@ -254,70 +254,66 @@ def rpn_target_assign(bbox_pred,
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 ] .
is [ xmin , ymin , xmax , ymax ] . The data type can be float32 or float64 .
cls_logits ( Variable ) : A 3 - D Tensor with shape [ N , M , 1 ] represents the
predicted confidence predictions . N is the batch size , 1 is the
frontground and background sigmoid , M is number of bounding boxes .
The data type can be float32 or float64 .
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 .
coordinate of the anchor box . The data type can be float32 or float64 .
anchor_var ( Variable ) : A 2 - D Tensor with shape [ M , 4 ] holds expanded
variances of anchors .
variances of anchors . The data type can be float32 or float64 .
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 .
bboxes of mini - batch input . The data type can be float32 or float64 .
is_crowd ( Variable ) : A 1 - D LoDTensor which indicates groud - truth is crowd .
The data type must be int32 .
im_info ( Variable ) : A 2 - D LoDTensor with shape [ N , 3 ] . N is the batch size ,
3 is the height , width and scale .
rpn_batch_size_per_im ( int ) : Total number of RPN examples per image .
The data type must be int32 .
rpn_straddle_thresh ( float ) : Remove RPN anchors that go outside the image
by straddle_thresh pixels .
by straddle_thresh pixels . The data type must be float32 .
rpn_fg_fraction ( float ) : Target fraction of RoI minibatch that is labeled
foreground ( i . e . class > 0 ) , 0 - th class is background .
foreground ( i . e . class > 0 ) , 0 - th class is background . The data type must be float32 .
rpn_positive_overlap ( float ) : Minimum overlap required between an anchor
and ground - truth box for the ( anchor , gt box ) pair to be a positive
example .
example . The data type must be float32 .
rpn_negative_overlap ( float ) : Maximum overlap allowed between an anchor
and ground - truth box for the ( anchor , gt box ) pair to be a negative
examples .
examples . The data type must be float32 .
Returns :
tuple :
A tuple ( predicted_scores , predicted_location , target_label ,
target_bbox , bbox_inside_weight ) is returned . The predicted_scores
and predicted_location is the predicted result of the RPN .
The target_label and target_bbox is 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 , 1 ] , and the shape of target_label is same as the shape
of the predicted_scores , 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 loc is fake_fg
or not and the shape is [ F , 4 ] .
A tuple ( predicted_scores , predicted_location , target_label ,
target_bbox , bbox_inside_weight ) is returned . The predicted_scores
and predicted_location is the predicted result of the RPN .
The target_label and target_bbox is 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 , 1 ] , and the shape of target_label is same as the shape
of the predicted_scores , 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 loc is fake_fg
or not and the shape is [ F , 4 ] .
Examples :
. . code - block : : python
import paddle . fluid as fluid
bbox_pred = fluid . layers . data ( name = ' bbox_pred ' , shape = [ 100 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
cls_logits = fluid . layers . data ( name = ' cls_logits ' , shape = [ 100 , 1 ] ,
append_batch_size = False , dtype = ' float32 ' )
anchor_box = fluid . layers . data ( name = ' anchor_box ' , shape = [ 20 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
anchor_var = fluid . layers . data ( name = ' anchor_var ' , shape = [ 20 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
gt_boxes = fluid . layers . data ( name = ' gt_boxes ' , shape = [ 10 , 4 ] ,
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 ' )
bbox_pred = fluid . data ( name = ' bbox_pred ' , shape = [ None , 4 ] , dtype = ' float32 ' )
cls_logits = fluid . data ( name = ' cls_logits ' , shape = [ None , 1 ] , dtype = ' float32 ' )
anchor_box = fluid . data ( name = ' anchor_box ' , shape = [ None , 4 ] , dtype = ' float32 ' )
anchor_var = fluid . data ( name = ' anchor_var ' , shape = [ None , 4 ] , dtype = ' float32 ' )
gt_boxes = fluid . data ( name = ' gt_boxes ' , shape = [ None , 4 ] , dtype = ' float32 ' )
is_crowd = fluid . data ( name = ' is_crowd ' , shape = [ None ] , dtype = ' float32 ' )
im_info = fluid . data ( name = ' im_infoss ' , shape = [ None , 3 ] , dtype = ' float32 ' )
loc , score , loc_target , score_target , inside_weight = fluid . layers . rpn_target_assign (
bbox_pred , cls_logits , anchor_box , anchor_var , gt_boxes , is_crowd , im_info )
@ -2217,8 +2213,7 @@ def generate_proposal_labels(rpn_rois,
is_cls_agnostic = False ,
is_cascade_rcnn = False ) :
"""
* * Generate Proposal Labels of Faster - RCNN * *
* * Generate Proposal Labels of Faster - RCNN * *
This operator can be , for given the GenerateProposalOp output bounding boxes and groundtruth ,
to sample foreground boxes and background boxes , and compute loss target .
