Python caffe2.python.core.ScopedBlobReference() Examples

def AffineChannelNd(self, blob_in, blob_out, dim_out, share_with=None,
                        inplace=False):
        if cfg.MODEL.USE_BN:
            return self.SpatialBNLayer(blob_in, blob_out, dim_out, share_with,
                                       inplace)
        blob_out = blob_out or self.net.NextName()
        is_not_sharing = share_with is None
        param_prefix = blob_out if is_not_sharing else share_with
        scale = core.ScopedBlobReference(
            param_prefix + '_s', self.param_init_net)
        bias = core.ScopedBlobReference(
            param_prefix + '_b', self.param_init_net)
        if is_not_sharing:
            self.net.Proto().external_input.extend([str(scale), str(bias)])
            self.params.extend([scale, bias])
            self.weights.append(scale)
            self.biases.append(bias)
        if inplace:
            return self.net.AffineChannelNd([blob_in, scale, bias], blob_in)
        else:
            return self.net.AffineChannelNd([blob_in, scale, bias], blob_out) 

def AffineChannel(self, blob_in, blob_out, dim_out, share_with=None,
                      inplace=False):
        if cfg.MODEL.USE_BN:
            return self.SpatialBNLayer(blob_in, blob_out, dim_out, share_with,
                                       inplace)
        blob_out = blob_out or self.net.NextName()
        is_not_sharing = share_with is None
        param_prefix = blob_out if is_not_sharing else share_with
        scale = core.ScopedBlobReference(
            param_prefix + '_s', self.param_init_net)
        bias = core.ScopedBlobReference(
            param_prefix + '_b', self.param_init_net)
        if is_not_sharing:
            self.net.Proto().external_input.extend([str(scale), str(bias)])
            self.params.extend([scale, bias])
            self.weights.append(scale)
            self.biases.append(bias)
        if inplace:
            return self.net.AffineChannel([blob_in, scale, bias], blob_in)
        else:
            return self.net.AffineChannel([blob_in, scale, bias], blob_out) 

def AffineChannel(self, blob_in, blob_out, share_with=None, inplace=False):
        """Affine transformation to replace BN in networks where BN cannot be
        used (e.g., because the minibatch size is too small).

        The AffineChannel parameters may be shared with another AffineChannelOp
        by specifying its blob name (excluding the '_{s,b}' suffix) in the
        share_with argument. The operations can be done in place to save memory.
        """
        blob_out = blob_out or self.net.NextName()
        is_not_sharing = share_with is None
        param_prefix = blob_out if is_not_sharing else share_with
        scale = core.ScopedBlobReference(
            param_prefix + '_s', self.param_init_net)
        bias = core.ScopedBlobReference(
            param_prefix + '_b', self.param_init_net)
        if is_not_sharing:
            self.net.Proto().external_input.extend([str(scale), str(bias)])
            self.params.extend([scale, bias])
            self.weights.append(scale)
            self.biases.append(bias)
        if inplace:
            return self.net.AffineChannel([blob_in, scale, bias], blob_in)
        else:
            return self.net.AffineChannel([blob_in, scale, bias], blob_out) 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        # blobs_in = ['rpn_rois', 'roidb', 'im_info']
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in])

        # Get output blob names from the data loader
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train)
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name)
        return blobs_out 

def GenerateProposalLabels(self, blobs_in):
        """Op for generating training labels for RPN proposals. This is used
        when training RPN jointly with Fast/Mask R-CNN (as in end-to-end
        Faster R-CNN training).

        blobs_in:
          - 'rpn_rois': 2D tensor of RPN proposals output by GenerateProposals
          - 'roidb': roidb entries that will be labeled
          - 'im_info': See GenerateProposals doc.

        blobs_out:
          - (variable set of blobs): returns whatever blobs are required for
            training the model. It does this by querying the data loader for
            the list of blobs that are needed.
        """
        name = 'GenerateProposalLabelsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # The list of blobs is not known before run-time because it depends on
        # the specific model being trained. Query the data loader to get the
        # list of output blob names.
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        self.net.Python(GenerateProposalLabelsOp().forward)(
            blobs_in, blobs_out, name=name
        )
        return blobs_out 

def CollectAndDistributeFpnRpnProposals(self):
        """Merges RPN proposals generated at various FPN levels and then
        redistributes those proposals to their appropriate FPN levels for use by
        the RoIFeatureTransform op.
        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
        If used during training, then the input blobs will also include
        [gt_boxes, roidb, im_info] and the output blobs will include (before
        rois) [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train)
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def DistributeCascadeProposals(self, stage):
        """Distribute proposals to their appropriate FPN levels.
        by Zhaowei Cai for Cascade R-CNN

