nets.py
def _forward(self, x):
        """Forward application of the module.

        Args:
            x: Tensor with shape [batch, channels, height, width].

        Returns:
            out_x: Output tensor with shape
                [batch, channels, self._target['height'], self._target['width']]
        """
        if not self.space_evaluated():
            if not self._target_evaluated():
                raise ValueError(
                    'Cannot compute the target size before the target it is '
                    'evaluated. Check to make sure that the target_evaluated '
                    'argument to the constructor of this instance is correct.')
            self._size = (self._target['height'], self._target['width'])

        out_x = torch.nn.functional.interpolate(
            x, size=self._size, mode=self._mode, align_corners=True)
        return out_x
Forward application of the module.

        Args:
            x: Tensor with shape [batch, channels, height, width].

        Returns:
            out_x: Output tensor with shape
                [batch, channels, self._target['height'], self._target['width']]
        
Forward application of the module.
Forward application of the module .
def _forward(self, x):
        if not self.space_evaluated():
            if not self._target_evaluated():
                raise ValueError(
                    'Cannot compute the target size before the target it is '
                    'evaluated. Check to make sure that the target_evaluated '
                    'argument to the constructor of this instance is correct.')
            self._size = (self._target['height'], self._target['width'])
        out_x = torch.nn.functional.interpolate(
            x, size=self._size, mode=self._mode, align_corners=True)
        return out_x
def _forward ( self , x ) : if not self . space_evaluated ( ) : if not self . _target_evaluated ( ) : raise ValueError ( 'Cannot compute the target size before the target it is ' 'evaluated. Check to make sure that the target_evaluated ' 'argument to the constructor of this instance is correct.' ) self . _size = ( self . _target [ 'height' ] , self . _target [ 'width' ] ) out_x = torch . nn . functional . interpolate ( x , size = self . _size , mode = self . _mode , align_corners = True ) return out_x
Forward application of the module.
Forward application of the module .
"""Forward application of the module.

        Args:
            x: Tensor with shape [batch, channels, height, width].

        Returns:
            out_x: Output tensor with shape
                [batch, channels, self._target['height'], self._target['width']]
        """
Output tensor with shape
[batch, channels, self._target['height'], self._target['width']]
Output tensor with shape [ batch channels self . _target [ ' height ' ] self . _target [ ' width ' ]]
out_x
self
x
Tensor with shape [batch, channels, height, width].
Tensor with shape [ batch channels height width ] .