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- # Copyright (c) OpenMMLab. All rights reserved.
- import torch
- import torch.nn as nn
- from mmcv.cnn import (bias_init_with_prob, build_activation_layer,
- build_norm_layer)
- from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
- from mmcv.runner import auto_fp16, force_fp32
-
- from mmdet.core import multi_apply
- from mmdet.models.builder import HEADS, build_loss
- from mmdet.models.dense_heads.atss_head import reduce_mean
- from mmdet.models.losses import accuracy
- from mmdet.models.utils import build_transformer
- from .bbox_head import BBoxHead
-
-
- @HEADS.register_module()
- class DIIHead(BBoxHead):
- r"""Dynamic Instance Interactive Head for `Sparse R-CNN: End-to-End Object
- Detection with Learnable Proposals <https://arxiv.org/abs/2011.12450>`_
-
- Args:
- num_classes (int): Number of class in dataset.
- Defaults to 80.
- num_ffn_fcs (int): The number of fully-connected
- layers in FFNs. Defaults to 2.
- num_heads (int): The hidden dimension of FFNs.
- Defaults to 8.
- num_cls_fcs (int): The number of fully-connected
- layers in classification subnet. Defaults to 1.
- num_reg_fcs (int): The number of fully-connected
- layers in regression subnet. Defaults to 3.
- feedforward_channels (int): The hidden dimension
- of FFNs. Defaults to 2048
- in_channels (int): Hidden_channels of MultiheadAttention.
- Defaults to 256.
- dropout (float): Probability of drop the channel.
- Defaults to 0.0
- ffn_act_cfg (dict): The activation config for FFNs.
- dynamic_conv_cfg (dict): The convolution config
- for DynamicConv.
- loss_iou (dict): The config for iou or giou loss.
-
- """
-
- def __init__(self,
- num_classes=80,
- num_ffn_fcs=2,
- num_heads=8,
- num_cls_fcs=1,
- num_reg_fcs=3,
- feedforward_channels=2048,
- in_channels=256,
- dropout=0.0,
- ffn_act_cfg=dict(type='ReLU', inplace=True),
- dynamic_conv_cfg=dict(
- type='DynamicConv',
- in_channels=256,
- feat_channels=64,
- out_channels=256,
- input_feat_shape=7,
- act_cfg=dict(type='ReLU', inplace=True),
- norm_cfg=dict(type='LN')),
- loss_iou=dict(type='GIoULoss', loss_weight=2.0),
- init_cfg=None,
- **kwargs):
- assert init_cfg is None, 'To prevent abnormal initialization ' \
- 'behavior, init_cfg is not allowed to be set'
- super(DIIHead, self).__init__(
- num_classes=num_classes,
- reg_decoded_bbox=True,
- reg_class_agnostic=True,
- init_cfg=init_cfg,
- **kwargs)
- self.loss_iou = build_loss(loss_iou)
- self.in_channels = in_channels
- self.fp16_enabled = False
- self.attention = MultiheadAttention(in_channels, num_heads, dropout)
- self.attention_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
-
- self.instance_interactive_conv = build_transformer(dynamic_conv_cfg)
- self.instance_interactive_conv_dropout = nn.Dropout(dropout)
- self.instance_interactive_conv_norm = build_norm_layer(
- dict(type='LN'), in_channels)[1]
-
- self.ffn = FFN(
- in_channels,
- feedforward_channels,
- num_ffn_fcs,
- act_cfg=ffn_act_cfg,
- dropout=dropout)
- self.ffn_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
-
- self.cls_fcs = nn.ModuleList()
- for _ in range(num_cls_fcs):
- self.cls_fcs.append(
- nn.Linear(in_channels, in_channels, bias=False))
- self.cls_fcs.append(
- build_norm_layer(dict(type='LN'), in_channels)[1])
- self.cls_fcs.append(
- build_activation_layer(dict(type='ReLU', inplace=True)))
-
- # over load the self.fc_cls in BBoxHead
- if self.loss_cls.use_sigmoid:
- self.fc_cls = nn.Linear(in_channels, self.num_classes)
- else:
- self.fc_cls = nn.Linear(in_channels, self.num_classes + 1)
-
- self.reg_fcs = nn.ModuleList()
- for _ in range(num_reg_fcs):
- self.reg_fcs.append(
- nn.Linear(in_channels, in_channels, bias=False))
- self.reg_fcs.append(
- build_norm_layer(dict(type='LN'), in_channels)[1])
- self.reg_fcs.append(
- build_activation_layer(dict(type='ReLU', inplace=True)))
- # over load the self.fc_cls in BBoxHead
- self.fc_reg = nn.Linear(in_channels, 4)
-
- assert self.reg_class_agnostic, 'DIIHead only ' \
- 'suppport `reg_class_agnostic=True` '
- assert self.reg_decoded_bbox, 'DIIHead only ' \
- 'suppport `reg_decoded_bbox=True`'
-
- def init_weights(self):
- """Use xavier initialization for all weight parameter and set
- classification head bias as a specific value when use focal loss."""
