def run_head(self, proposals, stage):
"""
Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes
"""
reg_weights = tf.constant(cfg.CASCADE.BBOX_REG_WEIGHTS[stage],
dtype=tf.float32)
pooled_feature = self.roi_func(proposals.boxes) # N,C,S,S
pooled_feature = self.scale_gradient(pooled_feature)
head_feature = self.fastrcnn_head_func('head', pooled_feature)
label_logits, box_logits = fastrcnn_outputs(
'outputs',
head_feature,
self.num_classes,
class_agnostic_regression=True)
head = FastRCNNHead(proposals, box_logits, label_logits, self.gt_boxes,
reg_weights)
refined_boxes = head.decoded_output_boxes_class_agnostic()
refined_boxes = clip_boxes(refined_boxes, self.image_shape2d)
return head, tf.stop_gradient(refined_boxes, name='output_boxes')
def run_head(self, proposals, stage):
"""
Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes
"""
reg_weights = tf.constant(cfg.CASCADE.BBOX_REG_WEIGHTS[stage], dtype=tf.float32) # 创建cascade的权重，是持久化常量浮点数
pooled_feature = self.roi_func(proposals.boxes) # N,C,S,S
if self.roi_func_extra != None:
# pooled_feature = tf.concat([pooled_feature, self.roi_func_extra(proposals.boxes)], 0)
pooled_feature = (self.roi_func_extra(proposals.boxes) + pooled_feature) / 2
pooled_feature = self.scale_gradient(pooled_feature) # 这里不太理解为什么重新赋值
head_feature = self.fastrcnn_head_func('head', pooled_feature)
# 82-87不太理解.....
# changed by Paul
label_logits, box_logits = fastrcnn_outputs(
'outputs_new', head_feature, self.num_classes, class_agnostic_regression=True)
head = FastRCNNHead(proposals, box_logits, label_logits, self.gt_boxes, reg_weights)
refined_boxes = head.decoded_output_boxes_class_agnostic()
refined_boxes = clip_boxes(refined_boxes, self.image_shape2d)
# tf.stop_gradient：停止梯度计算；参数 - 张量 + 操作名称
return head, tf.stop_gradient(refined_boxes, name='output_boxes')
def run_head(self, proposals, stage):
"""
Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes
"""
reg_weights = tf.constant(cfg.CASCADE.BBOX_REG_WEIGHTS[stage], dtype=tf.float32)
pooled_feature = self.roi_func(proposals.boxes) # N,C,S,S
pooled_feature = self.scale_gradient(pooled_feature)
head_feature = self.fastrcnn_head_func('head', pooled_feature)
label_logits, box_logits = fastrcnn_outputs(
'outputs', head_feature, self.num_classes, class_agnostic_regression=True)
head = FastRCNNHead(proposals, box_logits, label_logits, reg_weights)
refined_boxes = head.decoded_output_boxes_class_agnostic()
refined_boxes = clip_boxes(refined_boxes, self.image_shape2d)
return head, tf.stop_gradient(refined_boxes, name='output_boxes')
