def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
self._proj_name = layer_params['proj_name'] # loki
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(proj_name=self._proj_name,
scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print('feat_stride: {}'.format(
self._feat_stride)) # python3 # print
print('anchors:') # python3 # print
print(self._anchors) # python3 # print
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
## generate anchors the same way as we do in anchor target layer
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
## Add scale option
self._scale = np.array(layer_params.get('scale_param', False),
np.float32)
## Add global context option
self._global_context = np.array(
layer_params.get('global_context', False), np.float32)
assert self._scale == 0.0 or self._global_context == 0.0, 'Only one of them can be True'
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
## If scale_param is True, we need to add top output
if self._scale != 0.0 or self._global_context != 0.0:
top[1].reshape(1, 5)
def setup(self, bottom, top):
"""
bottom[0]: rpn score map
bottom[1]: det score map
bottom[2]: bbox_pred_delta
top[0]: ROIs
top[1]: scores
"""
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
params = layer_params['stride_scale_numcls']
feat_stride_str, scale_str, cls_str = params.split(',')
self._feat_stride = int(feat_stride_str)
anchor_scale = int(scale_str)
self._numclasses = int(cls_str)
self._anchors = generate_anchors(base_size=cfg.MINANCHOR,
scales=np.array(
[anchor_scale, anchor_scale + 1]),
ratios=[0.333, 0.5, 1, 2, 3])
self._num_anchors = self._anchors.shape[0]
# 1. Generate proposals from bbox deltas and shifted anchors
self._ndim = cfg.TEST.BATCH_SIZE
top[0].reshape(self._ndim, 1, 4)
top[1].reshape(self._ndim, 1, self._numclasses)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
# added by yzh for anchor ratio setting
ratios = layer_params.get('ratios', [0.5, 1, 2])
self._anchors = generate_anchors(scales=np.array(anchor_scales), ratios=ratios)
self._num_anchors = self._anchors.shape[0]
if WSX:
print '==== _anchors ==='
print self._anchors
#with open('/home/yzh/tmp/anchors.pkl','wb') as output:
# pickle.dump(self._anchors,output, pickle.HIGHEST_PROTOCOL)
print self._num_anchors
print '================'
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def __init__(self,
classifier,
anchor_scales=None,
negative_overlap=0.3,
positive_overlap=0.7,
fg_fraction=0.5,
batch_size=256,
nms_thresh=0.7,
min_size=16,
pre_nms_topN=12000,
post_nms_topN=2000):
super(RPN, self).__init__()
self.rpn_classifier = classifier
if anchor_scales is None:
anchor_scales = (8, 16, 32)
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
self.negative_overlap = negative_overlap
self.positive_overlap = positive_overlap
self.fg_fraction = fg_fraction
self.batch_size = batch_size
# used for both train and test
self.nms_thresh = nms_thresh
self.pre_nms_topN = pre_nms_topN
self.post_nms_topN = post_nms_topN
self.min_size = min_size
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
# layer_params = yaml.load(self.param_str_)
# print dir(self)
# param_str_ is wrong. In python it's param_str. C++ uses the extra _.
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
anchor_ratios = layer_params.get('ratios', ((0.5, 1, 2)))
self._anchors = generate_anchors(ratios=anchor_ratios,
scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print "anchor scales are", anchor_scales
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
# top[0].reshape(1, 5)
top[0].reshape(300, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
#anchor_scales = [layer_params['scale']]
params = layer_params['stride_scale_border_batchsize']
feat_stride_str, scale_str, border_str, batch_str = params.split(',')
self._feat_stride = float(feat_stride_str)
self._scales = float(scale_str)
self._allowed_border = float(border_str)
self._batch = float(batch_str)
self._anchors = generate_anchors(base_size=cfg.MINANCHOR,
scales=np.array(
[self._scales, self._scales + 1]),
ratios=[0.333, 0.5, 1, 2, 3])
self._num_anchors = self._anchors.shape[0]
height, width = bottom[0].data.shape[-2:]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
self._ndim = cfg.TRAIN.IMS_PER_BATCH
