Repository: Project-MONAI/MONAI
File: monai/apps/generation/maisi/utils/morphological_ops.py (Python)

Code before:
        mask_t: input 2D/3D binary mask, [N,C,M,N] or [N,C,M,N,P] torch tensor.
        filter_size: morphological filter size, has to be odd numbers.
        pad_value: the filled value for padding. We need to pad the input before filtering to keep the output with the same size as input.

    Return:
        Morphological filter result mask, [N,C,M,N] or [N,C,M,N,P] torch tensor
    """
    spatial_dims = len(mask_t.shape) - 2
    if spatial_dims not in [2, 3]:
        raise ValueError(
            f"spatial_dims must be either 2 or 3, yet got spatial_dims={spatial_dims} for mask tensor with shape of {mask_t.shape}."
        )

    # Define the structuring element
    filter_size = ensure_tuple_rep(filter_size, spatial_dims)
    if any(size % 2 == 0 for size in filter_size):
        raise ValueError(f"All dimensions in filter_size must be odd numbers, yet got {filter_size}.")

    filter_shape = [mask_t.shape[1], mask_t.shape[1]] + list(filter_size)
    structuring_element = torch.ones(filter_shape).to(mask_t.device)

    # Pad the input tensor to handle border pixels
    # Calculate padding size
    pad_size = []
    for size in filter_size:
        pad_size.extend([size // 2, size // 2])

    input_padded = F.pad(mask_t.float(), pad_size, mode="constant", value=pad_value)

    # Apply filter operation
    if spatial_dims == 2:
        output = F.conv2d(input_padded, structuring_element, padding=0) / torch.sum(structuring_element[0, ...])
    if spatial_dims == 3:
        output = F.conv3d(input_padded, structuring_element, padding=0) / torch.sum(structuring_element[0, ...])

    return output


def erode_t(mask_t: Tensor, filter_size: int | Sequence[int] = 3, pad_value: float = 1.0) -> Tensor:
    """
    Erode 2D/3D binary mask with data type as torch tensor.

    Args:
        mask_t: input 2D/3D binary mask, [N,C,M,N] or [N,C,M,N,P] torch tensor.
        filter_size: erosion filter size, has to be odd numbers, default to be 3.
        pad_value: the filled value for padding. We need to pad the input before filtering to keep the output with the same size as input. Usually use default value and not changed.

    Return:
        eroded mask, [N,C,M,N] or [N,C,M,N,P] torch tensor
    """


Review comment (suggestion):
```suggestion
    pad_size = [size // 2 for size in filter_size for _ in range(2)]
```

Code after:
    Apply a morphological filter to a 2D/3D binary mask tensor.

    Args:
        mask_t: input 2D/3D binary mask, [N,C,M,N] or [N,C,M,N,P] torch tensor.
        filter_size: morphological filter size, has to be odd numbers.
        pad_value: the filled value for padding. We need to pad the input before filtering
                   to keep the output with the same size as input.

    Return:
        Tensor: Morphological filter result mask, same shape as input.
    """
    spatial_dims = len(mask_t.shape) - 2
    if spatial_dims not in [2, 3]:
        raise ValueError(
            f"spatial_dims must be either 2 or 3, "
            f"got spatial_dims={spatial_dims} for mask tensor with shape of {mask_t.shape}."
        )

    # Define the structuring element
    filter_size = ensure_tuple_rep(filter_size, spatial_dims)
    if any(size % 2 == 0 for size in filter_size):
        raise ValueError(f"All dimensions in filter_size must be odd numbers, got {filter_size}.")

    structuring_element = torch.ones((mask_t.shape[1], mask_t.shape[1]) + filter_size).to(mask_t.device)

    # Pad the input tensor to handle border pixels
    # Calculate padding size
    pad_size = [size // 2 for size in filter_size for _ in range(2)]

    input_padded = F.pad(mask_t.float(), pad_size, mode="constant", value=pad_value)

    # Apply filter operation
    conv_fn = F.conv2d if spatial_dims == 2 else F.conv3d
    output = conv_fn(input_padded, structuring_element, padding=0) / torch.sum(structuring_element[0, ...])

    return output


def erode_t(mask_t: Tensor, filter_size: int | Sequence[int] = 3, pad_value: float = 1.0) -> Tensor:
    """
    Erode 2D/3D binary mask with data type as torch tensor.

    Args:
        mask_t: input 2D/3D binary mask, [N,C,M,N] or [N,C,M,N,P] torch tensor.
        filter_size: erosion filter size, has to be odd numbers, default to be 3.
        pad_value: the filled value for padding. We need to pad the input before filtering
                   to keep the output with the same size as input. Usually use default value
                   and not changed.

    Return:
        Tensor: eroded mask, same shape as input.