Merge branch 'main' into feature/packets-level-order

Author: v0lta

src/ptwt/packets.py

@@ -12,7 +12,7 @@
 import pywt
 import torch
 
-from ._util import Wavelet, _as_wavelet
+from ._util import Wavelet, _as_wavelet, _swap_axes, _undo_swap_axes
 from .constants import (
     ExtendedBoundaryMode,
     OrthogonalizeMethod,
@@ -114,9 +114,6 @@ def __init__(
         self.maxlevel: Optional[int] = None
         self.axis = axis
         if data is not None:
-            if len(data.shape) == 1:
-                # add a batch dimension.
-                data = data.unsqueeze(0)
             self.transform(data, maxlevel)
         else:
             self.data = {}
@@ -135,7 +132,7 @@ def transform(
         """
         self.data = {}
         if maxlevel is None:
-            maxlevel = pywt.dwt_max_level(data.shape[-1], self.wavelet.dec_len)
+            maxlevel = pywt.dwt_max_level(data.shape[self.axis], self.wavelet.dec_len)
         self.maxlevel = maxlevel
         self._recursive_dwt(data, level=0, path="")
         return self
@@ -167,13 +164,15 @@ def reconstruct(self) -> WaveletPacket:
             for node in self.get_level(level):
                 data_a = self[node + "a"]
                 data_b = self[node + "d"]
-                rec = self._get_waverec(data_a.shape[-1])([data_a, data_b])
+                rec = self._get_waverec(data_a.shape[self.axis])([data_a, data_b])
                 if level > 0:
-                    if rec.shape[-1] != self[node].shape[-1]:
+                    if rec.shape[self.axis] != self[node].shape[self.axis]:
                         assert (
-                            rec.shape[-1] == self[node].shape[-1] + 1
+                            rec.shape[self.axis] == self[node].shape[self.axis] + 1
                         ), "padding error, please open an issue on github"
-                        rec = rec[..., :-1]
+                        rec = rec.swapaxes(self.axis, -1)[..., :-1].swapaxes(
+                            -1, self.axis
+                        )
                 self[node] = rec
         return self
 
@@ -244,12 +243,12 @@ def _get_graycode_order(level: int, x: str = "a", y: str = "d") -> list[str]:
             return graycode_order
 
     def _recursive_dwt(self, data: torch.Tensor, level: int, path: str) -> None:
-        if not self.maxlevel:
+        if self.maxlevel is None:
             raise AssertionError
 
         self.data[path] = data
         if level < self.maxlevel:
-            res_lo, res_hi = self._get_wavedec(data.shape[-1])(data)
+            res_lo, res_hi = self._get_wavedec(data.shape[self.axis])(data)
             self._recursive_dwt(res_lo, level + 1, path + "a")
             self._recursive_dwt(res_hi, level + 1, path + "d")
 
@@ -357,13 +356,10 @@ def transform(
         """
         self.data = {}
         if maxlevel is None:
-            maxlevel = pywt.dwt_max_level(min(data.shape[-2:]), self.wavelet.dec_len)
+            min_transform_size = min(_swap_axes(data, self.axes).shape[-2:])
+            maxlevel = pywt.dwt_max_level(min_transform_size, self.wavelet.dec_len)
         self.maxlevel = maxlevel
 
-        if data.dim() == 2:
-            # add batch dim to unbatched input
-            data = data.unsqueeze(0)
-
         self._recursive_dwt2d(data, level=0, path="")
         return self
 
@@ -376,30 +372,33 @@ def reconstruct(self) -> WaveletPacket2D:
            a reconstruction from the leaves.
         """
         if self.maxlevel is None:
-            self.maxlevel = pywt.dwt_max_level(
-                min(self[""].shape[-2:]), self.wavelet.dec_len
-            )
+            min_transform_size = min(_swap_axes(self[""], self.axes).shape[-2:])
+            self.maxlevel = pywt.dwt_max_level(min_transform_size, self.wavelet.dec_len)
 
         for level in reversed(range(self.maxlevel)):
             for node in WaveletPacket2D.get_natural_order(level):
                 data_a = self[node + "a"]
                 data_h = self[node + "h"]
                 data_v = self[node + "v"]
                 data_d = self[node + "d"]
-                rec = self._get_waverec(data_a.shape[-2:])(
+                transform_size = _swap_axes(data_a, self.axes).shape[-2:]
+                rec = self._get_waverec(transform_size)(
                     (data_a, WaveletDetailTuple2d(data_h, data_v, data_d))
                 )
                 if level > 0:
-                    if rec.shape[-1] != self[node].shape[-1]:
+                    rec = _swap_axes(rec, self.axes)
+                    swapped_node = _swap_axes(self[node], self.axes)
+                    if rec.shape[-1] != swapped_node.shape[-1]:
                         assert (
-                            rec.shape[-1] == self[node].shape[-1] + 1
+                            rec.shape[-1] == swapped_node.shape[-1] + 1
                         ), "padding error, please open an issue on GitHub"
                         rec = rec[..., :-1]
-                    if rec.shape[-2] != self[node].shape[-2]:
+                    if rec.shape[-2] != swapped_node.shape[-2]:
                         assert (
-                            rec.shape[-2] == self[node].shape[-2] + 1
+                            rec.shape[-2] == swapped_node.shape[-2] + 1
                         ), "padding error, please open an issue on GitHub"
                         rec = rec[..., :-1, :]
+                    rec = _undo_swap_axes(rec, self.axes)
                 self[node] = rec
         return self
 
@@ -485,12 +484,13 @@ def _fsdict_func(coeffs: WaveletCoeff2d) -> torch.Tensor:
         return _fsdict_func
 
     def _recursive_dwt2d(self, data: torch.Tensor, level: int, path: str) -> None:
-        if not self.maxlevel:
+        if self.maxlevel is None:
             raise AssertionError
 
         self.data[path] = data
         if level < self.maxlevel:
-            result = self._get_wavedec(data.shape[-2:])(data)
+            transform_size = _swap_axes(data, self.axes).shape[-2:]
+            result = self._get_wavedec(transform_size)(data)
 
             # assert for type checking
             assert len(result) == 2