: Setup
     runs-on: ubuntu-latest
@@ -46,7 +44,6 @@ jobs:
           echo "pth_version=$(python -c "import configparser; cfg=configparser.ConfigParser(); cfg.read('docker.cfg'); print(cfg.get('DEFAULT', 'build_docker_image_pytorch_version'))")" >> $GITHUB_OUTPUT
           echo "hvd_version=$(python -c "import configparser; cfg=configparser.ConfigParser(); cfg.read('docker.cfg'); print(cfg.get('DEFAULT', 'build_docker_image_hvd_version'))")" >> $GITHUB_OUTPUT
 
-
   build-hvd:
     name: Build all Horovod flavoured PyTorch-Ignite images
     needs: setup
@@ -106,7 +103,6 @@ jobs:
           bash push_all.sh hvd-vision
           bash push_all.sh hvd-nlp
 
-
   build-hvd-apex:
     name: Build all Horovod + Apex flavoured PyTorch-Ignite images
     needs: setup
@@ -166,7 +162,6 @@ jobs:
           bash push_all.sh hvd-apex-vision
           bash push_all.sh hvd-apex-nlp
 
-
   build-main:
     name: Build all PyTorch-Ignite images
     needs: setup
@@ -226,7 +221,6 @@ jobs:
           bash push_all.sh vision
           bash push_all.sh nlp
 
-
   build-main-apex:
     name: Build all PyTorch-Ignite images with Apex
     needs: setup

.github/workflows/pytorch-version-tests.yml

@@ -16,8 +16,7 @@ jobs:
       fail-fast: false
       matrix:
         python-version: [3.9, "3.10", "3.11"]
-        pytorch-version:
-          [2.5.1, 2.4.1, 2.3.1, 2.2.2, 1.13.1, 1.12.1, 1.10.0]
+        pytorch-version: [2.5.1, 2.4.1, 2.3.1, 2.2.2, 1.13.1, 1.12.1, 1.10.0]
         exclude:
           - pytorch-version: 1.10.0
             python-version: "3.10"

.github/workflows/triage.yml

@@ -19,7 +19,6 @@ jobs:
         with:
           configuration-path: .github/pr-labeler-config.yml
           repo-token: "${{ secrets.GITHUB_TOKEN }}"
-
 # Turned off due to unexpected behavior on issue opening+labeling? https://github.com/pytorch/ignite/issues/1836
 #       - name: Welcome
 #         uses: actions/first-interaction@v1

docker/README.md

@@ -50,6 +50,7 @@ Available Tensor Operations:
   - `docker pull pytorchignite/hvd-apex-nlp:latest`
 
 **Deprecated images** (no version updates)
+
 - [msdp/Dockerfile.msdp-apex-base](msdp/Dockerfile.msdp-apex): multi-stage MSDeepSpeed build with latest Pytorch, Ignite image with minimal dependencies
   - `docker pull pytorchignite/msdp-apex:latest`
 - [msdp/Dockerfile.msdp-apex-vision](msdp/Dockerfile.msdp-apex-vision): base MSDeepSpeed build with useful computer vision libraries

examples/mnist/README.md

@@ -17,7 +17,6 @@ python mnist.py
 
 Same example with logging using TQDM progress bar
 
-
 ```bash
 python mnist_with_tqdm_logger.py
 ```
@@ -30,7 +29,6 @@ MNIST example with training and validation monitoring using Tensorboard
 
 - Tensorboard: `pip install tensorboard`
 
-
 Run the example:
 
 ```bash

examples/references/classification/imagenet/README.md

@@ -12,9 +12,9 @@ Features:
 | --------- | ----------------------- | ----------------------- | ------------------- | ------------------- |
 | ResNet-50 | 78%                     | 92%                     | 77%                 | 94%                 |
 
-Experiment | Model | Training Top-1 Accuracy | Training Top-5 Accuracy | Test Top-1 Accuracy | Test Top-5 Accuracy | ClearML Link
----|---|---|---|---|---|---
-configs/???.py |
+| Experiment     | Model | Training Top-1 Accuracy | Training Top-5 Accuracy | Test Top-1 Accuracy | Test Top-5 Accuracy | ClearML Link |
+| -------------- | ----- | ----------------------- | ----------------------- | ------------------- | ------------------- | ------------ |
+| configs/???.py |
 
 ## Setup
 
@@ -25,6 +25,7 @@ pip install -r requirements.txt
 ### Docker
 
 For docker users, you can use the following images to run the example:
+
 ```bash
 docker pull pytorchignite/vision:latest
 ```
@@ -47,9 +48,11 @@ export DATASET_PATH=/path/to/imagenet
 - Adjust batch size for your GPU type in the configuration file: `configs/baseline_resnet50.py` or `configs/baseline_resnet50.py`
 
 Run the following command:
+
 ```bash
 CUDA_VISIBLE_DEVICES=0 python -u main.py training configs/baseline_resnet50.py
 ```
+
 #### Multiple GPUs
 
 - Adjust total batch size for your GPUs in the configuration file: `configs/baseline_resnet50.py` or `configs/baseline_resnet50.py`
@@ -58,7 +61,6 @@ CUDA_VISIBLE_DEVICES=0 python -u main.py training configs/baseline_resnet50.py
 OMP_NUM_THREADS=1 torchrun --nproc_per_node=2 main.py training configs/baseline_resnet50.py
 ```
 
