@ -39,6 +39,7 @@ __all__ = [
' detection_output ' ,
' ssd_loss ' ,
' rpn_target_assign ' ,
' retinanet_target_assign ' ,
' anchor_generator ' ,
' roi_perspective_transform ' ,
' generate_proposal_labels ' ,
@ -57,6 +58,164 @@ __all__ = [
]
def retinanet_target_assign ( bbox_pred ,
cls_logits ,
anchor_box ,
anchor_var ,
gt_boxes ,
gt_labels ,
is_crowd ,
im_info ,
num_classes = 1 ,
positive_overlap = 0.5 ,
negative_overlap = 0.4 ) :
"""
* * Target Assign Layer for Retinanet . * *
This layer can be , for given the Intersection - over - Union ( IoU ) overlap
between anchors and ground truth boxes , to assign classification and
regression targets to each anchor , these target labels are used for training
retinanet . Every anchor is assigned with a length : attr : ` num_classes `
one - hot vector of classification targets , and a 4 - vector of box regression
targets . The assignment rules are as followed :
1. Anchors are assigned to ground - truth boxes when : ( i ) it has the highest
IoU overlap with a ground - truth box , or ( ii ) it has an IoU overlap higher
than positive_overlap ( 0.5 ) with any ground - truth box .
2. Anchors are assigned to background when its IoU ratio is lower than
negative_overlap ( 0.4 ) for all ground - truth boxes .
When an anchor is assigned with a ground - truth box which is the i - th category ,
the i - th entry in its C vector of targets is set to 1 and all other entries
are set to 0. When an anchor is assigned with background , all entries are set
to 0. Anchors that are not assigned do not contribute to the training
objective . The regression targets are the encoded ground - truth boxes
associated with the assigned anchors .
Args :
bbox_pred ( Variable ) : A 3 - D Tensor with shape [ N , M , 4 ] represents the
predicted locations of M bounding bboxes . N is the batch size ,
and each bounding box has four coordinate values and the layout
is [ xmin , ymin , xmax , ymax ] .
cls_logits ( Variable ) : A 3 - D Tensor with shape [ N , M , C ] represents the
predicted confidence predictions . N is the batch size , C is the
number of classes ( excluding background ) , M is number of bounding boxes .
anchor_box ( Variable ) : A 2 - D Tensor with shape [ M , 4 ] holds M boxes ,
each box is represented as [ xmin , ymin , xmax , ymax ] ,
[ xmin , ymin ] is the left top coordinate of the anchor box ,
if the input is image feature map , they are close to the origin
of the coordinate system . [ xmax , ymax ] is the right bottom
coordinate of the anchor box .
anchor_var ( Variable ) : A 2 - D Tensor with shape [ M , 4 ] holds expanded
variances of anchors .
gt_boxes ( Variable ) : The ground - truth bounding boxes ( bboxes ) are a 2 D
LoDTensor with shape [ Ng , 4 ] , Ng is the total number of ground - truth
bboxes of mini - batch input .
gt_labels ( variable ) : The ground - truth labels are a 2 D LoDTensor with
shape [ Ng , 1 ] , Ng is the total number of ground - truth labels of
mini - batch input .
is_crowd ( Variable ) : A 1 - D LoDTensor which indicates ground - truth is crowd .
im_info ( Variable ) : A 2 - D LoDTensor with shape [ N , 3 ] . N is the batch size ,
3 is the height , width and scale .
num_classes ( int32 ) : The number of classes .
positive_overlap ( float ) : Minimum overlap required between an anchor
and ground - truth box for the ( anchor , gt box ) pair to be a positive
example .
negative_overlap ( float ) : Maximum overlap allowed between an anchor
and ground - truth box for the ( anchor , gt box ) pair to be a negative
examples .
