cyto_dl.nn.vits.blocks.patchify.patchify_hiera module#
- class cyto_dl.nn.vits.blocks.patchify.patchify_hiera.PatchifyHiera(patch_size: List[int], n_patches: List[int], emb_dim: int = 64, spatial_dims: int = 3, context_pixels: List[int] = [0, 0, 0], input_channels: int = 1, tasks: List[str] | None = [], mask_units_per_dim: List[int] = [8, 8, 8])[source]#
Bases:
PatchifyBaseClass for converting images to a sequence of patches with positional embeddings, masked at the level of mask units (groups of patches specified by mask_units_per_dim).
- patch_size: List[int]
Size of each patch in pix (ZYX order for 3D, YX order for 2D)
- n_patches: List[int]
Number of patches in each spatial dimension (ZYX order for 3D, YX order for 2D)
- emb_dim: int
Dimension of encoder
- spatial_dims: int
Number of spatial dimensions
- context_pixels: List[int]
Number of extra pixels around each patch to include in convolutional embedding to encoder dimension.
- input_channels: int
Number of input channels
- tasks: List[str]
List of tasks to encode
- mask_units_per_dim: List[int]
Number of mask units in each spatial dimension (ZYX order for 3D, YX order for 2D)
- property img2token#