本文整理匯總了Python中utils.fpn.add_multilevel_roi_blobs方法的典型用法代碼示例。如果您正苦於以下問題：Python fpn.add_multilevel_roi_blobs方法的具體用法？Python fpn.add_multilevel_roi_blobs怎麽用？Python fpn.add_multilevel_roi_blobs使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils.fpn的用法示例。
在下文中一共展示了fpn.add_multilevel_roi_blobs方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _add_multilevel_rois_for_test
# 需要導入模塊: from utils import fpn [as 別名]
# 或者: from utils.fpn import add_multilevel_roi_blobs [as 別名]
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
) 示例2: _add_multilevel_rois
# 需要導入模塊: from utils import fpn [as 別名]
# 或者: from utils.fpn import add_multilevel_roi_blobs [as 別名]
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') 示例3: _add_rel_multilevel_rois
# 需要導入模塊: from utils import fpn [as 別名]
# 或者: from utils.fpn import add_multilevel_roi_blobs [as 別名]
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'])