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PaddleRS/paddlers/models/ppdet/modeling/proposal_generator/target_layer.py

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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import paddle
from paddlers.models.ppdet.core.workspace import register, serializable
from .target import rpn_anchor_target, generate_proposal_target, generate_mask_target, libra_generate_proposal_target
import numpy as np
@register
@serializable
class RPNTargetAssign(object):
__shared__ = ['assign_on_cpu']
"""
RPN targets assignment module
The assignment consists of three steps:
1. Match anchor and ground-truth box, label the anchor with foreground
or background sample
2. Sample anchors to keep the properly ratio between foreground and
background
3. Generate the targets for classification and regression branch
Args:
batch_size_per_im (int): Total number of RPN samples per image.
default 256
fg_fraction (float): Fraction of anchors that is labeled
foreground, default 0.5
positive_overlap (float): Minimum overlap required between an anchor
and ground-truth box for the (anchor, gt box) pair to be
a foreground sample. default 0.7
negative_overlap (float): Maximum overlap allowed between an anchor
and ground-truth box for the (anchor, gt box) pair to be
a background sample. default 0.3
ignore_thresh(float): Threshold for ignoring the is_crowd ground-truth
if the value is larger than zero.
use_random (bool): Use random sampling to choose foreground and
background boxes, default true.
assign_on_cpu (bool): In case the number of gt box is too large,
compute IoU on CPU, default false.
"""
def __init__(self,
batch_size_per_im=256,
fg_fraction=0.5,
positive_overlap=0.7,
negative_overlap=0.3,
ignore_thresh=-1.,
use_random=True,
assign_on_cpu=False):
super(RPNTargetAssign, self).__init__()
self.batch_size_per_im = batch_size_per_im
self.fg_fraction = fg_fraction
self.positive_overlap = positive_overlap
self.negative_overlap = negative_overlap
self.ignore_thresh = ignore_thresh
self.use_random = use_random
self.assign_on_cpu = assign_on_cpu
def __call__(self, inputs, anchors):
"""
inputs: ground-truth instances.
anchor_box (Tensor): [num_anchors, 4], num_anchors are all anchors in all feature maps.
"""
gt_boxes = inputs['gt_bbox']
is_crowd = inputs.get('is_crowd', None)
batch_size = len(gt_boxes)
tgt_labels, tgt_bboxes, tgt_deltas = rpn_anchor_target(
anchors,
gt_boxes,
self.batch_size_per_im,
self.positive_overlap,
self.negative_overlap,
self.fg_fraction,
self.use_random,
batch_size,
self.ignore_thresh,
is_crowd,
assign_on_cpu=self.assign_on_cpu)
norm = self.batch_size_per_im * batch_size
return tgt_labels, tgt_bboxes, tgt_deltas, norm
@register
class BBoxAssigner(object):
__shared__ = ['num_classes', 'assign_on_cpu']
"""
RCNN targets assignment module
The assignment consists of three steps:
1. Match RoIs and ground-truth box, label the RoIs with foreground
or background sample
2. Sample anchors to keep the properly ratio between foreground and
background
3. Generate the targets for classification and regression branch
Args:
batch_size_per_im (int): Total number of RoIs per image.
default 512
fg_fraction (float): Fraction of RoIs that is labeled
foreground, default 0.25
fg_thresh (float): Minimum overlap required between a RoI
and ground-truth box for the (roi, gt box) pair to be
a foreground sample. default 0.5
bg_thresh (float): Maximum overlap allowed between a RoI
and ground-truth box for the (roi, gt box) pair to be
a background sample. default 0.5
ignore_thresh(float): Threshold for ignoring the is_crowd ground-truth
if the value is larger than zero.
use_random (bool): Use random sampling to choose foreground and
background boxes, default true
cascade_iou (list[iou]): The list of overlap to select foreground and
background of each stage, which is only used In Cascade RCNN.
num_classes (int): The number of class.
assign_on_cpu (bool): In case the number of gt box is too large,
compute IoU on CPU, default false.
