Release 0.3.0

[BloodAxe]

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 performance of ImageSlicer weight=pyramid #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