# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. from typing import List, Optional, Sequence, Tuple import torch import torch.nn as nn from mmcv.cnn import ConvModule, Scale from mmengine.model import bias_init_with_prob, normal_init from mmengine.structures import InstanceData from torch import Tensor from mmdet.registry import MODELS, TASK_UTILS from mmdet.structures.bbox import bbox_overlaps from mmdet.utils import (ConfigType, InstanceList, OptConfigType, OptInstanceList, reduce_mean) from ..task_modules.prior_generators import anchor_inside_flags from ..utils import images_to_levels, multi_apply, unmap from .anchor_head import AnchorHead EPS = 1e-12 @MODELS.register_module() class DDODHead(AnchorHead): """Detection Head of `DDOD `_. DDOD head decomposes conjunctions lying in most current one-stage detectors via label assignment disentanglement, spatial feature disentanglement, and pyramid supervision disentanglement. 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): The number of stacked Conv. Defaults to 4. conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for convolution layer. Defaults to None. use_dcn (bool): Use dcn, Same as ATSS when False. Defaults to True. norm_cfg (:obj:`ConfigDict` or dict): Normal config of ddod head. Defaults to dict(type='GN', num_groups=32, requires_grad=True). loss_iou (:obj:`ConfigDict` or dict): Config of IoU loss. Defaults to dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0). """ def __init__(self, num_classes: int, in_channels: int, stacked_convs: int = 4, conv_cfg: OptConfigType = None, use_dcn: bool = True, norm_cfg: ConfigType = dict( type='GN', num_groups=32, requires_grad=True), loss_iou: ConfigType = dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), **kwargs) -> None: self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.use_dcn = use_dcn super().__init__(num_classes, in_channels, **kwargs) if self.train_cfg: self.cls_assigner = TASK_UTILS.build(self.train_cfg['assigner']) self.reg_assigner = TASK_UTILS.build( self.train_cfg['reg_assigner']) self.loss_iou = MODELS.build(loss_iou) def _init_layers(self) -> None: """Initialize layers of the head.""" self.relu = nn.ReLU(inplace=True) self.cls_convs = nn.ModuleList() self.reg_convs = nn.ModuleList() for i in range(self.stacked_convs): chn = self.in_channels if i == 0 else self.feat_channels self.cls_convs.append( ConvModule( chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=dict(type='DCN', deform_groups=1) if i == 0 and self.use_dcn else self.conv_cfg, norm_cfg=self.norm_cfg)) self.reg_convs.append( ConvModule( chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=dict(type='DCN', deform_groups=1) if i == 0 and self.use_dcn else self.conv_cfg, norm_cfg=self.norm_cfg)) self.atss_cls = nn.Conv2d( self.feat_channels, self.num_base_priors * self.cls_out_channels, 3, padding=1) self.atss_reg = nn.Conv2d( self.feat_channels, self.num_base_priors * 4, 3, padding=1) self.atss_iou = nn.Conv2d( self.feat_channels, self.num_base_priors * 1, 3, padding=1) self.scales = nn.ModuleList( [Scale(1.0) for _ in self.prior_generator.strides]) # we use the global list in loss self.cls_num_pos_samples_per_level = [ 0. for _ in range(len(self.prior_generator.strides)) ] self.reg_num_pos_samples_per_level = [ 0. for _ in range(len(self.prior_generator.strides)) ] def init_weights(self) -> None: """Initialize weights of the head.""" for m in self.cls_convs: normal_init(m.conv, std=0.01) for m in self.reg_convs: normal_init(m.conv, std=0.01) normal_init(self.atss_reg, std=0.01) normal_init(self.atss_iou, std=0.01) bias_cls = bias_init_with_prob(0.01) normal_init(self.atss_cls, std=0.01, bias=bias_cls) def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]: """Forward features from the upstream network. Args: x (tuple[Tensor]): Features from the upstream network, each is a 4D-tensor. Returns: tuple: A tuple of classification scores, bbox predictions, and iou predictions. - cls_scores (list[Tensor]): Classification scores for all \ scale levels, each is a 4D-tensor, the channels number is \ num_base_priors * num_classes. - bbox_preds (list[Tensor]): Box energies / deltas for all \ scale levels, each is a 4D-tensor, the channels number is \ num_base_priors * 4. - iou_preds (list[Tensor]): IoU scores for all scale levels, \ each is a 4D-tensor, the