def __init__(self,
feat_stride,
scales,
ratios,
is_train=False,
output_score=False):
super(ProposalOperator, self).__init__()
self._feat_stride = float(feat_stride)
self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
self._ratios = np.fromstring(ratios[1:-1], dtype=float,
sep=',').tolist()
self._anchors = generate_anchors(base_size=self._feat_stride,
scales=self._scales,
ratios=self._ratios)
self._num_anchors = self._anchors.shape[0]
self._output_score = output_score
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
if is_train:
self.cfg_key = 'TRAIN'
else:
self.cfg_key = 'TEST'
def __init__(self, feat_stride, scales, ratios, is_train=False, output_score=False):
super(ProposalOperator, self).__init__()
self._feat_stride = float(feat_stride)
self._scales = np.fromstring(scales[1:-1], dtype=float, sep=',')
self._ratios = np.fromstring(ratios[1:-1], dtype=float, sep=',').tolist()
self._anchors = generate_anchors(base_size=self._feat_stride, scales=self._scales, ratios=self._ratios)
self._num_anchors = self._anchors.shape[0]
self._output_score = output_score
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
if is_train:
self.cfg_key = 'TRAIN'
else:
self.cfg_key = 'TEST'
def assign_anchor(feat_shape,
gt_boxes,
im_info,
feat_stride=16,
scales=(8, 16, 32),
ratios=(0.5, 1, 2),
allowed_border=0):
"""
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
: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
def _compute_targets(ex_rois, gt_rois):
""" compute bbox targets for an image """
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32,
copy=False)
DEBUG = False
im_info = im_info[0]
scales = np.array(scales, dtype=np.float32)
base_anchors = generate_anchors(base_size=16,
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 config.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps < config.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 >= config.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if config.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < config.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
else:
labels[:] = 0
# subsample positive labels if we have too many
num_fg = int(config.TRAIN.RPN_FG_FRACTION * config.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 = config.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[:] = _compute_targets(anchors,
gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_inside_weights[labels == 1, :] = np.array(
config.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if config.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of exampling (given non-uniform sampling)
num_examples = np.sum(labels >= 0)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
else:
assert ((config.TRAIN.RPN_POSTIVE_WEIGHT > 0) &
(config.TRAIN.RPN_POSTIVE_WEIGHT < 1))
positive_weights = config.TRAIN.RPN_POSTIVE_WEIGHT / np.sum(
labels == 1)
negative_weights = (1.0 - config.TRAIN.RPN_POSTIVE_WEIGHT) / np.sum(
labels == 1)
bbox_outside_weights[labels == 1, :] = positive_weights
bbox_outside_weights[labels == 0, :] = negative_weights
if DEBUG:
_sums = bbox_targets[labels == 1, :].sum(axis=0)
_squared_sums = (bbox_targets[labels == 1, :]**2).sum(axis=0)
_counts = config.EPS + np.sum(labels == 1)
means = _sums / _counts
stds = np.sqrt(_squared_sums / _counts - means**2)
print 'means', means
print 'stdevs', stds
# 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_inside_weights = _unmap(bbox_inside_weights,
total_anchors,
inds_inside,
fill=0)
bbox_outside_weights = _unmap(bbox_outside_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_inside_weights = bbox_inside_weights.reshape(
(1, feat_height, feat_width, A * 4)).transpose((0, 3, 1, 2))
bbox_outside_weights = bbox_outside_weights.reshape(
(1, feat_height, feat_width, A * 4)).transpose((0, 3, 1, 2))
label = {
'label': labels,
'bbox_target': bbox_targets,
'bbox_inside_weight': bbox_inside_weights,
'bbox_outside_weight': bbox_outside_weights
}
return label
def assign_anchor(feat_shape, gt_boxes, im_info, feat_stride=16,
scales=(8, 16, 32), ratios=(0.5, 1, 2), allowed_border=0):
"""
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
: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
def _compute_targets(ex_rois, gt_rois):
""" compute bbox targets for an image """
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
DEBUG = False
im_info = im_info[0]
scales = np.array(scales, dtype=np.float32)
base_anchors = generate_anchors(base_size=16, 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 config.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels first so that positive labels can clobber them
labels[max_overlaps < config.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 >= config.TRAIN.RPN_POSITIVE_OVERLAP] = 1
if config.TRAIN.RPN_CLOBBER_POSITIVES:
# assign bg labels last so that negative labels can clobber positives
labels[max_overlaps < config.TRAIN.RPN_NEGATIVE_OVERLAP] = 0
else:
labels[:] = 0
# subsample positive labels if we have too many
num_fg = int(config.TRAIN.RPN_FG_FRACTION * config.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 = config.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[:] = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
bbox_inside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
bbox_inside_weights[labels == 1, :] = np.array(config.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if config.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of exampling (given non-uniform sampling)
num_examples = np.sum(labels >= 0)
positive_weights = np.ones((1, 4)) * 1.0 / num_examples
negative_weights = np.ones((1, 4)) * 1.0 / num_examples
else:
assert ((config.TRAIN.RPN_POSTIVE_WEIGHT > 0) & (config.TRAIN.RPN_POSTIVE_WEIGHT < 1))
positive_weights = config.TRAIN.RPN_POSTIVE_WEIGHT / np.sum(labels == 1)
negative_weights = (1.0 - config.TRAIN.RPN_POSTIVE_WEIGHT) / np.sum(labels == 1)
bbox_outside_weights[labels == 1, :] = positive_weights
bbox_outside_weights[labels == 0, :] = negative_weights
if DEBUG:
_sums = bbox_targets[labels == 1, :].sum(axis=0)
_squared_sums = (bbox_targets[labels == 1, :] ** 2).sum(axis=0)
_counts = config.EPS + np.sum(labels == 1)
means = _sums / _counts
stds = np.sqrt(_squared_sums / _counts - means ** 2)
print 'means', means
print 'stdevs', stds
# 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_inside_weights = _unmap(bbox_inside_weights, total_anchors, inds_inside, fill=0)
bbox_outside_weights = _unmap(bbox_outside_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_inside_weights = bbox_inside_weights.reshape((1, feat_height, feat_width, A * 4)).transpose((0, 3, 1, 2))
bbox_outside_weights = bbox_outside_weights.reshape((1, feat_height, feat_width, A * 4)).transpose((0, 3, 1, 2))
label = {'label': labels,
'bbox_target': bbox_targets,
'bbox_inside_weight': bbox_inside_weights,
'bbox_outside_weight': bbox_outside_weights}
return label