# labels
top[0].reshape(self._ndim, 1, self._num_anchors * height, width)
# bbox_targets
top[1].reshape(self._ndim, self._num_anchors * 4, height, width)
# bbox_inside_weights
top[2].reshape(self._ndim, self._num_anchors * 4, height, width)
# bbox_outside_weights
top[3].reshape(self._ndim, self._num_anchors * 4, height, width)
def setup(self, bottom, top):
"""
bottom[0]: rpn score map
bottom[1]: det score map
bottom[2]: bbox_pred_delta
top[0]: ROIs
top[1]: scores
"""
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
params = layer_params['stride_scale_numcls']
feat_stride_str, scale_str, cls_str = params.split(',')
self._feat_stride = int(feat_stride_str)
anchor_scale = int(scale_str)
self._numclasses = int(cls_str)
# self._anchors = generate_anchors(base_size=self._feat_stride, scales=np.array([anchor_scale, anchor_scale * cfg.ANCHOR_SCALE_OFFSET]),
# ratios=cfg.GENERATION_ANCHOR_RATIOS)
self._anchors = generate_anchors(
base_size=self._feat_stride,
scales=np.array(cfg.TRAIN.MULTI_SCALE_RPN_SCALE[anchor_scale]),
ratios=cfg.GENERATION_ANCHOR_RATIOS)
self._num_anchors = self._anchors.shape[0]
# 1. Generate proposals from bbox deltas and shifted anchors
self._ndim = cfg.TEST.BATCH_SIZE
top[0].reshape(self._ndim, 1, 4)
top[1].reshape(self._ndim, 1, 1)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
base_size = cfg.TRAIN.BASE_SIZE
anchor_scales = np.array(cfg.TRAIN.ANCHOR_SCALES)
anchor_ratios = np.array(cfg.TRAIN.ANCHOR_RATIOS)
self._anchors = generate_anchors(base_size=base_size,
ratios=anchor_ratios,
scales=anchor_scales)
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'.format(self._anchors)
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'anchors:'
print self._anchors
self._count = 0
self._fg_num = 0
self._bg_num = 0
#layer_params = yaml.load(self.param_str_)
layer_params = yaml.load(self.param_str)
self._num_classes = layer_params['num_classes']
# sampled rois (0, x1, y1, x2, y2)
top[0].reshape(1, 5)
# labels
top[1].reshape(1, 1)
# bbox_targets
top[2].reshape(1, self._num_classes * 4)
# bbox_inside_weights
top[3].reshape(1, self._num_classes * 4)
# bbox_outside_weights
top[4].reshape(1, self._num_classes * 4)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
#from IPython import embed; embed()
#anchor_scales = layer_params.get('scales', (8, 16, 32))
anchor_ratios = layer_params.get('ratios', cfg.ANCHOR_RATIOS)
anchor_scales = layer_params.get('scales', cfg.ANCHOR_SCALE)
anchor_base_size = cfg.ANCHOR_BASE_SIZE
#self._anchors = generate_anchors(base_size=anchor_base_size,)\
# scales=np.array(anchor_scales))
self._anchors = generate_anchors(base_size=anchor_base_size,ratios=np.array(anchor_ratios),\
scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
anchor_scales = (8, 16, 32)
if 'scales' in self.pythonargs:
anchor_scales = self.pythonargs['scales']
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
self._feat_stride = self.pythonargs['feat_stride']
# allow boxes to sit over the edge by a small amount
self._allowed_border = 0
if 'allowed_border' in self.pythonargs:
self._allowed_border = self.pythonargs['allowed_border']
height, width = bottom[0].data.shape[-2:]
A = self._num_anchors
# labels
top[0].reshape((1, 1, A * height, width))
# bbox_targets
top[1].reshape((1, A * 4, height, width))
# bbox_inside_weights
top[2].reshape((1, A * 4, height, width))
# bbox_outside_weights
top[3].reshape((1, A * 4, height, width))
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
params = layer_params['stride_scale_border_batchsize_numcls']
feat_stride_str, scale_str, border_str, batch_str, num_cls = params.split(
',')
self._feat_stride = float(feat_stride_str)
self._scales = float(scale_str)
self._num_classes = int(num_cls)
self._score_thresh = cfg.TRAIN.PROB
self._allowed_border = float(border_str)
self._batch = float(batch_str)
#self._anchors = generate_anchors(base_size = cfg.MINANCHOR, scales=np.array([self._scales, self._scales + 1]), ratios = [0.333, 0.5, 1, 2, 3])
self._anchors = generate_anchors(
base_size=self._feat_stride,
scales=np.array(
[self._scales, self._scales * cfg.ANCHOR_SCALE_OFFSET]),
ratios=cfg.GENERATION_ANCHOR_RATIOS)
self._num_anchors = self._anchors.shape[0]
height, width = bottom[0].data.shape[-2:]
self._ndim = cfg.TRAIN.IMS_PER_BATCH
