Source code for mmdet.models.dense_heads.free_anchor_retina_head

import torch
import torch.nn.functional as F

from mmdet.core import bbox_overlaps
from ..builder import HEADS
from .retina_head import RetinaHead

EPS = 1e-12

[docs]@HEADS.register_module() class FreeAnchorRetinaHead(RetinaHead): """FreeAnchor RetinaHead used in Args: num_classes (int): Number of categories excluding the background category. in_channels (int): Number of channels in the input feature map. stacked_convs (int): Number of conv layers in cls and reg tower. Default: 4. conv_cfg (dict): dictionary to construct and config conv layer. Default: None. norm_cfg (dict): dictionary to construct and config norm layer. Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True). pre_anchor_topk (int): Number of boxes that be token in each bag. bbox_thr (float): The threshold of the saturated linear function. It is usually the same with the IoU threshold used in NMS. gamma (float): Gamma parameter in focal loss. alpha (float): Alpha parameter in focal loss. """ # noqa: W605 def __init__(self, num_classes, in_channels, stacked_convs=4, conv_cfg=None, norm_cfg=None, pre_anchor_topk=50, bbox_thr=0.6, gamma=2.0, alpha=0.5, **kwargs): super(FreeAnchorRetinaHead, self).__init__(num_classes, in_channels, stacked_convs, conv_cfg, norm_cfg, **kwargs) self.pre_anchor_topk = pre_anchor_topk self.bbox_thr = bbox_thr self.gamma = gamma self.alpha = alpha
[docs] def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore=None): """Compute losses of the head. Args: cls_scores (list[Tensor]): Box scores for each scale level Has shape (N, num_anchors * num_classes, H, W) bbox_preds (list[Tensor]): Box energies / deltas for each scale level with shape (N, num_anchors * 4, H, W) gt_bboxes (list[Tensor]): each item are the truth boxes for each image in [tl_x, tl_y, br_x, br_y] format. gt_labels (list[Tensor]): class indices corresponding to each box img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. gt_bboxes_ignore (None | list[Tensor]): specify which bounding boxes can be ignored when computing the loss. Returns: dict[str, Tensor]: A dictionary of loss components. """ featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == len(self.anchor_generator.base_anchors) anchor_list, _ = self.get_anchors(featmap_sizes, img_metas) anchors = [ for anchor in anchor_list] # concatenate each level cls_scores = [ cls.permute(0, 2, 3, 1).reshape(cls.size(0), -1, self.cls_out_channels) for cls in cls_scores ] bbox_preds = [ bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4) for bbox_pred in bbox_preds ] cls_scores =, dim=1) bbox_preds =, dim=1) cls_prob = torch.sigmoid(cls_scores) box_prob = [] num_pos = 0 positive_losses = [] for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_, bbox_preds_) in enumerate( zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)): with torch.no_grad(): if len(gt_bboxes_) == 0: image_box_prob = torch.zeros( anchors_.size(0), self.cls_out_channels).type_as(bbox_preds_) else: # box_localization: a_{j}^{loc}, shape: [j, 4] pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_) # object_box_iou: IoU_{ij}^{loc}, shape: [i, j] object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes) # object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j] t1 = self.bbox_thr t2 = object_box_iou.max( dim=1, keepdim=True).values.clamp(min=t1 + 1e-12) object_box_prob = ((object_box_iou - t1) / (t2 - t1)).clamp( min=0, max=1) # object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j] num_obj = gt_labels_.size(0) indices = torch.stack([ torch.arange(num_obj).type_as(gt_labels_), gt_labels_ ], dim=0) object_cls_box_prob = torch.sparse_coo_tensor( indices, object_box_prob) # image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j] """ from "start" to "end" implement: image_box_iou = torch.sparse.max(object_cls_box_prob, dim=0).t() """ # start box_cls_prob = torch.sparse.sum( object_cls_box_prob, dim=0).to_dense() indices = torch.nonzero(box_cls_prob, as_tuple=False).t_() if indices.numel() == 0: image_box_prob = torch.zeros( anchors_.size(0), self.cls_out_channels).type_as(object_box_prob) else: nonzero_box_prob = torch.where( (gt_labels_.unsqueeze(dim=-1) == indices[0]), object_box_prob[:, indices[1]], torch.tensor([ 0 ]).type_as(object_box_prob)).max(dim=0).values # upmap to shape [j, c] image_box_prob = torch.sparse_coo_tensor( indices.flip([0]), nonzero_box_prob, size=(anchors_.size(0), self.cls_out_channels)).to_dense() # end box_prob.append(image_box_prob) # construct bags for objects match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_) _, matched = torch.topk( match_quality_matrix, self.pre_anchor_topk, dim=1, sorted=False) del match_quality_matrix # matched_cls_prob: P_{ij}^{cls} matched_cls_prob = torch.gather( cls_prob_[matched], 2, gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk, 1)).squeeze(2) # matched_box_prob: P_{ij}^{loc} matched_anchors = anchors_[matched] matched_object_targets = self.bbox_coder.encode( matched_anchors, gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors)) loss_bbox = self.loss_bbox( bbox_preds_[matched], matched_object_targets, reduction_override='none').sum(-1) matched_box_prob = torch.exp(-loss_bbox) # positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )} num_pos += len(gt_bboxes_) positive_losses.append( self.positive_bag_loss(matched_cls_prob, matched_box_prob)) positive_loss = / max(1, num_pos) # box_prob: P{a_{j} \in A_{+}} box_prob = torch.stack(box_prob, dim=0) # negative_loss: # \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B|| negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max( 1, num_pos * self.pre_anchor_topk) # avoid the absence of gradients in regression subnet # when no ground-truth in a batch if num_pos == 0: positive_loss = bbox_preds.sum() * 0 losses = { 'positive_bag_loss': positive_loss, 'negative_bag_loss': negative_loss } return losses
[docs] def positive_bag_loss(self, matched_cls_prob, matched_box_prob): """Compute positive bag loss. :math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`. :math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples. :math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples. Args: matched_cls_prob (Tensor): Classification probability of matched samples in shape (num_gt, pre_anchor_topk). matched_box_prob (Tensor): BBox probability of matched samples, in shape (num_gt, pre_anchor_topk). Returns: Tensor: Positive bag loss in shape (num_gt,). """ # noqa: E501, W605 # bag_prob = Mean-max(matched_prob) matched_prob = matched_cls_prob * matched_box_prob weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None) weight /= weight.sum(dim=1).unsqueeze(dim=-1) bag_prob = (weight * matched_prob).sum(dim=1) # positive_bag_loss = -self.alpha * log(bag_prob) return self.alpha * F.binary_cross_entropy( bag_prob, torch.ones_like(bag_prob), reduction='none')
[docs] def negative_bag_loss(self, cls_prob, box_prob): """Compute negative bag loss. :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`. :math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples. :math:`P_{j}^{bg}`: Classification probability of negative samples. Args: cls_prob (Tensor): Classification probability, in shape (num_img, num_anchors, num_classes). box_prob (Tensor): Box probability, in shape (num_img, num_anchors, num_classes). Returns: Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes). """ # noqa: E501, W605 prob = cls_prob * (1 - box_prob) # There are some cases when neg_prob = 0. # This will cause the neg_prob.log() to be inf without clamp. prob = prob.clamp(min=EPS, max=1 - EPS) negative_bag_loss = prob**self.gamma * F.binary_cross_entropy( prob, torch.zeros_like(prob), reduction='none') return (1 - self.alpha) * negative_bag_loss