New stuff

• All encoders & decoders & heads are now inherit from HasOutputFeaturesSpecification interface to query number of output channels and strides this module outputs.
• New loss class QualityFocalLoss from https://arxiv.org/abs/2006.04388
• New function pad_tensor_to_size -A generic padding function for N-dimensional tensors [B,C, ...] shape.
• Added DropPath layer (aka DropConnect)
• Pretrained weights for SegFormer backbones
• first_class_background_init for initializing last output convolution/linear block to have zeros in weights and bias layer set to [logit(bg_prob), logit(1-bg_prob), ...]

• New function instantiate_normalization_block to create normalization layer by name. This is used in some decoder layers / heads.

• Improvements

• Improve numeric accuracy of focal_loss_with_logits function by explicitly disabling AMP autocast for this function and casting preds & targets to float32.

• MultiscaleTTA now allows setting interpolation mode and align_corners for resizing input and predictions.
• BinaryFocalLoss now has __repr__
• name_for_stride now accepts stride argument to be None. In this case the function is noop and returns input argument name.
• RandomSubsetDataset now takes optional weights argument to select samples with given probability.

Bugfixes

• Implementation of get_collate_fn for RandomSubsetDataset is not correct and returns collate function instead of calling it

Breaking Changes

• Signature of decoders changes to require first argument input_spec to be of type FeatureMapsSpecification.
• Rewritten BiFPN decoder to support arbitrary number of input feature maps, user-defined normalization & activation & BiFPN block.

• Rewritten UNetDecoder to allow setting upsample block as string type

• WeightedLoss and JointLoss classes has been removed. If your code was using these classes here they are - copy paste them to your project and live happily, but I strongly suggest to use modern deep learning frameworks that support defining losses from configuration files.

  :::python class WeightedLoss(_Loss): """Wrapper class around loss function that applies weighted with fixed factor. This class helps to balance multiple losses if they have different scales """

  def __init__(self, loss, weight=1.0):
      super().__init__()
      self.loss = loss
      self.weight = weight
  
  def forward(self, *input):
      return self.loss(*input) * self.weight
  

  class JointLoss(_Loss): """ Wrap two loss functions into one. This class computes a weighted sum of two losses. """

  def __init__(self, first: nn.Module, second: nn.Module, first_weight=1.0, second_weight=1.0):
      super().__init__()
      self.first = WeightedLoss(first, first_weight)
      self.second = WeightedLoss(second, second_weight)
  
  def forward(self, *input):
      return self.first(*input) + self.second(*input)