# labels
top[0].reshape(self._ndim, 1, self._num_anchors * height, width)
def __init__(self, feat_stride, scales, ratios):
super(_ProposalLayer, self).__init__()
self._feat_stride = feat_stride
self._anchors = torch.from_numpy(generate_anchors(scales=np.array(scales),
ratios=np.array(ratios))).float()
self._num_anchors = self._anchors.size(0)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
anchor_ratios = layer_params.get('ratios', ((0.5, 1, 2)))
_base_size = layer_params.get('base_size', 16)
self._anchors = generate_anchors(base_size=_base_size,
ratios=anchor_ratios,
scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def generate_anchors_pre(height,
width,
feat_stride,
anchor_scales=(8, 16, 32),
anchor_ratios=(0.5, 1, 2),
base_size=16):
""" A wrapper function to generate anchors given different scales
Also return the number of anchors in variable 'length'
"""
anchors = generate_anchors(ratios=np.array(anchor_ratios),
scales=np.array(anchor_scales),
base_size=base_size)
A = anchors.shape[0]
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, 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()
K = shifts.shape[0]
# width changes faster, so here it is H, W, C
anchors = anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
length = np.int32(anchors.shape[0])
return anchors, length
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
print 'proposal_layer parameter file is {}'.format(self.param_str_)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
print 'number of anchors:'
print self._num_anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = json.loads(self.param_str)
self._feat_stride = layer_params['feat_stride']
# has extra training targets, so to sample more rois
self._has_extra = layer_params.get('has_extra', False)
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
if self.phase == caffe.TRAIN:
# rois_labels
top[1].reshape(1, )
# rois_gt_assignments
top[2].reshape(1, )
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
self._feat_stride = [
int(i) for i in layer_params['feat_stride'].split(',')
]
self._scales = cfg.FPNRSCALES
self._ratios = cfg.FPNRATIOS
#anchor_scales = layer_params.get('scales', (8, ))
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 1000)
s = 0
for i in range(5):
height, width = bottom[i].data.shape[-2:]
s = s + height * width
i_anchors = generate_anchors(base_size=self._feat_stride[i],
ratios=self._ratios,
scales=self._scales)
A = i_anchors.shape[0]
# labels
top[0].reshape(1, 1, A * s)
# bbox_targets
top[1].reshape(1, A * 4, s)
# bbox_inside_weights
top[2].reshape(1, A * 4, s)
# bbox_outside_weights
top[3].reshape(1, A * 4, s)
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
#anchor_scales = [layer_params['scale']]
params = layer_params['stride_scale_border_batchsize']
feat_stride_str, scale_str, border_str, batch_str = params.split(',')
self._feat_stride = float(feat_stride_str)
self._scales = float(scale_str)
self._allowed_border = float(border_str)
self._batch = float(batch_str)
self._anchors = generate_anchors(base_size = cfg.MINANCHOR, scales=np.array([self._scales, self._scales + 1]), ratios = [0.333, 0.5, 1, 2, 3])
self._num_anchors = self._anchors.shape[0]
height, width = bottom[0].data.shape[-2:]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
self._ndim = cfg.TRAIN.IMS_PER_BATCH
# labels
top[0].reshape(self._ndim, 1, self._num_anchors * height, width)
# bbox_targets
top[1].reshape(self._ndim, self._num_anchors * 4, height, width)
# bbox_inside_weights
top[2].reshape(self._ndim, self._num_anchors * 4, height, width)
# bbox_outside_weights
top[3].reshape(self._ndim, self._num_anchors * 4, height, width)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get(
'scales', (8, 16, 32)) #original hardcode scales as (8,16,32)
#anchor_scales = layer_params['scales'] # modified: load scales defined from prototxt
print(
"=================================anchor_scales in ProposalLayer:============="
+ str(anchor_scales))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def anchor_target_layer_ws(rpn_cls_score,
gt_boxes,
num_gt_boxes,
im_info,
data,
_feat_stride=[
16,
],
anchor_scales=[4, 8, 16, 32]):
"""
Assign anchors to ground-truth targets. Produces anchor classification
labels and bounding-box regression targets.
