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_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 _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)
ws = blobs[rois_blob_name][:, 3] - blobs[rois_blob_name][:, 1] + 1
hs = blobs[rois_blob_name][:, 4] - blobs[rois_blob_name][:, 2] + 1
areas = ws * hs
assert np.all(
areas >= 0
), 'Negative areas founds when add multilevel rois, negative rate: {} / {}, negative anchor: {}, idx of anchor: {}'.format(
np.where(areas < 0)[0].shape, areas.shape[0],
blobs[rois_blob_name][np.where(areas < 0)[0], :],
np.where(areas < 0)[0])
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 distribute(rois, label_blobs, outputs, train):
"""To understand the output blob order see return value of
detectron.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.map_rois_to_fpn_levels(rois[:, 1:5], lvl_min, lvl_max)
outputs[0].reshape(rois.shape)
outputs[0].data[...] = rois
# Create new roi blobs for each FPN level
# (See: modeling.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].reshape(blob_roi_level.shape)
outputs[output_idx + 1].data[...] = blob_roi_level
rois_idx_order = np.concatenate((rois_idx_order, idx_lvl))
rois_idx_restore = np.argsort(rois_idx_order)
blob_utils.py_op_copy_blob(rois_idx_restore.astype(np.int32), outputs[-1])
def distribute(rois, label_blobs, outputs, train):
"""To understand the output blob order see return value of
detectron.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.map_rois_to_fpn_levels(rois[:, 1:5], lvl_min, lvl_max)
outputs[0].reshape(rois.shape)
outputs[0].data[...] = rois
# Create new roi blobs for each FPN level
# (See: modeling.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].reshape(blob_roi_level.shape)
outputs[output_idx + 1].data[...] = blob_roi_level
rois_idx_order = np.concatenate((rois_idx_order, idx_lvl))
rois_idx_restore = np.argsort(rois_idx_order)
blob_utils.py_op_copy_blob(rois_idx_restore.astype(np.int32), outputs[-1])