(self, x)
return locs, objs, confs
Compute localization, objectness, and classification from a batch of images.

        This method computes three variables, :obj:`locs`, :obj:`objs`,
        and :obj:`confs`.
        :meth:`self._decode` converts these variables to bounding box
        coordinates and confidence scores.
        These variables are also used in training YOLOv2.

        Args:
            x (chainer.Variable): A variable holding a batch of images.

        Returns:
            tuple of chainer.Variable:
            This method returns three variables, :obj:`locs`,
            :obj:`objs`, and :obj:`confs`.

            * **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.
            * **objs**: A variable of float arrays of shape \
                :math:`(B, K)`.
            * **confs**: A variable of float arrays of shape \
                :math:`(B, K, n\_fg\_class)`.
Compute localization, objectness, and classification from a batch of images.
Compute localization objectness and classification from a batch of images .
def forward(self, x):
        """Compute localization, objectness, and classification from a batch of images.

        This method computes three variables, :obj:`locs`, :obj:`objs`,
        and :obj:`confs`.
        :meth:`self._decode` converts these variables to bounding box
        coordinates and confidence scores.
        These variables are also used in training YOLOv2.

        Args:
            x (chainer.Variable): A variable holding a batch of images.

        Returns:
            tuple of chainer.Variable:
            This method returns three variables, :obj:`locs`,
            :obj:`objs`, and :obj:`confs`.

            * **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.
            * **objs**: A variable of float arrays of shape \
                :math:`(B, K)`.
            * **confs**: A variable of float arrays of shape \
                :math:`(B, K, n\_fg\_class)`.
        """

        h = self.subnet(self.extractor(x))
        h = F.transpose(h, (0, 2, 3, 1))
        h = F.reshape(h, (h.shape[0], -1, 4 + 1 + self.n_fg_class))
        locs = h[:, :, :4]
        objs = h[:, :, 4]
        confs = h[:, :, 5:]
        return locs, objs, confs
def forward ( self , x ) : h = self . subnet ( self . extractor ( x ) ) h = F . transpose ( h , ( 0 , 2 , 3 , 1 ) ) h = F . reshape ( h , ( h . shape [ 0 ] , - 1 , 4 + 1 + self . n_fg_class ) ) locs = h [ : , : , : 4 ] objs = h [ : , : , 4 ] confs = h [ : , : , 5 : ] return locs , objs , confs