Use typing.Literal for boundary mode, padding mode, and orthogonaliza…

…tion mode (#77)

docs/ptwt.rst

@@ -102,4 +102,9 @@ ptwt.wavelets\_learnable module
    :undoc-members:
    :show-inheritance:
 
-
+ptwt.constants
+-------------------------------
+.. automodule:: ptwt.constants
+   :members:
+   :undoc-members:
+   :show-inheritance:

examples/speed_tests/timeitconv_2d.py

@@ -33,7 +33,7 @@ def _to_jit_wavedec_2(data, wavelet):
     means we have to stack the lists in the output.
     """
     assert data.shape == (32, 1e3, 1e3), "Changing the chape requires re-tracing."
-    coeff = ptwt.wavedec2(data, wavelet, "periodic", level=5)
+    coeff = ptwt.wavedec2(data, wavelet, mode="periodic", level=5)
     coeff2 = []
     for c in coeff:
         if isinstance(c, torch.Tensor):

src/ptwt/_util.py

@@ -1,11 +1,14 @@
 """Utility methods to compute wavelet decompositions from a dataset."""
 
+import typing
 from typing import Any, Callable, List, Optional, Protocol, Sequence, Tuple, Union
 
 import numpy as np
 import pywt
 import torch
 
+from ptwt.constants import OrthogonalizeMethod
+
 
 class Wavelet(Protocol):
     """Wavelet object interface, based on the pywt wavelet object."""
@@ -43,8 +46,8 @@ def _as_wavelet(wavelet: Union[Wavelet, str]) -> Wavelet:
         return wavelet
 
 
-def _is_boundary_mode_supported(boundary_mode: Optional[str]) -> bool:
-    return boundary_mode in ["qr", "gramschmidt"]
+def _is_boundary_mode_supported(boundary_mode: Optional[OrthogonalizeMethod]) -> bool:
+    return boundary_mode in typing.get_args(OrthogonalizeMethod)
 
 
 def _is_dtype_supported(dtype: torch.dtype) -> bool:

src/ptwt/constants.py

@@ -0,0 +1,38 @@
+"""Constants and types used throughout the PyTorch Wavelet Toolbox."""
+
+from typing import Literal, Union
+
+__all__ = [
+    "BoundaryMode",
+    "ExtendedBoundaryMode",
+    "PaddingMode",
+    "OrthogonalizeMethod",
+]
+
+BoundaryMode = Literal["constant", "zero", "reflect", "periodic", "symmetric"]
+"""
+This is a type literal for the way of padding.
+
+- Refection padding mirrors samples along the border.
+- Zero padding pads zeros.
+- Constant padding replicates border values.
+- Periodic padding cyclically repeats samples.
+- Symmetric padding mirrors samples along the border
+"""
+
+ExtendedBoundaryMode = Union[Literal["boundary"], BoundaryMode]
+
+PaddingMode = Literal["full", "valid", "same", "sameshift"]
+"""
+The padding mode is used when construction convolution matrices.
+"""
+
+OrthogonalizeMethod = Literal["qr", "gramschmidt"]
+"""
+The method for orthogonalizing a matrix.
+
+1. 'qr' relies on pytorch's dense qr implementation, it is fast but memory hungry.
+2. 'gramschmidt' option is sparse, memory efficient, and slow.
+
+Choose 'gramschmidt' if 'qr' runs out of memory.
+"""

src/ptwt/conv_transform.py

@@ -4,7 +4,7 @@
 """
 
 # Created by moritz wolter, 14.04.20
-from typing import List, Optional, Sequence, Tuple, Union
+from typing import List, Optional, Sequence, Tuple, Union, cast
 
 import pywt
 import torch
@@ -18,6 +18,7 @@
     _pad_symmetric,
     _unfold_axes,
 )
+from .constants import BoundaryMode
 
 
 def _create_tensor(
@@ -106,7 +107,7 @@ def _get_pad(data_len: int, filt_len: int) -> Tuple[int, int]:
     return padr, padl
 
 
-def _translate_boundary_strings(pywt_mode: str) -> str:
+def _translate_boundary_strings(pywt_mode: BoundaryMode) -> str:
     """Translate pywt mode strings to PyTorch mode strings.
 
     We support constant, zero, reflect, and periodic.
@@ -118,24 +119,25 @@ def _translate_boundary_strings(pywt_mode: str) -> str:
 
     """
     if pywt_mode == "constant":
-        pt_mode = "replicate"
+        return "replicate"
     elif pywt_mode == "zero":
-        pt_mode = "constant"
+        return "constant"
     elif pywt_mode == "reflect":
-        pt_mode = pywt_mode
+        return pywt_mode
     elif pywt_mode == "periodic":
-        pt_mode = "circular"
+        return "circular"
     elif pywt_mode == "symmetric":
         # pytorch does not support symmetric mode,
         # we have our own implementation.
-        pt_mode = pywt_mode
-    else:
-        raise ValueError("Padding mode not supported.")
-    return pt_mode
+        return pywt_mode
+    raise ValueError(f"Padding mode not supported: {pywt_mode}")
 
 
 def _fwt_pad(
-    data: torch.Tensor, wavelet: Union[Wavelet, str], mode: str = "reflect"
+    data: torch.Tensor,
+    wavelet: Union[Wavelet, str],
+    *,
+    mode: Optional[BoundaryMode] = None,
 ) -> torch.Tensor:
     """Pad the input signal to make the fwt matrix work.
 
