Python fpn.map_rois_to_fpn_levels方法代码示例

# 需要导入模块: from utils import fpn [as 别名]
# 或者: from utils.fpn import map_rois_to_fpn_levels [as 别名]
def _add_multilevel_rois_for_test(blobs, name):
    """Distributes a set of RoIs across FPN pyramid levels by creating new level
    specific RoI blobs.

    Arguments:
        blobs (dict): dictionary of blobs
        name (str): a key in 'blobs' identifying the source RoI blob

    Returns:
        [by ref] blobs (dict): new keys named by `name + 'fpn' + level`
            are added to dict each with a value that's an R_level x 5 ndarray of
            RoIs (see _get_rois_blob for format)
    """
    lvl_min = cfg.FPN.ROI_MIN_LEVEL
    lvl_max = cfg.FPN.ROI_MAX_LEVEL
    lvls = fpn_utils.map_rois_to_fpn_levels(blobs[name][:, 1:5], lvl_min, lvl_max)
    fpn_utils.add_multilevel_roi_blobs(
        blobs, name, blobs[name], lvls, lvl_min, lvl_max
    ) 

# 需要导入模块: from utils import fpn [as 别名]
# 或者: from utils.fpn import map_rois_to_fpn_levels [as 别名]
def _add_multilevel_rois(blobs):
    """By default training RoIs are added for a single feature map level only.
    When using FPN, the RoIs must be distributed over different FPN levels
    according the level assignment heuristic (see: modeling.FPN.
    map_rois_to_fpn_levels).
    """
    lvl_min = cfg.FPN.ROI_MIN_LEVEL
    lvl_max = cfg.FPN.ROI_MAX_LEVEL

    def _distribute_rois_over_fpn_levels(rois_blob_name):
        """Distribute rois over the different FPN levels."""
        # Get target level for each roi
        # Recall blob rois are in (batch_idx, x1, y1, x2, y2) format, hence take
        # the box coordinates from columns 1:5
        target_lvls = fpn_utils.map_rois_to_fpn_levels(
            blobs[rois_blob_name][:, 1:5], lvl_min, lvl_max
        )
        # Add per FPN level roi blobs named like: <rois_blob_name>_fpn<lvl>
        fpn_utils.add_multilevel_roi_blobs(
            blobs, rois_blob_name, blobs[rois_blob_name], target_lvls, lvl_min,
            lvl_max
        )

    _distribute_rois_over_fpn_levels('rois')
    if cfg.MODEL.MASK_ON:
        _distribute_rois_over_fpn_levels('mask_rois')
    if cfg.MODEL.KEYPOINTS_ON:
        _distribute_rois_over_fpn_levels('keypoint_rois') 

# 需要导入模块: from utils import fpn [as 别名]
# 或者: from utils.fpn import map_rois_to_fpn_levels [as 别名]
def distribute(rois, label_blobs):
    """To understand the output blob order see return value of
    roi_data.fast_rcnn.get_fast_rcnn_blob_names(is_training=False)
    """
    lvl_min = cfg.FPN.ROI_MIN_LEVEL
    lvl_max = cfg.FPN.ROI_MAX_LEVEL
    lvls = fpn_utils.map_rois_to_fpn_levels(rois[:, 1:5], lvl_min, lvl_max)

    # Delete roi entries that have negative area
    # idx_neg = np.where(lvls == -1)[0]
    # rois = np.delete(rois, idx_neg, axis=0)
    # lvls = np.delete(lvls, idx_neg, axis=0)

    output_blob_names = roi_data.fast_rcnn.get_fast_rcnn_blob_names(is_training=False)
    outputs = [None] * len(output_blob_names)
    outputs[0] = rois

    # Create new roi blobs for each FPN level
    # (See: utils.fpn.add_multilevel_roi_blobs which is similar but annoying
    # to generalize to support this particular case.)
    rois_idx_order = np.empty((0, ))
    for output_idx, lvl in enumerate(range(lvl_min, lvl_max + 1)):
        idx_lvl = np.where(lvls == lvl)[0]
        blob_roi_level = rois[idx_lvl, :]
        outputs[output_idx + 1] = blob_roi_level
        rois_idx_order = np.concatenate((rois_idx_order, idx_lvl))
    rois_idx_restore = np.argsort(rois_idx_order)
    outputs[-1] = rois_idx_restore.astype(np.int32)

    return dict(zip(output_blob_names, outputs)) 

# 需要导入模块: from utils import fpn [as 别名]
# 或者: from utils.fpn import map_rois_to_fpn_levels [as 别名]
def _add_rel_multilevel_rois(blobs):
    """By default training RoIs are added for a single feature map level only.
    When using FPN, the RoIs must be distributed over different FPN levels
    according the level assignment heuristic (see: modeling.FPN.
    map_rois_to_fpn_levels).
    """
    lvl_min = cfg.FPN.ROI_MIN_LEVEL
    lvl_max = cfg.FPN.ROI_MAX_LEVEL

    def _distribute_rois_over_fpn_levels(rois_blob_names):
        """Distribute rois over the different FPN levels."""
        # Get target level for each roi
        # Recall blob rois are in (batch_idx, x1, y1, x2, y2) format, hence take
        # the box coordinates from columns 1:5
        lowest_target_lvls = None
        for rois_blob_name in rois_blob_names:
            target_lvls = fpn_utils.map_rois_to_fpn_levels(
                blobs[rois_blob_name][:, 1:5], lvl_min, lvl_max)
            if lowest_target_lvls is None:
                lowest_target_lvls = target_lvls
            else:
                lowest_target_lvls = np.minimum(lowest_target_lvls, target_lvls)
        for rois_blob_name in rois_blob_names:
            # Add per FPN level roi blobs named like: <rois_blob_name>_fpn<lvl>
            fpn_utils.add_multilevel_roi_blobs(
                blobs, rois_blob_name, blobs[rois_blob_name], lowest_target_lvls, lvl_min,
                lvl_max)

    _distribute_rois_over_fpn_levels(['sbj_rois'])
    _distribute_rois_over_fpn_levels(['obj_rois'])
    _distribute_rois_over_fpn_levels(['rel_rois'])