# Copyright (c) OpenMMLab. All rights reserved. from typing import List, Optional, Tuple import torch import torch.nn as nn from mmcv.cnn import ConvModule, is_norm from mmengine.model import bias_init_with_prob, constant_init, normal_init from mmengine.structures import InstanceData from torch import Tensor from mmdet.registry import MODELS from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean from ..task_modules.prior_generators import anchor_inside_flags from ..utils import levels_to_images, multi_apply, unmap from .anchor_head import AnchorHead INF = 1e8 @MODELS.register_module() class YOLOFHead(AnchorHead): """Detection Head of `YOLOF `_ Args: num_classes (int): The number of object classes (w/o background) in_channels (list[int]): The number of input channels per scale. cls_num_convs (int): The number of convolutions of cls branch. Defaults to 2. reg_num_convs (int): The number of convolutions of reg branch. Defaults to 4. norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization layer. Defaults to ``dict(type='BN', requires_grad=True)``. """ def __init__(self, num_classes: int, in_channels: List[int], num_cls_convs: int = 2, num_reg_convs: int = 4, norm_cfg: ConfigType = dict(type='BN', requires_grad=True), **kwargs) -> None: self.num_cls_convs = num_cls_convs self.num_reg_convs = num_reg_convs self.norm_cfg = norm_cfg super().__init__( num_classes=num_classes, in_channels=in_channels, **kwargs) def _init_layers(self) -> None: cls_subnet = [] bbox_subnet = [] for i in range(self.num_cls_convs): cls_subnet.append( ConvModule( self.in_channels, self.in_channels, kernel_size=3, padding=1, norm_cfg=self.norm_cfg)) for i in range(self.num_reg_convs): bbox_subnet.append( ConvModule( self.in_channels, self.in_channels, kernel_size=3, padding=1, norm_cfg=self.norm_cfg)) self.cls_subnet = nn.Sequential(*cls_subnet) self.bbox_subnet = nn.Sequential(*bbox_subnet) self.cls_score = nn.Conv2d( self.in_channels, self.num_base_priors * self.num_classes, kernel_size=3, stride=1, padding=1) self.bbox_pred = nn.Conv2d( self.in_channels, self.num_base_priors * 4, kernel_size=3, stride=1, padding=1) self.object_pred = nn.Conv2d( self.in_channels, self.num_base_priors, kernel_size=3, stride=1, padding=1) def init_weights(self) -> None: for m in self.modules(): if isinstance(m, nn.Conv2d): normal_init(m, mean=0, std=0.01) if is_norm(m): constant_init(m, 1) # Use prior in model initialization to improve stability bias_cls = bias_init_with_prob(0.01) torch.nn.init.constant_(self.cls_score.bias, bias_cls) def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]: """Forward feature of a single scale level. Args: x (Tensor): Features of a single scale level. Returns: tuple: normalized_cls_score (Tensor): Normalized Cls scores for a \ single scale level, the channels number is \ num_base_priors * num_classes. bbox_reg (Tensor): Box energies / deltas for a single scale \ level, the channels number is num_base_priors * 4. """ cls_score = self.cls_score(self.cls_subnet(x)) N, _, H, W = cls_score.shape cls_score = cls_score.view(N, -1, self.num_classes, H, W) reg_feat = self.bbox_subnet(x) bbox_reg = self.bbox_pred(reg_feat) objectness = self.object_pred(reg_feat) # implicit objectness objectness = objectness.view(N, -1, 1, H, W) normalized_cls_score = cls_score + objectness - torch.log( 1. + torch.clamp(cls_score.exp(), max=INF) + torch.clamp(objectness.exp(), max=INF)) normalized_cls_score = normalized_cls_score.view(N, -1, H, W) return normalized_cls_score, bbox_reg def loss_by_feat( self, cls_scores: List[Tensor], bbox_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_anchors * num_classes, H, W). bbox_preds (list[Tensor]): Box energies / deltas for each scale level with shape (N, num_anchors * 4, 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: A dictionary of loss components. """ assert len(cls_scores) == 1 assert self.prior_generator.num_levels == 1 device = cls_scores[0].device featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, batch_img_metas, device=device) # The output level is always 1 anchor_list = [anchors[0] for anchors in anchor_list] valid_flag_list = [valid_flags[0] for valid_flags in valid_flag_list] cls_scores_list = levels_to_images(cls_scores) bbox_preds_list = levels_to_images(bbox_preds) cls_reg_targets = self.get_targets( cls_scores_list, bbox_preds_list, anchor_list, valid_flag_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore=batch_gt_instances_ignore) if cls_reg_targets is None: return None (batch_labels, batch_label_weights, avg_factor, batch_bbox_weights, batch_pos_predicted_boxes, batch_target_boxes) = cls_reg_targets flatten_labels = batch_labels.reshape(-1) batch_label_weights = batch_label_weights.reshape(-1) cls_score = cls_scores[0].permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) avg_factor = reduce_mean( torch.tensor(avg_factor, dtype=torch.float, device=device)).item() # classification loss loss_cls = self.loss_cls( cls_score, flatten_labels, batch_label_weights, avg_factor=avg_factor) # regression loss if batch_pos_predicted_boxes.shape[0] == 0: # no pos sample loss_bbox = batch_pos_predicted_boxes.sum() * 