(self, x, scales=None)
return roi_ag_seg_scores, roi_ag_locs, roi_cls_scores, \
            rois, roi_indices
Forward FCIS.

        Scaling paramter :obj:`scale` is used by RPN to determine the
        threshold to select small objects, which are going to be
        rejected irrespective of their confidence scores.

        Here are notations used.

        * :math:`N` is the number of batch size
        * :math:`R'` is the total number of RoIs produced across batches. \
            Given :math:`R_i` proposed RoIs from the :math:`i` th image, \
            :math:`R' = \\sum _{i=1} ^ N R_i`.
        * :math:`L` is the number of classes excluding the background.
        * :math:`RH` is the height of pooled image by Position Sensitive \
            ROI pooling.
        * :math:`RW` is the height of pooled image by Position Sensitive \
            ROI pooling.

        Classes are ordered by the background, the first class, ..., and
        the :math:`L` th class.

        Args:
            x (~chainer.Variable): 4D image variable.
            scales (tuple of floats): Amount of scaling applied to each input
                image during preprocessing.

        Returns:
            Variable, Variable, Variable, array, array:
            Returns tuple of five values listed below.

            * **roi_ag_seg_scores**: Class-agnostic clipped mask scores for \
                the proposed ROIs. Its shape is :math:`(R', 2, RH, RW)`
            * **ag_locs**: Class-agnostic offsets and scalings for \
                the proposed RoIs.  Its shape is :math:`(R', 2, 4)`.
            * **roi_cls_scores**: Class predictions for the proposed RoIs. \
                Its shape is :math:`(R', L + 1)`.
            * **rois**: RoIs proposed by RPN. Its shape is \
                :math:`(R', 4)`.
            * **roi_indices**: Batch indices of RoIs. Its shape is \
                :math:`(R',)`.
Forward FCIS.
Forward FCIS .
def forward(self, x, scales=None):
        """Forward FCIS.

        Scaling paramter :obj:`scale` is used by RPN to determine the
        threshold to select small objects, which are going to be
        rejected irrespective of their confidence scores.

        Here are notations used.

        * :math:`N` is the number of batch size
        * :math:`R'` is the total number of RoIs produced across batches. \
            Given :math:`R_i` proposed RoIs from the :math:`i` th image, \
            :math:`R' = \\sum _{i=1} ^ N R_i`.
        * :math:`L` is the number of classes excluding the background.
        * :math:`RH` is the height of pooled image by Position Sensitive \
            ROI pooling.
        * :math:`RW` is the height of pooled image by Position Sensitive \
            ROI pooling.

        Classes are ordered by the background, the first class, ..., and
        the :math:`L` th class.

        Args:
            x (~chainer.Variable): 4D image variable.
            scales (tuple of floats): Amount of scaling applied to each input
                image during preprocessing.

        Returns:
            Variable, Variable, Variable, array, array:
            Returns tuple of five values listed below.

            * **roi_ag_seg_scores**: Class-agnostic clipped mask scores for \
                the proposed ROIs. Its shape is :math:`(R', 2, RH, RW)`
            * **ag_locs**: Class-agnostic offsets and scalings for \
                the proposed RoIs.  Its shape is :math:`(R', 2, 4)`.
            * **roi_cls_scores**: Class predictions for the proposed RoIs. \
                Its shape is :math:`(R', L + 1)`.
            * **rois**: RoIs proposed by RPN. Its shape is \
                :math:`(R', 4)`.
            * **roi_indices**: Batch indices of RoIs. Its shape is \
                :math:`(R',)`.

        """
        img_size = x.shape[2:]

        # Feature Extractor
        rpn_features, roi_features = self.extractor(x)
        rpn_locs, rpn_scores, rois, roi_indices, anchor = self.rpn(
            rpn_features, img_size, scales)
        roi_ag_seg_scores, roi_ag_locs, roi_cls_scores, rois, roi_indices = \
            self.head(roi_features, rois, roi_indices, img_size)
        return roi_ag_seg_scores, roi_ag_locs, roi_cls_scores, \
            rois, roi_indices
def forward ( self , x , scales = None ) : img_size = x . shape [ 2 : ] # Feature Extractor rpn_features , roi_features = self . extractor ( x ) rpn_locs , rpn_scores , rois , roi_indices , anchor = self . rpn ( rpn_features , img_size , scales ) roi_ag_seg_scores , roi_ag_locs , roi_cls_scores , rois , roi_indices = self . head ( roi_features , rois , roi_indices , img_size ) return roi_ag_seg_scores , roi_ag_locs , roi_cls_scores , rois , roi_indices