Source code for mmdet.models.dense_heads.fsaf_head

import numpy as np
import torch
from mmcv.runner import force_fp32

from mmdet.core import (anchor_inside_flags, images_to_levels, multi_apply,
from ..builder import HEADS
from ..losses.accuracy import accuracy
from ..losses.utils import weight_reduce_loss
from .retina_head import RetinaHead

[docs]@HEADS.register_module() class FSAFHead(RetinaHead): """Anchor-free head used in `FSAF <>`_. The head contains two subnetworks. The first classifies anchor boxes and the second regresses deltas for the anchors (num_anchors is 1 for anchor- free methods) Args: *args: Same as its base class in :class:`RetinaHead` score_threshold (float, optional): The score_threshold to calculate positive recall. If given, prediction scores lower than this value is counted as incorrect prediction. Default to None. init_cfg (dict or list[dict], optional): Initialization config dict. Default: None **kwargs: Same as its base class in :class:`RetinaHead` Example: >>> import torch >>> self = FSAFHead(11, 7) >>> x = torch.rand(1, 7, 32, 32) >>> cls_score, bbox_pred = self.forward_single(x) >>> # Each anchor predicts a score for each class except background >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors >>> assert cls_per_anchor == self.num_classes >>> assert box_per_anchor == 4 """ def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs): # The positive bias in self.retina_reg conv is to prevent predicted \ # bbox with 0 area if init_cfg is None: init_cfg = dict( type='Normal', layer='Conv2d', std=0.01, override=[ dict( type='Normal', name='retina_cls', std=0.01, bias_prob=0.01), dict( type='Normal', name='retina_reg', std=0.01, bias=0.25) ]) super().__init__(*args, init_cfg=init_cfg, **kwargs) self.score_threshold = score_threshold
[docs] def forward_single(self, x): """Forward feature map of a single scale level. Args: x (Tensor): Feature map of a single scale level. Returns: tuple (Tensor): cls_score (Tensor): Box scores for each scale level Has shape (N, num_points * num_classes, H, W). bbox_pred (Tensor): Box energies / deltas for each scale level with shape (N, num_points * 4, H, W). """ cls_score, bbox_pred = super().forward_single(x) # relu: TBLR encoder only accepts positive bbox_pred return cls_score, self.relu(bbox_pred)
def _get_targets_single(self, flat_anchors, valid_flags, gt_bboxes, gt_bboxes_ignore, gt_labels, img_meta, label_channels=1, unmap_outputs=True): """Compute regression and classification targets for anchors in a single image. Most of the codes are the same with the base class :obj: `AnchorHead`, except that it also collects and returns the matched gt index in the image (from 0 to num_gt-1). If the anchor bbox is not matched to any gt, the corresponding value in pos_gt_inds is -1. """ inside_flags = anchor_inside_flags(flat_anchors, valid_flags, img_meta['img_shape'][:2], self.train_cfg.allowed_border) if not inside_flags.any(): return (None, ) * 7 # Assign gt and sample anchors anchors = flat_anchors[inside_flags.type(torch.bool), :] assign_result = self.assigner.assign( anchors, gt_bboxes, gt_bboxes_ignore, None if self.sampling else gt_labels) sampling_result = self.sampler.sample(assign_result, anchors, gt_bboxes) num_valid_anchors = anchors.shape[0] bbox_targets = torch.zeros_like(anchors) bbox_weights = torch.zeros_like(anchors) labels = anchors.new_full((num_valid_anchors, ), self.num_classes, dtype=torch.long) label_weights = anchors.new_zeros((num_valid_anchors, label_channels), dtype=torch.float) pos_gt_inds = anchors.new_full((num_valid_anchors, ), -1, dtype=torch.long) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: if not self.reg_decoded_bbox: pos_bbox_targets = self.bbox_coder.encode( sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) else: # When the regression loss (e.g. `IouLoss`, `GIouLoss`) # is applied directly on the decoded bounding boxes, both # the predicted boxes and regression targets should be with # absolute coordinate format. pos_bbox_targets = sampling_result.pos_gt_bboxes bbox_targets[pos_inds, :] = pos_bbox_targets bbox_weights[pos_inds, :] = 1.0 # The assigned gt_index for each anchor. (0-based) pos_gt_inds[pos_inds] = sampling_result.pos_assigned_gt_inds if gt_labels is None: # Only rpn gives gt_labels as None # Foreground is the first class labels[pos_inds] = 0 else: labels[pos_inds] = gt_labels[ sampling_result.pos_assigned_gt_inds] if self.train_cfg.pos_weight <= 0: label_weights[pos_inds] = 1.0 else: label_weights[pos_inds] = self.train_cfg.pos_weight