# Copyright (c) OpenMMLab. All rights reserved. import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import ConvModule from mmcv.runner import BaseModule, force_fp32 from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms from mmdet.models.builder import HEADS, build_loss from mmdet.models.losses import accuracy @HEADS.register_module() class SABLHead(BaseModule): """Side-Aware Boundary Localization (SABL) for RoI-Head. Side-Aware features are extracted by conv layers with an attention mechanism. Boundary Localization with Bucketing and Bucketing Guided Rescoring are implemented in BucketingBBoxCoder. Please refer to https://arxiv.org/abs/1912.04260 for more details. Args: cls_in_channels (int): Input channels of cls RoI feature. \ Defaults to 256. reg_in_channels (int): Input channels of reg RoI feature. \ Defaults to 256. roi_feat_size (int): Size of RoI features. Defaults to 7. reg_feat_up_ratio (int): Upsample ratio of reg features. \ Defaults to 2. reg_pre_kernel (int): Kernel of 2D conv layers before \ attention pooling. Defaults to 3. reg_post_kernel (int): Kernel of 1D conv layers after \ attention pooling. Defaults to 3. reg_pre_num (int): Number of pre convs. Defaults to 2. reg_post_num (int): Number of post convs. Defaults to 1. num_classes (int): Number of classes in dataset. Defaults to 80. cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024. reg_offset_out_channels (int): Hidden and output channel \ of reg offset branch. Defaults to 256. reg_cls_out_channels (int): Hidden and output channel \ of reg cls branch. Defaults to 256. num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1. num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0. reg_class_agnostic (bool): Class agnostic regression or not. \ Defaults to True. norm_cfg (dict): Config of norm layers. Defaults to None. bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'. loss_cls (dict): Config of classification loss. loss_bbox_cls (dict): Config of classification loss for bbox branch. loss_bbox_reg (dict): Config of regression loss for bbox branch. init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ def __init__(self, num_classes, cls_in_channels=256, reg_in_channels=256, roi_feat_size=7, reg_feat_up_ratio=2, reg_pre_kernel=3, reg_post_kernel=3, reg_pre_num=2, reg_post_num=1, cls_out_channels=1024, reg_offset_out_channels=256, reg_cls_out_channels=256, num_cls_fcs=1, num_reg_fcs=0, reg_class_agnostic=True, norm_cfg=None, bbox_coder=dict( type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox_reg=dict( type='SmoothL1Loss', beta=0.1, loss_weight=1.0), init_cfg=None): super(SABLHead, self).__init__(init_cfg) self.cls_in_channels = cls_in_channels self.reg_in_channels = reg_in_channels self.roi_feat_size = roi_feat_size self.reg_feat_up_ratio = int(reg_feat_up_ratio) self.num_buckets = bbox_coder['num_buckets'] assert self.reg_feat_up_ratio // 2 >= 1 self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio assert self.up_reg_feat_size == bbox_coder['num_buckets'] self.reg_pre_kernel = reg_pre_kernel self.reg_post_kernel = reg_post_kernel self.reg_pre_num = reg_pre_num self.reg_post_num = reg_post_num self.num_classes = num_classes self.cls_out_channels = cls_out_channels self.reg_offset_out_channels = reg_offset_out_channels self.reg_cls_out_channels = reg_cls_out_channels self.num_cls_fcs = num_cls_fcs self.num_reg_fcs = num_reg_fcs self.reg_class_agnostic = reg_class_agnostic assert self.reg_class_agnostic self.norm_cfg = norm_cfg self.bbox_coder = build_bbox_coder(bbox_coder) self.loss_cls = build_loss(loss_cls) self.loss_bbox_cls = build_loss(loss_bbox_cls) self.loss_bbox_reg = build_loss(loss_bbox_reg) self.cls_fcs = self._add_fc_branch(self.num_cls_fcs, self.cls_in_channels, self.roi_feat_size, self.cls_out_channels) self.side_num = int(np.ceil(self.num_buckets / 2)) if self.reg_feat_up_ratio > 1: self.upsample_x = nn.ConvTranspose1d( reg_in_channels, reg_in_channels, self.reg_feat_up_ratio, stride=self.reg_feat_up_ratio) self.upsample_y = nn.ConvTranspose1d( reg_in_channels, reg_in_channels, self.reg_feat_up_ratio, stride=self.reg_feat_up_ratio) self.reg_pre_convs = nn.ModuleList() for i in range(self.reg_pre_num): reg_pre_conv = ConvModule( reg_in_channels, reg_in_channels, kernel_size=reg_pre_kernel, padding=reg_pre_kernel // 2, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU')) self.reg_pre_convs.append(reg_pre_conv) self.reg_post_conv_xs = nn.ModuleList() for i in range(self.reg_post_num): reg_post_conv_x = ConvModule( reg_in_channels, reg_in_channels, kernel_size=(1, reg_post_kernel), padding=(0, reg_post_kernel // 2), norm_cfg=norm_cfg, act_cfg=dict(type='ReLU')) self.reg_post_conv_xs.append(reg_post_conv_x) self.reg_post_conv_ys = nn.ModuleList() for i in range(self.reg_post_num): reg_post_conv_y = ConvModule( reg_in_channels, reg_in_channels, kernel_size=(reg_post_kernel, 1), padding=(reg_post_kernel // 2, 0), norm_cfg=norm_cfg, act_cfg=dict(type='ReLU')) self.reg_post_conv_ys.append(reg_post_conv_y) self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1) self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1) self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1) self.relu = nn.ReLU(inplace=True) self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs, self.reg_in_channels, 1, self.reg_cls_out_channels) self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs, self.reg_in_channels, 1, self.reg_offset_out_channels) self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1) self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1) if init_cfg is None: self.init_cfg = [ dict( type='Xavier', layer='Linear', distribution='uniform', override=[ dict(type='Normal', name='reg_conv_att_x', std=0.01), dict(type='Normal', name='reg_conv_att_y', std=0.01), dict(type='Normal', name='fc_reg_cls', std=0.01), dict(type='Normal', name='fc_cls', std=0.01), dict(type='Normal', name='fc_reg_offset', std=0.001) ]) ] if self.reg_feat_up_ratio > 1: self.init_cfg += [ dict( type='Kaiming', distribution='normal', override=[ dict(name='upsample_x'), dict(name='upsample_y') ]) ] def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size, fc_out_channels): in_channels = in_channels * roi_feat_size * roi_feat_size branch_fcs = nn.ModuleList() for i in range(num_branch_fcs): fc_in_channels = (in_channels if i == 0 else fc_out_channels) branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels)) return branch_fcs def cls_forward(self, cls_x): cls_x = cls_x.view(cls_x.size(0), -1) for fc in self.cls_fcs: cls_x = self.relu(fc(cls_x)) cls_score = self.fc_cls(cls_x) return cls_score def attention_pool(self, reg_x): """Extract direction-specific features fx and fy with attention methanism.""" reg_fx = reg_x reg_fy = reg_x reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid() reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid() reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2) reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3) reg_fx = (reg_fx * reg_fx_att).sum(dim=2) reg_fy = (reg_fy * reg_fy_att).sum(dim=3) return reg_fx, reg_fy def side_aware_feature_extractor(self, reg_x): """Refine and extract side-aware features without split them.""" for reg_pre_conv in self.reg_pre_convs: reg_x = reg_pre_conv(reg_x) reg_fx, reg_fy = self.attention_pool(reg_x) if self.reg_post_num > 0: reg_fx = reg_fx.unsqueeze(2) reg_fy = reg_fy.unsqueeze(3) for i in range(self.reg_post_num): reg_fx = self.reg_post_conv_xs[i](reg_fx) reg_fy = self.reg_post_conv_ys[i](reg_fy) reg_fx = reg_fx.squeeze(2) reg_fy = reg_fy.squeeze(3) if self.reg_feat_up_ratio > 1: reg_fx = self.relu(self.upsample_x(reg_fx)) reg_fy = self.relu(self.upsample_y(reg_fy)) reg_fx = torch.transpose(reg_fx, 1, 2) reg_fy = torch.transpose(reg_fy, 1, 2) return reg_fx.contiguous(), reg_fy.contiguous() def reg_pred(self, x, offset_fcs, cls_fcs): """Predict bucketing estimation (cls_pred) and fine regression (offset pred) with side-aware features.""" x_offset = x.view(-1, self.reg_in_channels) x_cls = x.view(-1, self.reg_in_channels) for fc in offset_fcs: x_offset = self.relu(fc(x_offset)) for fc in cls_fcs: x_cls = self.relu(fc(x_cls)) offset_pred = self.fc_reg_offset(x_offset) cls_pred = self.fc_reg_cls(x_cls) offset_pred = offset_pred.view(x.size(0), -1) cls_pred = cls_pred.view(x.size(0), -1) return offset_pred, cls_pred def side_aware_split(self, feat): """Split side-aware features aligned with orders of bucketing targets.""" l_end = int(np.ceil(self.up_reg_feat_size / 2)) r_start = int(np.floor(self.up_reg_feat_size / 2)) feat_fl = feat[:, :l_end] feat_fr = feat[:, r_start:].flip(dims=(1, )) feat_fl = feat_fl.contiguous() feat_fr = feat_fr.contiguous() feat = torch.cat([feat_fl, feat_fr], dim=-1) return feat def bbox_pred_split(self, bbox_pred, num_proposals_per_img): """Split batch bbox prediction back to each