@ -2236,37 +2231,43 @@ def generate_proposal_labels(rpn_rois,
Finally BboxInsideWeights and BboxOutsideWeights are used to specify whether it would contribute to training loss .
Args :
rpn_rois ( Variable ) : A 2 - D LoDTensor with shape [ N , 4 ] . N is the number of the GenerateProposalOp ' s output, each element is a bounding box with [xmin, ymin, xmax, ymax] format.
gt_classes ( Variable ) : A 2 - D LoDTensor with shape [ M , 1 ] . M is the number of groundtruth , each element is a class label of groundtruth .
is_crowd ( Variable ) : A 2 - D LoDTensor with shape [ M , 1 ] . M is the number of groundtruth , each element is a flag indicates whether a groundtruth is crowd .
rpn_rois ( Variable ) : A 2 - D LoDTensor with shape [ N , 4 ] . N is the number of the GenerateProposalOp ' s output, each element is a bounding box with [xmin, ymin, xmax, ymax] format. The data type can be float32 or float64.
gt_classes ( Variable ) : A 2 - D LoDTensor with shape [ M , 1 ] . M is the number of groundtruth , each element is a class label of groundtruth . The data type must be int32 .
is_crowd ( Variable ) : A 2 - D LoDTensor with shape [ M , 1 ] . M is the number of groundtruth , each element is a flag indicates whether a groundtruth is crowd . The data type must be int32 .
gt_boxes ( Variable ) : A 2 - D LoDTensor with shape [ M , 4 ] . M is the number of groundtruth , each element is a bounding box with [ xmin , ymin , xmax , ymax ] format .
im_info ( Variable ) : A 2 - D LoDTensor with shape [ B , 3 ] . B is the number of input images , each element consists of im_height , im_width , im_scale .
batch_size_per_im ( int ) : Batch size of rois per images .
fg_fraction ( float ) : Foreground fraction in total batch_size_per_im .
fg_thresh ( float ) : Overlap threshold which is used to chose foreground sample .
bg_thresh_hi ( float ) : Overlap threshold upper bound which is used to chose background sample .
bg_thresh_lo ( float ) : Overlap threshold lower bound which is used to chose background sample .
bbox_reg_weights ( list | tuple ) : Box regression weights .
class_nums ( int ) : Class number .
batch_size_per_im ( int ) : Batch size of rois per images . The data type must be int32 .
fg_fraction ( float ) : Foreground fraction in total batch_size_per_im . The data type must be float32 .
fg_thresh ( float ) : Overlap threshold which is used to chose foreground sample . The data type must be float32 .
bg_thresh_hi ( float ) : Overlap threshold upper bound which is used to chose background sample . The data type must be float32 .
bg_thresh_lo ( float ) : Overlap threshold lower bound which is used to chose background sample . The data type must be float32 .
bbox_reg_weights ( list | tuple ) : Box regression weights . The data type must be float32 .
class_nums ( int ) : Class number . The data type must be int32 .
use_random ( bool ) : Use random sampling to choose foreground and background boxes .
is_cls_agnostic ( bool ) : bbox regression use class agnostic simply which only represent fg and bg boxes .
is_cascade_rcnn ( bool ) : it will filter some bbox crossing the image ' s boundary when setting True.
