def make_roi_box_post_processor(cfg):
use_fpn = cfg.MODEL.ROI_HEADS.USE_FPN
bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
box_coder = BoxCoder(weights=bbox_reg_weights)
score_thresh = cfg.MODEL.ROI_HEADS.SCORE_THRESH
nms_thresh = cfg.MODEL.ROI_HEADS.NMS
detections_per_img = cfg.MODEL.ROI_HEADS.DETECTIONS_PER_IMG
cls_agnostic_bbox_reg = cfg.MODEL.CLS_AGNOSTIC_BBOX_REG
bbox_aug_enabled = cfg.TEST.BBOX_AUG.ENABLED
postprocessor = PostProcessor(score_thresh, nms_thresh, detections_per_img,
box_coder, cls_agnostic_bbox_reg,
bbox_aug_enabled)
return postprocessor
def __init__(self, cfg, in_channels):
super(RetinaNetModule, self).__init__()
self.cfg = cfg.clone()
anchor_generator = make_anchor_generator_retinanet(cfg)
head = RetinaNetHead(cfg, in_channels)
box_coder = BoxCoder(weights=(10., 10., 5., 5.))
box_selector_test = make_retinanet_postprocessor(cfg,
box_coder,
is_train=False)
loss_evaluator = make_retinanet_loss_evaluator(cfg, box_coder)
self.anchor_generator = anchor_generator
self.head = head
self.box_selector_test = box_selector_test
self.loss_evaluator = loss_evaluator
def __init__(self,
score_thresh=0.05,
nms=0.5,
detections_per_img=100,
box_coder=None,
cls_agnostic_bbox_reg=False,
bbox_aug_enabled=False):
"""
Arguments:
score_thresh (float)
nms (float)
detections_per_img (int)
box_coder (BoxCoder)
"""
super(PostProcessor, self).__init__()
self.score_thresh = score_thresh
self.nms = nms
self.detections_per_img = detections_per_img
if box_coder is None:
box_coder = BoxCoder(weights=(10., 10., 5., 5.))
self.box_coder = box_coder
self.cls_agnostic_bbox_reg = cls_agnostic_bbox_reg
self.bbox_aug_enabled = bbox_aug_enabled
def __init__(self, cfg, in_channels):
super(RPNModule, self).__init__()
self.cfg = cfg.clone()
anchor_generator = make_anchor_generator(cfg)
rpn_head = registry.RPN_HEADS[cfg.MODEL.RPN.RPN_HEAD]
head = rpn_head(
cfg, in_channels, anchor_generator.num_anchors_per_location()[0]
)
rpn_box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
box_selector_train = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=True)
box_selector_test = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=False)
loss_evaluator = make_rpn_loss_evaluator(cfg, rpn_box_coder)
self.anchor_generator = anchor_generator
self.head = head
self.box_selector_train = box_selector_train
self.box_selector_test = box_selector_test
self.loss_evaluator = loss_evaluator
def make_roi_box_loss_evaluator(cfg):
matcher = Matcher(
cfg.MODEL.ROI_HEADS.FG_IOU_THRESHOLD,
cfg.MODEL.ROI_HEADS.BG_IOU_THRESHOLD,
allow_low_quality_matches=False,
)
bbox_reg_weights = cfg.MODEL.ROI_HEADS.BBOX_REG_WEIGHTS
box_coder = BoxCoder(weights=bbox_reg_weights)
fg_bg_sampler = BalancedPositiveNegativeSampler(
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE, cfg.MODEL.ROI_HEADS.POSITIVE_FRACTION
)
cls_agnostic_bbox_reg = cfg.MODEL.CLS_AGNOSTIC_BBOX_REG
loss_evaluator = FastRCNNLossComputation(
matcher,
fg_bg_sampler,
box_coder,
cls_agnostic_bbox_reg
)
return loss_evaluator
def __init__(self, cfg, in_channels):
super(RPNWithRefModule, self).__init__(cfg, in_channels)
rpn_box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
box_selector_ref = make_rpn_postprocessor(cfg, rpn_box_coder, is_train=True, version="ref")
self.box_selector_ref = box_selector_ref