(self, xs)
return mb_locs, mb_confs
Compute loc and conf from feature maps

        This method computes :obj:`mb_locs` and :obj:`mb_confs`
        from given feature maps.

        Args:
            xs (iterable of chainer.Variable): An iterable of feature maps.
                The number of feature maps must be same as the number of
                :obj:`aspect_ratios`.

        Returns:
            tuple of chainer.Variable:
            This method returns two :obj:`chainer.Variable`: :obj:`mb_locs` and
            :obj:`mb_confs`.

            * **mb_locs**: A variable of float arrays of shape \
                :math:`(B, K, 4)`, \
                where :math:`B` is the number of samples in the batch and \
                :math:`K` is the number of default bounding boxes.
            * **mb_confs**: A variable of float arrays of shape \
                :math:`(B, K, n\_fg\_class + 1)`.
Compute loc and conf from feature maps
Compute loc and conf from feature maps
def forward(self, xs):
        """Compute loc and conf from feature maps

        This method computes :obj:`mb_locs` and :obj:`mb_confs`
        from given feature maps.

        Args:
            xs (iterable of chainer.Variable): An iterable of feature maps.
                The number of feature maps must be same as the number of
                :obj:`aspect_ratios`.

        Returns:
            tuple of chainer.Variable:
            This method returns two :obj:`chainer.Variable`: :obj:`mb_locs` and
            :obj:`mb_confs`.

            * **mb_locs**: A variable of float arrays of shape \
                :math:`(B, K, 4)`, \
                where :math:`B` is the number of samples in the batch and \
                :math:`K` is the number of default bounding boxes.
            * **mb_confs**: A variable of float arrays of shape \
                :math:`(B, K, n\_fg\_class + 1)`.

        """

        mb_locs = []
        mb_confs = []
        for i, x in enumerate(xs):
            mb_loc = self.loc[i](x)
            mb_loc = F.transpose(mb_loc, (0, 2, 3, 1))
            mb_loc = F.reshape(mb_loc, (mb_loc.shape[0], -1, 4))
            mb_locs.append(mb_loc)

            mb_conf = self.conf[i](x)
            mb_conf = F.transpose(mb_conf, (0, 2, 3, 1))
            mb_conf = F.reshape(
                mb_conf, (mb_conf.shape[0], -1, self.n_class))
            mb_confs.append(mb_conf)

        mb_locs = F.concat(mb_locs, axis=1)
        mb_confs = F.concat(mb_confs, axis=1)

        return mb_locs, mb_confs
def forward ( self , xs ) : mb_locs = [ ] mb_confs = [ ] for i , x in enumerate ( xs ) : mb_loc = self . loc [ i ] ( x ) mb_loc = F . transpose ( mb_loc , ( 0 , 2 , 3 , 1 ) ) mb_loc = F . reshape ( mb_loc , ( mb_loc . shape [ 0 ] , - 1 , 4 ) ) mb_locs . append ( mb_loc ) mb_conf = self . conf [ i ] ( x ) mb_conf = F . transpose ( mb_conf , ( 0 , 2 , 3 , 1 ) ) mb_conf = F . reshape ( mb_conf , ( mb_conf . shape [ 0 ] , - 1 , self . n_class ) ) mb_confs . append ( mb_conf ) mb_locs = F . concat ( mb_locs , axis = 1 ) mb_confs = F . concat ( mb_confs , axis = 1 ) return mb_locs , mb_confs