(self, inputs, inference=False)
return self.outputs
Apply the forward pass transformation to the input data.

        Arguments:
            inputs (Tensor): input data

        Returns:
            Tensor: output data
Apply the forward pass transformation to the input data.
Apply the forward pass transformation to the input data .
def fprop(self, inputs, inference=False):
        """
        Apply the forward pass transformation to the input data.

        Arguments:
            inputs (Tensor): input data

        Returns:
            Tensor: output data
        """
        self.init_buffers(inputs)

        self.outputs.fill(0)
        self.max_idx.fill(0)

        # fprop through the roipooling layer
        self.be.roipooling_fprop(self.img, self.rois, self.outputs, self.max_idx,
                                 self.rois_per_img, self.fm_channel, self.fm_height,
                                 self.fm_width, self.roi_H, self.roi_W, self.spatial_scale)

        return self.outputs
def fprop ( self , inputs , inference = False ) : self . init_buffers ( inputs ) self . outputs . fill ( 0 ) self . max_idx . fill ( 0 ) # fprop through the roipooling layer self . be . roipooling_fprop ( self . img , self . rois , self . outputs , self . max_idx , self . rois_per_img , self . fm_channel , self . fm_height , self . fm_width , self . roi_H , self . roi_W , self . spatial_scale ) return self . outputs