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duplicate

Duplicate

Duplicate an input and apply two different transforms. Used for SimCLR primarily.

Source code in fmcib/transforms/duplicate.py 
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class Duplicate:
    """
    Duplicate an input and apply two different transforms. Used for SimCLR primarily.
    """

    def __init__(self, transforms1: Optional[Callable] = None, transforms2: Optional[Callable] = None):
        """
        Duplicates an input and applies the given transformations to each copy separately.

        Args:
            transforms1 (Optional[Callable]): _description_. Default is None.
            transforms2 (Optional[Callable]): _description_. Default is None.
        """
        # Wrapped into a list if it isn't one already to allow both a
        # list of transforms as well as `torchvision.transform.Compose` transforms.
        self.transforms1 = transforms1
        self.transforms2 = transforms2

    def __call__(self, input: Any) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            input (torch.Tensor or any other type supported by the given transforms): Input.

        Returns:
            Tuple[torch.Tensor, torch.Tensor]: A tuple of two tensors.
        """
        out1, out2 = input, deepcopy(input)
        if self.transforms1 is not None:
            out1 = self.transforms1(out1)
        if self.transforms2 is not None:
            out2 = self.transforms2(out2)
        return (out1, out2)

__call__(input)

Parameters:

Name Type Description Default
input torch.Tensor or any other type supported by the given transforms

Input.

 required

Returns:

Type Description
Tuple[Tensor, Tensor]

Tuple[torch.Tensor, torch.Tensor]: A tuple of two tensors.
Source code in fmcib/transforms/duplicate.py 
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def __call__(self, input: Any) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Args:
        input (torch.Tensor or any other type supported by the given transforms): Input.

    Returns:
        Tuple[torch.Tensor, torch.Tensor]: A tuple of two tensors.
    """
    out1, out2 = input, deepcopy(input)
    if self.transforms1 is not None:
        out1 = self.transforms1(out1)
    if self.transforms2 is not None:
        out2 = self.transforms2(out2)
    return (out1, out2)

__init__(transforms1=None, transforms2=None)

Duplicates an input and applies the given transformations to each copy separately.

Parameters:

Name Type Description Default
transforms1 Optional[Callable]

description. Default is None.

 None
transforms2 Optional[Callable]

description. Default is None.

 None
Source code in fmcib/transforms/duplicate.py 
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def __init__(self, transforms1: Optional[Callable] = None, transforms2: Optional[Callable] = None):
    """
    Duplicates an input and applies the given transformations to each copy separately.

    Args:
        transforms1 (Optional[Callable]): _description_. Default is None.
        transforms2 (Optional[Callable]): _description_. Default is None.
    """
    # Wrapped into a list if it isn't one already to allow both a
    # list of transforms as well as `torchvision.transform.Compose` transforms.
    self.transforms1 = transforms1
    self.transforms2 = transforms2