# Copyright (c) OpenMMLab. All rights reserved. from typing import List, Optional, Sequence, Tuple import torch import torch.nn as nn from mmcv.cnn import ConvModule, Scale from mmengine.structures import InstanceData from torch import Tensor from mmdet.registry import MODELS from mmdet.utils import (ConfigType, InstanceList, MultiConfig, 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 @MODELS.register_module() class ATSSHead(AnchorHead): """Detection Head of `ATSS `_. ATSS head structure is similar with FCOS, however ATSS use anchor boxes and assign label by Adaptive Training Sample Selection instead max-iou. Args: num_classes (int): Number of categories excluding the background category. in_channels (int): Number of channels in the input feature map. pred_kernel_size (int): Kernel size of ``nn.Conv2d`` stacked_convs (int): Number of stacking convs of the head. conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for convolution layer. Defaults to None. norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization layer. Defaults to ``dict(type='GN', num_groups=32, requires_grad=True)``. reg_decoded_bbox (bool): If true, the regression loss would be applied directly on decoded bounding boxes, converting both the predicted boxes and regression targets to absolute coordinates format. Defaults to False. It should be `True` when using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. loss_centerness (:obj:`ConfigDict` or dict): Config of centerness loss. Defaults to ``dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)``. init_cfg (:obj:`ConfigDict` or dict or list[dict] or list[:obj:`ConfigDict`]): Initialization config dict. """ def __init__(self, num_classes: int, in_channels: int, pred_kernel_size: int = 3, stacked_convs: int = 4, conv_cfg: OptConfigType = None, norm_cfg: ConfigType = dict( type='GN', num_groups=32, requires_grad=True), reg_decoded_bbox: bool = True, loss_centerness: ConfigType = dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), init_cfg: MultiConfig = dict( type='Normal', layer='Conv2d', std=0.01, override=dict( type='Normal', name='atss_cls', std=0.01, bias_prob=0.01)), **kwargs) -> None: self.pred_kernel_size = pred_kernel_size self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg super().__init__( num_classes=num_classes, in_channels=in_channels, reg_decoded_bbox=reg_decoded_bbox, init_cfg=init_cfg, **kwargs) self.sampling = False self.loss_centerness = MODELS.build(loss_centerness) 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=self.conv_cfg, norm_cfg=self.norm_cfg)) self.reg_convs.append( ConvModule( chn, self.feat_channels, 3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) pred_pad_size = self.pred_kernel_size // 2 self.atss_cls = nn.Conv2d( self.feat_channels, self.num_anchors * self.cls_out_channels, self.pred_kernel_size, padding=pred_pad_size) self.atss_reg = nn.Conv2d( self.feat_channels, self.num_base_priors * 4, self.pred_kernel_size, padding=pred_pad_size) self.atss_centerness = nn.Conv2d( self.feat_channels, self.num_base_priors * 1, self.pred_kernel_size, padding=pred_pad_size) self.scales = nn.ModuleList( [Scale(1.0) for _ in self.prior_generator.strides]) 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: Usually a tuple of classification scores and bbox prediction cls_scores (list[Tensor]): Classification scores for all scale levels, each is a 4D-tensor, the channels number is num_anchors * num_classes. bbox_preds (list[Tensor]): Box energies / deltas for all scale levels, each is a 4D-tensor, the channels number is num_anchors * 4. """ 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_anchors * num_classes. bbox_pred (Tensor): Box energies / deltas for a single scale level, the channels number is num_anchors * 4. centerness (Tensor): Centerness for a single scale level, the channel number is (N, num_anchors * 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() centerness = self.atss_centerness(reg_feat) return cls_score, bbox_pred, centerness def loss_by_feat_single(self, anchors: Tensor, cls_score: Tensor, bbox_pred: Tensor, centerness: Tensor, labels: Tensor, label_weights: Tensor, bbox_targets: Tensor, avg_factor: float) -> dict: """Calculate the loss of a single scale level based on the features extracted by the detection head. Args: cls_score (Tensor): Box scores for each scale level Has shape (N, num_anchors * num_classes, H, W). bbox_pred (Tensor): Box energies / deltas for each scale level with shape (N, num_anchors * 4, H, W). anchors (Tensor): Box reference for each scale level with shape (N, num_total_anchors, 4). 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). 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: dict[str, Tensor]: A dictionary of loss components. """ anchors = anchors.reshape(-1, 4) cls_score = cls_score.permute(0, 2, 3, 1).reshape( -1, self.cls_out_channels).contiguous() bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) centerness = centerness.permute(0, 2, 3, 1).reshape(-1) bbox_targets = bbox_targets.reshape(-1, 4) labels = labels.reshape(-1) label_weights = label_weights.reshape(-1) # classification loss loss_cls = self.loss_cls( cls_score, labels, label_weights, avg_factor=avg_factor) # 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().