"""
"""This function is for cases with empty 'gt_boxes' (weakly supervised)"""
_anchors = generate_anchors(scales=np.array(anchor_scales))
_num_anchors = _anchors.shape[0]
height, width = rpn_cls_score.shape[1:3]
A = _num_anchors
batch_size = rpn_cls_score.shape[0]
rpn_labels = np.empty((batch_size, 1, A * height, width), dtype=np.float32)
rpn_labels.fill(-1)
rpn_bbox_targets = np.zeros((batch_size, A * 4, height, width),
dtype=np.float32)
rpn_bbox_inside_weights = np.zeros((batch_size, A * 4, height, width),
dtype=np.float32)
rpn_bbox_outside_weights = np.zeros((batch_size, A * 4, height, width),
dtype=np.float32)
return rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights
def setup(self, bottom, gt):
#layer_params = yaml.load(self.param_str_)
#anchor_scales = ((0.5, 1, 2), (8, 16, 32))
self._anchors = generate_anchors()
#print self._anchors.shape
self._num_anchors = self._anchors.shape[0]
self._feat_stride = 16
if DEBUG:
print 'anchors:'
print self._anchors
print 'anchor shapes:'
print np.hstack((
self._anchors[:, 2::4] - self._anchors[:, 0::4],
self._anchors[:, 3::4] - self._anchors[:, 1::4],
))
self._counts = cfg.EPS
self._sums = np.zeros((1, 4))
self._squared_sums = np.zeros((1, 4))
self._fg_sum = 0
self._bg_sum = 0
self._count = 0
# allow boxes to sit over the edge by a small amount
self._allowed_border = 0
#height, width = 34, 62
#height, width = bottom.shape[-2:]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
A = self._num_anchors
def __generate_anchor(self, width, height, feat_stride):
anchors = generate_anchors(ratios=np.array(self._anchor_ratio),
scales=np.array(self._anchor_scale))
num_anchor = anchors.shape[0]
shift_x = tf.range(width) * feat_stride[0]
shift_y = tf.range(height) * feat_stride[0]
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
sx = tf.reshape(shift_x, shape=(-1, ))
sy = tf.reshape(shift_y, shape=(-1, ))
shifts = tf.transpose(tf.stack([sx, sy, sx, sy]))
rect = tf.multiply(width, height)
shifts = tf.transpose(tf.reshape(shifts, shape=[1, rect, 4]),
perm=(1, 0, 2))
anchor_constant = tf.constant(anchors.reshape((1, num_anchor, 4)),
dtype=tf.int32)
length = num_anchor * rect
anchors_tf = tf.cast(tf.reshape(tf.add(anchor_constant, shifts),
shape=(length, 4)),
dtype=tf.float32)
anchors_tf.set_shape([None, 4])
length.set_shape([])
self._anchors = anchors_tf
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0] #anchor的总个数
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
"""
输出Top[0]: R个roi, 每一个均是 5-tuple (n, x1, y1, x2, y2),
其中n 代表batch index； x1, y1, x2, y2表示矩形的4个点的坐标。
输出Top[1]: Ｒ个proposal的得分，即是一个物体的可能性。
"""
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def __init__(self, feat_stride, scales):
super(_AnchorTargetLayer, self).__init__()
self._feat_stride = feat_stride
self._scales = scales
anchor_scales = scales
self._anchors = torch.from_numpy(
generate_anchors(scales=np.array(anchor_scales))).float()
self._num_anchors = self._anchors.size(0)
if DEBUG:
print 'anchors:'
print self._anchors
print 'anchor shapes:'
print np.hstack((
self._anchors[:, 2::4] - self._anchors[:, 0::4],
self._anchors[:, 3::4] - self._anchors[:, 1::4],
))
self._counts = cfg.EPS
self._sums = np.zeros((1, 4))
self._squared_sums = np.zeros((1, 4))
self._fg_sum = 0
self._bg_sum = 0
self._count = 0
# allow boxes to sit over the edge by a small amount
self._allowed_border = 0 # default is 0
def __init__(self, feat_stride, scales, ratios):
super(_ProposalLayer, self).__init__()
self._feat_stride = feat_stride
self._anchors = torch.from_numpy(generate_anchors(scales=np.array(scales),
ratios=np.array(ratios))).float()
self._num_anchors = self._anchors.size(0)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
if 'a_size1' in layer_params and 'a_size2' in layer_params and 'a_size3' in layer_params:
a_size1_scale = int(layer_params['a_size1']) * (8.0 / 128.0)
a_size2_scale = int(layer_params['a_size2']) * (8.0 / 128.0)
a_size3_scale = int(layer_params['a_size3']) * (8.0 / 128.0)
anchor_scales = layer_params.get(
'scales', (a_size1_scale, a_size2_scale, a_size3_scale))
else:
# 8: area 128*128, 16: area 256*256, 32: area 512*512
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
# top[0].reshape(1, 5)
cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
top[0].reshape(post_nms_topN, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params['feat_stride']
#anchor_scales = layer_params.get('scales', (8, 16, 32))
#self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
self._bbox_para_num = 5 # parameter number of bbox
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 6-tuple
