(self, imagedata)
return torch.nn.functional.interpolate(
                renormalizer(imagedata).float()[None,...],
                size=self.size)[0]
Scales the given image to the visualized size.
Scales the given image to the visualized size.
Scales the given image to the visualized size .
def scaled_image(self, imagedata):
        '''
        Scales the given image to the visualized size.
        '''
        if len(imagedata.shape) == 4:
            imagedata = imagedata[0]
        renormalizer = self.renormalizer_for(imagedata)
        return torch.nn.functional.interpolate(
                renormalizer(imagedata).float()[None,...],
                size=self.size)[0]
def scaled_image ( self , imagedata ) : if len ( imagedata . shape ) == 4 : imagedata = imagedata [ 0 ] renormalizer = self . renormalizer_for ( imagedata ) return torch . nn . functional . interpolate ( renormalizer ( imagedata ) . float ( ) [ None , ... ] , size = self . size ) [ 0 ]