def sample_rois(rois,
fg_rois_per_image,
rois_per_image,
num_classes,
cfg,
labels=None,
overlaps=None,
bbox_targets=None,
gt_boxes=None):
"""
generate random sample of ROIs comprising foreground and background examples
:param rois: all_rois [n, 4]; e2e: [n, 5] with batch_index
:param fg_rois_per_image: foreground roi number
:param rois_per_image: total roi number
:param num_classes: number of classes
:param labels: maybe precomputed
:param overlaps: maybe precomputed (max_overlaps)
:param bbox_targets: maybe precomputed
:param gt_boxes: optional for e2e [n, 5] (x1, y1, x2, y2, cls)
:return: (labels, rois, bbox_targets, bbox_weights)
"""
if labels is None:
overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
gt_boxes[:, :4].astype(np.float))
gt_assignment = overlaps.argmax(axis=1)
overlaps = overlaps.max(axis=1)
labels = gt_boxes[gt_assignment, 4]
# foreground RoI with FG_THRESH overlap
fg_indexes = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
# guard against the case when an image has fewer than fg_rois_per_image foreground RoIs
fg_rois_per_this_image = np.minimum(fg_rois_per_image, fg_indexes.size)
# Sample foreground regions without replacement
if len(fg_indexes) > fg_rois_per_this_image:
fg_indexes = npr.choice(fg_indexes,
size=fg_rois_per_this_image,
replace=False)
# Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
bg_indexes = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI)
& (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
# Compute number of background RoIs to take from this image (guarding against there being fewer than desired)
bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image
bg_rois_per_this_image = np.minimum(bg_rois_per_this_image,
bg_indexes.size)
# Sample foreground regions without replacement
if len(bg_indexes) > bg_rois_per_this_image:
bg_indexes = npr.choice(bg_indexes,
size=bg_rois_per_this_image,
replace=False)
# indexes selected
keep_indexes = np.append(fg_indexes, bg_indexes)
# pad more to ensure a fixed minibatch size
while keep_indexes.shape[0] < rois_per_image:
gap = np.minimum(len(rois), rois_per_image - keep_indexes.shape[0])
gap_indexes = npr.choice(range(len(rois)), size=gap, replace=False)
keep_indexes = np.append(keep_indexes, gap_indexes)
# select labels
labels = labels[keep_indexes]
# set labels of bg_rois to be 0
labels[fg_rois_per_this_image:] = 0
rois = rois[keep_indexes]
# load or compute bbox_target
if bbox_targets is not None:
bbox_target_data = bbox_targets[keep_indexes, :]
else:
targets = bbox_transform(rois[:, 1:],
gt_boxes[gt_assignment[keep_indexes], :4])
if cfg.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED:
targets = ((targets - np.array(cfg.TRAIN.BBOX_MEANS)) /
np.array(cfg.TRAIN.BBOX_STDS))
bbox_target_data = np.hstack((labels[:, np.newaxis], targets))
bbox_targets, bbox_weights = \
expand_bbox_regression_targets(bbox_target_data, num_classes, cfg)
return rois, labels, bbox_targets, bbox_weights
def assign_anchor(feat_shape, gt_boxes, im_info, cfg, feat_stride=16,
scales=(8, 16, 32), ratios=(0.5, 1, 2), allowed_border=0,
normalize_target=False, bbox_mean=(0.0, 0.0, 0.0, 0.0),
bbox_std=(0.1, 0.1, 0.4, 0.4)):
"""
assign ground truth boxes to anchor positions
:param feat_shape: infer output shape
:param gt_boxes: assign ground truth
:param im_info: filter out anchors overlapped with edges
:param feat_stride: anchor position step
:param scales: used to generate anchors, affects num_anchors (per location)
:param ratios: aspect ratios of generated anchors
:param allowed_border: filter out anchors with edge overlap > allowed_border
:param normalize_target: normalize rpn target
:param bbox_mean: anchor target mean
:param bbox_std: anchor target std
:return: dict of label
'label': of shape (batch_size, 1) <- (batch_size, num_anchors, feat_height, feat_width)
'bbox_target': of shape (batch_size, num_anchors * 4, feat_height, feat_width)
'bbox_inside_weight': *todo* mark the assigned anchors
'bbox_outside_weight': used to normalize the bbox_loss, all weights sums to RPN_POSITIVE_WEIGHT
"""
def _unmap(data, count, inds, fill=0):
"""" unmap a subset inds of data into original data of size count """
if len(data.shape) == 1:
ret = np.empty((count,), dtype=np.float32)
ret.fill(fill)
ret[inds] = data
else:
ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
ret.fill(fill)
ret[inds, :] = data
return ret
DEBUG = False