"""
def __init__(self,
batch_size_per_im=512,
fg_fraction=.25,
fg_thresh=.5,
bg_thresh=.5,
ignore_thresh=-1.,
use_random=True,
cascade_iou=[0.5, 0.6, 0.7],
num_classes=80,
assign_on_cpu=False):
super(BBoxAssigner, self).__init__()
self.batch_size_per_im = batch_size_per_im
self.fg_fraction = fg_fraction
self.fg_thresh = fg_thresh
self.bg_thresh = bg_thresh
self.ignore_thresh = ignore_thresh
self.use_random = use_random
self.cascade_iou = cascade_iou
self.num_classes = num_classes
self.assign_on_cpu = assign_on_cpu
def __call__(self,
rpn_rois,
rpn_rois_num,
inputs,
stage=0,
is_cascade=False):
gt_classes = inputs['gt_class']
gt_boxes = inputs['gt_bbox']
is_crowd = inputs.get('is_crowd', None)
# rois, tgt_labels, tgt_bboxes, tgt_gt_inds
# new_rois_num
outs = generate_proposal_target(
rpn_rois, gt_classes, gt_boxes, self.batch_size_per_im,
self.fg_fraction, self.fg_thresh, self.bg_thresh, self.num_classes,
self.ignore_thresh, is_crowd, self.use_random, is_cascade,
self.cascade_iou[stage], self.assign_on_cpu)
rois = outs[0]
rois_num = outs[-1]
# tgt_labels, tgt_bboxes, tgt_gt_inds
targets = outs[1:4]
return rois, rois_num, targets
@register
class BBoxLibraAssigner(object):
__shared__ = ['num_classes']
"""
Libra-RCNN targets assignment module
The assignment consists of three steps:
1. Match RoIs and ground-truth box, label the RoIs with foreground
or background sample
2. Sample anchors to keep the properly ratio between foreground and
background
3. Generate the targets for classification and regression branch
Args:
batch_size_per_im (int): Total number of RoIs per image.
default 512
fg_fraction (float): Fraction of RoIs that is labeled
foreground, default 0.25
fg_thresh (float): Minimum overlap required between a RoI
and ground-truth box for the (roi, gt box) pair to be
a foreground sample. default 0.5
bg_thresh (float): Maximum overlap allowed between a RoI
and ground-truth box for the (roi, gt box) pair to be
a background sample. default 0.5
use_random (bool): Use random sampling to choose foreground and
background boxes, default true
cascade_iou (list[iou]): The list of overlap to select foreground and
background of each stage, which is only used In Cascade RCNN.
num_classes (int): The number of class.
num_bins (int): The number of libra_sample.
"""
def __init__(self,
batch_size_per_im=512,
fg_fraction=.25,
fg_thresh=.5,
bg_thresh=.5,
use_random=True,
cascade_iou=[0.5, 0.6, 0.7],
num_classes=80,
num_bins=3):
super(BBoxLibraAssigner, self).__init__()
self.batch_size_per_im = batch_size_per_im
self.fg_fraction = fg_fraction
self.fg_thresh = fg_thresh
self.bg_thresh = bg_thresh
self.use_random = use_random
self.cascade_iou = cascade_iou
self.num_classes = num_classes
self.num_bins = num_bins
def __call__(self,
rpn_rois,
rpn_rois_num,
inputs,
stage=0,
is_cascade=False):
gt_classes = inputs['gt_class']
gt_boxes = inputs['gt_bbox']
# rois, tgt_labels, tgt_bboxes, tgt_gt_inds
outs = libra_generate_proposal_target(
rpn_rois, gt_classes, gt_boxes, self.batch_size_per_im,
self.fg_fraction, self.fg_thresh, self.bg_thresh, self.num_classes,
self.use_random, is_cascade, self.cascade_iou[stage], self.num_bins)
rois = outs[0]
rois_num = outs[-1]
# tgt_labels, tgt_bboxes, tgt_gt_inds
targets = outs[1:4]
return rois, rois_num, targets
@register
@serializable
class MaskAssigner(object):
__shared__ = ['num_classes', 'mask_resolution']
"""
Mask targets assignment module
The assignment consists of three steps:
