IRFFT fix (#126)

* Add output shape to irfft.

* Expose fixed parameter.

* Update CHANGELOG.

* Fix assertion.

* Fix typo.

CHANGELOG.rst

@@ -40,6 +40,7 @@ Bugfix
 - Local path for DRUNet download.
 - APGD input handling (float32).
 - Multimask handling.
+- Passing shape to IRFFT so that it matches shape of input to RFFT.
 
 1.0.6 - (2024-02-21)
 --------------------

lensless/recon/admm.py

@@ -282,10 +282,14 @@ def _image_update(self):
 
         if self.is_torch:
             freq_space_result = self._R_divmat * torch.fft.rfft2(rk, dim=(-3, -2))
-            self._image_est = torch.fft.irfft2(freq_space_result, dim=(-3, -2))
+            self._image_est = torch.fft.irfft2(
+                freq_space_result, dim=(-3, -2), s=self._convolver._padded_shape[-3:-1]
+            )
         else:
             freq_space_result = self._R_divmat * fft.rfft2(rk, axes=(-3, -2))
-            self._image_est = fft.irfft2(freq_space_result, axes=(-3, -2))
+            self._image_est = fft.irfft2(
+                freq_space_result, axes=(-3, -2), s=self._convolver._padded_shape[-3:-1]
+            )
 
         # self._image_est = self._convolver._crop(res)
 

lensless/recon/rfft_convolve.py

@@ -139,20 +139,28 @@ def convolve(self, x):
         if self.is_torch:
             conv_output = torch.fft.ifftshift(
                 torch.fft.irfft2(
-                    torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._H, dim=(-3, -2)
+                    torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._H,
+                    dim=(-3, -2),
+                    s=self._padded_shape[-3:-1],
                 ),
                 dim=(-3, -2),
             )
 
         else:
             conv_output = fft.ifftshift(
-                fft.irfft2(fft.rfft2(self._padded_data, axes=(-3, -2)) * self._H, axes=(-3, -2)),
+                fft.irfft2(
+                    fft.rfft2(self._padded_data, axes=(-3, -2)) * self._H,
+                    axes=(-3, -2),
+                    s=self._padded_shape[-3:-1],
+                ),
                 axes=(-3, -2),
             )
         if self.pad:
-            return self._crop(conv_output)
-        else:
-            return conv_output
+            conv_output = self._crop(conv_output)
+
+        # ensure shape stays the same
+        assert conv_output.shape[-3:-1] == x.shape[-3:-1]
+        return conv_output
 
     def deconvolve(self, y):
         """
@@ -165,21 +173,30 @@ def deconvolve(self, y):
                 self._padded_data = y  # .type(self.dtype).to(self._psf.device)
             else:
                 self._padded_data[:] = y  # .astype(self.dtype)
+
         if self.is_torch:
             deconv_output = torch.fft.ifftshift(
                 torch.fft.irfft2(
-                    torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._Hadj, dim=(-3, -2)
+                    torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._Hadj,
+                    dim=(-3, -2),
+                    s=self._padded_shape[-3:-1],
                 ),
                 dim=(-3, -2),
             )
 
         else:
             deconv_output = fft.ifftshift(
-                fft.irfft2(fft.rfft2(self._padded_data, axes=(-3, -2)) * self._Hadj, axes=(-3, -2)),
+                fft.irfft2(
+                    fft.rfft2(self._padded_data, axes=(-3, -2)) * self._Hadj,
+                    axes=(-3, -2),
+                    s=self._padded_shape[-3:-1],
+                ),
                 axes=(-3, -2),
             )
 
         if self.pad:
-            return self._crop(deconv_output)
-        else:
-            return deconv_output
+            deconv_output = self._crop(deconv_output)
+
+        # ensure shape stays the same
+        assert deconv_output.shape[-3:-1] == y.shape[-3:-1]
+        return deconv_output

lensless/recon/unrolled_admm.py

@@ -198,7 +198,9 @@ def _image_update(self, iter):
             + self._convolver.deconvolve(self._mu1[iter] * self._X - self._xi)
         )
         freq_space_result = self._R_divmat[iter] * torch.fft.rfft2(rk, dim=(-3, -2))
-        self._image_est = torch.fft.irfft2(freq_space_result, dim=(-3, -2))
+        self._image_est = torch.fft.irfft2(
+            freq_space_result, dim=(-3, -2), s=self._convolver._padded_shape[-3:-1]
+        )
 
     def _W_update(self, iter):
         """Non-negativity update"""

lensless/utils/dataset.py

@@ -968,6 +968,7 @@ def __init__(
         psf="psf.tiff",
         downsample=2,
         flip_ud=True,
+        dtype="float32",
         **kwargs,
     ):
         """
@@ -981,9 +982,6 @@ def __init__(
             If True, data is flipped up-down, by default ``True``. Otherwise data is upside-down.
         """
 
-        # fixed parameters
-        dtype = "float32"
-
         # get dataset
         self.dataset = load_dataset(repo_id, split=split)