Returns :
tuple :
A tuple with format ` ` ( rois , labels_int32 , bbox_targets , bbox_inside_weights , bbox_outside_weights ) ` ` .
- * * rois * * : 2 - D LoDTensor with shape ` ` [ batch_size_per_im * batch_size , 4 ] ` ` . The data type is the same as ` ` rpn_rois ` ` .
- * * labels_int32 * * : 2 - D LoDTensor with shape ` ` [ batch_size_per_im * batch_size , 1 ] ` ` . The data type must be int32 .
- * * bbox_targets * * : 2 - D LoDTensor with shape ` ` [ batch_size_per_im * batch_size , 4 * class_num ] ` ` . The regression targets of all RoIs . The data type is the same as ` ` rpn_rois ` ` .
- * * bbox_inside_weights * * : 2 - D LoDTensor with shape ` ` [ batch_size_per_im * batch_size , 4 * class_num ] ` ` . The weights of foreground boxes ' regression loss. The data type is the same as ``rpn_rois``.
- * * bbox_outside_weights * * : 2 - D LoDTensor with shape ` ` [ batch_size_per_im * batch_size , 4 * class_num ] ` ` . The weights of regression loss . The data type is the same as ` ` rpn_rois ` ` .
Examples :
. . code - block : : python
import paddle . fluid as fluid
rpn_rois = fluid . layers . data ( name = ' rpn_rois ' , shape = [ 2 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
gt_classes = fluid . layers . data ( name = ' gt_classes ' , shape = [ 8 , 1 ] ,
append_batch_size = False , dtype = ' float32 ' )
is_crowd = fluid . layers . data ( name = ' is_crowd ' , shape = [ 8 , 1 ] ,
append_batch_size = False , dtype = ' float32 ' )
gt_boxes = fluid . layers . data ( name = ' gt_boxes ' , shape = [ 8 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
im_info = fluid . layers . data ( name = ' im_info ' , shape = [ 10 , 3 ] ,
append_batch_size = False , dtype = ' float32 ' )
rpn_rois = fluid . data ( name = ' rpn_rois ' , shape = [ None , 4 ] , dtype = ' float32 ' )
gt_classes = fluid . data ( name = ' gt_classes ' , shape = [ None , 1 ] , dtype = ' float32 ' )
is_crowd = fluid . data ( name = ' is_crowd ' , shape = [ None , 1 ] , dtype = ' float32 ' )
gt_boxes = fluid . data ( name = ' gt_boxes ' , shape = [ None , 4 ] , dtype = ' float32 ' )
im_info = fluid . data ( name = ' im_info ' , shape = [ None , 3 ] , dtype = ' float32 ' )
rois , labels , bbox , inside_weights , outside_weights = fluid . layers . generate_proposal_labels (
rpn_rois , gt_classes , is_crowd , gt_boxes , im_info ,
class_nums = 10 )
@ -2496,44 +2497,47 @@ def generate_proposals(scores,
scores ( Variable ) : A 4 - D Tensor with shape [ N , A , H , W ] represents
the probability for each box to be an object .
N is batch size , A is number of anchors , H and W are height and
width of the feature map .
width of the feature map . The data type must be float32 .
bbox_deltas ( Variable ) : A 4 - D Tensor with shape [ N , 4 * A , H , W ]
represents the differece between predicted box locatoin and
anchor location .
anchor location . The data type must be float32 .
im_info ( Variable ) : A 2 - D Tensor with shape [ N , 3 ] represents origin
image information for N batch . Info contains height , width and scale
between origin image size and the size of feature map .