0 else: loss_bbox = self.loss_bbox( batch_pos_predicted_boxes, batch_target_boxes, batch_bbox_weights.float(), avg_factor=avg_factor) return dict(loss_cls=loss_cls, loss_bbox=loss_bbox) def get_targets(self, cls_scores_list: List[Tensor], bbox_preds_list: List[Tensor], anchor_list: List[Tensor], valid_flag_list: List[Tensor], batch_gt_instances: InstanceList, batch_img_metas: List[dict], batch_gt_instances_ignore: OptInstanceList = None, unmap_outputs: bool = True): """Compute regression and classification targets for anchors in multiple images. Args: cls_scores_list (list[Tensor])： Classification scores of each image. each is a 4D-tensor, the shape is (h * w, num_anchors * num_classes). bbox_preds_list (list[Tensor])： Bbox preds of each image. each is a 4D-tensor, the shape is (h * w, num_anchors * 4). anchor_list (list[Tensor]): Anchors of each image. Each element of is a tensor of shape (h * w * num_anchors, 4). valid_flag_list (list[Tensor]): Valid flags of each image. Each element of is a tensor of shape (h * w * num_anchors, ) 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. Returns: tuple: Usually returns a tuple containing learning targets. - batch_labels (Tensor): Label of all images. Each element \ of is a tensor of shape (batch, h * w * num_anchors) - batch_label_weights (Tensor): Label weights of all images \ of is a tensor of shape (batch, h * w * num_anchors) - num_total_pos (int): Number of positive samples in all \ images. - num_total_neg (int): Number of negative samples in all \ images. additional_returns: This function enables user-defined returns from `self._get_targets_single`. These returns are currently refined to properties at each feature map (i.e. having HxW dimension). The results will be concatenated after the end """ num_imgs = len(batch_img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs # compute targets for each image if batch_gt_instances_ignore is None: batch_gt_instances_ignore = [None] * num_imgs results = multi_apply( self._get_targets_single, bbox_preds_list, anchor_list, valid_flag_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore, unmap_outputs=unmap_outputs) (all_labels, all_label_weights, pos_inds, neg_inds, sampling_results_list) = results[:5] # 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]) rest_results = list(results[5:]) # user-added return values batch_labels = torch.stack(all_labels, 0) batch_label_weights = torch.stack(all_label_weights, 0) res = (batch_labels, batch_label_weights, avg_factor) for i, rests in enumerate(rest_results): # user-added return values rest_results[i] = torch.cat(rests, 0) return res + tuple(rest_results) def _get_targets_single(self, bbox_preds: Tensor, flat_anchors: Tensor, valid_flags: Tensor, gt_instances: InstanceData, img_meta: dict, gt_instances_ignore: Optional[InstanceData] = None, unmap_outputs: bool = True) -> tuple: """Compute regression and classification targets for anchors in a single image. Args: bbox_preds (Tensor): Bbox prediction of the image, which shape is (h * w ,4) flat_anchors (Tensor): Anchors of the image, which shape is (h * w * num_anchors ,4) valid_flags (Tensor): Valid flags of the image, which shape is (h * w * num_anchors,). gt_instances (:obj:`InstanceData`): Ground truth of instance annotations. It should 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. Returns: tuple: labels (Tensor): Labels of image, which shape is (h * w * num_anchors, ). label_weights (Tensor): Label weights of image, which shape is (h * w * num_anchors, ). pos_inds (Tensor): Pos index of image. neg_inds (Tensor): Neg index of image. sampling_result (obj:`SamplingResult`): Sampling result. pos_bbox_weights (Tensor): The Weight of using to calculate the bbox branch loss, which shape is (num, ). pos_predicted_boxes (Tensor): boxes predicted value of using to calculate the bbox branch loss, which shape is (num, 4). pos_target_boxes (Tensor): boxes target value of using to calculate the bbox branch loss, which shape is (num, 4). """ 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, :] bbox_preds = bbox_preds.reshape(-1, 4) bbox_preds = bbox_preds[inside_flags, :] # decoded bbox decoder_bbox_preds = self.bbox_coder.decode(anchors, bbox_preds) pred_instances = InstanceData( priors=anchors, decoder_priors=decoder_bbox_preds) assign_result = self.assigner.assign(pred_instances, gt_instances, gt_instances_ignore) pos_bbox_weights = assign_result.get_extra_property('pos_idx') pos_predicted_boxes = assign_result.get_extra_property( 'pos_predicted_boxes') pos_target_boxes = assign_result.get_extra_property('target_boxes') sampling_result = self.sampler.sample(assign_result, pred_instances, gt_instances) num_valid_anchors = anchors.shape[0] 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: 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) labels = unmap( labels, num_total_anchors, inside_flags, fill=self.num_classes) # fill bg label label_weights = unmap(label_weights, num_total_anchors, inside_flags) return (labels, label_weights, pos_inds, neg_inds, sampling_result, pos_bbox_weights, pos_predicted_boxes, pos_target_boxes)