@@ -145,29 +147,26 @@ def _fwt_pad(
         data (torch.Tensor): Input data ``[batch_size, 1, time]``
         wavelet (Wavelet or str): A pywt wavelet compatible object or
             the name of a pywt wavelet.
-        mode (str): The desired way to pad. The following methods are supported::
-
-                "reflect", "zero", "constant", "periodic", "symmetric".
-
-            Refection padding mirrors samples along the border.
-            Zero padding pads zeros.
-            Constant padding replicates border values.
-            Periodic padding cyclically repeats samples.
-            This function defaults to reflect.
+        mode :
+            The desired padding mode for extending the signal along the edges.
+            Defaults to "reflect". See :data:`ptwt.constants.BoundaryMode`.
 
     Returns:
         torch.Tensor: A PyTorch tensor with the padded input data
 
     """
     wavelet = _as_wavelet(wavelet)
+
     # convert pywt to pytorch convention.
-    mode = _translate_boundary_strings(mode)
+    if mode is None:
+        mode = cast(BoundaryMode, "reflect")
+    pytorch_mode = _translate_boundary_strings(mode)
 
     padr, padl = _get_pad(data.shape[-1], _get_len(wavelet))
-    if mode == "symmetric":
+    if pytorch_mode == "symmetric":
         data_pad = _pad_symmetric(data, [(padl, padr)])
     else:
-        data_pad = torch.nn.functional.pad(data, [padl, padr], mode=mode)
+        data_pad = torch.nn.functional.pad(data, [padl, padr], mode=pytorch_mode)
     return data_pad
 
 
@@ -263,7 +262,8 @@ def _preprocess_result_list_rec1d(
 def wavedec(
     data: torch.Tensor,
     wavelet: Union[Wavelet, str],
-    mode: str = "reflect",
+    *,
+    mode: BoundaryMode = "reflect",
     level: Optional[int] = None,
     axis: int = -1,
 ) -> List[torch.Tensor]:
@@ -276,18 +276,9 @@ def wavedec(
             the name of a pywt wavelet.
             Please consider the output from ``pywt.wavelist(kind='discrete')``
             for possible choices.
-        mode (str): The desired padding mode. Padding extends the signal along
-            the edges. Supported methods are::
-
-                "reflect", "zero", "constant", "periodic", "symmetric".
-
-            Defaults to "reflect".
-
-            Symmetric padding mirrors samples along the border.
-            Refection padding reflects samples along the border.
-            Zero padding pads zeros.
-            Constant padding replicates border values.
-            Periodic padding cyclically repeats samples.
+        mode :
+            The desired padding mode for extending the signal along the edges.
+            Defaults to "reflect". See :data:`ptwt.constants.BoundaryMode`.
         level (int): The scale level to be computed.
                                Defaults to None.
         axis (int): Compute the transform over this axis instead of the

src/ptwt/conv_transform_2.py

@@ -5,7 +5,7 @@
 """
 
 from functools import partial
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union, cast
 
 import pywt
 import torch
@@ -25,6 +25,7 @@
     _undo_swap_axes,
     _unfold_axes,
 )
+from .constants import BoundaryMode
 from .conv_transform import (
     _adjust_padding_at_reconstruction,
     _get_filter_tensors,
@@ -58,7 +59,10 @@ def _construct_2d_filt(lo: torch.Tensor, hi: torch.Tensor) -> torch.Tensor:
 
 
 def _fwt_pad2(
-    data: torch.Tensor, wavelet: Union[Wavelet, str], mode: str = "reflect"
+    data: torch.Tensor,
+    wavelet: Union[Wavelet, str],
+    *,
+    mode: Optional[BoundaryMode] = None,
 ) -> torch.Tensor:
     """Pad data for the 2d FWT.
 
@@ -68,25 +72,26 @@ def _fwt_pad2(
         data (torch.Tensor): Input data with 4 dimensions.
         wavelet (Wavelet or str): A pywt wavelet compatible object or
             the name of a pywt wavelet.
-        mode (str): The padding mode.
-            Supported modes are::
-
-                "reflect", "zero", "constant", "periodic", "symmetric".
-
-            "reflect" is the default mode.
+        mode :
+            The desired padding mode for extending the signal along the edges.
+            Defaults to "reflect". See :data:`ptwt.constants.BoundaryMode`.
 
     Returns:
         The padded output tensor.
 