- super(DIIHead, self).init_weights()
- for p in self.parameters():
- if p.dim() > 1:
- nn.init.xavier_uniform_(p)
- else:
- # adopt the default initialization for
- # the weight and bias of the layer norm
- pass
- if self.loss_cls.use_sigmoid:
- bias_init = bias_init_with_prob(0.01)
- nn.init.constant_(self.fc_cls.bias, bias_init)
-
- @auto_fp16()
- def forward(self, roi_feat, proposal_feat):
- """Forward function of Dynamic Instance Interactive Head.
-
- Args:
- roi_feat (Tensor): Roi-pooling features with shape
- (batch_size*num_proposals, feature_dimensions,
- pooling_h , pooling_w).
- proposal_feat (Tensor): Intermediate feature get from
- diihead in last stage, has shape
- (batch_size, num_proposals, feature_dimensions)
-
- Returns:
- tuple[Tensor]: Usually a tuple of classification scores
- and bbox prediction and a intermediate feature.
-
- - cls_scores (Tensor): Classification scores for
- all proposals, has shape
- (batch_size, num_proposals, num_classes).
- - bbox_preds (Tensor): Box energies / deltas for
- all proposals, has shape
- (batch_size, num_proposals, 4).
- - obj_feat (Tensor): Object feature before classification
- and regression subnet, has shape
- (batch_size, num_proposal, feature_dimensions).
- """
- N, num_proposals = proposal_feat.shape[:2]
-
- # Self attention
- proposal_feat = proposal_feat.permute(1, 0, 2)
- proposal_feat = self.attention_norm(self.attention(proposal_feat))
- attn_feats = proposal_feat.permute(1, 0, 2)
-
- # instance interactive
- proposal_feat = attn_feats.reshape(-1, self.in_channels)
- proposal_feat_iic = self.instance_interactive_conv(
- proposal_feat, roi_feat)
- proposal_feat = proposal_feat + self.instance_interactive_conv_dropout(
- proposal_feat_iic)
- obj_feat = self.instance_interactive_conv_norm(proposal_feat)
-
- # FFN
- obj_feat = self.ffn_norm(self.ffn(obj_feat))
-
- cls_feat = obj_feat
- reg_feat = obj_feat
-
- for cls_layer in self.cls_fcs:
- cls_feat = cls_layer(cls_feat)
- for reg_layer in self.reg_fcs:
- reg_feat = reg_layer(reg_feat)
-
- cls_score = self.fc_cls(cls_feat).view(
- N, num_proposals, self.num_classes
- if self.loss_cls.use_sigmoid else self.num_classes + 1)
- bbox_delta = self.fc_reg(reg_feat).view(N, num_proposals, 4)
-
- return cls_score, bbox_delta, obj_feat.view(
- N, num_proposals, self.in_channels), attn_feats
-
- @force_fp32(apply_to=('cls_score', 'bbox_pred'))
- def loss(self,
- cls_score,
- bbox_pred,
- labels,
- label_weights,
- bbox_targets,
- bbox_weights,
- imgs_whwh=None,
- reduction_override=None,
- **kwargs):
- """"Loss function of DIIHead, get loss of all images.
-
- Args:
- cls_score (Tensor): Classification prediction
- results of all class, has shape
- (batch_size * num_proposals_single_image, num_classes)
- bbox_pred (Tensor): Regression prediction results,
- has shape
- (batch_size * num_proposals_single_image, 4), the last
- dimension 4 represents [tl_x, tl_y, br_x, br_y].
