New features and bug fixes

Core

Various improvements in the core part of the library:

• Add epoch_bound parameter to RunningAverage (#488)

• Bug fixes with Confusion matrix, new implementation (#572) - BC breaking

• Added event_to_attr in register_events (#523)

• Added accumulative single variable metrics (#524)

• should_terminate is reset between runs (#525)

• to_onehot returns tensor with uint8 dtype (#571) - may be BC breaking

• Removable handle returned from Engine.add_event_handler() to enable single-shot events (#588)

• New documentation style 🎉

Distributed

We removed mnist distrib example as being misleading and provided distrib branch(XX/YY/2020: distrib branch merged to master) to adapt metrics for distributed computation. Code is working and is under testing. Please, try it in your use-case and leave us a feedback.

Now in Contributions module

• Added mlflow logger (#558)
• R-Squared Metric in regression metrics module (#496)
• Add tag field to OptimizerParamsHandler (#502)
• Improved ProgressBar with TerminateOnNan (#506)
• Support for layer freezing with Tensorboard integration (#515)
• Improved OutputHandler API (#531)
• Improved create_lr_scheduler_with_warmup (#556)
• Added "all" option to metric_names in contrib loggers (#565)
• Added GPU usage info as metric (#569)
• Other bug fixes

Notebook examples

• Added Cycle-GAN notebook (#500)
• Finetune EfficientNet-B0 on CIFAR100 (#544)
• Added Fashion MNIST jupyter notebook (#549)

Updated nighlty builds

From pip:

pip install --pre pytorch-ignite

From conda (this suggests to install pytorch nightly release instead of stable version as dependency):

conda install ignite -c pytorch-nightly

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Acknowledgments

🎉 Thanks to our community and all our contributors for the issues, PRs and 🌟 ⭐️ 🌟 !
💯 We really appreciate your implication into the project (in alphabetical order):

@ANUBHAVNATANI, @Bibonaut, @Evpok, @Hiroshiba, @JeroenDelcour, @Mxbonn, @anmolsjoshi, @asford, @bosr, @johnstill, @marrrcin, @vfdev-5, @willfrey

New features and enhanced contrib module

Core

• We removed deprecated metric classes BinaryAccuracy and CategoricalAccuracy and which are replaced by Accuracy.

• Multilabel option for Accuracy, Precision, Recall metrics.

• Added other metrics:

  • ConfusionMatrix, IoU, mean IoU

• Operations on metrics: p = Precision(average=False)

  • apply PyTorch operators: mean_precision = p.mean()
  • indexing: precision_no_bg = p[1:]

• Improved our docs with more examples.

• Added FAQ section with best practices.

• Bug fixes

Now in Contributions module

• added TensorboardLogger
• added VisdomLogger
• added PolyaxonLogger
• improved ProgressBar
• New regression metrics
  • Median Absolute Error
  • Median Relative Absolute Error
  • Median Absolute Percentage Error
  • Geometric Mean Relative Absolute Error
  • Canberra Metric
  • Fractional Absolute Error
  • Wave Hedges Distance
  • Geometric Mean Absolute Error
• added new parameter scheduling classes and improved parameters:
  • PiecewiseLinear
  • LRScheduler
  • other helper methods
• added custom events support: CustomPeriodicEvent

Notebook examples

• VAE on MNIST
• CNN for text classification

Nighlty builds with pytorch-nightly as dependency

We also provide pip/conda nighlty builds with pytorch-nightly as dependency:

pip install pytorch-ignite-nightly

or

conda install -c pytorch ignite-nightly 

---

Acknowledgments

🎉 Thanks to our community and all our contributors for the issues, PRs and 🌟 ⭐️ 🌟 !
💯 We really appreciate your implication into the project (in alphabetical order):

Bibonaut, IlyaOvodov, TheCodez, anmolsjoshi, fabianschilling, maaario, snowyday, vfdev-5, willprice, zasdfgbnm, zippeurfou

vfdev-5 would like also to thank his wife and newborn baby girl Nina for their support while working on this release !

Bug fixes and features

• Improve and fix bug with binary accuracy, precision, recall
• Metrics arithmetics
• ParamScheduler to support multiple optimizers/multiple parameter groups

Thanks to all our contributors !

New features and bug fixes

What's new in this release:

• Contrib module with
  • Parameter schedule
  • TQDM ProgressBar
  • ROC/AUC, AP, MaxAE metrics
  • TBPTT Engine
• New handlers:
  • Terminate on Nan
• New metrics:
  • RunningAverage
  • Merged Categorical/Binary -> Accuracy
• Refactor of examples
• New examples:
  • Fast Neural Style
  • RL

Thanks to all our contributors !

First release

Introduced Engine, Handlers and Metrics.

Metrics:

• BinaryAccuracy
• CategoricalAccuracy
• Loss
• Precision
• Recall
• etc

Handlers:

• ModelCheckpoint
• EarlyStopping
• Timer

Features:

• PyTorch 0.4 support

Examples:

• mnist.py
• mnist_with_tensorboardx.py
• mnist_with_visdom.py
• dcgan.py