okunator/cellseg_models.pytorch: v0.1.23

add a stem-skip module. (Long skip for the input image resolution feature map)

add UnetTR transformer encoder wrapper class

add a new Encoder wrapper for timm and unetTR based encoders

Add stem skip support and upsampling block options to all current model architectures

Add masking option to all the criterions

Add BCELoss

Add base class for transformer based backbones

Add SAM-VitDet image encoder with support to load pre-trained SAM weights

Add CellVIT-SAM model.

Add notebook example on training Hover-Net with lightning from scratch.

Add notebook example on training StarDist with lightning from scratch.

Add notebook example on training OmniPose with accelerate from scratch.

Add notebook example on finetuning CellVIT-SAM with accelerate.

Fix current TimmEncoder to store feature info

Fix Up block to support transconv and bilinear upsampling and fix data flow issues.

Fix StardistUnet class to output all the decoder features.

Fix Decoder, DecoderStage and long-skip modules to work with up scale factors instead of output dimensions.