refactor: change function _get_convol_img_fn for more clarity

ot/bregman/_convolutional.py

@@ -31,13 +31,7 @@ def _get_convol_img_fn(nx, width, height, reg, type_as, log_domain=False):
     t2 = nx.linspace(0, 1, height, type_as=type_as)
     Y2, X2 = nx.meshgrid(t2, t2)
     M2 = -((X2 - Y2) ** 2) / reg
-
-    # As M1 and M2 are computed first, we can use them to compute the convolution in log-domain
-    def convol_imgs(log_imgs):
-        log_imgs = nx.logsumexp(M1[:, :, None] + log_imgs[None], axis=1)
-        log_imgs = nx.logsumexp(M2[:, :, None] + log_imgs.T[None], axis=1).T
-        return log_imgs
-
+
     # If normal domain is selected, we can use M1 and M2 to compute the convolution
     if not log_domain:
         K1, K2 = nx.exp(M1), nx.exp(M2)
@@ -47,6 +41,13 @@ def convol_imgs(imgs):
             kxy = nx.einsum("...ij,klj->kli", K2, kx)
             return kxy
 
+    # Else, we can use M1 and M2 to compute the convolution in log-domain
+    else:
+        def convol_imgs(log_imgs):
+            log_imgs = nx.logsumexp(M1[:, :, None] + log_imgs[None], axis=1)
+            log_imgs = nx.logsumexp(M2[:, :, None] + log_imgs.T[None], axis=1).T
+            return log_imgs
+
     return convol_imgs