channels number is num_base_priors * 1. """ return multi_apply(self.forward_single, x, self.scales) def forward_single(self, x: Tensor, scale: Scale) -> Sequence[Tensor]: """Forward feature of a single scale level. Args: x (Tensor): Features of a single scale level. scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize the bbox prediction. Returns: tuple: - cls_score (Tensor): Cls scores for a single scale level \ the channels number is num_base_priors * num_classes. - bbox_pred (Tensor): Box energies / deltas for a single \ scale level, the channels number is num_base_priors * 4. - iou_pred (Tensor): Iou for a single scale level, the \ channel number is (N, num_base_priors * 1, H, W). """ cls_feat = x reg_feat = x for cls_conv in self.cls_convs: cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: reg_feat = reg_conv(reg_feat) cls_score = self.atss_cls(cls_feat) # we just follow atss, not apply exp in bbox_pred bbox_pred = scale(self.atss_reg(reg_feat)).float() iou_pred = self.atss_iou(reg_feat) return cls_score, bbox_pred, iou_pred def loss_cls_by_feat_single(self, cls_score: Tensor, labels: Tensor, label_weights: Tensor, reweight_factor: List[float], avg_factor: float) -> Tuple[Tensor]: """Compute cls loss of a single scale level. Args: cls_score (Tensor): Box scores for each scale level Has shape (N, num_base_priors * num_classes, H, W). labels (Tensor): Labels of each anchors with shape (N, num_total_anchors). label_weights (Tensor): Label weights of each anchor with shape (N, num_total_anchors) reweight_factor (List[float]): Reweight factor for cls and reg loss. avg_factor (float): Average factor that is used to average the loss. When using sampling method, avg_factor is usually the sum of positive and negative priors. When using `PseudoSampler`, `avg_factor` is usually equal to the number of positive priors. Returns: Tuple[Tensor]: A tuple of loss components. """ cls_score = cls_score.permute(0, 2, 3, 1).reshape( -1, self.cls_out_channels).contiguous() labels = labels.reshape(-1) label_weights = label_weights.reshape(-1) loss_cls = self.loss_cls( cls_score, labels, label_weights, avg_factor=avg_factor) return reweight_factor * loss_cls, def loss_reg_by_feat_single(self, anchors: Tensor, bbox_pred: Tensor, iou_pred: Tensor, labels, label_weights: Tensor, bbox_targets: Tensor, bbox_weights: Tensor, reweight_factor: List[float], avg_factor: float) -> Tuple[Tensor, Tensor]: """Compute reg loss of a single scale level based on the features extracted by the detection head. Args: anchors (Tensor): Box reference for each scale level with shape (N, num_total_anchors, 4). bbox_pred (Tensor): Box energies / deltas for each scale level with shape (N, num_base_priors * 4, H, W). iou_pred (Tensor): Iou for a single scale level, the channel number is (N, num_base_priors * 1, H, W). labels (Tensor): Labels of each anchors with shape (N, num_total_anchors). label_weights (Tensor): Label weights of each anchor with shape (N, num_total_anchors) bbox_targets (Tensor): BBox regression targets of each anchor with shape (N, num_total_anchors, 4). bbox_weights (Tensor): BBox weights of all anchors in the image with shape (N, 4) reweight_factor (List[float]): Reweight factor for cls and reg loss. avg_factor (float): Average factor that is used to average the loss. When using sampling method, avg_factor is usually the sum of positive and negative priors. When using `PseudoSampler`, `avg_factor` is usually equal to the number of positive priors. Returns: Tuple[Tensor, Tensor]: A tuple of loss components. """ anchors = anchors.reshape(-1, 4) bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) iou_pred = iou_pred.permute(0, 2, 3, 1).reshape(-1, ) bbox_targets = bbox_targets.reshape(-1, 4) bbox_weights = bbox_weights.reshape(-1, 4) labels = labels.reshape(-1) label_weights = label_weights.reshape(-1) iou_targets = label_weights.new_zeros(labels.shape) iou_weights = label_weights.new_zeros(labels.shape) iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero( as_tuple=False)] = 1. # FG cat_id: [0, num_classes -1], BG cat_id: num_classes bg_class_ind = self.num_classes pos_inds = ((labels >= 0) & (labels < bg_class_ind)).nonzero(as_tuple=False).squeeze(1) if len(pos_inds) > 0: pos_bbox_targets = bbox_targets[pos_inds] pos_bbox_pred = bbox_pred[pos_inds] pos_anchors = anchors[pos_inds] pos_decode_bbox_pred = self.bbox_coder.decode( pos_anchors, pos_bbox_pred) pos_decode_bbox_targets = self.bbox_coder.decode( pos_anchors, pos_bbox_targets) # regression loss loss_bbox = self.loss_bbox( pos_decode_bbox_pred, pos_decode_bbox_targets, avg_factor=avg_factor) iou_targets[pos_inds] = bbox_overlaps( pos_decode_bbox_pred.detach(), pos_decode_bbox_targets, is_aligned=True) loss_iou = self.loss_iou( iou_pred, iou_targets, iou_weights, avg_factor=avg_factor) else: loss_bbox = bbox_pred.sum() * 0 loss_iou = iou_pred.sum() * 0 return reweight_factor * loss_bbox, reweight_factor * loss_iou def calc_reweight_factor(self, labels_list: List[Tensor]) -> List[float]: """Compute reweight_factor for regression and classification loss.""" # get pos samples for each level bg_class_ind = self.num_classes for ii, each_level_label in enumerate(labels_list): pos_inds = ((each_level_label >= 0) & (each_level_label < bg_class_ind)).nonzero( as_tuple=False).squeeze(1) self.cls_num_pos_samples_per_level[ii] += len(pos_inds) # get reweight factor from 1 ~ 2 with bilinear interpolation min_pos_samples = min(self.cls_num_pos_samples_per_level) max_pos_samples = max(self.cls_num_pos_samples_per_level) interval = 1. / (max_pos_samples - min_pos_samples + 1e-10) reweight_factor_per_level = [] for pos_samples in self.cls_num_pos_samples_per_level: factor = 2. - (pos_samples - min_pos_samples) * interval reweight_factor_per_level.append(factor) return reweight_factor_per_level def loss_by_feat( self, cls_scores: List[Tensor], bbox_preds: List[Tensor], iou_preds: List[Tensor], batch_gt_instances: InstanceList, batch_img_metas: List[dict], batch_gt_instances_ignore: OptInstanceList = None) -> dict: """Calculate the loss based on the features extracted by the detection head. Args: cls_scores (list[Tensor]): Box scores for each scale level Has shape (N, num_base_priors * num_classes, H, W) bbox_preds (list[Tensor]): Box energies / deltas for each scale level with shape (N, num_base_priors * 4, H, W) iou_preds (list[Tensor]): Score factor for all scale level, each is a 4D-tensor, has shape (batch_size, 1, H, W). batch_gt_instances (list[:obj:`InstanceData`]): Batch of gt_instance. It usually includes ``bboxes`` and ``labels`` attributes. batch_img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional): Batch of gt_instances_ignore. It includes ``bboxes`` attribute data that is ignored during training and testing. Defaults to None. Returns: dict[str, Tensor]: A dictionary of loss components. """ featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == self.prior_generator.num_levels device = cls_scores[0].device anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, batch_img_metas, device=device) # calculate common vars for cls and reg assigners at once targets_com = self.process_predictions_and_anchors( anchor_list, valid_flag_list, cls_scores, bbox_preds, batch_img_metas, batch_gt_instances_ignore) (anchor_list, valid_flag_list, num_level_anchors_list, cls_score_list, bbox_pred_list, batch_gt_instances_ignore) = targets_com # classification branch assigner cls_targets = self.get_cls_targets( anchor_list, valid_flag_list, num_level_anchors_list, cls_score_list, bbox_pred_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore=batch_gt_instances_ignore) (cls_anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, avg_factor) = cls_targets avg_factor = reduce_mean( torch.tensor(avg_factor, dtype=torch.float, device=device)).item() avg_factor = max(avg_factor, 1.0) reweight_factor_per_level = self.calc_reweight_factor(labels_list) cls_losses_cls, = multi_apply( self.loss_cls_by_feat_single, cls_scores, labels_list, label_weights_list, reweight_factor_per_level, avg_factor=avg_factor) # regression branch assigner reg_targets = self.get_reg_targets( anchor_list, valid_flag_list, num_level_anchors_list, cls_score_list, bbox_pred_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore=batch_gt_instances_ignore) (reg_anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, avg_factor) = reg_targets avg_factor = reduce_mean( torch.tensor(avg_factor, dtype=torch.float, device=device)).item() avg_factor = max(avg_factor, 1.0) reweight_factor_per_level = self.calc_reweight_factor(labels_list) reg_losses_bbox, reg_losses_iou = multi_apply( self.loss_reg_by_feat_single, reg_anchor_list, bbox_preds, iou_preds, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, reweight_factor_per_level, avg_factor=avg_factor) return dict( loss_cls=cls_losses_cls, loss_bbox=reg_losses_bbox, loss_iou=reg_losses_iou) def process_predictions_and_anchors( self, anchor_list: List[List[Tensor]], valid_flag_list: List[List[Tensor]], cls_scores: List[Tensor], bbox_preds: List[Tensor], batch_img_metas: List[dict], batch_gt_instances_ignore: OptInstanceList = None) -> tuple: """Compute common vars for regression and classification targets. Args: anchor_list (List[List[Tensor]]): anchors of each image. valid_flag_list (List[List[Tensor]]): Valid flags of each image. cls_scores (List[Tensor]): Classification scores for all scale levels, each is a 4D-tensor, the channels number is num_base_priors * num_classes. bbox_preds (list[Tensor]): Box energies / deltas for all scale levels, each is a 4D-tensor, the channels number is num_base_priors * 4. batch_img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional): Batch of gt_instances_ignore. It includes ``bboxes`` attribute data that is ignored during training and testing. Defaults to None. Return: tuple[Tensor]: A tuple of common loss vars. """ num_imgs = len(batch_img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs # anchor number of multi levels num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] num_level_anchors_list = [num_level_anchors] * num_imgs anchor_list_ = [] valid_flag_list_ = [] # concat all level anchors and flags to a single tensor for i in range(num_imgs): assert len(anchor_list[i]) == len(valid_flag_list[i]) anchor_list_.append(torch.cat(anchor_list[i])) valid_flag_list_.append(torch.cat(valid_flag_list[i])) # compute targets for each image if batch_gt_instances_ignore is None: batch_gt_instances_ignore = [None for _ in range(num_imgs)] num_levels = len(cls_scores) cls_score_list = [] bbox_pred_list = [] mlvl_cls_score_list = [ cls_score.permute(0, 2, 3, 1).reshape( num_imgs, -1, self.num_base_priors * self.cls_out_channels) for cls_score in cls_scores ] mlvl_bbox_pred_list = [ bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, self.num_base_priors * 4) for bbox_pred in bbox_preds ] for i in range(num_imgs): mlvl_cls_tensor_list = [ mlvl_cls_score_list[j][i] for j in range(num_levels) ] mlvl_bbox_tensor_list = [ mlvl_bbox_pred_list[j][i] for j in range(num_levels) ] cat_mlvl_cls_score = torch.cat(mlvl_cls_tensor_list, dim=0) cat_mlvl_bbox_pred = torch.cat(mlvl_bbox_tensor_list, dim=0) cls_score_list.append(cat_mlvl_cls_score) bbox_pred_list.append(cat_mlvl_bbox_pred) return (anchor_list_, valid_flag_list_, num_level_anchors_list, cls_score_list, bbox_pred_list, batch_gt_instances_ignore) def get_cls_targets(self, anchor_list: List[Tensor], valid_flag_list: List[Tensor], num_level_anchors_list: List[int], cls_score_list: List[Tensor], bbox_pred_list: List[Tensor], batch_gt_instances: InstanceList, batch_img_metas: List[dict], batch_gt_instances_ignore: OptInstanceList = None, unmap_outputs: bool = True) -> tuple: """Get cls targets for DDOD head. This method is almost the same as `AnchorHead.get_targets()`. Besides returning the targets as the parent method does, it also returns the anchors as the first element of the returned tuple. Args: anchor_list (list[Tensor]): anchors of each image. valid_flag_list (list[Tensor]): Valid flags of each image. num_level_anchors_list (list[Tensor]): Number of anchors of each scale level of all image. cls_score_list (list[Tensor]): Classification scores for all scale levels, each is a 4D-tensor, the channels number is num_base_priors * num_classes. bbox_pred_list (list[Tensor]): Box energies / deltas for all scale levels, each is a 4D-tensor, the channels number is num_base_priors * 4. batch_gt_instances (list[:obj:`InstanceData`]): Batch of gt_instance. It usually includes ``bboxes`` and ``labels`` attributes. batch_img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. batch_gt_instances_ignore (list[:obj:`InstanceData`], optional): Batch of gt_instances_ignore. It includes ``bboxes`` attribute data that is ignored during training and testing. Defaults to None. unmap_outputs (bool): Whether to map outputs back to the original set of anchors. Return: tuple[Tensor]: A tuple of cls targets components. """ (all_anchors, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, pos_inds_list, neg_inds_list, sampling_results_list) = multi_apply( self._get_targets_single, anchor_list, valid_flag_list, cls_score_list, bbox_pred_list, num_level_anchors_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore, unmap_outputs=unmap_outputs, is_cls_assigner=True) # Get `avg_factor` of all images, which calculate in `SamplingResult`. # When using sampling method, avg_factor is usually the sum of # positive and negative priors. When using `PseudoSampler`, # `avg_factor` is usually equal to the number of positive priors. avg_factor = sum( [results.avg_factor for results in sampling_results_list]) # split targets to a list w.r.t. multiple levels anchors_list = images_to_levels(all_anchors, num_level_anchors_list[0]) labels_list = images_to_levels(all_labels, num_level_anchors_list[0]) label_weights_list = images_to_levels(all_label_weights, num_level_anchors_list[0]) bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors_list[0]) bbox_weights_list = images_to_levels(all_bbox_weights, num_level_anchors_list[0]) return (anchors_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, avg_factor) def get_reg_targets(self, anchor_list: List[Tensor], valid_flag_list: List[Tensor], num_level_anchors_list: List[int], cls_score_list: List[Tensor], bbox_pred_list: List[Tensor], batch_gt_instances: InstanceList, batch_img_metas: List[dict], batch_gt_instances_ignore: OptInstanceList = None, unmap_outputs: bool = True) -> tuple: """Get reg targets for DDOD head. This method is almost the same as `AnchorHead.get_targets()` when is_cls_assigner is False. Besides returning the targets as the parent method does, it also returns the anchors as the first element of the returned tuple. Args: anchor_list (list[Tensor]): anchors of each image. valid_flag_list (list[Tensor]): Valid flags of each image. num_level_anchors_list (list[Tensor]): Number of anchors of each scale level of all image. cls_score_list (list[Tensor]): Classification scores for all scale levels, each is a 4D-tensor, the channels number is num_base_priors * num_classes. bbox_pred_list (list[Tensor]): Box energies / deltas for all scale levels, each is a 4D-tensor, the channels number is num_base_priors * 4. batch_gt_instances (list[:obj:`InstanceData`]): Batch of gt_instance. It usually includes ``bboxes`` and ``labels`` attributes. batch_img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. batch_gt_instances_ignore (list[:obj:`InstanceData`], optional): Batch of gt_instances_ignore. It includes ``bboxes`` attribute data that is ignored during training and testing. Defaults to None. unmap_outputs (bool): Whether to map outputs back to the original set of anchors. Return: tuple[Tensor]: A tuple of reg targets components. """ (all_anchors, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, pos_inds_list, neg_inds_list, sampling_results_list) = multi_apply( self._get_targets_single, anchor_list, valid_flag_list, cls_score_list, bbox_pred_list, num_level_anchors_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore, unmap_outputs=unmap_outputs, is_cls_assigner=False) # Get `avg_factor` of all images, which calculate in `SamplingResult`. # When using sampling method, avg_factor is usually the sum of # positive and negative priors. When using `PseudoSampler`, # `avg_factor` is usually equal to the number of positive priors. avg_factor = sum( [results.avg_factor for results in sampling_results_list]) # split targets to a list w.r.t. multiple levels anchors_list = images_to_levels(all_anchors, num_level_anchors_list[0]) labels_list = images_to_levels(all_labels, num_level_anchors_list[0]) label_weights_list = images_to_levels(all_label_weights, num_level_anchors_list[0]) bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors_list[0]) bbox_weights_list = images_to_levels(all_bbox_weights, num_level_anchors_list[0]) return (anchors_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, avg_factor) def _get_targets_single(self, flat_anchors: Tensor, valid_flags: Tensor, cls_scores: Tensor, bbox_preds: Tensor, num_level_anchors: List[int], gt_instances: InstanceData, img_meta: dict, gt_instances_ignore: Optional[InstanceData] = None, unmap_outputs: bool = True, is_cls_assigner: bool = True) -> tuple: """Compute regression, classification