Vision data

Prediction to mask

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pred_to_multiclass_mask

 pred_to_multiclass_mask (pred:torch.Tensor)

*Apply Softmax on the predicted tensor to rescale the values in the range [0, 1] and sum to 1. Then apply argmax to get the indices of the maximum value of all elements in the predicted Tensor.

Args: pred: [C,W,H,D] activation tensor.

Returns: Predicted mask.*

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batch_pred_to_multiclass_mask

 batch_pred_to_multiclass_mask (pred:torch.Tensor)

*Convert a batch of predicted activation tensors to masks.

Args: pred: [B, C, W, H, D] batch of activations.

Returns: Tuple of batch of predicted masks and number of classes.*

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pred_to_binary_mask

 pred_to_binary_mask (pred:torch.Tensor)

*Apply Sigmoid function that squishes activations into a range between 0 and 1. Then we classify all values greater than or equal to 0.5 to 1, and the values below 0.5 to 0.

Args: pred: [B, C, W, H, D] or [C, W, H, D] activation tensor

Returns: Predicted binary mask(s).*

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MedDataBlock

 MedDataBlock (blocks:list=None, dl_type:fastai.data.core.TfmdDL=None,
               getters:list=None, n_inp:int=None, item_tfms:list=None,
               batch_tfms:list=None, reorder:bool=False,
               resample:(<class'int'>,<class'list'>)=None, **kwargs)

Container to quickly build dataloaders.

TransformBlock for segmentation

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MedMaskBlock

 MedMaskBlock ()

Create a TransformBlock for medical masks.

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MedImageDataLoaders

 MedImageDataLoaders (*loaders, path:str|pathlib.Path='.', device=None)

Higher-level MedDataBlock API.