-
 ## Acknowledgements
 
 Trainings were done using credits provided by [trainml.ai](trainml.ai) platform.

examples/references/segmentation/pascal_voc2012/README.md

@@ -8,11 +8,10 @@ Features:
 - Distributed training with native automatic mixed precision
 - Experiments tracking with [ClearML](https://github.com/allegroai/clearml)
 
-Experiment | Model | Dataset | Val Avg IoU | ClearML Link
----|---|---|---|---
-configs/baseline_dplv3_resnet101.py | DeepLabV3 Resnet101 | VOC Only | 0.659161 | [link](https://app.clear.ml/projects/0e9a3a92d3134283b7d5572d516d60c5/experiments/a7254f084a9e47ca9380dfd739f89520/output/execution)
-configs/baseline_dplv3_resnet101_sbd.py | DeepLabV3 Resnet101 | VOC+SBD | 0.6853087 | [link](https://app.clear.ml/projects/0e9a3a92d3134283b7d5572d516d60c5/experiments/dc4cee3377a74d19bc2d0e0e4d638c1f/output/execution)
-
+| Experiment                              | Model               | Dataset  | Val Avg IoU | ClearML Link                                                                                                                         |
+| --------------------------------------- | ------------------- | -------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------ |
+| configs/baseline_dplv3_resnet101.py     | DeepLabV3 Resnet101 | VOC Only | 0.659161    | [link](https://app.clear.ml/projects/0e9a3a92d3134283b7d5572d516d60c5/experiments/a7254f084a9e47ca9380dfd739f89520/output/execution) |
+| configs/baseline_dplv3_resnet101_sbd.py | DeepLabV3 Resnet101 | VOC+SBD  | 0.6853087   | [link](https://app.clear.ml/projects/0e9a3a92d3134283b7d5572d516d60c5/experiments/dc4cee3377a74d19bc2d0e0e4d638c1f/output/execution) |
 
 ## Setup
 
@@ -23,10 +22,13 @@ pip install -r requirements.txt
 ### Docker
 
 For docker users, you can use the following images to run the example:
+
 ```bash
 docker pull pytorchignite/vision:latest
 ```
+
 or
+
 ```bash
 docker pull pytorchignite/hvd-vision:latest
 ```
@@ -51,7 +53,6 @@ This script will download and extract the following datasets into `/path/to/data
 - The [Pascal VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) dataset
 - Optionally, the [SBD](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz) evaluation dataset
 
-
 ## Usage
 
 Please, export the `DATASET_PATH` environment variable for the Pascal VOC2012 dataset.
@@ -75,6 +76,7 @@ export SBD_DATASET_PATH=/path/to/SBD/
 - Adjust batch size for your GPU type in the configuration file: `configs/baseline_dplv3_resnet101_sbd.py` or `configs/baseline_dplv3_resnet101.py`
 
 Run the following command:
+
 ```bash
 CUDA_VISIBLE_DEVICES=0 python -u main.py training configs/baseline_dplv3_resnet101_sbd.py
 # or without SBD
@@ -121,7 +123,6 @@ torchrun --nproc_per_node=2 main.py eval configs/eval_baseline_dplv3_resnet101_s
 horovodrun -np=2 python -u main.py eval configs/eval_baseline_dplv3_resnet101_sbd.py --backend="horovod"
 ```
 
-
 ## Acknowledgements
 
 Trainings were done using credits provided by AWS for open-source development via NumFOCUS

examples/super_resolution/README.md

@@ -41,9 +41,11 @@ This example trains a super-resolution network on the [Caltech101 dataset](https
 ### Super-resolve example on a Cifar-10 image
 
 #### Input Image
+
 ![Cifar input image](./images/input_cifar.png)
 
 #### Output Images
-| Output image from Model | Output from bicubic sampling |
-|-------------------------------|------------------------------------|
-| ![Cifar output image](./images/out_cifar.png) | ![Cifar output from bicubic sampling](./images/bicubic_image_cifar.png)|
+
+| Output image from Model                       | Output from bicubic sampling                                            |
+| --------------------------------------------- | ----------------------------------------------------------------------- |
+| ![Cifar output image](./images/out_cifar.png) | ![Cifar output from bicubic sampling](./images/bicubic_image_cifar.png) |

examples/transformers/README.md

@@ -32,24 +32,24 @@ Run the example on a single GPU:
 ```bash
 python main.py run
 ```
+
 If needed, please, adjust the batch size to your GPU device with `--batch_size` argument.
 
 The default model is `bert-base-uncased` incase you need to change that use the `--model` argument, for details on which models can be used refer [here](https://huggingface.co/transformers/v3.0.2/model_doc/auto.html#automodelforsequenceclassification)
 
 Example:
+
 ```bash
 #Using DistilBERT which has 40% less parameters than bert-base-uncased
 python main.py run --model="distilbert-base-uncased"
 ```
 
-
 For details on accepted arguments:
 
 ```bash
 python main.py run -- --help
 ```
 
-
 ### Distributed training
 
 #### Single node, multiple GPUs
@@ -93,6 +93,7 @@ python -u main.py run --backend="horovod" --nproc_per_node=2
 import os
 assert os.environ['COLAB_TPU_ADDR'], 'Make sure to select TPU from Edit > Notebook settings > Hardware accelerator'
 ```
+
 ```bash
 VERSION = "nightly"
 !curl -q https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py