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- # 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
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