def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h,
gtboxes_batch_r,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
# 1. build base network
feature_pyramid = self.build_base_network(input_img_batch)
# 2. build rpn
rpn_box_pred, rpn_cls_score, rpn_cls_prob = self.rpn_net(
feature_pyramid)
# 3. generate_anchors
anchors = self.make_anchors(feature_pyramid)
# 4. postprocess rpn proposals. such as: decode, clip, filter
if not self.is_training:
with tf.variable_scope('postprocess_detctions'):
boxes, scores, category = postprocess_detctions(
rpn_bbox_pred=rpn_box_pred,
rpn_cls_prob=rpn_cls_prob,
anchors=anchors,
is_training=self.is_training)
return boxes, scores, category
# 5. build loss
else:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes = tf.py_func(
func=anchor_target_layer,
inp=[gtboxes_batch_h, gtboxes_batch_r, anchors, gpu_id],
Tout=[tf.float32, tf.float32, tf.float32, tf.float32])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 1)
cls_loss = losses.focal_loss(labels, rpn_cls_score,
anchor_states)
if cfgs.REG_LOSS_MODE == 0:
reg_loss = losses.iou_smooth_l1_loss(
target_delta, rpn_box_pred, anchor_states,
target_boxes, anchors)
elif cfgs.REG_LOSS_MODE == 1:
reg_loss = losses.smooth_l1_loss_atan(
target_delta, rpn_box_pred, anchor_states)
else:
reg_loss = losses.smooth_l1_loss(target_delta,
rpn_box_pred,
anchor_states)
losses_dict = {
'cls_loss': cls_loss * cfgs.CLS_WEIGHT,
'reg_loss': reg_loss * cfgs.REG_WEIGHT
}
with tf.variable_scope('postprocess_detctions'):
boxes, scores, category = postprocess_detctions(
rpn_bbox_pred=rpn_box_pred,
rpn_cls_prob=rpn_cls_prob,
anchors=anchors,
is_training=self.is_training)
boxes = tf.stop_gradient(boxes)
scores = tf.stop_gradient(scores)
category = tf.stop_gradient(category)
return boxes, scores, category, losses_dict
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h,
gtboxes_batch_r,
gthead_quadrant,
gt_smooth_label,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
gthead_quadrant = tf.reshape(gthead_quadrant, [-1, 1])
gthead_quadrant = tf.cast(gthead_quadrant, tf.int32)
gt_smooth_label = tf.reshape(gt_smooth_label,
[-1, self.angle_range])
gt_smooth_label = tf.cast(gt_smooth_label, tf.float32)
img_shape = tf.shape(input_img_batch)
# 1. build base network
feature_pyramid = self.build_base_network(input_img_batch)
# 2. build rpn
rpn_box_pred, rpn_cls_score, rpn_cls_prob, rpn_head_cls, rpn_angle_cls = self.rpn_net(
feature_pyramid)
# 3. generate_anchors
anchors = self.make_anchors(feature_pyramid)
# 4. postprocess rpn proposals. such as: decode, clip, filter
if self.is_training:
with tf.variable_scope('build_loss'):
labels, target_delta, anchor_states, target_boxes, target_head_quadrant, target_smooth_label = tf.py_func(
func=anchor_target_layer,
inp=[
gtboxes_batch_h, gtboxes_batch_r, gthead_quadrant,
gt_smooth_label, anchors, gpu_id
],
Tout=[
tf.float32, tf.float32, tf.float32, tf.float32,
tf.float32, tf.float32
])
if self.method == 'H':
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 0)
else:
self.add_anchor_img_smry(input_img_batch, anchors,
anchor_states, 1)
cls_loss = losses.focal_loss(labels, rpn_cls_score,
anchor_states)
if cfgs.REG_LOSS_MODE == 0:
reg_loss = losses.iou_smooth_l1_loss(
target_delta, rpn_box_pred, anchor_states,
target_boxes, anchors)
elif cfgs.REG_LOSS_MODE == 1:
reg_loss = losses.smooth_l1_loss_atan(
target_delta, rpn_box_pred, anchor_states)
else:
reg_loss = losses.smooth_l1_loss(target_delta,
rpn_box_pred,
anchor_states)
if cfgs.DATASET_NAME.startswith('DOTA'):
head_cls_loss = losses.head_specific_cls_focal_loss(
target_head_quadrant,
rpn_head_cls,
anchor_states,
labels,
specific_cls=[6, 7, 8, 9, 10, 11])
else:
head_cls_loss = losses.head_focal_loss(
target_head_quadrant, rpn_head_cls, anchor_states)
angle_cls_loss = losses.angle_focal_loss(
target_smooth_label, rpn_angle_cls, anchor_states)
self.losses_dict = {
'cls_loss': cls_loss * cfgs.CLS_WEIGHT,
'reg_loss': reg_loss * cfgs.REG_WEIGHT,
'head_cls_loss': head_cls_loss * cfgs.HEAD_WEIGHT,
'angle_cls_loss': angle_cls_loss * cfgs.ANGLE_WEIGHT
}
with tf.variable_scope('postprocess_detctions'):
boxes, scores, category, boxes_head, boxes_angle = postprocess_detctions(
rpn_bbox_pred=rpn_box_pred,
rpn_cls_prob=rpn_cls_prob,
rpn_angle_prob=tf.sigmoid(rpn_angle_cls),
rpn_head_prob=tf.sigmoid(rpn_head_cls),
anchors=anchors,
is_training=self.is_training)
boxes = tf.stop_gradient(boxes)
scores = tf.stop_gradient(scores)
category = tf.stop_gradient(category)
boxes_head = tf.stop_gradient(boxes_head)
boxes_angle = tf.stop_gradient(boxes_angle)
if self.is_training:
return boxes, scores, category, boxes_head, boxes_angle, self.losses_dict
else:
return boxes, scores, category, boxes_head, boxes_angle
