# Copyright (c) OpenMMLab. All rights reserved. import torch import torch.nn as nn from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm, normal_init) from mmcv.runner import force_fp32 from mmdet.core import anchor_inside_flags, multi_apply, reduce_mean, unmap from ..builder import HEADS from .anchor_head import AnchorHead INF = 1e8 def levels_to_images(mlvl_tensor): """Concat multi-level feature maps by image. [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] Convert the shape of each element in mlvl_tensor from (N, C, H, W) to (N, H*W , C), then split the element to N elements with shape (H*W, C), and concat elements in same image of all level along first dimension. Args: mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from corresponding level. Each element is of shape (N, C, H, W) Returns: list[torch.Tensor]: A list that contains N tensors and each tensor is of shape (num_elements, C) """ batch_size = mlvl_tensor[0].size(0) batch_list = [[] for _ in range(batch_size)] channels = mlvl_tensor[0].size(1) for t in mlvl_tensor: t = t.permute(0, 2, 3, 1) t = t.view(batch_size, -1, channels).contiguous() for img in range(batch_size): batch_list[img].append(t[img]) return [torch.cat(item, 0) for item in batch_list] @HEADS.register_module() class YOLOFHead(AnchorHead): """YOLOFHead Paper link: https://arxiv.org/abs/2103.09460. 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. Default 2. reg_num_convs (int): The number of convolutions of reg branch. Default 4. norm_cfg (dict): Dictionary to construct and config norm layer. """ def __init__(self, num_classes, in_channels, num_cls_convs=2, num_reg_convs=4, norm_cfg=dict(type='BN', requires_grad=True), **kwargs): self.num_cls_convs = num_cls_convs self.num_reg_convs = num_reg_convs self.norm_cfg = norm_cfg super(YOLOFHead, self).__init__(num_classes, in_channels, **kwargs) def _init_layers(self): 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): 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, feature): cls_score = self.cls_score(self.cls_subnet(feature)) N, _, H, W = cls_score.shape cls_score = cls_score.view(N, -1, self.num_classes, H, W) reg_feat = self.bbox_subnet(feature) 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 @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 losses of the head. Args: cls_scores (list[Tensor]): Box scores for each scale level Has shape (batch, num_anchors * num_classes, h, w) bbox_preds (list[Tensor]): Box energies / deltas for each scale level with shape (batch, num_anchors * 4, h, w) gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) 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. Default: None Returns: dict[str, Tensor]: 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, 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) label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 cls_reg_targets = self.get_targets( cls_scores_list, bbox_preds_list, 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 (batch_labels, batch_label_weights, num_total_pos, num_total_neg, 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) num_total_samples = (num_total_pos + num_total_neg) if self.sampling else num_total_pos num_total_samples = reduce_mean( cls_score.new_tensor(num_total_samples)).clamp_(1.0).item() # classification loss loss_cls = self.loss_cls( cls_score, flatten_labels, batch_label_weights, avg_factor=num_total_samples) # 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=num_total_samples) return dict(loss_cls=loss_cls, loss_bbox=loss_bbox) def get_targets(self, cls_scores_list, bbox_preds_list, anchor_list, valid_flag_list, gt_bboxes_list, img_metas, gt_bboxes_ignore_list=None, gt_labels_list=None, label_channels=1, unmap_outputs=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, ) gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. img_metas (list[dict]): Meta info of each image. gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be ignored. gt_labels_list (list[Tensor]): Ground truth labels of each box. label_channels (int): Channel of label. 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(img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs # compute targets for each image if gt_bboxes_ignore_list is None: gt_bboxes_ignore_list = [None for _ in range(num_imgs)] if gt_labels_list is None: gt_labels_list = [None for _ in range(num_imgs)] results = multi_apply( self._get_targets_single, bbox_preds_list, anchor_list, valid_flag_list, gt_bboxes_list, gt_bboxes_ignore_list, gt_labels_list, img_metas, label_channels=label_channels, unmap_outputs=unmap_outputs) (all_labels, all_label_weights, pos_inds_list, neg_inds_list, sampling_results_list) = results[:5] rest_results = list(results[5:]) # user-added return values # no valid anchors if any([labels is None for labels in all_labels]): return None # sampled anchors of all images num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) batch_labels = torch.stack(all_labels, 0) batch_label_weights = torch.stack(all_label_weights, 0) res = (batch_labels, batch_label_weights, num_total_pos, num_total_neg) 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, 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. 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_bboxes (Tensor): Ground truth bboxes of the image, shape (num_gts, 4). gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). img_meta (dict): Meta info of the image. gt_labels (Tensor): Ground truth labels of each box, shape (num_gts,). label_channels (int): Channel of label. 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(): return (None, ) * 8 # 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) assign_result = self.assigner.assign( decoder_bbox_preds, anchors, gt_bboxes, gt_bboxes_ignore, None if self.sampling else gt_labels) 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, anchors, gt_bboxes) 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: if gt_labels is None: # Only rpn gives gt_labels as None # Foreground is the first class since v2.5.0 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 # 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)