1. Select RoIs labels with foreground.
2. Encode the RoIs and corresponding gt polygons to generate
mask target
Args:
num_classes (int): The number of class
mask_resolution (int): The resolution of mask target, default 14
"""
def __init__(self, num_classes=80, mask_resolution=14):
super(MaskAssigner, self).__init__()
self.num_classes = num_classes
self.mask_resolution = mask_resolution
def __call__(self, rois, tgt_labels, tgt_gt_inds, inputs):
gt_segms = inputs['gt_poly']
outs = generate_mask_target(gt_segms, rois, tgt_labels, tgt_gt_inds,
self.num_classes, self.mask_resolution)
# mask_rois, mask_rois_num, tgt_classes, tgt_masks, mask_index, tgt_weights
return outs
@register
class RBoxAssigner(object):
"""
assigner of rbox
Args:
pos_iou_thr (float): threshold of pos samples
neg_iou_thr (float): threshold of neg samples
min_iou_thr (float): the min threshold of samples
ignore_iof_thr (int): the ignored threshold
"""
def __init__(self,
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_iou_thr=0.0,
ignore_iof_thr=-2):
super(RBoxAssigner, self).__init__()
self.pos_iou_thr = pos_iou_thr
self.neg_iou_thr = neg_iou_thr
self.min_iou_thr = min_iou_thr
self.ignore_iof_thr = ignore_iof_thr
def anchor_valid(self, anchors):
"""
Args:
anchor: M x 4
Returns:
"""
if anchors.ndim == 3:
anchors = anchors.reshape(-1, anchors.shape[-1])
assert anchors.ndim == 2
anchor_num = anchors.shape[0]
anchor_valid = np.ones((anchor_num), np.int32)
anchor_inds = np.arange(anchor_num)
return anchor_inds
def rbox2delta(self,
proposals,
gt,
means=[0, 0, 0, 0, 0],
stds=[1, 1, 1, 1, 1]):
"""
Args:
proposals: tensor [N, 5]
gt: gt [N, 5]
means: means [5]
stds: stds [5]
Returns:
"""
proposals = proposals.astype(np.float64)
PI = np.pi
gt_widths = gt[..., 2]
gt_heights = gt[..., 3]
gt_angle = gt[..., 4]
proposals_widths = proposals[..., 2]
proposals_heights = proposals[..., 3]
proposals_angle = proposals[..., 4]
coord = gt[..., 0:2] - proposals[..., 0:2]
dx = (np.cos(proposals[..., 4]) * coord[..., 0] +
np.sin(proposals[..., 4]) * coord[..., 1]) / proposals_widths
dy = (-np.sin(proposals[..., 4]) * coord[..., 0] +
np.cos(proposals[..., 4]) * coord[..., 1]) / proposals_heights
dw = np.log(gt_widths / proposals_widths)
dh = np.log(gt_heights / proposals_heights)
da = (gt_angle - proposals_angle)
da = (da + PI / 4) % PI - PI / 4
da /= PI
deltas = np.stack([dx, dy, dw, dh, da], axis=-1)
means = np.array(means, dtype=deltas.dtype)
stds = np.array(stds, dtype=deltas.dtype)
deltas = (deltas - means) / stds
deltas = deltas.astype(np.float32)
return deltas
def assign_anchor(self,
anchors,
gt_bboxes,
gt_lables,
pos_iou_thr,
neg_iou_thr,
min_iou_thr=0.0,
ignore_iof_thr=-2):
"""
Args:
anchors:
gt_bboxes:[M, 5] rc,yc,w,h,angle
gt_lables:
Returns:
"""
assert anchors.shape[1] == 4 or anchors.shape[1] == 5
assert gt_bboxes.shape[1] == 4 or gt_bboxes.shape[1] == 5
anchors_xc_yc = anchors
gt_bboxes_xc_yc = gt_bboxes
# calc rbox iou
anchors_xc_yc = anchors_xc_yc.astype(np.float32)
gt_bboxes_xc_yc = gt_bboxes_xc_yc.astype(np.float32)
anchors_xc_yc = paddle.to_tensor(anchors_xc_yc)
gt_bboxes_xc_yc = paddle.to_tensor(gt_bboxes_xc_yc)
try:
from rbox_iou_ops import rbox_iou
