BloodAxe / pytorch-toolbelt

PyTorch extensions for fast R&D prototyping and Kaggle farming

MIT License

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Release 0.3.0 #35

Closed BloodAxe closed 4 years ago

BloodAxe commented 4 years ago

Pytorch Toolbel 0.3.0

This release has a huge set of new features, bugfixes and breaking changes. So be careful, when upgrading.

New features

Encoders

HRNetV2
DenseNets
EfficientNet
Encoder class has change_input_channels method to change number of channels in input image

New losses

BCELoss with support of ignore_index
SoftBCELoss (Label smoothing loss for binary case with support of ignore_index)
SoftCrossEntropyLoss (Label smoothing loss for multiclass case with support of ignore_index)

Catalyst goodies

Online pseudolabeling callback
Training signal annealing callback

Other

New activation functions support in ABN block: Swish, Mish, HardSigmoid
New decoders (Unet, FPN, DeeplabV3, PPM) to simplify creation of segmentation models
CREDITS.md to include all the references to code/articles. Existing list is definitely not complete, so feel free to make PR's
Object context block from OCNet

API changes

Focal loss now supports normalized focal loss and reduced focal loss extensions.
Optimize computation of pyramid weight matrix #34
Default value align_corners=False in F.interpolate when doing bilinear upsampling.

Bugfixes

Fix missing call to batch normalization block in FPNBottleneckBN
Fix numerical stability for DiceLoss and JaccardLoss when log_loss=True
Fix numerical stability when computing normalized focal loss