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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List
import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer, build_norm_layer
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
from mmengine.config import ConfigDict
from mmengine.model import bias_init_with_prob
from torch import Tensor
from mmdet.models.losses import accuracy
from mmdet.models.task_modules import SamplingResult
from mmdet.models.utils import multi_apply
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, reduce_mean
from .bbox_head import BBoxHead
@MODELS.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 (:obj:`ConfigDict` or dict): The activation config
for FFNs.
dynamic_conv_cfg (:obj:`ConfigDict` or dict): The convolution
config for DynamicConv.
loss_iou (:obj:`ConfigDict` or dict): The config for iou or
giou loss.
init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
dict]): Initialization config dict. Defaults to None.
"""
def __init__(self,
num_classes: int = 80,
num_ffn_fcs: int = 2,
num_heads: int = 8,
num_cls_fcs: int = 1,
num_reg_fcs: int = 3,
feedforward_channels: int = 2048,
in_channels: int = 256,
dropout: float = 0.0,
ffn_act_cfg: ConfigType = dict(type='ReLU', inplace=True),
dynamic_conv_cfg: ConfigType = 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: ConfigType = dict(type='GIoULoss', loss_weight=2.0),
init_cfg: OptConfigType = None,
**kwargs) -> None:
assert init_cfg is None, 'To prevent abnormal initialization ' \
'behavior, init_cfg is not allowed to be set'
super().__init__(
num_classes=num_classes,
reg_decoded_bbox=True,
reg_class_agnostic=True,
init_cfg=init_cfg,
**kwargs)
self.loss_iou = MODELS.build(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 = MODELS.build(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) -> None:
"""Use xavier initialization for all weight parameter and set
classification head bias as a specific value when use focal loss."""
super().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)
def forward(self, roi_feat: Tensor, proposal_feat: Tensor) -> tuple:
"""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).
- attn_feats (Tensor): Intermediate feature.
"""
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
def loss_and_target(self,
cls_score: Tensor,
bbox_pred: Tensor,
sampling_results: List[SamplingResult],
rcnn_train_cfg: ConfigType,
imgs_whwh: Tensor,
concat: bool = True,
reduction_override: str = None) -> dict:
"""Calculate the loss based on the features extracted by the DIIHead.
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].
sampling_results (List[obj:SamplingResult]): Assign results of
all images in a batch after sampling.
rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
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].
concat (bool): Whether to concatenate the results of all
the images in a single batch. Defaults to True.
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: A dictionary of loss and targets components.
The targets are only used for cascade rcnn.
"""
cls_reg_targets = self.get_targets(
sampling_results=sampling_results,
rcnn_train_cfg=rcnn_train_cfg,
concat=concat)
(labels, label_weights, bbox_targets, bbox_weights) = cls_reg_targets
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 dict(loss_bbox=losses, bbox_targets=cls_reg_targets)
def _get_targets_single(self, pos_inds: Tensor, neg_inds: Tensor,
pos_priors: Tensor, neg_priors: Tensor,
pos_gt_bboxes: Tensor, pos_gt_labels: Tensor,
cfg: ConfigDict) -> tuple:
"""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_priors (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_priors (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 gt_boxes for
all positive samples, has shape (num_pos, 4),
the last dimension 4
represents [tl_x, tl_y, br_x, br_y].
pos_gt_labels (Tensor): Contains gt_labels for
all positive samples, has shape (num_pos, ).
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_priors.size(0)
num_neg = neg_priors.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_priors.new_full((num_samples, ),
self.num_classes,
dtype=torch.long)
label_weights = pos_priors.new_zeros(num_samples)
bbox_targets = pos_priors.new_zeros(num_samples, 4)
bbox_weights = pos_priors.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_priors, 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: List[SamplingResult],
rcnn_train_cfg: ConfigDict,
concat: bool = True) -> tuple:
"""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_targets_single` function.
Args:
sampling_results (List[obj:SamplingResult]): Assign results of
all images in a batch after sampling.
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_priors_list = [res.pos_priors for res in sampling_results]
neg_priors_list = [res.neg_priors 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_targets_single,
pos_inds_list,
neg_inds_list,
pos_priors_list,
neg_priors_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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