The data type must be int32 .
anchors ( Variable ) : A 4 - D Tensor represents the anchors with a layout
of [ H , W , A , 4 ] . H and W are height and width of the feature map ,
num_anchors is the box count of each position . Each anchor is
in ( xmin , ymin , xmax , ymax ) format an unnormalized .
variances ( Variable ) : expanded variances of anchors with a layout of
in ( xmin , ymin , xmax , ymax ) format an unnormalized . The data type must be float32 .
variances ( Variable ) : A 4 - D Tensor . The expanded variances of anchors with a layout of
[ H , W , num_priors , 4 ] . Each variance is in
( xcenter , ycenter , w , h ) format .
( xcenter , ycenter , w , h ) format . The data type must be float32 .
pre_nms_top_n ( float ) : Number of total bboxes to be kept per
image before NMS . 6000 by default .
image before NMS . The data type must be float32 . ` 6000 ` by default .
post_nms_top_n ( float ) : Number of total bboxes to be kept per
image after NMS . 1000 by default .
nms_thresh ( float ) : Threshold in NMS , 0.5 by default .
image after NMS . The data type must be float32 . ` 1000 ` by default .
nms_thresh ( float ) : Threshold in NMS . The data type must be float32 . ` 0.5 ` by default .
min_size ( float ) : Remove predicted boxes with either height or
width < min_size . 0.1 by default .
eta ( float ) : Apply in adaptive NMS , if adaptive threshold > 0.5 ,
adaptive_threshold = adaptive_threshold * eta in each iteration .
width < min_size . The data type must be float32 . ` 0.1 ` by default .
eta ( float ) : Apply in adaptive NMS , if adaptive ` threshold > 0.5 ` ,
` adaptive_threshold = adaptive_threshold * eta ` in each iteration .
Returns :
tuple :
A tuple with format ` ` ( rpn_rois , rpn_roi_probs ) ` ` .
- * * rpn_rois * * : The generated RoIs . 2 - D Tensor with shape ` ` [ N , 4 ] ` ` while ` ` N ` ` is the number of RoIs . The data type is the same as ` ` scores ` ` .
- * * rpn_roi_probs * * : The scores of generated RoIs . 2 - D Tensor with shape ` ` [ N , 1 ] ` ` while ` ` N ` ` is the number of RoIs . The data type is the same as ` ` scores ` ` .
Examples :
. . code - block : : python
import paddle . fluid as fluid
scores = fluid . layers . data ( name = ' scores ' , shape = [ 2 , 4 , 5 , 5 ] ,
append_batch_size = False , dtype = ' float32 ' )
bbox_deltas = fluid . layers . data ( name = ' bbox_deltas ' , shape = [ 2 , 16 , 5 , 5 ] ,
append_batch_size = False , dtype = ' float32 ' )
im_info = fluid . layers . data ( name = ' im_info ' , shape = [ 2 , 3 ] ,
append_batch_size = False , dtype = ' float32 ' )
anchors = fluid . layers . data ( name = ' anchors ' , shape = [ 5 , 5 , 4 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
variances = fluid . layers . data ( name = ' variances ' , shape = [ 5 , 5 , 10 , 4 ] ,
append_batch_size = False , dtype = ' float32 ' )
scores = fluid . data ( name = ' scores ' , shape = [ None , 4 , 5 , 5 ] , dtype = ' float32 ' )
bbox_deltas = fluid . data ( name = ' bbox_deltas ' , shape = [ None , 16 , 5 , 5 ] , dtype = ' float32 ' )
im_info = fluid . data ( name = ' im_info ' , shape = [ None , 3 ] , dtype = ' float32 ' )
anchors = fluid . data ( name = ' anchors ' , shape = [ None , 5 , 4 , 4 ] , dtype = ' float32 ' )
variances = fluid . data ( name = ' variances ' , shape = [ None , 5 , 10 , 4 ] , dtype = ' float32 ' )
rois , roi_probs = fluid . layers . generate_proposals ( scores , bbox_deltas ,
im_info , anchors , variances )
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