if len(neg_inds) > 0: label_weights[neg_inds] = 1.0 # shadowed_labels is a tensor composed of tuples # (anchor_inds, class_label) that indicate those anchors lying in the # outer region of a gt or overlapped by another gt with a smaller # area. # # Therefore, only the shadowed labels are ignored for loss calculation. # the key `shadowed_labels` is defined in :obj:`CenterRegionAssigner` shadowed_labels = assign_result.get_extra_property('shadowed_labels') if shadowed_labels is not None and shadowed_labels.numel(): if len(shadowed_labels.shape) == 2: idx_, label_ = shadowed_labels[:, 0], shadowed_labels[:, 1] assert (labels[idx_] != label_).all(), \ 'One label cannot be both positive and ignored' label_weights[idx_, label_] = 0 else: label_weights[shadowed_labels] = 0 # map up to original set of anchors if unmap_outputs: num_total_anchors = flat_anchors.size(0) labels = unmap(labels, num_total_anchors, inside_flags) label_weights = unmap(label_weights, num_total_anchors, inside_flags) bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) pos_gt_inds = unmap( pos_gt_inds, num_total_anchors, inside_flags, fill=-1) return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, neg_inds, sampling_result, pos_gt_inds)
[docs] @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore=None): """Compute loss of the head. Args: cls_scores (list[Tensor]): Box scores for each scale level Has shape (N, num_points * num_classes, H, W). bbox_preds (list[Tensor]): Box energies / deltas for each scale level with shape (N, num_points * 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. """ for i in range(len(bbox_preds)): # loop over fpn level # avoid 0 area of the predicted bbox bbox_preds[i] = bbox_preds[i].clamp(min=1e-4) # TODO: It may directly use the base-class loss function. featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == self.anchor_generator.num_levels batch_size = len(gt_bboxes) device = cls_scores[0].device anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, img_metas, device=device) label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 cls_reg_targets = self.get_targets( anchor_list, valid_flag_list, gt_bboxes, img_metas, gt_bboxes_ignore_list=gt_bboxes_ignore, gt_labels_list=gt_labels, label_channels=label_channels) if cls_reg_targets is None: return None (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg, pos_assigned_gt_inds_list) = cls_reg_targets num_gts = np.array(list(map(len, gt_labels))) num_total_samples = ( num_total_pos + num_total_neg if self.sampling else num_total_pos) # anchor number of multi levels num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] # concat all level anchors and flags to a single tensor concat_anchor_list = [] for i in range(len(anchor_list)): concat_anchor_list.append([i])) all_anchor_list = images_to_levels(concat_anchor_list, num_level_anchors) losses_cls, losses_bbox = multi_apply( self.loss_single, cls_scores, bbox_preds, all_anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_samples=num_total_samples) # `pos_assigned_gt_inds_list` (length: fpn_levels) stores the assigned # gt index of each anchor bbox in each fpn level. cum_num_gts = list(np.cumsum(num_gts)) # length of batch_size for i, assign in enumerate(pos_assigned_gt_inds_list): # loop over fpn levels for j in range(1, batch_size): # loop over batch size # Convert gt indices in each img to those in the batch assign[j][assign[j] >= 0] += int(cum_num_gts[j - 1]) pos_assigned_gt_inds_list[i] = assign.flatten() labels_list[i] = labels_list[i].flatten() num_gts = sum(map(len, gt_labels)) # total number of gt in the batch # The unique label index of each gt in the batch label_sequence = torch.arange(num_gts, device=device) # Collect the average loss of each gt in each level with torch.no_grad(): loss_levels, = multi_apply( self.collect_loss_level_single, losses_cls, losses_bbox, pos_assigned_gt_inds_list, labels_seq=label_sequence) # Shape: (fpn_levels, num_gts). Loss of each gt at each fpn level loss_levels = torch.stack(loss_levels, dim=0) # Locate the best fpn level for loss back-propagation if loss_levels.numel() == 0: # zero gt argmin = loss_levels.new_empty((num_gts, ), dtype=torch.long) else: _, argmin = loss_levels.min(dim=0) # Reweight the loss of each (anchor, label) pair, so that only those # at the best gt level are back-propagated. losses_cls, losses_bbox, pos_inds = multi_apply( self.reweight_loss_single, losses_cls, losses_bbox, pos_assigned_gt_inds_list, labels_list, list(range(len(losses_cls))), min_levels=argmin) num_pos =, 0).sum().float() pos_recall = self.calculate_pos_recall(cls_scores, labels_list, pos_inds) if num_pos == 0: # No gt avg_factor = num_pos + float(num_total_neg) else: avg_factor = num_pos for i in range(len(losses_cls)): losses_cls[i] /= avg_factor losses_bbox[i] /= avg_factor return dict( loss_cls=losses_cls, loss_bbox=losses_bbox, num_pos=num_pos / batch_size, pos_recall=pos_recall)
