def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
with tf.variable_scope(name) as scope:
rois, rpn_scores = proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred, self._im_info,
self._feat_stride, self._anchors, self._num_anchors)
rois.set_shape([None, 6])
rpn_scores.set_shape([None, 1])
rpn_scores = tf.to_float(rpn_scores)
return rois, rpn_scores
def _proposal_layer(rpn_cls_prob, rpn_bbox_pred, name):
with tf.variable_scope(name) as scope:
rois, rpn_scores = proposal_layer_tf(rpn_cls_prob, rpn_bbox_pred,
imgsize_placeholder, mode,
feat_stride, anchors, num_anchors)
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return rois, rpn_scores
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
rois, rpn_scores = proposal_layer_tf(rpn_cls_prob,rpn_bbox_pred,self._im_info,self._mode,self._feat_stride,
self._anchors,self._num_anchors) #筛选超越边界的候选框，进行nms处理，得到前6k个候选框
else:
rois, rpn_scores = tf.py_func(proposal_layer,[rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors],
[tf.float32, tf.float32], name="proposal")
rois.set_shape([None, 5]) #[w*h*9,5]
rpn_scores.set_shape([None, 1]) #[w*h*9,1]
return rois, rpn_scores
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
# 生成rois，roi_scores
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
rois, rpn_scores = proposal_layer_tf(
rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors)
else:
rois, rpn_scores = tf.py_func(proposal_layer, [
rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors
], [tf.float32, tf.float32],
name='proposal')
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return rois, rpn_scores
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
'''given rpn classification prob and bbox offset prediction to get 2000 rois'''
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
rois, rpn_scores = proposal_layer_tf(
rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors)
else:
rois, rpn_scores = tf.py_func(proposal_layer, [
rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors
], [tf.float32, tf.float32],
name="proposal")
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return rois, rpn_scores
def _proposal_layer(inputs):
build_rpn_cls_prob, build_rpn_bbox_pred, build_anchors, im_info = inputs
im_info = im_info[0]
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
rois, rpn_scores = proposal_layer_tf(
build_rpn_cls_prob, build_rpn_bbox_pred, im_info,
self._mode, self._feat_stride, build_anchors,
self._num_anchors)
else:
rois, rpn_scores = tf.py_func(proposal_layer, [
build_rpn_cls_prob, build_rpn_bbox_pred, im_info,
self._mode, self._feat_stride, build_anchors,
self._num_anchors
], [tf.float32, tf.float32],
name="proposal")
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return [rois, rpn_scores]
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
rois, rpn_scores = proposal_layer_tf(
rpn_cls_prob,
rpn_bbox_pred,
self._im_info,
self._mode,
self._feat_stride,
self._anchors,
self._num_anchors
)
else:
rois, rpn_scores = tf.py_func(proposal_layer,
[rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors],
[tf.float32, tf.float32], name="proposal")
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return rois, rpn_scores
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
#筛选出大约2k个预测的候选框
rois, rpn_scores = proposal_layer_tf(
rpn_cls_prob,
rpn_bbox_pred,
self._im_info,
self._mode,
self._feat_stride,
self._anchors,
self._num_anchors
)
else:
rois, rpn_scores = tf.py_func(proposal_layer,
[rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors],
[tf.float32, tf.float32], name="proposal")
rois.set_shape([None, 5]) #(2k,5):[0,x1,y1,x2,y2]
rpn_scores.set_shape([None, 1]) #(2k,1)
return rois, rpn_scores
def _proposal_layer(self, rpn_cls_prob, rpn_bbox_pred, name):
"""
# get anchor score and coor, anchors filter，mns提取rois
:param rpn_cls_prob:
:param rpn_bbox_pred:
:param name:
:return:
"""
with tf.variable_scope(name) as scope:
if cfg.USE_E2E_TF:
rois, rpn_scores = proposal_layer_tf(
rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors)
else:
rois, rpn_scores = tf.py_func(proposal_layer, [
rpn_cls_prob, rpn_bbox_pred, self._im_info, self._mode,
self._feat_stride, self._anchors, self._num_anchors
], [tf.float32, tf.float32],
name="proposal")
rois.set_shape([None, 5])
rpn_scores.set_shape([None, 1])
return rois, rpn_scores