🐛 Fix JITing of complex submodule

The `complex` function confused the compiler.

piqa/__init__.py

@@ -5,7 +5,7 @@
 specific image quality assessement metric.
 """
 
-__version__ = '1.1.6'
+__version__ = '1.1.7'
 
 from .tv import TV
 from .psnr import PSNR

piqa/fsim.py

@@ -105,7 +105,7 @@ def fsim(
         s_q = (2 * q_x * q_y + t4) / (q_x ** 2 + q_y ** 2 + t4)
 
         s_iq = s_i * s_q
-        s_iq = cx.complex(s_iq, torch.zeros_like(s_iq))
+        s_iq = cx.complx(s_iq, torch.zeros_like(s_iq))
         s_iq_lambda =  cx.real(cx.pow(s_iq, lmbda))
 
         s_l = s_l * s_iq_lambda

piqa/mdsi.py

@@ -101,8 +101,8 @@ def mdsi(
     cs = cs_num / cs_den
 
     # Gradient-chromaticity similarity
-    gs = cx.complex(gs, torch.zeros_like(gs))
-    cs = cx.complex(cs, torch.zeros_like(cs))
+    gs = cx.complx(gs, torch.zeros_like(gs))
+    cs = cx.complx(cs, torch.zeros_like(cs))
 
     if combination == 'prod':
         gcs = cx.prod(cx.pow(gs, gamma), cx.pow(cs, beta))

piqa/utils/complex.py

@@ -4,7 +4,7 @@
 import torch
 
 
-def complex(real: torch.Tensor, imag: torch.Tensor) -> torch.Tensor:
+def complx(real: torch.Tensor, imag: torch.Tensor) -> torch.Tensor:
     r"""Returns a complex tensor with its real part equal to \(\Re\) and
     its imaginary part equal to \(\Im\).
 
@@ -20,7 +20,7 @@ def complex(real: torch.Tensor, imag: torch.Tensor) -> torch.Tensor:
     Example:
         >>> x = torch.tensor([2., 0.7071])
         >>> y = torch.tensor([0., 0.7071])
-        >>> complex(x, y)
+        >>> complx(x, y)
         tensor([[2.0000, 0.0000],
                 [0.7071, 0.7071]])
     """
@@ -103,7 +103,7 @@ def turn(x: torch.Tensor) -> torch.Tensor:
                 [-0.7071,  0.7071]])
     """
 
-    return complex(-imag(x), real(x))
+    return complx(-imag(x), real(x))
 
 
 def polar(r: torch.Tensor, phi: torch.Tensor) -> torch.Tensor:
@@ -127,7 +127,7 @@ def polar(r: torch.Tensor, phi: torch.Tensor) -> torch.Tensor:
                 [0.7071, 0.7071]])
     """
 
-    return complex(r * torch.cos(phi), r * torch.sin(phi))
+    return complx(r * torch.cos(phi), r * torch.sin(phi))
 
 
 def mod(x: torch.Tensor, squared: bool = False) -> torch.Tensor:
@@ -200,7 +200,7 @@ def prod(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
     x_r, x_i = x[..., 0], x[..., 1]
     y_r, y_i = y[..., 0], y[..., 1]
 
-    return complex(x_r * y_r - x_i * y_i, x_i * y_r + x_r * y_i)
+    return complx(x_r * y_r - x_i * y_i, x_i * y_r + x_r * y_i)
 
 
 def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:

piqa/vsi.py

@@ -101,7 +101,7 @@ def vsi(
         s_c = (2 * mn_x * mn_y + c3) / (mn_x ** 2 + mn_y ** 2 + c3)
         s_c = s_c.prod(dim=1)
 
-        s_c = cx.complex(s_c, torch.zeros_like(s_c))
+        s_c = cx.complx(s_c, torch.zeros_like(s_c))
         s_c_beta = cx.real(cx.pow(s_c, beta))
 
         s_vs = s_vs * s_c_beta

tests/benchmark.py

@@ -44,7 +44,7 @@
     'PSNR': (2, {
         'sk.psnr-np': sk.peak_signal_noise_ratio,
         'piq.psnr': piq.psnr,
-        'kornia.PSNR': kornia.PSNRLoss(max_val=1.),
+        'kornia.PSNR': kornia.PSNRLoss(1.),
         'piqa.PSNR': piqa.PSNR(),
     }),
     'SSIM': (2, {
@@ -55,10 +55,7 @@
             gaussian_weights=True,
         ),
         'piq.ssim': lambda x, y: piq.ssim(x, y, downsample=False),
-        'kornia.SSIM-halfloss': kornia.SSIM(
-            window_size=11,
-            reduction='mean',
-        ),
+        'kornia.SSIM-halfloss': kornia.SSIMLoss(11),
         'IQA.SSIM-loss': IQA.SSIM(),
         'vainf.SSIM': vainf.SSIM(data_range=1.),
         'piqa.SSIM': piqa.SSIM(),