[docs] def calculate_pos_recall(self, cls_scores, labels_list, pos_inds): """Calculate positive recall with score threshold. Args: cls_scores (list[Tensor]): Classification scores at all fpn levels. Each tensor is in shape (N, num_classes * num_anchors, H, W) labels_list (list[Tensor]): The label that each anchor is assigned to. Shape (N * H * W * num_anchors, ) pos_inds (list[Tensor]): List of bool tensors indicating whether the anchor is assigned to a positive label. Shape (N * H * W * num_anchors, ) Returns: Tensor: A single float number indicating the positive recall. """ with torch.no_grad(): num_class = self.num_classes scores = [ cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos] for cls, pos in zip(cls_scores, pos_inds) ] labels = [ label.reshape(-1)[pos] for label, pos in zip(labels_list, pos_inds) ] scores =, dim=0) labels =, dim=0) if self.use_sigmoid_cls: scores = scores.sigmoid() else: scores = scores.softmax(dim=1) return accuracy(scores, labels, thresh=self.score_threshold)
[docs] def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds, labels_seq): """Get the average loss in each FPN level w.r.t. each gt label. Args: cls_loss (Tensor): Classification loss of each feature map pixel, shape (num_anchor, num_class) reg_loss (Tensor): Regression loss of each feature map pixel, shape (num_anchor, 4) assigned_gt_inds (Tensor): It indicates which gt the prior is assigned to (0-based, -1: no assignment). shape (num_anchor), labels_seq: The rank of labels. shape (num_gt) Returns: shape: (num_gt), average loss of each gt in this level """ if len(reg_loss.shape) == 2: # iou loss has shape (num_prior, 4) reg_loss = reg_loss.sum(dim=-1) # sum loss in tblr dims if len(cls_loss.shape) == 2: cls_loss = cls_loss.sum(dim=-1) # sum loss in class dims loss = cls_loss + reg_loss assert loss.size(0) == assigned_gt_inds.size(0) # Default loss value is 1e6 for a layer where no anchor is positive # to ensure it will not be chosen to back-propagate gradient losses_ = loss.new_full(labels_seq.shape, 1e6) for i, l in enumerate(labels_seq): match = assigned_gt_inds == l if match.any(): losses_[i] = loss[match].mean() return losses_,
[docs] def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds, labels, level, min_levels): """Reweight loss values at each level. Reassign loss values at each level by masking those where the pre-calculated loss is too large. Then return the reduced losses. Args: cls_loss (Tensor): Element-wise classification loss. Shape: (num_anchors, num_classes) reg_loss (Tensor): Element-wise regression loss. Shape: (num_anchors, 4) assigned_gt_inds (Tensor): The gt indices that each anchor bbox is assigned to. -1 denotes a negative anchor, otherwise it is the gt index (0-based). Shape: (num_anchors, ), labels (Tensor): Label assigned to anchors. Shape: (num_anchors, ). level (int): The current level index in the pyramid (0-4 for RetinaNet) min_levels (Tensor): The best-matching level for each gt. Shape: (num_gts, ), Returns: tuple: - cls_loss: Reduced corrected classification loss. Scalar. - reg_loss: Reduced corrected regression loss. Scalar. - pos_flags (Tensor): Corrected bool tensor indicating the final positive anchors. Shape: (num_anchors, ). """ loc_weight = torch.ones_like(reg_loss) cls_weight = torch.ones_like(cls_loss) pos_flags = assigned_gt_inds >= 0 # positive pixel flag pos_indices = torch.nonzero(pos_flags, as_tuple=False).flatten() if pos_flags.any(): # pos pixels exist pos_assigned_gt_inds = assigned_gt_inds[pos_flags] zeroing_indices = (min_levels[pos_assigned_gt_inds] != level) neg_indices = pos_indices[zeroing_indices] if neg_indices.numel(): pos_flags[neg_indices] = 0 loc_weight[neg_indices] = 0 # Only the weight corresponding to the label is # zeroed out if not selected zeroing_labels = labels[neg_indices] assert (zeroing_labels >= 0).all() cls_weight[neg_indices, zeroing_labels] = 0 # Weighted loss for both cls and reg loss cls_loss = weight_reduce_loss(cls_loss, cls_weight, reduction='sum') reg_loss = weight_reduce_loss(reg_loss, loc_weight, reduction='sum') return cls_loss, reg_loss, pos_flags