def forward()

in pytorch/sagemakercv/detection/roi_heads/roi_heads.py [0:0]

• 23 lines of code
• 14 McCabe index (conditional complexity)

    def forward(self, features, proposals, targets=None):
        losses = {}
        # TODO rename x to roi_box_features, if it doesn't increase memory consumption
        x, detections, loss_box = self.box(features, proposals, targets)
        losses.update(loss_box)
        if self.cfg.MODEL.MASK_ON:
            mask_features = features
            # optimization: during training, if we share the feature extractor between
            # the box and the mask heads, then we can reuse the features already computed
            if (
                self.training
                and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR
            ):
                mask_features = x
            # During training, self.box() will return the unaltered proposals as "detections"
            # this makes the API consistent during training and testing
            x, detections, loss_mask = self.mask(mask_features, detections, targets)
            losses.update(loss_mask)

        if self.cfg.MODEL.KEYPOINT_ON:
            keypoint_features = features
            # optimization: during training, if we share the feature extractor between
            # the box and the mask heads, then we can reuse the features already computed
            if (
                self.training
                and self.cfg.MODEL.ROI_KEYPOINT_HEAD.SHARE_BOX_FEATURE_EXTRACTOR
            ):
                keypoint_features = x
            # During training, self.box() will return the unaltered proposals as "detections"
            # this makes the API consistent during training and testing
            x, detections, loss_keypoint = self.keypoint(keypoint_features, detections, targets)
            losses.update(loss_keypoint)
        return x, detections, losses