def __init__(self, anchor_generator, head,
fg_iou_thresh, bg_iou_thresh,
batch_size_per_image, positive_fraction,
pre_nms_top_n, post_nms_top_n, nms_thresh):
super(RegionProposalNetwork, self).__init__()
self.anchor_generator = anchor_generator
self.head = head
self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
# use during training
# 计算anchors与真实bbox的iou
self.box_similarity = box_ops.box_iou
self.proposal_matcher = det_utils.Matcher(
fg_iou_thresh, # 当iou大于fg_iou_thresh(0.7)时视为正样本
bg_iou_thresh, # 当iou小于bg_iou_thresh(0.3)时视为负样本
allow_low_quality_matches=True
)
self.fg_bg_sampler = det_utils.BalancedPositiveNegativeSampler(
batch_size_per_image, positive_fraction # 256, 0.5
)
# use during testing
self._pre_nms_top_n = pre_nms_top_n
self._post_nms_top_n = post_nms_top_n
self.nms_thresh = nms_thresh
self.min_size = 1.
def __init__(
self,
box_roi_pool,
box_head,
box_predictor,
# Faster R-CNN training
fg_iou_thresh,
bg_iou_thresh,
batch_size_per_image,
positive_fraction,
bbox_reg_weights,
# Faster R-CNN inference
score_thresh,
nms_thresh,
detection_per_img):
super(RoIHeads, self).__init__()
self.box_similarity = box_ops.box_iou
# assign ground-truth boxes for each proposal
self.proposal_matcher = det_utils.Matcher(
fg_iou_thresh, # 0.5
bg_iou_thresh, # 0.5
allow_low_quality_matches=False)
self.fg_bg_sampler = det_utils.BalancedPositiveNegativeSampler(
batch_size_per_image, # 512
positive_fraction) # 0.25
if bbox_reg_weights is None:
bbox_reg_weights = (10., 10., 5., 5.)
self.box_coder = det_utils.BoxCoder(bbox_reg_weights)
self.box_roi_pool = box_roi_pool
self.box_head = box_head
self.box_predictor = box_predictor
self.score_thresh = score_thresh
self.nms_thresh = nms_thresh
self.detection_per_img = detection_per_img
def __init__(
self,
box_roi_pool, # Multi-scale RoIAlign pooling
box_head, # TwoMLPHead
box_predictor, # FastRCNNPredictor
# Faster R-CNN training
fg_iou_thresh,
bg_iou_thresh, # default: 0.5, 0.5
batch_size_per_image,
positive_fraction, # default: 512, 0.25
bbox_reg_weights, # None
# Faster R-CNN inference
score_thresh, # default: 0.05
nms_thresh, # default: 0.5
detection_per_img): # default: 100
super(RoIHeads, self).__init__()
self.box_similarity = box_ops.box_iou
# assign ground-truth boxes for each proposal
self.proposal_matcher = det_utils.Matcher(
fg_iou_thresh, # default: 0.5
bg_iou_thresh, # default: 0.5
allow_low_quality_matches=False)
self.fg_bg_sampler = det_utils.BalancedPositiveNegativeSampler(
batch_size_per_image, # default: 512
positive_fraction) # default: 0.25
if bbox_reg_weights is None:
bbox_reg_weights = (10., 10., 5., 5.)
self.box_coder = det_utils.BoxCoder(bbox_reg_weights)
self.box_roi_pool = box_roi_pool # Multi-scale RoIAlign pooling
self.box_head = box_head # TwoMLPHead
self.box_predictor = box_predictor # FastRCNNPredictor
self.score_thresh = score_thresh # default: 0.05
self.nms_thresh = nms_thresh # default: 0.5
self.detection_per_img = detection_per_img # default: 100