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_centerness = centerness[pos_inds] centerness_targets = self.centerness_target( pos_anchors, pos_bbox_targets) pos_decode_bbox_pred = self.bbox_coder.decode( pos_anchors, pos_bbox_pred) # regression loss loss_bbox = self.loss_bbox( pos_decode_bbox_pred, pos_bbox_targets, weight=centerness_targets, avg_factor=1.0) # centerness loss loss_centerness = self.loss_centerness( pos_centerness, centerness_targets, avg_factor=avg_factor) else: loss_bbox = bbox_pred.sum() * 0 loss_centerness = centerness.sum() * 0 centerness_targets = bbox_targets.new_tensor(0.) return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum() def loss_by_feat( self, cls_scores: List[Tensor], bbox_preds: List[Tensor], centernesses: 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) centernesses (list[Tensor]): Centerness for each scale level with shape (N, num_anchors * 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 bbox_preds] 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) cls_reg_targets = self.get_targets( anchor_list, valid_flag_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore=batch_gt_instances_ignore) (anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, avg_factor) = cls_reg_targets avg_factor = reduce_mean( torch.tensor(avg_factor, dtype=torch.float, device=device)).item() losses_cls, losses_bbox, loss_centerness, \ bbox_avg_factor = multi_apply( self.loss_by_feat_single, anchor_list, cls_scores, bbox_preds, centernesses, labels_list, label_weights_list, bbox_targets_list, avg_factor=avg_factor) bbox_avg_factor = sum(bbox_avg_factor) bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item() losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox)) return dict( loss_cls=losses_cls, loss_bbox=losses_bbox, loss_centerness=loss_centerness) def centerness_target(self, anchors: Tensor, gts: Tensor) -> Tensor: """Calculate the centerness between anchors and gts. Only calculate pos centerness targets, otherwise there may be nan. Args: anchors (Tensor): Anchors with shape (N, 4), "xyxy" format. gts (Tensor): Ground truth bboxes with shape (N, 4), "xyxy" format. Returns: Tensor: Centerness between anchors and gts. """ anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2 anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2 l_ = anchors_cx - gts[:, 0] t_ = anchors_cy - gts[:, 1] r_ = gts[:, 2] - anchors_cx b_ = gts[:, 3] - anchors_cy left_right = torch.stack([l_, r_], dim=1) top_bottom = torch.stack([t_, b_], dim=1) centerness = torch.sqrt( (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])) assert not torch.isnan(centerness).any() return centerness def get_targets(self, anchor_list: List[List[Tensor]], valid_flag_list: List[List[Tensor]], batch_gt_instances: InstanceList, batch_img_metas: List[dict], batch_gt_instances_ignore: OptInstanceList = None, unmap_outputs: bool = True) -> tuple: """Get targets for ATSS 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. """ 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 # 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[i] = torch.cat(anchor_list[i]) valid_flag_list[i] = torch.cat(valid_flag_list[i]) # compute targets for each image if batch_gt_instances_ignore is None: batch_gt_instances_ignore = [None] * num_imgs (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, num_level_anchors_list, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore, unmap_outputs=unmap_outputs) # 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) labels_list = images_to_levels(all_labels, num_level_anchors) label_weights_list = images_to_levels(all_label_weights, num_level_anchors) bbox_targets_list = images_to_levels(all_bbox_targets, num_level_anchors) bbox_weights_list = images_to_levels(all_bbox_weights, num_level_anchors) 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, num_level_anchors: List[int], gt_instances: InstanceData, img_meta: dict, gt_instances_ignore: Optional[InstanceData] = None, unmap_outputs: 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_anchors ,4) valid_flags (Tensor): Multi level valid flags of the image, which are concatenated into a single tensor of shape (num_anchors,). 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. Returns: tuple: N is the number of total anchors in the image. 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) pred_instances = InstanceData(priors=anchors) assign_result = self.assigner.assign(pred_instances, num_level_anchors_inside, gt_instances, gt_instances_ignore) sampling_result = self.sampler.sample(assign_result, pred_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: if self.reg_decoded_bbox: pos_bbox_targets = sampling_result.pos_gt_bboxes else: pos_bbox_targets = self.bbox_coder.encode( sampling_result.pos_priors, 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, inside_flags): """Get the number of valid anchors in every level.""" 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