- labels (Tensor): Label of each proposals, has shape
- (batch_size * num_proposals_single_image
- label_weights (Tensor): Classification loss
- weight of each proposals, has shape
- (batch_size * num_proposals_single_image
- bbox_targets (Tensor): Regression targets of each
- proposals, has shape
- (batch_size * num_proposals_single_image, 4),
- the last dimension 4 represents
- [tl_x, tl_y, br_x, br_y].
- bbox_weights (Tensor): Regression loss weight of each
- proposals's coordinate, has shape
- (batch_size * num_proposals_single_image, 4),
- imgs_whwh (Tensor): imgs_whwh (Tensor): Tensor with\
- shape (batch_size, num_proposals, 4), the last
- dimension means
- [img_width,img_height, img_width, img_height].
- reduction_override (str, optional): The reduction
- method used to override the original reduction
- method of the loss. Options are "none",
- "mean" and "sum". Defaults to None,
-
- Returns:
- dict[str, Tensor]: Dictionary of loss components
- """
- losses = dict()
- bg_class_ind = self.num_classes
- # note in spare rcnn num_gt == num_pos
- pos_inds = (labels >= 0) & (labels < bg_class_ind)
- num_pos = pos_inds.sum().float()
- avg_factor = reduce_mean(num_pos)
- if cls_score is not None:
- if cls_score.numel() > 0:
- losses['loss_cls'] = self.loss_cls(
- cls_score,
- labels,
- label_weights,
- avg_factor=avg_factor,
- reduction_override=reduction_override)
- losses['pos_acc'] = accuracy(cls_score[pos_inds],
- labels[pos_inds])
- if bbox_pred is not None:
- # 0~self.num_classes-1 are FG, self.num_classes is BG
- # do not perform bounding box regression for BG anymore.
- if pos_inds.any():
- pos_bbox_pred = bbox_pred.reshape(bbox_pred.size(0),
- 4)[pos_inds.type(torch.bool)]
- imgs_whwh = imgs_whwh.reshape(bbox_pred.size(0),
- 4)[pos_inds.type(torch.bool)]
- losses['loss_bbox'] = self.loss_bbox(
- pos_bbox_pred / imgs_whwh,
- bbox_targets[pos_inds.type(torch.bool)] / imgs_whwh,
- bbox_weights[pos_inds.type(torch.bool)],
- avg_factor=avg_factor)
- losses['loss_iou'] = self.loss_iou(
- pos_bbox_pred,
- bbox_targets[pos_inds.type(torch.bool)],
- bbox_weights[pos_inds.type(torch.bool)],
- avg_factor=avg_factor)
- else:
- losses['loss_bbox'] = bbox_pred.sum() * 0
- losses['loss_iou'] = bbox_pred.sum() * 0
- return losses
-
- def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes,
- pos_gt_bboxes, pos_gt_labels, cfg):
- """Calculate the ground truth for proposals in the single image
- according to the sampling results.
-
- Almost the same as the implementation in `bbox_head`,
- we add pos_inds and neg_inds to select positive and
- negative samples instead of selecting the first num_pos
- as positive samples.
-
- Args:
- pos_inds (Tensor): The length is equal to the
- positive sample numbers contain all index
- of the positive sample in the origin proposal set.
- neg_inds (Tensor): The length is equal to the
- negative sample numbers contain all index
- of the negative sample in the origin proposal set.
- pos_bboxes (Tensor): Contains all the positive boxes,
- has shape (num_pos, 4), the last dimension 4
- represents [tl_x, tl_y, br_x, br_y].
- neg_bboxes (Tensor): Contains all the negative boxes,
- has shape (num_neg, 4), the last dimension 4
- represents [tl_x, tl_y, br_x, br_y].
- pos_gt_bboxes (Tensor): Contains all the gt_boxes,
- has shape (num_gt, 4), the last dimension 4
- represents [tl_x, tl_y, br_x, br_y].
- pos_gt_labels (Tensor): Contains all the gt_labels,
- has shape (num_gt).
- cfg (obj:`ConfigDict`): `train_cfg` of R-CNN.