image.""" bucket_cls_preds, bucket_offset_preds = bbox_pred bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0) bucket_offset_preds = bucket_offset_preds.split( num_proposals_per_img, 0) bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds)) return bbox_pred def reg_forward(self, reg_x): outs = self.side_aware_feature_extractor(reg_x) edge_offset_preds = [] edge_cls_preds = [] reg_fx = outs[0] reg_fy = outs[1] offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs, self.reg_cls_fcs) offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs, self.reg_cls_fcs) offset_pred_x = self.side_aware_split(offset_pred_x) offset_pred_y = self.side_aware_split(offset_pred_y) cls_pred_x = self.side_aware_split(cls_pred_x) cls_pred_y = self.side_aware_split(cls_pred_y) edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1) edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1) return (edge_cls_preds, edge_offset_preds) def forward(self, x): bbox_pred = self.reg_forward(x) cls_score = self.cls_forward(x) return cls_score, bbox_pred def get_targets(self, sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg): pos_proposals = [res.pos_bboxes for res in sampling_results] neg_proposals = [res.neg_bboxes for res in sampling_results] pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results] pos_gt_labels = [res.pos_gt_labels for res in sampling_results] cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals, pos_gt_bboxes, pos_gt_labels, rcnn_train_cfg) (labels, label_weights, bucket_cls_targets, bucket_cls_weights, bucket_offset_targets, bucket_offset_weights) = cls_reg_targets return (labels, label_weights, (bucket_cls_targets, bucket_offset_targets), (bucket_cls_weights, bucket_offset_weights)) def bucket_target(self, pos_proposals_list, neg_proposals_list, pos_gt_bboxes_list, pos_gt_labels_list, rcnn_train_cfg, concat=True): (labels, label_weights, bucket_cls_targets, bucket_cls_weights, bucket_offset_targets, bucket_offset_weights) = multi_apply( self._bucket_target_single, pos_proposals_list, neg_proposals_list, pos_gt_bboxes_list, pos_gt_labels_list, cfg=rcnn_train_cfg) if concat: labels = torch.cat(labels, 0) label_weights = torch.cat(label_weights, 0) bucket_cls_targets = torch.cat(bucket_cls_targets, 0) bucket_cls_weights = torch.cat(bucket_cls_weights, 0) bucket_offset_targets = torch.cat(bucket_offset_targets, 0) bucket_offset_weights = torch.cat(bucket_offset_weights, 0) return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, bucket_offset_targets, bucket_offset_weights) def _bucket_target_single(self, pos_proposals, neg_proposals, pos_gt_bboxes, pos_gt_labels, cfg): """Compute bucketing estimation targets and fine regression targets for a single image. Args: pos_proposals (Tensor): positive proposals of a single image, Shape (n_pos, 4) neg_proposals (Tensor): negative proposals of a single image, Shape (n_neg, 4). pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals of a single image, Shape (n_pos, 4). pos_gt_labels (Tensor): gt labels assigned to positive proposals of a single image, Shape (n_pos, ). cfg (dict): Config of calculating targets Returns: tuple: - labels (Tensor): Labels in a single image. \ Shape (n,). - label_weights (Tensor): Label weights in a single image.\ Shape (n,) - bucket_cls_targets (Tensor): Bucket cls targets in \ a single image. Shape (n, num_buckets*2). - bucket_cls_weights (Tensor): Bucket cls weights in \ a single image. Shape (n, num_buckets*2). - bucket_offset_targets (Tensor): Bucket offset targets \ in a single image. Shape (n, num_buckets*2). - bucket_offset_targets (Tensor): Bucket offset weights \ in a single image. Shape (n, num_buckets*2). """ num_pos = pos_proposals.size(0) num_neg = neg_proposals.size(0) num_samples = num_pos + num_neg labels = pos_gt_bboxes.new_full((num_samples, ), self.num_classes, dtype=torch.long) label_weights = pos_proposals.new_zeros(num_samples) bucket_cls_targets = pos_proposals.new_zeros(num_samples, 4 * self.side_num) bucket_cls_weights = pos_proposals.new_zeros(num_samples, 4 * self.side_num) bucket_offset_targets = pos_proposals.new_zeros( num_samples, 4 * self.side_num) bucket_offset_weights = pos_proposals.new_zeros( num_samples, 4 * self.side_num) if num_pos > 0: labels[:num_pos] = pos_gt_labels label_weights[:num_pos] = 1.0 (pos_bucket_offset_targets, pos_bucket_offset_weights, pos_bucket_cls_targets, pos_bucket_cls_weights) = self.bbox_coder.encode( pos_proposals, pos_gt_bboxes) bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights if num_neg > 0: label_weights[-num_neg:] = 1.0 return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, bucket_offset_targets, bucket_offset_weights) def loss(self, cls_score, bbox_pred, rois, labels, label_weights, bbox_targets, bbox_weights, reduction_override=None): losses = dict() if cls_score is not None: avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) losses['loss_cls'] = self.loss_cls( cls_score, labels, label_weights, avg_factor=avg_factor, reduction_override=reduction_override) losses['acc'] = accuracy(cls_score, labels) if bbox_pred is not None: bucket_cls_preds, bucket_offset_preds = bbox_pred bucket_cls_targets, bucket_offset_targets = bbox_targets bucket_cls_weights, bucket_offset_weights = bbox_weights # edge cls bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num) bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num) bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num) losses['loss_bbox_cls'] = self.loss_bbox_cls( bucket_cls_preds, bucket_cls_targets, bucket_cls_weights, avg_factor=bucket_cls_targets.size(0), reduction_override=reduction_override) losses['loss_bbox_reg'] = self.loss_bbox_reg( bucket_offset_preds, bucket_offset_targets, bucket_offset_weights, avg_factor=bucket_offset_targets.size(0), reduction_override=reduction_override) return losses @force_fp32(apply_to=('cls_score', 'bbox_pred')) def get_bboxes(self, rois, cls_score, bbox_pred, img_shape, scale_factor, rescale=False, cfg=None): if isinstance(cls_score, list): cls_score = sum(cls_score) / float(len(cls_score)) scores = F.softmax(cls_score, dim=1) if cls_score is not None else None if bbox_pred is not None: bboxes, confidences = self.bbox_coder.decode( rois[:, 1:], bbox_pred, img_shape) else: bboxes = rois[:, 1:].clone() confidences = None if img_shape is not None: bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1) bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1) if rescale and bboxes.size(0) > 0: if isinstance(scale_factor, float): bboxes /= scale_factor else: bboxes /= torch.from_numpy(scale_factor).to(bboxes.device) if cfg is None: return bboxes, scores else: det_bboxes, det_labels = multiclass_nms( bboxes, scores, cfg.score_thr, cfg.nms, cfg.max_per_img, score_factors=confidences) return det_bboxes, det_labels @force_fp32(apply_to=('bbox_preds', )) def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): """Refine bboxes during training. Args: rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, and bs is the sampled RoIs per image. labels (Tensor): Shape (n*bs, ). bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \ (n*bs, num_buckets*2)]. pos_is_gts (list[Tensor]): Flags indicating if each positive bbox is a gt bbox. img_metas (list[dict]): Meta info of each image. Returns: list[Tensor]: Refined bboxes of each image in a mini-batch. """ img_ids = rois[:, 0].long().unique(sorted=True) assert img_ids.numel() == len(img_metas) bboxes_list = [] for i in range(len(img_metas)): inds = torch.nonzero( rois[:, 0] == i, as_tuple=False).squeeze(dim=1) num_rois = inds.numel() bboxes_ = rois[inds, 1:] label_ = labels[inds] edge_cls_preds, edge_offset_preds = bbox_preds edge_cls_preds_ = edge_cls_preds[inds] edge_offset_preds_ = edge_offset_preds[inds] bbox_pred_ = [edge_cls_preds_, edge_offset_preds_] img_meta_ = img_metas[i] pos_is_gts_ = pos_is_gts[i] bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, img_meta_) # filter gt bboxes pos_keep = 1 - pos_is_gts_ keep_inds = pos_is_gts_.new_ones(num_rois) keep_inds[:len(pos_is_gts_)] = pos_keep bboxes_list.append(bboxes[keep_inds.type(torch.bool)]) return bboxes_list @force_fp32(apply_to=('bbox_pred', )) def regress_by_class(self, rois, label, bbox_pred, img_meta): """Regress the bbox for the predicted class. Used in Cascade R-CNN. Args: rois (Tensor): shape (n, 4) or (n, 5) label (Tensor): shape (n, ) bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \ (n, num_buckets *2)] img_meta (dict): Image meta info. Returns: Tensor: Regressed bboxes, the same shape as input rois. """ assert rois.size(1) == 4 or rois.size(1) == 5 if rois.size(1) == 4: new_rois, _ = self.bbox_coder.decode(rois, bbox_pred, img_meta['img_shape']) else: bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred, img_meta['img_shape']) new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) return new_rois