use numpy.ufuncs instead of xarray.ufuncs

Author: FynnBe

bioimageio/core/prediction_pipeline/_measure_groups.py

@@ -105,7 +105,7 @@ def compute(self, sample: Sample) -> Dict[TensorName, Dict[Measure, MeasureValue
         c = tensor - mean
         n = tensor.size if self.axes is None else numpy.prod([tensor.sizes[d] for d in self.axes])
         var = xr.dot(c, c, dims=self.axes) / n
-        std = xr.ufuncs.sqrt(var)
+        std = numpy.ufuncs.sqrt(var)
         return {self.tensor_name: {Mean(axes=self.axes): mean, Var(axes=self.axes): var, Std(axes=self.axes): std}}
 
     def reset(self):
@@ -146,7 +146,7 @@ def finalize(self) -> Dict[TensorName, Dict[Measure, MeasureValue]]:
                 self.tensor_name: {
                     Mean(axes=self.axes): self.mean,
                     Var(axes=self.axes): var,
-                    Std(axes=self.axes): xr.ufuncs.sqrt(var),
+                    Std(axes=self.axes): numpy.ufuncs.sqrt(var),
                 }
             }
 

bioimageio/core/prediction_pipeline/_processing.py

@@ -187,7 +187,7 @@ def __post_init__(self):
 @dataclass
 class Sigmoid(Processing):
     def apply(self, tensor: xr.DataArray) -> xr.DataArray:
-        return 1.0 / (1.0 + xr.ufuncs.exp(-tensor))
+        return 1.0 / (1.0 + np.ufuncs.exp(-tensor))
 
 
 @dataclass