except Exception as e:
print("import custom_ops error, try install rbox_iou_ops " \
"following ppdet/ext_op/README.md", e)
sys.stdout.flush()
sys.exit(-1)
iou = rbox_iou(gt_bboxes_xc_yc, anchors_xc_yc)
iou = iou.numpy()
iou = iou.T
# every gt's anchor's index
gt_bbox_anchor_inds = iou.argmax(axis=0)
gt_bbox_anchor_iou = iou[gt_bbox_anchor_inds, np.arange(iou.shape[1])]
gt_bbox_anchor_iou_inds = np.where(iou == gt_bbox_anchor_iou)[0]
# every anchor's gt bbox's index
anchor_gt_bbox_inds = iou.argmax(axis=1)
anchor_gt_bbox_iou = iou[np.arange(iou.shape[0]), anchor_gt_bbox_inds]
# (1) set labels=-2 as default
labels = np.ones((iou.shape[0], ), dtype=np.int32) * ignore_iof_thr
# (2) assign ignore
labels[anchor_gt_bbox_iou < min_iou_thr] = ignore_iof_thr
# (3) assign neg_ids -1
assign_neg_ids1 = anchor_gt_bbox_iou >= min_iou_thr
assign_neg_ids2 = anchor_gt_bbox_iou < neg_iou_thr
assign_neg_ids = np.logical_and(assign_neg_ids1, assign_neg_ids2)
labels[assign_neg_ids] = -1
# anchor_gt_bbox_iou_inds
# (4) assign max_iou as pos_ids >=0
anchor_gt_bbox_iou_inds = anchor_gt_bbox_inds[gt_bbox_anchor_iou_inds]
# gt_bbox_anchor_iou_inds = np.logical_and(gt_bbox_anchor_iou_inds, anchor_gt_bbox_iou >= min_iou_thr)
labels[gt_bbox_anchor_iou_inds] = gt_lables[anchor_gt_bbox_iou_inds]
# (5) assign >= pos_iou_thr as pos_ids
iou_pos_iou_thr_ids = anchor_gt_bbox_iou >= pos_iou_thr
iou_pos_iou_thr_ids_box_inds = anchor_gt_bbox_inds[iou_pos_iou_thr_ids]
labels[iou_pos_iou_thr_ids] = gt_lables[iou_pos_iou_thr_ids_box_inds]
return anchor_gt_bbox_inds, anchor_gt_bbox_iou, labels
def __call__(self, anchors, gt_bboxes, gt_labels, is_crowd):
assert anchors.ndim == 2
assert anchors.shape[1] == 5
assert gt_bboxes.ndim == 2
assert gt_bboxes.shape[1] == 5
pos_iou_thr = self.pos_iou_thr
neg_iou_thr = self.neg_iou_thr
min_iou_thr = self.min_iou_thr
ignore_iof_thr = self.ignore_iof_thr
anchor_num = anchors.shape[0]
gt_bboxes = gt_bboxes
is_crowd_slice = is_crowd
not_crowd_inds = np.where(is_crowd_slice == 0)
# Step1: match anchor and gt_bbox
anchor_gt_bbox_inds, anchor_gt_bbox_iou, labels = self.assign_anchor(
anchors, gt_bboxes,
gt_labels.reshape(-1), pos_iou_thr, neg_iou_thr, min_iou_thr,
ignore_iof_thr)
# Step2: sample anchor
pos_inds = np.where(labels >= 0)[0]
neg_inds = np.where(labels == -1)[0]
# Step3: make output
anchors_num = anchors.shape[0]
bbox_targets = np.zeros_like(anchors)
bbox_weights = np.zeros_like(anchors)
bbox_gt_bboxes = np.zeros_like(anchors)
pos_labels = np.zeros(anchors_num, dtype=np.int32)
pos_labels_weights = np.zeros(anchors_num, dtype=np.float32)
pos_sampled_anchors = anchors[pos_inds]
pos_sampled_gt_boxes = gt_bboxes[anchor_gt_bbox_inds[pos_inds]]
if len(pos_inds) > 0:
pos_bbox_targets = self.rbox2delta(pos_sampled_anchors,
pos_sampled_gt_boxes)
bbox_targets[pos_inds, :] = pos_bbox_targets
bbox_gt_bboxes[pos_inds, :] = pos_sampled_gt_boxes
bbox_weights[pos_inds, :] = 1.0
pos_labels[pos_inds] = labels[pos_inds]
pos_labels_weights[pos_inds] = 1.0
if len(neg_inds) > 0:
pos_labels_weights[neg_inds] = 1.0
return (pos_labels, pos_labels_weights, bbox_targets, bbox_weights,
bbox_gt_bboxes, pos_inds, neg_inds)