     """
-    mode = _translate_boundary_strings(mode)
+    if mode is None:
+        mode = cast(BoundaryMode, "reflect")
+    pytorch_mode = _translate_boundary_strings(mode)
     wavelet = _as_wavelet(wavelet)
     padb, padt = _get_pad(data.shape[-2], _get_len(wavelet))
     padr, padl = _get_pad(data.shape[-1], _get_len(wavelet))
-    if mode == "symmetric":
+    if pytorch_mode == "symmetric":
         data_pad = _pad_symmetric(data, [(padt, padb), (padl, padr)])
     else:
-        data_pad = torch.nn.functional.pad(data, [padl, padr, padt, padb], mode=mode)
+        data_pad = torch.nn.functional.pad(
+            data, [padl, padr, padt, padb], mode=pytorch_mode
+        )
     return data_pad
 
 
@@ -122,7 +127,8 @@ def _preprocess_tensor_dec2d(
 def wavedec2(
     data: torch.Tensor,
     wavelet: Union[Wavelet, str],
-    mode: str = "reflect",
+    *,
+    mode: BoundaryMode = "reflect",
     level: Optional[int] = None,
     axes: Tuple[int, int] = (-2, -1),
 ) -> List[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]]:
@@ -140,11 +146,9 @@ def wavedec2(
         wavelet (Wavelet or str): A pywt wavelet compatible object or
             the name of a pywt wavelet. Refer to the output of
             ``pywt.wavelist(kind="discrete")`` for a list of possible choices.
-        mode (str): The padding mode. Options are::
-
-                "reflect", "zero", "constant", "periodic", "symmetric".
-
-            This function defaults to "reflect".
+        mode :
+            The desired padding mode for extending the signal along the edges.
+            Defaults to "reflect". See :data:`ptwt.constants.BoundaryMode`.
         level (int): The number of desired scales.
             Defaults to None.
         axes (Tuple[int, int]): Compute the transform over these axes instead of the

src/ptwt/conv_transform_3.py

@@ -24,6 +24,7 @@
     _undo_swap_axes,
     _unfold_axes,
 )
+from .constants import BoundaryMode
 from .conv_transform import (
     _adjust_padding_at_reconstruction,
     _get_filter_tensors,
@@ -63,7 +64,7 @@ def _construct_3d_filt(lo: torch.Tensor, hi: torch.Tensor) -> torch.Tensor:
 
 
 def _fwt_pad3(
-    data: torch.Tensor, wavelet: Union[Wavelet, str], mode: str
+    data: torch.Tensor, wavelet: Union[Wavelet, str], *, mode: BoundaryMode
 ) -> torch.Tensor:
     """Pad data for the 3d-FWT.
 
@@ -73,37 +74,38 @@ def _fwt_pad3(
         data (torch.Tensor): Input data with 4 dimensions.
         wavelet (Wavelet or str): A pywt wavelet compatible object or
             the name of a pywt wavelet.
-        mode (str): The padding mode. Supported modes are::
-
-            "reflect", "zero", "constant", "periodic", "symmetric".
+        mode :
+            The desired padding mode for extending the signal along the edges.
+            See :data:`ptwt.constants.BoundaryMode`.
 
     Returns:
         The padded output tensor.
 
     """
-    mode = _translate_boundary_strings(mode)
+    pytorch_mode = _translate_boundary_strings(mode)
 
     wavelet = _as_wavelet(wavelet)
     pad_back, pad_front = _get_pad(data.shape[-3], _get_len(wavelet))
     pad_bottom, pad_top = _get_pad(data.shape[-2], _get_len(wavelet))
     pad_right, pad_left = _get_pad(data.shape[-1], _get_len(wavelet))
-    if mode == "symmetric":
+    if pytorch_mode == "symmetric":
         data_pad = _pad_symmetric(
             data, [(pad_front, pad_back), (pad_top, pad_bottom), (pad_left, pad_right)]
         )
     else:
         data_pad = torch.nn.functional.pad(
             data,
             [pad_left, pad_right, pad_top, pad_bottom, pad_front, pad_back],
-            mode=mode,
+            mode=pytorch_mode,
         )
     return data_pad
 
 
 def wavedec3(
     data: torch.Tensor,
     wavelet: Union[Wavelet, str],
-    mode: str = "zero",
+    *,
+    mode: BoundaryMode = "zero",
     level: Optional[int] = None,
     axes: Tuple[int, int, int] = (-3, -2, -1),
 ) -> List[Union[torch.Tensor, Dict[str, torch.Tensor]]]:
@@ -114,11 +116,9 @@ def wavedec3(
             [batch_size, length, height, width]
         wavelet (Union[Wavelet, str]): The wavelet to transform with.
             ``pywt.wavelist(kind='discrete')`` lists possible choices.
-        mode (str): The padding mode. Possible options are::
-
-                "reflect", "zero", "constant", "periodic", "symmetric".
-
-            Defaults to "zero".
+        mode :
+            The desired padding mode for extending the signal along the edges.
+            Defaults to "zero". See :data:`ptwt.constants.BoundaryMode`.
         level (Optional[int]): The maximum decomposition level.
             This argument defaults to None.
         axes (Tuple[int, int, int]): Compute the transform over these axes