# (n, ctr_x, ctr_y, h, w, theta) specifying an image batch index n and a
# rectangle (ctr_x, ctr_y, h, w, theta)
top[0].reshape(1, self._bbox_para_num + 1) # D
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1, 1) # D
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
if cfg.CUSTOM_ANCHORS:
self._anchors = load_custom_anchors()
else:
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
self._feat_stride = layer_params['feat_stride']
print 'anchors:'
print self._anchors
print 'anchor shapes:'
print np.hstack((
self._anchors[:, 2::4] - self._anchors[:, 0::4],
self._anchors[:, 3::4] - self._anchors[:, 1::4],
))
# if DEBUG:
# print 'feat_stride: {}'.format(self._feat_stride)
# print 'anchors:'
# print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
self._feat_stride = layer_params['feat_stride']
if DEBUG:
print 'anchors:'
print self._anchors
print 'anchor shapes:'
print np.hstack((
self._anchors[:, 2::4] - self._anchors[:, 0::4],
self._anchors[:, 3::4] - self._anchors[:, 1::4],
))
self._counts = cfg.EPS
self._sums = np.zeros((1, 4))
self._squared_sums = np.zeros((1, 4))
self._fg_sum = 0
self._bg_sum = 0
self._count = 0
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
height, width = bottom[0].data.shape[-2:]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
A = self._num_anchors
# labels
top[0].reshape(1, 1, A * height, width)
# bbox_targets
top[1].reshape(1, A * 4, height, width)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
p = int(np.log2(self._feat_stride) + 1)
if p == 5:
anchor_scales = layer_params.get('scales', (8, 16, 32))
if p == 4:
anchor_scales = layer_params.get('scales', (4, 8, 16))
if p == 3:
anchor_scales = layer_params.get('scales', (2, 4, 8))
if p == 2:
anchor_scales = layer_params.get('scales', (1, 2, 4))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def __init__(self, feat_stride, scales, ratios):
super(_AnchorTargetLayer, self).__init__()
self._feat_stride = feat_stride
self._scales = scales
anchor_scales = scales
self._anchors = torch.from_numpy(generate_anchors(scales=np.array(anchor_scales), ratios=np.array(ratios))).float()
self._num_anchors = self._anchors.size(0)
# allow boxes to sit over the edge by a small amount
self._allowed_border = 0 # default is 0
def setup(self, bottom, top):
layer_params = json.loads(self.param_str_)
self._scales = layer_params['scales']
self._base_size = layer_params['base_size']
self._anchors = generate_anchors(base_size=self._base_size,scales=np.array(self._scales))
self._num_anchors = self._anchors.shape[0]
self._feat_stride = layer_params['feat_stride']
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
height, width = bottom[0].data.shape[-2:]
if DEBUG:
print 'GenerateAnchorLayer: height', height, 'width', width
A = self._num_anchors
# bbox_anchors
top[0].reshape(height * width * A, 4)
def setup(self, bottom, top):
self._feat_stride = self.pythonargs['feat_stride']
anchor_scales = (8, 16, 32)
if 'scales' in self.pythonargs:
anchor_scales = self.pythonargs['scales']
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape((1, 5))
self._output_scores = 'output_scores' in self.pythonargs
# scores blob: holds scores for R regions of interest
if self._output_scores:
top[1].reshape((1, 1, 1, 1))
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str)
params = layer_params['stride_scale_border_batchsize_numcls']
feat_stride_str, scale_str, border_str, batch_str, num_cls = params.split(',')
self._feat_stride = float(feat_stride_str)
self._scales = float(scale_str)
self._num_classes = int(num_cls)
self._score_thresh = cfg.TRAIN.PROB
self._allowed_border = float(border_str)
self._batch = float(batch_str)
self._anchors = generate_anchors(base_size = cfg.MINANCHOR, scales=np.array([self._scales, self._scales + 1]), ratios = [0.333, 0.5, 1, 2, 3])
self._num_anchors = self._anchors.shape[0]
height, width = bottom[0].data.shape[-2:]
self._ndim = cfg.TRAIN.IMS_PER_BATCH
# labels
top[0].reshape(self._ndim, 1, self._num_anchors * height, width)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
self._anchors = generate_anchors(cfg.TRAIN.RPN_BASE_SIZE, cfg.TRAIN.RPN_ASPECTS, cfg.TRAIN.RPN_SCALES)
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params['feat_stride']
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'feat_stride: {}'.format(self._feat_stride)
print 'anchors:'
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print 'anchors:'
print self._anchors
print 'anchor shapes:'
print np.hstack((
self._anchors[:, 2::4] - self._anchors[:, 0::4],
self._anchors[:, 3::4] - self._anchors[:, 1::4],
))
self._counts = cfg.EPS
self._sums = np.zeros((1, 4))
self._squared_sums = np.zeros((1, 4))
self._fg_sum = 0
self._bg_sum = 0
self._count = 0