-
- Returns:
- Tuple[Tensor]: Ground truth for proposals in a single image.
- Containing the following Tensors:
-
- - labels(Tensor): Gt_labels for all proposals, has
- shape (num_proposals,).
- - label_weights(Tensor): Labels_weights for all proposals, has
- shape (num_proposals,).
- - bbox_targets(Tensor):Regression target for all proposals, has
- shape (num_proposals, 4), the last dimension 4
- represents [tl_x, tl_y, br_x, br_y].
- - bbox_weights(Tensor):Regression weights for all proposals,
- has shape (num_proposals, 4).
- """
- num_pos = pos_bboxes.size(0)
- num_neg = neg_bboxes.size(0)
- num_samples = num_pos + num_neg
-
- # original implementation uses new_zeros since BG are set to be 0
- # now use empty & fill because BG cat_id = num_classes,
- # FG cat_id = [0, num_classes-1]
- labels = pos_bboxes.new_full((num_samples, ),
- self.num_classes,
- dtype=torch.long)
- label_weights = pos_bboxes.new_zeros(num_samples)
- bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
- bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
- if num_pos > 0:
- labels[pos_inds] = pos_gt_labels
- pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
- label_weights[pos_inds] = pos_weight
- if not self.reg_decoded_bbox:
- pos_bbox_targets = self.bbox_coder.encode(
- pos_bboxes, pos_gt_bboxes)
- else:
- pos_bbox_targets = pos_gt_bboxes
- bbox_targets[pos_inds, :] = pos_bbox_targets
- bbox_weights[pos_inds, :] = 1
- if num_neg > 0:
- label_weights[neg_inds] = 1.0
-
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_targets(self,
- sampling_results,
- gt_bboxes,
- gt_labels,
- rcnn_train_cfg,
- concat=True):
- """Calculate the ground truth for all samples in a batch according to
- the sampling_results.
-
- Almost the same as the implementation in bbox_head, we passed
- additional parameters pos_inds_list and neg_inds_list to
- `_get_target_single` function.
-
- Args:
- sampling_results (List[obj:SamplingResults]): Assign results of
- all images in a batch after sampling.
- gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch,
- each tensor has shape (num_gt, 4), the last dimension 4
- represents [tl_x, tl_y, br_x, br_y].
- gt_labels (list[Tensor]): Gt_labels of all images in a batch,
- each tensor has shape (num_gt,).
- rcnn_train_cfg (obj:`ConfigDict`): `train_cfg` of RCNN.
- concat (bool): Whether to concatenate the results of all
- the images in a single batch.
-
- Returns:
- Tuple[Tensor]: Ground truth for proposals in a single image.
- Containing the following list of Tensors:
-
- - labels (list[Tensor],Tensor): Gt_labels for all
- proposals in a batch, each tensor in list has
- shape (num_proposals,) when `concat=False`, otherwise just
- a single tensor has shape (num_all_proposals,).
- - label_weights (list[Tensor]): Labels_weights for
- all proposals in a batch, each tensor in list has shape
- (num_proposals,) when `concat=False`, otherwise just a
- single tensor has shape (num_all_proposals,).
- - bbox_targets (list[Tensor],Tensor): Regression target
- for all proposals in a batch, each tensor in list has
- shape (num_proposals, 4) when `concat=False`, otherwise
- just a single tensor has shape (num_all_proposals, 4),
- the last dimension 4 represents [tl_x, tl_y, br_x, br_y].
- - bbox_weights (list[tensor],Tensor): Regression weights for
- all proposals in a batch, each tensor in list has shape
- (num_proposals, 4) when `concat=False`, otherwise just a
- single tensor has shape (num_all_proposals, 4).
- """
- pos_inds_list = [res.pos_inds for res in sampling_results]
- neg_inds_list = [res.neg_inds for res in sampling_results]
- pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
- neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
- pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
- pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
- labels, label_weights, bbox_targets, bbox_weights = multi_apply(
- self._get_target_single,
- pos_inds_list,
- neg_inds_list,
- pos_bboxes_list,
- neg_bboxes_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)
- bbox_targets = torch.cat(bbox_targets, 0)
- bbox_weights = torch.cat(bbox_weights, 0)
- return labels, label_weights, bbox_targets, bbox_weights
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