Fastestimator

Apphub:
• New Apphub: foundation_model - Lora, GPT image_generation – Stable Diffusion
• Updating levit tensorflow link

Dataset:
• Allowing custom directories

NumpyOp:
• Extending MinMax numpyop to take custom user defined min and max.
• Updating masks_in for multivariate op

Release Note
With the compatibility issues between Tensorflow and PyTorch, future releases might not include Tensorflow.

Backend:

This is a new major release of FastEstimator. Here are the highlights of new release:

Backend:
* New framework backend: TF 2.11, torch 2.0.1
* Introduced `IfElse` Helper function to help create simplified code

Apphub:
* New Apphub: keypoint detection - HRNet, 3D segmentation - 3DUnet+, Line Search, LeViT
* Added use your own dataset section to help with adapting to new task
* Updated SimCLR apphub for better efficiency
* Fix in-place operation in PGGAN in pytorch that cause graph to fail randomly

Dataset:
* New dataset class - Interleave dataset to switch dataset on per-step basis for multi-task learning.
* Added keypoint to mscoco dataset
* Added Pascal VOC dataset, MedMnist dataset
* Add filtering functionality to the csv dataset class
* Created custom pycocotools API to resolve pycocotool compilation issue
* Migrated several dataset hosting to google drive for stability
* Improved stability of batchdataset probability sampling

Pipeline:
* Fixed eval logging for multi-ds users
* Pipeline can now be instantiated without datasets

NumpyOp:
* Fixed issue encountered with Onehot encoding
* Added probability to OneOf NumpyOp

CLI:
* enabled warmup, eager, summary argument in run cli

Network:
* Fixed an issue when model's optimizer is None, the model_lr will not be printed at the end of training
* Fixed a performance issue of torch unet
* New patch-based inferencing class - Slicer

TensorOp:
* enabled multi-dimensional support for cross entropy loss
* Since Mixup and Cutmix no longer uses MixLoss, remove the class and update docstring
* Update focal loss's default mode to be consistent with other lossOp
* Resolved L1 loss dimension mismatch issue
* Focal loss rework
* Introduced RepeatOp for tensor operations
* Added probability to OneOf TensorOp

Visualization:
* Add BatchDisplay and GridDisplay traces
* FE logging visualization will work with single file
* Extending visualization to keypoints and masks

Trace:
* CSV logger rework
* added classification AUC trace
* Dice Trace rework
* fixed an issue of restore with unhashable param loading

Traceability:
* Updated pytorch model summary for traceability report
* Fixed multi-gpu model traceability graph
* Traceability Report now displays hardware information

Others:
* Updated yapf setting to work with recent yapf versions
* new benchmarking tool for better speed and resource monitoring
* New support matrix added to help user install past FE versions
* Fix ipython version due to its recent release upgrade that no longer supports python 3.8 below
* Updated Mac installation guide

Thank everyone who provided their feedbacks and made contribution to FE.

This release features several dependency bug fixes which caused issue during installation. Specifically, here is a list of notable changes in dependencies:
* numpy: specified a maximum numpy version due to numpy stopped supporting np.bool in recent versions.
* scikit-learn: changed sklearn to scikit-learn due to their recent name change and occasional downtime in the installation
* uncertainty-calibration: upgraded to a more recent version due to their dependency of sklearn

Moreover, this release will increase the stability of the package by resolving all open-ended dependency list. This will avoid future issues installing this release.

Finally, this release also incorporates a critical bug fix: TensorOp Oneof. TensorOp version of Oneof works properly now.