        Input blobs:
          - proposals_<j> are the decoded proposals from stage j; see
            documentation from DecodeBBoxes.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights,
          mapped_gt_boxes].
        """
        stage_name = '_{}'.format(stage)

        # Prepare input blobs
        blobs_in = ['proposals' + stage_name]
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'DistributeCascadeProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = cascade_rcnn_roi_data.get_cascade_rcnn_blob_names(
            stage, is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            DistributeCascadeProposalsOp(self.train, stage).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def DistributeCascadeProposals(self, stage):
        """Distribute proposals to their appropriate FPN levels.
        by Zhaowei Cai for Cascade R-CNN

        Input blobs:
          - proposals_<j> are the decoded proposals from stage j; see
            documentation from DecodeBBoxes.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights,
          mapped_gt_boxes].
        """
        stage_name = '_{}'.format(stage)

        # Prepare input blobs
        blobs_in = ['proposals' + stage_name]
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'DistributeCascadeProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = cascade_rcnn_roi_data.get_cascade_rcnn_blob_names(
            stage, is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            DistributeCascadeProposalsOp(self.train, stage).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposalsRec(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsRecOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsRecOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = roi_data.fast_rcnn.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def DistributeCascadeProposals(self, stage):
        """Distribute proposals to their appropriate FPN levels.
        by Zhaowei Cai for Cascade R-CNN

        Input blobs:
          - proposals_<j> are the decoded proposals from stage j; see
            documentation from DecodeBBoxes.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights,
          mapped_gt_boxes].
        """
        stage_name = '_{}'.format(stage)

        # Prepare input blobs
        blobs_in = ['proposals' + stage_name]
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'DistributeCascadeProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = cascade_rcnn_roi_data.get_cascade_rcnn_blob_names(
            stage, is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            DistributeCascadeProposalsOp(self.train, stage).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnClusterProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """


        # Prepare input blobs
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnClusterProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = cluster_rcnn_roi_data.get_cluster_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnClusterProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs 

def CollectAndDistributeFpnRpnProposals(self):
        """Merge RPN proposals generated at multiple FPN levels and then
        distribute those proposals to their appropriate FPN levels. An anchor
        at one FPN level may predict an RoI that will map to another level,
        hence the need to redistribute the proposals.

        This function assumes standard blob names for input and output blobs.

        Input blobs: [rpn_rois_fpn<min>, ..., rpn_rois_fpn<max>,
                      rpn_roi_probs_fpn<min>, ..., rpn_roi_probs_fpn<max>]
          - rpn_rois_fpn<i> are the RPN proposals for FPN level i; see rpn_rois
            documentation from GenerateProposals.
          - rpn_roi_probs_fpn<i> are the RPN objectness probabilities for FPN
            level i; see rpn_roi_probs documentation from GenerateProposals.

        If used during training, then the input blobs will also include:
          [roidb, im_info] (see GenerateProposalLabels).

        Output blobs: [rois_fpn<min>, ..., rois_rpn<max>, rois,
                       rois_idx_restore]
          - rois_fpn<i> are the RPN proposals for FPN level i
          - rois_idx_restore is a permutation on the concatenation of all
            rois_fpn<i>, i=min...max, such that when applied the RPN RoIs are
            restored to their original order in the input blobs.

        If used during training, then the output blobs will also include:
          [labels, bbox_targets, bbox_inside_weights, bbox_outside_weights].
        """
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL

        # Prepare input blobs
        rois_names = ['rpn_rois_fpn' + str(l) for l in range(k_min, k_max + 1)]
        score_names = [
            'rpn_roi_probs_fpn' + str(l) for l in range(k_min, k_max + 1)
        ]
        blobs_in = rois_names + score_names
        if self.train:
            blobs_in += ['roidb', 'im_info']
        blobs_in = [core.ScopedBlobReference(b) for b in blobs_in]
        name = 'CollectAndDistributeFpnRpnProposalsOp:' + ','.join(
            [str(b) for b in blobs_in]
        )

        # Prepare output blobs
        blobs_out = fast_rcnn_roi_data.get_fast_rcnn_blob_names(
            is_training=self.train
        )
        blobs_out = [core.ScopedBlobReference(b) for b in blobs_out]

        outputs = self.net.Python(
            CollectAndDistributeFpnRpnProposalsOp(self.train).forward
        )(blobs_in, blobs_out, name=name)

        return outputs