Returns :
tuple :
A tuple ( predicted_scores , predicted_location , target_label ,
target_bbox , bbox_inside_weight , fg_num ) is returned . The
predicted_scores and predicted_location are the predicted result
of the retinanet . The target_label and target_bbox are the ground
truth , respectively . The predicted_location is a 2 D Tensor with
shape [ F , 4 ] , and the shape of target_bbox is same as the shape of
the predicted_location , F is the number of the foreground
anchors . The predicted_scores is a 2 D Tensor with shape
[ F + B , C ] , and the shape of target_label is [ F + B , 1 ] , B is the
number of the background anchors , the F and B is depends on the
input of this operator . Bbox_inside_weight represents whether the
predicted location is fake foreground or not and the shape is [ F , 4 ] .
Fg_num is the foreground number ( including fake foreground ) which
is needed by focal loss .
Examples :
. . code - block : : python
import paddle . fluid as fluid
bbox_pred = layers . data ( name = ' bbox_pred ' , shape = [ 1 , 100 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
cls_logits = layers . data ( name = ' cls_logits ' , shape = [ 1 , 100 , 10 ] ,
append_batch_size = False , dtype = ' float32 ' )
anchor_box = layers . data ( name = ' anchor_box ' , shape = [ 100 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
anchor_var = layers . data ( name = ' anchor_var ' , shape = [ 100 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
gt_boxes = layers . data ( name = ' gt_boxes ' , shape = [ 10 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
gt_labels = layers . data ( name = ' gt_labels ' , shape = [ 10 , 1 ] ,
append_batch_size = False , dtype = ' float32 ' )
is_crowd = fluid . layers . data ( name = ' is_crowd ' , shape = [ 1 ] ,
append_batch_size = False , dtype = ' float32 ' )
im_info = fluid . layers . data ( name = ' im_infoss ' , shape = [ 1 , 3 ] ,
append_batch_size = False , dtype = ' float32 ' )
loc_pred , score_pred , loc_target , score_target , bbox_inside_weight , fg_num =
fluid . layers . retinanet_target_assign ( bbox_pred , cls_logits , anchor_box ,
anchor_var , gt_boxes , gt_labels , is_crowd , im_info , 10 )
"""
helper = LayerHelper ( ' retinanet_target_assign ' , * * locals ( ) )
# Assign target label to anchors
loc_index = helper . create_variable_for_type_inference ( dtype = ' int32 ' )
score_index = helper . create_variable_for_type_inference ( dtype = ' int32 ' )
target_label = helper . create_variable_for_type_inference ( dtype = ' int32 ' )
target_bbox = helper . create_variable_for_type_inference (
dtype = anchor_box . dtype )
bbox_inside_weight = helper . create_variable_for_type_inference (
dtype = anchor_box . dtype )
fg_num = helper . create_variable_for_type_inference ( dtype = ' int32 ' )
helper . append_op (
type = " retinanet_target_assign " ,
inputs = {
' Anchor ' : anchor_box ,
' GtBoxes ' : gt_boxes ,
' GtLabels ' : gt_labels ,
' IsCrowd ' : is_crowd ,
' ImInfo ' : im_info
} ,
outputs = {
' LocationIndex ' : loc_index ,
' ScoreIndex ' : score_index ,
' TargetLabel ' : target_label ,
' TargetBBox ' : target_bbox ,
' BBoxInsideWeight ' : bbox_inside_weight ,
' ForegroundNumber ' : fg_num
} ,
attrs = {
' positive_overlap ' : positive_overlap ,
' negative_overlap ' : negative_overlap
} )
loc_index . stop_gradient = True
score_index . stop_gradient = True
target_label . stop_gradient = True
target_bbox . stop_gradient = True
bbox_inside_weight . stop_gradient = True
fg_num . stop_gradient = True
cls_logits = nn . reshape ( x = cls_logits , shape = ( - 1 , num_classes ) )
bbox_pred = nn . reshape ( x = bbox_pred , shape = ( - 1 , 4 ) )
predicted_cls_logits = nn . gather ( cls_logits , score_index )
predicted_bbox_pred = nn . gather ( bbox_pred , loc_index )
return predicted_cls_logits , predicted_bbox_pred , target_label , target_bbox , bbox_inside_weight , fg_num
def rpn_target_assign ( bbox_pred ,
cls_logits ,
anchor_box ,
FlyingQianMM
6 years ago
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