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.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.add_multilevel_roi_blobs(blobs, rois_blob_name,
blobs[rois_blob_name], target_lvls,
lvl_min, lvl_max)
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.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.add_multilevel_roi_blobs(
blobs, rois_blob_name, blobs[rois_blob_name], target_lvls, lvl_min,
lvl_max
)
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
rois_blob_name = 'mask_rois'
"""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.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.add_multilevel_roi_blobs(blobs, rois_blob_name, blobs[rois_blob_name],
target_lvls, lvl_min, lvl_max)
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.map_rois_to_fpn_levels(blobs[name][:, 1:5], lvl_min, lvl_max)
fpn.add_multilevel_roi_blobs(blobs, name, blobs[name], lvls, lvl_min,
lvl_max)
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.map_rois_to_fpn_levels(blobs[name][:, 1:], lvl_min, lvl_max)
fpn.add_multilevel_roi_blobs(
blobs, name, blobs[name], lvls, lvl_min, lvl_max)
def _add_multilevel_rois(blobs):
lvl_min = cfg.FPN.ROI_MIN_LEVEL
lvl_max = cfg.FPN.ROI_MAX_LEVEL
# The map_rois_to_fpn_levels and add_multilevel_roi_blobs functions are
# shared among the 2D and 3D models.
lvls = fpn.map_rois_to_fpn_levels(blobs['rois'][:, 1:], lvl_min, lvl_max)
fpn.add_multilevel_roi_blobs(blobs, 'rois', blobs['rois'], lvls, lvl_min,
lvl_max)
if cfg.MODEL.MASK_ON:
# Masks use the same rois as the box/cls head
fpn.add_multilevel_roi_blobs(blobs,
'mask_rois',
blobs['rois'],
lvls,
lvl_min,
lvl_max,
valid_levels=blobs['roi_has_mask_int32'])
if cfg.MODEL.KEYPOINTS_ON:
# Keypoints use a separate set of training rois
lvls = fpn.map_rois_to_fpn_levels(blobs['keypoint_rois'][:, 1:],
lvl_min, lvl_max)
fpn.add_multilevel_roi_blobs(blobs, 'keypoint_rois',
blobs['keypoint_rois'], lvls, lvl_min,
lvl_max)
def _add_multilevel_rois(blobs):
lvl_min = cfg.FPN.ROI_MIN_LEVEL
lvl_max = cfg.FPN.ROI_MAX_LEVEL
# The map_rois_to_fpn_levels and add_multilevel_roi_blobs functions are
# shared among the 2D and 3D models.
lvls = fpn.map_rois_to_fpn_levels(blobs['rois'][:, 1:], lvl_min, lvl_max)
fpn.add_multilevel_roi_blobs(
blobs, 'rois', blobs['rois'], lvls, lvl_min, lvl_max)
if cfg.MODEL.MASK_ON:
# Masks use the same rois as the box/cls head
fpn.add_multilevel_roi_blobs(
blobs, 'mask_rois', blobs['rois'], lvls, lvl_min, lvl_max,
valid_levels=blobs['roi_has_mask_int32'])
if cfg.MODEL.KEYPOINTS_ON:
# Keypoints use a separate set of training rois
lvls = fpn.map_rois_to_fpn_levels(
blobs['keypoint_rois'][:, 1:], lvl_min, lvl_max)
fpn.add_multilevel_roi_blobs(
blobs, 'keypoint_rois', blobs['keypoint_rois'], lvls, lvl_min,
lvl_max)