targets for anchors in a single image. Args: flat_anchors (Tensor): Multi-level anchors of the image, which are concatenated into a single tensor of shape (num_base_priors, 4). valid_flags (Tensor): Multi level valid flags of the image, which are concatenated into a single tensor of shape (num_base_priors,). cls_scores (Tensor): Classification scores for all scale levels of the image. bbox_preds (Tensor): Box energies / deltas for all scale levels of the image. num_level_anchors (List[int]): Number of anchors of each scale level. gt_instances (:obj:`InstanceData`): Ground truth of instance annotations. It usually includes ``bboxes`` and ``labels`` attributes. img_meta (dict): Meta information for current image. gt_instances_ignore (:obj:`InstanceData`, optional): Instances to be ignored during training. It includes ``bboxes`` attribute data that is ignored during training and testing. Defaults to None. unmap_outputs (bool): Whether to map outputs back to the original set of anchors. Defaults to True. is_cls_assigner (bool): Classification or regression. Defaults to True. Returns: tuple: N is the number of total anchors in the image. - anchors (Tensor): all anchors in the image with shape (N, 4). - labels (Tensor): Labels of all anchors in the image with \ shape (N, ). - label_weights (Tensor): Label weights of all anchor in the \ image with shape (N, ). - bbox_targets (Tensor): BBox targets of all anchors in the \ image with shape (N, 4). - bbox_weights (Tensor): BBox weights of all anchors in the \ image with shape (N, 4) - pos_inds (Tensor): Indices of positive anchor with shape \ (num_pos, ). - neg_inds (Tensor): Indices of negative anchor with shape \ (num_neg, ). - sampling_result (:obj:`SamplingResult`): Sampling results. """ inside_flags = anchor_inside_flags(flat_anchors, valid_flags, img_meta['img_shape'][:2], self.train_cfg['allowed_border']) if not inside_flags.any(): raise ValueError( 'There is no valid anchor inside the image boundary. Please ' 'check the image size and anchor sizes, or set ' '``allowed_border`` to -1 to skip the condition.') # assign gt and sample anchors anchors = flat_anchors[inside_flags, :] num_level_anchors_inside = self.get_num_level_anchors_inside( num_level_anchors, inside_flags) bbox_preds_valid = bbox_preds[inside_flags, :] cls_scores_valid = cls_scores[inside_flags, :] assigner = self.cls_assigner if is_cls_assigner else self.reg_assigner # decode prediction out of assigner bbox_preds_valid = self.bbox_coder.decode(anchors, bbox_preds_valid) pred_instances = InstanceData( priors=anchors, bboxes=bbox_preds_valid, scores=cls_scores_valid) assign_result = assigner.assign( pred_instances=pred_instances, num_level_priors=num_level_anchors_inside, gt_instances=gt_instances, gt_instances_ignore=gt_instances_ignore) sampling_result = self.sampler.sample( assign_result=assign_result, pred_instances=pred_instances, gt_instances=gt_instances) 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, dtype=torch.float) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: pos_bbox_targets = self.bbox_coder.encode( sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) bbox_targets[pos_inds, :] = pos_bbox_targets bbox_weights[pos_inds, :] = 1.0 labels[pos_inds] = sampling_result.pos_gt_labels 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 # map up to original set of anchors if unmap_outputs: num_total_anchors = flat_anchors.size(0) anchors = unmap(anchors, num_total_anchors, inside_flags) labels = unmap( labels, num_total_anchors, inside_flags, fill=self.num_classes) 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) return (anchors, labels, label_weights, bbox_targets, bbox_weights, pos_inds, neg_inds, sampling_result) def get_num_level_anchors_inside(self, num_level_anchors: List[int], inside_flags: Tensor) -> List[int]: """Get the anchors of each scale level inside. Args: num_level_anchors (list[int]): Number of anchors of each scale level. inside_flags (Tensor): Multi level inside flags of the image, which are concatenated into a single tensor of shape (num_base_priors,). Returns: list[int]: Number of anchors of each scale level inside. """ split_inside_flags = torch.split(inside_flags, num_level_anchors) num_level_anchors_inside = [ int(flags.sum()) for flags in split_inside_flags ] return num_level_anchors_inside