#layer_params = yaml.load(self.param_str_)
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params['feat_stride']
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
height, width = bottom[0].data.shape[-2:]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
A = self._num_anchors
# labels
top[0].reshape(1, 1, A * height, width)
# bbox_targets
top[1].reshape(1, A * 4, height, width)
# bbox_inside_weights
top[2].reshape(1, A * 4, height, width)
# bbox_outside_weights
top[3].reshape(1, A * 4, height, width)
def setup(self, bottom, top):
# parse the layer parameter string, which must be valid YAML
# layer_params = yaml.load(self.param_str_)
layer_params = yaml.load(self.param_str)
self._feat_stride = layer_params["feat_stride"]
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
if DEBUG:
print "feat_stride: {}".format(self._feat_stride)
print "anchors:"
print self._anchors
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
top[0].reshape(1, 5)
# scores blob: holds scores for R regions of interest
if len(top) > 1:
top[1].reshape(1, 1, 1, 1)
def setup(self, bottom, top):
# layer_params = yaml.load(self.param_str_)
layer_params = dict()
layer_params['feat_stride'] = 16
layer_params['scales'] = (8, 16, 32)
layer_params['allowed_border'] = 0
self.RPN_NEGATIVE_OVERLAP = 0.3
self.RPN_POSITIVE_OVERLAP = 0.7
self.RPN_FG_FRACTION = 0.5
self.RPN_BATCHSIZE = 256
self.EPS = 1e-14
self.RPN_BBOX_INSIDE_WEIGHTS = 1
self.RPN_POSITIVE_WEIGHT = -1
anchor_scales = layer_params.get('scales', (8, 16, 32))
self._anchors = generate_anchors(scales=np.array(anchor_scales))
self._num_anchors = self._anchors.shape[0]
self._feat_stride = layer_params['feat_stride']
if DEBUG:
# print 'anchors:'
# print self._anchors
# print 'anchor shapes:'
# print np.hstack((
# self._anchors[:, 2::4] - self._anchors[:, 0::4],
# self._anchors[:, 3::4] - self._anchors[:, 1::4],
# ))
self._counts = self.EPS
self._sums = np.zeros((1, 4))
self._squared_sums = np.zeros((1, 4))
self._fg_sum = 0
self._bg_sum = 0
self._count = 0
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
def reshape(self, bottom, top):
"""Reshaping happens during the call to forward."""
pass
def _filter_boxes(boxes, min_size):
"""Remove all boxes with any side smaller than min_size."""
ws = boxes[:, 3] # D
hs = boxes[:, 2] # D
keep = np.where((ws >= min_size) & (hs >= min_size))[0]
return keep
if __name__ == "__main__":
anchor_scales = (8, 16, 32)
_anchors = generate_anchors(scales=np.array(anchor_scales))
_num_anchors = _anchors.shape[0]
_feat_stride = 16
print _anchors
_bbox_para_num = 5
height, width = (10,10)
A = _num_anchors
# labels
#top0 = np.zeros((1, 6))
# im_info
im_info = [160,160,1]
def __init__(self, feat_stride=16):
self.feat_stride = feat_stride
self.anchors = generate_anchors()
self.n_anchors = self.anchors.shape[0]
self.allowed_border = 0
def __init__(self, feat_stride=16):
self.feat_stride = 16
self.anchors = generate_anchors()
self.num_anchors = self.anchors.shape[0]
self.train = False
def proposal_layer(rpn_cls_prob_reshape,rpn_bbox_pred,im_info,cfg_key,_feat_stride = [16,],anchor_scales = [8, 16, 32]):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
#layer_params = yaml.load(self.param_str_)
_anchors = generate_anchors(scales=np.array(anchor_scales))
_num_anchors = _anchors.shape[0]
rpn_cls_prob_reshape = np.transpose(rpn_cls_prob_reshape,[0,3,1,2])
rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,1,2])
#rpn_cls_prob_reshape = np.transpose(np.reshape(rpn_cls_prob_reshape,[1,rpn_cls_prob_reshape.shape[0],rpn_cls_prob_reshape.shape[1],rpn_cls_prob_reshape.shape[2]]),[0,3,2,1])
#rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,2,1])
im_info = im_info[0]
assert rpn_cls_prob_reshape.shape[0] == 1, \
'Only single item batches are supported'
# cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
#cfg_key = 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = cfg[cfg_key].RPN_MIN_SIZE
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :]
bbox_deltas = rpn_bbox_pred
#im_info = bottom[2].data[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print 'score map size: {}'.format(scores.shape)
# Enumerate all shifts
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, 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()
# Enumerate all shifted anchors:
#
# 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]
anchors = _anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob
def anchor_target_layer(rpn_cls_score, gt_boxes, im_info, data, _feat_stride = [16,], anchor_scales = [4 ,8, 16, 32]):
"""
Assign anchors to ground-truth targets. Produces anchor classification
labels and bounding-box regression targets.