im_info = im_info[0]
scales = np.array(scales, dtype=np.float32)
base_anchors = generate_anchors(base_size=feat_stride, ratios=list(ratios), scales=scales)
num_anchors = base_anchors.shape[0]
feat_height, feat_width = feat_shape[-2:]
if DEBUG:
print('anchors:')
print(base_anchors)
print('anchor shapes:')
print(np.hstack((base_anchors[:, 2::4] - base_anchors[:, 0::4],
base_anchors[:, 3::4] - base_anchors[:, 1::4])))
print('im_info', im_info)
print('height', feat_height, 'width', feat_width)
print('gt_boxes shape', gt_boxes.shape)
print('gt_boxes', gt_boxes)
# 1. generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, feat_width) * feat_stride
shift_y = np.arange(0, feat_height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = num_anchors
K = shifts.shape[0]
all_anchors = base_anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
all_anchors = all_anchors.reshape((K * A, 4))
total_anchors = int(K * A)
# only keep anchors inside the image
inds_inside = np.where((all_anchors[:, 0] >= -allowed_border) &
(all_anchors[:, 1] >= -allowed_border) &
(all_anchors[:, 2] < im_info[1] + allowed_border) &
(all_anchors[:, 3] < im_info[0] + allowed_border))[0]
if DEBUG:
print('total_anchors', total_anchors)
print('inds_inside', len(inds_inside))
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
if DEBUG:
print('anchors shape', anchors.shape)
# label: 1 is positive, 0 is negative, -1 is dont care
labels = np.empty((len(inds_inside),), dtype=np.float32)
labels.fill(-1)
if gt_boxes.size > 0:
# overlap between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(anchors.astype(np.float), gt_boxes.astype(np.float))
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
gt_argmax_overlaps = overlaps.argmax(axis=0)
gt_max_overlaps = overlaps[gt_argmax_overlaps, np.arange(overlaps.shape[1])]
gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
if not cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
# fg label: for each gt, anchor with highest overlap
labels[gt_argmax_overlaps] = 1
# fg label: above threshold IoU
labels[max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if cfg.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < cfg.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
else:
labels[:] = 0
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCH_SIZE)
fg_inds = np.where(labels == 1)[0]
if len(fg_inds) > num_fg:
disable_inds = npr.choice(fg_inds, size=(len(fg_inds) - num_fg), replace=False)
if DEBUG:
disable_inds = fg_inds[:(len(fg_inds) - num_fg)]
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCH_SIZE - np.sum(labels == 1)
bg_inds = np.where(labels == 0)[0]
if len(bg_inds) > num_bg:
disable_inds = npr.choice(bg_inds, size=(len(bg_inds) - num_bg), replace=False)
if DEBUG:
disable_inds = bg_inds[:(len(bg_inds) - num_bg)]
labels[disable_inds] = -1
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
if gt_boxes.size > 0:
bbox_targets[:] = bbox_transform(anchors, gt_boxes[argmax_overlaps, :4])
bbox_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_weights[labels == 1, :] = np.array(cfg.TRAIN.RPN_BBOX_WEIGHTS)
if DEBUG:
_sums = bbox_targets[labels == 1, :].sum(axis=0)
_squared_sums = (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
_counts = np.sum(labels == 1)
means = _sums / (_counts + 1e-14)
stds = np.sqrt(_squared_sums / _counts - means ** 2)
print('means', means)
print('stdevs', stds)
if normalize_target:
bbox_targets = ((bbox_targets - np.array(bbox_mean))
/ np.array(bbox_std))
# map up to original set of anchors
labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
bbox_weights = _unmap(bbox_weights, total_anchors, inds_inside, fill=0)
if DEBUG:
print('rpn: max max_overlaps', np.max(max_overlaps))
print('rpn: num_positives', np.sum(labels == 1))
print('rpn: num_negatives', np.sum(labels == 0))
_fg_sum = np.sum(labels == 1)
_bg_sum = np.sum(labels == 0)
_count = 1
print('rpn: num_positive avg', _fg_sum / _count)
print('rpn: num_negative avg', _bg_sum / _count)
labels = labels.reshape((1, feat_height, feat_width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, A * feat_height * feat_width))
bbox_targets = bbox_targets.reshape((1, feat_height, feat_width, A * 4)).transpose(0, 3, 1, 2)
bbox_weights = bbox_weights.reshape((1, feat_height, feat_width, A * 4)).transpose((0, 3, 1, 2))
label = {'label': labels,
'bbox_target': bbox_targets,
'bbox_weight': bbox_weights}
return label