"""
_anchors = generate_anchors(scales=np.array(anchor_scales))
_num_anchors = _anchors.shape[0]
if DEBUG:
print 'anchors:'
print _anchors
print 'anchor shapes:'
print np.hstack((
_anchors[:, 2::4] - _anchors[:, 0::4],
_anchors[:, 3::4] - _anchors[:, 1::4],
))
_counts = cfg.EPS
_sums = np.zeros((1, 4))
_squared_sums = np.zeros((1, 4))
_fg_sum = 0
_bg_sum = 0
_count = 0
# allow boxes to sit over the edge by a small amount
_allowed_border = 0
# map of shape (..., H, W)
#height, width = rpn_cls_score.shape[1:3]
im_info = im_info[0]
# Algorithm:
#
# for each (H, W) location i
# generate 9 anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the 9 anchors
# filter out-of-image anchors
# measure GT overlap
assert rpn_cls_score.shape[0] == 1, \
'Only single item batches are supported'
# map of shape (..., H, W)
height, width = rpn_cls_score.shape[1:3]
if DEBUG:
print 'AnchorTargetLayer: height', height, 'width', width
print ''
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
print 'height, width: ({}, {})'.format(height, width)
print 'rpn: gt_boxes.shape', gt_boxes.shape
print 'rpn: gt_boxes', gt_boxes
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, 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 = (_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) & # width
(all_anchors[:, 3] < im_info[0] + _allowed_border) # height
)[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)
# overlaps between the anchors and the gt boxes
# overlaps (ex, gt)
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=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
# subsample positive labels if we have too many
num_fg = int(cfg.TRAIN.RPN_FG_FRACTION * cfg.TRAIN.RPN_BATCHSIZE)
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)
labels[disable_inds] = -1
# subsample negative labels if we have too many
num_bg = cfg.TRAIN.RPN_BATCHSIZE - 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)
labels[disable_inds] = -1
#print "was %s inds, disabling %s, now %s inds" % (
#len(bg_inds), len(disable_inds), np.sum(labels == 0))
bbox_targets = np.zeros((len(inds_inside), 4), dtype=np.float32)
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(cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS)
bbox_outside_weights = np.zeros((len(inds_inside), 4), dtype=np.float32)
if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
# uniform weighting of examples (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 ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
(cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))
positive_weights = (cfg.TRAIN.RPN_POSITIVE_WEIGHT /
np.sum(labels == 1))
negative_weights = ((1.0 - cfg.TRAIN.RPN_POSITIVE_WEIGHT) /
np.sum(labels == 0))
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 += np.sum(labels == 1)
means = _sums / _counts
stds = np.sqrt(_squared_sums / _counts - means ** 2)
print 'means:'
print means
print 'stdevs:'
print 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_overlap', np.max(max_overlaps)
print 'rpn: num_positive', np.sum(labels == 1)
print 'rpn: num_negative', 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
#pdb.set_trace()
labels = labels.reshape((1, height, width, A)).transpose(0, 3, 1, 2)
labels = labels.reshape((1, 1, A * height, width))
rpn_labels = labels
# bbox_targets
bbox_targets = bbox_targets \
.reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
rpn_bbox_targets = bbox_targets
# bbox_inside_weights
bbox_inside_weights = bbox_inside_weights \
.reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
#assert bbox_inside_weights.shape[2] == height
#assert bbox_inside_weights.shape[3] == width
rpn_bbox_inside_weights = bbox_inside_weights
# bbox_outside_weights
bbox_outside_weights = bbox_outside_weights \
.reshape((1, height, width, A * 4)).transpose(0, 3, 1, 2)
#assert bbox_outside_weights.shape[2] == height
#assert bbox_outside_weights.shape[3] == width
rpn_bbox_outside_weights = bbox_outside_weights
return rpn_labels,rpn_bbox_targets,rpn_bbox_inside_weights,rpn_bbox_outside_weights
def forward(self, bottom, top):
cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = self._min_sizes
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
im_info = bottom[0].data[0, :]
batch_size = bottom[1].data.shape[0]
if batch_size > 1:
raise ValueError("Sorry, multiple images each device is not implemented")
cls_prob_dict = {
'stride64': bottom[10].data,
'stride32': bottom[9].data,
'stride16': bottom[8].data,
'stride8': bottom[7].data,
'stride4': bottom[6].data,
}
bbox_pred_dict = {
'stride64': bottom[5].data,
'stride32': bottom[4].data,
'stride16': bottom[3].data,
'stride8': bottom[2].data,
'stride4': bottom[1].data,
}
proposal_list = []
score_list = []
for s in self._feat_stride:
stride = int(s)
sub_anchors = generate_anchors(base_size=stride, scales=self._scales, ratios=self._ratios)
scores = cls_prob_dict['stride' + str(s)][:, self._num_anchors:, :, :]
bbox_deltas = bbox_pred_dict['stride' + str(s)]
# 1. Generate proposals from bbox_deltas and shifted anchors
# use real image size instead of padded feature map sizes
height, width = int(im_info[0] / stride), int(im_info[1] / stride)
# Enumerate all shifts
shift_x = np.arange(0, width) * stride
shift_y = np.arange(0, height) * 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()
# Enumerate all shifted anchors:
#
# 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 = self._num_anchors
K = shifts.shape[0]
anchors = sub_anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = _clip_pad(bbox_deltas, (height, width))
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = _clip_pad(scores, (height, width))
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
proposal_list.append(proposals)
score_list.append(scores)
proposals = np.vstack(proposal_list)
scores = np.vstack(score_list)
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
det = np.hstack((proposals, scores)).astype(np.float32)
keep = nms(det,nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
# pad to ensure output size remains unchanged
if len(keep) < post_nms_topN:
pad = npr.choice(keep, size=post_nms_topN - len(keep))
keep = np.hstack((keep, pad))
# pad to ensure output size remains unchanged
if len(keep) < post_nms_topN:
try:
pad = npr.choice(keep, size=post_nms_topN - len(keep))
except:
proposals = np.zeros((post_nms_topN, 4), dtype=np.float32)
proposals[:,2] = 16
proposals[:,3] = 16
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
top[0].reshape(*(blob.shape))
top[0].data[...] = blob
return
keep = np.hstack((keep, pad))
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois array
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
# if is_train:
top[0].reshape(*(blob.shape))
top[0].data[...] = blob
def imdb_rpn_compute_stats(net, imdb, anchor_scales=(8,16,32),
feature_stride=16):
raw_anchors = generate_anchors(scales=np.array(anchor_scales))
print raw_anchors.shape
sums = 0
squred_sums = 0
counts = 0
roidb = filter_roidb(imdb.roidb)
# Compute a map of input image size and output feature map blob
map_w = {}
map_h = {}
for i in xrange(50, cfg.TRAIN.MAX_SIZE + 10):
blobs = {
'data': np.zeros((1, 3, i, i)),
'im_info': np.asarray([[i, i, 1.0]])
}
net.blobs['data'].reshape(*(blobs['data'].shape))
net.blobs['im_info'].reshape(*(blobs['im_info'].shape))
blobs_out = net.forward(
data=blobs['data'].astype(np.float32, copy=False),
im_info=blobs['im_info'].astype(np.float32, copy=False))
height, width = net.blobs['rpn/output'].data.shape[-2:]
map_w[i] = width
map_h[i] = height
for i in xrange(len(roidb)):
if not i % 5000:
print 'computing %d/%d' % (i, imdb.num_images)
im = None
if cfg.TRAIN.FORMAT == 'pickle':
with open(roidb[i]['image'], 'rb') as f:
im = cPickle.load(f)
else:
im = cv2.imread(roidb[i]['image'])
im_data, im_info = _get_image_blob(im)
gt_boxes = roidb[i]['boxes']
gt_boxes = gt_boxes * im_info[0, 2]
height = map_h[im_data.shape[2]]
width = map_w[im_data.shape[3]]
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * feature_stride
shift_y = np.arange(0, height) * feature_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 = raw_anchors.shape[0]
K = shifts.shape[0]
all_anchors = (raw_anchors.reshape((1, A, 4)) +
shifts.reshape((1, K, 4)).transpose((1, 0, 2)))
all_anchors = all_anchors.reshape((K * A, 4))
# only keep anchors inside the image
inds_inside = np.where(
(all_anchors[:, 0] >= 0) &
(all_anchors[:, 1] >= 0) &
(all_anchors[:, 2] < im_info[0, 1]) & # width
(all_anchors[:, 3] < im_info[0, 0]) # height
)[0]
# keep only inside anchors
anchors = all_anchors[inds_inside, :]
overlaps = bbox_overlaps(
np.ascontiguousarray(anchors, dtype=np.float),
np.ascontiguousarray(gt_boxes, dtype=np.float))
# There are 2 types of bbox targets
# 1. anchor whose overlaps with gt is greater than RPN_POSITIVE_OVERLAP
argmax_overlaps = overlaps.argmax(axis=1)
max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
fg_inds = np.where(max_overlaps >= cfg.TRAIN.RPN_POSITIVE_OVERLAP)[0]
# 2. anchors which best match certain gt
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]
fg_inds = np.unique(np.hstack((fg_inds, gt_argmax_overlaps)))
gt_rois = gt_boxes[argmax_overlaps, :]
anchors = anchors[fg_inds, :]
gt_rois = gt_rois[fg_inds, :]
targets = bbox_transform(anchors, gt_rois[:, :4]).astype(np.float32, copy=False)
sums += targets.sum(axis=0)
squred_sums += (targets ** 2).sum(axis=0)
counts += targets.shape[0]
means = sums / counts
stds = np.sqrt(squred_sums / counts - means ** 2)
print means
print stds
return means, stds